Spark Optimizations

Over at the DZone blog, we learn how to use Distribute By and Cluster By to optimize Spark performance:

Your DataFrame is skewed if most of its rows are located on a small number of partitions, while the majority of the partitions remain empty. You really should avoid such a situation. Why? This makes your application virtually not parallel – most of the time you will be waiting for a single task to finish. Even worse, in some cases you can run out of memory on some executors or cause an excessive spill of data to a disk. All of this can happen if your data is not evenly distributed.

To deal with the skew, you can repartition your data using distribute by. For the expression to partition by, choose something that you know will evenly distribute the data. You can even use the primary key of the DataFrame!

It’s interesting to see how cluster by, distribute by, and sort by can have such different performance consequences.

Related Posts

Optimizer Imperfections With Complex Filters

Erik Darling shows a couple examples of how the optimizer will sometimes pick a superior plan when dealing with complicated filters but not always: Sometimes, the optimizer can take a query with a complex where clause, and turn it into two queries. This only happens up to a certain point in complexity, and only if […]

Read More

Bayesian Modeling Of Hardware Failure Rates

Sean Owen shows how you can use Bayesian statistical approaches with Spark Streaming, using the example of hard drive failure rates: This data doesn’t arrive all at once, in reality. It arrives in a stream, and so it’s natural to run these kind of queries continuously. This is simple with Apache Spark’s Structured Streaming, and proceeds […]

Read More

Categories

May 2016
MTWTFSS
« Apr Jun »
 1
2345678
9101112131415
16171819202122
23242526272829
3031