solrium
A general purpose R interface to Solr
Development is now following Solr v7 and greater - which introduced many changes, which means many functions here may not work with your Solr installation older than v7.
Be aware that currently some functions will only work in certain Solr modes, e.g, collection_create() won't work when you are not in Solrcloud mode. But, you should get an error message stating that you aren't.
Currently developing against Solr
v7.0.0
Note that we recently changed the package name to
solrium. A previous version of this package is on CRAN assolr, but next version will be up assolrium.
Solr info
- Solr home page
- Highlighting help
- Faceting help
- Solr stats
- 'More like this' searches
- Grouping/Feild collapsing
- Install and Setup SOLR in OSX, including running Solr
- Solr csv writer
Package API and ways of using the package
The first thing to look at is SolrClient to instantiate a client connection
to your Solr instance. ping and schema are helpful functions to look
at after instantiating your client.
There are two ways to use solrium:
- Call functions on the
SolrClientobject - Pass the
SolrClientobject to functions
For example, if we instantiate a client like conn <- SolrClient$new(), then
to use the first way we can do conn$search(...), and the second way by doing
solr_search(conn, ...). These two ways of using the package hopefully
make the package more user friendly for more people, those that prefer a more
object oriented approach, and those that prefer more of a functional approach.
Collections
Functions that start with collection work with Solr collections when in
cloud mode. Note that these functions won't work when in Solr standard mode
Cores
Functions that start with core work with Solr cores when in standard Solr
mode. Note that these functions won't work when in Solr cloud mode
Documents
The following functions work with documents in Solr
#> - add
#> - delete_by_id
#> - delete_by_query
#> - update_atomic_json
#> - update_atomic_xml
#> - update_csv
#> - update_json
#> - update_xmlSearch
Search functions, including solr_parse for parsing results from different
functions appropriately
#> - solr_all
#> - solr_facet
#> - solr_get
#> - solr_group
#> - solr_highlight
#> - solr_mlt
#> - solr_parse
#> - solr_search
#> - solr_statsInstall
Stable version from CRAN
install.packages("solrium")Or development version from GitHub
devtools::install_github("ropensci/solrium")library("solrium")Setup
Use SolrClient$new() to initialize your connection. These examples use a remote Solr server, but work on any local Solr server.
(cli <- SolrClient$new(host = "api.plos.org", path = "search", port = NULL))
#> <Solr Client>
#> host: api.plos.org
#> path: search
#> port:
#> scheme: http
#> errors: simple
#> proxy:You can also set whether you want simple or detailed error messages (via errors), and whether you want URLs used in each function call or not (via verbose), and your proxy settings (via proxy) if needed. For example:
SolrClient$new(errors = "complete")Your settings are printed in the print method for the connection object
cli
#> <Solr Client>
#> host: api.plos.org
#> path: search
#> port:
#> scheme: http
#> errors: simple
#> proxy:For local Solr server setup:
bin/solr start -e cloud -noprompt
bin/post -c gettingstarted example/exampledocs/*.xmlSearch
cli$search(params = list(q='*:*', rows=2, fl='id'))
#> # A tibble: 2 x 1
#> id
#> <chr>
#> 1 10.1371/journal.pone.0079536/title
#> 2 10.1371/journal.pone.0079536/abstractSearch grouped data
Most recent publication by journal
cli$group(params = list(q='*:*', group.field='journal', rows=5, group.limit=1,
group.sort='publication_date desc',
fl='publication_date, score'))
#> groupValue numFound start publication_date
#> 1 plos one 1572163 0 2017-11-01T00:00:00Z
#> 2 plos neglected tropical diseases 47510 0 2017-11-01T00:00:00Z
#> 3 plos genetics 59871 0 2017-11-01T00:00:00Z
#> 4 plos pathogens 53246 0 2017-11-01T00:00:00Z
#> 5 none 63561 0 2012-10-23T00:00:00Z
#> score
#> 1 1
#> 2 1
#> 3 1
#> 4 1
#> 5 1First publication by journal
cli$group(params = list(q = '*:*', group.field = 'journal', group.limit = 1,
group.sort = 'publication_date asc',
fl = c('publication_date', 'score'),
fq = "publication_date:[1900-01-01T00:00:00Z TO *]"))
#> groupValue numFound start publication_date
#> 1 plos one 1572163 0 2006-12-20T00:00:00Z
#> 2 plos neglected tropical diseases 47510 0 2007-08-30T00:00:00Z
#> 3 plos pathogens 53246 0 2005-07-22T00:00:00Z
#> 4 plos computational biology 45582 0 2005-06-24T00:00:00Z
#> 5 none 57532 0 2005-08-23T00:00:00Z
#> 6 plos clinical trials 521 0 2006-04-21T00:00:00Z
#> 7 plos genetics 59871 0 2005-06-17T00:00:00Z
#> 8 plos medicine 23519 0 2004-09-07T00:00:00Z
#> 9 plos medicin 9 0 2012-04-17T00:00:00Z
#> 10 plos biology 32513 0 2003-08-18T00:00:00Z
#> score
#> 1 1
#> 2 1
#> 3 1
#> 4 1
#> 5 1
#> 6 1
#> 7 1
#> 8 1
#> 9 1
#> 10 1Search group query : Last 3 publications of 2013.
gq <- 'publication_date:[2013-01-01T00:00:00Z TO 2013-12-31T00:00:00Z]'
cli$group(
params = list(q='*:*', group.query = gq,
group.limit = 3, group.sort = 'publication_date desc',
fl = 'publication_date'))
#> numFound start publication_date
#> 1 307076 0 2013-12-31T00:00:00Z
#> 2 307076 0 2013-12-31T00:00:00Z
#> 3 307076 0 2013-12-31T00:00:00ZSearch group with format simple
cli$group(params = list(q='*:*', group.field='journal', rows=5,
group.limit=3, group.sort='publication_date desc',
group.format='simple', fl='journal, publication_date'))
#> numFound start publication_date journal
#> 1 1898495 0 2012-10-23T00:00:00Z <NA>
#> 2 1898495 0 2012-10-23T00:00:00Z <NA>
#> 3 1898495 0 2012-10-23T00:00:00Z <NA>
#> 4 1898495 0 2017-11-01T00:00:00Z PLOS ONE
#> 5 1898495 0 2017-11-01T00:00:00Z PLOS ONEFacet
cli$facet(params = list(q='*:*', facet.field='journal', facet.query=c('cell', 'bird')))
#> $facet_queries
#> # A tibble: 2 x 2
#> term value
#> <chr> <int>
#> 1 cell 157652
#> 2 bird 16385
#>
#> $facet_fields
#> $facet_fields$journal
#> # A tibble: 9 x 2
#> term value
#> <chr> <chr>
#> 1 plos one 1572163
#> 2 plos genetics 59871
#> 3 plos pathogens 53246
#> 4 plos neglected tropical diseases 47510
#> 5 plos computational biology 45582
#> 6 plos biology 32513
#> 7 plos medicine 23519
#> 8 plos clinical trials 521
#> 9 plos medicin 9
#>
#>
#> $facet_pivot
#> NULL
#>
#> $facet_dates
#> NULL
#>
#> $facet_ranges
#> NULLHighlight
cli$highlight(params = list(q='alcohol', hl.fl = 'abstract', rows=2))
#> # A tibble: 2 x 2
#> names
#> <chr>
#> 1 10.1371/journal.pone.0185457
#> 2 10.1371/journal.pone.0071284
#> # ... with 1 more variables: abstract <chr>Stats
out <- cli$stats(params = list(q='ecology', stats.field=c('counter_total_all','alm_twitterCount'), stats.facet='journal'))out$data
#> min max count missing sum sumOfSquares
#> counter_total_all 0 920716 40497 0 219020039 7.604567e+12
#> alm_twitterCount 0 3401 40497 0 281128 7.300081e+07
#> mean stddev
#> counter_total_all 5408.302813 12591.07462
#> alm_twitterCount 6.941946 41.88646More like this
solr_mlt is a function to return similar documents to the one
out <- cli$mlt(params = list(q='title:"ecology" AND body:"cell"', mlt.fl='title', mlt.mindf=1, mlt.mintf=1, fl='counter_total_all', rows=5))out$docs
#> # A tibble: 5 x 2
#> id counter_total_all
#> <chr> <int>
#> 1 10.1371/journal.pbio.1001805 21824
#> 2 10.1371/journal.pbio.0020440 25424
#> 3 10.1371/journal.pbio.1002559 9746
#> 4 10.1371/journal.pone.0087217 11502
#> 5 10.1371/journal.pbio.1002191 22013out$mlt
#> $`10.1371/journal.pbio.1001805`
#> # A tibble: 5 x 4
#> numFound start id counter_total_all
#> <int> <int> <chr> <int>
#> 1 3822 0 10.1371/journal.pone.0098876 3590
#> 2 3822 0 10.1371/journal.pone.0082578 2893
#> 3 3822 0 10.1371/journal.pone.0102159 2028
#> 4 3822 0 10.1371/journal.pcbi.1002652 3819
#> 5 3822 0 10.1371/journal.pcbi.1003408 9920
#>
#> $`10.1371/journal.pbio.0020440`
#> # A tibble: 5 x 4
#> numFound start id counter_total_all
#> <int> <int> <chr> <int>
#> 1 1115 0 10.1371/journal.pone.0162651 2828
#> 2 1115 0 10.1371/journal.pone.0003259 3225
#> 3 1115 0 10.1371/journal.pntd.0003377 4267
#> 4 1115 0 10.1371/journal.pone.0101568 4603
#> 5 1115 0 10.1371/journal.pone.0068814 9042
#>
#> $`10.1371/journal.pbio.1002559`
#> # A tibble: 5 x 4
#> numFound start id counter_total_all
#> <int> <int> <chr> <int>
#> 1 5482 0 10.1371/journal.pone.0155989 2519
#> 2 5482 0 10.1371/journal.pone.0023086 8442
#> 3 5482 0 10.1371/journal.pone.0155028 1547
#> 4 5482 0 10.1371/journal.pone.0041684 22057
#> 5 5482 0 10.1371/journal.pone.0164330 969
#>
#> $`10.1371/journal.pone.0087217`
#> # A tibble: 5 x 4
#> numFound start id counter_total_all
#> <int> <int> <chr> <int>
#> 1 4576 0 10.1371/journal.pone.0175497 1088
#> 2 4576 0 10.1371/journal.pone.0159131 4937
#> 3 4576 0 10.1371/journal.pcbi.0020092 24786
#> 4 4576 0 10.1371/journal.pone.0133941 1336
#> 5 4576 0 10.1371/journal.pone.0131665 1207
#>
#> $`10.1371/journal.pbio.1002191`
#> # A tibble: 5 x 4
#> numFound start id counter_total_all
#> <int> <int> <chr> <int>
#> 1 12585 0 10.1371/journal.pbio.1002232 3055
#> 2 12585 0 10.1371/journal.pone.0070448 2203
#> 3 12585 0 10.1371/journal.pone.0131700 2493
#> 4 12585 0 10.1371/journal.pone.0121680 4980
#> 5 12585 0 10.1371/journal.pone.0041534 5701Parsing
solr_parse is a general purpose parser function with extension methods solr_parse.sr_search, solr_parse.sr_facet, and solr_parse.sr_high, for parsing solr_search, solr_facet, and solr_highlight function output, respectively. solr_parse is used internally within those three functions (solr_search, solr_facet, solr_highlight) to do parsing. You can optionally get back raw json or xml from solr_search, solr_facet, and solr_highlight setting parameter raw=TRUE, and then parsing after the fact with solr_parse. All you need to know is solr_parse can parse
For example:
(out <- cli$highlight(params = list(q='alcohol', hl.fl = 'abstract', rows=2),
raw=TRUE))
#> [1] "{\"response\":{\"numFound\":25987,\"start\":0,\"maxScore\":4.705177,\"docs\":[{\"id\":\"10.1371/journal.pone.0185457\",\"journal\":\"PLOS ONE\",\"eissn\":\"1932-6203\",\"publication_date\":\"2017-09-28T00:00:00Z\",\"article_type\":\"Research Article\",\"author_display\":[\"Jacqueline Willmore\",\"Terry-Lynne Marko\",\"Darcie Taing\",\"Hugues Sampasa-Kanyinga\"],\"abstract\":[\"Objectives: Alcohol-related morbidity and mortality are significant public health issues. The purpose of this study was to describe the prevalence and trends over time of alcohol consumption and alcohol-related morbidity and mortality; and public attitudes of alcohol use impacts on families and the community in Ottawa, Canada. Methods: Prevalence (2013–2014) and trends (2000–2001 to 2013–2014) of alcohol use were obtained from the Canadian Community Health Survey. Data on paramedic responses (2015), emergency department (ED) visits (2013–2015), hospitalizations (2013–2015) and deaths (2007–2011) were used to quantify the acute and chronic health effects of alcohol in Ottawa. Qualitative data were obtained from the “Have Your Say” alcohol survey, an online survey of public attitudes on alcohol conducted in 2016. Results: In 2013–2014, an estimated 595,300 (83%) Ottawa adults 19 years and older drank alcohol, 42% reported binge drinking in the past year. Heavy drinking increased from 15% in 2000–2001 to 20% in 2013–2014. In 2015, the Ottawa Paramedic Service responded to 2,060 calls directly attributable to alcohol. Between 2013 and 2015, there were an average of 6,100 ED visits and 1,270 hospitalizations per year due to alcohol. Annually, alcohol use results in at least 140 deaths in Ottawa. Men have higher rates of alcohol-attributable paramedic responses, ED visits, hospitalizations and deaths than women, and young adults have higher rates of alcohol-attributable paramedic responses. Qualitative data of public attitudes indicate that alcohol misuse has greater repercussions not only on those who drink, but also on the family and community. Conclusions: Results highlight the need for healthy public policy intended to encourage a culture of drinking in moderation in Ottawa to support lower risk alcohol use, particularly among men and young adults. \"],\"title_display\":\"The burden of alcohol-related morbidity and mortality in Ottawa, Canada\",\"score\":4.705177},{\"id\":\"10.1371/journal.pone.0071284\",\"journal\":\"PLoS ONE\",\"eissn\":\"1932-6203\",\"publication_date\":\"2013-08-20T00:00:00Z\",\"article_type\":\"Research Article\",\"author_display\":[\"Petra Suchankova\",\"Pia Steensland\",\"Ida Fredriksson\",\"Jörgen A. Engel\",\"Elisabet Jerlhag\"],\"abstract\":[\"\\nAlcohol dependence is a heterogeneous disorder where several signalling systems play important roles. Recent studies implicate that the gut-brain hormone ghrelin, an orexigenic peptide, is a potential mediator of alcohol related behaviours. Ghrelin increases whereas a ghrelin receptor (GHS-R1A) antagonist decreases alcohol consumption as well as operant self-administration of alcohol in rodents that have consumed alcohol for twelve weeks. In the present study we aimed at investigating the effect of acute and repeated treatment with the GHS-R1A antagonist JMV2959 on alcohol intake in a group of rats following voluntarily alcohol consumption for two, five and eight months. After approximately ten months of voluntary alcohol consumption the expression of the GHS-R1A gene (Ghsr) as well as the degree of methylation of a CpG island found in Ghsr was examined in reward related brain areas. In a separate group of rats, we examined the effect of the JMV2959 on alcohol relapse using the alcohol deprivation paradigm. Acute JMV2959 treatment was found to decrease alcohol intake and the effect was more pronounced after five, compared to two months of alcohol exposure. In addition, repeated JMV2959 treatment decreased alcohol intake without inducing tolerance or rebound increase in alcohol intake after the treatment. The GHS-R1A antagonist prevented the alcohol deprivation effect in rats. There was a significant down-regulation of the Ghsr expression in the ventral tegmental area (VTA) in high- compared to low-alcohol consuming rats after approximately ten months of voluntary alcohol consumption. Further analysis revealed a negative correlation between Ghsr expression in the VTA and alcohol intake. No differences in methylation degree were found between high- compared to low-alcohol consuming rats. These findings support previous studies showing that the ghrelin signalling system may constitute a potential target for development of novel treatment strategies for alcohol dependence.\\n\"],\"title_display\":\"Ghrelin Receptor (GHS-R1A) Antagonism Suppresses Both Alcohol Consumption and the Alcohol Deprivation Effect in Rats following Long-Term Voluntary Alcohol Consumption\",\"score\":4.7050986}]},\"highlighting\":{\"10.1371/journal.pone.0185457\":{\"abstract\":[\"Objectives: <em>Alcohol</em>-related morbidity and mortality are significant public health issues\"]},\"10.1371/journal.pone.0071284\":{\"abstract\":[\"\\n<em>Alcohol</em> dependence is a heterogeneous disorder where several signalling systems play important\"]}}}\n"
#> attr(,"class")
#> [1] "sr_high"
#> attr(,"wt")
#> [1] "json"Then parse
solr_parse(out, 'df')
#> # A tibble: 2 x 2
#> names
#> <chr>
#> 1 10.1371/journal.pone.0185457
#> 2 10.1371/journal.pone.0071284
#> # ... with 1 more variables: abstract <chr>Advanced: Function Queries
Function Queries allow you to query on actual numeric fields in the SOLR database, and do addition, multiplication, etc on one or many fields to stort results. For example, here, we search on the product of counter_total_all and alm_twitterCount, using a new temporary field "val"
cli$search(params = list(q='_val_:"product(counter_total_all,alm_twitterCount)"',
rows=5, fl='id,title', fq='doc_type:full'))
#> # A tibble: 5 x 2
#> id
#> <chr>
#> 1 10.1371/journal.pmed.0020124
#> 2 10.1371/journal.pone.0141854
#> 3 10.1371/journal.pone.0073791
#> 4 10.1371/journal.pone.0153419
#> 5 10.1371/journal.pone.0115069
#> # ... with 1 more variables: title <chr>Here, we search for the papers with the most citations
cli$search(params = list(q='_val_:"max(counter_total_all)"',
rows=5, fl='id,counter_total_all', fq='doc_type:full'))
#> # A tibble: 5 x 2
#> id
#> <chr>
#> 1 10.1371/journal.pmed.0020124
#> 2 10.1371/annotation/80bd7285-9d2d-403a-8e6f-9c375bf977ca
#> 3 10.1371/journal.pcbi.1003149
#> 4 10.1371/journal.pone.0141854
#> 5 10.1371/journal.pcbi.0030102
#> # ... with 1 more variables: counter_total_all <int>Or with the most tweets
cli$search(params = list(q='_val_:"max(alm_twitterCount)"',
rows=5, fl='id,alm_twitterCount', fq='doc_type:full'))
#> # A tibble: 5 x 2
#> id alm_twitterCount
#> <chr> <int>
#> 1 10.1371/journal.pone.0141854 3401
#> 2 10.1371/journal.pmed.0020124 3207
#> 3 10.1371/journal.pone.0115069 2873
#> 4 10.1371/journal.pmed.1001953 2821
#> 5 10.1371/journal.pone.0061981 2392Using specific data sources
USGS BISON service
The occurrences service
conn <- SolrClient$new(scheme = "https", host = "bison.usgs.gov", path = "solr/occurrences/select", port = NULL)
conn$search(params = list(q = '*:*', fl = c('decimalLatitude','decimalLongitude','scientificName'), rows = 2))
#> # A tibble: 2 x 3
#> decimalLongitude scientificName decimalLatitude
#> <dbl> <chr> <dbl>
#> 1 -116.5694 Zonotrichia leucophrys 34.05072
#> 2 -116.5694 Tyrannus vociferans 34.05072The species names service
conn <- SolrClient$new(scheme = "https", host = "bison.usgs.gov", path = "solr/scientificName/select", port = NULL)
conn$search(params = list(q = '*:*'))
#> # A tibble: 10 x 2
#> scientificName `_version_`
#> <chr> <dbl>
#> 1 Dictyopteris polypodioides 1.565325e+18
#> 2 Lonicera iberica 1.565325e+18
#> 3 Epuraea ambigua 1.565325e+18
#> 4 Pseudopomala brachyptera 1.565325e+18
#> 5 Didymosphaeria populina 1.565325e+18
#> 6 Sanoarca 1.565325e+18
#> 7 Celleporina ventricosa 1.565325e+18
#> 8 Trigonurus crotchi 1.565325e+18
#> 9 Ceraticelus laticeps 1.565325e+18
#> 10 Micraster acutus 1.565325e+18PLOS Search API
Most of the examples above use the PLOS search API... :)
Solr server management
This isn't as complete as searching functions show above, but we're getting there.
Cores
conn <- SolrClient$new()Many functions, e.g.:
core_create()core_rename()core_status()- ...
Create a core
conn$core_create(name = "foo_bar")Collections
Many functions, e.g.:
collection_create()collection_list()collection_addrole()- ...
Create a collection
conn$collection_create(name = "hello_world")Add documents
Add documents, supports adding from files (json, xml, or csv format), and from R objects (including data.frame and list types so far)
df <- data.frame(id = c(67, 68), price = c(1000, 500000000))
conn$add(df, name = "books")Delete documents, by id
conn$delete_by_id(name = "books", ids = c(3, 4))Or by query
conn$delete_by_query(name = "books", query = "manu:bank")Meta
- Please report any issues or bugs
- License: MIT
- Get citation information for
solriumin R doingcitation(package = 'solrium') - Please note that this project is released with a Contributor Code of Conduct. By participating in this project you agree to abide by its terms.