Using Sqoop’s Logic To Improve Spark JDBC Performance

Avi Yehuda analyzes how Sqoop works to make relational database access from Spark faster:

Sqoop performed so much better almost instantly, all you needed to do is to set the number of mappers according to the size of the data and it was working perfectly.
Since both Spark and Sqoop are based on the Hadoop map-reduce framework, it’s clear that Spark can work at least as good as Sqoop, I only needed to find out how to do it. I decided to look closer at what Sqoop does to see if I can imitate that with Spark.
By turning on the verbose flag of Sqoop, you can get a lot more details. What I found was that Sqoop is splitting the input to the different mappers which makes sense, this is map-reduce after all, Spark does the same thing. But before doing that, Sqoop does something smart that Spark doesn’t do.

Read on to see what in particular Sqoop does, and how you can use that in your Spark code.

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