Parquet

Parquet is a columnar storage format that supports nested data.

Parquet metadata is encoded using Apache Thrift.

The Parquet-format project contains all Thrift definitions that are necessary to create readers
and writers for Parquet files.

Motivation

We created Parquet to make the advantages of compressed, efficient columnar data representation available to any project in the Hadoop ecosystem.

Parquet is built from the ground up with complex nested data structures in mind, and uses the record shredding and assembly algorithm described in the Dremel paper. We believe this approach is superior to simple flattening of nested name spaces.

Parquet is built to support very efficient compression and encoding schemes. Multiple projects have demonstrated the performance impact of applying the right compression and encoding scheme to the data. Parquet allows compression schemes to be specified on a per-column level, and is future-proofed to allow adding more encodings as they are invented and implemented.

Parquet is built to be used by anyone. The Hadoop ecosystem is rich with data processing frameworks, and we are not interested in playing favorites. We believe that an efficient, well-implemented columnar storage substrate should be useful to all frameworks without the cost of extensive and difficult to set up dependencies.

Modules

The parquet-format project contains format specifications and Thrift definitions of metadata required to properly read Parquet files.

The parquet-mr project contains multiple sub-modules, which implement the core components of reading and writing a nested, column-oriented data stream, map this core onto the parquet format, and provide Hadoop Input/Output Formats, Pig loaders, and other java-based utilities for interacting with Parquet.

The parquet-compatibility project contains compatibility tests that can be used to verify that implementations in different languages can read and write each other's files.

Building

Java resources can be built using mvn package. The current stable version should always be available from Maven Central.

C++ thrift resources can be generated via make.

Thrift can be also code-generated into any other thrift-supported language.

Glossary

• Block (HDFS block): This means a block in HDFS and the meaning is
  unchanged for describing this file format. The file format is
  designed to work well on top of HDFS.

• File: A HDFS file that must include the metadata for the file.
  It does not need to actually contain the data.

• Row group: A logical horizontal partitioning of the data into rows.
  There is no physical structure that is guaranteed for a row group.
  A row group consists of a column chunk for each column in the dataset.

• Column chunk: A chunk of the data for a particular column. They live
  in a particular row group and are guaranteed to be contiguous in the file.

• Page: Column chunks are divided up into pages. A page is conceptually
  an indivisible unit (in terms of compression and encoding). There can
  be multiple page types which are interleaved in a column chunk.

Hierarchically, a file consists of one or more row groups. A row group
contains exactly one column chunk per column. Column chunks contain one or
more pages.

Unit of parallelization

• MapReduce - File/Row Group
• IO - Column chunk
• Encoding/Compression - Page

File format

This file and the thrift definition should be read together to understand the format.

4-byte magic number "PAR1"
<Column 1 Chunk 1 + Column Metadata>
<Column 2 Chunk 1 + Column Metadata>
...
<Column N Chunk 1 + Column Metadata>
<Column 1 Chunk 2 + Column Metadata>
<Column 2 Chunk 2 + Column Metadata>
...
<Column N Chunk 2 + Column Metadata>
...
<Column 1 Chunk M + Column Metadata>
<Column 2 Chunk M + Column Metadata>
...
<Column N Chunk M + Column Metadata>
File Metadata
4-byte length in bytes of file metadata (little endian)
4-byte magic number "PAR1"

In the above example, there are N columns in this table, split into M row
groups. The file metadata contains the locations of all the column metadata
start locations. More details on what is contained in the metadata can be found
in the thrift definition.

Metadata is written after the data to allow for single pass writing.

Readers are expected to first read the file metadata to find all the column
chunks they are interested in. The columns chunks should then be read sequentially.

Metadata

There are three types of metadata: file metadata, column (chunk) metadata and page
header metadata. All thrift structures are serialized using the TCompactProtocol.

Types

The types supported by the file format are intended to be as minimal as possible,
with a focus on how the types effect on disk storage. For example, 16-bit ints
are not explicitly supported in the storage format since they are covered by
32-bit ints with an efficient encoding. This reduces the complexity of implementing
readers and writers for the format. The types are:

• BOOLEAN: 1 bit boolean
• INT32: 32 bit signed ints
• INT64: 64 bit signed ints
• INT96: 96 bit signed ints
• FLOAT: IEEE 32-bit floating point values
• DOUBLE: IEEE 64-bit floating point values
• BYTE_ARRAY: arbitrarily long byte arrays.

Logical Types

Logical types are used to extend the types that parquet can be used to store,
by specifying how the primitive types should be interpreted. This keeps the set
of primitive types to a minimum and reuses parquet's efficient encodings. For
example, strings are stored as byte arrays (binary) with a UTF8 annotation.
These annotations define how to further decode and interpret the data.
Annotations are stored as ConvertedType fields in the file metadata and are
documented in
LogicalTypes.md.

Nested Encoding

To encode nested columns, Parquet uses the Dremel encoding with definition and
repetition levels. Definition levels specify how many optional fields in the
path for the column are defined. Repetition levels specify at what repeated field
in the path has the value repeated. The max definition and repetition levels can
be computed from the schema (i.e. how much nesting there is). This defines the
maximum number of bits required to store the levels (levels are defined for all
values in the column).

Two encodings for the levels are supported BIT_PACKED and RLE. Only RLE is now used as it supersedes BIT_PACKED.

Nulls

Nullity is encoded in the definition levels (which is run-length encoded). NULL values
are not encoded in the data. For example, in a non-nested schema, a column with 1000 NULLs
would be encoded with run-length encoding (0, 1000 times) for the definition levels and
nothing else.

Data Pages

For data pages, the 3 pieces of information are encoded back to back, after the page
header.
In order we have:

1. repetition levels data
2. definition levels data
3. encoded values

The value of uncompressed_page_size specified in the header is for all the 3 pieces combined.

The encoded values for the data page is always required. The definition and repetition levels
are optional, based on the schema definition. If the column is not nested (i.e.
the path to the column has length 1), we do not encode the repetition levels (it would
always have the value 1). For data that is required, the definition levels are
skipped (if encoded, it will always have the value of the max definition level).

For example, in the case where the column is non-nested and required, the data in the
page is only the encoded values.

The supported encodings are described in Encodings.md

Column chunks

Column chunks are composed of pages written back to back. The pages share a common
header and readers can skip over pages they are not interested in. The data for the
page follows the header and can be compressed and/or encoded. The compression and
encoding is specified in the page metadata.

Additionally, files can contain an optional column index to allow readers to
skip pages more efficiently. See PageIndex.md for details and
the reasoning behind adding these to the format.

Checksumming

Data pages can be individually checksummed. This allows disabling of checksums at the
HDFS file level, to better support single row lookups. Data page checksums are calculated
using the standard CRC32 algorithm on the compressed data of a page (not including the
page header itself).

Error recovery

If the file metadata is corrupt, the file is lost. If the column metadata is corrupt,
that column chunk is lost (but column chunks for this column in other row groups are
okay). If a page header is corrupt, the remaining pages in that chunk are lost. If
the data within a page is corrupt, that page is lost. The file will be more
resilient to corruption with smaller row groups.

Potential extension: With smaller row groups, the biggest issue is placing the file
metadata at the end. If an error happens while writing the file metadata, all the
data written will be unreadable. This can be fixed by writing the file metadata
every Nth row group.
Each file metadata would be cumulative and include all the row groups written so
far. Combining this with the strategy used for rc or avro files using sync markers,
a reader could recover partially written files.

Separating metadata and column data.

The format is explicitly designed to separate the metadata from the data. This
allows splitting columns into multiple files, as well as having a single metadata
file reference multiple parquet files.

Configurations

• Row group size: Larger row groups allow for larger column chunks which makes it
  possible to do larger sequential IO. Larger groups also require more buffering in
  the write path (or a two pass write). We recommend large row groups (512MB - 1GB).
  Since an entire row group might need to be read, we want it to completely fit on
  one HDFS block. Therefore, HDFS block sizes should also be set to be larger. An
  optimized read setup would be: 1GB row groups, 1GB HDFS block size, 1 HDFS block
  per HDFS file.
• Data page size: Data pages should be considered indivisible so smaller data pages
  allow for more fine grained reading (e.g. single row lookup). Larger page sizes
  incur less space overhead (less page headers) and potentially less parsing overhead
  (processing headers). Note: for sequential scans, it is not expected to read a page
  at a time; this is not the IO chunk. We recommend 8KB for page sizes.

Extensibility

There are many places in the format for compatible extensions:

• File Version: The file metadata contains a version.
• Encodings: Encodings are specified by enum and more can be added in the future.
• Page types: Additional page types can be added and safely skipped.

Contributing

Comment on the issue and/or contact the parquet-dev mailing list with your questions and ideas.
Changes to this core format definition are proposed and discussed in depth on the mailing list. You may also be interested in contributing to the Parquet-MR subproject, which contains all the Java-side implementation and APIs. See the "How To Contribute" section of the Parquet-MR project

Code of Conduct

We hold ourselves and the Parquet developer community to a code of conduct as described by Twitter OSS: https://github.com/twitter/code-of-conduct/blob/master/code-of-conduct.md.

License

Copyright 2013 Twitter, Cloudera and other contributors.

Licensed under the Apache License, Version 2.0: http://www.apache.org/licenses/LICENSE-2.0