Apache Spark supportedit

 

Apache Spark is a fast and general-purpose cluster computing system. It provides high-level APIs in Java, Scala and Python, and an optimized engine that supports general execution graphs.

 
 -- Spark website

Spark provides fast iterative/functional-like capabilities over large data sets, typically by caching data in memory. As opposed to the rest of the libraries mentioned in this documentation, Apache Spark is computing framework that is not tied to Map/Reduce itself however it does integrate with Hadoop, mainly to HDFS. elasticsearch-hadoop allows Elasticsearch to be used in Spark in two ways: through the dedicated support available since 2.1 or through the Map/Reduce bridge since 2.0

Installationedit

Just like other libraries, elasticsearch-hadoop needs to be available in Spark’s classpath. As Spark has multiple deployment modes, this can translate to the target classpath, whether it is on only one node (as is the case with the local mode - which will be used through-out the documentation) or per-node depending on the desired infrastructure.

Native supportedit

Note

Added in 2.1.

elasticsearch-hadoop provides native integration between Elasticsearch and Apache Spark, in the form of an RDD (Resilient Distributed Dataset) (or Pair RDD to be precise) that can read data from Elasticsearch. The RDD is offered in two flavors: one for Scala (which returns the data as Tuple2 with Scala collections) and one for Java (which returns the data as Tuple2 containing java.util collections).

Important

Whenever possible, consider using the native integration as it offers the best performance and maximum flexibility.

Configurationedit

To configure elasticsearch-hadoop for Apache Spark, one can set the various properties described in the Configuration chapter in the SparkConf object:

import org.apache.spark.SparkConf

val conf = new SparkConf().setAppName(appName).setMaster(master)
conf.set("es.index.auto.create", "true")
SparkConf conf = new SparkConf().setAppName(appName).setMaster(master);
conf.set("es.index.auto.create", "true");

Command-line. For those that want to set the properties through the command-line (either directly or by loading them from a file), note that Spark only accepts those that start with the "spark." prefix and will ignore the rest (and depending on the version a warning might be thrown). To work around this limitation, define the elasticsearch-hadoop properties by appending the spark. prefix (thus they become spark.es.) and elasticsearch-hadoop will automatically resolve them:

$ ./bin/spark-submit --conf spark.es.resource=index/type ...

Notice the es.resource property which became spark.es.resource

Writing data to Elasticsearchedit

With elasticsearch-hadoop, any RDD can be saved to Elasticsearch as long as its content can be translated into documents. In practice this means the RDD type needs to be a Map (whether a Scala or a Java one), a JavaBean or a Scala case class. When that is not the case, one can easily transform the data in Spark or plug-in their own custom ValueWriter.

Scalaedit

When using Scala, simply import the org.elasticsearch.spark package which, through the pimp my library pattern, enriches the any RDD API with saveToEs methods:

import org.apache.spark.SparkContext    
import org.apache.spark.SparkContext._

import org.elasticsearch.spark._        

...

val conf = ...
val sc = new SparkContext(conf)         

val numbers = Map("one" -> 1, "two" -> 2, "three" -> 3)
val airports = Map("arrival" -> "Otopeni", "SFO" -> "San Fran")

sc.makeRDD(Seq(numbers, airports)).saveToEs("spark/docs")

Spark Scala imports

elasticsearch-hadoop Scala imports

start Spark through its Scala API

makeRDD creates an ad-hoc RDD based on the collection specified; any other RDD (in Java or Scala) can be passed in

index the content (namely the two documents (numbers and airports)) in Elasticsearch under spark/docs

Note

Scala users might be tempted to use Seq and the notation for declaring root objects (that is the JSON document) instead of using a Map. While similar, the first notation results in slightly different types that cannot be matched to a JSON document: Seq is an order sequence (in other words a list) while creates a Tuple which is more or less an ordered, fixed number of elements. As such, a list of lists cannot be used as a document since it cannot be mapped to a JSON object; however it can be used freely within one. Hence why in the example above Map(k→v) was used instead of Seq(k→v)

As an alternative to the implicit import above, one can use elasticsearch-hadoop Spark support in Scala through EsSpark in the org.elasticsearch.spark.rdd package which acts as a utility class allowing explicit method invocations. Additionally instead of Maps (which are convenient but require one mapping per instance due to their difference in structure), use a case class :

import org.apache.spark.SparkContext
import org.elasticsearch.spark.rdd.EsSpark                        

// define a case class
case class Trip(departure: String, arrival: String)               

val upcomingTrip = Trip("OTP", "SFO")
val lastWeekTrip = Trip("MUC", "OTP")

val rdd = sc.makeRDD(Seq(upcomingTrip, lastWeekTrip))             
EsSpark.saveToEs(rdd, "spark/docs")                               

EsSpark import

Define a case class named Trip

Create an RDD around the Trip instances

Index the RDD explicitly through EsSpark

For cases where the id (or other metadata fields like ttl or timestamp) of the document needs to be specified, one can do so by setting the appropriate mapping namely es.mapping.id. Following the previous example, to indicate to Elasticsearch to use the field id as the document id, update the RDD configuration (it is also possible to set the property on the SparkConf though due to its global effect it is discouraged):

EsSpark.saveToEs(rdd, "spark/docs", Map("es.mapping.id" -> "id"))
Javaedit

Java users have a dedicated class that provides a similar functionality to EsSpark, namely JavaEsSpark in the org.elasticsearch.spark.rdd.api.java (a package similar to Spark’s Java API):

import org.apache.spark.api.java.JavaSparkContext;                              
import org.apache.spark.api.java.JavaRDD;
import org.apache.spark.SparkConf;

import org.elasticsearch.spark.rdd.api.java.JavaEsSpark;                        
...

SparkConf conf = ...
JavaSparkContext jsc = new JavaSparkContext(conf);                              

Map<String, ?> numbers = ImmutableMap.of("one", 1, "two", 2);                   
Map<String, ?> airports = ImmutableMap.of("OTP", "Otopeni", "SFO", "San Fran");

JavaRDD<Map<String, ?>> javaRDD = jsc.parallelize(ImmutableList.of(numbers, airports));
JavaEsSpark.saveToEs(javaRDD, "spark/docs");                                    

Spark Java imports

elasticsearch-hadoop Java imports

start Spark through its Java API

to simplify the example, use Guava(a dependency of Spark) Immutable* methods for simple Map, List creation

create a simple RDD over the two collections; any other RDD (in Java or Scala) can be passed in

index the content (namely the two documents (numbers and airports)) in Elasticsearch under spark/docs

The code can be further simplified by using Java 5 static imports. Additionally, the Map (who’s mapping is dynamic due to its loose structure) can be replaced with a JavaBean:

public class TripBean implements Serializable {
   private String departure, arrival;

   public TripBean(String departure, String arrival) {
       setDeparture(departure);
       setArrival(arrival);
   }

   public TripBean() {}

   public String getDeparture() { return departure; }
   public String getArrival() { return arrival; }
   public void setDeparture(String dep) { departure = dep; }
   public void setArrival(String arr) { arrival = arr; }
}
import static org.elasticsearch.spark.rdd.java.api.JavaEsSpark;                
...

TripBean upcoming = new TripBean("OTP", "SFO");
TripBean lastWeek = new TripBean("MUC", "OTP");

JavaRDD<TripBean> javaRDD = jsc.parallelize(
                            ImmutableList.of(upcoming, lastWeek));        
saveToEs(javaRDD, "spark/docs");                                          

statically import JavaEsSpark

define an RDD containing TripBean instances (TripBean is a JavaBean)

call saveToEs method without having to type JavaEsSpark again

Setting the document id (or other metadata fields like ttl or timestamp) is similar to its Scala counterpart, though potentially a bit more verbose depending on whether you are using the JDK classes or some other utilities (like Guava):

JavaEsSpark.saveToEs(javaRDD, "spark/docs", ImmutableMap.of("es.mapping.id", "id"));

Writing existing JSON to Elasticsearchedit

For cases where the data in the RDD is already in JSON, elasticsearch-hadoop allows direct indexing without applying any transformation; the data is taken as is and sent directly to Elasticsearch. As such, in this case, elasticsearch-hadoop expects either an RDD containing String or byte arrays (byte[]/Array[Byte]), assuming each entry represents a JSON document. If the RDD does not have the proper signature, the saveJsonToEs methods cannot be applied (in Scala they will not be available).

Scalaedit
val json1 = """{"reason" : "business", "airport" : "SFO"}"""      
val json2 = """{"participants" : 5, "airport" : "OTP"}"""

new SparkContext(conf).makeRDD(Seq(json1, json2))
                      .saveJsonToEs("spark/json-trips") 

example of an entry within the RDD - the JSON is written as is, without any transformation

index the JSON data through the dedicated saveJsonToEs method

Javaedit
String json1 = "{\"reason\" : \"business\",\"airport\" : \"SFO\"}";  
String json2 = "{\"participants\" : 5,\"airport\" : \"OTP\"}";

JavaContextSpark jsc = ...
JavaRDD<String> stringRDD = jsc.parallelize(ImmutableList.of(json1, json2));
JavaEsSpark.saveJsonToEs(stringRDD, "spark/json-trips");             

example of an entry within the RDD - the JSON is written as is, without any transformation

notice the RDD<String> signature

index the JSON data through the dedicated saveJsonToEs method

Writing to dynamic/multi-resourcesedit

For cases when the data being written to Elasticsearch needs to be indexed under different buckets (based on the data content) one can use the es.resource.write field which accepts a pattern that is resolved from the document content, at runtime. Following the aforementioned media example, one could configure it as follows:

Scalaedit
val game = Map("media_type"->"game","title" -> "FF VI","year" -> "1994")
val book = Map("media_type" -> "book","title" -> "Harry Potter","year" -> "2010")
val cd = Map("media_type" -> "music","title" -> "Surfing With The Alien")

sc.makeRDD(Seq(game, book, cd)).saveToEs("my-collection/{media_type}")  

Document key used for splitting the data. Any field can be declared (but make sure it is available in all documents)

Save each object based on its resource pattern, in this example based on media_type

For each document/object about to be written, elasticsearch-hadoop will extract the media_type field and use its value to determine the target resource.

Javaedit

As expected, things in Java are strikingly similar:

Map<String, ?> game =
  ImmutableMap.of("media_type", "game", "title", "FF VI", "year", "1994");
Map<String, ?> book = ...
Map<String, ?> cd = ...

JavaRDD<Map<String, ?>> javaRDD =
                jsc.parallelize(ImmutableList.of(game, book, cd));
saveToEs(javaRDD, "my-collection/{media_type}");  

Save each object based on its resource pattern, media_type in this example

Handling document metadataedit

Elasticsearch allows each document to have its own metadata. As explained above, through the various mapping options one can customize these parameters so that their values are extracted from their belonging document. Further more, one can even include/exclude what parts of the data are sent back to Elasticsearch. In Spark, elasticsearch-hadoop extends this functionality allowing metadata to be supplied outside the document itself through the use of pair RDDs. In other words, for RDDs containing a key-value tuple, the metadata can be extracted from the key and the value used as the document source.

The metadata is described through the Metadata Java enum within org.elasticsearch.spark.rdd package which identifies its type - id, ttl, version, etc… Thus an RDD keys can be a Map containing the Metadata for each document and its associated values. If RDD key is not of type Map, elasticsearch-hadoop will consider the object as representing the document id and use it accordingly. This sounds more complicated than it is, so let us see some examples.

Scalaedit

Pair RDDs, or simply put RDDs with the signature RDD[(K,V)] can take advantage of the saveToEsWithMeta methods that are available either through the implicit import of org.elasticsearch.spark package or EsSpark object. To manually specify the id for each document, simply pass in the Object (not of type Map) in your RDD:

val otp = Map("iata" -> "OTP", "name" -> "Otopeni")
val muc = Map("iata" -> "MUC", "name" -> "Munich")
val sfo = Map("iata" -> "SFO", "name" -> "San Fran")

// instance of SparkContext
val sc = ...

val airportsRDD = sc.makeRDD(Seq((1, otp), (2, muc), (3, sfo)))  
pairRDD.saveToEsWithMeta(airportsRDD, "airports/2015")

airportsRDD is a key-value pair RDD; it is created from a Seq of tuples

The key of each tuple within the Seq represents the id of its associated value/document; in other words, document otp has id 1, muc 2 and sfo 3

Since airportsRDD is a pair RDD, it has the saveToEsWithMeta method available. This tells elasticsearch-hadoop to pay special attention to the RDD keys and use them as metadata, in this case as document ids. If saveToEs would have been used instead, then elasticsearch-hadoop would consider the RDD tuple, that is both the key and the value, as part of the document.

When more than just the id needs to be specified, one should use a scala.collection.Map with keys of type org.elasticsearch.spark.rdd.Metadata:

import org.elasticsearch.spark.rdd.Metadata._          

val otp = Map("iata" -> "OTP", "name" -> "Otopeni")
val muc = Map("iata" -> "MUC", "name" -> "Munich")
val sfo = Map("iata" -> "SFO", "name" -> "San Fran")

// metadata for each document
// note it's not required for them to have the same structure
val otpMeta = Map(ID -> 1, TTL -> "3h")                
val mucMeta = Map(ID -> 2, VERSION -> "23")            
val sfoMeta = Map(ID -> 3)                             

// instance of SparkContext
val sc = ...

val airportsRDD = sc.makeRDD(Seq((otpMeta, otp), (mucMeta, muc), (sfoMeta, sfo)))
pairRDD.saveToEsWithMeta(airportsRDD, "airports/2015") 

Import the Metadata enum

The metadata used for otp document. In this case, ID with a value of 1 and TTL with a value of 3h

The metadata used for muc document. In this case, ID with a value of 2 and VERSION with a value of 23

The metadata used for sfo document. In this case, ID with a value of 3

The metadata and the documents are assembled into a pair RDD

The RDD is saved accordingly using the saveToEsWithMeta method

Javaedit

In a similar fashion, on the Java side, JavaEsSpark provides saveToEsWithMeta methods that are applied to JavaPairRDD (the equivalent in Java of RDD[(K,V)]). Thus to save documents based on their ids one can use:

import org.elasticsearch.spark.rdd.java.api.JavaEsSpark;

// data to be saved
Map<String, ?> otp = ImmutableMap.of("iata", "OTP", "name", "Otopeni");
Map<String, ?> jfk = ImmutableMap.of("iata", "JFK", "name", "JFK NYC");

JavaSparkContext jsc = ...

// create a pair RDD between the id and the docs
JavaPairRDD<?, ?> pairRdd = jsc.parallelizePairs(ImmutableList.of(
        new Tuple2<Object, Object>(1, otp),          
        new Tuple2<Object, Object>(2, jfk)));        
JavaEsSpark.saveToEsWithMeta(pairRDD, target);       

Create a JavaPairRDD by using Scala Tuple2 class wrapped around the document id and the document itself

Tuple for the first document wrapped around the id (1) and the doc (otp) itself

Tuple for the second document wrapped around the id (2) and jfk

The JavaPairRDD is saved accordingly using the keys as a id and the values as documents

When more than just the id needs to be specified, one can choose to use a java.util.Map populated with keys of type org.elasticsearch.spark.rdd.Metadata:

import org.elasticsearch.spark.rdd.java.api.JavaEsSpark;
import org.elasticsearch.spark.rdd.Metadata;          

import static org.elasticsearch.spark.rdd.Metadata.*; 

// data to be saved
Map<String, ?> otp = ImmutableMap.of("iata", "OTP", "name", "Otopeni");
Map<String, ?> sfo = ImmutableMap.of("iata", "SFO", "name", "San Fran");

// metadata for each document
// note it's not required for them to have the same structure
Map<Metadata, Object> otpMeta = ImmutableMap.<Metadata, Object> of(ID, 1, TTL, "1d");
Map<Metadata, Object> sfoMeta = ImmutableMap.<Metadata, Object> of(ID, "2", VERSION, "23");

JavaSparkContext jsc = ...

// create a pair RDD between the id and the docs
JavaPairRDD<?, ?> pairRdd = jsc.parallelizePairs<(ImmutableList.of(
        new Tuple2<Object, Object>(otpMeta, otp),    
        new Tuple2<Object, Object>(sfoMeta, sfo)));  
JavaEsSpark.saveToEsWithMeta(pairRDD, target);       

Metadata enum describing the document metadata that can be declared

static import for the enum to refer to its values in short format (ID, TTL, etc…)

Metadata for otp document

Boiler-plate construct for forcing the of method generic signature

Metadata for sfo document

Tuple between otp (as the value) and its metadata (as the key)

Tuple associating sfo and its metadata

saveToEsWithMeta invoked over the JavaPairRDD containing documents and their respective metadata

Reading data from Elasticsearchedit

For reading, one should define the Elasticsearch RDD that streams data from Elasticsearch to Spark.

Scalaedit

Similar to writing, the org.elasticsearch.spark package, enriches the SparkContext API with esRDD methods:

import org.apache.spark.SparkContext    
import org.apache.spark.SparkContext._

import org.elasticsearch.spark._        

...

val conf = ...
val sc = new SparkContext(conf)         

val RDD = sc.esRDD("radio/artists")     

Spark Scala imports

elasticsearch-hadoop Scala imports

start Spark through its Scala API

a dedicated RDD for Elasticsearch is created for index radio/artists

The method can be overloaded to specify an additional query or even a configuration Map (overriding SparkConf):

...
import org.elasticsearch.spark._

...
val conf = ...
val sc = new SparkContext(conf)

sc.esRDD("radio/artists", "?q=me*") 

create an RDD streaming all the documents matching me* from index radio/artists

The documents from Elasticsearch are returned, by default, as a Tuple2 containing as the first element the document id and the second element the actual document represented through Scala collections, namely one `Map[String, Any]`where the keys represent the field names and the value their respective values.

Javaedit

Java users have a dedicated JavaPairRDD that works the same as its Scala counterpart however the returned Tuple2 values (or second element) returns the documents as native, java.util collections.

import org.apache.spark.api.java.JavaSparkContext;               
import org.elasticsearch.spark.rdd.java.api.JavaEsSpark;             
...

SparkConf conf = ...
JavaSparkContext jsc = new JavaSparkContext(conf);               

JavaPairRDD<String, Map<String, Object>> esRDD =
                        JavaEsSpark.esRDD(jsc, "radio/artists"); 

Spark Java imports

elasticsearch-hadoop Java imports

start Spark through its Java API

a dedicated JavaPairRDD for Elasticsearch is created for index radio/artists

In a similar fashion one can use the overloaded esRDD methods to specify a query or pass a Map object for advanced configuration. Let us see how this looks, but this time around using Java static imports. Further more, let us discard the documents ids and retrieve only the RDD values:

import static org.elasticsearch.spark.rdd.java.api.JavaEsSpark.*;   

...
JavaRDD<Map<String, Object>> esRDD =
                        esRDD(jsc, "radio/artists", "?q=me*").values();

statically import JavaEsSpark class

create an RDD streaming all the documents starting with me from index radio/artists. Note the method does not have to be fully qualified due to the static import

return only values of the PairRDD - hence why the result is of type JavaRDD and not JavaPairRDD

By using the JavaEsSpark API, one gets a hold of Spark’s dedicated JavaPairRDD which are better suited in Java environments than the base RDD (due to its Scala signatures). Moreover, the dedicated RDD returns Elasticsearch documents as proper Java collections so one does not have to deal with Scala collections (which is typically the case with RDDs). This is particularly powerful when using Java 8, which we strongly recommend as its lambda expressions make collection processing extremely concise.

To wit, let us assume one wants to filter the documents from the RDD and return only those that contain a value that contains mega (please ignore the fact one can and should do the filtering directly through Elasticsearch).

In versions prior to Java 8, the code would look something like this:

JavaRDD<Map<String, Object>> esRDD =
                        esRDD(jsc, "radio/artists", "?q=me*").values();
JavaRDD<Map<String, Object>> filtered = esRDD.filter(
    new Function<Map<String, Object>, Boolean>() {
      @Override
      public Boolean call(Map<String, Object> map) throws Exception {
          returns map.contains("mega");
      }
    });

with Java 8, the filtering becomes a one liner:

JavaRDD<Map<String, Object>> esRDD =
                        esRDD(jsc, "radio/artists", "?q=me*").values();
JavaRDD<Map<String, Object>> filtered = esRDD.filter(doc ->
                                                doc.contains("mega"));
Reading data in JSON formatedit

In case where the results from Elasticsearch need to be in JSON format (typically to be sent down the wire to some other system), one can use the dedicated esJsonRDD methods. In this case, the connector will return the documents content as it is received from Elasticsearch without any processing as an RDD[(String, String)] in Scala or JavaPairRDD[String, String] in Java with the keys representing the document id and the value its actual content in JSON format.

Type conversionedit

Important

When dealing with multi-value/array fields, please see this section and in particular these configuration options. IMPORTANT: If automatic index creation is used, please review this section for more information.

elasticsearch-hadoop automatically converts Spark built-in types to Elasticsearch types (and back) as shown in the table below:

Table 5. Scala Types Conversion Table

Scala type Elasticsearch type

None

null

Unit

null

Nil

empty array

Some[T]

T according to the table

Map

object

Traversable

array

case class

object (see Map)

Product

array


in addition, the following implied conversion applies for Java types:

Table 6. Java Types Conversion Table

Java type Elasticsearch type

null

null

String

string

Boolean

boolean

Byte

byte

Short

short

Integer

int

Long

long

Double

double

Float

float

Number

float or double (depending on size)

java.util.Calendar

date (string format)

java.util.Date

date (string format)

java.util.Timestamp

date (string format)

byte[]

string (BASE64)

Object[]

array

Iterable

array

Map

object

Java Bean

object (see Map)


The conversion is done as a best effort; built-in Java and Scala types are guaranteed to be properly converted, however there are no guarantees for user types whether in Java or Scala. As mentioned in the tables above, when a case class is encountered in Scala or JavaBean in Java, the converters will try to unwrap its content and save it as an object. Note this works only for top-level user objects - if the user object has other user objects nested in, the conversion is likely to fail since the converter does not perform nested unwrapping. This is done on purpose since the converter has to serialize and deserialize the data and user types introduce ambiguity due to data loss; this can be addressed through some type of mapping however that takes the project way too close to the realm of ORMs and arguably introduces too much complexity for little to no gain; thanks to the processing functionality in Spark and the plugability in elasticsearch-hadoop one can easily transform objects into other types, if needed with minimal effort and maximum control.

Geo types. It is worth mentioning that rich data types available only in Elasticsearch, such as GeoPoint or GeoShape are supported by converting their structure into the primitives available in the table above. For example, based on its storage a geo_point might be returned as a String or a Traversable.

Spark SQL supportedit

Note

Added in 2.1.

 

Spark SQL is a Spark module for structured data processing. It provides a programming abstraction called DataFrames and can also act as distributed SQL query engine.

 
 -- Spark website

On top of the core Spark support, elasticsearch-hadoop also provides integration with Spark SQL. In other words, Elasticsearch becomes a native source for Spark SQL so that data can be indexed and queried from Spark SQL transparently.

Important

Spark SQL works with structured data - in other words, all entries are expected to have the same structure (same number of fields, of the same type and name). Using unstructured data (documents with different structures) is not supported and will cause problems. For such cases, use PairRDDs.

Supported Spark SQL versionsedit

Spark SQL is a young component, going through significant changes between releases. Spark SQL became a stable component in version 1.3, however it is not backwards compatible with the previous releases. elasticsearch-hadoop supports both version Spark SQL 1.1-1.2 and 1.3 (and higher) through two different jars: elasticsearch-spark-<version>.jar and elasticsearch-hadoop-<version>.jar support Spark SQL 1.3 (or higher) while elasticsearch-spark-1.2-<version>.jar supports Spark SQL 1.1 and 1.2. In other words, if you are not using Spark SQL 1.3, append the -1.2 suffix to the elasticsearch-hadoop artifact id.

Spark SQL support is available under org.elasticsearch.spark.sql package.

API differences. From the elasticsearch-hadoop user perspectives, the differences between Spark SQL 1.3 and its previous versions are fairly trivial. This document describes at length the differences which are briefly mentioned below:

DataFrame vs SchemaRDD
The core unit of Spark SQL in 1.3+ is a DataFrame while previously it was a SchemaRDD
Unified API vs dedicated Java/Scala APIs
In Spark SQL 1.3+ there is only one API for both Java and Scala, previous versions had dedicated APIs in particular with regards to data types.

The documentation below will focus on Spark SQL 1.3+ however accompanies each example with the suitable Spark SQL 1.1-1.2 code.

Writing DataFrame (Spark SQL 1.3+) to Elasticsearchedit

With elasticsearch-hadoop, DataFrames can be indexed to Elasticsearch.

Scalaedit

In Scala, simply import org.elasticsearch.spark.sql package which enriches the given DataFrame class with saveToEs methods; while these have the same signature as the org.elasticsearch.spark package, they are designed for DataFrame implementations:

// reusing the example from Spark SQL documentation

import org.apache.spark.sql.SQLContext    
import org.apache.spark.sql.SQLContext._

import org.elasticsearch.spark.sql._      

...

// sc = existing SparkContext
val sqlContext = new SQLContext(sc)

// case class used to define the DataFrame
case class Person(name: String, surname: String, age: Int)

//  create DataFrame
val people = sc.textFile("people.txt")    
        .map(_.split(","))
        .map(p => Person(p(0), p(1), p(2).trim.toInt))
        .toDF()

people.saveToEs("spark/people")           

Spark SQL package import

elasticsearch-hadoop Spark package import

Read a text file as normal RDD and map it to a DataFrame (using the Person case class)

Index the resulting DataFrame to Elasticsearch through the saveToEs method

Note

By default, elasticsearch-hadoop will ignore null values in favor of not writing any field at all. Since a DataFrame is meant to be treated as structured tabular data, you can enable writing nulls as null valued fields for DataFrame Objects only by toggling the es.spark.dataframe.write.null setting to true.

Javaedit

In a similar fashion, for Java usage the dedicated package org.elasticsearch.spark.sql.java.api provides similar functionality through the JavaEsSpark SQL :

import org.apache.spark.sql.api.java.*;                      
import org.elasticsearch.spark.sql.java.api.JavaEsSpark SQL;  
...

DataFrame people = ...
JavaEsSparkSQL.saveToEs(people, "spark/people");                     

Spark SQL Java imports

elasticsearch-hadoop Spark SQL Java imports

index the DataFrame in Elasticsearch under spark/people

Again, with Java 5 static imports this can be further simplied to:

import static org.elasticsearch.spark.sql.java.api.JavaEsSpark SQL; 
...
saveToEs("spark/people");                                          

statically import JavaEsSpark SQL

call saveToEs method without having to type JavaEsSpark again

Important

For maximum control over the mapping of your DataFrame in Elasticsearch, it is highly recommended to create the mapping before hand. See this chapter for more information.

Writing SchemaRDD (Spark SQL 1.2) to Elasticsearchedit

When dealing with Spark SQL 1.1/1.2 simply interchange DataFrame with SchemaRDD as the Java and Scala APIs are the same.

Scalaedit
// reusing the example from Spark SQL documentation

import org.apache.spark.sql.SQLContext    
import org.apache.spark.sql.SQLContext._

import org.elasticsearch.spark.sql._      

...

// sc = existing SparkContext
val sqlContext = new SQLContext(sc)

// case class used to define the RDD schema
case class Person(name: String, surname: String, age: Int)

//  create SchemaRDD
val people = sc.textFile("people.txt")    
        .map(_.split(","))
        .map(p => Person(p(0), p(1), p(2).trim.toInt))

people.saveToEs("spark/people")           

Spark SQL package import

elasticsearch-hadoop Spark package import

Read a text file as normal RDD and map it to a SchemaRDD (using the Person case class)

Index the resulting SchemaRDD to Elasticsearch through the saveToEs method

Javaedit

As expected, the Java example is identical :

import org.apache.spark.sql.api.java.*;                      
import org.elasticsearch.spark.sql.java.api.JavaEsSpark SQL;  
...

JavaSchemaRDD people = ...
JavaEsSpark SQL.saveToEs("spark/people");                     

Spark SQL Java imports

elasticsearch-hadoop Spark SQL Java imports

index the JavaSchemaRDD in Elasticsearch under spark/people

Again, with Java 5 static imports this can be further simplied to:

import static org.elasticsearch.spark.sql.java.api.JavaEsSpark SQL; 
...
saveToEs("spark/people");                                          

statically import JavaEsSpark SQL

call saveToEs method without having to type JavaEsSpark again

Important

For maximum control over the mapping of your SchemaRDD in Elasticsearch, it is highly recommended to create the mapping before hand. See this chapter for more information.

Writing existing JSON to Elasticsearchedit

When using Spark SQL, if the input data is in JSON format, simply convert it to a DataFrame (in Spark SQL 1.3) or a SchemaRDD (for Spark SQL 1.1/1.2) (as described in Spark documentation) through SQLContext/JavaSQLContext jsonFile methods.

Using pure SQL to read from Elasticsearchedit

Important

Available in Apache Spark SQL 1.2 (or higher)

Important

The index and its mapping, have to exist prior to creating the temporary table

Spark SQL 1.2 introduced a new API for reading from external data sources, which is supported by elasticsearch-hadoop simplifying the SQL configured needed for interacting with Elasticsearch. Further more, behind the scenes it understands the operations executed by Spark and thus can optimize the data and queries made (such as filtering or pruning), improving performance.

Data Sources in Spark SQL 1.3edit

When using Spark SQL 1.3, elasticsearch-hadoop allows access to Elasticsearch through SQLContext load method. In other words, to create a DataFrame backed by Elasticsearch in a declarative manner:

val sql = new SQLContext...
// Spark 1.3 style
val df = sql.load("spark/index", "org.elasticsearch.spark.sql")

SQLContext experimental load method for arbitrary data sources

path or resource to load - in this case the index/type in Elasticsearch

the data source provider - org.elasticsearch.spark.sql

In Spark 1.4, one would use the following similar API calls:

// Spark 1.4 style
val df = sql.read.format("org.elasticsearch.spark.sql").load("spark/index")

SQLContext experimental read method for arbitrary data sources

the data source provider - org.elasticsearch.spark.sql

path or resource to load - in this case the index/type in Elasticsearch

In Spark 1.5, this can be further simplified to:

// Spark 1.5 style
val df = sql.read.format("es").load("spark/index")

Use es as an alias instead of the full package name for the DataSource provider

Whatever API is used, once created, the DataFrame can be accessed freely to manipulate the data.

The sources declaration also allows specific options to be passed in, namely:

Name Default value Description

path

required

Elasticsearch index/type

pushdown

true

Whether to translate (push-down) Spark SQL into Elasticsearch Query DSL

strict

false

Whether to use exact (not analyzed) matching or not (analyzed)

Usable in Spark 1.6 or higher

double.filtering

true

Whether to tell Spark apply its own filtering on the filters pushed down

Both options are explained in the next section. To specify the options (including the generic elasticsearch-hadoop ones), one simply passes a Map to the aforementioned methods:

For example:

val sql = new SQLContext...
// options for Spark 1.3 need to include the target path/resource
val options13 = Map("path" -> "spark/index",
                    "pushdown" -> "true",
                    "es.nodes" -> "someNode", "es.port" -> "9200")

// Spark 1.3 style
val spark13DF = sql.load("org.elasticsearch.spark.sql", options13)

// options for Spark 1.4 - the path/resource is specified separately
val options = Map("pushdown" -> "true", "es.nodes" -> "someNode", "es.port" -> "9200")

// Spark 1.4 style
val spark14DF = sql.read.format("org.elasticsearch.spark.sql")
                        .options(options).load("spark/index")

pushdown option - specific to Spark data sources

es.nodes configuration option

pass the options when definition/loading the source

sqlContext.sql(
   "CREATE TEMPORARY TABLE myIndex    " + 
   "USING org.elasticsearch.spark.sql " + 
   "OPTIONS ( resource 'spark/index', nodes 'spark/index')" ) "

Spark’s temporary table name

USING clause identifying the data source provider, in this case org.elasticsearch.spark.sql

elasticsearch-hadoop configuration options, the mandatory one being resource. The es. prefix is fixed due to the SQL parser

Do note that due to the SQL parser, the . (among other common characters used for delimiting) is not allowed; the connector tries to work around it by append the es. prefix automatically however this works only for specifying the configuration options with only one . (like es.nodes above). Because of this, if properties with multiple . are needed, one should use the SQLContext.load or SQLContext.read methods above and pass the properties as a Map.

Push-Down operationsedit

Note

Available only in Spark 1.3 or higher

An important hidden feature of using elasticsearch-hadoop as a Spark source is that the connector understand the operations performed within the DataFrame/SQL and, by default, will translate them into the appropriate QueryDSL. In other words, the connector pushes down the operations directly at the source, where the data is efficiently filtered out so that only the required data is streamed back to Spark. This significantly increases the queries performance and minimizes the CPU, memory and I/O on both Spark and Elasticsearch clusters as only the needed data is returned (as oppose to returning the data in bulk only to be processed and discarded by Spark). Note the push down operations apply even when one specifies a query - the connector will enhance it according to the specified SQL.

As a side note, elasticsearch-hadoop supports all the `Filter`s available in Spark (1.3.0 and higher) while retaining backwards binary-compatibility with Spark 1.3.0, pushing down to full extent the SQL operations to Elasticsearch without any user interference.

To wit, consider the following Spark SQL:

// as a DataFrame
val df = sqlContext.read().format("org.elasticsearch.spark.sql").load("spark/trips")

df.printSchema()
// root
//|-- departure: string (nullable = true)
//|-- arrival: string (nullable = true)
//|-- days: long (nullable = true)

val filter = df.filter(df("arrival").equalTo("OTP").and(df("days").gt(3))

or in pure SQL:

CREATE TEMPORARY TABLE trips USING org.elasticsearch.spark.sql OPTIONS (path "spark/trips")
SELECT departure FROM trips WHERE arrival = "OTP" and days > 3

The connector translates the query into:

{
  "query" : {
    "filtered" : {
      "query" : {
        "match_all" : {}

      },
      "filter" : {
        "and" : [{
            "query" : {
              "match" : {
                "arrival" : "OTP"
              }
            }
          }, {
            "days" : {
              "gt" : 3
            }
          }
        ]
      }
    }
  }
}

Further more, the pushdown filters can work on analyzed terms (the default) or can be configured to be strict and provide exact matches (work only on not-analyzed fields). Unless one manually specifies the mapping, it is highly recommended to leave the defaults as they are. This and other topics are discussed at length in the Elasticsearch Reference Documentation.

Note that double.filtering, available since elasticsearch-hadoop 2.2 for Spark 1.6 or higher, allows filters that are already pushed down to Elasticsearch to be processed/evaluated by Spark as well (default) or not. Turning this feature off, especially when dealing with large data sizes speed things up. However one should pay attention to the semantics as turning this off, might return different results (depending on how the data is indexed, analyzed vs not_analyzed). In general, when turning strict on, one can disable double.filtering as well.

Data Sources in Spark SQL 1.2edit

Available since Spark SQL 1.2, one can also access a data source by declaring it as a Spark temporary table (backed by elasticsearch-hadoop):

sqlContext.sql(
   "CREATE TEMPORARY TABLE myIndex    " + 
   "USING org.elasticsearch.spark.sql " + 
   "OPTIONS (resource 'spark/index', scroll_size '20')" )

Spark’s temporary table name

USING clause identifying the data source provider, in this case org.elasticsearch.spark.sql

elasticsearch-hadoop configuration options, the mandatory one being resource. One can use the es prefix or skip it for convenience.

Since using . can cause syntax exceptions, one should replace it instead with _ style. Thus, in this example es.scroll.size becomes scroll_size (as the leading es can be removed). Do note this only works in Spark 1.3 as the Spark 1.4 has a stricter parser. See the chapter above for more information.

Once defined, the schema is picked up automatically. So one can issue queries, right away:

val all = sqlContext.sql("SELECT * FROM myIndex WHERE id <= 10")

As elasticsearch-hadoop is aware of the queries being made, it can optimize the requests done to Elasticsearch. For example, given the following query:

val names = sqlContext.sql("SELECT name FROM myIndex WHERE id >=1 AND id <= 10")

it knows only the name and id fields are required (the first to be returned to the user, the second for Spark’s internal filtering) and thus will ask only for this data, making the queries quite efficient.

Reading DataFrames (Spark SQL 1.3) from Elasticsearchedit

As you might have guessed, one can define a DataFrame backed by Elasticsearch documents. Or even better, have them backed by a query result, effectively creating dynamic, real-time views over your data.

Scalaedit

Through the org.elasticsearch.spark.sql package, esDF methods are available on the SQLContext API:

import org.apache.spark.sql.SQLContext        

import org.elasticsearch.spark.sql._          
...

val sql = new SQLContext(sc)

val people = sql.esDF("spark/people")         

// check the associated schema
println(people.schema.treeString)             
// root
//  |-- name: string (nullable = true)
//  |-- surname: string (nullable = true)
//  |-- age: long (nullable = true)           

Spark SQL Scala imports

elasticsearch-hadoop SQL Scala imports

create a DataFrame backed by the spark/people index in Elasticsearch

the DataFrame associated schema discovered from Elasticsearch

notice how the age field was transformed into a Long when using the default Elasticsearch mapping as discussed in the Mapping and Types chapter.

And just as with the Spark core support, additional parameters can be specified such as a query. This is quite a powerful concept as one can filter the data at the source (Elasticsearch) and use Spark only on the results:

// get only the Smiths
val smiths = sqlContext.esDF("spark/people","?q=Smith" )

Elasticsearch query whose results comprise the DataFrame

Controlling the DataFrame schema. In some cases, especially when the index in Elasticsearch contains a lot of fields, it is desireable to create a DataFrame that contains only a subset of them. While one can modify the DataFrame (by working on its backing RDD) through the official Spark API or through dedicated queries, elasticsearch-hadoop allows the user to specify what fields to include and exclude from Elasticsearch when creating the DataFrame.

Through es.read.field.include and es.read.field.exclude properties, one can indicate what fields to include or exclude from the index mapping. The syntax is similar to that of Elasticsearch include/exclude. Multiple values can be specified by using a comma. By default, no value is specified meaning all properties/fields are included and no properties/fields are excluded.

For example:

# include
es.read.field.include = *name, address.*
# exclude
es.read.field.exclude = *.created
Important

Due to the way SparkSQL works with a DataFrame schema, elasticsearch-hadoop needs to be aware of what fields are returned from Elasticsearch before executing the actual queries. While one can restrict the fields manually through the underlying Elasticsearch query, elasticsearch-hadoop is unaware of this and the results are likely to be different or worse, errors will occur. Use the properties above instead, which Elasticsearch will properly use alongside the user query.

Javaedit

For Java users, a dedicated API exists through JavaEsSpark SQL. It is strikingly similar to EsSpark SQL however it allows configuration options to be passed in through Java collections instead of Scala ones; other than that using the two is exactly the same:

import org.apache.spark.sql.api.java.JavaSQLContext;          
import org.elasticsearch.spark.sql.java.api.JavaEsSparkSQL;   
...
SQLContext sql = new SQLContext(sc);

DataFrame people = JavaEsSparkSQL.esDF(sql, "spark/people");  

Spark SQL import

elasticsearch-hadoop import

create a Java DataFrame backed by an Elasticsearch index

Better yet, the DataFrame can be backed by a query result:

DataFrame people = JavaEsSparkSQL.esDF(sql, "spark/people", "?q=Smith"  );

Elasticsearch query backing the elasticsearch-hadoop DataFrame

Reading SchemaRDDs (Spark SQL 1.2) from Elasticsearchedit

And again, if you are using Spark SQL 1.1/1.2 simply interchange DataFrame with SchemaRDD and esDF with esRDD:

Scalaedit

Through the org.elasticsearch.spark.sql package, esRDD methods are available on the SQLContext API:

import org.apache.spark.sql.SQLContext        

import org.elasticsearch.spark.sql._          
...

val sql = new SQLContext(sc)

val people = sql.esRDD("spark/people")        

// check the associated schema
println(people.schema)                        
// root
//  |-- name: string (nullable = true)
//  |-- surname: string (nullable = true)
//  |-- age: long (nullable = true)           

Spark SQL Scala imports

elasticsearch-hadoop SQL Scala imports

create a SchemaRDD backed by the spark/people index in Elasticsearch

the SchemaRDD associated schema discovered from Elasticsearch

notice how the age field was transformed into a Long when using the default Elasticsearch mapping as discussed in the Mapping and Types chapter.

And just as with the Spark core support, additional parameters can be specified such as a query. This is quite a powerful concept as one can filter the data at the source (Elasticsearch) and use Spark only on the results:

// get only the Smiths
val smiths = sqlContext.esRDD("spark/people","?q=Smith" )

Elasticsearch query whose results comprise the RDD

Javaedit
import org.apache.spark.sql.api.java.JavaSQLContext;               
import org.elasticsearch.spark.sql.java.api.JavaEsSparkSQL;        
...
JavaSQLContext jsql = new JavaSQLContext(sc);

JavaSchemaRDD people = JavaEsSparkSQL.esRDD(jsql, "spark/people"); 

Spark SQL import

elasticsearch-hadoop import

create a Java SchemaRDD backed by an Elasticsearch index

Better yet, the JavaSchemaRDD can be backed by a query result:

JavaSchemaRDD people = JavaEsSparkSQL.esRDD(jsql, "spark/people", "?q=Smith" );

Elasticsearch query backing the elasticsearch-hadoop SchemaRDD

Spark SQL Type conversionedit

Important

When dealing with multi-value/array fields, please see this section and in particular these configuration options. IMPORTANT: If automatic index creation is used, please review this section for more information.

elasticsearch-hadoop automatically converts Spark built-in types to Elasticsearch types (and back) as shown in the table below:

While Spark SQL DataTypes have an equivalent in both Scala and Java and thus the RDD conversion can apply, there are slightly different semantics - in particular with the java.sql types due to the way Spark SQL handles them:

Table 7. Spark SQL 1.3+ Conversion Table

Spark SQL DataType Elasticsearch type

null

null

ByteType

byte

ShortType

short

IntegerType

int

LongType

long

FloatType

float

DoubleType

double

StringType

string

BinaryType

string (BASE64)

BooleanType

boolean

TimestampType

long (unix time)

ArrayType

array

MapType

object

StructType

object

Available only in Spark 1.2+

DateType

date (string format)


Geo Types Conversion Table. In addition to the table above, for Spark SQL 1.3 or higher, elasticsearch-hadoop performs automatic schema detection for geo types, namely Elasticsearch geo_point and geo_shape. Since each type allows multiple formats (geo_point accepts latitude and longitude to be specified in 4 different ways, while geo_shape allows a variety of types (currently 9)) and the mapping does not provide such information, elasticsearch-hadoop will sample the determined geo fields at startup and retrieve an arbitrary document that contains all the relevant fields; it will parse it and thus determine the necessary schema (so for example it can tell whether a geo_point is specified as a StringType or as an ArrayType).

Important

Since Spark SQL is strongly-typed, each geo field needs to have the same format across all documents. Shy of that, the returned data will not fit the detected schema and thus lead to errors.

Using the Map/Reduce layeredit

Another way of using Spark with Elasticsearch is through the Map/Reduce layer, that is by leveraging the dedicated Input/OuputFormat in elasticsearch-hadoop. However, unless one is stuck on elasticsearch-hadoop 2.0, we strongly recommend using the native integration as it offers significantly better performance and flexibility.

Configurationedit

Through elasticsearch-hadoop, Spark can integrate with Elasticsearch through its dedicated InputFormat, and in case of writing, through OutputFormat. These are described at length in the Map/Reduce chapter so please refer to that for an in-depth explanation.

In short, one needs to setup a basic Hadoop Configuration object with the target Elasticsearch cluster and index, potentially a query, and she’s good to go.

From Spark’s perspective, the only thing required is setting up serialization - Spark relies by default on Java serialization which is convenient but fairly inefficient. This is the reason why Hadoop itself introduced its own serialization mechanism and its own types - namely Writables. As such, InputFormat and OutputFormats are required to return Writables which, out of the box, Spark does not understand. The good news is, one can easily enable a different serialization (Kryo) which handles the conversion automatically and also does this quite efficiently.

SparkConf sc = new SparkConf(); //.setMaster("local");
sc.set("spark.serializer", KryoSerializer.class.getName()); 

// needed only when using the Java API
JavaSparkContext jsc = new JavaSparkContext(sc);

Enable the Kryo serialization support with Spark

Or if you prefer Scala

val sc = new SparkConf(...)
sc.set("spark.serializer", classOf[KryoSerializer].getName) 

Enable the Kryo serialization support with Spark

Note that the Kryo serialization is used as a work-around for dealing with Writable types; one can choose to convert the types directly (from Writable to Serializable types) - which is fine however for getting started, the one liner above seems to be the most effective.

Reading data from Elasticsearchedit

To read data, simply pass in the org.elasticsearch.hadoop.mr.EsInputFormat class - since it supports both the old and the new Map/Reduce APIs, you are free to use either method on SparkContext's, hadoopRDD (which we recommend for conciseness reasons) or newAPIHadoopRDD. Which ever you chose, stick with it to avoid confusion and problems down the road.

Old (org.apache.hadoop.mapred) APIedit
JobConf conf = new JobConf();                             
conf.set("es.resource", "radio/artists");                 
conf.set("es.query", "?q=me*");                           

JavaPairRDD esRDD = jsc.hadoopRDD(conf, EsInputFormat.class,
                          Text.class, MapWritable.class); 
long docCount = esRDD.count();

Create the Hadoop object (use the old API)

Configure the source (index)

Setup the query (optional)

Create a Spark RDD on top of Elasticsearch through EsInputFormat - the key represents the doc id, the value the doc itself

The Scala version is below:

val conf = new JobConf()                                   
conf.set("es.resource", "radio/artists")                   
conf.set("es.query", "?q=me*")                             
val esRDD = sc.hadoopRDD(conf,
                classOf[EsInputFormat[Text, MapWritable]], 
                classOf[Text], classOf[MapWritable]))
val docCount = esRDD.count();

Create the Hadoop object (use the old API)

Configure the source (index)

Setup the query (optional)

Create a Spark RDD on top of Elasticsearch through EsInputFormat

New (org.apache.hadoop.mapreduce) APIedit

As expected, the mapreduce API version is strikingly similar - replace hadoopRDD with newAPIHadoopRDD and JobConf with Configuration. That’s about it.

Configuration conf = new Configuration();       
conf.set("es.resource", "radio/artists");       
conf.set("es.query", "?q=me*");                 

JavaPairRDD esRDD = jsc.newAPIHadoopRDD(conf, EsInputFormat.class,
                Text.class, MapWritable.class); 
long docCount = esRDD.count();

Create the Hadoop object (use the new API)

Configure the source (index)

Setup the query (optional)

Create a Spark RDD on top of Elasticsearch through EsInputFormat - the key represent the doc id, the value the doc itself

The Scala version is below:

val conf = new Configuration()                             
conf.set("es.resource", "radio/artists")                   
conf.set("es.query", "?q=me*")                             
val esRDD = sc.newAPIHadoopRDD(conf,
                classOf[EsInputFormat[Text, MapWritable]], 
                classOf[Text], classOf[MapWritable]))
val docCount = esRDD.count();

Create the Hadoop object (use the new API)

Configure the source (index)

Setup the query (optional)

Create a Spark RDD on top of Elasticsearch through EsInputFormat

Using the connector from PySparkedit

Thanks to its Map/Reduce layer, elasticsearch-hadoop can be used from PySpark as well to both read and write data to Elasticsearch. To wit, below is a snippet from the Spark documentation (make sure to switch to the Python snippet):

$ ./bin/pyspark --driver-class-path=/path/to/elasticsearch-hadoop.jar
>>> conf = {"es.resource" : "index/type"}   # assume Elasticsearch is running on localhost defaults
>>> rdd = sc.newAPIHadoopRDD("org.elasticsearch.hadoop.mr.EsInputFormat",\
    "org.apache.hadoop.io.NullWritable", "org.elasticsearch.hadoop.mr.LinkedMapWritable", conf=conf)
>>> rdd.first()         # the result is a MapWritable that is converted to a Python dict
(u'Elasticsearch ID',
 {u'field1': True,
  u'field2': u'Some Text',
  u'field3': 12345})

Also, the SQL loader can be used as well:

from pyspark.sql import SQLContext
sqlContext = SQLContext(sc)
df = sqlContext.read.format("org.elasticsearch.spark.sql").load("index/type")
df.printSchema()