How do you optimize a Spark query?

To improve the Spark SQL performance, you should optimize the file system. File size should not be too small, as it will take lots of time to open all those small files. If you consider too big, the Spark will spend some time in splitting that file when it reads. Optimal file size should be 64MB to 1GB.

What is Spark optimizer?

The Spark SQL Catalyst Optimizer improves developer productivity and the performance of their written queries. Catalyst automatically transforms relational queries to execute them more efficiently using techniques such as filtering, indexes and ensuring that data source joins are performed in the most efficient order.

How do you optimize pipeline?

At this step, you need to perform several tasks:

1. Design partitioning strategies.
2. Optimize transformation design.
3. Filtering data early on in the pipeline to reduce overall data movement.
4. Using the right data types for intensive operations.
5. Forward projection of only necessary columns.

How does Spark catalyst Optimizer work?

What is shuffling in Spark?

In Apache Spark, Spark Shuffle describes the procedure in between reduce task and map task. Shuffling refers to the shuffle of data given. This operation is considered the costliest. Parallelising effectively of the spark shuffle operation gives performance output as good for spark jobs.

Which supports cost-based optimization and rule based optimization in spark?

Catalyst Optimizer supports both rule-based and cost-based optimization. In rule-based optimization the rule based optimizer use set of rule to determine how to execute the query.

What is cost-based optimization in spark?

Cost-Based Optimization (aka Cost-Based Query Optimization or CBO Optimizer) is an optimization technique in Spark SQL that uses table statistics to determine the most efficient query execution plan of a structured query (given the logical query plan). Cost-based optimization is disabled by default.