Overview of Parquet and Why It Gels with PySpark

Parquet is a columnar storage format for big data. It is a self-describing format that allows for efficient compression and encoding schemes. Parquet is designed to work well with big data processing frameworks like Apache Hadoop and Apache Spark.

PySpark, the Python library for Spark, works well with Parquet because it allows for efficient reading and writing of data. When data is stored in Parquet format, —

1. PySpark can read only the necessary columns and rows from the data, rather than reading the entire file. This makes it more efficient for big data processing and can significantly reduce the amount of memory required to perform a given operation.
2. Additionally, Parquet files are splittable, which means that they can be split into smaller chunks for parallel processing.

Pros & Cons

One of the main advantages of using Parquet is its efficient compression and encoding schemes. It uses various encoding techniques like run length encoding, dictionary encoding, and bit-packing to compress data, which allows for faster data processing and reduces storage costs. Additionally, because Parquet is a columnar storage format, it is well-suited for analytical queries that only require a subset of the columns in a table.

Some of the disadvantages of using Parquet include that it can have a high overhead when performing small read-write operations and that it may not be as performant as row-based storage formats like Avro or ORC for transactional workloads.

Best practices using Parquet

• Use predicate pushdown: This allows the Parquet reader to skip irrelevant data and read only the necessary columns and rows, which can improve performance.
• Use partitioning: Partitioning data in Parquet can improve query performance by allowing you to query only a subset of the data.
• Use snappy compression: Snappy is a high-performance compression algorithm that works well with Parquet. It balances compression ratio and CPU usage, making it suitable for big data processing.
• Use vectorized readers: Vectorized readers can process large amounts of data in a single batch, resulting in improved performance.
• Use Parquet for analytical workloads: As mentioned earlier, Parquet is well-suited for analytical workloads that only require a subset of the columns in a table.

It’s worth noting that these are general best practices and you should test and evaluate the different options to find the best solution for your use case.

What is Snappy compression ?

Snappy is a fast and efficient compression algorithm that is designed for real-time data compression and decompression. It was developed by Google and is now an open-source project. Snappy is a high-performance compression algorithm that works well with big data processing frameworks like Hadoop and Spark.

Snappy uses a combination of techniques, including LZ77, Huffman coding, and a byte-aligned representation, to compress data. It also uses a fast and efficient decompression algorithm, which makes it well-suited for real-time data processing scenarios.

One of the main benefits of Snappy is that it balances compression ratio and CPU usage, making it suitable for big data processing. It is generally faster than other popular compression algorithms like Gzip and LZO, but it may not compress the data as much as those algorithms.

Snappy is also well-suited for use cases where data needs to be decompressed quickly, such as real-time data processing, because it has a low CPU overhead and fast decompression times.

Snappy can be used with a variety of file formats like Avro, Parquet, and SequenceFiles to compress data and improve the performance of big data processing tasks.

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