Archive for : March, 2015

Loading database data into Spark using Data Sources API

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Update: Read this new post for Spark 2.0 example.

With Spark 1.3 release, it is easy to load database data into Spark using Spark SQL data sources API. In my last blog post, I explained about using JdbcRDD to do the same. With this new release, JdbcRDD is no longer the preferred approach. I will explain the differences later in another blog post. Now we’ll look at the new APIs.

Data sources API which provides a unified interface to query external data sources from Spark SQL is introduced in Spark 1.2. However an official JDBC data source API is released only in version 1.3. This release also introduced a very important feature of Spark – The DataFrame API which is a higher level abstraction over RDD and provides a simpler API for data processing.

There are two ways to call data sources API

  1. Programmatically using SQLContext load function
  2. Using SQL – We’ll look at this in another blog post

SQLContext load

The load function is defined in Scala as follows,

First parameter ‘source’ specifies the type of data source API. In our case, it is ‘jdbc’. 2nd parameter is a map of options required by the data source implementation specified in the first parameter. It varies from data source to data source. For JDBC datasource following parameters are required (Reproduced from Spark SQL documentation).

  1. url:
    The JDBC URL to connect to.
  2. dbtable:
    The JDBC table that should be read. Note that anything that is valid in a ‘FROM’ clause of a SQL query can be used. For example, instead of a full table you could also use a subquery in parentheses.
  3. driver:
    The class name of the JDBC driver needed to connect to this URL. This class with be loaded on the master and workers before running an JDBC commands to allow the driver to register itself with the JDBC subsystem.
  4. partitionColumn, lowerBound, upperBound, numPartitions:
    These options must all be specified if any of them is specified. They describe how to partition the table when reading in parallel from multiple workers. partitionColumn must be a numeric column from the table in question.

I’m going explain this using the same MySQL example application used in JdbcRDD example. It uses MySQL sample Employees database https://dev.mysql.com/doc/employee/en/. First it loads part of MySQL data into Spark using Data sources API and prints it in the log.

  1. MySQL connection parameters are defined as constants
  2. Initializing SQLContext from SparkContext
  3. JDBC data source options for MySQL database

    As of writing this, it is not possible to separately specify username and password. So it has been specified as part of connection URL.

    The most interesting part here is the ‘dbtable’ option. I have used a derived table created from a subquery. The subquery only selects the required columns from ’employees’ table. It also concatenates first name and last name and returns it as full name. Since this transformation happens inside the MySQL, overall performance of this app will be improved. Please note that similar optimization is also possible in JdbcRDD. Here I’m doing it this way to demontrate that ‘dbtable’ is not just limited to name of an existing table.

    Last 4 parameters are similar to JdbcRDD. Only difference is, ‘partitionColumn’ is an option here. In JdbcRDD, it has to be part of the SQL query.

  4. Loading MySQL query result as DataFrame

    Like RDD, DataFrame is also lazily executed. So calling load function will not immediately load the data from MySQL. It will wait until an action is executed on the returned DataFrame.

  5. Executing DataFrame by invoking an action

Complete Java application is available at my GitHub repo https://github.com/sujee81/SparkApps.

If you’d like to know about saving dataframe to database, read this post

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