Note this is an currently incompatible fork of the original Spark MongoDB connector.

It has the following changes:

• Support for additional predicate pushdowns (Not Null, String Contains, String Starts With, String Ends With)
• Support for additional data types (Long, Decimal, Date, Double)
• Forced partitioning by _id (MongoDB's default primary key)

The plan is to push these upstream.

NSMC: A Native MongoDB Connector for Apache Spark

This is a native connector for reading and writing MongoDB collections directly from Apache Spark. In Spark, the data from MongoDB is represented as an RDD[MongoDBObject]. Starting with Release 0.5.0, you can also use NSMC through Spark SQL by registering a MongpDB collection as a temporary table.

Fore more details about this project, check the following blog posts:

NSMC: A Native MongoDB Connector for Apache Spark

Spark SQL Integration for MongoDB

Current Limitations

• No Java or Python API bindings
• Can only read (no updates)
• Only tested with the following configurations:
• MongoDB: 2.6
• Scala: 2.10
• Spark: 1.4.0
• Casbah 2.7
• Not tested with MongoDB's hash-based partitioning

Other Warnings

• The APIs will probably change several times before an official release

Release Status

A pre-release alpha version (0.5.3) is available.

Licensing

See the top-level LICENSE file

Getting Started

Perhaps the easiest way to get started is to use the companion example project. It contains an appropriately configured sbt project, together with sample code for using NSMC from both core Spark and Spark SQL. Be sure to check out that project's README to to make sure you're using the right branch for the NSMC and Apache Spark versions you want to use.

sbt configuration

Add the following to your build.sbt for the latest stable release:

scalaVersion := "2.10.4" // any 2.10 is OK -- support for 2.11 coming soon

libraryDependencies += "org.apache.spark" %% "spark-core" % "1.4.0" % "provided"

libraryDependencies += "org.apache.spark" %% "spark-sql" % "1.4.0" % "provided"

libraryDependencies += "com.github.spirom" %% "spark-mongodb-connector" % "0.5.3"

Configuration

Setting	Meaning	Units	Default
spark.nsmc.connection.host	MongoDB host or IP address		localhost
spark.nsmc.connection.port	MongoDB port		27017
spark.nsmc.user	MongoDB user name		no authentication
spark.nsmc.password	MongoDB password		no authentication
spark.nsmc.split.indexed.collections	Should indexed collections be partitioned using MongoDB's [internal] splitVector command?	boolean	false
spark.nsmc.split.chunk.size	Maximum chunk size, in megabytes, passed to MongoDB's splitVector command, if used.	MB	4
spark.nsmc.partition.on.shard.chunks	Should collections that are already sharded in MongoDB retain this as their partitioning in Spark? If not, the entire collection will be read as a single Spark partition.	boolean	false
spark.nsmc.direct.to.shards	If sharding of collections is being observed, should the mongos server be bypassed? (Don't do this unless you understand MongoDB really well, or you may obtain incorrect results -- if MongoDB is rebalancing the shards when your query executes.)	boolean	false

Usage from core Spark

In your code, you need to add:

import nsmc._

import com.mongodb.DBObject

Then to actually read from a collection:

val conf = new SparkConf()
    .setAppName("My MongoApp").setMaster("local[4]")
    .set("spark.nsmc.connection.host", "myMongoHost")
    .set("spark.nsmc.connection.port", "myMongoPort")
    .set("spark.nsmc.user", "yourUsernameHere")
    .set("spark.nsmc.password", "yourPasswordHere")
val sc = new SparkContext(conf)
val data: MongoRDD[DBObject] =
    sc.mongoCollection[DBObject]("myDB", "myCollection")

Usage from Spark SQL

Register a temporary table as follows

sqlContext.sql(
    s"""
    |CREATE TEMPORARY TABLE dataTable
    |USING nsmc.sql.MongoRelationProvider
    |OPTIONS (db 'myDB', collection 'myCollection')
  """.stripMargin)

Query the temporary table as follows

val data = sqlContext.sql("SELECT * FROM dataTable")

Summary of releases

NSMC Release	Status	Apache Spark Release	Scala Version	Features Added
0.5.3	Alpha	1.4.0	2.10	Spark 1.4.0
0.5.2	Alpha	1.3.0	2.10	Configuration parameters are prefixed with "spark." to enable use of various Spark tools like the Thrift server and spark-submit.
0.5.1	Alpha	1.3.0	2.10	Apache Spark dependency is marked as "provided", making it easier to create assemblies that include NSMC.
0.5.0	Alpha	1.3.0	2.10	Spark 1.3.0
0.4.1	Alpha	1.2.0	2.10	Filter and Projection push-down for Spark SQL
0.4.0	Alpha	1.2.0	2.10	Spark 1.2.0 and Spark SQL Support
0.3.0	Alpha	1.1.0	2.10	Initial Release