![]() ![]() Because of the natural columnar format of Parquet, this is very fast!ĭuckDB will read the Parquet files in a streaming fashion, which means you can perform queries on large Parquet files that do not fit in your main memory.ĭuckDB is able to automatically detect which columns and rows are required for any given query. This can often turn Big Data into Medium Data.ĭuckDB’s zero-dependency Parquet reader is able to directly execute SQL queries on Parquet files without any import or analysis step. The columnar compression significantly reduces the file size of the format, which in turn reduces the storage requirement of data sets.These statistics allow the reader to skip row groups if they are not required. The file contains per-column statistics in every row group (min/max value, and the number of NULL values).The columnar representation means that individual columns can be (efficiently) read.The Parquet format has a number of properties that make it suitable for analytical use cases: Within a row group, the table data is stored in a columnar fashion. The table is partitioned into row groups, which each contain a subset of the rows of the table. In Parquet files, data is stored in a columnar-compressed binary format. TLDR: DuckDB, a free and open source analytical data management system, can run SQL queries directly on Parquet files and automatically take advantage of the advanced features of the Parquet format.Īpache Parquet is the most common “Big Data” storage format for analytics. Hannes Mühleisen and Mark Raasveldt Querying Parquet with Precision using DuckDB ![]()
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