Apache Spark support

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).

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-lineFor 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 ... 

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.

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") 

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")                               

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"))

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");                                    

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.api.java.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");                                          

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"));

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).

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

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

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

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

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:

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}/doc")  

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.

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}/doc");  

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.

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)))  
airportsRDD.saveToEsWithMeta("airports/2015")    

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)))
airportsRDD.saveToEsWithMeta("airports/2015") 

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.api.java.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);       

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.api.java.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);       

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

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")     

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*") 

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.

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.api.java.JavaEsSpark;             
...

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

JavaPairRDD<String, Map<String, Object>> esRDD =
                        JavaEsSpark.esRDD(jsc, "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.api.java.JavaEsSpark.*;   

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

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"));

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.

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

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

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 typesIt 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 Streaming is an extension on top of the core Spark functionality that allows near real time processing of stream data. Spark Streaming works around the idea of DStreams, or Discretized Streams. DStreams operate by collecting newly arrived records into a small RDD and executing it. This repeats every few seconds with a new RDD in a process called microbatching. The DStream api includes many of the same processing operations as the RDD api, plus a few other streaming specific methods. elasticsearch-hadoop provides native integration with Spark Streaming as of version 5.0.

When using the elasticsearch-hadoop Spark Streaming support, Elasticsearch can be targeted as an output location to index data into from a Spark Streaming job in the same way that one might persist the results from an RDD. Though, unlike RDDs, you are unable to read data out of Elasticsearch using a DStream due to the continuous nature of it.

Like RDDs, any DStream can be saved to Elasticsearch as long as its content can be translated into documents. In practice this means the DStream type needs to be a Map (either 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.

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

import org.apache.spark.SparkContext
import org.apache.spark.SparkContext._               
import org.apache.spark.streaming.StreamingContext
import org.apache.spark.streaming.StreamingContext._

import org.elasticsearch.spark.streaming._           

...

val conf = ...
val sc = new SparkContext(conf)                      
val ssc = new StreamingContext(sc, Seconds(1))       

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

val rdd = sc.makeRDD(Seq(numbers, airports))
val microbatches = mutable.Queue(rdd)                

ssc.queueStream(microbatches).saveToEs("spark/docs") 

ssc.start()
ssc.awaitTermination() 

As an alternative to the implicit import above, one can use elasticsearch-hadoop Spark Streaming support in Scala through EsSparkStreaming in the org.elasticsearch.spark.streaming 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.streaming.EsSparkStreaming         

// 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))
val microbatches = mutable.Queue(rdd)                             
val dstream = ssc.queueStream(microbatches)

EsSparkStreaming.saveToEs(dstream, "spark/docs")                  

ssc.start()                                                       

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 DStream configuration (it is also possible to set the property on the SparkConf though due to its global effect it is discouraged):

EsSparkStreaming.saveToEs(dstream, "spark/docs", Map("es.mapping.id" -> "id"))

Java users have a dedicated class that provides a similar functionality to EsSparkStreaming, namely JavaEsSparkStreaming in the package org.elasticsearch.spark.streaming.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.apache.spark.streaming.api.java.JavaStreamingContext;
import org.apache.spark.streaming.api.java.JavaDStream;

import org.elasticsearch.spark.streaming.api.java.JavaEsSparkStreaming;         
...

SparkConf conf = ...
JavaSparkContext jsc = new JavaSparkContext(conf);                              
JavaStreamingContext jssc = new JavaSparkStreamingContext(jsc, Seconds.apply(1));

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));
Queue<JavaRDD<Map<String, ?>>> microbatches = new LinkedList<>();
microbatches.add(javaRDD);                                                      
JavaDStream<Map<String, ?>> javaDStream = jssc.queueStream(microbatches);

JavaEsSparkStreaming.saveToEs(javaDStream, "spark/docs");                       

jssc.start()                                                                    

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.api.java.JavaEsSparkStreaming;  
...

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

JavaRDD<TripBean> javaRDD = jsc.parallelize(ImmutableList.of(upcoming, lastWeek));
Queue<JavaRDD<TripBean>> microbatches = new LinkedList<JavaRDD<TripBean>>();
microbatches.add(javaRDD);
JavaDStream<TripBean> javaDStream = jssc.queueStream(microbatches);       

saveToEs(javaDStream, "spark/docs");                                          

jssc.start()                                                              

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):

JavaEsSparkStreaming.saveToEs(javaDStream, "spark/docs", ImmutableMap.of("es.mapping.id", "id"));

For cases where the data being streamed by the DStream is already serialized as 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 a DStream containing String or byte arrays (byte[]/Array[Byte]), assuming each entry represents a JSON document. If the DStream does not have the proper signature, the saveJsonToEs methods cannot be applied (in Scala they will not be available).

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

val sc = new SparkContext(conf)
val ssc = new StreamingContext(sc, Seconds(1))

val rdd = sc.makeRDD(Seq(json1, json2))
val microbatch = mutable.Queue(rdd)
ssc.queueStream(microbatch).saveJsonToEs("spark/json-trips")      

ssc.start()                                                       

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

JavaSparkContext jsc = ...
JavaStreamingContext jssc = ...
JavaRDD<String> stringRDD = jsc.parallelize(ImmutableList.of(json1, json2));
Queue<JavaRDD<String>> microbatches = new LinkedList<JavaRDD<String>>();      
microbatches.add(stringRDD);
JavaDStream<String> stringDStream = jssc.queueStream(microbatches);  

JavaEsSparkStreaming.saveJsonToEs(stringRDD, "spark/json-trips");    

jssc.start()                                                         

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:

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")

val batch = sc.makeRDD(Seq(game, book, cd))
val microbatches = mutable.Queue(batch)
ssc.queueStream(microbatches).saveToEs("my-collection-{media_type}/doc")  
ssc.start()

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.

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));
Queue<JavaRDD<Map<String, ?>>> microbatches = ...
JavaDStream<Map<String, ?>> javaDStream =
                jssc.queueStream(microbatches);

saveToEs(javaDStream, "my-collection-{media_type}/doc");  
jssc.start();

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.

This is no different in Spark Streaming. For DStreamss 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 a DStream's keys can be a Map containing the Metadata for each document and its associated values. If the DStream 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.

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

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 = ...
// instance of StreamingContext
val ssc = ...

val airportsRDD = 
  sc.makeRDD(Seq((1, otp), (2, muc), (3, sfo)))  
val microbatches = mutable.Queue(airportsRDD)

ssc.queueStream(microbatches)        
  .saveToEsWithMeta("airports/2015") 
ssc.start()

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 = ...
// instance of StreamingContext
val ssc = ...

val airportsRDD = sc.makeRDD( 
  Seq((otpMeta, otp), (mucMeta, muc), (sfoMeta, sfo)))
val microbatches = mutable.Queue(airportsRDD)

ssc.queueStream(microbatches)        
  .saveToEsWithMeta("airports/2015") 
ssc.start()

In a similar fashion, on the Java side, JavaEsSparkStreaming provides saveToEsWithMeta methods that are applied to JavaPairDStream (the equivalent in Java of DStream[(K,V)]).

This tends to involve a little more work due to the Java API’s limitations. For instance, you cannot create a JavaPairDStream directly from a queue of JavaPairRDDs. Instead, you must create a regular JavaDStream of Tuple2 objects and convert the JavaDStream into a JavaPairDStream. This sounds complex, but it’s a simple work around for a limitation of the API.

First, we’ll create a pair function, that takes a Tuple2 object in, and returns it right back to the framework:

public static class ExtractTuples implements PairFunction<Tuple2<Object, Object>, Object, Object>, Serializable {
    @Override
    public Tuple2<Object, Object> call(Tuple2<Object, Object> tuple2) throws Exception {
        return tuple2;
    }
}

Then we’ll apply the pair function to a JavaDStream of Tuple2s to create a JavaPairDStream and save it:

import org.elasticsearch.spark.streaming.api.java.JavaEsSparkStreaming;

// 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 = ...
JavaStreamingContext jssc = ...

// create an RDD of between the id and the docs
JavaRDD<Tuple2<?, ?>> rdd = jsc.parallelize(         
      ImmutableList.of(
        new Tuple2<Object, Object>(1, otp),          
        new Tuple2<Object, Object>(2, jfk)));        

Queue<JavaRDD<Tuple2<?, ?>>> microbatches = ...
JavaDStream<Tuple2<?, ?>> dStream = jssc.queueStream(microbatches); 

JavaPairDStream<?, ?> pairDStream = dstream.mapToPair(new ExtractTuples()); 

JavaEsSparkStreaming.saveToEsWithMeta(pairDStream, target);       
jssc.start();

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. We’ll use the same typing trick to repack the JavaDStream as a JavaPairDStream:

import org.elasticsearch.spark.streaming.api.java.JavaEsSparkStreaming;
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
JavaRDD<Tuple2<?, ?>> pairRdd = jsc.parallelize<(ImmutableList.of(
        new Tuple2<Object, Object>(otpMeta, otp),    
        new Tuple2<Object, Object>(sfoMeta, sfo)));  

Queue<JavaRDD<Tuple2<?, ?>>> microbatches = ...
JavaDStream<Tuple2<?, ?>> dStream = jssc.queueStream(microbatches); 

JavaPairDStream<?, ?> pairDStream = dstream.mapToPair(new ExtractTuples()) 

JavaEsSparkStreaming.saveToEsWithMeta(pairDStream, target);       
jssc.start();

The elasticsearch-hadoop Spark Streaming support leverages the same type mapping as the regular Spark type mapping. The mappings are repeated here for consistency:

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

Geo typesIt is worth re-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.

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.

Spark SQL while becoming a mature component, is still 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. Further more Spark 2.0 introduced significant changed which broke backwards compatibility, through the Dataset API. elasticsearch-hadoop supports both version Spark SQL 1.3-1.6 and Spark SQL 2.0 through two different jars: elasticsearch-spark-1.x-<version>.jar and elasticsearch-hadoop-<version>.jar support Spark SQL 1.3-1.6 (or higher) while elasticsearch-spark-2.0-<version>.jar supports Spark SQL 2.0. In other words, unless you are using Spark 2.0, use elasticsearch-spark-1.x-<version>.jar

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

API differencesFrom the elasticsearch-hadoop user perspectives, the differences between Spark SQL 1.3-1.6 and Spark 2.0 are fairly consolidated. This document describes at length the differences which are briefly mentioned below:

As conceptually, a DataFrame is a Dataset[Row], the documentation below will focus on Spark SQL 1.3-1.6.

With elasticsearch-hadoop, DataFrames (or any Dataset for that matter) can be indexed to Elasticsearch.

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")           

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

import org.apache.spark.sql.api.java.*;                      
import org.elasticsearch.spark.sql.api.java.JavaEsSparkSQL;  
...

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

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

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

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 Dataset (for Spark SQL 2.0) (as described in Spark documentation) through SQLContext/JavaSQLContext jsonFile methods.

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.

When using Spark SQL, elasticsearch-hadoop allows access to Elasticsearch through SQLContext load method. In other words, to create a DataFrame/Dataset 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") 

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") 

In Spark 1.5, this can be further simplified to:

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

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:

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")

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

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.

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.

Operators those have been optimized as pushdown filters:

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.

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')" ) 

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.

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.

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)           

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") 

Controlling the DataFrame schemaIn 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. Note that these properties can include leading and trailing wildcards. Including part of a hierarchy of fields without a trailing wildcard does not imply that the entire hierarcy is included. However in most cases it does not make sense to include only part of a hierarchy, so a trailing wildcard should be included.

For example:

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

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.api.java.JavaEsSparkSQL;   
...
SQLContext sql = new SQLContext(sc);

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

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

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

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:

Geo Types Conversion TableIn 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).

Released as an experimental feature in Spark 2.0, Spark Structured Streaming provides a unified streaming and batch interface built into the Spark SQL integration. As of elasticsearch-hadoop 6.0, we provide native functionality to index streaming data into Elasticsearch.

Spark Structured Streaming is considered generally available as of Spark v2.2.0. As such, elasticsearch-hadoop support for Structured Streaming (available in elasticsearch-hadoop 6.0+) is only compatible with Spark versions 2.2.0 and onward. Similar to Spark SQL before it, Structured Streaming may be subject to significant changes between releases before its interfaces are considered stable.

Spark Structured Streaming support is available under the org.elasticsearch.spark.sql and org.elasticsearch.spark.sql.streaming packages. It shares a unified interface with Spark SQL in the form of the Dataset[_] api. Clients can interact with streaming Datasets in almost exactly the same way as regular batch Datasets with only a few exceptions.

With elasticsearch-hadoop, Stream-backed Datasets can be indexed to Elasticsearch.

In Scala, to save your streaming based Datasets and DataFrames to Elasticsearch, simply configure the stream to write out using the "es" format, like so:

import org.apache.spark.sql.SparkSession    

...

val spark = SparkSession.builder()
   .appName("EsStreamingExample")           
   .getOrCreate()

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

//  create DataFrame
val people = spark.readStream                   
        .textFile("/path/to/people/files/*")    
        .map(_.split(","))
        .map(p => Person(p(0), p(1), p(2).trim.toInt))

people.writeStream
      .option("checkpointLocation", "/save/location")      
      .format("es")
      .start("spark/people")                               

In a similar fashion, the "es" format is available for Java usage as well:

import org.apache.spark.sql.SparkSession    

...

SparkSession spark = SparkSession
  .builder()
  .appName("JavaStructuredNetworkWordCount")     
  .getOrCreate();

// java bean style class
public static class PersonBean {
  private String name;
  private String surname;                        
  private int age;

  ...
}

Dataset<PersonBean> people = spark.readStream()         
        .textFile("/path/to/people/files/*")
        .map(new MapFunction<String, PersonBean>() {
            @Override
            public PersonBean call(String value) throws Exception {
                return someFunctionThatParsesStringToJavaBeans(value.split(","));         
            }
        }, Encoders.<PersonBean>bean(PersonBean.class));

people.writeStream()
    .option("checkpointLocation", "/save/location")       
    .format("es")
    .start("spark/people");                               

When using Spark SQL, if the input data is in JSON format, simply convert it to a Dataset (for Spark SQL 2.0) (as described in Spark documentation) through the DataStreamReader's json format.

Spark Structured Streaming advertises an end-to-end fault-tolerant exactly-once processing model that is made possible through the usage of offset checkpoints and maintaining commit logs for each streaming query. When executing a streaming query, most sources and sinks require you to specify a "checkpointLocation" in order to persist the state of your job. In the event of an interruption, launching a new streaming query with the same checkpoint location will recover the state of the job and pick up where it left off. We maintain a commit log for elasticsearch-hadoop’s Elasticsearch sink implementation in a special directory under the configured checkpoint location:

$> ls /path/to/checkpoint/location
metadata  offsets/  sinks/
$> ls /path/to/checkpoint/location/sinks
elasticsearch/
$> ls /path/to/checkpoint/location/sinks/elasticsearch
12.compact  13  14  15 16  17  18

Each file in the commit log directory corresponds to a batch id that has been committed. The log implementation periodically compacts the logs down to avoid clutter. You can set the location for the log directory a number of ways:

Here is a list of configurations that affect the behavior of Elasticsearch’s commit log:

Structured Streaming uses the exact same type conversion rules as the Spark SQL integration.

elasticsearch-hadoop automatically converts Spark built-in types to Elasticsearch types 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:

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.

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);

Or if you prefer Scala

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

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.

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.

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();

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();

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();

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();

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()