We would like to introduce Parquet, a columnar file format for Hadoop. Performance and compression benefits of using columnar storage formats for storing and processing large amounts of data are well documented in academic literature as well as several commercial analytical databases. Parquet supports deeply nested structures, efficient encoding and column compression schemes, and is designed to be compatible with a variety of higher-level type systems. It is available as a standalone library, allowing any Hadoop framework or tool to build support for it with minimal dependencies. As of this release, Parquet is supported by Apache Pig, plain Hadoop Map-Reduce, and Cloudera’s Impala, and is being put into production at Twitter. We will discuss Parquet’s design and share performance numbers.
Julien is the lead for Parquet’s java implementation. He also leads Pig development at Twitter and is the Apache Pig PMC Chair. His French accent makes his talks attractive.
Working on impala
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