Introducing Parquet: Efficient Columnar Storage for Apache Hadoop
Below you’ll find the official announcement from Cloudera and Twitter about Parquet, an efficient general-purpose columnar file format for Apache Hadoop.
Parquet is designed to bring efficient columnar storage to Hadoop. Compared to, and learning from, the initial work done toward this goal in Trevni, Parquet includes the following enhancements:
- Efficiently encode nested structures and sparsely populated data based on the Google Dremel definition/repetition levels
- Provide extensible support for per-column encodings (e.g. delta, run length, etc)
- Provide extensibility of storing multiple types of data in column data (e.g. indexes, bloom filters, statistics)
- Offer better write performance by storing metadata at the end of the file
Based on feedback from the Impala beta and after a joint evaluation with Twitter, we determined that these further improvements to the Trevni design were necessary to provide a more efficient format that we can evolve going forward for production usage. Furthermore, we found it appropriate to host and develop the columnar file format outside of the Avro project (unlike Trevni, which is part of Avro) because Avro is just one of many input data formats that can be used with Parquet.
We’d like to introduce a new columnar storage format for Hadoop called Parquet, which started as a joint project between Twitter and Cloudera engineers.
We created Parquet to make the advantages of compressed, efficient columnar data representation available to any project in the Hadoop ecosystem, regardless of the choice of data processing framework, data model, or programming language.
Parquet is built from the ground up with complex nested data structures in mind. We adopted the repetition/definition level approach to encoding such data structures, as described in Google’s Dremel paper; we have found this to be a very efficient method of encoding data in non-trivial object schemas.
Parquet is built to support very efficient compression and encoding schemes. 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. We separate the concepts of encoding and compression, allowing Parquet consumers to implement operators that work directly on encoded data without paying decompression and decoding penalty when possible.
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.
The initial code defines the file format, provides Java building blocks for processing columnar data, and implements Hadoop Input/Output Formats, Pig Storers/Loaders, and an example of a complex integration — Input/Output formats that can convert Parquet-stored data directly to and from Thrift objects.
A preview version of Parquet support will be available in Cloudera’s Impala 0.7.
Twitter is starting to convert some of its major data source to Parquet in order to take advantage of the compression and deserialization savings.
Parquet is currently under heavy development. Parquet’s near-term roadmap includes:
- Hive SerDes (Criteo)
- Cascading Taps (Criteo)
- Support for dictionary encoding, zigzag encoding, and RLE encoding of data (Cloudera and Twitter)
- Further improvements to Pig support (Twitter)
Company names in parenthesis indicate whose engineers signed up to do the work — others can feel free to jump in too, of course.
We’ve also heard requests to provide an Avro container layer, similar to what we do with Thrift. Seeking volunteers!
We welcome all feedback, patches, and ideas; to foster community development, we plan to contribute Parquet to the Apache Incubator when the development is farther along.
Nong Li (Cloudera)
Julien Le Dem (Twitter)
Marcel Kornacker (Cloudera)
Todd Lipcon (Cloudera)
Dmitriy Ryaboy (Twitter)
Jonathan Coveney (Twitter)
Justin Coffey (Criteo)
Mickaël Lacour (Criteo)