Name: elastic
Owner: Zapier
Description: R client for the Elasticsearch HTTP API
Forked from: ropensci/elastic
Created: 2016-11-30 19:28:16.0
Updated: 2016-11-30 19:28:17.0
Pushed: 2016-11-10 02:15:04.0
Size: 1553
Language: R
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A general purpose R interface to Elasticsearch
Also check out elasticdsl
- an R DSL for Elasticsearch - https://github.com/ropensci/elasticdsl
This client is developed following the latest stable releases, currently v5.0.0
. It is generally compatible with older versions of Elasticsearch. Unlike the Python client, we try to keep as much compatibility as possible within a single version of this client, as that's an easier setup in R world.
You're fine running ES locally on your machine, but be careful just throwing up ES on a server with a public IP address - make sure to think about security.
Stable version from CRAN
all.packages("elastic")
Development version from GitHub
all.packages("devtools")
ools::install_github("ropensci/elastic")
ary('elastic')
w/ Docker
Pull the official elasticsearch image
er pull elasticsearch
Then start up a container
er run -d -p 9200:9200 elasticsearch
Then elasticsearch should be available on port 9200, try curl localhost:9200
and you should get the familiar message indicating ES is on.
If you're using boot2docker, you'll need to use the IP address in place of localhost. Get it by doing boot2docker ip
.
on OSX
curl -L -O https://artifacts.elastic.co/downloads/elasticsearch/elasticsearch-5.0.0.tar.gz
tar -zxvf elasticsearch-5.0.0.tar.gz
sudo mv elasticsearch-5.0.0 /usr/local
(replace version with your version)cd /usr/local
elasticsearch
directory: rm -rf elasticsearch
sudo ln -s elasticsearch-5.0.0 elasticsearch
(replace version with your version)You can also install via Homebrew: brew install elasticsearch
Note: for the 1.6 and greater upgrades of Elasticsearch, they want you to have java 8 or greater. I downloaded Java 8 from here http://www.oracle.com/technetwork/java/javase/downloads/jdk8-downloads-2133151.html and it seemed to work great.
I am not totally clear on best practice here, but from what I understand, when you upgrade to a new version of Elasticsearch, place old elasticsearch/data
and elasticsearch/config
directories into the new installation (elasticsearch/
dir). The new elasticsearch instance with replaced data and config directories should automatically update data to the new version and start working. Maybe if you use homebrew on a Mac to upgrade it takes care of this for you - not sure.
Obviously, upgrading Elasticsearch while keeping it running is a different thing (some help here from Elastic).
cd /usr/local/elasticsearch
bin/elasticsearch
I create a little bash shortcut called es
that does both of the above commands in one step (cd /usr/local/elasticsearch && bin/elasticsearch
).
Elasticsearch has a bulk load API to load data in fast. The format is pretty weird though. It's sort of JSON, but would pass no JSON linter. I include a few data sets in elastic
so it's easy to get up and running, and so when you run examples in this package they'll actually run the same way (hopefully).
I have prepare a non-exported function useful for preparing the weird format that Elasticsearch wants for bulk data loads, that is somewhat specific to PLOS data (See below), but you could modify for your purposes. See make_bulk_plos()
and make_bulk_gbif()
here.
Elasticsearch provides some data on Shakespeare plays. I've provided a subset of this data in this package. Get the path for the file specific to your machine:
espeare <- system.file("examples", "shakespeare_data.json", package = "elastic")
Then load the data into Elasticsearch:
_bulk(shakespeare)
If you need some big data to play with, the shakespeare dataset is a good one to start with. You can get the whole thing and pop it into Elasticsearch (beware, may take up to 10 minutes or so.):
-XGET https://www.elastic.co/guide/en/kibana/3.0/snippets/shakespeare.json > shakespeare.json
-XPUT localhost:9200/_bulk --data-binary @shakespeare.json
A dataset inluded in the elastic
package is metadata for PLOS scholarly articles. Get the file path, then load:
dat <- system.file("examples", "plos_data.json", package = "elastic")
_bulk(plosdat)
A dataset inluded in the elastic
package is data for GBIF species occurrence records. Get the file path, then load:
dat <- system.file("examples", "gbif_data.json", package = "elastic")
_bulk(gbifdat)
GBIF geo data with a coordinates element to allow geo_shape
queries
geo <- system.file("examples", "gbif_geo.json", package = "elastic")
_bulk(gbifgeo)
There are more datasets formatted for bulk loading in the ropensci/elastic_data
GitHub repository. Find it at https://github.com/ropensci/elastic_data
The function connect()
is used before doing anything else to set the connection details to your remote or local elasticsearch store. The details created by connect()
are written to your options for the current session, and are used by elastic
functions.
ect(es_port = 9200)
ransport: http
ost: 127.0.0.1
ort: 9200
ath: NULL
sername: NULL
assword: <secret>
rrors: simple
eaders (names): NULL
Search the plos
index and only return 1 result
ch(index = "plos", size = 1)$hits$hits
[1]]
[1]]$`_index`
1] "plos"
[1]]$`_type`
1] "article"
[1]]$`_id`
1] "0"
[1]]$`_score`
1] 1
[1]]$`_source`
[1]]$`_source`$id
1] "10.1371/journal.pone.0007737"
[1]]$`_source`$title
1] "Phospholipase C-?4 Is Essential for the Progression of the Normal Sleep Sequence and Ultradian Body Temperature Rhythms in Mice"
Search the plos
index, and the article
document type, sort by title, and query for antibody, limit to 1 result
ch(index = "plos", type = "article", sort = "title", q = "antibody", size = 1)$hits$hits
[1]]
[1]]$`_index`
1] "plos"
[1]]$`_type`
1] "article"
[1]]$`_id`
1] "568"
[1]]$`_score`
ULL
[1]]$`_source`
[1]]$`_source`$id
1] "10.1371/journal.pone.0085002"
[1]]$`_source`$title
1] "Evaluation of 131I-Anti-Angiotensin II Type 1 Receptor Monoclonal Antibody as a Reporter for Hepatocellular Carcinoma"
[1]]$sort
[1]]$sort[[1]]
1] "1"
Get document with id=1
_get(index = 'plos', type = 'article', id = 4)
`_index`
1] "plos"
`_type`
1] "article"
`_id`
1] "4"
`_version`
1] 1
found
1] TRUE
`_source`
`_source`$id
1] "10.1371/journal.pone.0107758"
`_source`$title
1] "Lactobacilli Inactivate Chlamydia trachomatis through Lactic Acid but Not H2O2"
Get certain fields
_get(index = 'plos', type = 'article', id = 4, fields = 'id')
`_index`
1] "plos"
`_type`
1] "article"
`_id`
1] "4"
`_version`
1] 1
found
1] TRUE
fields
fields$id
fields$id[[1]]
1] "10.1371/journal.pone.0107758"
Same index and type, different document ids
_mget(index = "plos", type = "article", id = 1:2)
docs
docs[[1]]
docs[[1]]$`_index`
1] "plos"
docs[[1]]$`_type`
1] "article"
docs[[1]]$`_id`
1] "1"
docs[[1]]$`_version`
1] 1
docs[[1]]$found
1] TRUE
docs[[1]]$`_source`
docs[[1]]$`_source`$id
1] "10.1371/journal.pone.0098602"
docs[[1]]$`_source`$title
1] "Population Genetic Structure of a Sandstone Specialist and a Generalist Heath Species at Two Levels of Sandstone Patchiness across the Strait of Gibraltar"
docs[[2]]
docs[[2]]$`_index`
1] "plos"
docs[[2]]$`_type`
1] "article"
docs[[2]]$`_id`
1] "2"
docs[[2]]$`_version`
1] 1
docs[[2]]$found
1] TRUE
docs[[2]]$`_source`
docs[[2]]$`_source`$id
1] "10.1371/journal.pone.0107757"
docs[[2]]$`_source`$title
1] "Cigarette Smoke Extract Induces a Phenotypic Shift in Epithelial Cells; Involvement of HIF1? in Mesenchymal Transition"
Different indeces, types, and ids
_mget(index_type_id = list(c("plos", "article", 1), c("gbif", "record", 1)))$docs[[1]]
`_index`
1] "plos"
`_type`
1] "article"
`_id`
1] "1"
`_version`
1] 1
found
1] TRUE
`_source`
`_source`$id
1] "10.1371/journal.pone.0098602"
`_source`$title
1] "Population Genetic Structure of a Sandstone Specialist and a Generalist Heath Species at Two Levels of Sandstone Patchiness across the Strait of Gibraltar"
You can optionally get back raw json
from Search()
, docs_get()
, and docs_mget()
setting parameter raw=TRUE
.
For example:
<- docs_mget(index = "plos", type = "article", id = 1:2, raw = TRUE))
1] "{\"docs\":[{\"_index\":\"plos\",\"_type\":\"article\",\"_id\":\"1\",\"_version\":1,\"found\":true,\"_source\":{\"id\":\"10.1371/journal.pone.0098602\",\"title\":\"Population Genetic Structure of a Sandstone Specialist and a Generalist Heath Species at Two Levels of Sandstone Patchiness across the Strait of Gibraltar\"}},{\"_index\":\"plos\",\"_type\":\"article\",\"_id\":\"2\",\"_version\":1,\"found\":true,\"_source\":{\"id\":\"10.1371/journal.pone.0107757\",\"title\":\"Cigarette Smoke Extract Induces a Phenotypic Shift in Epithelial Cells; Involvement of HIF1? in Mesenchymal Transition\"}}]}"
ttr(,"class")
1] "elastic_mget"
Then parse
lite::fromJSON(out)
docs
_index _type _id _version found _source.id
plos article 1 1 TRUE 10.1371/journal.pone.0098602
plos article 2 1 TRUE 10.1371/journal.pone.0107757
_source.title
Population Genetic Structure of a Sandstone Specialist and a Generalist Heath Species at Two Levels of Sandstone Patchiness across the Strait of Gibraltar
Cigarette Smoke Extract Induces a Phenotypic Shift in Epithelial Cells; Involvement of HIF1? in Mesenchymal Transition
HEAD
requests don't seem to work, not sure whyGET
requests, a number of functions that require
POST
requests obviously then won't work. A big one is Search()
, but
you can use Search_uri()
to get around this, which uses GET
instead
of POST
, but you can't pass a more complicated query via the bodyelastic
in R doing citation(package = 'elastic')