Delta Lake is a storage layer that brings scalable, ACID transactions to Apache Spark and other big-data engines.
See the Delta Lake Documentation for details.
See the Quick Start Guide to get started with Scala, Java and Python.
Starting from 0.7.0, Delta Lake is only available with Scala version 2.12.
<dependency> <groupId>io.delta</groupId> <artifactId>delta-core_2.12</artifactId> <version>0.8.0</version> </dependency>
You include Delta Lake in your SBT project by adding the following line to your build.sbt file:
libraryDependencies += "io.delta" %% "delta-core" % "0.8.0"
Compatibility with Apache Spark Versions
Delta Lake currently requires Apache Spark 3.0.0
The only stable public APIs, currently provided by Delta Lake, are through the
df.writeStream). Options to these APIs will remain stable within a major release of Delta Lake (e.g., 1.x.x).
All other interfaces in this library are considered internal, and they are subject to change across minor/patch releases.
Data Storage Compatibility
Delta Lake guarantees backward compatibility for all Delta Lake tables (i.e., newer versions of Delta Lake will always be able to read tables written by older versions of Delta Lake). However, we reserve the right to break forward compatibility as new features are introduced to the transaction protocol (i.e., an older version of Delta Lake may not be able to read a table produced by a newer version).
Breaking changes in the protocol are indicated by incrementing the minimum reader/writer version in the
Delta Lake is a recent open-source project based on technology developed at Databricks. We plan to open-source all APIs that are required to correctly run Spark programs that read and write Delta tables. For a detailed timeline on this effort see the project roadmap.
Delta Lake Core is compiled using SBT.
To compile, run
To generate artifacts, run
To execute tests, run
Refer to SBT docs for more commands.
Delta Transaction Log Protocol document provides a specification of the transaction protocol.
Requirements for Underlying Storage Systems
Delta Lake ACID guarantees are predicated on the atomicity and durability guarantees of the storage system. Specifically, we require the storage system to provide the following.
- Atomic visibility: There must be a way for a file to be visible in its entirety or not visible at all.
- Mutual exclusion: Only one writer must be able to create (or rename) a file at the final destination.
- Consistent listing: Once a file has been written in a directory, all future listings for that directory must return that file.
Given that storage systems do not necessarily provide all of these guarantees out-of-the-box, Delta Lake transactional operations typically go through the LogStore API instead of accessing the storage system directly. We can plug in custom
LogStore implementations in order to provide the above guarantees for different storage systems. Delta Lake has built-in
LogStore implementations for HDFS, Amazon S3, Azure, Google Cloud Storage, Oracle Cloud Infrastructure, and IBM Cloud Object Storage. Please see Delta Lake Storage Configuration for more details. If you are interested in adding a custom
LogStore implementation for your storage system, you can start discussions in the community mailing group.
As an optimization, storage systems can also allow partial listing of a directory, given a start marker. Delta Lake can use this ability to efficiently discover the latest version of a table, without listing all of the files in the transaction log.
Delta Lake ensures serializability for concurrent reads and writes. Please see Delta Lake Concurrency Control for more details.
We welcome contributions to Delta Lake. See our CONTRIBUTING.md for more details.
We also adhere to the Delta Lake Code of Conduct.
Apache License 2.0, see LICENSE.
There are two mediums of communication within the Delta Lake community.