Memory Efficient Data Streaming to Parquet Files

Estuary Flow has developed a method for efficiently streaming data into Apache Parquet files, addressing the challenges posed by memory constraints in connectors. Parquet, a columnar storage format, requires data to be written in row groups, which typically necessitates significant memory usage. Estuary Flow's solution involves a "2-pass write" approach, which minimizes memory consumption while maintaining performance. In the first pass, data is streamed row-by-row into a scratch file on disk, using small row groups to limit RAM usage. This scratch file is then read column-by-column in the second pass, consolidating the smaller row groups into larger ones for output.

The 2-pass write method effectively transposes incoming data from a row-oriented to a column-oriented structure, allowing for efficient handling of large datasets. While this approach introduces some overhead due to the need for encoding and decoding data across two passes, it remains faster than alternatives that require more memory. However, the method does have limitations, such as potential performance bottlenecks with very large datasets and excessive metadata sizes when dealing with numerous columns. To mitigate these issues, Estuary Flow employs heuristics to manage scratch file sizes and metadata. Overall, this innovative approach enables memory-efficient data streaming into Parquet files, making it suitable for real-time data integration and analytics.