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(mongo-spark
Example application on how to use mongo-hadoop connector with Apache Spark.
Prerequisites
- MongoDB installed and running on local or remote machine
- Scala 2.10 and SBT installed
- Sbt assemly plugin installed
- Apache spark 1.4 installed. (Also should work with 1.0 + version, just adjusb build.sbt )
How to
First of all thanks to original repository and blog post.
This example provides instructions on how to run you application on stadalone spark cluster running on ec2.
- Because by default ec2 scipt installs standalone spark cluster with hadoop version 2.4 you should build you mongo-hadoop connector against this version of hadoop (here the prebuilded version is located at lib/). Please check mongo-hadoop-conector repository to learn how to build mongo-hadoop connector against your version of hadoop.
- The strightforward way to run your application is to make "fat" jar using assembly sbt plugin:
sbt assembly
and pass it to submit script. Please review the build.sbt file and check mergeStrategy and libraryDependencies. - Copy your "fat jar" ( mongo-spark-assembly-1.0.jar ) to spark master:
scp mongo-spark-assembly-1.0.jar root@hostSparkMaster:~/jobs/
Read and write from mongodb / bson / serialized files
Based on my experience most of the time you have to read or write whole collection. And preferably from bson dump or serialized RDD[BSONObject]. For these tasks I wrote simple methods - readBsonRDD and writeBsonRDD.
// create spark context
val sparkConf = new SparkConf()
.set("spark.executor.memory", "12g")
.set("spark.storage.memoryFraction", "0.1")
.set("spark.akka.frameSize", "16")
val sc = new SparkContext(sparkConf)
val config = new Configuration()
// read from mongodb localted at localhost from db DB and collection COLLECTION
// mongoRDD result will be RDD[BSONObject]
val mongoRDD = readBsonRDD("mongodb://127.0.0.1:27017/DB.COLLECTION", sc, "mongodb")
val mongoRDD = readBsonRDD("s3n://scorr-spark/normalized_profiles_20150324", sc, "bson")
val mongoRDD = readBsonRDD("s3n://scorr-spark/normalized_profiles_spark_20150325", sc, "serialized_file")
// write to diffrent sources
// for serialized file save only BSONObject
saveBsonRDD[BSONObject](mongoRDD, path = "s3n://scorr-spark/normalized_profiles_serialized", format = "serialized_file")
// for bson or mongodb db we HAVE TO SAVE PAIR: (Object, org.bson.BSONObject)
saveBsonRDD[(Object, org.bson.BSONObject)](mongoRDD.map(x => (null, x)), path = "s3n://scorr-spark/normalized_profiles_bson", format = "bson")
saveBsonRDD[(Object, org.bson.BSONObject)](mongoRDD.map(x => (null, x)), path = "mongodb://127.0.0.1:27017/DB.COLLECTION", format = "mongodb")
#stop spark context
sc.stop()
Running
Generate sample data (or use existing), run ScalaWordCount and print the results. For example you can add some data using mongo shell
for (var i = 1; i <= 25; i++) db.INPUT_COLLECTION.insert( { TEXT_FIELD_NAME_TO_COUNT_WORDS : "bla blaa blaaa" } )
-
Run your job on spark master:
~/spark/bin/spark-submit --class ScalaWordCount --master spark://hostSparkMaster:7077 ~/jobs/mongo-spark-assembly-1.0.jar <mongo-host:port> <DB_NAME.INPUT_COLLECTION> <DB_NAME.OUTPUT_COLLECTION> <TEXT_FIELD_NAME_TO_COUNT_WORDS> -
check result in DB_NAME.OUTPUT_COLLECTION