How to leverage compression and data skipping in Parquet and ORC file formats

Introduction

This tutorial will guide you through the process of leveraging compression and data skipping techniques in the Parquet and ORC file formats to optimize your Hadoop data storage and querying. By understanding the benefits and implementation of these features, you can improve the performance and reduce the storage costs of your Hadoop-based applications.

Skills Graph

Understanding Parquet and ORC File Formats

Parquet File Format

Parquet is an open-source, column-oriented data storage format developed by Hadoop community. It is designed to efficiently store and process large datasets by leveraging columnar data layout and various compression techniques.

Some key features of Parquet format:

• Columnar Data Layout: Parquet stores data in a columnar format, which means that values for each column are stored together. This allows for efficient data querying and processing, as only the required columns need to be read from disk.

• Compression: Parquet supports various compression codecs, such as Snappy, Gzip, and LZO, which can significantly reduce the storage space required for data.

• Nested Data Structures: Parquet can handle complex, nested data structures, such as arrays and maps, making it suitable for a wide range of data types.

• Efficient Data Querying: The columnar data layout and compression techniques used by Parquet enable efficient data querying, as only the necessary columns need to be read from disk.

ORC File Format

ORC (Optimized Row Columnar) is another open-source, column-oriented data storage format developed by the Apache Hive community. It is designed to provide efficient storage and processing of large datasets, similar to Parquet.

Key features of ORC format:

• Columnar Data Layout: Like Parquet, ORC stores data in a columnar format, which enables efficient data querying and processing.

• Compression: ORC supports various compression codecs, including Snappy, Zlib, and LZO, to reduce the storage space required for data.

• Efficient Data Querying: The columnar data layout and compression techniques used by ORC allow for efficient data querying, as only the necessary columns need to be read from disk.

• Predicate Pushdown: ORC supports predicate pushdown, which means that filters and predicates can be pushed down to the storage layer, further improving query performance.

• Metadata: ORC stores detailed metadata about the data, such as column statistics and index information, which can be used to optimize query execution.

Both Parquet and ORC file formats are widely used in big data ecosystems, such as Apache Hadoop, Apache Spark, and Apache Hive, for efficient storage and processing of large datasets.

Compression Techniques in Parquet and ORC

Compression in Parquet

Parquet supports several compression codecs, each with its own trade-offs in terms of compression ratio and CPU usage. The available compression codecs in Parquet include:

• Snappy: A fast compression algorithm that provides a good balance between compression ratio and speed.
• Gzip: A popular lossless compression algorithm that offers a higher compression ratio but slower performance compared to Snappy.
• LZO: A lightweight compression algorithm that provides a moderate compression ratio with fast decompression speed.

You can specify the compression codec to use when writing Parquet files. For example, in PySpark, you can set the compression codec as follows:

df.write.format("parquet")
      .option("compression", "snappy")
      .save("path/to/parquet/file")

Compression in ORC

ORC also supports several compression codecs, similar to Parquet. The available compression codecs in ORC include:

• Snappy: A fast compression algorithm that provides a good balance between compression ratio and speed.
• Zlib: A popular lossless compression algorithm that offers a higher compression ratio but slower performance compared to Snappy.
• LZO: A lightweight compression algorithm that provides a moderate compression ratio with fast decompression speed.

You can specify the compression codec to use when writing ORC files. For example, in Hive, you can set the compression codec as follows:

CREATE TABLE my_table (
  col1 INT,
  col2 STRING
)
STORED AS ORC
TBLPROPERTIES ("orc.compress" = "snappy");

Both Parquet and ORC provide efficient compression capabilities, allowing you to reduce the storage requirements for your data while maintaining fast query performance.

Efficient Data Querying with Parquet and ORC

Data Skipping in Parquet

Parquet provides a feature called data skipping, which allows the query engine to skip reading unnecessary data blocks during query execution. This is achieved by storing metadata about the data in the Parquet file, such as the minimum and maximum values for each column in each row group.

When a query is executed, the query engine can use this metadata to determine which row groups are relevant to the query and only read those row groups, significantly improving query performance.

Here's an example of how data skipping works in Parquet:

## PySpark example
df = spark.read.parquet("path/to/parquet/file")
df.filter(df.col1 > 100).select("col1", "col2").show()

In this example, the query engine can use the column statistics stored in the Parquet file to determine that only the row groups with col1 > 100 need to be read, skipping the irrelevant row groups and improving query performance.

Predicate Pushdown in ORC

ORC supports a feature called predicate pushdown, which allows the query engine to push down filters and predicates to the storage layer, further improving query performance.

When a query is executed, the query engine can analyze the predicates and push them down to the ORC file reader, which can then use the metadata stored in the ORC file to skip reading unnecessary data.

Here's an example of how predicate pushdown works in ORC:

-- Hive example
SELECT col1, col2
FROM my_table
WHERE col1 > 100 AND col2 LIKE 'abc%';

In this example, the Hive query engine can push down the predicates col1 > 100 and col2 LIKE 'abc%' to the ORC file reader, which can then use the column statistics and index information stored in the ORC file to skip reading irrelevant data, improving query performance.

Both Parquet and ORC provide efficient data querying capabilities through features like data skipping and predicate pushdown, allowing you to optimize the performance of your big data workloads.

Summary

In this Hadoop-focused tutorial, you have learned how to leverage compression and data skipping techniques in the Parquet and ORC file formats to optimize your data storage and querying. By understanding the capabilities of these file formats, you can improve the performance and reduce the storage costs of your Hadoop-based applications, leading to more efficient and cost-effective data processing workflows.