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 build 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-genned 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. These live in a particular row group and is 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 is interleaved in a column chunk.

Hierarchically, a file consists of one or more rows 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
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 metdata can be found in the thrift files.

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.

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 is there). 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 in the initial version.

Bit-packed

The first is a bit-packed encoding. Each level is encoding in the minimum number of bits and simply encoded back to back. This is no padding between values (except the last byte). For example, if the max repetition level was 3 (2 bits) and the max definition level as 3 (2 bits), to encode 30 values, we would have 30 * 2 = 60 bits = 8 bytes.

RLE

The second encoding is bit-packed run-length-encoding. The run length encoding is serialized as follows:

let max be the maximum definition level (determined by the schema)
let w be the width in bits required to encode a definition level value. w = ceil(log2(max + 1))
If the value is repeated we store:
1 as one bit
the value encoded in w bits
the repetition count as an unsigned var int. (see ULEB128: http://en.wikipedia.org/wiki/Variable-length_quantity)
If the value is not repeated (or not repeated enough so that the above scheme would be more compact)
0 as one bit
the value encoded in w bits

To sum up:

the first bit is 1 if we're storing a repeated value [1][value][count]
it is 0 if we're storing the value without repetition count [0][value]
0 or 1 is stored as 1 bit
value is stored as w bits
count is stored as var int

The size of all the RLE data comes before the encoded data. The length is encoded in little endian.

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. We have the

definition levels data,
repetition levels data,
encoded values. The size of specified in the header is for all 3 pieces combined.

The data for the data page is always required. The definition and reptition 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 reptition 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.

Column chunks

Column chunks are composed of pages written back to back. The pages share a common header and readers can skip over page 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.

Checksumming

Data pages can be individually checksummed. This allows disabling of checksums at the HDFS file level, to better support single row lookups.

Error recovery

If the file metadata is corrupt, the file is lost. If the column metdata is corrupt, that column chunk is lost (but column chunks for this column in order 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 lowing the file metadata at the end. If this happens in the write path, 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 recovery 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 (512GB - 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.

License

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

baeeq / parquet-format Goto Github PK

parquet-format's Introduction