elastic4s - Elasticsearch Scala Client

Elastic4s is mostly a wrapper around the standard Elasticsearch Java client with the intention of creating a concise, idiomatic, reactive, type safe DSL for applications in Scala that use Elasticsearch. The Java client, which can of course be used directly in Scala, is more verbose due to Java's nature. Scala lets us do better.

Elastic4s's DSL allows you to to construct your requests programatically, with syntatic and semantic errors manifested at compile time, and uses standard Scala futures to enable you to easily integrate into your existing asynchronous workflow. The aim of the DSL is that requests are written in an SQL-like way, while staying true to the Java API or Rest API.

Elastic4s supports Scala collections so you don't have to do tedious conversions from your Scala domain classes into Java collections. It also allows you to index documents directly without having to extract and set fields manually - eg from a case class, a JSON document, or a Map (or a custom source). Due to its type safe nature, it is easy to see what operations are available for any request type, because your IDE can use type information to show what methods are available.

Key points

• Type safe concise DSL
• Integrates with standard Scala futures
• Uses Scala collections library over Java collections
• Returns Option where the java methods would return null
• Uses Scala Durations instead of strings/longs for time values
• Uses typeclasses for marshalling and unmarshalling of classes to/from elasticsearch documents, backed by Jackson, Circe, Json4s and PlayJson implementations
• Leverages the built-in Java client
• Provides reactive-streams implementation
• Provides embedded node and testkit subprojects, ideal for your tests

Release

The latest release is 5.0.4 which is compatible with Elasticsearch 5.0.x. There are releases for both Scala 2.11 and Scala 2.12. Scala 2.10 support has been dropped starting with the 5.0.x release train. For releases that are compatible with earlier versions of Elasticsearch, search maven central. For more information read Using Elastic4s in your project.

Starting from version 5.0.0, the underlying Elasticsearch Java client has dependencies on Netty, Lucene and others that it does not bring in transitively. I do not know the reasoning behind this, as they are needed for the Java client to work. The elastic4s client brings in the dependencies for you, but in case anything is missed, add it to your build yourself.

The second issue is that it uses Netty 4.1. However some popular projects such as Spark and Play currently use 4.0 and there is a breaking change between the two versions. Therefore if you bring in elastic4s (or even just the raw Java transport client) you will get NoSuchMethodExceptions if you try to use it with Play or Spark. I am unable of a workaround at present.

Elastic4s Release	Target Elasticsearch version
5.0.x	5.0.x
2.4.x	2.4.X
2.3.x	2.3.X
2.2.1	2.2.X
2.1.2	2.1.X
2.0.1	2.0.X
1.7.5	1.7.X
1.6.6	1.6.X
1.5.17	1.5.X
1.4.14	1.4.x
1.3.3	1.3.x
1.2.3.0	1.2.x
1.1.2.0	1.1.x
1.0.3.0	1.0.x
0.90.13.2	0.90.x

Changelog

5.1.5

• Fixed filter in add alias

• Added com.vividsolutions.jts.geom to build to avoid "stub issues"

5.1.3

• Added explicit search scroll close in reactive streams #695
• Added phrase suggestion collate query

5.1.1

• Upgrade to elasticsearch 5.1.1

5.0.4

• elastic4s-xpack-security module.
• Added missing geoShapeQuery for indexed shape

5.0.3

• Fixed issue with search scroll id using a scala Duration

5.0.2

• Elastic4s now brings in the required netty and lucene dependencies that the elasticsearch-transport module needs, but doesn't bring in transitively.
• Added strongly typed listener to elastic4s-streams.

5.0.1

• Released elastic4s-play-json for Elasticsearch 5.

5.0.0

Elasticsearch 5.0 is a huge release from the people at Elastic. There have been some queries and actions removed completely, and plenty of methods have been renamed or changed. The full breaking changes log in Elasticsearch itself is here: https://www.elastic.co/guide/en/elasticsearch/reference/current/breaking-changes-5.0.html

These are the majority of changes in the scala client. As part of upgrading, there will certainly be some tweaking required.

• TTL has been removed. As a replacement, you should use time based indexes or cron a delete-by-query with a range query on a timestamp field.
• Scala specific enums have (mostly) been removed in favour of using the Java Enums provided by the Java client.
• Infix notation has (mostly) been deprecated
• New typeclass HitReader for reading data from searches. This typeclass handles errors (it returns Either[Throwable, T]) and now works for search, get, multisearch and multiget.
• PercolatorQuery has been added (Elasticsearch have removed the previous percolator actions)
• Scala specific re-index has been removed and the elasticsearch re-index action has been added #556
• Update by query is now a plugin in Elasticsearch and so has been added to the client #616
• Delete by query is now a plugin in Elasticsearch and so has been added to the client #616
• Rollover has been added #658
• Suggestions action has been removed in Elasticsearch, suggestions are now available on the search action
• Search API has methods for getting the correct suggestion result back, eg resp.termSuggestion("mysugg1")
• Multimatchquery now supports boosting individual fields #545
• Existing bool syntax has been deprecated in favour of a more explicit syntax #580
• Terms lookup query has been added #631
• Terms query now supports Iterables #597 #599
• New module added for embedded nodes
• successFn has been added to elastic-streams. This is only invoked if there are no errors. #615
• Cluster health supports parameters #600
• Added indices options to GetSettings
• Removed most of the methods deprecated prior to 2.0
• Create template now supports alias #652
• Fixed bug in docAsUpsert #651 #592
• Support predefined Analyzers / Filters #602
• Support for predefined language-specific stopwords #596
• Support geo_shape queries #639
• More like this request has been removed, use more like this query
• Added better name for update in index into #535
• Allow querying mappings for all types in an index #619
• Add shutdown listener to close local node on JVM exit #655

2.1.1

• #484 Fixed bug in es-streams throwing ClassCastException
• #483 Added overloaded doc method to update to accept indexables

2.1.0

• Optimize was renamed to ForceMerge. The existing optimize method are deprecated and forceMerge(indexes*) has been added in its place.
• #395 Added pipeline aggregation definitions
• #458 Added parameters to clear cache operation
• #475 Fixed breaking change in terms query
• rewrite was removed from Elasticsearch's matchXXX queries so has been removed in the dsl
• Added GeoCentroid aggregation
• Added terminateAfter to search definition
• SearchType.SCAN is now deprecated in Elasticsearch
• count is deprecated in Elasticsearch and should be replaced with a search with size 0

2.0.1

• #473 Added missing "filter" clause from bool query
• #475 Fixed breaking change in terms query
• #474 Added "item" likes to more like this query

2.0.0

Major upgrade to Elasticsearch 2.0.0 including breaking changes. Please raise a PR if I've missed any breaking changes.

• In elasticsearch 2.0.0 one of the major changes has been filters have become queries. So in elastic4s this means all methods xxxFilter are now xxxQuery, eg hasChildrenFilter is now hasChildrenQuery.
• Some options that existed only on filters like cache and cache key are now removed.
• Fuzzy like this query has been removed (this was removed in elasticsearch itself)
• Script dsl has changed. To create a script to pass into a method, you use script(script) or script(name, script) with further parameters set using the builder pattern.
• DynamicTemplate dsl template name <name> has been removed. Now you supply the full field definition in the dsl method, as such template(field("price_*", DoubleType))
• Index_analyzer has been removed in elasticsearch. Use analyzer and then override the analyzer for search with search_analyzer
• MoreLikeThis was removed from elasticsearch in favour of a moreLikeThisQuery on a search request.
• moreLikeThisQuery has changed camel case (capital L), also now requires the 'like' text as the 2nd method, eg moreLikeThisQuery("field").text("a") (both can take varargs as well).
• Search requests now return a richer response type. Previously it returned the java type. The richer type has java style methods so your code will continue to compile, but with deprecation warnings.
• The sorting DSL has changed in that the previous infix style methods are deprecated. So field sort x becomes fieldSort(x) etc.
• Or and And filters have been removed completely (not changed into queries like other filters). Use a bool query with must clauses for and's and should clauses for or's.
• Highlight dsl has changed slightly, highlight field x is now deprecated in favour of highlight(x)
• Delete mapping has been removed (this is removed in elasticsearch itself)
• IndexStatus api has been removed (this was removed in elasticsearch itself)
• Template has been renamed dynamic template (to better match the terminology in elasticsesarch)
• Field and mapping syntax has changed slightly. The implicit "fieldname" as StringType ... has been deprecated in favour of field("fieldname", StringType) or stringField(), longField, etc
• In es-streams the ResponseListener has changed to accept a BulkItemResult instead of a BulkItemResponse
• Multiget now returns a rich scala wrapper in the form of MultiGetResult. The richer type has java style methods so your code will continue to compile, but with deprecation warnings.
• GetSegments returns a scala wrapper in the form of GetSegmentsResult
• IndexStats returns a scala wrapper in the form IndexStatsResult

1.7.0

• Works with Elasticsearch 1.7.x
• Removed sync client (deprecated since 1.3.0)

1.6.6

• Fix for race condition in elastic-streams subscriber

1.6.5

• Added sourceAsUpsert to allow Indexable as upsert in update queries
• Added geohash aggregation
• Added geohash cell filter
• Added cluster state api
• Added support for unmapped_type property
• Added block until index/type exists testkit helpers
• Added raw query to count dsl
• Added show typeclass for count
• Added InFilter and IndicesFilter
• Added shorter syntax for field types, eg stringField(name) vs field name <name> typed StringType

1.6.4

• Added reactive streams implementation for elastic4s.
• Support explicit field types in the update dsl
• Added missing options to restore snapshot dsl
• Added show typeclass for percolate register

1.5.17

• Added clear scroll api
• Added missing options to restore snapshot dsl

1.6.3

• Added clear scroll api
• Added show typeclass for multisearch
• Allow update dsl to use explicit field values

1.5.16

• Added HitAs as a replacement for the Reader typeclass
• Added indices option to mapping, count and search dsl
• Added docValuesFormat to timestamp mapping

1.6.2

• Added new methods to testkit
• Introduced simplier syntax for sorts
• Added HitAs as a replacement for the Reader typeclass
• Fixed validate query for block queries
• Added show typeclasses for search, create index, into into, validate, count, and percolate to allow easy debugging of the json of requests.

1.5.15

• Added matched_fields and highlight filter to highlighter

1.6.1

• Added IterableSearch for iterating over a scroll
• Enhanced multiget dsl to include routing, version and field options
• Added rich result for GetAliasResponse
• Added context queries to suggestions
• Breaking change: Changed syntax of suggestions to be clearer and allow for type safe results
• Allow setting analyzer by name on matchphraseprefix
• Added singleMethodSyntax variant, eg indexInto(index) rather than index into index
• Added re-write to validate
• Added filter support to alias (previously only the java client filters were supported)
• Added cluster settings api
• Added field stats api
• Addd docValuesFormat to timestamp mapping
• Added matched_fields and highlight filter to highlighter
• Supported stopwords_list in filter
• Reworked testkit to allow more configuration over the creating of the test clients

Introduction

The basic usage of the Scala driver is that you create an instance of ElasticClient and then invoke the various execute methods with the requests you want to perform. The execute methods are asynchronous and will return a standard Scala Future[T] where T is the response type appropriate for your request type. For example a search request will return a response of type SearchResponse which contains the results of the search.

Requests, such as inserting a document, searching, creating an index, etc, are created using the DSL syntax that is similar in style to SQL queries. For example to create a search request, you would do: search in "index/type" query "findthistext"

The response objects are, for the most part, the exact same type the Java API returns. This is because there is mostly no reason to wrap these as they are fairly easy to use in Scala.

All the DSL keywords are located in the ElasticDsl trait which needs to be imported or extended.

An example is worth 1000 characters so here is a quick example of how to create a local node with a client and index a one field document. Then we will search for that document using a simple text query.

import com.sksamuel.elastic4s.ElasticClient
import com.sksamuel.elastic4s.ElasticDsl._

object Test extends App {

  val client = ElasticClient.transport(ElasticsearchClientUri(host, port))

  // await is a helper method to make this operation synchronous instead of async
  // You would normally avoid doing this in a real program as it will block your thread
  client.execute { index into "bands" / "artists" fields "name"->"coldplay" }.await

  // we need to wait until the index operation has been flushed by the server.
  // this is an important point - when the index future completes, that doesn't mean that the doc
  // is necessarily searchable. It simply means the server has processed your request and the doc is
  // queued to be flushed to the indexes. Elasticsearch is eventually consistent.
  // For this demo, we'll simply wait for 2 seconds (default refresh interval is 1 second).
  Thread.sleep(2000)

  // now we can search for the document we indexed earlier
  val resp = client.execute { search in "bands" / "artists" query "coldplay" }.await
  println(resp)

}

For more in depth examples keep reading.

Syntax

Here is a list of the common requests and the syntax used to create them. For more details on each request click through to the readme page. For options that are not yet documented, refer to the Elasticsearch documentation as the DSL closely mirrors the standard Java API / REST API.

Operation	Syntax
Add Alias	addAlias(<alias>).on(<index>)
Cancel Tasks	cancelTasks(<nodeIds>)
Clear index cache	clearCache(<index>)
Close index	closeIndex(<name>)
Cluster health	clusterHealth()
Cluster stats	clusterStats()
Create Index	createIndex(<name>).mappings( mapping(<name>).as( ... fields ... ) )
Create Repository	createRepository(<repo>).type(<type>)
Create Snapshot	createSnapshot(<name>).in(<repo>)
Create Template	createTemplate(<name>).pattern(<pattern>).mappings {...}
Delete by id	delete(<id>).from(<index> / <type>)
Delete index	deleteIndex(<index>) [settings]
Delete Snapshot	deleteSnapshot(<name>).in(<repo>)
Delete Template	deleteTemplate(<name>)
Explain	explain(<index>, <type>, <id>)
Field stats	fieldStats(<indexes>)
Flush Index	flushIndex(<index>)
Get	get(<id>).from(<index> / <type>)
Get Alias	getAlias(<name>).on(<index>)
Get Mapping	getMapping(<index> / <type>)
Get Segments	getSegments(<indexes>)
Get Snapshot	getSnapshot <name> from <repo>
Get Template	getTemplate(<name>)
Index	indexInto(<index> / <type>).doc(<doc>)
Index exists	indexExists(<name>)
Index Status	indexStatus(<index>)
List Tasks	listTasks(nodeIds)
Multiget	multiget( get(1).from(<index> / <type>), get(2).from(<index> / <type>) )
Multisearch	multi( search(...), search(...) )
Open index	openIndex(<name>)
Force Merge	forceMerge(<indexes>)
Put mapping	putMapping(<index> / <type>) as { mappings block }
Recover Index	recoverIndex(<name>)
Refresh index	refreshIndex(<name>)
Register Query	register(<query>).into(<index> / <type>, <field>)
Remove Alias	removeAlias(<alias>).on(<index>)
Restore Snapshot	restoreSnapshot(<name>).from(<repo>)
Search	search(<index> / <type>).query(<query>)
Search scroll	searchScroll(<scrollId>)
Type Exists	typesExists(<types>) in <index>
Update	update(<id>).in(<index> / <type>)
Validate	validateIn(<index/type>).query(<query>)

Please also note some java interoperability notes.

Connecting to a Cluster

To connect to a stand alone elasticsearch cluster then you need to use the transport method on the ElasticClient object specifying the uri of the cluster. The uri is an instance of ElasticsearchClientUri which can be created from a host and port or from a string. Please note that the uri uses the port of the TCP interface (normally 9300) and NOT the port you connect with when using HTTP (normally 9200).

val client = ElasticClient.transport(ElasticsearchClientUri("host1", 9300))

For multiple nodes it's better to use the elasticsearch client uri connection string. This is in the format "elasticsearch://host1:port2,host2:port2,...?param=value&param2=value2". For example:

val uri = ElasticsearchClientUri("elasticsearch://foo:1234,boo:9876?cluster.name=mycluster")
val client = ElasticClient.transport(uri)

If you need to pass settings to the client, then you need to invoke transport with a settings object. For example to specify the cluster name (if you changed the default then you must specify the cluster name).

import org.elasticsearch.common.settings.Settings
val settings = Settings.builder().put("cluster.name", "myClusterName").build()
val client = ElasticClient.transport(settings, ElasticsearchClientUri("elasticsearch://somehost:9300"))

If you already have a handle to a Node in the Java API then you can create a client from it easily:

val node = ... // node from the java API somewhere
val client = ElasticClient.fromNode(node)

X-Pack-Security

Elastic4s also supports the xpack-security add on. To use this, add the elastic-xpack-security dependency to your build and create a client using the XPackElasticClient object instead of the ElasticClient object. Eg,

val client = XPackElasticClient(settings, uri, <plugins>...)

Embedded Node

A locally configured node and client can be created be including the elatic4s-embedded module. Then a local node can be started by invoking LocalNode() with the cluster name and data path. From the local node we can return a handle to the client by invoking the elastic4sclient function.

import com.sksamuel.elastic4s.ElasticClient
val node = LocalNode(clusterName, pathHome)
val client = node.elastic4sclient()

To specify settings for the local node you can pass in a settings object like this:

val settings = Settings.builder()
      .put("cluster.name", "elasticsearch")
      .put("path.home", "mypath")
      .put("http.enabled", false)
      .build()
val node = LocalNode(settings)
val client = node.elastic4sclient(<shutdownNodeOnClose>)

If shutdownNodeOnClose is true, then once close is called on the client, the local node will be stopped. Otherwise you will manage the lifecycle of the local node yourself (stopping it before exiting the process).

Create Index

All documents in Elasticsearch are stored in an index. We do not need to tell Elasticsearch in advance what an index will look like (eg what fields it will contain) as Elasticsearch will adapt the index dynamically as more documents are added, but we must at least create the index first.

To create an index called "places" that is fully dynamic we can simply use:

client.execute { createIndex("places") }

We can optionally set the number of shards and / or replicas

client.execute { createIndex("places") shards 3 replicas 2 }

Sometimes we want to specify the properties of the fields in the index in advance. This allows us to manually set the type of the field (where Elasticsearch might infer something else) or set the analyzer used, or multiple other options

To do this we add mappings:

import com.sksamuel.elastic4s.mappings.FieldType._
import com.sksamuel.elastic4s.StopAnalyzer

client.execute {
    createIndex("places") mappings (
        mapping("cities") as (
            keywordField("id"),
            textField("name") boost 4,
            textField("content") analyzer StopAnalyzer
        )
    )
}

Then Elasticsearch is configured with those mappings for those fields only. It is still fully dynamic and other fields will be created as needed with default options. Only the fields specified will have their type preset.

More examples on the create index syntax can be found here.

Analyzers

Analyzers control how Elasticsearch parses the fields for indexing. For example, you might decide that you want whitespace to be important, so that "band of brothers" is indexed as a single "word" rather than the default which is to split on whitespace. There are many advanced options available in analayzers. Elasticsearch also allows us to create custom analyzers. For more details read about the DSL support for analyzers.

Indexing

To index a document we need to specify the index and type and optionally we can set an id. If we don't include an id then elasticsearch will generate one for us. We must also include at least one field. Fields are specified as standard tuples.

client.execute {
  indexInto("places" / "cities") id "uk" fields (
    "name" -> "London",
    "country" -> "United Kingdom",
    "continent" -> "Europe",
    "status" -> "Awesome"
  )
}

There are many additional options we can set such as routing, version, parent, timestamp and op type. See official documentation for additional options, all of which exist in the DSL as keywords that reflect their name in the official API.

Indexing Typeclass

Sometimes it is useful to index directly from your domain model, and not have to create maps of fields inline. For this elastic4s provides the Indexable typeclass. Simply provide an implicit instance of Indexable[T] in scope for any class T that you wish to index, and then you can use doc(t) on the index request. For example:

// a simple example of a domain model
case class Character(name: String, location: String)

// how you turn the type into json is up to you
implicit object CharacterIndexable extends Indexable[Character] {
  override def json(t: Character): String = s""" { "name" : "${t.name}", "location" : "${t.location}" } """
}

// now the index request reads much cleaner
val jonsnow = Character("jon snow", "the wall")
client.execute {
  indexInto("gameofthrones" / "characters").doc(jonsnow)
}

Some people prefer to write typeclasses manually for the types they need to support. Other people like to just have it done automagically. For those people, elastic4s provides extensions for the well known Scala Json libraries that can be used to generate JSon generically.

Simply add the import for your chosen library below and then with those implicits in scope, you can now pass any type you like to doc and an Indexable will be derived automatically.

Library	Elastic4s Module	Import
Jackson	[elastic4s-jackson](http://search.maven.org/#search	ga
Json4s	[elastic4s-json4s](http://search.maven.org/#search	ga
Circe	[elastic4s-circe](http://search.maven.org/#search	ga

                                                                                                                     import com.sksamuel.elastic4s.circe._|

Searching

Searching is naturally the most involved operation. There are many ways to do searching in elastic search and that is reflected in the higher complexity of the query DSL.

To do a simple text search, where the query is parsed from a single string

search("places" / "cities").query("London")

That is actually an example of a SimpleStringQueryDefinition. The string is implicitly converted to that type of query. It is the same as specifying the query type directly:

search("places" / "cities"),query(simpleStringQuery("London"))

The simple string example is the only time we don't need to specify the query type. We can search for everything by not specifying a query at all.

search("places" / "cities")

We might want to limit the number of results and / or set the offset.

search("places" / "cities") query "paris" start 5 limit 10

We can search against certain fields only:

search("places" / "cities") query termQuery("country", "France")

Or by a prefix:

search("places" / "cities") query prefixQuery("country", "France")

Or by a regular expression (slow, but handy sometimes!):

search("places" / "cities") query regexQuery("country", "France")

There are many other types, such as range for numeric fields, wildcards, distance, geo shapes, matching.

Read more about search syntax here. Read about multisearch here. Read about suggestions here.

HitReader Typeclass

By default Elasticsearch search responses contain an array of SearchHit instances which contain things like the id, index, type, version, etc as well as the document source as a string or map. Elastic4s provides a means to convert these back to meaningful domain types quite easily using the HitReader[T] typeclass.

Provide an implementation of this typeclass, as an in scope implicit, for whatever type you wish to marshall search responses into, and then you can call to[T] or safeTo[T] on the response. The difference between to and safeTo is that to will drop any errors and just return successful conversions, whereas safeTo returns a sequence of Either[Throwable, T].

A full example:

case class Character(name: String, location: String)

implicit object CharacterHitReader extends HitReader[Character] {
  override def read(hit: Hit): Either[Throwable, Character] = {
    Character(hit.sourceAsMap("name").toString, hit.sourceAsMap("location").toString)
  }
}

val resp = client.execute {
  search("gameofthrones" / "characters").query("kings landing")
}.await // don't block in real code

// .as[Character] will look for an implicit HitAs[Character] in scope
// and then convert all the hits into Characters for us.
val characters :Seq[Character] = resp.as[Character]

This is basically the inverse of the Indexable typeclass. And just like Indexable, the json modules provide implementations out of the box for any types. The imports are the same as for the Indexable typeclasses.

As a bonus feature of the Jackson implementation, if your domain object has fields called _timestamp, _id, _type, _index, or _version then those special fields will be automatically populated as well.

Highlighting

Elasticsearch can annotate results to show which part of the results matched the queries by using highlighting. Just think when you're in google and you see the snippets underneath your results - that's what highlighting does.

We can use this very easily, just add a highlighting definition to your search request, where you set the field or fields to be highlighted. Viz:

search in "music" / "bios" query "kate bush" highlighting (
  highlight field "body" fragmentSize 20
)

All very straightforward. There are many options you can use to tweak the results. In the example above I have simply set the snippets to be taken from the field called "body" and to have max length 20. You can set the number of fragments to return, seperate queries to generate them and other things. See the elasticsearch page on highlighting for more info.

Get

Sometimes we don't want to search and want to retrieve a document directly from the index by id. In this example we are retrieving the document with id 'coldplay' from the bands/rock index and type.

client.execute {
 get("coldplay").from("bands" / "rock")
}

We can get multiple documents at once too. Notice the following multiget wrapping block.

client.execute {
  multiget(
    get("coldplay").from("bands" / "rock"),
    get("keane").from("bands" / "rock")
  )
}

See more get examples and usage of multiget here

Deleting

In the rare case that we become tired of a band we might want to remove them. Naturally we wouldn't want to remove Chris Martin and boys so we're going to remove U2 instead. We think they're a little past their best (controversial). This operation assumes the id of the document is "u2".

client.execute {
  delete("u2").from("bands/rock")
}

We can take this a step further by deleting by a query rather than id. In this example we're deleting all bands where their type is pop.

client.execute {
  deleteIn("bands").by(termQuery("type", "pop"))
}

See more about delete on the delete page

Updates

We can update existing documents without having to do a full index, by updating a partial set of fields.

client.execute {
  update(25).in("scifi" / "starwars"). docAsUpsert (
    "character" -> "chewie",
    "race" -> "wookie"
  )
}

Read more about updates and see more examples.

More like this

If you want to return documents that are "similar" to a current document we can do that very easily with the more like this query.

client.execute {
  search("drinks" / "beer") query {
    moreLikeThisQuery("name").likeTexts("coors", "beer", "molson") minTermFreq 1 minDocFreq 1
  }
}

For all the options see here.

Bulk Operations

Elasticsearch is fast. Roundtrips are not. Sometimes we want to wrestle every last inch of performance and a useful way to do this is to batch up requests. Elastic has guessed our wishes and created the bulk API. To do this we simply wrap index, delete and update requests using the bulk keyword and pass to the execute method in the client.

client.execute {
  bulk (
    index into "bands/rock" fields "name"->"coldplay",
    index into "bands/rock" fields "name"->"kings of leon",
    index into "bands/pop" fields (
      "name" -> "elton john",
      "best_album" -> "tumbleweed connection"
    )
  )
}

A single HTTP or TCP request is now needed for 4 operations. In addition Elasticsearch can now optimize the requests, by combinging inserts or using aggressive caching.

The example above uses simple documents just for clarity of reading; the usual optional settings can still be used. See more information on the bulk page.

Json Output

It can be useful to see the json output of requests in case you wish to tinker with the request in a REST client or your browser. It can be much easier to tweak a complicated query when you have the instant feedback of the HTTP interface.

Elastic4s makes it easy to get this json where possible. Simply call .show on a request to get back a json string. Eg:

val req = search in "music" / "bands" query "coldplay" ...
println(req.show) // would output json
client.execute { req } // now executes that request

Not all requests have a json body. For example get-by-id is modelled purely by http query parameters, there is no json body to output. And some requests don't convert to json in the Java client so aren't yet supported by the show typeclass. Also, for clarity, it should be pointed out that the client doesn't send JSON to the server, it uses a binary protocol. So the provided json format should be treated as a debugging tool only.

The requests that support .show are search, multi, createIndex, index into, validate, percolate, count.

Synchronous Operations

All operations are normally asynchronous. Sometimes though you might want to block - for example when doing snapshots or when creating the initial index. You can call .await on any operation to block until the result is ready. This is especially useful when testing.

val resp = client.execute {
  index("bands" / "rock") fields ("name"->"coldplay", "debut"->"parachutes")
}.await

DSL Completeness

As it stands the Scala DSL covers all of the common operations - index, create, delete, delete by query, search, validate, percolate, update, explain, get, and bulk operations. There is good support for the various settings for each of these - more so than the Java client provides in the sense that more settings are provided in a type safe manner.

However there are settings and operations (mostly admin / cluster related) that the DSL does not yet cover (pull requests welcome!). In these cases it is necessary to drop back to the Java API. This can be done by calling .java on the client object to get the underlying java elastic client,

client.java.admin.cluster.prepareHealth.setWaitForEvents(Priority.LANGUID).setWaitForGreenStatus().execute().actionGet

This way you can still access everything the normal Java client covers in the cases where the Scala DSL is missing a construct, or where there is no need to provide a DSL.

Elastic Reactive Streams

Elastic4s has an implementation of the reactive streams api for both publishing and subscribing that is built using Akka. To use this, you need to add a dependency on the elastic4s-streams module.

There are two things you can do with the reactive streams implementation. You can create an elastic subscriber, and have that stream data from some publisher into elasticsearch. Or you can create an elastic publisher and have documents streamed out to subscribers.

Integrate

First you have to add an additional dependeny to your build.sbt

libraryDependencies += "com.sksamuel.elastic4s" %% "elastic4s-streams" % "x.x.x"

Import the new API with

import com.sksamuel.elastic4s.streams.ReactiveElastic._

Publisher

An elastic publisher can be created for any arbitrary query you wish, and then using the efficient search scroll API, the entire dataset that matches your query is streamed out to subscribers.

And make sure you have an Akka Actor System in implicit scope

implicit val system = ActorSystem()

Then create a publisher from the client using any query you want. You must specify the scroll parameter, as the publisher uses the scroll API.

val publisher = client.publisher(search in "myindex" query "sometext" scroll "1m")

Now you can add subscribers to this publisher. They can of course be any type that adheres to the reactive-streams api, so you could stream out to a mongo database, or a filesystem, or whatever custom type you want.

publisher.subscribe(someSubscriber)

If you just want to stream out an entire index then you can use the overloaded form:

val publisher = client.publisher("index1", keepAlive = "1m")

Subscription

An elastic subcriber can be created that will stream a request to elasticsearch for each item produced by a publisher. The subscriber can create index, update, or delete requests, so is a good way to synchronize datasets.

import ReactiveElastic._

And make sure you have an Akka Actor System in implicit scope.

implicit val system = ActorSystem()

Then create a subscriber, specifying the following parameters:

• A type parameter that is the type of object that the publisher will provide
• How many documents should be included per index batch (10-100 is usually good)
• How many concurrent batches should be in flight (usually around the number of cores)
• An optional ResponseListener that will be notified for each item that was successfully acknowledged by the es cluster
• An optional function that will be called once the subscriber has received all data. Defaults to a no-op
• An optional function to call if the subscriber encouters an error. Defaults to a no-op.

In addition there should be a further implicit in scope of type RequestBuilder[T] that will accept objects of T (the type produced by your publisher) and build an index, update, or delete request suitable for dispatchin to elasticsearch.

implicit val builder = new RequestBuilder[SomeType] {
  import ElasticDsl._
  // the request returned doesn't have to be an index - it can be anything supported by the bulk api
  def request(t: T): BulkCompatibleDefinition =  index into "index" / "type" fields ....
}

Then the subscriber can be created, and attached to a publisher:

val subscriber = client.subscriber[SomeType](batchSize, concurrentBatches, () => println "all done")
publisher.subscribe(subscriber)

Using Elastic4s in your project

For gradle users, add (replace 2.12 with 2.11 for Scala 2.11):

compile 'com.sksamuel.elastic4s:elastic4s-core_2.12:x.x.x'

For SBT users simply add:

libraryDependencies += "com.sksamuel.elastic4s" %% "elastic4s-core" % "x.x.x"

For Maven users simply add (replace 2.12 with 2.11 for Scala 2.11):

<dependency>
    <groupId>com.sksamuel.elastic4s</groupId>
    <artifactId>elastic4s-core_2.12</artifactId>
    <version>x.x.x</version>
</dependency>

Check for the latest released versions on maven central

Building and Testing

This project is built with SBT. So to build

sbt compile

And to test

sbt test

Integration tests run on a local elastic that is created and torn down as part of the tests inside your standard temp folder. There is no need to configure anything externally.

Used By

• Barclays Bank
• HSBC
• Shazaam
• Graphflow
• Hotel Urbano
• Immobilien Scout
• Deutsche Bank
• Goldman Sachs
• HMRC
• Canal+
• AOE

Raise a PR to add your company here

YourKit supports open source projects with its full-featured Java Profiler. YourKit, LLC is the creator of YourKit Java Profiler and YourKit .NET Profiler, innovative and intelligent tools for profiling Java and .NET applications.

Contributions

Contributions to elastic4s are always welcome. Good ways to contribute include:

• Raising bugs and feature requests
• Fixing bugs and enhancing the DSL
• Improving the performance of elastic4s
• Adding to the documentation

License

This software is licensed under the Apache 2 license, quoted below.

Copyright 2013-2016 Stephen Samuel

Licensed under the Apache License, Version 2.0 (the "License"); you may not
use this file except in compliance with the License. You may obtain a copy of
the License at

    http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
License for the specific language governing permissions and limitations under
the License.