, rOpenSci [fnd]
(https://ropensci.org/)| Title: | General Purpose R Interface to 'Solr' |
|---|---|
| Description: | Provides a set of functions for querying and parsing data from 'Solr' (<https://solr.apache.org/>) 'endpoints' (local and remote), including search, 'faceting', 'highlighting', 'stats', and 'more like this'. In addition, some functionality is included for creating, deleting, and updating documents in a 'Solr' 'database'. |
| Authors: | Scott Chamberlain [aut, cre]
, rOpenSci [fnd]
(https://ropensci.org/) |
| Maintainer: | Scott Chamberlain <[email protected]> |
| License: | MIT + file LICENSE |
| Version: | 1.2.0 |
| Built: | 2024-11-19 06:37:25 UTC |
| Source: | CRAN |
This package has support for all the search endpoints, as well as a suite of functions for managing a Solr database, including adding and deleting documents.
solr_search() - General search, only returns documents
solr_all() - General search, including all non-documents
in addition to documents: facets, highlights, groups, mlt, stats.
solr_facet() - Faceting only (w/o general search)
solr_highlight() - Highlighting only (w/o general search)
solr_mlt() - More like this (w/o general search)
solr_group() - Group search (w/o general search)
solr_stats() - Stats search (w/o general search)
update_json() - Add or delete documents using json in a file
add() - Add documents via an R list or data.frame
delete_by_id() - Delete documents by ID
delete_by_query() - Delete documents by query
See the vignettes for help browseVignettes(package = "solrium")
v0.2 and above of this package will have wt=csv as the default.
This should give significant performance improvement over the previous
default of wt=json, which pulled down json, parsed to an R list,
then to a data.frame. With wt=csv, we pull down csv, and read that
in directly to a data.frame.
The http library we use, crul, sets gzip compression header by default. As long as compression is used server side, you're good to go on compression, which should be a good peformance boost. See https://wiki.apache.org/solr/SolrPerformanceFactors#Query_Response_Compression for notes on how to enable compression.
There are other notes about Solr performance at https://wiki.apache.org/solr/SolrPerformanceFactors that can be used server side/in your Solr config, but aren't things to tune here in this R client.
Let us know if there's any further performance improvements we can make.
Scott Chamberlain [email protected]
Add documents from R objects
add( x, conn, name, commit = TRUE, commit_within = NULL, overwrite = TRUE, boost = NULL, wt = "json", raw = FALSE, ... )add( x, conn, name, commit = TRUE, commit_within = NULL, overwrite = TRUE, boost = NULL, wt = "json", raw = FALSE, ... )
x |
Documents, either as rows in a data.frame, or a list. |
conn |
A solrium connection object, see SolrClient |
name |
(character) A collection or core name. Required. |
commit |
(logical) If |
commit_within |
(numeric) Milliseconds to commit the change, the document will be added within that time. Default: NULL |
overwrite |
(logical) Overwrite documents with matching keys.
Default: |
boost |
(numeric) Boost factor. Default: NULL |
wt |
(character) One of json (default) or xml. If json, uses
|
raw |
(logical) If |
... |
curl options passed on to crul::HttpClient |
Works for Collections as well as Cores (in SolrCloud and Standalone modes, respectively)
update_json, update_xml,
update_csv for adding documents from files
## Not run: (conn <- SolrClient$new()) # create the boooks collection if (!collection_exists(conn, "books")) { collection_create(conn, name = "books", numShards = 1) } # Documents in a list ss <- list(list(id = 1, price = 100), list(id = 2, price = 500)) add(ss, conn, name = "books") conn$get(c(1, 2), "books") # Documents in a data.frame ## Simple example df <- data.frame(id = c(67, 68), price = c(1000, 500000000)) add(df, conn, "books") df <- data.frame(id = c(77, 78), price = c(1, 2.40)) add(df, conn, "books") ## More complex example, get file from package examples # start Solr in Schemaless mode first: bin/solr start -e schemaless file <- system.file("examples", "books.csv", package = "solrium") x <- read.csv(file, stringsAsFactors = FALSE) class(x) head(x) if (!collection_exists(conn, "mybooks")) { collection_create(conn, name = "mybooks", numShards = 2) } add(x, conn, "mybooks") # Use modifiers add(x, conn, "mybooks", commit_within = 5000) # Get back XML instead of a list ss <- list(list(id = 1, price = 100), list(id = 2, price = 500)) # parsed XML add(ss, conn, name = "books", wt = "xml") # raw XML add(ss, conn, name = "books", wt = "xml", raw = TRUE) ## End(Not run)## Not run: (conn <- SolrClient$new()) # create the boooks collection if (!collection_exists(conn, "books")) { collection_create(conn, name = "books", numShards = 1) } # Documents in a list ss <- list(list(id = 1, price = 100), list(id = 2, price = 500)) add(ss, conn, name = "books") conn$get(c(1, 2), "books") # Documents in a data.frame ## Simple example df <- data.frame(id = c(67, 68), price = c(1000, 500000000)) add(df, conn, "books") df <- data.frame(id = c(77, 78), price = c(1, 2.40)) add(df, conn, "books") ## More complex example, get file from package examples # start Solr in Schemaless mode first: bin/solr start -e schemaless file <- system.file("examples", "books.csv", package = "solrium") x <- read.csv(file, stringsAsFactors = FALSE) class(x) head(x) if (!collection_exists(conn, "mybooks")) { collection_create(conn, name = "mybooks", numShards = 2) } add(x, conn, "mybooks") # Use modifiers add(x, conn, "mybooks", commit_within = 5000) # Get back XML instead of a list ss <- list(list(id = 1, price = 100), list(id = 2, price = 500)) # parsed XML add(ss, conn, name = "books", wt = "xml") # raw XML add(ss, conn, name = "books", wt = "xml", raw = TRUE) ## End(Not run)
Add a replica to a shard in a collection. The node name can be specified if the replica is to be created in a specific node
collection_addreplica( conn, name, shard = NULL, route = NULL, node = NULL, instanceDir = NULL, dataDir = NULL, async = NULL, raw = FALSE, callopts = list(), ... )collection_addreplica( conn, name, shard = NULL, route = NULL, node = NULL, instanceDir = NULL, dataDir = NULL, async = NULL, raw = FALSE, callopts = list(), ... )
conn |
A solrium connection object, see SolrClient |
name |
(character) The name of the core to be created. Required |
shard |
(character) The name of the shard to which replica is to be added.
If |
route |
(character) If the exact shard name is not known, users may pass
the |
node |
(character) The name of the node where the replica should be created |
instanceDir |
(character) The instanceDir for the core that will be created |
dataDir |
(character) The directory in which the core should be created |
async |
(character) Request ID to track this action which will be processed asynchronously |
raw |
(logical) If |
callopts |
curl options passed on to crul::HttpClient |
... |
You can pass in parameters like |
## Not run: (conn <- SolrClient$new()) # create collection if (!conn$collection_exists("foobar")) { conn$collection_create(name = "foobar", numShards = 2) # OR bin/solr create -c foobar } # status conn$collection_clusterstatus()$cluster$collections$foobar # add replica if (!conn$collection_exists("foobar")) { conn$collection_addreplica(name = "foobar", shard = "shard1") } # status again conn$collection_clusterstatus()$cluster$collections$foobar conn$collection_clusterstatus()$cluster$collections$foobar$shards conn$collection_clusterstatus()$cluster$collections$foobar$shards$shard1 ## End(Not run)## Not run: (conn <- SolrClient$new()) # create collection if (!conn$collection_exists("foobar")) { conn$collection_create(name = "foobar", numShards = 2) # OR bin/solr create -c foobar } # status conn$collection_clusterstatus()$cluster$collections$foobar # add replica if (!conn$collection_exists("foobar")) { conn$collection_addreplica(name = "foobar", shard = "shard1") } # status again conn$collection_clusterstatus()$cluster$collections$foobar conn$collection_clusterstatus()$cluster$collections$foobar$shards conn$collection_clusterstatus()$cluster$collections$foobar$shards$shard1 ## End(Not run)
Assign an arbitrary property to a particular replica and give it the value specified. If the property already exists, it will be overwritten with the new value.
collection_addreplicaprop( conn, name, shard, replica, property, property.value, shardUnique = FALSE, raw = FALSE, callopts = list() )collection_addreplicaprop( conn, name, shard, replica, property, property.value, shardUnique = FALSE, raw = FALSE, callopts = list() )
conn |
A solrium connection object, see SolrClient |
name |
(character) The name of the core to be created. Required |
shard |
(character) Required. The name of the shard the replica belongs to |
replica |
(character) Required. The replica, e.g. core_node1. |
property |
(character) Required. The property to add. Note: this will
have the literal 'property.' prepended to distinguish it from
system-maintained properties. So these two forms are equivalent:
|
property.value |
(character) Required. The value to assign to the property |
shardUnique |
(logical) If |
raw |
(logical) If |
callopts |
curl options passed on to crul::HttpClient |
## Not run: (conn <- SolrClient$new()) # create collection if (!conn$collection_exists("addrep")) { conn$collection_create(name = "addrep", numShards = 1) # OR bin/solr create -c addrep } # status conn$collection_clusterstatus()$cluster$collections$addrep$shards # add the value world to the property hello conn$collection_addreplicaprop(name = "addrep", shard = "shard1", replica = "core_node1", property = "hello", property.value = "world") # check status conn$collection_clusterstatus()$cluster$collections$addrep$shards conn$collection_clusterstatus()$cluster$collections$addrep$shards$shard1$replicas$core_node1 ## End(Not run)## Not run: (conn <- SolrClient$new()) # create collection if (!conn$collection_exists("addrep")) { conn$collection_create(name = "addrep", numShards = 1) # OR bin/solr create -c addrep } # status conn$collection_clusterstatus()$cluster$collections$addrep$shards # add the value world to the property hello conn$collection_addreplicaprop(name = "addrep", shard = "shard1", replica = "core_node1", property = "hello", property.value = "world") # check status conn$collection_clusterstatus()$cluster$collections$addrep$shards conn$collection_clusterstatus()$cluster$collections$addrep$shards$shard1$replicas$core_node1 ## End(Not run)
Assign a role to a given node in the cluster. The only supported role as of 4.7 is 'overseer' . Use this API to dedicate a particular node as Overseer. Invoke it multiple times to add more nodes. This is useful in large clusters where an Overseer is likely to get overloaded . If available, one among the list of nodes which are assigned the 'overseer' role would become the overseer. The system would assign the role to any other node if none of the designated nodes are up and running
collection_addrole(conn, role = "overseer", node, raw = FALSE, ...)collection_addrole(conn, role = "overseer", node, raw = FALSE, ...)
conn |
A solrium connection object, see SolrClient |
role |
(character) Required. The name of the role. The only supported role as of now is overseer (set as default). |
node |
(character) Required. The name of the node. It is possible to assign a role even before that node is started. |
raw |
(logical) If |
... |
curl options passed on to crul::HttpClient |
## Not run: (conn <- SolrClient$new()) # get list of nodes nodes <- conn$collection_clusterstatus()$cluster$live_nodes collection_addrole(conn, node = nodes[1]) ## End(Not run)## Not run: (conn <- SolrClient$new()) # get list of nodes nodes <- conn$collection_clusterstatus()$cluster$live_nodes collection_addrole(conn, node = nodes[1]) ## End(Not run)
Insures that a particular property is distributed evenly amongst the physical nodes that make up a collection. If the property already exists on a replica, every effort is made to leave it there. If the property is not on any replica on a shard one is chosen and the property is added.
collection_balanceshardunique( conn, name, property, onlyactivenodes = TRUE, shardUnique = NULL, raw = FALSE, ... )collection_balanceshardunique( conn, name, property, onlyactivenodes = TRUE, shardUnique = NULL, raw = FALSE, ... )
conn |
A solrium connection object, see SolrClient |
name |
(character) The name of the core to be created. Required |
property |
(character) Required. The property to balance. The literal "property." is prepended to this property if not specified explicitly. |
onlyactivenodes |
(logical) Normally, the property is instantiated
on active nodes only. If |
shardUnique |
(logical) Something of a safety valve. There is one
pre-defined property (preferredLeader) that defaults this value to |
raw |
(logical) If |
... |
You can pass in parameters like |
## Not run: (conn <- SolrClient$new()) # create collection if (!conn$collection_exists("addrep")) { conn$collection_create(name = "mycollection") # OR: bin/solr create -c mycollection } # balance preferredLeader property conn$collection_balanceshardunique("mycollection", property = "preferredLeader") # examine cluster status conn$collection_clusterstatus()$cluster$collections$mycollection ## End(Not run)## Not run: (conn <- SolrClient$new()) # create collection if (!conn$collection_exists("addrep")) { conn$collection_create(name = "mycollection") # OR: bin/solr create -c mycollection } # balance preferredLeader property conn$collection_balanceshardunique("mycollection", property = "preferredLeader") # examine cluster status conn$collection_clusterstatus()$cluster$collections$mycollection ## End(Not run)
Important: whether add, edit, or delete is used is determined
by the value passed to the val parameter. If the property name is
new, it will be added. If the property name exists, and the value is
different, it will be edited. If the property name exists, and the value
is NULL or empty the property is deleted (unset).
collection_clusterprop(conn, name, val, raw = FALSE, callopts = list())collection_clusterprop(conn, name, val, raw = FALSE, callopts = list())
conn |
A solrium connection object, see SolrClient |
name |
(character) Name of the core or collection |
val |
(character) Required. The value of the property. If the value is empty or null, the property is unset. |
raw |
(logical) If |
callopts |
curl options passed on to crul::HttpClient |
## Not run: (conn <- SolrClient$new()) # add the value https to the property urlScheme collection_clusterprop(conn, name = "urlScheme", val = "https") # status again collection_clusterstatus(conn)$cluster$properties # delete the property urlScheme by setting val to NULL or a 0 length string collection_clusterprop(conn, name = "urlScheme", val = "") ## End(Not run)## Not run: (conn <- SolrClient$new()) # add the value https to the property urlScheme collection_clusterprop(conn, name = "urlScheme", val = "https") # status again collection_clusterstatus(conn)$cluster$properties # delete the property urlScheme by setting val to NULL or a 0 length string collection_clusterprop(conn, name = "urlScheme", val = "") ## End(Not run)
Fetch the cluster status including collections, shards, replicas, configuration name as well as collection aliases and cluster properties.
collection_clusterstatus(conn, name = NULL, shard = NULL, raw = FALSE, ...)collection_clusterstatus(conn, name = NULL, shard = NULL, raw = FALSE, ...)
conn |
A solrium connection object, see SolrClient |
name |
(character) The name of the core to be created. Required |
shard |
(character) The shard(s) for which information is requested. Multiple shard names can be specified as a character vector. |
raw |
(logical) If |
... |
You can pass in parameters like |
## Not run: (conn <- SolrClient$new()) conn$collection_clusterstatus() res <- conn$collection_clusterstatus() res$responseHeader res$cluster res$cluster$collections res$cluster$collections$gettingstarted res$cluster$live_nodes ## End(Not run)## Not run: (conn <- SolrClient$new()) conn$collection_clusterstatus() res <- conn$collection_clusterstatus() res$responseHeader res$cluster res$cluster$collections res$cluster$collections$gettingstarted res$cluster$live_nodes ## End(Not run)
Add a collection
collection_create( conn, name, numShards = 1, maxShardsPerNode = 1, createNodeSet = NULL, collection.configName = NULL, replicationFactor = 1, router.name = NULL, shards = NULL, createNodeSet.shuffle = TRUE, router.field = NULL, autoAddReplicas = FALSE, async = NULL, raw = FALSE, callopts = list(), ... )collection_create( conn, name, numShards = 1, maxShardsPerNode = 1, createNodeSet = NULL, collection.configName = NULL, replicationFactor = 1, router.name = NULL, shards = NULL, createNodeSet.shuffle = TRUE, router.field = NULL, autoAddReplicas = FALSE, async = NULL, raw = FALSE, callopts = list(), ... )
conn |
A solrium connection object, see SolrClient |
name |
(character) The name of the core to be created. Required |
numShards |
(integer) The number of shards to be created as part of the collection. This is a required parameter when using the 'compositeId' router. |
maxShardsPerNode |
(integer) When creating collections, the shards and/or replicas are spread across all available (i.e., live) nodes, and two replicas of the same shard will never be on the same node. If a node is not live when the CREATE operation is called, it will not get any parts of the new collection, which could lead to too many replicas being created on a single live node. Defining maxShardsPerNode sets a limit on the number of replicas CREATE will spread to each node. If the entire collection can not be fit into the live nodes, no collection will be created at all. Default: 1 |
createNodeSet |
(logical) Allows defining the nodes to spread the new collection
across. If not provided, the CREATE operation will create shard-replica spread across all
live Solr nodes. The format is a comma-separated list of node_names, such as
localhost:8983_solr, localhost:8984_solr, localhost:8985_solr. Default: |
collection.configName |
(character) Defines the name of the configurations (which
must already be stored in ZooKeeper) to use for this collection. If not provided, Solr
will default to the collection name as the configuration name. Default: |
replicationFactor |
(integer) The number of replicas to be created for each shard. Default: 1 |
router.name |
(character) The router name that will be used. The router defines
how documents will be distributed among the shards. The value can be either |
shards |
(character) A comma separated list of shard names, e.g., shard-x,shard-y,shard-z . This is a required parameter when using the 'implicit' router. |
createNodeSet.shuffle |
(logical) Controls whether or not the shard-replicas created
for this collection will be assigned to the nodes specified by the createNodeSet in a
sequential manner, or if the list of nodes should be shuffled prior to creating individual
replicas. A 'false' value makes the results of a collection creation predictible and
gives more exact control over the location of the individual shard-replicas, but 'true'
can be a better choice for ensuring replicas are distributed evenly across nodes. Ignored
if createNodeSet is not also specified. Default: |
router.field |
(character) If this field is specified, the router will look at the value of the field in an input document to compute the hash and identify a shard instead of looking at the uniqueKey field. If the field specified is null in the document, the document will be rejected. Please note that RealTime Get or retrieval by id would also require the parameter route (or shard.keys) to avoid a distributed search. |
autoAddReplicas |
(logical) When set to true, enables auto addition of replicas on
shared file systems. See the section autoAddReplicas Settings for more details on settings
and overrides. Default: |
async |
(character) Request ID to track this action which will be processed asynchronously |
raw |
(logical) If |
callopts |
curl options passed on to crul::HttpClient |
... |
You can pass in parameters like |
## Not run: # connect (conn <- SolrClient$new()) if (!conn$collection_exists("helloWorld")) { conn$collection_create(name = "helloWorld") } if (!conn$collection_exists("tablesChairs")) { conn$collection_create(name = "tablesChairs") } ## End(Not run)## Not run: # connect (conn <- SolrClient$new()) if (!conn$collection_exists("helloWorld")) { conn$collection_create(name = "helloWorld") } if (!conn$collection_exists("tablesChairs")) { conn$collection_create(name = "tablesChairs") } ## End(Not run)
Create a new alias pointing to one or more collections. If an alias by the same name already exists, this action will replace the existing alias, effectively acting like an atomic "MOVE" command.
collection_createalias( conn, alias, collections, raw = FALSE, callopts = list() )collection_createalias( conn, alias, collections, raw = FALSE, callopts = list() )
conn |
A solrium connection object, see SolrClient |
alias |
(character) Required. The alias name to be created |
collections |
(character) Required. A character vector of collections to be aliased |
raw |
(logical) If |
callopts |
curl options passed on to |
## Not run: (conn <- SolrClient$new()) if (!conn$collection_exists("thingsstuff")) { conn$collection_create(name = "thingsstuff") } conn$collection_createalias("tstuff", "thingsstuff") conn$collection_clusterstatus()$cluster$collections$thingsstuff$aliases ## End(Not run)## Not run: (conn <- SolrClient$new()) if (!conn$collection_exists("thingsstuff")) { conn$collection_create(name = "thingsstuff") } conn$collection_createalias("tstuff", "thingsstuff") conn$collection_clusterstatus()$cluster$collections$thingsstuff$aliases ## End(Not run)
Create a shard
collection_createshard( conn, name, shard, createNodeSet = NULL, raw = FALSE, ... )collection_createshard( conn, name, shard, createNodeSet = NULL, raw = FALSE, ... )
conn |
A solrium connection object, see SolrClient |
name |
(character) The name of the core to be created. Required |
shard |
(character) Required. The name of the shard to be created. |
createNodeSet |
(character) Allows defining the nodes to spread the new collection across. If not provided, the CREATE operation will create shard-replica spread across all live Solr nodes. The format is a comma-separated list of node_names, such as localhost:8983_solr, localhost:8984_s olr, localhost:8985_solr. |
raw |
(logical) If |
... |
You can pass in parameters like |
## Not run: (conn <- SolrClient$new()) ## FIXME - doesn't work right now # conn$collection_create(name = "trees") # conn$collection_createshard(name = "trees", shard = "newshard") ## End(Not run)## Not run: (conn <- SolrClient$new()) ## FIXME - doesn't work right now # conn$collection_create(name = "trees") # conn$collection_createshard(name = "trees", shard = "newshard") ## End(Not run)
Add a collection
collection_delete(conn, name, raw = FALSE, callopts = list())collection_delete(conn, name, raw = FALSE, callopts = list())
conn |
A solrium connection object, see SolrClient |
name |
(character) The name of the core to be created. Required |
raw |
(logical) If |
callopts |
curl options passed on to |
## Not run: (conn <- SolrClient$new()) if (!conn$collection_exists("helloWorld")) { conn$collection_create(name = "helloWorld") } collection_delete(conn, name = "helloWorld") ## End(Not run)## Not run: (conn <- SolrClient$new()) if (!conn$collection_exists("helloWorld")) { conn$collection_create(name = "helloWorld") } collection_delete(conn, name = "helloWorld") ## End(Not run)
Delete a collection alias
collection_deletealias(conn, alias, raw = FALSE, callopts = list())collection_deletealias(conn, alias, raw = FALSE, callopts = list())
conn |
A solrium connection object, see SolrClient |
alias |
(character) Required. The alias name to be created |
raw |
(logical) If |
callopts |
curl options passed on to crul::HttpClient |
## Not run: (conn <- SolrClient$new()) if (!conn$collection_exists("thingsstuff")) { conn$collection_create(name = "thingsstuff") } conn$collection_createalias("tstuff", "thingsstuff") conn$collection_clusterstatus()$cluster$collections$thingsstuff$aliases # new alias conn$collection_deletealias("tstuff") conn$collection_clusterstatus()$cluster$collections$thingsstuff$aliases # gone ## End(Not run)## Not run: (conn <- SolrClient$new()) if (!conn$collection_exists("thingsstuff")) { conn$collection_create(name = "thingsstuff") } conn$collection_createalias("tstuff", "thingsstuff") conn$collection_clusterstatus()$cluster$collections$thingsstuff$aliases # new alias conn$collection_deletealias("tstuff") conn$collection_clusterstatus()$cluster$collections$thingsstuff$aliases # gone ## End(Not run)
Delete a replica from a given collection and shard. If the corresponding core is up and running the core is unloaded and the entry is removed from the clusterstate. If the node/core is down , the entry is taken off the clusterstate and if the core comes up later it is automatically unregistered.
collection_deletereplica( conn, name, shard = NULL, replica = NULL, onlyIfDown = FALSE, raw = FALSE, callopts = list(), ... )collection_deletereplica( conn, name, shard = NULL, replica = NULL, onlyIfDown = FALSE, raw = FALSE, callopts = list(), ... )
conn |
A solrium connection object, see SolrClient |
name |
(character) Required. The name of the collection. |
shard |
(character) Required. The name of the shard that includes the replica to be removed. |
replica |
(character) Required. The name of the replica to remove. |
onlyIfDown |
(logical) When |
raw |
(logical) If |
callopts |
curl options passed on to crul::HttpClient |
... |
You can pass in parameters like |
## Not run: (conn <- SolrClient$new()) # create collection if (!conn$collection_exists("foobar2")) { conn$collection_create(name = "foobar2", maxShardsPerNode = 2) } # status conn$collection_clusterstatus()$cluster$collections$foobar2$shards$shard1 # add replica conn$collection_addreplica(name = "foobar2", shard = "shard1") # delete replica ## get replica name nms <- names(conn$collection_clusterstatus()$cluster$collections$foobar2$shards$shard1$replicas) conn$collection_deletereplica(name = "foobar2", shard = "shard1", replica = nms[1]) # status again conn$collection_clusterstatus()$cluster$collections$foobar2$shards$shard1 ## End(Not run)## Not run: (conn <- SolrClient$new()) # create collection if (!conn$collection_exists("foobar2")) { conn$collection_create(name = "foobar2", maxShardsPerNode = 2) } # status conn$collection_clusterstatus()$cluster$collections$foobar2$shards$shard1 # add replica conn$collection_addreplica(name = "foobar2", shard = "shard1") # delete replica ## get replica name nms <- names(conn$collection_clusterstatus()$cluster$collections$foobar2$shards$shard1$replicas) conn$collection_deletereplica(name = "foobar2", shard = "shard1", replica = nms[1]) # status again conn$collection_clusterstatus()$cluster$collections$foobar2$shards$shard1 ## End(Not run)
Deletes an arbitrary property from a particular replica.
collection_deletereplicaprop( conn, name, shard, replica, property, raw = FALSE, callopts = list() )collection_deletereplicaprop( conn, name, shard, replica, property, raw = FALSE, callopts = list() )
conn |
A solrium connection object, see SolrClient |
name |
(character) The name of the core to be created. Required |
shard |
(character) Required. The name of the shard the replica belongs to. |
replica |
(character) Required. The replica, e.g. core_node1. |
property |
(character) Required. The property to delete. Note: this
will have the literal 'property.' prepended to distinguish it from
system-maintained properties. So these two forms are equivalent:
|
raw |
(logical) If |
callopts |
curl options passed on to crul::HttpClient |
## Not run: (conn <- SolrClient$new()) # create collection if (!conn$collection_exists("deleterep")) { conn$collection_create(name = "deleterep") # OR bin/solr create -c deleterep } # status conn$collection_clusterstatus()$cluster$collections$deleterep$shards # add the value bar to the property foo conn$collection_addreplicaprop(name = "deleterep", shard = "shard1", replica = "core_node1", property = "foo", property.value = "bar") # check status conn$collection_clusterstatus()$cluster$collections$deleterep$shards conn$collection_clusterstatus()$cluster$collections$deleterep$shards$shard1$replicas$core_node1 # delete replica property conn$collection_deletereplicaprop(name = "deleterep", shard = "shard1", replica = "core_node1", property = "foo") # check status - foo should be gone conn$collection_clusterstatus()$cluster$collections$deleterep$shards$shard1$replicas$core_node1 ## End(Not run)## Not run: (conn <- SolrClient$new()) # create collection if (!conn$collection_exists("deleterep")) { conn$collection_create(name = "deleterep") # OR bin/solr create -c deleterep } # status conn$collection_clusterstatus()$cluster$collections$deleterep$shards # add the value bar to the property foo conn$collection_addreplicaprop(name = "deleterep", shard = "shard1", replica = "core_node1", property = "foo", property.value = "bar") # check status conn$collection_clusterstatus()$cluster$collections$deleterep$shards conn$collection_clusterstatus()$cluster$collections$deleterep$shards$shard1$replicas$core_node1 # delete replica property conn$collection_deletereplicaprop(name = "deleterep", shard = "shard1", replica = "core_node1", property = "foo") # check status - foo should be gone conn$collection_clusterstatus()$cluster$collections$deleterep$shards$shard1$replicas$core_node1 ## End(Not run)
Deleting a shard will unload all replicas of the shard and remove them from clusterstate.json. It will only remove shards that are inactive, or which have no range given for custom sharding.
collection_deleteshard(conn, name, shard, raw = FALSE, ...)collection_deleteshard(conn, name, shard, raw = FALSE, ...)
conn |
A solrium connection object, see SolrClient |
name |
(character) Required. The name of the collection that includes the shard to be deleted |
shard |
(character) Required. The name of the shard to be deleted |
raw |
(logical) If |
... |
curl options passed on to crul::HttpClient |
## Not run: (conn <- SolrClient$new()) # create collection if (!conn$collection_exists("buffalo")) { conn$collection_create(name = "buffalo") # OR: bin/solr create -c buffalo } # find shard names names(conn$collection_clusterstatus()$cluster$collections$buffalo$shards) # split a shard by name collection_splitshard(conn, name = "buffalo", shard = "shard1") # now we have three shards names(conn$collection_clusterstatus()$cluster$collections$buffalo$shards) # delete shard conn$collection_deleteshard(name = "buffalo", shard = "shard1_1") ## End(Not run)## Not run: (conn <- SolrClient$new()) # create collection if (!conn$collection_exists("buffalo")) { conn$collection_create(name = "buffalo") # OR: bin/solr create -c buffalo } # find shard names names(conn$collection_clusterstatus()$cluster$collections$buffalo$shards) # split a shard by name collection_splitshard(conn, name = "buffalo", shard = "shard1") # now we have three shards names(conn$collection_clusterstatus()$cluster$collections$buffalo$shards) # delete shard conn$collection_deleteshard(name = "buffalo", shard = "shard1_1") ## End(Not run)
Check if a collection exists
collection_exists(conn, name, ...)collection_exists(conn, name, ...)
conn |
A solrium connection object, see SolrClient |
name |
(character) The name of the core. If not given, all cores. |
... |
curl options passed on to crul::HttpClient |
Simply calls collection_list() internally
A single boolean, TRUE or FALSE
## Not run: # start Solr with Cloud mode via the schemaless eg: bin/solr -e cloud # you can create a new core like: bin/solr create -c corename # where <corename> is the name for your core - or creaate as below (conn <- SolrClient$new()) # exists conn$collection_exists("gettingstarted") # doesn't exist conn$collection_exists("hhhhhh") ## End(Not run)## Not run: # start Solr with Cloud mode via the schemaless eg: bin/solr -e cloud # you can create a new core like: bin/solr create -c corename # where <corename> is the name for your core - or creaate as below (conn <- SolrClient$new()) # exists conn$collection_exists("gettingstarted") # doesn't exist conn$collection_exists("hhhhhh") ## End(Not run)
List collections
collection_list(conn, raw = FALSE, ...)collection_list(conn, raw = FALSE, ...)
conn |
A solrium connection object, see SolrClient |
raw |
(logical) If |
... |
curl options passed on to crul::HttpClient |
## Not run: (conn <- SolrClient$new()) conn$collection_list() conn$collection_list()$collections collection_list(conn) ## End(Not run)## Not run: (conn <- SolrClient$new()) conn$collection_list() conn$collection_list()$collections collection_list(conn) ## End(Not run)
Migrate documents to another collection
collection_migrate( conn, name, target.collection, split.key, forward.timeout = NULL, async = NULL, raw = FALSE, callopts = list() )collection_migrate( conn, name, target.collection, split.key, forward.timeout = NULL, async = NULL, raw = FALSE, callopts = list() )
conn |
A solrium connection object, see SolrClient |
name |
(character) The name of the core to be created. Required |
target.collection |
(character) Required. The name of the target collection to which documents will be migrated |
split.key |
(character) Required. The routing key prefix. For example, if uniqueKey is a!123, then you would use split.key=a! |
forward.timeout |
(integer) The timeout (seconds), until which write requests
made to the source collection for the given |
async |
(character) Request ID to track this action which will be processed asynchronously |
raw |
(logical) If |
callopts |
curl options passed on to crul::HttpClient |
## Not run: (conn <- SolrClient$new()) # create collection if (!conn$collection_exists("migrate_from")) { conn$collection_create(name = "migrate_from") # OR: bin/solr create -c migrate_from } # create another collection if (!conn$collection_exists("migrate_to")) { conn$collection_create(name = "migrate_to") # OR bin/solr create -c migrate_to } # add some documents file <- system.file("examples", "books.csv", package = "solrium") x <- read.csv(file, stringsAsFactors = FALSE) conn$add(x, "migrate_from") # migrate some documents from one collection to the other ## FIXME - not sure if this is actually working.... # conn$collection_migrate("migrate_from", "migrate_to", split.key = "05535") ## End(Not run)## Not run: (conn <- SolrClient$new()) # create collection if (!conn$collection_exists("migrate_from")) { conn$collection_create(name = "migrate_from") # OR: bin/solr create -c migrate_from } # create another collection if (!conn$collection_exists("migrate_to")) { conn$collection_create(name = "migrate_to") # OR bin/solr create -c migrate_to } # add some documents file <- system.file("examples", "books.csv", package = "solrium") x <- read.csv(file, stringsAsFactors = FALSE) conn$add(x, "migrate_from") # migrate some documents from one collection to the other ## FIXME - not sure if this is actually working.... # conn$collection_migrate("migrate_from", "migrate_to", split.key = "05535") ## End(Not run)
Returns the current status of the overseer, performance statistics of various overseer APIs as well as last 10 failures per operation type.
collection_overseerstatus(conn, raw = FALSE, ...)collection_overseerstatus(conn, raw = FALSE, ...)
conn |
A solrium connection object, see SolrClient |
raw |
(logical) If |
... |
You can pass in parameters like |
## Not run: (conn <- SolrClient$new()) conn$collection_overseerstatus() res <- conn$collection_overseerstatus() res$responseHeader res$leader res$overseer_queue_size res$overseer_work_queue_size res$overseer_operations res$collection_operations res$overseer_queue res$overseer_internal_queue res$collection_queue ## End(Not run)## Not run: (conn <- SolrClient$new()) conn$collection_overseerstatus() res <- conn$collection_overseerstatus() res$responseHeader res$leader res$overseer_queue_size res$overseer_work_queue_size res$overseer_operations res$collection_operations res$overseer_queue res$overseer_internal_queue res$collection_queue ## End(Not run)
Reassign leaders in a collection according to the preferredLeader property across active nodes
collection_rebalanceleaders( conn, name, maxAtOnce = NULL, maxWaitSeconds = NULL, raw = FALSE, ... )collection_rebalanceleaders( conn, name, maxAtOnce = NULL, maxWaitSeconds = NULL, raw = FALSE, ... )
conn |
A solrium connection object, see SolrClient |
name |
(character) The name of the core to be created. Required |
maxAtOnce |
(integer) The maximum number of reassignments to have queue up at once. Values <=0 are use the default value Integer.MAX_VALUE. When this number is reached, the process waits for one or more leaders to be successfully assigned before adding more to the queue. |
maxWaitSeconds |
(integer) Timeout value when waiting for leaders to be reassigned. NOTE: if maxAtOnce is less than the number of reassignments that will take place, this is the maximum interval that any single wait for at least one reassignment. For example, if 10 reassignments are to take place and maxAtOnce is 1 and maxWaitSeconds is 60, the upper bound on the time that the command may wait is 10 minutes. Default: 60 |
raw |
(logical) If |
... |
You can pass in parameters like |
## Not run: (conn <- SolrClient$new()) # create collection if (!conn$collection_exists("mycollection2")) { conn$collection_create(name = "mycollection2") # OR: bin/solr create -c mycollection2 } # balance preferredLeader property conn$collection_balanceshardunique("mycollection2", property = "preferredLeader") # balance preferredLeader property conn$collection_rebalanceleaders("mycollection2") # examine cluster status conn$collection_clusterstatus()$cluster$collections$mycollection2 ## End(Not run)## Not run: (conn <- SolrClient$new()) # create collection if (!conn$collection_exists("mycollection2")) { conn$collection_create(name = "mycollection2") # OR: bin/solr create -c mycollection2 } # balance preferredLeader property conn$collection_balanceshardunique("mycollection2", property = "preferredLeader") # balance preferredLeader property conn$collection_rebalanceleaders("mycollection2") # examine cluster status conn$collection_clusterstatus()$cluster$collections$mycollection2 ## End(Not run)
Reload a collection
collection_reload(conn, name, raw = FALSE, callopts)collection_reload(conn, name, raw = FALSE, callopts)
conn |
A solrium connection object, see SolrClient |
name |
(character) The name of the core to be created. Required |
raw |
(logical) If |
callopts |
curl options passed on to crul::HttpClient |
## Not run: (conn <- SolrClient$new()) if (!conn$collection_exists("helloWorld")) { conn$collection_create(name = "helloWorld") } conn$collection_reload(name = "helloWorld") ## End(Not run)## Not run: (conn <- SolrClient$new()) if (!conn$collection_exists("helloWorld")) { conn$collection_create(name = "helloWorld") } conn$collection_reload(name = "helloWorld") ## End(Not run)
Remove an assigned role. This API is used to undo the roles
assigned using collection_addrole
collection_removerole(conn, role = "overseer", node, raw = FALSE, ...)collection_removerole(conn, role = "overseer", node, raw = FALSE, ...)
conn |
A solrium connection object, see SolrClient |
role |
(character) Required. The name of the role. The only supported role as of now is overseer (set as default). |
node |
(character) Required. The name of the node. |
raw |
(logical) If |
... |
You can pass in parameters like |
## Not run: (conn <- SolrClient$new()) # get list of nodes nodes <- conn$collection_clusterstatus()$cluster$live_nodes conn$collection_addrole(node = nodes[1]) conn$collection_removerole(node = nodes[1]) ## End(Not run)## Not run: (conn <- SolrClient$new()) # get list of nodes nodes <- conn$collection_clusterstatus()$cluster$live_nodes conn$collection_addrole(node = nodes[1]) conn$collection_removerole(node = nodes[1]) ## End(Not run)
Request the status of an already submitted Asynchronous Collection API call. This call is also used to clear up the stored statuses.
collection_requeststatus(conn, requestid, raw = FALSE, ...)collection_requeststatus(conn, requestid, raw = FALSE, ...)
conn |
A solrium connection object, see SolrClient |
requestid |
(character) Required. The user defined request-id for the
request. This can be used to track the status of the submitted asynchronous
task. |
raw |
(logical) If |
... |
You can pass in parameters like |
Create a shard
collection_splitshard( conn, name, shard, ranges = NULL, split.key = NULL, async = NULL, raw = FALSE, callopts = list() )collection_splitshard( conn, name, shard, ranges = NULL, split.key = NULL, async = NULL, raw = FALSE, callopts = list() )
conn |
A solrium connection object, see SolrClient |
name |
(character) The name of the core to be created. Required |
shard |
(character) Required. The name of the shard to be split |
ranges |
(character) A comma-separated list of hash ranges in hexadecimal e.g. ranges=0-1f4,1f5-3e8,3e9-5dc |
split.key |
(character) The key to use for splitting the index |
async |
(character) Request ID to track this action which will be processed asynchronously |
raw |
(logical) If |
callopts |
curl options passed on to crul::HttpClient |
## Not run: (conn <- SolrClient$new()) # create collection if (!conn$collection_exists("trees")) { conn$collection_create("trees") } # find shard names names(conn$collection_clusterstatus()$cluster$collections$trees$shards) # split a shard by name conn$collection_splitshard(name = "trees", shard = "shard1") # now we have three shards names(conn$collection_clusterstatus()$cluster$collections$trees$shards) ## End(Not run)## Not run: (conn <- SolrClient$new()) # create collection if (!conn$collection_exists("trees")) { conn$collection_create("trees") } # find shard names names(conn$collection_clusterstatus()$cluster$collections$trees$shards) # split a shard by name conn$collection_splitshard(name = "trees", shard = "shard1") # now we have three shards names(conn$collection_clusterstatus()$cluster$collections$trees$shards) ## End(Not run)
List collections or cores
collections(conn, ...) cores(conn, ...)collections(conn, ...) cores(conn, ...)
conn |
A solrium connection object, see SolrClient |
... |
curl options passed on to crul::HttpClient |
Calls collection_list() or core_status() internally,
and parses out names for you.
character vector
## Not run: # connect (conn <- SolrClient$new()) # list collections conn$collection_list() collections(conn) # list cores conn$core_status() cores(conn) ## End(Not run)## Not run: # connect (conn <- SolrClient$new()) # list collections conn$collection_list() collections(conn) # list cores conn$core_status() cores(conn) ## End(Not run)
Commit
commit( conn, name, expunge_deletes = FALSE, wait_searcher = TRUE, soft_commit = FALSE, wt = "json", raw = FALSE, ... )commit( conn, name, expunge_deletes = FALSE, wait_searcher = TRUE, soft_commit = FALSE, wt = "json", raw = FALSE, ... )
conn |
A solrium connection object, see SolrClient |
name |
(character) A collection or core name. Required. |
expunge_deletes |
merge segments with deletes away. Default: |
wait_searcher |
block until a new searcher is opened and registered as
the main query searcher, making the changes visible. Default: |
soft_commit |
perform a soft commit - this will refresh the 'view' of
the index in a more performant manner, but without "on-disk" guarantees.
Default: |
wt |
(character) One of json (default) or xml. If json, uses
|
raw |
(logical) If |
... |
curl options passed on to crul::HttpClient |
<>
## Not run: (conn <- SolrClient$new()) conn$commit("gettingstarted") conn$commit("gettingstarted", wait_searcher = FALSE) # get xml back conn$commit("gettingstarted", wt = "xml") ## raw xml conn$commit("gettingstarted", wt = "xml", raw = TRUE) ## End(Not run)## Not run: (conn <- SolrClient$new()) conn$commit("gettingstarted") conn$commit("gettingstarted", wait_searcher = FALSE) # get xml back conn$commit("gettingstarted", wt = "xml") ## raw xml conn$commit("gettingstarted", wt = "xml", raw = TRUE) ## End(Not run)
Get Solr configuration details
config_get(conn, name, what = NULL, wt = "json", raw = FALSE, ...)config_get(conn, name, what = NULL, wt = "json", raw = FALSE, ...)
conn |
A solrium connection object, see SolrClient |
name |
(character) The name of the core. If not given, all cores. |
what |
(character) What you want to look at. One of solrconfig or schema. Default: solrconfig |
wt |
(character) One of json (default) or xml. Data type returned.
If json, uses |
raw |
(logical) If |
... |
curl options passed on to crul::HttpClient |
Note that if raw=TRUE, what is ignored. That is,
you get all the data when raw=TRUE.
A list, xml_document, or character
## Not run: # start Solr with Cloud mode via the schemaless eg: bin/solr -e cloud # you can create a new core like: bin/solr create -c corename # where <corename> is the name for your core - or creaate as below # connect (conn <- SolrClient$new()) # all config settings conn$config_get("gettingstarted") # just znodeVersion conn$config_get("gettingstarted", "znodeVersion") # just znodeVersion conn$config_get("gettingstarted", "luceneMatchVersion") # just updateHandler conn$config_get("gettingstarted", "updateHandler") # just updateHandler conn$config_get("gettingstarted", "requestHandler") ## Get XML conn$config_get("gettingstarted", wt = "xml") conn$config_get("gettingstarted", "updateHandler", wt = "xml") conn$config_get("gettingstarted", "requestHandler", wt = "xml") ## Raw data - what param ignored when raw=TRUE conn$config_get("gettingstarted", raw = TRUE) ## End(Not run)## Not run: # start Solr with Cloud mode via the schemaless eg: bin/solr -e cloud # you can create a new core like: bin/solr create -c corename # where <corename> is the name for your core - or creaate as below # connect (conn <- SolrClient$new()) # all config settings conn$config_get("gettingstarted") # just znodeVersion conn$config_get("gettingstarted", "znodeVersion") # just znodeVersion conn$config_get("gettingstarted", "luceneMatchVersion") # just updateHandler conn$config_get("gettingstarted", "updateHandler") # just updateHandler conn$config_get("gettingstarted", "requestHandler") ## Get XML conn$config_get("gettingstarted", wt = "xml") conn$config_get("gettingstarted", "updateHandler", wt = "xml") conn$config_get("gettingstarted", "requestHandler", wt = "xml") ## Raw data - what param ignored when raw=TRUE conn$config_get("gettingstarted", raw = TRUE) ## End(Not run)
Get Solr configuration overlay
config_overlay(conn, name, omitHeader = FALSE, ...)config_overlay(conn, name, omitHeader = FALSE, ...)
conn |
A solrium connection object, see SolrClient |
name |
(character) The name of the core. If not given, all cores. |
omitHeader |
(logical) If |
... |
curl options passed on to crul::HttpClient |
A list with response from server
## Not run: # start Solr with Cloud mode via the schemaless eg: bin/solr -e cloud # you can create a new core like: bin/solr create -c corename # where <corename> is the name for your core - or creaate as below # connect (conn <- SolrClient$new()) # get config overlay conn$config_overlay("gettingstarted") # without header conn$config_overlay("gettingstarted", omitHeader = TRUE) ## End(Not run)## Not run: # start Solr with Cloud mode via the schemaless eg: bin/solr -e cloud # you can create a new core like: bin/solr create -c corename # where <corename> is the name for your core - or creaate as below # connect (conn <- SolrClient$new()) # get config overlay conn$config_overlay("gettingstarted") # without header conn$config_overlay("gettingstarted", omitHeader = TRUE) ## End(Not run)
Set Solr configuration params
config_params( conn, name, param = NULL, set = NULL, unset = NULL, update = NULL, ... )config_params( conn, name, param = NULL, set = NULL, unset = NULL, update = NULL, ... )
conn |
A solrium connection object, see SolrClient |
name |
(character) The name of the core. If not given, all cores. |
param |
(character) Name of a parameter |
set |
(list) List of key:value pairs of what to set. Create or overwrite a parameter set map. Default: NULL (nothing passed) |
unset |
(list) One or more character strings of keys to unset.
Default: |
update |
(list) List of key:value pairs of what to update. Updates a parameter set map. This essentially overwrites the old parameter set, so all parameters must be sent in each update request. |
... |
curl options passed on to crul::HttpClient |
The Request Parameters API allows creating parameter sets that can override or take the place of parameters defined in solrconfig.xml. It is really another endpoint of the Config API instead of a separate API, and has distinct commands. It does not replace or modify any sections of solrconfig.xml, but instead provides another approach to handling parameters used in requests. It behaves in the same way as the Config API, by storing parameters in another file that will be used at runtime. In this case, the parameters are stored in a file named params.json. This file is kept in ZooKeeper or in the conf directory of a standalone Solr instance.
A list with response from server
## Not run: # start Solr in standard or Cloud mode # connect (conn <- SolrClient$new()) # set a parameter set myFacets <- list(myFacets = list(facet = TRUE, facet.limit = 5)) config_params(conn, "gettingstarted", set = myFacets) # check a parameter config_params(conn, "gettingstarted", param = "myFacets") ## End(Not run)## Not run: # start Solr in standard or Cloud mode # connect (conn <- SolrClient$new()) # set a parameter set myFacets <- list(myFacets = list(facet = TRUE, facet.limit = 5)) config_params(conn, "gettingstarted", set = myFacets) # check a parameter config_params(conn, "gettingstarted", param = "myFacets") ## End(Not run)
Set Solr configuration details
config_set(conn, name, set = NULL, unset = NULL, ...)config_set(conn, name, set = NULL, unset = NULL, ...)
conn |
A solrium connection object, see SolrClient |
name |
(character) The name of the core. If not given, all cores. |
set |
(list) List of key:value pairs of what to set.
Default: |
unset |
(list) One or more character strings of keys to unset.
Default: |
... |
curl options passed on to crul::HttpClient |
A list with response from server
## Not run: # start Solr with Cloud mode via the schemaless eg: bin/solr -e cloud # you can create a new core like: bin/solr create -c corename # where <corename> is the name for your core - or creaate as below # connect (conn <- SolrClient$new()) # set a property conn$config_set("gettingstarted", set = list(query.filterCache.autowarmCount = 1000)) # unset a property conn$config_set("gettingstarted", unset = "query.filterCache.size", verbose = TRUE) # many properties conn$config_set("gettingstarted", set = list( query.filterCache.autowarmCount = 1000, query.commitWithin.softCommit = 'false' ) ) ## End(Not run)## Not run: # start Solr with Cloud mode via the schemaless eg: bin/solr -e cloud # you can create a new core like: bin/solr create -c corename # where <corename> is the name for your core - or creaate as below # connect (conn <- SolrClient$new()) # set a property conn$config_set("gettingstarted", set = list(query.filterCache.autowarmCount = 1000)) # unset a property conn$config_set("gettingstarted", unset = "query.filterCache.size", verbose = TRUE) # many properties conn$config_set("gettingstarted", set = list( query.filterCache.autowarmCount = 1000, query.commitWithin.softCommit = 'false' ) ) ## End(Not run)
Create a core
core_create( conn, name, instanceDir = NULL, config = NULL, schema = NULL, dataDir = NULL, configSet = NULL, collection = NULL, shard = NULL, async = NULL, raw = FALSE, callopts = list(), ... )core_create( conn, name, instanceDir = NULL, config = NULL, schema = NULL, dataDir = NULL, configSet = NULL, collection = NULL, shard = NULL, async = NULL, raw = FALSE, callopts = list(), ... )
conn |
A solrium connection object, see SolrClient |
name |
(character) The name of the core to be created. Required |
instanceDir |
(character) Path to instance directory |
config |
(character) Path to config file |
schema |
(character) Path to schema file |
dataDir |
(character) Name of the data directory relative to instanceDir. |
configSet |
(character) Name of the configset to use for this core. For more information, see https://lucene.apache.org/solr/guide/6_6/config-sets.html |
collection |
(character) The name of the collection to which this core
belongs. The default is the name of the |
shard |
(character) The shard id this core represents. Normally you want to be auto-assigned a shard id. |
async |
(character) Request ID to track this action which will be processed asynchronously |
raw |
(logical) If |
callopts |
curl options passed on to crul::HttpClient |
... |
You can pass in parameters like |
## Not run: # start Solr with Schemaless mode via the schemaless eg: # bin/solr start -e schemaless # you can create a new core like: bin/solr create -c corename # where <corename> is the name for your core - or create as below # connect (conn <- SolrClient$new()) # Create a core path <- "~/solr-8.2.0/server/solr/newcore/conf" dir.create(path, recursive = TRUE) files <- list.files("~/solr-8.2.0/server/solr/configsets/sample_techproducts_configs/conf/", full.names = TRUE) invisible(file.copy(files, path, recursive = TRUE)) conn$core_create(name = "newcore", instanceDir = "newcore", configSet = "sample_techproducts_configs") ## End(Not run)## Not run: # start Solr with Schemaless mode via the schemaless eg: # bin/solr start -e schemaless # you can create a new core like: bin/solr create -c corename # where <corename> is the name for your core - or create as below # connect (conn <- SolrClient$new()) # Create a core path <- "~/solr-8.2.0/server/solr/newcore/conf" dir.create(path, recursive = TRUE) files <- list.files("~/solr-8.2.0/server/solr/configsets/sample_techproducts_configs/conf/", full.names = TRUE) invisible(file.copy(files, path, recursive = TRUE)) conn$core_create(name = "newcore", instanceDir = "newcore", configSet = "sample_techproducts_configs") ## End(Not run)
Check if a core exists
core_exists(conn, name, callopts = list())core_exists(conn, name, callopts = list())
conn |
A solrium connection object, see SolrClient |
name |
(character) The name of the core to be created. Required |
callopts |
curl options passed on to crul::HttpClient |
Simply calls core_status() internally
A single boolean, TRUE or FALSE
## Not run: # start Solr with Schemaless mode via the schemaless eg: # bin/solr start -e schemaless # you can create a new core like: bin/solr create -c corename # where <corename> is the name for your core - or create as below # connect (conn <- SolrClient$new()) # exists conn$core_exists("gettingstarted") # doesn't exist conn$core_exists("hhhhhh") ## End(Not run)## Not run: # start Solr with Schemaless mode via the schemaless eg: # bin/solr start -e schemaless # you can create a new core like: bin/solr create -c corename # where <corename> is the name for your core - or create as below # connect (conn <- SolrClient$new()) # exists conn$core_exists("gettingstarted") # doesn't exist conn$core_exists("hhhhhh") ## End(Not run)
Merges one or more indexes to another index. The indexes must have completed commits, and should be locked against writes until the merge is complete or the resulting merged index may become corrupted. The target core index must already exist and have a compatible schema with the one or more indexes that will be merged to it.
core_mergeindexes( conn, name, indexDir = NULL, srcCore = NULL, async = NULL, raw = FALSE, callopts = list() )core_mergeindexes( conn, name, indexDir = NULL, srcCore = NULL, async = NULL, raw = FALSE, callopts = list() )
conn |
A solrium connection object, see SolrClient |
name |
(character) The name of the core to be created. Required |
indexDir |
(character) Multi-valued, directories that would be merged. |
srcCore |
(character) Multi-valued, source cores that would be merged. |
async |
(character) Request ID to track this action which will be processed asynchronously |
raw |
(logical) If |
callopts |
curl options passed on to crul::HttpClient |
## Not run: # start Solr with Schemaless mode via the schemaless eg: # bin/solr start -e schemaless # connect (conn <- SolrClient$new()) ## FIXME: not tested yet # use indexDir parameter # conn$core_mergeindexes(core="new_core_name", # indexDir = c("/solr_home/core1/data/index", # "/solr_home/core2/data/index")) # use srcCore parameter # conn$core_mergeindexes(name = "new_core_name", srcCore = c('core1', 'core2')) ## End(Not run)## Not run: # start Solr with Schemaless mode via the schemaless eg: # bin/solr start -e schemaless # connect (conn <- SolrClient$new()) ## FIXME: not tested yet # use indexDir parameter # conn$core_mergeindexes(core="new_core_name", # indexDir = c("/solr_home/core1/data/index", # "/solr_home/core2/data/index")) # use srcCore parameter # conn$core_mergeindexes(name = "new_core_name", srcCore = c('core1', 'core2')) ## End(Not run)
Reload a core
core_reload(conn, name, raw = FALSE, callopts = list())core_reload(conn, name, raw = FALSE, callopts = list())
conn |
A solrium connection object, see SolrClient |
name |
(character) The name of the core to be created. Required |
raw |
(logical) If |
callopts |
curl options passed on to crul::HttpClient |
## Not run: # start Solr with Schemaless mode via the schemaless eg: # bin/solr start -e schemaless # you can create a new core like: bin/solr create -c corename # where <corename> is the name for your core - or creaate as below # connect (conn <- SolrClient$new()) # Status of particular cores if (conn$core_exists("gettingstarted")) { conn$core_reload("gettingstarted") conn$core_status("gettingstarted") } ## End(Not run)## Not run: # start Solr with Schemaless mode via the schemaless eg: # bin/solr start -e schemaless # you can create a new core like: bin/solr create -c corename # where <corename> is the name for your core - or creaate as below # connect (conn <- SolrClient$new()) # Status of particular cores if (conn$core_exists("gettingstarted")) { conn$core_reload("gettingstarted") conn$core_status("gettingstarted") } ## End(Not run)
Rename a core
core_rename(conn, name, other, async = NULL, raw = FALSE, callopts = list())core_rename(conn, name, other, async = NULL, raw = FALSE, callopts = list())
conn |
A solrium connection object, see SolrClient |
name |
(character) The name of the core to be created. Required |
other |
(character) The new name of the core. Required. |
async |
(character) Request ID to track this action which will be processed asynchronously |
raw |
(logical) If |
callopts |
curl options passed on to crul::HttpClient |
## Not run: # start Solr with Schemaless mode via the schemaless eg: # bin/solr start -e schemaless # you can create a new core like: bin/solr create -c corename # where <corename> is the name for your core - or creaate as below # connect (conn <- SolrClient$new()) # Status of particular cores path <- "~/solr-8.2.0/server/solr/testcore/conf" dir.create(path, recursive = TRUE) files <- list.files( "~/solr-8.2.0/server/solr/configsets/sample_techproducts_configs/conf/", full.names = TRUE) invisible(file.copy(files, path, recursive = TRUE)) conn$core_create("testcore") # or create in CLI: bin/solr create -c testcore # rename conn$core_rename("testcore", "newtestcore") ## status conn$core_status("testcore") # core missing conn$core_status("newtestcore", FALSE) # not missing # cleanup conn$core_unload("newtestcore") ## End(Not run)## Not run: # start Solr with Schemaless mode via the schemaless eg: # bin/solr start -e schemaless # you can create a new core like: bin/solr create -c corename # where <corename> is the name for your core - or creaate as below # connect (conn <- SolrClient$new()) # Status of particular cores path <- "~/solr-8.2.0/server/solr/testcore/conf" dir.create(path, recursive = TRUE) files <- list.files( "~/solr-8.2.0/server/solr/configsets/sample_techproducts_configs/conf/", full.names = TRUE) invisible(file.copy(files, path, recursive = TRUE)) conn$core_create("testcore") # or create in CLI: bin/solr create -c testcore # rename conn$core_rename("testcore", "newtestcore") ## status conn$core_status("testcore") # core missing conn$core_status("newtestcore", FALSE) # not missing # cleanup conn$core_unload("newtestcore") ## End(Not run)
Request status of asynchronous CoreAdmin API call
core_requeststatus(conn, requestid, raw = FALSE, callopts = list())core_requeststatus(conn, requestid, raw = FALSE, callopts = list())
conn |
A solrium connection object, see SolrClient |
requestid |
The name of one of the cores to be removed. Required |
raw |
(logical) If |
callopts |
curl options passed on to crul::HttpClient |
## Not run: # start Solr with Schemaless mode via the schemaless eg: # bin/solr start -e schemaless # FIXME: not tested yet... # (conn <- SolrClient$new()) # conn$core_requeststatus(requestid = 1) ## End(Not run)## Not run: # start Solr with Schemaless mode via the schemaless eg: # bin/solr start -e schemaless # FIXME: not tested yet... # (conn <- SolrClient$new()) # conn$core_requeststatus(requestid = 1) ## End(Not run)
SPLIT splits an index into two or more indexes. The index being split can continue to handle requests. The split pieces can be placed into a specified directory on the server's filesystem or it can be merged into running Solr cores.
core_split( conn, name, path = NULL, targetCore = NULL, ranges = NULL, split.key = NULL, async = NULL, raw = FALSE, callopts = list() )core_split( conn, name, path = NULL, targetCore = NULL, ranges = NULL, split.key = NULL, async = NULL, raw = FALSE, callopts = list() )
conn |
A solrium connection object, see SolrClient |
name |
(character) The name of the core to be created. Required |
path |
(character) Two or more target directory paths in which a piece of the index will be written |
targetCore |
(character) Two or more target Solr cores to which a piece of the index will be merged |
ranges |
(character) A list of number ranges, or hash ranges in hexadecimal format. If numbers, they get converted to hexidecimal format before being passed to your Solr server. |
split.key |
(character) The key to be used for splitting the index |
async |
(character) Request ID to track this action which will be processed asynchronously |
raw |
(logical) If |
callopts |
curl options passed on to crul::HttpClient |
The core index will be split into as many pieces as the number of
path or targetCore parameters.
Either path or targetCore parameter must be specified but not
both. The ranges and split.key parameters are optional and only one of
the two should be specified, if at all required.
## Not run: # start Solr with Schemaless mode via the schemaless eg: bin/solr start -e schemaless # you can create a new core like: bin/solr create -c corename # where <corename> is the name for your core - or creaate as below # connect (conn <- SolrClient$new()) # Swap a core ## First, create two cores # conn$core_split("splitcoretest0") # or create in the CLI: bin/solr create -c splitcoretest0 # conn$core_split("splitcoretest1") # or create in the CLI: bin/solr create -c splitcoretest1 # conn$core_split("splitcoretest2") # or create in the CLI: bin/solr create -c splitcoretest2 ## check status conn$core_status("splitcoretest0", FALSE) conn$core_status("splitcoretest1", FALSE) conn$core_status("splitcoretest2", FALSE) ## split core using targetCore parameter conn$core_split("splitcoretest0", targetCore = c("splitcoretest1", "splitcoretest2")) ## split core using split.key parameter ### Here all documents having the same route key as the split.key i.e. 'A!' ### will be split from the core index and written to the targetCore conn$core_split("splitcoretest0", targetCore = "splitcoretest1", split.key = "A!") ## split core using ranges parameter ### Solr expects hash ranges in hexidecimal, but since we're in R, ### let's not make our lives any harder, so you can pass in numbers ### but you can still pass in hexidecimal if you want. rgs <- c('0-1f4', '1f5-3e8') conn$core_split("splitcoretest0", targetCore = c("splitcoretest1", "splitcoretest2"), ranges = rgs) rgs <- list(c(0, 500), c(501, 1000)) conn$core_split("splitcoretest0", targetCore = c("splitcoretest1", "splitcoretest2"), ranges = rgs) ## End(Not run)## Not run: # start Solr with Schemaless mode via the schemaless eg: bin/solr start -e schemaless # you can create a new core like: bin/solr create -c corename # where <corename> is the name for your core - or creaate as below # connect (conn <- SolrClient$new()) # Swap a core ## First, create two cores # conn$core_split("splitcoretest0") # or create in the CLI: bin/solr create -c splitcoretest0 # conn$core_split("splitcoretest1") # or create in the CLI: bin/solr create -c splitcoretest1 # conn$core_split("splitcoretest2") # or create in the CLI: bin/solr create -c splitcoretest2 ## check status conn$core_status("splitcoretest0", FALSE) conn$core_status("splitcoretest1", FALSE) conn$core_status("splitcoretest2", FALSE) ## split core using targetCore parameter conn$core_split("splitcoretest0", targetCore = c("splitcoretest1", "splitcoretest2")) ## split core using split.key parameter ### Here all documents having the same route key as the split.key i.e. 'A!' ### will be split from the core index and written to the targetCore conn$core_split("splitcoretest0", targetCore = "splitcoretest1", split.key = "A!") ## split core using ranges parameter ### Solr expects hash ranges in hexidecimal, but since we're in R, ### let's not make our lives any harder, so you can pass in numbers ### but you can still pass in hexidecimal if you want. rgs <- c('0-1f4', '1f5-3e8') conn$core_split("splitcoretest0", targetCore = c("splitcoretest1", "splitcoretest2"), ranges = rgs) rgs <- list(c(0, 500), c(501, 1000)) conn$core_split("splitcoretest0", targetCore = c("splitcoretest1", "splitcoretest2"), ranges = rgs) ## End(Not run)
Get core status
core_status( conn, name = NULL, indexInfo = TRUE, raw = FALSE, callopts = list() )core_status( conn, name = NULL, indexInfo = TRUE, raw = FALSE, callopts = list() )
conn |
A solrium connection object, see SolrClient |
name |
(character) The name of the core to be created. Required |
indexInfo |
(logical) |
raw |
(logical) If |
callopts |
curl options passed on to crul::HttpClient |
## Not run: # start Solr with Schemaless mode via the schemaless eg: # bin/solr start -e schemaless # you can create a new core like: bin/solr create -c corename # where <corename> is the name for your core - or creaate as below # connect (conn <- SolrClient$new()) # Status of all cores conn$core_status() # Status of particular cores conn$core_status("gettingstarted") # Get index info or not ## Default: TRUE conn$core_status("gettingstarted", indexInfo = TRUE) conn$core_status("gettingstarted", indexInfo = FALSE) ## End(Not run)## Not run: # start Solr with Schemaless mode via the schemaless eg: # bin/solr start -e schemaless # you can create a new core like: bin/solr create -c corename # where <corename> is the name for your core - or creaate as below # connect (conn <- SolrClient$new()) # Status of all cores conn$core_status() # Status of particular cores conn$core_status("gettingstarted") # Get index info or not ## Default: TRUE conn$core_status("gettingstarted", indexInfo = TRUE) conn$core_status("gettingstarted", indexInfo = FALSE) ## End(Not run)
SWAP atomically swaps the names used to access two existing Solr cores. This can be used to swap new content into production. The prior core remains available and can be swapped back, if necessary. Each core will be known by the name of the other, after the swap
core_swap(conn, name, other, async = NULL, raw = FALSE, callopts = list())core_swap(conn, name, other, async = NULL, raw = FALSE, callopts = list())
conn |
A solrium connection object, see SolrClient |
name |
(character) The name of the core to be created. Required |
other |
(character) The name of one of the cores to be swapped. Required. |
async |
(character) Request ID to track this action which will be processed asynchronously |
raw |
(logical) If |
callopts |
curl options passed on to crul::HttpClient |
Do not use core_swap with a SolrCloud node. It is not
supported and can result in the core being unusable. We'll try to stop
you if you try.
## Not run: # start Solr with Schemaless mode via the schemaless eg: # bin/solr start -e schemaless # you can create a new core like: bin/solr create -c corename # where <corename> is the name for your core - or creaate as below # connect (conn <- SolrClient$new()) # Swap a core ## First, create two cores conn$core_create("swapcoretest1") # - or create on CLI: bin/solr create -c swapcoretest1 conn$core_create("swapcoretest2") # - or create on CLI: bin/solr create -c swapcoretest2 ## check status conn$core_status("swapcoretest1", FALSE) conn$core_status("swapcoretest2", FALSE) ## swap core conn$core_swap("swapcoretest1", "swapcoretest2") ## check status again conn$core_status("swapcoretest1", FALSE) conn$core_status("swapcoretest2", FALSE) ## End(Not run)## Not run: # start Solr with Schemaless mode via the schemaless eg: # bin/solr start -e schemaless # you can create a new core like: bin/solr create -c corename # where <corename> is the name for your core - or creaate as below # connect (conn <- SolrClient$new()) # Swap a core ## First, create two cores conn$core_create("swapcoretest1") # - or create on CLI: bin/solr create -c swapcoretest1 conn$core_create("swapcoretest2") # - or create on CLI: bin/solr create -c swapcoretest2 ## check status conn$core_status("swapcoretest1", FALSE) conn$core_status("swapcoretest2", FALSE) ## swap core conn$core_swap("swapcoretest1", "swapcoretest2") ## check status again conn$core_status("swapcoretest1", FALSE) conn$core_status("swapcoretest2", FALSE) ## End(Not run)
Unload (delete) a core
core_unload( conn, name, deleteIndex = FALSE, deleteDataDir = FALSE, deleteInstanceDir = FALSE, async = NULL, raw = FALSE, callopts = list() )core_unload( conn, name, deleteIndex = FALSE, deleteDataDir = FALSE, deleteInstanceDir = FALSE, async = NULL, raw = FALSE, callopts = list() )
conn |
A solrium connection object, see SolrClient |
name |
(character) The name of the core to be created. Required |
deleteIndex |
(logical) If |
deleteDataDir |
(logical) If |
deleteInstanceDir |
(logical) If |
async |
(character) Request ID to track this action which will be processed asynchronously |
raw |
(logical) If |
callopts |
curl options passed on to crul::HttpClient |
## Not run: # start Solr with Schemaless mode via the schemaless eg: # bin/solr start -e schemaless # connect (conn <- SolrClient$new()) # Create a core conn$core_create(name = "books") # Unload a core conn$core_unload(name = "books") ## not found # conn$core_unload(name = "books") # > Error: 400 - Cannot unload non-existent core [books] ## End(Not run)## Not run: # start Solr with Schemaless mode via the schemaless eg: # bin/solr start -e schemaless # connect (conn <- SolrClient$new()) # Create a core conn$core_create(name = "books") # Unload a core conn$core_unload(name = "books") ## not found # conn$core_unload(name = "books") # > Error: 400 - Cannot unload non-existent core [books] ## End(Not run)
Delete documents by ID or query
delete_by_id( conn, ids, name, commit = TRUE, commit_within = NULL, overwrite = TRUE, boost = NULL, wt = "json", raw = FALSE, ... ) delete_by_query( conn, query, name, commit = TRUE, commit_within = NULL, overwrite = TRUE, boost = NULL, wt = "json", raw = FALSE, ... )delete_by_id( conn, ids, name, commit = TRUE, commit_within = NULL, overwrite = TRUE, boost = NULL, wt = "json", raw = FALSE, ... ) delete_by_query( conn, query, name, commit = TRUE, commit_within = NULL, overwrite = TRUE, boost = NULL, wt = "json", raw = FALSE, ... )
conn |
A solrium connection object, see SolrClient |
ids |
Document IDs, one or more in a vector or list |
name |
(character) A collection or core name. Required. |
commit |
(logical) If |
commit_within |
(numeric) Milliseconds to commit the change, the
document will be added within that time. Default: |
overwrite |
(logical) Overwrite documents with matching keys.
Default: |
boost |
(numeric) Boost factor. Default: |
wt |
(character) One of json (default) or xml. If json, uses
|
raw |
(logical) If |
... |
curl options passed on to crul::HttpClient |
query |
Query to use to delete documents |
We use json internally as data interchange format for this function.
## Not run: (cli <- SolrClient$new()) # add some documents first ss <- list(list(id = 1, price = 100), list(id = 2, price = 500)) cli$add(ss, name = "gettingstarted") # Now, delete them # Delete by ID cli$delete_by_id(ids = 1, "gettingstarted") ## Many IDs cli$delete_by_id(ids = c(3, 4), "gettingstarted") # Delete by query cli$search("gettingstarted", params=list(q="*:*")) # apple is there cli$delete_by_query(query = 'id:apple', "gettingstarted") # delete it cli$search("gettingstarted", params=list(q='id:apple')) # apple is now gone ## End(Not run)## Not run: (cli <- SolrClient$new()) # add some documents first ss <- list(list(id = 1, price = 100), list(id = 2, price = 500)) cli$add(ss, name = "gettingstarted") # Now, delete them # Delete by ID cli$delete_by_id(ids = 1, "gettingstarted") ## Many IDs cli$delete_by_id(ids = c(3, 4), "gettingstarted") # Delete by query cli$search("gettingstarted", params=list(q="*:*")) # apple is there cli$delete_by_query(query = 'id:apple', "gettingstarted") # delete it cli$search("gettingstarted", params=list(q='id:apple')) # apple is now gone ## End(Not run)
Test for sr_facet class
Test for sr_high class
Test for sr_search class
is.sr_facet(x) is.sr_high(x) is.sr_search(x)is.sr_facet(x) is.sr_high(x) is.sr_search(x)
x |
Input |
Function to make make multiple args of the same name from a single input with length > 1
makemultiargs(x)makemultiargs(x)
x |
Value |
Ping a Solr instance
ping(conn, name, wt = "json", raw = FALSE, ...)ping(conn, name, wt = "json", raw = FALSE, ...)
conn |
A solrium connection object, see SolrClient |
name |
(character) Name of a collection or core. Required. |
wt |
(character) One of json (default) or xml. If json, uses
[xml2::read_xml)]: R:xml2::read_xml) |
raw |
(logical) If |
... |
curl options passed on to crul::HttpClient |
You likely may not be able to run this function against many public Solr services as they hopefully don't expose their admin interface to the public, but works locally.
if wt="xml" an object of class xml_document, if
wt="json" an object of class list
## Not run: # start Solr, in your CLI, run: `bin/solr start -e cloud -noprompt` # after that, if you haven't run `bin/post -c gettingstarted docs/` yet, # do so # connect: by default we connect to localhost, port 8983 (cli <- SolrClient$new()) # ping the gettingstarted index cli$ping("gettingstarted") ping(cli, "gettingstarted") ping(cli, "gettingstarted", wt = "xml") ping(cli, "gettingstarted", verbose = FALSE) ping(cli, "gettingstarted", raw = TRUE) ping(cli, "gettingstarted", wt="xml", verbose = TRUE) ## End(Not run)## Not run: # start Solr, in your CLI, run: `bin/solr start -e cloud -noprompt` # after that, if you haven't run `bin/post -c gettingstarted docs/` yet, # do so # connect: by default we connect to localhost, port 8983 (cli <- SolrClient$new()) # ping the gettingstarted index cli$ping("gettingstarted") ping(cli, "gettingstarted") ping(cli, "gettingstarted", wt = "xml") ping(cli, "gettingstarted", verbose = FALSE) ping(cli, "gettingstarted", raw = TRUE) ping(cli, "gettingstarted", wt="xml", verbose = TRUE) ## End(Not run)
Get the schema for a collection or core
schema(conn, name, what = "", raw = FALSE, ...)schema(conn, name, what = "", raw = FALSE, ...)
conn |
A solrium connection object, see SolrClient |
name |
(character) Name of a collection or core. Required. |
what |
(character) What to retrieve. By default, we retrieve the entire schema. Options include: fields, dynamicfields, fieldtypes, copyfields, name, version, uniquekey, similarity, "solrqueryparser/defaultoperator" |
raw |
(logical) If |
... |
curl options passed on to crul::HttpClient |
## Not run: # start Solr, in your CLI, run: `bin/solr start -e cloud -noprompt` # after that, if you haven't run `bin/post -c gettingstarted docs/` yet, do so # connect: by default we connect to localhost, port 8983 (cli <- SolrClient$new()) # get the schema for the gettingstarted index schema(cli, name = "gettingstarted") # Get parts of the schema schema(cli, name = "gettingstarted", "fields") schema(cli, name = "gettingstarted", "dynamicfields") schema(cli, name = "gettingstarted", "fieldtypes") schema(cli, name = "gettingstarted", "copyfields") schema(cli, name = "gettingstarted", "name") schema(cli, name = "gettingstarted", "version") schema(cli, name = "gettingstarted", "uniquekey") schema(cli, name = "gettingstarted", "similarity") schema(cli, name = "gettingstarted", "solrqueryparser/defaultoperator") # get raw data schema(cli, name = "gettingstarted", "similarity", raw = TRUE) schema(cli, name = "gettingstarted", "uniquekey", raw = TRUE) # start Solr in Schemaless mode: bin/solr start -e schemaless # schema(cli, "gettingstarted") # start Solr in Standalone mode: bin/solr start # then add a core: bin/solr create -c helloWorld # schema(cli, "helloWorld") ## End(Not run)## Not run: # start Solr, in your CLI, run: `bin/solr start -e cloud -noprompt` # after that, if you haven't run `bin/post -c gettingstarted docs/` yet, do so # connect: by default we connect to localhost, port 8983 (cli <- SolrClient$new()) # get the schema for the gettingstarted index schema(cli, name = "gettingstarted") # Get parts of the schema schema(cli, name = "gettingstarted", "fields") schema(cli, name = "gettingstarted", "dynamicfields") schema(cli, name = "gettingstarted", "fieldtypes") schema(cli, name = "gettingstarted", "copyfields") schema(cli, name = "gettingstarted", "name") schema(cli, name = "gettingstarted", "version") schema(cli, name = "gettingstarted", "uniquekey") schema(cli, name = "gettingstarted", "similarity") schema(cli, name = "gettingstarted", "solrqueryparser/defaultoperator") # get raw data schema(cli, name = "gettingstarted", "similarity", raw = TRUE) schema(cli, name = "gettingstarted", "uniquekey", raw = TRUE) # start Solr in Schemaless mode: bin/solr start -e schemaless # schema(cli, "gettingstarted") # start Solr in Standalone mode: bin/solr start # then add a core: bin/solr create -c helloWorld # schema(cli, "helloWorld") ## End(Not run)
Includes documents, facets, groups, mlt, stats, and highlights
solr_all( conn, name = NULL, params = NULL, body = NULL, callopts = list(), raw = FALSE, parsetype = "df", concat = ",", optimizeMaxRows = TRUE, minOptimizedRows = 50000L, progress = NULL, ... )solr_all( conn, name = NULL, params = NULL, body = NULL, callopts = list(), raw = FALSE, parsetype = "df", concat = ",", optimizeMaxRows = TRUE, minOptimizedRows = 50000L, progress = NULL, ... )
conn |
A solrium connection object, see SolrClient |
name |
Name of a collection or core. Or leave as |
params |
(list) a named list of parameters, results in a GET request as long as no body parameters given |
body |
(list) a named list of parameters, if given a POST request will be performed |
callopts |
Call options passed on to crul::HttpClient |
raw |
(logical) If TRUE, returns raw data in format specified by wt param |
parsetype |
(character) One of 'list' or 'df' |
concat |
(character) Character to concatenate elements of longer than length 1. Note that this only works reliably when data format is json (wt='json'). The parsing is more complicated in XML format, but you can do that on your own. |
optimizeMaxRows |
(logical) If |
minOptimizedRows |
(numeric) used by |
progress |
a function with logic for printing a progress
bar for an HTTP request, ultimately passed down to curl. only supports
|
... |
Further args to be combined into query |
XML, JSON, a list, or data.frame
q Query terms, defaults to ':', or everything.
sort Field to sort on. You can specify ascending (e.g., score desc) or descending (e.g., score asc), sort by two fields (e.g., score desc, price asc), or sort by a function (e.g., sum(x_f, y_f) desc, which sorts by the sum of x_f and y_f in a descending order).
start Record to start at, default to beginning.
rows Number of records to return. Default: 10.
pageDoc If you expect to be paging deeply into the results (say beyond page 10,
assuming rows=10) and you are sorting by score, you may wish to add the pageDoc
and pageScore parameters to your request. These two parameters tell Solr (and Lucene)
what the last result (Lucene internal docid and score) of the previous page was,
so that when scoring the query for the next set of pages, it can ignore any results
that occur higher than that item. To get the Lucene internal doc id, you will need
to add docid to the &fl list.
pageScore See pageDoc notes.
fq Filter query, this does not affect the search, only what gets returned. This parameter can accept multiple items in a lis or vector. You can't pass more than one parameter of the same name, so we get around it by passing multiple queries and we parse internally
fl Fields to return, can be a character vector like c('id', 'title'),
or a single character vector with one or more comma separated names, like
'id,title'
defType Specify the query parser to use with this request.
timeAllowed The time allowed for a search to finish. This value only applies
to the search and not to requests in general. Time is in milliseconds. Values <= 0
mean no time restriction. Partial results may be returned (if there are any).
qt Which query handler used. Options: dismax, others?
NOW Set a fixed time for evaluating Date based expresions
TZ Time zone, you can override the default.
echoHandler If TRUE, Solr places the name of the handle used in the
response to the client for debugging purposes. Default:
echoParams The echoParams parameter tells Solr what kinds of Request parameters should be included in the response for debugging purposes, legal values include:
none - don't include any request parameters for debugging
explicit - include the parameters explicitly specified by the client in the request
all - include all parameters involved in this request, either specified explicitly by the client, or implicit because of the request handler configuration.
wt (character) One of json, xml, or csv. Data type returned, defaults
to 'csv'. If json, uses jsonlite::fromJSON() to parse. If xml,
uses xml2::read_xml() to parse. If csv, uses read.table() to parse.
wt=csv gives the fastest performance at least in all the cases we have
tested in, thus it's the default value for wt
See https://lucene.apache.org/solr/guide/8_2/searching.html for more information.
solr_highlight(), solr_facet()
## Not run: # connect (cli <- SolrClient$new(host = "api.plos.org", path = "search", port = NULL)) solr_all(cli, params = list(q='*:*', rows=2, fl='id')) # facets solr_all(cli, params = list(q='*:*', rows=2, fl='id', facet="true", facet.field="journal")) # mlt solr_all(cli, params = list(q='ecology', rows=2, fl='id', mlt='true', mlt.count=2, mlt.fl='abstract')) # facets and mlt solr_all(cli, params = list(q='ecology', rows=2, fl='id', facet="true", facet.field="journal", mlt='true', mlt.count=2, mlt.fl='abstract')) # stats solr_all(cli, params = list(q='ecology', rows=2, fl='id', stats='true', stats.field='counter_total_all')) # facets, mlt, and stats solr_all(cli, params = list(q='ecology', rows=2, fl='id', facet="true", facet.field="journal", mlt='true', mlt.count=2, mlt.fl='abstract', stats='true', stats.field='counter_total_all')) # group solr_all(cli, params = list(q='ecology', rows=2, fl='id', group='true', group.field='journal', group.limit=3)) # facets, mlt, stats, and groups solr_all(cli, params = list(q='ecology', rows=2, fl='id', facet="true", facet.field="journal", mlt='true', mlt.count=2, mlt.fl='abstract', stats='true', stats.field='counter_total_all', group='true', group.field='journal', group.limit=3)) # using wt = xml solr_all(cli, params = list(q='*:*', rows=50, fl=c('id','score'), fq='doc_type:full', wt="xml"), raw=TRUE) ## End(Not run)## Not run: # connect (cli <- SolrClient$new(host = "api.plos.org", path = "search", port = NULL)) solr_all(cli, params = list(q='*:*', rows=2, fl='id')) # facets solr_all(cli, params = list(q='*:*', rows=2, fl='id', facet="true", facet.field="journal")) # mlt solr_all(cli, params = list(q='ecology', rows=2, fl='id', mlt='true', mlt.count=2, mlt.fl='abstract')) # facets and mlt solr_all(cli, params = list(q='ecology', rows=2, fl='id', facet="true", facet.field="journal", mlt='true', mlt.count=2, mlt.fl='abstract')) # stats solr_all(cli, params = list(q='ecology', rows=2, fl='id', stats='true', stats.field='counter_total_all')) # facets, mlt, and stats solr_all(cli, params = list(q='ecology', rows=2, fl='id', facet="true", facet.field="journal", mlt='true', mlt.count=2, mlt.fl='abstract', stats='true', stats.field='counter_total_all')) # group solr_all(cli, params = list(q='ecology', rows=2, fl='id', group='true', group.field='journal', group.limit=3)) # facets, mlt, stats, and groups solr_all(cli, params = list(q='ecology', rows=2, fl='id', facet="true", facet.field="journal", mlt='true', mlt.count=2, mlt.fl='abstract', stats='true', stats.field='counter_total_all', group='true', group.field='journal', group.limit=3)) # using wt = xml solr_all(cli, params = list(q='*:*', rows=50, fl=c('id','score'), fq='doc_type:full', wt="xml"), raw=TRUE) ## End(Not run)
Returns only facet items
solr_facet( conn, name = NULL, params = list(q = "*:*"), body = NULL, callopts = list(), raw = FALSE, parsetype = "df", concat = ",", progress = NULL, ... )solr_facet( conn, name = NULL, params = list(q = "*:*"), body = NULL, callopts = list(), raw = FALSE, parsetype = "df", concat = ",", progress = NULL, ... )
conn |
A solrium connection object, see SolrClient |
name |
Name of a collection or core. Or leave as |
params |
(list) a named list of parameters, results in a GET request as long as no body parameters given |
body |
(list) a named list of parameters, if given a POST request will be performed |
callopts |
Call options passed on to crul::HttpClient |
raw |
(logical) If TRUE (default) raw json or xml returned. If FALSE, parsed data returned. |
parsetype |
(character) One of 'list' or 'df' |
concat |
(character) Character to concatenate elements of longer than length 1. Note that this only works reliably when data format is json (wt='json'). The parsing is more complicated in XML format, but you can do that on your own. |
progress |
a function with logic for printing a progress
bar for an HTTP request, ultimately passed down to curl. only supports
|
... |
Further args, usually per field arguments for faceting. |
A number of fields can be specified multiple times, in which case you can separate
them by commas, like facet.field='journal,subject'. Those fields are:
facet.field
facet.query
facet.date
facet.date.other
facet.date.include
facet.range
facet.range.other
facet.range.include
facet.pivot
Options for some parameters:
facet.sort: This param determines the ordering of the facet field constraints.
count sort the constraints by count (highest count first)
index to return the constraints sorted in their index order (lexicographic by indexed term). For terms in the ascii range, this will be alphabetically sorted.
The default is count if facet.limit is greater than 0, index otherwise. This parameter can be specified on a per field basis.
facet.method: This parameter indicates what type of algorithm/method to use when faceting a field.
enum Enumerates all terms in a field, calculating the set intersection of documents that match the term with documents that match the query. This was the default (and only) method for faceting multi-valued fields prior to Solr 1.4.
fc (Field Cache) The facet counts are calculated by iterating over documents that match the query and summing the terms that appear in each document. This was the default method for single valued fields prior to Solr 1.4.
fcs (Field Cache per Segment) works the same as fc except the underlying cache data structure is built for each segment of the index individually
The default value is fc (except for BoolField which uses enum) since it tends to use less memory and is faster then the enumeration method when a field has many unique terms in the index. For indexes that are changing rapidly in NRT situations, fcs may be a better choice because it reduces the overhead of building the cache structures on the first request and/or warming queries when opening a new searcher – but tends to be somewhat slower then fc for subsequent requests against the same searcher. This parameter can be specified on a per field basis.
facet.date.other: This param indicates that in addition to the counts for each date range constraint between facet.date.start and facet.date.end, counts should also be computed for...
before All records with field values lower then lower bound of the first range
after All records with field values greater then the upper bound of the last range
between All records with field values between the start and end bounds of all ranges
none Compute none of this information
all Shortcut for before, between, and after
This parameter can be specified on a per field basis. In addition to the all option, this parameter can be specified multiple times to indicate multiple choices – but none will override all other options.
facet.date.include: By default, the ranges used to compute date faceting between facet.date.start and facet.date.end are all inclusive of both endpoints, while the "before" and "after" ranges are not inclusive. This behavior can be modified by the facet.date.include param, which can be any combination of the following options...
lower All gap based ranges include their lower bound
upper All gap based ranges include their upper bound
edge The first and last gap ranges include their edge bounds (ie: lower for the first one, upper for the last one) even if the corresponding upper/lower option is not specified
outer The "before" and "after" ranges will be inclusive of their bounds, even if the first or last ranges already include those boundaries.
all Shorthand for lower, upper, edge, outer
This parameter can be specified on a per field basis. This parameter can be specified multiple times to indicate multiple choices.
facet.date.include: This param indicates that in addition to the counts for each range constraint between facet.range.start and facet.range.end, counts should also be computed for...
before All records with field values lower then lower bound of the first range
after All records with field values greater then the upper bound of the last range
between All records with field values between the start and end bounds of all ranges
none Compute none of this information
all Shortcut for before, between, and after
This parameter can be specified on a per field basis. In addition to the all option, this parameter can be specified multiple times to indicate multiple choices – but none will override all other options.
facet.range.include: By default, the ranges used to compute range faceting between facet.range.start and facet.range.end are inclusive of their lower bounds and exclusive of the upper bounds. The "before" range is exclusive and the "after" range is inclusive. This default, equivalent to lower below, will not result in double counting at the boundaries. This behavior can be modified by the facet.range.include param, which can be any combination of the following options...
lower All gap based ranges include their lower bound
upper All gap based ranges include their upper bound
edge The first and last gap ranges include their edge bounds (ie: lower for the first one, upper for the last one) even if the corresponding upper/lower option is not specified
outer The "before" and "after" ranges will be inclusive of their bounds, even if the first or last ranges already include those boundaries.
all Shorthand for lower, upper, edge, outer
Can be specified on a per field basis. Can be specified multiple times to indicate multiple choices. If you want to ensure you don't double-count, don't choose both lower & upper, don't choose outer, and don't choose all.
Raw json or xml, or a list of length 4 parsed elements (usually data.frame's).
name Name of a collection or core. Or leave as NULL if not needed.
q Query terms. See examples.
facet.query This param allows you to specify an arbitrary query in the
Lucene default syntax to generate a facet count. By default, faceting returns
a count of the unique terms for a "field", while facet.query allows you to
determine counts for arbitrary terms or expressions. This parameter can be
specified multiple times to indicate that multiple queries should be used as
separate facet constraints. It can be particularly useful for numeric range
based facets, or prefix based facets – see example below (i.e.
price:[* TO 500] and price:[501 TO *]).
facet.field This param allows you to specify a field which should be treated as a facet. It will iterate over each Term in the field and generate a facet count using that Term as the constraint. This parameter can be specified multiple times to indicate multiple facet fields. None of the other params in this section will have any effect without specifying at least one field name using this param.
facet.prefix Limits the terms on which to facet to those starting with the given string prefix. Note that unlike fq, this does not change the search results – it merely reduces the facet values returned to those beginning with the specified prefix. This parameter can be specified on a per field basis.
facet.sort See Details.
facet.limit This param indicates the maximum number of constraint counts that should be returned for the facet fields. A negative value means unlimited. Default: 100. Can be specified on a per field basis.
facet.offset This param indicates an offset into the list of constraints to allow paging. Default: 0. This parameter can be specified on a per field basis.
facet.mincount This param indicates the minimum counts for facet fields should be included in the response. Default: 0. This parameter can be specified on a per field basis.
facet.missing Set to "true" this param indicates that in addition to the Term based constraints of a facet field, a count of all matching results which have no value for the field should be computed. Default: FALSE. This parameter can be specified on a per field basis.
facet.method See Details.
facet.enum.cache.minDf This param indicates the minimum document frequency (number of documents matching a term) for which the filterCache should be used when determining the constraint count for that term. This is only used when facet.method=enum method of faceting. A value greater than zero will decrease memory usage of the filterCache, but increase the query time. When faceting on a field with a very large number of terms, and you wish to decrease memory usage, try a low value of 25 to 50 first. Default: 0, causing the filterCache to be used for all terms in the field. This parameter can be specified on a per field basis.
facet.threads This param will cause loading the underlying fields used in faceting to be executed in parallel with the number of threads specified. Specify as facet.threads=# where # is the maximum number of threads used. Omitting this parameter or specifying the thread count as 0 will not spawn any threads just as before. Specifying a negative number of threads will spin up to Integer.MAX_VALUE threads. Currently this is limited to the fields, range and query facets are not yet supported. In at least one case this has reduced warmup times from 20 seconds to under 5 seconds.
facet.date Specify names of fields (of type DateField) which should be treated as date facets. Can be specified multiple times to indicate multiple date facet fields.
facet.date.start The lower bound for the first date range for all Date Faceting on this field. This should be a single date expression which may use the DateMathParser syntax. Can be specified on a per field basis.
facet.date.end The minimum upper bound for the last date range for all Date Faceting on this field (see facet.date.hardend for an explanation of what the actual end value may be greater). This should be a single date expression which may use the DateMathParser syntax. Can be specified on a per field basis.
facet.date.gap The size of each date range expressed as an interval to be added to the lower bound using the DateMathParser syntax. Eg: facet.date.gap=+1DAY. Can be specified on a per field basis.
facet.date.hardend A Boolean parameter instructing Solr what to do in the event that facet.date.gap does not divide evenly between facet.date.start and facet.date.end. If this is true, the last date range constraint will have an upper bound of facet.date.end; if false, the last date range will have the smallest possible upper bound greater then facet.date.end such that the range is exactly facet.date.gap wide. Default: FALSE. This parameter can be specified on a per field basis.
facet.date.other See Details.
facet.date.include See Details.
facet.range Indicates what field to create range facets for. Example: facet.range=price&facet.range=age
facet.range.start The lower bound of the ranges. Can be specified on a per field basis. Example: f.price.facet.range.start=0.0&f.age.facet.range.start=10
facet.range.end The upper bound of the ranges. Can be specified on a per field basis. Example: f.price.facet.range.end=1000.0&f.age.facet.range.start=99
facet.range.gap The size of each range expressed as a value to be added to the lower bound. For date fields, this should be expressed using the DateMathParser syntax. (ie: facet.range.gap=+1DAY). Can be specified on a per field basis. Example: f.price.facet.range.gap=100&f.age.facet.range.gap=10
facet.range.hardend A Boolean parameter instructing Solr what to do in the event that facet.range.gap does not divide evenly between facet.range.start and facet.range.end. If this is true, the last range constraint will have an upper bound of facet.range.end; if false, the last range will have the smallest possible upper bound greater then facet.range.end such that the range is exactly facet.range.gap wide. Default: FALSE. This parameter can be specified on a per field basis.
facet.range.other See Details.
facet.range.include See Details.
facet.pivot This param allows you to specify a single comma-separated string of fields to allow you to facet within the results of the parent facet to return counts in the format of SQL group by operation
facet.pivot.mincount This param indicates the minimum counts for facet fields to be included in the response. Default: 0. This parameter should only be specified once.
start Record to start at, default to beginning.
rows Number of records to return.
key API key, if needed.
wt (character) Data type returned, defaults to 'json'. One of json or xml. If json,
uses fromJSON to parse. If xml, uses xmlParse to
parse. csv is only supported in solr_search and solr_all.
See https://lucene.apache.org/solr/guide/8_2/faceting.html for more information on faceting.
solr_search(), solr_highlight(), solr_parse()
## Not run: # connect - local Solr instance (cli <- SolrClient$new()) cli$facet("gettingstarted", params = list(q="*:*", facet.field='name')) cli$facet("gettingstarted", params = list(q="*:*", facet.field='name'), callopts = list(verbose = TRUE)) cli$facet("gettingstarted", body = list(q="*:*", facet.field='name'), callopts = list(verbose = TRUE)) # Facet on a single field solr_facet(cli, "gettingstarted", params = list(q='*:*', facet.field='name')) # Remote instance (cli <- SolrClient$new(host = "api.plos.org", path = "search", port = NULL)) # Facet on multiple fields solr_facet(cli, params = list(q='alcohol', facet.field = c('journal','subject'))) # Using mincount solr_facet(cli, params = list(q='alcohol', facet.field='journal', facet.mincount='500')) # Using facet.query to get counts solr_facet(cli, params = list(q='*:*', facet.field='journal', facet.query=c('cell','bird'))) # Using facet.pivot to simulate SQL group by counts solr_facet(cli, params = list(q='alcohol', facet.pivot='journal,subject', facet.pivot.mincount=10)) ## two or more fields are required - you can pass in as a single ## character string solr_facet(cli, params = list(q='*:*', facet.pivot = "journal,subject", facet.limit = 3)) ## Or, pass in as a vector of length 2 or greater solr_facet(cli, params = list(q='*:*', facet.pivot = c("journal", "subject"), facet.limit = 3)) # Date faceting solr_facet(cli, params = list(q='*:*', facet.date='publication_date', facet.date.start='NOW/DAY-5DAYS', facet.date.end='NOW', facet.date.gap='+1DAY')) ## two variables solr_facet(cli, params = list(q='*:*', facet.date=c('publication_date', 'timestamp'), facet.date.start='NOW/DAY-5DAYS', facet.date.end='NOW', facet.date.gap='+1DAY')) # Range faceting solr_facet(cli, params = list(q='*:*', facet.range='counter_total_all', facet.range.start=5, facet.range.end=1000, facet.range.gap=10)) # Range faceting with > 1 field, same settings solr_facet(cli, params = list(q='*:*', facet.range=c('counter_total_all','alm_twitterCount'), facet.range.start=5, facet.range.end=1000, facet.range.gap=10)) # Range faceting with > 1 field, different settings solr_facet(cli, params = list(q='*:*', facet.range=c('counter_total_all','alm_twitterCount'), f.counter_total_all.facet.range.start=5, f.counter_total_all.facet.range.end=1000, f.counter_total_all.facet.range.gap=10, f.alm_twitterCount.facet.range.start=5, f.alm_twitterCount.facet.range.end=1000, f.alm_twitterCount.facet.range.gap=10)) # Get raw json or xml ## json solr_facet(cli, params = list(q='*:*', facet.field='journal'), raw=TRUE) ## xml solr_facet(cli, params = list(q='*:*', facet.field='journal', wt='xml'), raw=TRUE) # Get raw data back, and parse later, same as what goes on internally if # raw=FALSE (Default) out <- solr_facet(cli, params = list(q='*:*', facet.field='journal'), raw=TRUE) solr_parse(out) out <- solr_facet(cli, params = list(q='*:*', facet.field='journal', wt = 'xml'), raw=TRUE) solr_parse(out) # Using the USGS BISON API (https://bison.usgs.gov/#solr) ## The occurrence endpoint (cli <- SolrClient$new(host = "bison.usgs.gov", scheme = "https", path = "solr/occurrences/select", port = NULL)) solr_facet(cli, params = list(q='*:*', facet.field='year')) solr_facet(cli, params = list(q='*:*', facet.field='computedStateFips')) # using a proxy # cli <- SolrClient$new(host = "api.plos.org", path = "search", port = NULL, # proxy = list(url = "http://54.195.48.153:8888")) # solr_facet(cli, params = list(facet.field='journal'), # callopts=list(verbose=TRUE)) ## End(Not run)## Not run: # connect - local Solr instance (cli <- SolrClient$new()) cli$facet("gettingstarted", params = list(q="*:*", facet.field='name')) cli$facet("gettingstarted", params = list(q="*:*", facet.field='name'), callopts = list(verbose = TRUE)) cli$facet("gettingstarted", body = list(q="*:*", facet.field='name'), callopts = list(verbose = TRUE)) # Facet on a single field solr_facet(cli, "gettingstarted", params = list(q='*:*', facet.field='name')) # Remote instance (cli <- SolrClient$new(host = "api.plos.org", path = "search", port = NULL)) # Facet on multiple fields solr_facet(cli, params = list(q='alcohol', facet.field = c('journal','subject'))) # Using mincount solr_facet(cli, params = list(q='alcohol', facet.field='journal', facet.mincount='500')) # Using facet.query to get counts solr_facet(cli, params = list(q='*:*', facet.field='journal', facet.query=c('cell','bird'))) # Using facet.pivot to simulate SQL group by counts solr_facet(cli, params = list(q='alcohol', facet.pivot='journal,subject', facet.pivot.mincount=10)) ## two or more fields are required - you can pass in as a single ## character string solr_facet(cli, params = list(q='*:*', facet.pivot = "journal,subject", facet.limit = 3)) ## Or, pass in as a vector of length 2 or greater solr_facet(cli, params = list(q='*:*', facet.pivot = c("journal", "subject"), facet.limit = 3)) # Date faceting solr_facet(cli, params = list(q='*:*', facet.date='publication_date', facet.date.start='NOW/DAY-5DAYS', facet.date.end='NOW', facet.date.gap='+1DAY')) ## two variables solr_facet(cli, params = list(q='*:*', facet.date=c('publication_date', 'timestamp'), facet.date.start='NOW/DAY-5DAYS', facet.date.end='NOW', facet.date.gap='+1DAY')) # Range faceting solr_facet(cli, params = list(q='*:*', facet.range='counter_total_all', facet.range.start=5, facet.range.end=1000, facet.range.gap=10)) # Range faceting with > 1 field, same settings solr_facet(cli, params = list(q='*:*', facet.range=c('counter_total_all','alm_twitterCount'), facet.range.start=5, facet.range.end=1000, facet.range.gap=10)) # Range faceting with > 1 field, different settings solr_facet(cli, params = list(q='*:*', facet.range=c('counter_total_all','alm_twitterCount'), f.counter_total_all.facet.range.start=5, f.counter_total_all.facet.range.end=1000, f.counter_total_all.facet.range.gap=10, f.alm_twitterCount.facet.range.start=5, f.alm_twitterCount.facet.range.end=1000, f.alm_twitterCount.facet.range.gap=10)) # Get raw json or xml ## json solr_facet(cli, params = list(q='*:*', facet.field='journal'), raw=TRUE) ## xml solr_facet(cli, params = list(q='*:*', facet.field='journal', wt='xml'), raw=TRUE) # Get raw data back, and parse later, same as what goes on internally if # raw=FALSE (Default) out <- solr_facet(cli, params = list(q='*:*', facet.field='journal'), raw=TRUE) solr_parse(out) out <- solr_facet(cli, params = list(q='*:*', facet.field='journal', wt = 'xml'), raw=TRUE) solr_parse(out) # Using the USGS BISON API (https://bison.usgs.gov/#solr) ## The occurrence endpoint (cli <- SolrClient$new(host = "bison.usgs.gov", scheme = "https", path = "solr/occurrences/select", port = NULL)) solr_facet(cli, params = list(q='*:*', facet.field='year')) solr_facet(cli, params = list(q='*:*', facet.field='computedStateFips')) # using a proxy # cli <- SolrClient$new(host = "api.plos.org", path = "search", port = NULL, # proxy = list(url = "http://54.195.48.153:8888")) # solr_facet(cli, params = list(facet.field='journal'), # callopts=list(verbose=TRUE)) ## End(Not run)
Get documents by id
solr_get(conn, ids, name, fl = NULL, wt = "json", raw = FALSE, ...)solr_get(conn, ids, name, fl = NULL, wt = "json", raw = FALSE, ...)
conn |
A solrium connection object, see SolrClient |
ids |
Document IDs, one or more in a vector or list |
name |
(character) A collection or core name. Required. |
fl |
Fields to return, can be a character vector like
|
wt |
(character) One of json (default) or xml. Data type returned.
If json, uses |
raw |
(logical) If |
... |
curl options passed on to crul::HttpClient |
We use json internally as data interchange format for this function.
## Not run: (cli <- SolrClient$new()) # add some documents first ss <- list(list(id = 1, price = 100), list(id = 2, price = 500)) add(cli, ss, name = "gettingstarted") # Now, get documents by id solr_get(cli, ids = 1, "gettingstarted") solr_get(cli, ids = 2, "gettingstarted") solr_get(cli, ids = c(1, 2), "gettingstarted") solr_get(cli, ids = "1,2", "gettingstarted") # Get raw JSON solr_get(cli, ids = 1, "gettingstarted", raw = TRUE, wt = "json") solr_get(cli, ids = 1, "gettingstarted", raw = TRUE, wt = "xml") ## End(Not run)## Not run: (cli <- SolrClient$new()) # add some documents first ss <- list(list(id = 1, price = 100), list(id = 2, price = 500)) add(cli, ss, name = "gettingstarted") # Now, get documents by id solr_get(cli, ids = 1, "gettingstarted") solr_get(cli, ids = 2, "gettingstarted") solr_get(cli, ids = c(1, 2), "gettingstarted") solr_get(cli, ids = "1,2", "gettingstarted") # Get raw JSON solr_get(cli, ids = 1, "gettingstarted", raw = TRUE, wt = "json") solr_get(cli, ids = 1, "gettingstarted", raw = TRUE, wt = "xml") ## End(Not run)
Returns only group items
solr_group( conn, name = NULL, params = NULL, body = NULL, callopts = list(), raw = FALSE, parsetype = "df", concat = ",", progress = NULL, ... )solr_group( conn, name = NULL, params = NULL, body = NULL, callopts = list(), raw = FALSE, parsetype = "df", concat = ",", progress = NULL, ... )
conn |
A solrium connection object, see SolrClient |
name |
Name of a collection or core. Or leave as |
params |
(list) a named list of parameters, results in a GET request as long as no body parameters given |
body |
(list) a named list of parameters, if given a POST request will be performed |
callopts |
Call options passed on to crul::HttpClient |
raw |
(logical) If TRUE, returns raw data in format specified by wt param |
parsetype |
(character) One of 'list' or 'df' |
concat |
(character) Character to concatenate elements of longer than length 1. Note that this only works reliably when data format is json (wt='json'). The parsing is more complicated in XML format, but you can do that on your own. |
progress |
a function with logic for printing a progress
bar for an HTTP request, ultimately passed down to curl. only supports
|
... |
Further args to be combined into query |
XML, JSON, a list, or data.frame
q Query terms, defaults to ':', or everything.
fq Filter query, this does not affect the search, only what gets returned
fl Fields to return
wt (character) Data type returned, defaults to 'json'. One of json or xml. If json,
uses fromJSON to parse. If xml, uses xmlParse to
parse. csv is only supported in solr_search and solr_all.
key API key, if needed.
group.field (fieldname) Group based on the unique values of a field. The field must currently be single-valued and must be either indexed, or be another field type that has a value source and works in a function query - such as ExternalFileField. Note: for Solr 3.x versions the field must by a string like field such as StrField or TextField, otherwise a http status 400 is returned.
group.func (function query) Group based on the unique values of a function query. Solr4.0 This parameter only is supported on 4.0
group.query (query) Return a single group of documents that also match the given query.
rows (number) The number of groups to return. Defaults to 10.
start (number) The offset into the list of groups.
group.limit (number) The number of results (documents) to return for each group. Defaults to 1.
group.offset (number) The offset into the document list of each group.
sort How to sort the groups relative to each other. For example, sort=popularity desc will cause the groups to be sorted according to the highest popularity doc in each group. Defaults to "score desc".
group.sort How to sort documents within a single group. Defaults to the same value as the sort parameter.
group.format One of grouped or simple. If simple, the grouped documents are presented in a single flat list. The start and rows parameters refer to numbers of documents instead of numbers of groups.
group.main (logical) If true, the result of the last field grouping command is used as the main result list in the response, using group.format=simple
group.ngroups (logical) If true, includes the number of groups that have matched the query. Default is false. Solr4.1 WARNING: If this parameter is set to true on a sharded environment, all the documents that belong to the same group have to be located in the same shard, otherwise the count will be incorrect. If you are using SolrCloud, consider using "custom hashing"
group.cache.percent (0-100) If > 0 enables grouping cache. Grouping is executed actual two searches. This option caches the second search. A value of 0 disables grouping caching. Default is 0. Tests have shown that this cache only improves search time with boolean queries, wildcard queries and fuzzy queries. For simple queries like a term query or a match all query this cache has a negative impact on performance
See https://lucene.apache.org/solr/guide/8_2/collapse-and-expand-results.html for more information.
solr_highlight(), solr_facet()
## Not run: # connect (cli <- SolrClient$new()) # by default we do a GET request cli$group("gettingstarted", params = list(q='*:*', group.field='compName_s')) # OR solr_group(cli, "gettingstarted", params = list(q='*:*', group.field='compName_s')) # connect (cli <- SolrClient$new(host = "api.plos.org", path = "search", port = NULL)) # Basic group query solr_group(cli, params = list(q='ecology', group.field='journal', group.limit=3, fl=c('id','score'))) solr_group(cli, params = list(q='ecology', group.field='journal', group.limit=3, fl='article_type')) # Different ways to sort (notice diff btw sort of group.sort) # note that you can only sort on a field if you return that field solr_group(cli, params = list(q='ecology', group.field='journal', group.limit=3, fl=c('id','score'))) solr_group(cli, params = list(q='ecology', group.field='journal', group.limit=3, fl=c('id','score','alm_twitterCount'), group.sort='alm_twitterCount desc')) solr_group(cli, params = list(q='ecology', group.field='journal', group.limit=3, fl=c('id','score','alm_twitterCount'), sort='score asc', group.sort='alm_twitterCount desc')) # Two group.field values out <- solr_group(cli, params = list(q='ecology', group.field=c('journal','article_type'), group.limit=3, fl='id'), raw=TRUE) solr_parse(out) solr_parse(out, 'df') # Get two groups, one with alm_twitterCount of 0-10, and another group # with 10 to infinity solr_group(cli, params = list(q='ecology', group.limit=3, fl=c('id','alm_twitterCount'), group.query=c('alm_twitterCount:[0 TO 10]','alm_twitterCount:[10 TO *]'))) # Use of group.format and group.simple. ## The raw data structure of these two calls are slightly different, but ## the parsing inside the function outputs the same results. You can ## of course set raw=TRUE to get back what the data actually look like solr_group(cli, params = list(q='ecology', group.field='journal', group.limit=3, fl=c('id','score'), group.format='simple')) solr_group(cli, params = list(q='ecology', group.field='journal', group.limit=3, fl=c('id','score'), group.format='grouped')) solr_group(cli, params = list(q='ecology', group.field='journal', group.limit=3, fl=c('id','score'), group.format='grouped', group.main='true')) # xml back solr_group(cli, params = list(q='ecology', group.field='journal', group.limit=3, fl=c('id','score'), wt = "xml")) solr_group(cli, params = list(q='ecology', group.field='journal', group.limit=3, fl=c('id','score'), wt = "xml"), parsetype = "list") res <- solr_group(cli, params = list(q='ecology', group.field='journal', group.limit=3, fl=c('id','score'), wt = "xml"), raw = TRUE) library("xml2") xml2::read_xml(unclass(res)) solr_group(cli, params = list(q='ecology', group.field='journal', group.limit=3, fl='article_type', wt = "xml")) solr_group(cli, params = list(q='ecology', group.field='journal', group.limit=3, fl='article_type', wt = "xml"), parsetype = "list") ## End(Not run)## Not run: # connect (cli <- SolrClient$new()) # by default we do a GET request cli$group("gettingstarted", params = list(q='*:*', group.field='compName_s')) # OR solr_group(cli, "gettingstarted", params = list(q='*:*', group.field='compName_s')) # connect (cli <- SolrClient$new(host = "api.plos.org", path = "search", port = NULL)) # Basic group query solr_group(cli, params = list(q='ecology', group.field='journal', group.limit=3, fl=c('id','score'))) solr_group(cli, params = list(q='ecology', group.field='journal', group.limit=3, fl='article_type')) # Different ways to sort (notice diff btw sort of group.sort) # note that you can only sort on a field if you return that field solr_group(cli, params = list(q='ecology', group.field='journal', group.limit=3, fl=c('id','score'))) solr_group(cli, params = list(q='ecology', group.field='journal', group.limit=3, fl=c('id','score','alm_twitterCount'), group.sort='alm_twitterCount desc')) solr_group(cli, params = list(q='ecology', group.field='journal', group.limit=3, fl=c('id','score','alm_twitterCount'), sort='score asc', group.sort='alm_twitterCount desc')) # Two group.field values out <- solr_group(cli, params = list(q='ecology', group.field=c('journal','article_type'), group.limit=3, fl='id'), raw=TRUE) solr_parse(out) solr_parse(out, 'df') # Get two groups, one with alm_twitterCount of 0-10, and another group # with 10 to infinity solr_group(cli, params = list(q='ecology', group.limit=3, fl=c('id','alm_twitterCount'), group.query=c('alm_twitterCount:[0 TO 10]','alm_twitterCount:[10 TO *]'))) # Use of group.format and group.simple. ## The raw data structure of these two calls are slightly different, but ## the parsing inside the function outputs the same results. You can ## of course set raw=TRUE to get back what the data actually look like solr_group(cli, params = list(q='ecology', group.field='journal', group.limit=3, fl=c('id','score'), group.format='simple')) solr_group(cli, params = list(q='ecology', group.field='journal', group.limit=3, fl=c('id','score'), group.format='grouped')) solr_group(cli, params = list(q='ecology', group.field='journal', group.limit=3, fl=c('id','score'), group.format='grouped', group.main='true')) # xml back solr_group(cli, params = list(q='ecology', group.field='journal', group.limit=3, fl=c('id','score'), wt = "xml")) solr_group(cli, params = list(q='ecology', group.field='journal', group.limit=3, fl=c('id','score'), wt = "xml"), parsetype = "list") res <- solr_group(cli, params = list(q='ecology', group.field='journal', group.limit=3, fl=c('id','score'), wt = "xml"), raw = TRUE) library("xml2") xml2::read_xml(unclass(res)) solr_group(cli, params = list(q='ecology', group.field='journal', group.limit=3, fl='article_type', wt = "xml")) solr_group(cli, params = list(q='ecology', group.field='journal', group.limit=3, fl='article_type', wt = "xml"), parsetype = "list") ## End(Not run)
Returns only highlight items
solr_highlight( conn, name = NULL, params = NULL, body = NULL, callopts = list(), raw = FALSE, parsetype = "df", progress = NULL, ... )solr_highlight( conn, name = NULL, params = NULL, body = NULL, callopts = list(), raw = FALSE, parsetype = "df", progress = NULL, ... )
conn |
A solrium connection object, see SolrClient |
name |
Name of a collection or core. Or leave as |
params |
(list) a named list of parameters, results in a GET request as long as no body parameters given |
body |
(list) a named list of parameters, if given a POST request will be performed |
callopts |
Call options passed on to crul::HttpClient |
raw |
(logical) If TRUE (default) raw json or xml returned. If FALSE, parsed data returned. |
parsetype |
One of list of df (data.frame) |
progress |
a function with logic for printing a progress
bar for an HTTP request, ultimately passed down to curl. only supports
|
... |
Further args to be combined into query |
XML, JSON, a list, or data.frame
q Query terms. See examples.
hl.fl A comma-separated list of fields for which to generate highlighted snippets. If left blank, the fields highlighted for the LuceneQParser are the defaultSearchField (or the df param if used) and for the DisMax parser the qf fields are used. A '' can be used to match field globs, e.g. 'text_' or even '' to highlight on all fields where highlighting is possible. When using '', consider adding hl.requireFieldMatch=TRUE.
hl.snippets Max no. of highlighted snippets to generate per field. Note: it is possible for any number of snippets from zero to this value to be generated. This parameter accepts per-field overrides. Default: 1.
hl.fragsize The size, in characters, of the snippets (aka fragments) created by the highlighter. In the original Highlighter, "0" indicates that the whole field value should be used with no fragmenting.
hl.q Set a query request to be highlighted. It overrides q parameter for highlighting. Solr query syntax is acceptable for this parameter.
hl.mergeContiguous Collapse contiguous fragments into a single fragment. "true" indicates contiguous fragments will be collapsed into single fragment. This parameter accepts per-field overrides. This parameter makes sense for the original Highlighter only. Default: FALSE.
hl.requireFieldMatch If TRUE, then a field will only be highlighted if the query matched in this particular field (normally, terms are highlighted in all requested fields regardless of which field matched the query). This only takes effect if "hl.usePhraseHighlighter" is TRUE. Default: FALSE.
hl.maxAnalyzedChars How many characters into a document to look for suitable snippets. This parameter makes sense for the original Highlighter only. Default: 51200. You can assign a large value to this parameter and use hl.fragsize=0 to return highlighting in large fields that have size greater than 51200 characters.
hl.alternateField If a snippet cannot be generated (due to no terms matching), you can specify a field to use as the fallback. This parameter accepts per-field overrides.
hl.maxAlternateFieldLength If hl.alternateField is specified, this parameter specifies the maximum number of characters of the field to return. Any value less than or equal to 0 means unlimited. Default: unlimited.
hl.preserveMulti Preserve order of values in a multiValued list. Default: FALSE.
hl.maxMultiValuedToExamine When highlighting a multiValued field, stop examining the individual entries after looking at this many of them. Will potentially return 0 snippets if this limit is reached before any snippets are found. If maxMultiValuedToMatch is also specified, whichever limit is hit first will terminate looking for more. Default: Integer.MAX_VALUE
hl.maxMultiValuedToMatch When highlighting a multiValued field, stop examining the individual entries after looking at this many matches are found. If maxMultiValuedToExamine is also specified, whichever limit is hit first will terminate looking for more. Default: Integer.MAX_VALUE
hl.formatter Specify a formatter for the highlight output. Currently the only legal value is "simple", which surrounds a highlighted term with a customizable pre- and post text snippet. This parameter accepts per-field overrides. This parameter makes sense for the original Highlighter only.
hl.simple.pre The text which appears before and after a highlighted term when using
the simple formatter. This parameter accepts per-field overrides. The default values are
<em> and </em> This parameter makes sense for the original Highlighter only. Use
hl.tag.pre and hl.tag.post for FastVectorHighlighter (see example under hl.fragmentsBuilder)
hl.simple.post The text which appears before and after a highlighted term when using
the simple formatter. This parameter accepts per-field overrides. The default values are
<em> and </em> This parameter makes sense for the original Highlighter only. Use
hl.tag.pre and hl.tag.post for FastVectorHighlighter (see example under hl.fragmentsBuilder)
hl.fragmenter Specify a text snippet generator for highlighted text. The standard fragmenter is gap (which is so called because it creates fixed-sized fragments with gaps for multi-valued fields). Another option is regex, which tries to create fragments that "look like" a certain regular expression. This parameter accepts per-field overrides. Default: "gap"
hl.fragListBuilder Specify the name of SolrFragListBuilder. This parameter makes sense for FastVectorHighlighter only. To create a fragSize=0 with the FastVectorHighlighter, use the SingleFragListBuilder. This field supports per-field overrides.
hl.fragmentsBuilder Specify the name of SolrFragmentsBuilder. This parameter makes sense for FastVectorHighlighter only.
hl.boundaryScanner Configures how the boundaries of fragments are determined. By default, boundaries will split at the character level, creating a fragment such as "uick brown fox jumps over the la". Valid entries are breakIterator or simple, with breakIterator being the most commonly used. This parameter makes sense for FastVectorHighlighter only.
hl.bs.maxScan Specify the length of characters to be scanned by SimpleBoundaryScanner. Default: 10. This parameter makes sense for FastVectorHighlighter only.
hl.bs.chars Specify the boundary characters, used by SimpleBoundaryScanner. This parameter makes sense for FastVectorHighlighter only.
hl.bs.type Specify one of CHARACTER, WORD, SENTENCE and LINE, used by BreakIteratorBoundaryScanner. Default: WORD. This parameter makes sense for FastVectorHighlighter only.
hl.bs.language Specify the language for Locale that is used by BreakIteratorBoundaryScanner. This parameter makes sense for FastVectorHighlighter only. Valid entries take the form of ISO 639-1 strings.
hl.bs.country Specify the country for Locale that is used by BreakIteratorBoundaryScanner. This parameter makes sense for FastVectorHighlighter only. Valid entries take the form of ISO 3166-1 alpha-2 strings.
hl.useFastVectorHighlighter Use FastVectorHighlighter. FastVectorHighlighter requires the field is termVectors=on, termPositions=on and termOffsets=on. This parameter accepts per-field overrides. Default: FALSE
hl.usePhraseHighlighter Use SpanScorer to highlight phrase terms only when they appear within the query phrase in the document. Default: TRUE.
hl.highlightMultiTerm If the SpanScorer is also being used, enables highlighting for range/wildcard/fuzzy/prefix queries. Default: FALSE. This parameter makes sense for the original Highlighter only.
hl.regex.slop Factor by which the regex fragmenter can stray from the ideal fragment size (given by hl.fragsize) to accomodate the regular expression. For instance, a slop of 0.2 with fragsize of 100 should yield fragments between 80 and 120 characters in length. It is usually good to provide a slightly smaller fragsize when using the regex fragmenter. Default: .6. This parameter makes sense for the original Highlighter only.
hl.regex.pattern The regular expression for fragmenting. This could be used to extract sentences (see example solrconfig.xml) This parameter makes sense for the original Highlighter only.
hl.regex.maxAnalyzedChars Only analyze this many characters from a field when using the regex fragmenter (after which, the fragmenter produces fixed-sized fragments). Applying a complicated regex to a huge field is expensive. Default: 10000. This parameter makes sense for the original Highlighter only.
start Record to start at, default to beginning.
rows Number of records to return.
wt (character) Data type returned, defaults to 'json'. One of json or xml. If json,
uses fromJSON to parse. If xml, uses xmlParse to
parse. csv is only supported in solr_search and solr_all.
fl Fields to return
fq Filter query, this does not affect the search, only what gets returned
See https://lucene.apache.org/solr/guide/8_2/highlighting.html for more information on highlighting.
## Not run: # connect (conn <- SolrClient$new(host = "api.plos.org", path = "search", port = NULL)) # highlight search solr_highlight(conn, params = list(q='alcohol', hl.fl = 'abstract', rows=10), parsetype = "list") solr_highlight(conn, params = list(q='alcohol', hl.fl = c('abstract','title'), rows=3), parsetype = "list") # Raw data back ## json solr_highlight(conn, params = list(q='alcohol', hl.fl = 'abstract', rows=10), raw=TRUE) ## xml solr_highlight(conn, params = list(q='alcohol', hl.fl = 'abstract', rows=10, wt='xml'), raw=TRUE) ## parse after getting data back out <- solr_highlight(conn, params = list(q='theoretical math', hl.fl = c('abstract','title'), hl.fragsize=30, rows=10, wt='xml'), raw=TRUE) solr_parse(out, parsetype='list') ## End(Not run)## Not run: # connect (conn <- SolrClient$new(host = "api.plos.org", path = "search", port = NULL)) # highlight search solr_highlight(conn, params = list(q='alcohol', hl.fl = 'abstract', rows=10), parsetype = "list") solr_highlight(conn, params = list(q='alcohol', hl.fl = c('abstract','title'), rows=3), parsetype = "list") # Raw data back ## json solr_highlight(conn, params = list(q='alcohol', hl.fl = 'abstract', rows=10), raw=TRUE) ## xml solr_highlight(conn, params = list(q='alcohol', hl.fl = 'abstract', rows=10, wt='xml'), raw=TRUE) ## parse after getting data back out <- solr_highlight(conn, params = list(q='theoretical math', hl.fl = c('abstract','title'), hl.fragsize=30, rows=10, wt='xml'), raw=TRUE) solr_parse(out, parsetype='list') ## End(Not run)
search using the JSON request API
solr_json_request( conn, name = NULL, body = NULL, callopts = list(), progress = NULL )solr_json_request( conn, name = NULL, body = NULL, callopts = list(), progress = NULL )
conn |
A solrium connection object, see SolrClient |
name |
Name of a collection or core. Or leave as |
body |
(list) a named list, or a valid JSON character string |
callopts |
Call options passed on to crul::HttpClient |
progress |
a function with logic for printing a progress
bar for an HTTP request, ultimately passed down to curl.
only supports |
JSON character string
SOLR v7.1 was first version to support this. See https://issues.apache.org/jira/browse/SOLR-11244
POST request only, no GET request available
See https://lucene.apache.org/solr/guide/7_6/json-request-api.html for more information.
## Not run: # Connect to a local Solr instance (conn <- SolrClient$new()) ## body as JSON a <- conn$json_request("gettingstarted", body = '{"query":"*:*"}') jsonlite::fromJSON(a) ## body as named list b <- conn$json_request("gettingstarted", body = list(query = "*:*")) jsonlite::fromJSON(b) ## body as JSON a <- solr_json_request(conn, "gettingstarted", body = '{"query":"*:*"}') jsonlite::fromJSON(a) ## body as named list b <- solr_json_request(conn, "gettingstarted", body = list(query = "*:*")) jsonlite::fromJSON(b) ## End(Not run)## Not run: # Connect to a local Solr instance (conn <- SolrClient$new()) ## body as JSON a <- conn$json_request("gettingstarted", body = '{"query":"*:*"}') jsonlite::fromJSON(a) ## body as named list b <- conn$json_request("gettingstarted", body = list(query = "*:*")) jsonlite::fromJSON(b) ## body as JSON a <- solr_json_request(conn, "gettingstarted", body = '{"query":"*:*"}') jsonlite::fromJSON(a) ## body as named list b <- solr_json_request(conn, "gettingstarted", body = list(query = "*:*")) jsonlite::fromJSON(b) ## End(Not run)
Returns only more like this items
solr_mlt( conn, name = NULL, params = NULL, body = NULL, callopts = list(), raw = FALSE, parsetype = "df", concat = ",", optimizeMaxRows = TRUE, minOptimizedRows = 50000L, progress = NULL, ... )solr_mlt( conn, name = NULL, params = NULL, body = NULL, callopts = list(), raw = FALSE, parsetype = "df", concat = ",", optimizeMaxRows = TRUE, minOptimizedRows = 50000L, progress = NULL, ... )
conn |
A solrium connection object, see SolrClient |
name |
Name of a collection or core. Or leave as |
params |
(list) a named list of parameters, results in a GET request as long as no body parameters given |
body |
(list) a named list of parameters, if given a POST request will be performed |
callopts |
Call options passed on to crul::HttpClient |
raw |
(logical) If TRUE, returns raw data in format specified by wt param |
parsetype |
(character) One of 'list' or 'df' |
concat |
(character) Character to concatenate elements of longer than length 1. Note that this only works reliably when data format is json (wt='json'). The parsing is more complicated in XML format, but you can do that on your own. |
optimizeMaxRows |
(logical) If |
minOptimizedRows |
(numeric) used by |
progress |
a function with logic for printing a progress
bar for an HTTP request, ultimately passed down to curl. only supports
|
... |
Further args to be combined into query |
XML, JSON, a list, or data.frame
q Query terms, defaults to ':', or everything.
fq Filter query, this does not affect the search, only what gets returned
mlt.count The number of similar documents to return for each result. Default is 5.
mlt.fl The fields to use for similarity. NOTE: if possible these should have a stored TermVector DEFAULT_FIELD_NAMES = new String[] "contents"
mlt.mintf Minimum Term Frequency - the frequency below which terms will be ignored in the source doc. DEFAULT_MIN_TERM_FREQ = 2
mlt.mindf Minimum Document Frequency - the frequency at which words will be ignored which do not occur in at least this many docs. DEFAULT_MIN_DOC_FREQ = 5
mlt.minwl minimum word length below which words will be ignored. DEFAULT_MIN_WORD_LENGTH = 0
mlt.maxwl maximum word length above which words will be ignored. DEFAULT_MAX_WORD_LENGTH = 0
mlt.maxqt maximum number of query terms that will be included in any generated query. DEFAULT_MAX_QUERY_TERMS = 25
mlt.maxntp maximum number of tokens to parse in each example doc field that is not stored with TermVector support. DEFAULT_MAX_NUM_TOKENS_PARSED = 5000
mlt.boost (true/false) set if the query will be boosted by the interesting term relevance. DEFAULT_BOOST = false
mlt.qf Query fields and their boosts using the same format as that used in DisMaxQParserPlugin. These fields must also be specified in mlt.fl.
fl Fields to return. We force 'id' to be returned so that there is a unique identifier with each record.
wt (character) Data type returned, defaults to 'json'. One of json or xml. If json,
uses fromJSON to parse. If xml, uses xmlParse to
parse. csv is only supported in solr_search and solr_all.
start Record to start at, default to beginning.
rows Number of records to return. Defaults to 10.
key API key, if needed.
See https://lucene.apache.org/solr/guide/8_2/morelikethis.html for more information.
## Not run: # connect (conn <- SolrClient$new(host = "api.plos.org", path = "search", port = NULL)) # more like this search conn$mlt(params = list(q='*:*', mlt.count=2, mlt.fl='abstract', fl='score', fq="doc_type:full")) conn$mlt(params = list(q='*:*', rows=2, mlt.fl='title', mlt.mindf=1, mlt.mintf=1, fl='alm_twitterCount')) conn$mlt(params = list(q='title:"ecology" AND body:"cell"', mlt.fl='title', mlt.mindf=1, mlt.mintf=1, fl='counter_total_all', rows=5)) conn$mlt(params = list(q='ecology', mlt.fl='abstract', fl='title', rows=5)) solr_mlt(conn, params = list(q='ecology', mlt.fl='abstract', fl=c('score','eissn'), rows=5)) solr_mlt(conn, params = list(q='ecology', mlt.fl='abstract', fl=c('score','eissn'), rows=5, wt = "xml")) # get raw data, and parse later if needed out <- solr_mlt(conn, params=list(q='ecology', mlt.fl='abstract', fl='title', rows=2), raw=TRUE) solr_parse(out, "df") ## End(Not run)## Not run: # connect (conn <- SolrClient$new(host = "api.plos.org", path = "search", port = NULL)) # more like this search conn$mlt(params = list(q='*:*', mlt.count=2, mlt.fl='abstract', fl='score', fq="doc_type:full")) conn$mlt(params = list(q='*:*', rows=2, mlt.fl='title', mlt.mindf=1, mlt.mintf=1, fl='alm_twitterCount')) conn$mlt(params = list(q='title:"ecology" AND body:"cell"', mlt.fl='title', mlt.mindf=1, mlt.mintf=1, fl='counter_total_all', rows=5)) conn$mlt(params = list(q='ecology', mlt.fl='abstract', fl='title', rows=5)) solr_mlt(conn, params = list(q='ecology', mlt.fl='abstract', fl=c('score','eissn'), rows=5)) solr_mlt(conn, params = list(q='ecology', mlt.fl='abstract', fl=c('score','eissn'), rows=5, wt = "xml")) # get raw data, and parse later if needed out <- solr_mlt(conn, params=list(q='ecology', mlt.fl='abstract', fl='title', rows=2), raw=TRUE) solr_parse(out, "df") ## End(Not run)
Optimize
solr_optimize( conn, name, max_segments = 1, wait_searcher = TRUE, soft_commit = FALSE, wt = "json", raw = FALSE, ... )solr_optimize( conn, name, max_segments = 1, wait_searcher = TRUE, soft_commit = FALSE, wt = "json", raw = FALSE, ... )
conn |
A solrium connection object, see SolrClient |
name |
(character) A collection or core name. Required. |
max_segments |
optimizes down to at most this number of segments. Default: 1 |
wait_searcher |
block until a new searcher is opened and registered
as the main query searcher, making the changes visible. Default: |
soft_commit |
perform a soft commit - this will refresh the 'view'
of the index in a more performant manner, but without "on-disk" guarantees.
Default: |
wt |
(character) One of json (default) or xml. If json, uses
|
raw |
(logical) If |
... |
curl options passed on to crul::HttpClient |
## Not run: (conn <- SolrClient$new()) solr_optimize(conn, "gettingstarted") solr_optimize(conn, "gettingstarted", max_segments = 2) solr_optimize(conn, "gettingstarted", wait_searcher = FALSE) # get xml back solr_optimize(conn, "gettingstarted", wt = "xml") ## raw xml solr_optimize(conn, "gettingstarted", wt = "xml", raw = TRUE) ## End(Not run)## Not run: (conn <- SolrClient$new()) solr_optimize(conn, "gettingstarted") solr_optimize(conn, "gettingstarted", max_segments = 2) solr_optimize(conn, "gettingstarted", wait_searcher = FALSE) # get xml back solr_optimize(conn, "gettingstarted", wt = "xml") ## raw xml solr_optimize(conn, "gettingstarted", wt = "xml", raw = TRUE) ## End(Not run)
Parse raw data from solr_search, solr_facet, or solr_highlight.
solr_parse(input, parsetype = NULL, concat) ## S3 method for class 'sr_high' solr_parse(input, parsetype = "list", concat = ",") ## S3 method for class 'sr_search' solr_parse(input, parsetype = "list", concat = ",") ## S3 method for class 'sr_all' solr_parse(input, parsetype = "list", concat = ",") ## S3 method for class 'sr_mlt' solr_parse(input, parsetype = "list", concat = ",") ## S3 method for class 'sr_stats' solr_parse(input, parsetype = "list", concat = ",") ## S3 method for class 'sr_group' solr_parse(input, parsetype = "list", concat = ",")solr_parse(input, parsetype = NULL, concat) ## S3 method for class 'sr_high' solr_parse(input, parsetype = "list", concat = ",") ## S3 method for class 'sr_search' solr_parse(input, parsetype = "list", concat = ",") ## S3 method for class 'sr_all' solr_parse(input, parsetype = "list", concat = ",") ## S3 method for class 'sr_mlt' solr_parse(input, parsetype = "list", concat = ",") ## S3 method for class 'sr_stats' solr_parse(input, parsetype = "list", concat = ",") ## S3 method for class 'sr_group' solr_parse(input, parsetype = "list", concat = ",")
input |
Output from solr_facet |
parsetype |
One of 'list' or 'df' (data.frame) |
concat |
Character to conactenate strings by, e.g,. ',' (character). Used in solr_parse.sr_search only. |
This is the parser used internally in solr_facet, but if you output raw data from solr_facet using raw=TRUE, then you can use this function to parse that data (a sr_facet S3 object) after the fact to a list of data.frame's for easier consumption. The data format type is detected from the attribute "wt" on the sr_facet object.
Returns only matched documents, and doesn't return other items, including facets, groups, mlt, stats, and highlights.
solr_search( conn, name = NULL, params = list(q = "*:*"), body = NULL, callopts = list(), raw = FALSE, parsetype = "df", concat = ",", optimizeMaxRows = TRUE, minOptimizedRows = 50000L, progress = NULL, ... )solr_search( conn, name = NULL, params = list(q = "*:*"), body = NULL, callopts = list(), raw = FALSE, parsetype = "df", concat = ",", optimizeMaxRows = TRUE, minOptimizedRows = 50000L, progress = NULL, ... )
conn |
A solrium connection object, see SolrClient |
name |
Name of a collection or core. Or leave as |
params |
(list) a named list of parameters, results in a GET request as long as no body parameters given |
body |
(list) a named list of parameters, if given a POST request will be performed |
callopts |
Call options passed on to crul::HttpClient |
raw |
(logical) If TRUE, returns raw data in format specified by wt param |
parsetype |
(character) One of 'list' or 'df' |
concat |
(character) Character to concatenate elements of longer than length 1. Note that this only works reliably when data format is json (wt='json'). The parsing is more complicated in XML format, but you can do that on your own. |
optimizeMaxRows |
(logical) If |
minOptimizedRows |
(numeric) used by |
progress |
a function with logic for printing a progress
bar for an HTTP request, ultimately passed down to curl. only supports
|
... |
Further args to be combined into query |
XML, JSON, a list, or data.frame
q Query terms, defaults to ':', or everything.
sort Field to sort on. You can specify ascending (e.g., score desc) or descending (e.g., score asc), sort by two fields (e.g., score desc, price asc), or sort by a function (e.g., sum(x_f, y_f) desc, which sorts by the sum of x_f and y_f in a descending order).
start Record to start at, default to beginning.
rows Number of records to return. Default: 10.
pageDoc If you expect to be paging deeply into the results (say beyond page 10,
assuming rows=10) and you are sorting by score, you may wish to add the pageDoc
and pageScore parameters to your request. These two parameters tell Solr (and Lucene)
what the last result (Lucene internal docid and score) of the previous page was,
so that when scoring the query for the next set of pages, it can ignore any results
that occur higher than that item. To get the Lucene internal doc id, you will need
to add docid to the &fl list.
pageScore See pageDoc notes.
fq Filter query, this does not affect the search, only what gets returned. This parameter can accept multiple items in a lis or vector. You can't pass more than one parameter of the same name, so we get around it by passing multiple queries and we parse internally
fl Fields to return, can be a character vector like c('id', 'title'),
or a single character vector with one or more comma separated names, like
'id,title'
defType Specify the query parser to use with this request.
timeAllowed The time allowed for a search to finish. This value only applies
to the search and not to requests in general. Time is in milliseconds. Values <= 0
mean no time restriction. Partial results may be returned (if there are any).
qt Which query handler used. Options: dismax, others?
NOW Set a fixed time for evaluating Date based expresions
TZ Time zone, you can override the default.
echoHandler If TRUE, Solr places the name of the handle used in the
response to the client for debugging purposes. Default:
echoParams The echoParams parameter tells Solr what kinds of Request parameters should be included in the response for debugging purposes, legal values include:
none - don't include any request parameters for debugging
explicit - include the parameters explicitly specified by the client in the request
all - include all parameters involved in this request, either specified explicitly by the client, or implicit because of the request handler configuration.
wt (character) One of json, xml, or csv. Data type returned, defaults
to 'csv'. If json, uses jsonlite::fromJSON() to parse. If xml,
uses xml2::read_xml() to parse. If csv, uses read.table() to parse.
wt=csv gives the fastest performance at least in all the cases we have
tested in, thus it's the default value for wt
Because solr_search() returns a data.frame, metadata doesn't fit into the
output data.frame itself. You can access number of results (numFound) in
the attributes of the results. For example, attr(x, "numFound") for
number of results, and attr(x, "start") for the offset value (if one
was given). Or you can get all attributes like attributes(x). These
metadata are not in the attributes when raw=TRUE as those metadata
are in the payload (unless wt="csv").
SOLR v1.2 was first version to support csv. See https://issues.apache.org/jira/browse/SOLR-66
See https://lucene.apache.org/solr/guide/8_2/searching.html for more information.
solr_highlight(), solr_facet()
## Not run: # Connect to a local Solr instance (cli <- SolrClient$new()) cli$search("gettingstarted", params = list(q = "features:notes")) solr_search(cli, "gettingstarted") solr_search(cli, "gettingstarted", params = list(q = "features:notes")) solr_search(cli, "gettingstarted", body = list(query = "features:notes")) (cli <- SolrClient$new(host = "api.plos.org", path = "search", port = NULL)) cli$search(params = list(q = "*:*")) cli$search(params = list(q = "title:golgi", fl = c('id', 'title'))) cli$search(params = list(q = "*:*", facet = "true")) # search solr_search(cli, params = list(q='*:*', rows=2, fl='id')) # search and return all rows solr_search(cli, params = list(q='*:*', rows=-1, fl='id')) # Search for word ecology in title and cell in the body solr_search(cli, params = list(q='title:"ecology" AND body:"cell"', fl='title', rows=5)) # Search for word "cell" and not "body" in the title field solr_search(cli, params = list(q='title:"cell" -title:"lines"', fl='title', rows=5)) # Wildcards ## Search for word that starts with "cell" in the title field solr_search(cli, params = list(q='title:"cell*"', fl='title', rows=5)) # Proximity searching ## Search for words "sports" and "alcohol" within four words of each other solr_search(cli, params = list(q='everything:"sports alcohol"~7', fl='abstract', rows=3)) # Range searches ## Search for articles with Twitter count between 5 and 10 solr_search(cli, params = list(q='*:*', fl=c('alm_twitterCount','id'), fq='alm_twitterCount:[5 TO 50]', rows=10)) # Boosts ## Assign higher boost to title matches than to body matches ## (compare the two calls) solr_search(cli, params = list(q='title:"cell" abstract:"science"', fl='title', rows=3)) solr_search(cli, params = list(q='title:"cell"^1.5 AND abstract:"science"', fl='title', rows=3)) # FunctionQuery queries ## This kind of query allows you to use the actual values of fields to ## calculate relevancy scores for returned documents ## Here, we search on the product of counter_total_all and alm_twitterCount ## metrics for articles in PLOS Journals solr_search(cli, params = list(q="{!func}product($v1,$v2)", v1 = 'sqrt(counter_total_all)', v2 = 'log(alm_twitterCount)', rows=5, fl=c('id','title'), fq='doc_type:full')) ## here, search on the product of counter_total_all and alm_twitterCount, ## using a new temporary field "_val_" solr_search(cli, params = list(q='_val_:"product(counter_total_all,alm_twitterCount)"', rows=5, fl=c('id','title'), fq='doc_type:full')) ## papers with most citations solr_search(cli, params = list(q='_val_:"max(counter_total_all)"', rows=5, fl=c('id','counter_total_all'), fq='doc_type:full')) ## papers with most tweets solr_search(cli, params = list(q='_val_:"max(alm_twitterCount)"', rows=5, fl=c('id','alm_twitterCount'), fq='doc_type:full')) ## many fq values solr_search(cli, params = list(q="*:*", fl=c('id','alm_twitterCount'), fq=list('doc_type:full','subject:"Social networks"', 'alm_twitterCount:[100 TO 10000]'), sort='counter_total_month desc')) ## using wt = csv solr_search(cli, params = list(q='*:*', rows=50, fl=c('id','score'), fq='doc_type:full', wt="csv")) solr_search(cli, params = list(q='*:*', rows=50, fl=c('id','score'), fq='doc_type:full')) # using a proxy # cli <- SolrClient$new(host = "api.plos.org", path = "search", port = NULL, # proxy = list(url = "http://186.249.1.146:80")) # solr_search(cli, q='*:*', rows=2, fl='id', callopts=list(verbose=TRUE)) # Pass on curl options to modify request ## verbose solr_search(cli, params = list(q='*:*', rows=2, fl='id'), callopts = list(verbose=TRUE)) # using a cursor for deep paging (cli <- SolrClient$new(host = "api.plos.org", path = "search", port = NULL)) ## json, raw data res <- solr_search(cli, params = list(q = '*:*', rows = 100, sort = "id asc", cursorMark = "*"), parsetype = "json", raw = TRUE, callopts=list(verbose=TRUE)) res ## data.frame res <- solr_search(cli, params = list(q = '*:*', rows = 100, sort = "id asc", cursorMark = "*")) res attributes(res) attr(res, "nextCursorMark") ## list res <- solr_search(cli, params = list(q = '*:*', rows = 100, sort = "id asc", cursorMark = "*"), parsetype = "list") res attributes(res) attr(res, "nextCursorMark") ## End(Not run)## Not run: # Connect to a local Solr instance (cli <- SolrClient$new()) cli$search("gettingstarted", params = list(q = "features:notes")) solr_search(cli, "gettingstarted") solr_search(cli, "gettingstarted", params = list(q = "features:notes")) solr_search(cli, "gettingstarted", body = list(query = "features:notes")) (cli <- SolrClient$new(host = "api.plos.org", path = "search", port = NULL)) cli$search(params = list(q = "*:*")) cli$search(params = list(q = "title:golgi", fl = c('id', 'title'))) cli$search(params = list(q = "*:*", facet = "true")) # search solr_search(cli, params = list(q='*:*', rows=2, fl='id')) # search and return all rows solr_search(cli, params = list(q='*:*', rows=-1, fl='id')) # Search for word ecology in title and cell in the body solr_search(cli, params = list(q='title:"ecology" AND body:"cell"', fl='title', rows=5)) # Search for word "cell" and not "body" in the title field solr_search(cli, params = list(q='title:"cell" -title:"lines"', fl='title', rows=5)) # Wildcards ## Search for word that starts with "cell" in the title field solr_search(cli, params = list(q='title:"cell*"', fl='title', rows=5)) # Proximity searching ## Search for words "sports" and "alcohol" within four words of each other solr_search(cli, params = list(q='everything:"sports alcohol"~7', fl='abstract', rows=3)) # Range searches ## Search for articles with Twitter count between 5 and 10 solr_search(cli, params = list(q='*:*', fl=c('alm_twitterCount','id'), fq='alm_twitterCount:[5 TO 50]', rows=10)) # Boosts ## Assign higher boost to title matches than to body matches ## (compare the two calls) solr_search(cli, params = list(q='title:"cell" abstract:"science"', fl='title', rows=3)) solr_search(cli, params = list(q='title:"cell"^1.5 AND abstract:"science"', fl='title', rows=3)) # FunctionQuery queries ## This kind of query allows you to use the actual values of fields to ## calculate relevancy scores for returned documents ## Here, we search on the product of counter_total_all and alm_twitterCount ## metrics for articles in PLOS Journals solr_search(cli, params = list(q="{!func}product($v1,$v2)", v1 = 'sqrt(counter_total_all)', v2 = 'log(alm_twitterCount)', rows=5, fl=c('id','title'), fq='doc_type:full')) ## here, search on the product of counter_total_all and alm_twitterCount, ## using a new temporary field "_val_" solr_search(cli, params = list(q='_val_:"product(counter_total_all,alm_twitterCount)"', rows=5, fl=c('id','title'), fq='doc_type:full')) ## papers with most citations solr_search(cli, params = list(q='_val_:"max(counter_total_all)"', rows=5, fl=c('id','counter_total_all'), fq='doc_type:full')) ## papers with most tweets solr_search(cli, params = list(q='_val_:"max(alm_twitterCount)"', rows=5, fl=c('id','alm_twitterCount'), fq='doc_type:full')) ## many fq values solr_search(cli, params = list(q="*:*", fl=c('id','alm_twitterCount'), fq=list('doc_type:full','subject:"Social networks"', 'alm_twitterCount:[100 TO 10000]'), sort='counter_total_month desc')) ## using wt = csv solr_search(cli, params = list(q='*:*', rows=50, fl=c('id','score'), fq='doc_type:full', wt="csv")) solr_search(cli, params = list(q='*:*', rows=50, fl=c('id','score'), fq='doc_type:full')) # using a proxy # cli <- SolrClient$new(host = "api.plos.org", path = "search", port = NULL, # proxy = list(url = "http://186.249.1.146:80")) # solr_search(cli, q='*:*', rows=2, fl='id', callopts=list(verbose=TRUE)) # Pass on curl options to modify request ## verbose solr_search(cli, params = list(q='*:*', rows=2, fl='id'), callopts = list(verbose=TRUE)) # using a cursor for deep paging (cli <- SolrClient$new(host = "api.plos.org", path = "search", port = NULL)) ## json, raw data res <- solr_search(cli, params = list(q = '*:*', rows = 100, sort = "id asc", cursorMark = "*"), parsetype = "json", raw = TRUE, callopts=list(verbose=TRUE)) res ## data.frame res <- solr_search(cli, params = list(q = '*:*', rows = 100, sort = "id asc", cursorMark = "*")) res attributes(res) attr(res, "nextCursorMark") ## list res <- solr_search(cli, params = list(q = '*:*', rows = 100, sort = "id asc", cursorMark = "*"), parsetype = "list") res attributes(res) attr(res, "nextCursorMark") ## End(Not run)
Returns only stat items
solr_stats( conn, name = NULL, params = list(q = "*:*", stats.field = NULL, stats.facet = NULL), body = NULL, callopts = list(), raw = FALSE, parsetype = "df", progress = NULL, ... )solr_stats( conn, name = NULL, params = list(q = "*:*", stats.field = NULL, stats.facet = NULL), body = NULL, callopts = list(), raw = FALSE, parsetype = "df", progress = NULL, ... )
conn |
A solrium connection object, see SolrClient |
name |
Name of a collection or core. Or leave as |
params |
(list) a named list of parameters, results in a GET request as long as no body parameters given |
body |
(list) a named list of parameters, if given a POST request will be performed |
callopts |
Call options passed on to crul::HttpClient |
raw |
(logical) If TRUE, returns raw data in format specified by wt param |
parsetype |
(character) One of 'list' or 'df' |
progress |
a function with logic for printing a progress
bar for an HTTP request, ultimately passed down to curl. only supports
|
... |
Further args to be combined into query |
XML, JSON, a list, or data.frame
q Query terms, defaults to ':', or everything.
stats.field The number of similar documents to return for each result.
stats.facet You can not facet on multi-valued fields.
wt (character) Data type returned, defaults to 'json'. One of json
or xml. If json, uses fromJSON to parse. If xml,
uses xmlParse to parse. csv is only supported in
solr_search and solr_all.
start Record to start at, default to beginning.
rows Number of records to return. Defaults to 10.
key API key, if needed.
See https://lucene.apache.org/solr/guide/8_2/the-stats-component.html for more information on Solr stats.
solr_highlight(), solr_facet(), solr_search(), solr_mlt()
## Not run: # connect (cli <- SolrClient$new(host = "api.plos.org", path = "search", port = NULL)) # get stats solr_stats(cli, params = list(q='science', stats.field='counter_total_all'), raw=TRUE) solr_stats(cli, params = list(q='title:"ecology" AND body:"cell"', stats.field=c('counter_total_all','alm_twitterCount'))) solr_stats(cli, params = list(q='ecology', stats.field=c('counter_total_all','alm_twitterCount'), stats.facet='journal')) solr_stats(cli, params = list(q='ecology', stats.field=c('counter_total_all','alm_twitterCount'), stats.facet=c('journal','volume'))) # Get raw data, then parse later if you feel like it ## json out <- solr_stats(cli, params = list(q='ecology', stats.field=c('counter_total_all','alm_twitterCount'), stats.facet=c('journal','volume')), raw=TRUE) library("jsonlite") jsonlite::fromJSON(out) solr_parse(out) # list solr_parse(out, 'df') # data.frame ## xml out <- solr_stats(cli, params = list(q='ecology', stats.field=c('counter_total_all','alm_twitterCount'), stats.facet=c('journal','volume'), wt="xml"), raw=TRUE) library("xml2") xml2::read_xml(unclass(out)) solr_parse(out) # list solr_parse(out, 'df') # data.frame # Get verbose http call information solr_stats(cli, params = list(q='ecology', stats.field='alm_twitterCount'), callopts=list(verbose=TRUE)) ## End(Not run)## Not run: # connect (cli <- SolrClient$new(host = "api.plos.org", path = "search", port = NULL)) # get stats solr_stats(cli, params = list(q='science', stats.field='counter_total_all'), raw=TRUE) solr_stats(cli, params = list(q='title:"ecology" AND body:"cell"', stats.field=c('counter_total_all','alm_twitterCount'))) solr_stats(cli, params = list(q='ecology', stats.field=c('counter_total_all','alm_twitterCount'), stats.facet='journal')) solr_stats(cli, params = list(q='ecology', stats.field=c('counter_total_all','alm_twitterCount'), stats.facet=c('journal','volume'))) # Get raw data, then parse later if you feel like it ## json out <- solr_stats(cli, params = list(q='ecology', stats.field=c('counter_total_all','alm_twitterCount'), stats.facet=c('journal','volume')), raw=TRUE) library("jsonlite") jsonlite::fromJSON(out) solr_parse(out) # list solr_parse(out, 'df') # data.frame ## xml out <- solr_stats(cli, params = list(q='ecology', stats.field=c('counter_total_all','alm_twitterCount'), stats.facet=c('journal','volume'), wt="xml"), raw=TRUE) library("xml2") xml2::read_xml(unclass(out)) solr_parse(out) # list solr_parse(out, 'df') # data.frame # Get verbose http call information solr_stats(cli, params = list(q='ecology', stats.field='alm_twitterCount'), callopts=list(verbose=TRUE)) ## End(Not run)
Solr connection client
host |
(character) Host url. Deafault: 127.0.0.1 |
path |
(character) url path. |
port |
(character/numeric) Port. Default: 8389 |
scheme |
(character) http scheme, one of http or https. Default: http |
proxy |
List of arguments for a proxy connection, including one or more of: url, port, username, password, and auth. See crul::proxy for help, which is used to construct the proxy connection. |
errors |
(character) One of |
SolrClient creates a R6 class object. The object is
not cloneable and is portable, so it can be inherited across packages
without complication.
SolrClient is used to initialize a client that knows about your
Solr instance, with options for setting host, port, http scheme,
and simple vs. complete error reporting
Various output, see help files for each grouping of methods.
Each of these methods also has a matching standalone exported
function that you can use by passing in the connection object made
by calling SolrClient$new(). Also, see the docs for each method for
parameter definitions and their default values.
ping(name, wt = 'json', raw = FALSE, ...)
schema(name, what = '', raw = FALSE, ...)
commit(name, expunge_deletes = FALSE, wait_searcher = TRUE, soft_commit = FALSE, wt = 'json', raw = FALSE, ...)
optimize(name, max_segments = 1, wait_searcher = TRUE, soft_commit = FALSE, wt = 'json', raw = FALSE, ...)
config_get(name, what = NULL, wt = "json", raw = FALSE, ...)
config_params(name, param = NULL, set = NULL, unset = NULL, update = NULL, ...)
config_overlay(name, omitHeader = FALSE, ...)
config_set(name, set = NULL, unset = NULL, ...)
collection_exists(name, ...)
collection_list(raw = FALSE, ...)
collection_create(name, numShards = 1, maxShardsPerNode = 1, createNodeSet = NULL, collection.configName = NULL, replicationFactor = 1, router.name = NULL, shards = NULL, createNodeSet.shuffle = TRUE, router.field = NULL, autoAddReplicas = FALSE, async = NULL, raw = FALSE, callopts=list(), ...)
collection_addreplica(name, shard = NULL, route = NULL, node = NULL, instanceDir = NULL, dataDir = NULL, async = NULL, raw = FALSE, callopts=list(), ...)
collection_addreplicaprop(name, shard, replica, property, property.value, shardUnique = FALSE, raw = FALSE, callopts=list())
collection_addrole(role = "overseer", node, raw = FALSE, ...)
collection_balanceshardunique(name, property, onlyactivenodes = TRUE, shardUnique = NULL, raw = FALSE, ...)
collection_clusterprop(name, val, raw = FALSE, callopts=list())
collection_clusterstatus(name = NULL, shard = NULL, raw = FALSE, ...)
collection_createalias(alias, collections, raw = FALSE, ...)
collection_createshard(name, shard, createNodeSet = NULL, raw = FALSE, ...)
collection_delete(name, raw = FALSE, ...)
collection_deletealias(alias, raw = FALSE, ...)
collection_deletereplica(name, shard = NULL, replica = NULL, onlyIfDown = FALSE, raw = FALSE, callopts=list(), ...)
collection_deletereplicaprop(name, shard, replica, property, raw = FALSE, callopts=list())
collection_deleteshard(name, shard, raw = FALSE, ...)
collection_migrate(name, target.collection, split.key, forward.timeout = NULL, async = NULL, raw = FALSE, ...)
collection_overseerstatus(raw = FALSE, ...)
collection_rebalanceleaders(name, maxAtOnce = NULL, maxWaitSeconds = NULL, raw = FALSE, ...)
collection_reload(name, raw = FALSE, ...)
collection_removerole(role = "overseer", node, raw = FALSE, ...)
collection_requeststatus(requestid, raw = FALSE, ...)
collection_splitshard(name, shard, ranges = NULL, split.key = NULL, async = NULL, raw = FALSE, ...)
core_status(name = NULL, indexInfo = TRUE, raw = FALSE, callopts=list())
core_exists(name, callopts = list())
core_create(name, instanceDir = NULL, config = NULL, schema = NULL, dataDir = NULL, configSet = NULL, collection = NULL, shard = NULL, async=NULL, raw = FALSE, callopts=list(), ...)
core_unload(name, deleteIndex = FALSE, deleteDataDir = FALSE, deleteInstanceDir = FALSE, async = NULL, raw = FALSE, callopts = list())
core_rename(name, other, async = NULL, raw = FALSE, callopts=list())
core_reload(name, raw = FALSE, callopts=list())
core_swap(name, other, async = NULL, raw = FALSE, callopts=list())
core_mergeindexes(name, indexDir = NULL, srcCore = NULL, async = NULL, raw = FALSE, callopts = list())
core_requeststatus(requestid, raw = FALSE, callopts = list())
core_split(name, path = NULL, targetCore = NULL, ranges = NULL, split.key = NULL, async = NULL, raw = FALSE, callopts=list())
search(name = NULL, params = NULL, body = NULL, callopts = list(), raw = FALSE, parsetype = 'df', concat = ',', optimizeMaxRows = TRUE, minOptimizedRows = 50000L, progress = NULL, ...)
facet(name = NULL, params = NULL, body = NULL, callopts = list(), raw = FALSE, parsetype = 'df', concat = ',', progress = NULL, ...)
stats(name = NULL, params = list(q = '*:*', stats.field = NULL, stats.facet = NULL), body = NULL, callopts=list(), raw = FALSE, parsetype = 'df', progress = NULL, ...)
highlight(name = NULL, params = NULL, body = NULL, callopts=list(), raw = FALSE, parsetype = 'df', progress = NULL, ...)
group(name = NULL, params = NULL, body = NULL, callopts=list(), raw=FALSE, parsetype='df', concat=',', progress = NULL, ...)
mlt(name = NULL, params = NULL, body = NULL, callopts=list(), raw=FALSE, parsetype='df', concat=',', optimizeMaxRows = TRUE, minOptimizedRows = 50000L, progress = NULL, ...)
all(name = NULL, params = NULL, body = NULL, callopts=list(), raw=FALSE, parsetype='df', concat=',', optimizeMaxRows = TRUE, minOptimizedRows = 50000L, progress = NULL, ...)
json_request(name = NULL, body = NULL, callopts=list(), progress = NULL)
get(ids, name, fl = NULL, wt = 'json', raw = FALSE, ...)
add(x, name, commit = TRUE, commit_within = NULL, overwrite = TRUE, boost = NULL, wt = 'json', raw = FALSE, ...)
delete_by_id(ids, name, commit = TRUE, commit_within = NULL, overwrite = TRUE, boost = NULL, wt = 'json', raw = FALSE, ...)
delete_by_query(query, name, commit = TRUE, commit_within = NULL, overwrite = TRUE, boost = NULL, wt = 'json', raw = FALSE, ...)
update_json(files, name, commit = TRUE, optimize = FALSE, max_segments = 1, expunge_deletes = FALSE, wait_searcher = TRUE, soft_commit = FALSE, prepare_commit = NULL, wt = 'json', raw = FALSE, ...)
update_xml(files, name, commit = TRUE, optimize = FALSE, max_segments = 1, expunge_deletes = FALSE, wait_searcher = TRUE, soft_commit = FALSE, prepare_commit = NULL, wt = 'json', raw = FALSE, ...)
update_csv(files, name, separator = ',', header = TRUE, fieldnames = NULL, skip = NULL, skipLines = 0, trim = FALSE, encapsulator = NULL, escape = NULL, keepEmpty = FALSE, literal = NULL, map = NULL, split = NULL, rowid = NULL, rowidOffset = NULL, overwrite = NULL, commit = NULL, wt = 'json', raw = FALSE, ...)
update_atomic_json(body, name, wt = 'json', raw = FALSE, ...)
update_atomic_xml(body, name, wt = 'json', raw = FALSE, ...)
When the $search() method returns a data.frame, metadata doesn't fit
into the output data.frame itself. You can access number of results
(numFound) in the attributes of the results. For example,
attr(x, "numFound") for number of results, and attr(x, "start")
for the offset value (if one was given). Or you can get all
attributes like attributes(x). These metadata are not in the
attributes when requesting raw xml or json though as those metadata
are in the payload (unless wt="csv").
## Not run: # make a client (cli <- SolrClient$new()) # variables cli$host cli$port cli$path cli$scheme # ping ## ping to make sure it's up cli$ping("gettingstarted") # version ## get Solr version information cli$schema("gettingstarted") cli$schema("gettingstarted", "fields") cli$schema("gettingstarted", "name") cli$schema("gettingstarted", "version")$version # Search cli$search("gettingstarted", params = list(q = "*:*")) cli$search("gettingstarted", body = list(query = "*:*")) # set a different host SolrClient$new(host = 'stuff.com') # set a different port SolrClient$new(host = 3456) # set a different http scheme SolrClient$new(scheme = 'https') # set a proxy SolrClient$new(proxy = list(url = "187.62.207.130:3128")) prox <- list(url = "187.62.207.130:3128", user = "foo", pwd = "bar") cli <- SolrClient$new(proxy = prox) cli$proxy # A remote Solr instance to which you don't have admin access (cli <- SolrClient$new(host = "api.plos.org", path = "search", port = NULL)) res <- cli$search(params = list(q = "memory")) res attr(res, "numFound") attr(res, "start") attr(res, "maxScore") ## End(Not run)## Not run: # make a client (cli <- SolrClient$new()) # variables cli$host cli$port cli$path cli$scheme # ping ## ping to make sure it's up cli$ping("gettingstarted") # version ## get Solr version information cli$schema("gettingstarted") cli$schema("gettingstarted", "fields") cli$schema("gettingstarted", "name") cli$schema("gettingstarted", "version")$version # Search cli$search("gettingstarted", params = list(q = "*:*")) cli$search("gettingstarted", body = list(query = "*:*")) # set a different host SolrClient$new(host = 'stuff.com') # set a different port SolrClient$new(host = 3456) # set a different http scheme SolrClient$new(scheme = 'https') # set a proxy SolrClient$new(proxy = list(url = "187.62.207.130:3128")) prox <- list(url = "187.62.207.130:3128", user = "foo", pwd = "bar") cli <- SolrClient$new(proxy = prox) cli$proxy # A remote Solr instance to which you don't have admin access (cli <- SolrClient$new(host = "api.plos.org", path = "search", port = NULL)) res <- cli$search(params = list(q = "memory")) res attr(res, "numFound") attr(res, "start") attr(res, "maxScore") ## End(Not run)
Atomic updates to parts of Solr documents
update_atomic_json(conn, body, name, wt = "json", raw = FALSE, ...)update_atomic_json(conn, body, name, wt = "json", raw = FALSE, ...)
conn |
A solrium connection object, see SolrClient |
body |
(character) JSON as a character string |
name |
(character) Name of the core or collection |
wt |
(character) One of json (default) or xml. If json, uses
|
raw |
(logical) If |
... |
curl options passed on to crul::HttpClient |
https://lucene.apache.org/solr/guide/7_0/updating-parts-of-documents.html
## Not run: # start Solr in Cloud mode: bin/solr start -e cloud -noprompt # connect (conn <- SolrClient$new()) # create a collection if (!conn$collection_exists("books")) { conn$collection_delete("books") conn$collection_create("books") } # Add documents file <- system.file("examples", "books2.json", package = "solrium") cat(readLines(file), sep = "\n") conn$update_json(file, "books") # get a document conn$get(ids = 343334534545, "books") # atomic update body <- '[{ "id": "343334534545", "genre_s": {"set": "mystery" }, "pages_i": {"inc": 1 } }]' conn$update_atomic_json(body, "books") # get the document again conn$get(ids = 343334534545, "books") # another atomic update body <- '[{ "id": "343334534545", "price": {"remove": "12.5" } }]' conn$update_atomic_json(body, "books") # get the document again conn$get(ids = 343334534545, "books") ## End(Not run)## Not run: # start Solr in Cloud mode: bin/solr start -e cloud -noprompt # connect (conn <- SolrClient$new()) # create a collection if (!conn$collection_exists("books")) { conn$collection_delete("books") conn$collection_create("books") } # Add documents file <- system.file("examples", "books2.json", package = "solrium") cat(readLines(file), sep = "\n") conn$update_json(file, "books") # get a document conn$get(ids = 343334534545, "books") # atomic update body <- '[{ "id": "343334534545", "genre_s": {"set": "mystery" }, "pages_i": {"inc": 1 } }]' conn$update_atomic_json(body, "books") # get the document again conn$get(ids = 343334534545, "books") # another atomic update body <- '[{ "id": "343334534545", "price": {"remove": "12.5" } }]' conn$update_atomic_json(body, "books") # get the document again conn$get(ids = 343334534545, "books") ## End(Not run)
Atomic updates to parts of Solr documents
update_atomic_xml(conn, body, name, wt = "json", raw = FALSE, ...)update_atomic_xml(conn, body, name, wt = "json", raw = FALSE, ...)
conn |
A solrium connection object, see SolrClient |
body |
(character) XML as a character string |
name |
(character) Name of the core or collection |
wt |
(character) One of json (default) or xml. If json, uses
|
raw |
(logical) If |
... |
curl options passed on to crul::HttpClient |
https://lucene.apache.org/solr/guide/7_0/updating-parts-of-documents.html
## Not run: # start Solr in Cloud mode: bin/solr start -e cloud -noprompt # connect (conn <- SolrClient$new()) # create a collection if (!conn$collection_exists("books")) { conn$collection_delete("books") conn$collection_create("books") } # Add documents file <- system.file("examples", "books.xml", package = "solrium") cat(readLines(file), sep = "\n") conn$update_xml(file, "books") # get a document conn$get(ids = '978-0641723445', "books", wt = "xml") # atomic update body <- ' <add> <doc> <field name="id">978-0641723445</field> <field name="genre_s" update="set">mystery</field> <field name="pages_i" update="inc">1</field> </doc> </add>' conn$update_atomic_xml(body, name="books") # get the document again conn$get(ids = '978-0641723445', "books", wt = "xml") # another atomic update body <- ' <add> <doc> <field name="id">978-0641723445</field> <field name="price" update="remove">12.5</field> </doc> </add>' conn$update_atomic_xml(body, "books") # get the document again conn$get(ids = '978-0641723445', "books", wt = "xml") ## End(Not run)## Not run: # start Solr in Cloud mode: bin/solr start -e cloud -noprompt # connect (conn <- SolrClient$new()) # create a collection if (!conn$collection_exists("books")) { conn$collection_delete("books") conn$collection_create("books") } # Add documents file <- system.file("examples", "books.xml", package = "solrium") cat(readLines(file), sep = "\n") conn$update_xml(file, "books") # get a document conn$get(ids = '978-0641723445', "books", wt = "xml") # atomic update body <- ' <add> <doc> <field name="id">978-0641723445</field> <field name="genre_s" update="set">mystery</field> <field name="pages_i" update="inc">1</field> </doc> </add>' conn$update_atomic_xml(body, name="books") # get the document again conn$get(ids = '978-0641723445', "books", wt = "xml") # another atomic update body <- ' <add> <doc> <field name="id">978-0641723445</field> <field name="price" update="remove">12.5</field> </doc> </add>' conn$update_atomic_xml(body, "books") # get the document again conn$get(ids = '978-0641723445', "books", wt = "xml") ## End(Not run)
Update documents with CSV data
update_csv( conn, files, name, separator = ",", header = TRUE, fieldnames = NULL, skip = NULL, skipLines = 0, trim = FALSE, encapsulator = NULL, escape = NULL, keepEmpty = FALSE, literal = NULL, map = NULL, split = NULL, rowid = NULL, rowidOffset = NULL, overwrite = NULL, commit = NULL, wt = "json", raw = FALSE, ... )update_csv( conn, files, name, separator = ",", header = TRUE, fieldnames = NULL, skip = NULL, skipLines = 0, trim = FALSE, encapsulator = NULL, escape = NULL, keepEmpty = FALSE, literal = NULL, map = NULL, split = NULL, rowid = NULL, rowidOffset = NULL, overwrite = NULL, commit = NULL, wt = "json", raw = FALSE, ... )
conn |
A solrium connection object, see SolrClient |
files |
Path to a single file to load into Solr |
name |
(character) Name of the core or collection |
separator |
Specifies the character to act as the field separator. Default: ',' |
header |
TRUE if the first line of the CSV input contains field or column names.
Default: |
fieldnames |
Specifies a comma separated list of field names to use when adding documents to the Solr index. If the CSV input already has a header, the names specified by this parameter will override them. Example: fieldnames=id,name,category |
skip |
A comma separated list of field names to skip in the input. An alternate
way to skip a field is to specify it's name as a zero length string in fieldnames.
For example, |
skipLines |
Specifies the number of lines in the input stream to discard
before the CSV data starts (including the header, if present). Default: |
trim |
If true remove leading and trailing whitespace from values. CSV parsing
already ignores leading whitespace by default, but there may be trailing whitespace,
or there may be leading whitespace that is encapsulated by quotes and is thus not
removed. This may be specified globally, or on a per-field basis.
Default: |
encapsulator |
The character optionally used to surround values to preserve characters such as the CSV separator or whitespace. This standard CSV format handles the encapsulator itself appearing in an encapsulated value by doubling the encapsulator. |
escape |
The character used for escaping CSV separators or other reserved characters. If an escape is specified, the encapsulator is not used unless also explicitly specified since most formats use either encapsulation or escaping, not both. |
keepEmpty |
Keep and index empty (zero length) field values. This may be specified
globally, or on a per-field basis. Default: |
literal |
Adds fixed field name/value to all documents. Example: Adds a "datasource"
field with value equal to "products" for every document indexed from the CSV
|
map |
Specifies a mapping between one value and another. The string on the LHS of
the colon will be replaced with the string on the RHS. This parameter can be specified
globally or on a per-field basis. Example: replaces "Absolutely" with "true" in every
field |
split |
If TRUE, the field value is split into multiple values by another CSV parser. The CSV parsing rules such as separator and encapsulator may be specified as field parameters. |
rowid |
If not null, add a new field to the document where the passed in parameter name is the field name to be added and the current line/rowid is the value. This is useful if your CSV doesn't have a unique id already in it and you want to use the line number as one. Also useful if you simply want to index where exactly in the original CSV file the row came from |
rowidOffset |
In conjunction with the rowid parameter, this integer value will be added to the rowid before adding it the field. |
overwrite |
If true (the default), check for and overwrite duplicate documents, based on the uniqueKey field declared in the solr schema. If you know the documents you are indexing do not contain any duplicates then you may see a considerable speed up with &overwrite=false. |
commit |
Commit changes after all records in this request have been indexed. The default is commit=false to avoid the potential performance impact of frequent commits. |
wt |
(character) One of json (default) or xml. If json, uses
|
raw |
(logical) If |
... |
curl options passed on to crul::HttpClient |
SOLR v1.2 was first version to support csv. See https://issues.apache.org/jira/browse/SOLR-66
Other update:
update_json(),
update_xml()
## Not run: # start Solr: bin/solr start -f -c -p 8983 # connect (conn <- SolrClient$new()) if (!conn$collection_exists("helloWorld")) { conn$collection_create(name = "helloWorld", numShards = 2) } df <- data.frame(id=1:3, name=c('red', 'blue', 'green')) write.csv(df, file="df.csv", row.names=FALSE, quote = FALSE) conn$update_csv("df.csv", "helloWorld", verbose = TRUE) # give back raw xml conn$update_csv("df.csv", "helloWorld", wt = "xml") ## raw json conn$update_csv("df.csv", "helloWorld", wt = "json", raw = TRUE) ## End(Not run)## Not run: # start Solr: bin/solr start -f -c -p 8983 # connect (conn <- SolrClient$new()) if (!conn$collection_exists("helloWorld")) { conn$collection_create(name = "helloWorld", numShards = 2) } df <- data.frame(id=1:3, name=c('red', 'blue', 'green')) write.csv(df, file="df.csv", row.names=FALSE, quote = FALSE) conn$update_csv("df.csv", "helloWorld", verbose = TRUE) # give back raw xml conn$update_csv("df.csv", "helloWorld", wt = "xml") ## raw json conn$update_csv("df.csv", "helloWorld", wt = "json", raw = TRUE) ## End(Not run)
Update documents with JSON data
update_json( conn, files, name, commit = TRUE, optimize = FALSE, max_segments = 1, expunge_deletes = FALSE, wait_searcher = TRUE, soft_commit = FALSE, prepare_commit = NULL, wt = "json", raw = FALSE, ... )update_json( conn, files, name, commit = TRUE, optimize = FALSE, max_segments = 1, expunge_deletes = FALSE, wait_searcher = TRUE, soft_commit = FALSE, prepare_commit = NULL, wt = "json", raw = FALSE, ... )
conn |
A solrium connection object, see SolrClient |
files |
Path to a single file to load into Solr |
name |
(character) Name of the core or collection |
commit |
(logical) If |
optimize |
Should index optimization be performed before the method returns.
Default: |
max_segments |
optimizes down to at most this number of segments. Default: 1 |
expunge_deletes |
merge segments with deletes away. Default: |
wait_searcher |
block until a new searcher is opened and registered as the
main query searcher, making the changes visible. Default: |
soft_commit |
perform a soft commit - this will refresh the 'view' of the
index in a more performant manner, but without "on-disk" guarantees.
Default: |
prepare_commit |
The prepareCommit command is an expert-level API that calls Lucene's IndexWriter.prepareCommit(). Not passed by default |
wt |
(character) One of json (default) or xml. If json, uses
|
raw |
(logical) If |
... |
curl options passed on to |
You likely may not be able to run this function against many public Solr services, but should work locally.
Other update:
update_csv(),
update_xml()
## Not run: # start Solr: bin/solr start -f -c -p 8983 # connect (conn <- SolrClient$new()) # Add documents file <- system.file("examples", "books2.json", package = "solrium") cat(readLines(file), sep = "\n") conn$update_json(files = file, name = "books") update_json(conn, files = file, name = "books") # Update commands - can include many varying commands ## Add file file <- system.file("examples", "updatecommands_add.json", package = "solrium") cat(readLines(file), sep = "\n") conn$update_json(file, "books") ## Delete file file <- system.file("examples", "updatecommands_delete.json", package = "solrium") cat(readLines(file), sep = "\n") conn$update_json(file, "books") # Add and delete in the same document ## Add a document first, that we can later delete ss <- list(list(id = 456, name = "cat")) conn$add(ss, "books") ## End(Not run)## Not run: # start Solr: bin/solr start -f -c -p 8983 # connect (conn <- SolrClient$new()) # Add documents file <- system.file("examples", "books2.json", package = "solrium") cat(readLines(file), sep = "\n") conn$update_json(files = file, name = "books") update_json(conn, files = file, name = "books") # Update commands - can include many varying commands ## Add file file <- system.file("examples", "updatecommands_add.json", package = "solrium") cat(readLines(file), sep = "\n") conn$update_json(file, "books") ## Delete file file <- system.file("examples", "updatecommands_delete.json", package = "solrium") cat(readLines(file), sep = "\n") conn$update_json(file, "books") # Add and delete in the same document ## Add a document first, that we can later delete ss <- list(list(id = 456, name = "cat")) conn$add(ss, "books") ## End(Not run)
Update documents with XML data
update_xml( conn, files, name, commit = TRUE, optimize = FALSE, max_segments = 1, expunge_deletes = FALSE, wait_searcher = TRUE, soft_commit = FALSE, prepare_commit = NULL, wt = "json", raw = FALSE, ... )update_xml( conn, files, name, commit = TRUE, optimize = FALSE, max_segments = 1, expunge_deletes = FALSE, wait_searcher = TRUE, soft_commit = FALSE, prepare_commit = NULL, wt = "json", raw = FALSE, ... )
conn |
A solrium connection object, see SolrClient |
files |
Path to a single file to load into Solr |
name |
(character) Name of the core or collection |
commit |
(logical) If |
optimize |
Should index optimization be performed before the method returns.
Default: |
max_segments |
optimizes down to at most this number of segments. Default: 1 |
expunge_deletes |
merge segments with deletes away. Default: |
wait_searcher |
block until a new searcher is opened and registered as the
main query searcher, making the changes visible. Default: |
soft_commit |
perform a soft commit - this will refresh the 'view' of the
index in a more performant manner, but without "on-disk" guarantees.
Default: |
prepare_commit |
The prepareCommit command is an expert-level API that calls Lucene's IndexWriter.prepareCommit(). Not passed by default |
wt |
(character) One of json (default) or xml. If json, uses
|
raw |
(logical) If |
... |
curl options passed on to |
You likely may not be able to run this function against many public Solr services, but should work locally.
Other update:
update_csv(),
update_json()
## Not run: # start Solr: bin/solr start -f -c -p 8983 # connect (conn <- SolrClient$new()) # create a collection if (!conn$collection_exists("books")) { conn$collection_create(name = "books", numShards = 2) } # Add documents file <- system.file("examples", "books.xml", package = "solrium") cat(readLines(file), sep = "\n") conn$update_xml(file, "books") # Update commands - can include many varying commands ## Add files file <- system.file("examples", "books2_delete.xml", package = "solrium") cat(readLines(file), sep = "\n") conn$update_xml(file, "books") ## Delete files file <- system.file("examples", "updatecommands_delete.xml", package = "solrium") cat(readLines(file), sep = "\n") conn$update_xml(file, "books") ## Add and delete in the same document ## Add a document first, that we can later delete ss <- list(list(id = 456, name = "cat")) conn$add(ss, "books") ## Now add a new document, and delete the one we just made file <- system.file("examples", "add_delete.xml", package = "solrium") cat(readLines(file), sep = "\n") conn$update_xml(file, "books") ## End(Not run)## Not run: # start Solr: bin/solr start -f -c -p 8983 # connect (conn <- SolrClient$new()) # create a collection if (!conn$collection_exists("books")) { conn$collection_create(name = "books", numShards = 2) } # Add documents file <- system.file("examples", "books.xml", package = "solrium") cat(readLines(file), sep = "\n") conn$update_xml(file, "books") # Update commands - can include many varying commands ## Add files file <- system.file("examples", "books2_delete.xml", package = "solrium") cat(readLines(file), sep = "\n") conn$update_xml(file, "books") ## Delete files file <- system.file("examples", "updatecommands_delete.xml", package = "solrium") cat(readLines(file), sep = "\n") conn$update_xml(file, "books") ## Add and delete in the same document ## Add a document first, that we can later delete ss <- list(list(id = 456, name = "cat")) conn$add(ss, "books") ## Now add a new document, and delete the one we just made file <- system.file("examples", "add_delete.xml", package = "solrium") cat(readLines(file), sep = "\n") conn$update_xml(file, "books") ## End(Not run)