Parquet - chunhualiao/public-docs GitHub Wiki
Parquet is a columnar storage file format that is optimized for use with big data processing frameworks such as Apache Spark, Apache Hadoop, and others. It is designed to efficiently store and process large datasets by organizing data in a way that minimizes I/O operations and maximizes query performance.
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Columnar Storage
- Data is stored in columns rather than rows. This allows for efficient reading of specific columns, reducing the amount of data that needs to be read from disk.
- For example, if you only need to query a few columns out of a large dataset, Parquet can read just those columns, significantly speeding up the query.
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Compression and Encoding
- Parquet supports various compression algorithms (e.g., Snappy, Gzip) and encoding schemes (e.g., Dictionary Encoding, Run Length Encoding) to reduce the size of the data on disk.
- Compression not only saves storage space but also speeds up data transfer and I/O operations.
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Schema Evolution
- Parquet supports schema evolution, allowing you to add, remove, or modify columns in your dataset without having to rewrite the entire file.
- This flexibility is crucial for evolving data schemas in big data environments.
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Efficient Data Encoding
- Parquet uses efficient data encoding techniques to store data in a compact form. For example, it can store repeated values using run-length encoding, which saves space and speeds up reads.
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Support for Complex Data Types
- Parquet supports a wide range of data types, including nested and complex data structures such as arrays, maps, and structs.
- This makes it suitable for storing and processing complex datasets, such as those generated by JSON or Avro.
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Row Groups
- A Parquet file is divided into row groups, which are collections of rows stored together. Each row group contains a subset of the data and is stored in a columnar format.
- Row groups allow for efficient parallel processing, as different row groups can be processed independently.
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Columns
- Within each row group, data is stored in columns. Each column contains the values for a specific field across all rows in the row group.
- Columns are stored contiguously on disk, which enables efficient reading of specific columns.
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Metadata
- Parquet files contain metadata that describes the schema of the data, including the names and types of columns, as well as statistics about the data (e.g., min/max values, null counts).
- This metadata allows for efficient query planning and optimization.
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Big Data Processing
- Parquet is widely used in big data processing frameworks such as Apache Spark and Apache Hadoop to store and process large datasets efficiently.
- Its columnar storage and compression features make it ideal for data warehousing and analytics workloads.
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Data Lakes
- Parquet is a popular choice for storing data in data lakes, where large volumes of structured and semi-structured data are stored and analyzed.
- Its support for schema evolution and complex data types makes it suitable for evolving data schemas in data lakes.
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Machine Learning
- Parquet can be used to store and preprocess data for machine learning tasks. Its efficient storage and retrieval capabilities make it suitable for large-scale machine learning pipelines.
Consider a dataset with the following schema:
id | name | age | city
-- | -- | -- | --
1 | Alice | 30 | New York
2 | Bob | 25 | Los Angeles
3 | Carol | 35 | Chicago
In Parquet format, this data would be stored in columns:
- id: [1, 2, 3]
- name: ["Alice", "Bob", "Carol"]
- age: [30, 25, 35]
- city: ["New York", "Los Angeles", "Chicago"]
If you only need to query the
age column, Parquet can read just the age column data, significantly reducing the amount of data read from disk.Parquet is a powerful and efficient file format for storing and processing large datasets. Its columnar storage, compression, and schema evolution features make it a popular choice for big data processing, data lakes, and machine learning applications.