Parquet-MR contains the java implementation of the Parquet format. Parquet is a columnar storage format for Hadoop; it provides efficient storage and encoding of data. Parquet uses the record shredding and assembly algorithm described in the Dremel paper to represent nested structures.

You can find some details about the format and intended use cases in our Hadoop Summit 2013 presentation

Parquet-MR uses Maven to build and depends on both the thrift and protoc compilers.

To build and install the protobuf compiler, run:

wget https://github.com/google/protobuf/releases/download/v2.5.0/protobuf-2.5.0.tar.gz
tar xzf protobuf-2.5.0.tar.gz
cd  protobuf-2.5.0
./configure
make
sudo make install
sudo ldconfig

To build and install the thrift compiler, run:

wget -nv https://archive.apache.org/dist/thrift/0.7.0/thrift-0.7.0.tar.gz
tar xzf thrift-0.7.0.tar.gz
cd thrift-0.7.0
chmod +x ./configure
./configure --disable-gen-erl --disable-gen-hs --without-ruby --without-haskell --without-erlang
sudo make install

Once protobuf and thrift are available in your path, you can build the project by running:

LC_ALL=C mvn clean install

Parquet is a very active project, and new features are being added quickly; below is the state as of June 2013.

Input and Output formats. Note that to use an Input or Output format, you need to implement a WriteSupport or ReadSupport class, which will implement the conversion of your object to and from a Parquet schema.

We've implemented this for 2 popular data formats to provide a clean migration path as well:

Thrift integration is provided by the parquet-thrift sub-project. If you are using Thrift through Scala, you may be using Twitter's Scrooge. If that's the case, not to worry -- we took care of the Scrooge/Apache Thrift glue for you in the parquet-scrooge sub-project.

Avro conversion is implemented via the parquet-avro sub-project.

• The ParquetOutputFormat can be provided a WriteSupport to write your own objects to an event based RecordConsumer.
• the ParquetInputFormat can be provided a ReadSupport to materialize your own objects by implementing a RecordMaterializer

See the APIs:

• Record conversion API
• Hadoop API

A Loader and a Storer are provided to read and write Parquet files with Apache Pig

Storing data into Parquet in Pig is simple:

-- options you might want to fiddle with
SET parquet.page.size 1048576 -- default. this is your min read/write unit.
SET parquet.block.size 134217728 -- default. your memory budget for buffering data
SET parquet.compression lzo -- or you can use none, gzip, snappy
STORE mydata into '/some/path' USING parquet.pig.ParquetStorer;

Reading in Pig is also simple:

mydata = LOAD '/some/path' USING parquet.pig.ParquetLoader();

If the data was stored using Pig, things will "just work". If the data was stored using another method, you will need to provide the Pig schema equivalent to the data you stored (you can also write the schema to the file footer while writing it -- but that's pretty advanced). We will provide a basic automatic schema conversion soon.

Hive integration is provided via the parquet-hive sub-project.

to run the unit tests: mvn test

to build the jars: mvn package

The build runs in Travis CI:

The current release is version 1.8.1

  <dependencies>
    <dependency>
      <groupId>org.apache.parquet</groupId>
      <artifactId>parquet-common</artifactId>
      <version>1.8.1</version>
    </dependency>
    <dependency>
      <groupId>org.apache.parquet</groupId>
      <artifactId>parquet-encoding</artifactId>
      <version>1.8.1</version>
    </dependency>
    <dependency>
      <groupId>org.apache.parquet</groupId>
      <artifactId>parquet-column</artifactId>
      <version>1.8.1</version>
    </dependency>
    <dependency>
      <groupId>org.apache.parquet</groupId>
      <artifactId>parquet-hadoop</artifactId>
      <version>1.8.1</version>
    </dependency>
  </dependencies>

We prefer to receive contributions in the form of GitHub pull requests. Please send pull requests against the github.com/apache/parquet-mr repository. If you've previously forked Parquet from its old location, you will need to add a remote or update your origin remote to https://github.com/apache/parquet-mr.git

If you are looking for some ideas on what to contribute, check out jira issues for this project labeled "pick-me-up". Comment on the issue and/or contact [email protected] with your questions and ideas.

If you’d like to report a bug but don’t have time to fix it, you can still post it to our issue tracker, or email the mailing list [email protected]

To contribute a patch:

1. Break your work into small, single-purpose patches if possible. It’s much harder to merge in a large change with a lot of disjoint features.
2. Create a JIRA for your patch on the Parquet Project JIRA.
3. Submit the patch as a GitHub pull request against the master branch. For a tutorial, see the GitHub guides on forking a repo and sending a pull request. Prefix your pull request name with the JIRA name (ex: apache#240).
4. Make sure that your code passes the unit tests. You can run the tests with mvn test in the root directory.
5. Add new unit tests for your code.

We tend to do fairly close readings of pull requests, and you may get a lot of comments. Some common issues that are not code structure related, but still important:

• Use 2 spaces for whitespace. Not tabs, not 4 spaces. The number of the spacing shall be 2.
• Give your operators some room. Not a+b but a + b and not foo(int a,int b) but foo(int a, int b).
• Generally speaking, stick to the Sun Java Code Conventions
• Make sure tests pass!

Thank you for getting involved!

• Contributors
• Committers

We hold ourselves and the Parquet developer community to two codes of conduct:

1. The Apache Software Foundation Code of Conduct
2. The Twitter OSS Code of Conduct

• Mailing list: [email protected]
• Bug trackter: jira
• Discussions also take place in github pull requests

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