Performance Tuning and Cost Optimization / Internals, Research, Consulting
It is quite common today to convert incoming JSON data into Parquet format to improve the performance of analytical queries.
When JSON data has an arbitrary schema i.e. different records can contain different key-value pairs, it is common to parse such JSON payloads into a map column in Parquet.
How is it stored? What read performance can you expect? Will json_map["key"] read only data for key or the entire JSON?
It is quite common to have a streaming Flink application that reads incoming data and puts them into Parquet files with low latency (a couple of minutes) for analysts to be able to run both near-realtime and historical ad-hoc analysis mostly using SQL queries.
But let’s review write patterns and problems that can appear for such applications at scale.
Parquet is one of the most popular columnar file formats used in many tools including Apache Hive, Spark, Presto, Flink and many others.
For tuning Parquet file writes for various workloads and scenarios let’s see how the Parquet writer works in detail (as of Parquet 1.10 but most concepts apply to later versions as well).
I have a Spark job that transforms incoming data from compressed text files into Parquet format and loads them into a daily partition of a Hive table. This is a typical job in a data lake, it is quite simple but in my case it was very slow.
Initially it took about 4 hours to convert ~2,100 input .gz files (~1.9 TB of data) into Parquet, while the actual Spark job took just 38 minutes to run and the remaining time was spent on loading data into a Hive partition.
Let’s see what is the reason of such behavior and how we can improve the performance.