Getting Spark, Python, and MongoDB to work together

Updates:

2016-07-04

Since the last update MongoDB Spark Connector matured quite a lot. It provides up-to-date binaries and data source based API but it is using SparkConf configuration so it is subjectively less flexible than the Stratio/Spark-MongoDB.

2016-03-30

Since the original answer I found two different ways to connect to MongoDB from Spark:

• mongodb/mongo-spark
• Stratio/Spark-MongoDB

While the former one seems to be relatively immature the latter one looks like a much better choice than a Mongo-Hadoop connector and provides a Spark SQL API.

# Adjust Scala and package version according to your setup
# although officially 0.11 supports only Spark 1.5
# I haven't encountered any issues on 1.6.1
bin/pyspark --packages com.stratio.datasource:spark-mongodb_2.11:0.11.0

df = (sqlContext.read
  .format("com.stratio.datasource.mongodb")
  .options(host="mongo:27017", database="foo", collection="bar")
  .load())

df.show()

## +---+----+--------------------+
## |  x|   y|                 _id|
## +---+----+--------------------+
## |1.0|-1.0|56fbe6f6e4120712c...|
## |0.0| 4.0|56fbe701e4120712c...|
## +---+----+--------------------+

It seems to be much more stable than mongo-hadoop-spark, supports predicate pushdown without static configuration and simply works.

The original answer:

Indeed, there are quite a few moving parts here. I tried to make it a little bit more manageable by building a simple Docker image which roughly matches described configuration (I've omitted Hadoop libraries for brevity though). You can find complete source on GitHub (DOI 10.5281/zenodo.47882) and build it from scratch:

git clone https://github.com/zero323/docker-mongo-spark.git
cd docker-mongo-spark
docker build -t zero323/mongo-spark .

or download an image I've pushed to Docker Hub so you can simply docker pull zero323/mongo-spark):

Start images:

docker run -d --name mongo mongo:2.6
docker run -i -t --link mongo:mongo zero323/mongo-spark /bin/bash

Start PySpark shell passing --jars and --driver-class-path:

pyspark --jars ${JARS} --driver-class-path ${SPARK_DRIVER_EXTRA_CLASSPATH}

And finally see how it works:

import pymongo
import pymongo_spark

mongo_url = 'mongodb://mongo:27017/'

client = pymongo.MongoClient(mongo_url)
client.foo.bar.insert_many([
    {"x": 1.0, "y": -1.0}, {"x": 0.0, "y": 4.0}])
client.close()

pymongo_spark.activate()
rdd = (sc.mongoRDD('{0}foo.bar'.format(mongo_url))
    .map(lambda doc: (doc.get('x'), doc.get('y'))))
rdd.collect()

## [(1.0, -1.0), (0.0, 4.0)]

Please note that mongo-hadoop seems to close the connection after the first action. So calling for example rdd.count() after the collect will throw an exception.

Based on different problems I've encountered creating this image I tend to believe that passing mongo-hadoop-1.5.0-SNAPSHOT.jar and mongo-hadoop-spark-1.5.0-SNAPSHOT.jar to both --jars and --driver-class-path is the only hard requirement.

Notes:

• This image is loosely based on jaceklaskowski/docker-spark so please be sure to send some good karma to @jacek-laskowski if it helps.
• If don't require a development version including new API then using --packages is most likely a better option.

Can you try using --package option instead of --jars ... in your spark-submit command:

spark-submit --packages org.mongodb.mongo-hadoop:mongo-hadoop-core:1.3.1,org.mongodb:mongo-java-driver:3.1.0 [REST OF YOUR OPTIONS]

Some of these jar files are not Uber jars and need more dependencies to be downloaded before that can get to work.