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Hadoop: The Definitive Guide, 4th Edition by Tom White

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Chapter 13. Parquet

Apache Parquet is a columnar storage format that can efficiently store nested data.

Columnar formats are attractive since they enable greater efficiency, in terms of both file size and query performance. File sizes are usually smaller than row-oriented equivalents since in a columnar format the values from one column are stored next to each other, which usually allows a very efficient encoding. A column storing a timestamp, for example, can be encoded by storing the first value and the differences between subsequent values (which tend to be small due to temporal locality: records from around the same time are stored next to each other). Query performance is improved too since a query engine can skip over columns that are not needed to answer a query. (This idea is illustrated in Figure 5-4.) This chapter looks at Parquet in more depth, but there are other columnar formats that work with Hadoop—notably ORCFile (Optimized Record Columnar File), which is a part of the Hive project.

A key strength of Parquet is its ability to store data that has a deeply nested structure in true columnar fashion. This is important since schemas with several levels of nesting are common in real-world systems. Parquet uses a novel technique for storing nested structures in a flat columnar format with little overhead, which was introduced by Google engineers in the Dremel paper.[86] The result is that even nested fields can be read independently of other fields, resulting in significant ...

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