Skip to content

spotify/scio

Folders and files

NameName
Last commit message
Last commit date

Latest commit

b974fc8 · Jun 3, 2020
May 11, 2020
Jun 3, 2020
May 27, 2020
May 13, 2020
Nov 20, 2019
May 21, 2020
May 13, 2020
May 13, 2020
Jun 1, 2020
May 13, 2020
May 15, 2020
Jun 1, 2020
Feb 25, 2020
May 13, 2020
May 11, 2020
May 13, 2020
May 13, 2020
May 13, 2020
May 21, 2020
May 13, 2020
May 13, 2020
May 13, 2020
Jun 1, 2020
May 13, 2020
May 20, 2020
Dec 6, 2019
Oct 1, 2019
May 11, 2020
Dec 21, 2018
May 26, 2020
Jul 12, 2019
Oct 3, 2019
Feb 19, 2016
Feb 23, 2016
Jan 15, 2020
Aug 30, 2019
Jun 3, 2020
Nov 19, 2018
Jun 3, 2020

Repository files navigation

Scio

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

Scio Logo

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., best effort support
  • 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