Using Apache Spark to Query a Remote Authenticated MongoDB Server

Using Apache Spark to Query a Remote Authenticated MongoDB Server

<strong>Apache Spark is one of the most popular open source tools for big data. Learn how to use it to ingest data from a remote MongoDB server.</strong>

Apache Spark is one of the most popular open source tools for big data. Learn how to use it to ingest data from a remote MongoDB server.

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1. Download and Extract Spark

$ wget http://apache.spinellicreations.com/spark/spark-2.4.0/spark-2.4.0-bin-hadoop2.7.tgz
$ tar -xf spark-2.4.0-bin-hadoop2.7.tgz
$ cd spark-2.4.0-bin-hadoop2.7

Create a spark-defaults.conf file by copying spark-defaults.conf.template in conf/.

Add the below line to the conf file.

spark.debug.maxToStringFields=1000

2. Connect to Mongo via a Remote Server

We use the MongoDB Spark Connector.

First, make sure the Mongo instance in the remote server has the bindIp set to the appropriate value and the correct local IP (not just localhost). Use the authentication root and password below to indicate the credentials of your authenticated Mongo database. 192.168.1.32 is your remote server’s private IP (i.e., the server where Mongo is running). We are reading the oplog.rs collection in the local database. Change these accordingly. Similarly, we are writing the outputs to the database, sparkoutput.

spark-2.4.0-bin-hadoop2.7]$ ./bin/pyspark --conf "spark.mongodb.input.uri=mongodb://root:[email protected]:27017/local.oplog.rs?readPreference=primaryPreferred" --conf "spark.mongodb.output.uri=mongodb://root:[email protected]:27017/sparkoutput" --packages org.mongodb.spark:mongo-spark-connector_2.11:2.4.0
Python 2.7.5 (default, Oct 30 2018, 23:45:53)

[GCC 4.8.5 20150623 (Red Hat 4.8.5-36)] on linux2

Type "help", "copyright", "credits" or "license" for more information.

Ivy Default Cache set to: /home/pkathi2/.ivy2/cache

The jars for the packages stored in: /home/pkathi2/.ivy2/jars

:: loading settings :: url = jar:file:/home/pkathi2/spark-2.4.0-bin-hadoop2.7/jars/ivy-2.4.0.jar!/org/apache/ivy/core/settings/ivysettings.xml

org.mongodb.spark#mongo-spark-connector_2.11 added as a dependency

:: resolving dependencies :: org.apache.spark#spark-submit-parent-33a37e02-1a24-498d-9217-e7025eeebd10;1.0

confs: [default]

found org.mongodb.spark#mongo-spark-connector_2.11;2.4.0 in central

found org.mongodb#mongo-java-driver;3.9.0 in central

:: resolution report :: resolve 256ms :: artifacts dl 5ms

:: modules in use:

org.mongodb#mongo-java-driver;3.9.0 from central in [default]

org.mongodb.spark#mongo-spark-connector_2.11;2.4.0 from central in [default]


| | modules || artifacts |

| conf | number| search|dwnlded|evicted|| number|dwnlded|


| default | 2 | 0 | 0 | 0 || 2 | 0 |


:: retrieving :: org.apache.spark#spark-submit-parent-33a37e02-1a24-498d-9217-e7025eeebd10

confs: [default]

0 artifacts copied, 2 already retrieved (0kB/6ms)

WARN NativeCodeLoader: This message means the systme is unable to load native-hadoop library for your platform… using built-in Java classes where applicable.

Set the default log level to “WARN”.

To adjust logging level, use sc.setLogLevel(newLevel). For SparkR, use setLogLevel(newLevel).

Welcome to


/ / _ ___/ /__

\ / _ / _ `/ __/ '/

/_ / .__/\,// /_/_\ version 2.4.0

/_/

Using Python version 2.7.5

SparkSession is available as ‘spark’.

>>> from pyspark.sql import SparkSession

>>> my_spark = SparkSession
... .builder
... .appName("myApp")
... .config("spark.mongodb.input.uri", "mongodb://root:[email protected]:27017/local.oplog.rs?authSource=admin")
... .config("spark.mongodb.output.uri", "mongodb://root:[email protected]:27017/sparkoutput?authSource=admin")
... .getOrCreate()

Make sure you are using the correct authentication source (i.e., where you authenticate yourself in the Mongo server).

3. Perform Queries on the Mongo Collection

Now you can perform queries on your remote Mongo collection through the Spark instance. For example, the below query finds the schema from the collection.

>>> df = spark.read.format("com.mongodb.spark.sql.DefaultSource").load()
>>> df.printSchema()
root
|-- h: long (nullable = true)
|-- ns: string (nullable = true)
|-- o: struct (nullable = true)
| |-- $set: struct (nullable = true)
| | |-- lastUse: timestamp (nullable = true)
| |-- $v: integer (nullable = true)

Originally published by Pradeeban Kathiravelu at https://dzone.com

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