elastic

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A general purpose R interface to Elasticsearch

Elasticsearch DSL

Also check out elasticdsl - an R DSL for Elasticsearch - https://github.com/ropensci/elasticdsl

Elasticsearch info

Compatibility

This client is developed following the latest stable releases, currently v5.6.0. 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.

Security

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.

Installation

Stable version from CRAN

install.packages("elastic")

Development version from GitHub

install.packages("devtools")
devtools::install_github("ropensci/elastic")
library('elastic')

Install Elasticsearch

w/ Docker

Pull the official elasticsearch image

docker pull elasticsearch

Then start up a container

docker 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

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.

Upgrading Elasticsearch

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).

Start 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).

Get some data

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.

Shakespeare data

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:

shakespeare <- system.file("examples", "shakespeare_data.json", package = "elastic")

Then load the data into Elasticsearch:

invisible(docs_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.):

curl -XGET https://www.elastic.co/guide/en/kibana/3.0/snippets/shakespeare.json > shakespeare.json
curl -XPUT localhost:9200/_bulk --data-binary @shakespeare.json

Public Library of Science (PLOS) data

A dataset inluded in the elastic package is metadata for PLOS scholarly articles. Get the file path, then load:

plosdat <- system.file("examples", "plos_data.json", package = "elastic")
invisible(docs_bulk(plosdat))

Global Biodiversity Information Facility (GBIF) data

A dataset inluded in the elastic package is data for GBIF species occurrence records. Get the file path, then load:

gbifdat <- system.file("examples", "gbif_data.json", package = "elastic")
invisible(docs_bulk(gbifdat))

GBIF geo data with a coordinates element to allow geo_shape queries

gbifgeo <- system.file("examples", "gbif_geo.json", package = "elastic")
invisible(docs_bulk(gbifgeo))

More data sets

There are more datasets formatted for bulk loading in the ropensci/elastic_data GitHub repository. Find it at https://github.com/ropensci/elastic_data

Initialization

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.

connect(es_port = 9200)
#> transport:  http 
#> host:       127.0.0.1 
#> port:       9200 
#> path:       NULL 
#> username:   NULL 
#> password:   <secret> 
#> errors:     simple 
#> headers (names):  NULL

For AWS hosted elasticsearch, make sure to specify es_path = “” and the correct port - transport schema pair.

connect(es_host = <aws_es_endpoint>, es_path = "", es_port = 80, es_transport_schema  = "http")
  # or
connect(es_host = <aws_es_endpoint>, es_path = "", es_port = 443, es_transport_schema  = "https")

If you are using Elastic Cloud or an installation with authentication (X-pack), make sure to specify es_path = “”, es_user = “”, es_pwd = “” and the correct port - transport schema pair.

connect(es_host = <ec_endpoint>, es_path = "", es_user="test", es_pwd = "1234", es_port = 9243, es_transport_schema  = "https")

Search the plos index and only return 1 result

Search(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-β4 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, and query for antibody, limit to 1 result

Search(index = "plos", type = "article", q = "antibody", size = 1)$hits$hits
#> [[1]]
#> [[1]]$`_index`
#> [1] "plos"
#> 
#> [[1]]$`_type`
#> [1] "article"
#> 
#> [[1]]$`_id`
#> [1] "568"
#> 
#> [[1]]$`_score`
#> [1] 4.165291
#> 
#> [[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"

Get documents

Get document with id=1

docs_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

docs_get(index = 'plos', type = 'article', id = 4, fields = 'id')
#> $`_index`
#> [1] "plos"
#> 
#> $`_type`
#> [1] "article"
#> 
#> $`_id`
#> [1] "4"
#> 
#> $`_version`
#> [1] 1
#> 
#> $found
#> [1] TRUE

Get multiple documents via the multiget API

Same index and type, different document ids

docs_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 HIF1α in Mesenchymal Transition"

Different indeces, types, and ids

docs_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"

Parsing

You can optionally get back raw json from Search(), docs_get(), and docs_mget() setting parameter raw=TRUE.

For example:

(out <- 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 HIF1α in Mesenchymal Transition\"}}]}"
#> attr(,"class")
#> [1] "elastic_mget"

Then parse

jsonlite::fromJSON(out)
#> $docs
#>   _index   _type _id _version found                   _source.id
#> 1   plos article   1        1  TRUE 10.1371/journal.pone.0098602
#> 2   plos article   2        1  TRUE 10.1371/journal.pone.0107757
#>                                                                                                                                                _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
#> 2                                     Cigarette Smoke Extract Induces a Phenotypic Shift in Epithelial Cells; Involvement of HIF1α in Mesenchymal Transition

Known pain points

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