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Programming language: Scala
Tags: Big Data    
Latest version: v0.8.0

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README

Scio

Build Status codecov.io [GitHub license](./LICENSE) Maven Central Scaladoc Join the chat at https://gitter.im/spotify/scio Scala Steward badge

Ecclesiastical Latin IPA: /ˈʃi.o/, [ˈʃiː.o], [ˈʃi.i̯o] Verb: I can, know, understand, have knowledge.

Scio is a Scala API for Apache Beam and Google Cloud Dataflow inspired by Apache Spark and Scalding.

Scio 0.3.0 and future versions depend on Apache Beam (org.apache.beam) while earlier versions depend on Google Cloud Dataflow SDK (com.google.cloud.dataflow). See this page for a list of breaking changes.

Features

  • Scala API close to that of Spark and Scalding core APIs
  • Unified batch and streaming programming model
  • Fully managed service*
  • Integration with Google Cloud products: Cloud Storage, BigQuery, Pub/Sub, Datastore, Bigtable
  • JDBC, TensorFlow TFRecords, Cassandra, Elasticsearch and Parquet I/O
  • Interactive mode with Scio REPL
  • Type safe BigQuery
  • Integration with Algebird and Breeze
  • Pipeline orchestration with Scala Futures
  • Distributed cache

* provided by Google Cloud Dataflow

Quick Start

Download and install the Java Development Kit (JDK) version 8.

Use our giter8 template to quickly create a new Scio job repository:

sbt new spotify/scio.g8

Switch to the new repo (default scio-job) and build it:

cd scio-job
sbt pack

Run the included word count example:

target/pack/bin/word-count --output=wc

List result files and inspect content:

ls -l wc
cat wc/part-00000-of-00001.txt

Documentation

Getting Started is the best place to start with Scio. If you are new to Apache Beam and distributed data processing, check out the Beam Programming Guide first for a detailed explanation of the Beam programming model and concepts. If you have experience with other Scala data processing libraries, check out this comparison between Scio, Scalding and Spark. Finally check out this document about the relationship between Scio, Beam and Dataflow.

Example Scio pipelines and tests can be found under scio-examples. A lot of them are direct ports from Beam's Java examples. See this page for some of them with side-by-side explanation. Also see Big Data Rosetta Code for common data processing code snippets in Scio, Scalding and Spark.

Artifacts

Scio includes the following artifacts:

  • scio-core: core library
  • scio-test: test utilities, add to your project as a "test" dependency
  • scio-avro: add-on for Avro, can also be used standalone
  • scio-bigquery: add-on for BigQuery, can also be used standalone
  • scio-bigtable: add-on for Bigtable
  • scio-cassandra*: add-ons for Cassandra
  • scio-elasticsearch*: add-ons for Elasticsearch
  • scio-extra: extra utilities for working with collections, Breeze, etc.
  • scio-jdbc: add-on for JDBC IO
  • scio-parquet: add-on for Parquet
  • scio-tensorflow: add-on for TensorFlow TFRecords IO and prediction

License

Copyright 2016 Spotify AB.

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


*Note that all licence references and agreements mentioned in the Scio README section above are relevant to that project's source code only.