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It’s all too easy to overlook the importance of storage and IO in the performance and optimization of Spark jobs.

However, the choice of file format has drastic implications to everything from the ongoing stability to compute cost of compute jobs.

These file formats also employ a number of optimization techniques to minimize data exchange, permit predicate pushdown, and prune unnecessary partitions.

This session from the Spark + AI Summit introduces and concisely explains the key concepts behind some of the most widely used file formats in the Spark ecosystem – namely Parquet, ORC, and Avro.

From the abstract:

We’ll discuss the history of the advent of these file formats from their origins in the Hadoop / Hive ecosystems to their functionality and use today. We’ll then deep dive into the core data structures that back these formats, covering specifics around the row groups of Parquet (including the recently deprecated summary metadata files), stripes and footers of ORC, and the schema evolution capabilities of Avro. We’ll continue to describe the specific SparkConf / SQLConf settings that developers can use to tune the settings behind these file formats. We’ll conclude with specific industry examples of the impact of the file on the performance of the job or the stability of a job (with examples around incorrect partition pruning introduced by a Parquet bug), and look forward to emerging technologies (Apache Arrow).

After this presentation, attendees should understand the core concepts behind the prevalent file formats, the relevant file-format specific settings, and finally how to select the correct file format for their jobs. This presentation is relevant to Spark+AI summit because as more AI/ML workflows move into the Spark ecosystem (especially IO intensive deep learning) leveraging the correct file format is paramount in performant model training.

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