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**Goal:** Add the ability to start structured streaming from a user provided version as outlined in #474

**Desc:** This change will introduce `startingVersion` and `startingTimestamp` to the Delta Stream Reader so that users can define a starting point of their stream source without processing the entire table.

**What's the behavior if both startingVersion and startingTimestamp are set?** Both options cannot be provided together. An error will be thrown telling you to choose only one.

**When a query restarts, how does the new options work?** If a query restarts and a checkpoint location is provided, the stream will start from the checkpoints offset as opposed to the provided option.

**If the user uses an old Delta Lake version to read the checkpoint, what will happen?** If we use a newer version of Delta with this option, we will store an offset that looks like: Process up to this version and this index. If the stream fails and someone reverts to an older version of Delta, we would try to process ALL the data in the snapshot up to the end offset which we came up with.

Credit to brkyvz for a large percentage of the work.

Closes #478

Signed-off-by: Burak Yavuz <brkyvz@gmail.com>

Author: Burak Yavuz <brkyvz@gmail.com>
Author: Wesley Hoffman <wesleyhoffman109@gmail.com>

GitOrigin-RevId: c654217a70b9f6bbee6e96a079223a693dd85e8a
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README.md

Delta Lake Logo

CircleCI

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.

Latest Binaries

Maven

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>0.7.0</version>
</dependency>

SBT

You include Delta Lake in your SBT project by adding the following line to your build.sbt file:

libraryDependencies += "io.delta" %% "delta-core" % "0.7.0"

API Documentation

Compatibility

Compatibility with Apache Spark Versions

Delta Lake currently requires Apache Spark 3.0.0

API Compatibility

The only stable public APIs, currently provided by Delta Lake, are through the DataFrameReader/Writer (i.e. spark.read, df.write, spark.readStream and 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 forwards 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 Protocol action.

Roadmap

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.

Building

Delta Lake Core is compiled using SBT.

To compile, run

build/sbt compile

To generate artifacts, run

build/sbt package

To execute tests, run

build/sbt test

Refer to SBT docs for more commands.

Transaction Protocol

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.

  1. Atomic visibility: There must be a way for a file to be visible in its entirety or not visible at all.
  2. Mutual exclusion: Only one writer must be able to create (or rename) a file at the final destination.
  3. 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 and Azure storage services. 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.

Concurrency Control

Delta Lake ensures serializability for concurrent reads and writes. Please see Delta Lake Concurrency Control for more details.

Reporting issues

We use GitHub Issues to track community reported issues. You can also contact the community for getting answers.

Contributing

We welcome contributions to Delta Lake. See our CONTRIBUTING.md for more details.

License

Apache License 2.0, see LICENSE.

Community

There are two mediums of communication within the Delta Lake community.

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An open-source storage layer that brings scalable, ACID transactions to Apache Spark™ and big data workloads.

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