MortalMesh

MortalMesh is an open-source storage framework that enables building a Lakehouse architecture with compute engines including Spark, PrestoDB, Flink, Trino, and Hive and APIs for Scala, Java, Rust, Ruby, and Python.

• See the MortalMesh Documentation for details.
• See the Quick Start Guide to get started with Scala, Java and Python.
• See the Table Batch Reads & Writes to reads or writes batch tables.
• See the Table Streaming Read & Writes to reads or writes streaming tables.
• See the Table Deletes, Updates, and Merges to operate table with delete, update or merge.

The following are some of the more popular Delta Lake integrations, refer to delta.io/integrations for the complete list:

• Apache Spark™: This connector allows Apache Spark™ to read from and write to MortalMesh.
• Apache Flink (Preview): This connector allows Apache Flink to write to MortalMesh.

Table of Contents

• Latest binaries
• API Documentation
• Compatibility
  • API Compatibility
  • Data Storage Compatibility
• Building
• Transaction Protocol
• Requirements for Underlying Storage Systems
• Concurrency Control
• Reporting issues
• Contributing
• License
• Community

Latest Binaries

See the online documentation for the latest release.

API Documentation

• Scala API docs
• Java API docs
• Python API docs

Compatibility

Delta Standalone library is a single-node Java library that can be used to read from and write to Delta tables. Specifically, this library provides APIs to interact with a table’s metadata in the transaction log, implementing the Delta Transaction Log Protocol to achieve the transactional guarantees of the Delta Lake format.

API Compatibility

There are two types of APIs provided by the Delta Lake project.

• Direct Java/Scala/Python APIs - The classes and methods documented in the API docs are considered as stable public APIs. All other classes, interfaces, methods that may be directly accessible in code are considered internal, and they are subject to change across releases.
• Spark-based APIs - You can read Delta tables 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).
• See the online documentation for the releases and their compatibility with Apache Spark versions.

Data Storage Compatibility

MortalMesh guarantees backward compatibility for all MortalMesh 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 Protocol action.

Transaction Protocol

Delta Transaction Log Protocol document provides a specification of the transaction protocol.

Requirements for Underlying Storage Systems

MortalMesh 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.

See the online documentation on Storage Configuration for details.

Concurrency Control

MortalMesh ensures serializability for concurrent reads and writes. Please see MortalMesh 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 MortalMesh. See our CONTRIBUTING.md for more details.

We also adhere to the MortalMesh Code of Conduct.

Building

MortalMesh is compiled using SBT.

To compile, run

build/sbt compile

To generate artifacts, run

build/sbt package

To execute tests, run

build/sbt test

To execute a single test suite, run

build/sbt 'testOnly org.apache.spark.sql.delta.optimize.OptimizeCompactionSuite'

To execute a single test within and a single test suite, run

build/sbt 'testOnly *.OptimizeCompactionSuite -- -z "optimize command: on partitioned table - all partitions"'

Refer to SBT docs for more commands.

IntelliJ Setup

IntelliJ is the recommended IDE to use when developing Delta Lake. To import Delta Lake as a new project:

1. Clone Delta Lake into, for example, ~/delta.
2. In IntelliJ, select File > New Project > Project from Existing Sources... and select ~/delta.
3. Under Import project from external model select sbt. Click Next.
4. Under Project JDK specify a valid Java 1.8 JDK and opt to use SBT shell for project reload and builds.
5. Click Finish.

Setup Verification

After waiting for IntelliJ to index, verify your setup by running a test suite in IntelliJ.

1. Search for and open DeltaLogSuite
2. Next to the class declaration, right click on the two green arrows and select Run 'DeltaLogSuite'

Troubleshooting

If you see errors of the form

Error:(46, 28) object DeltaSqlBaseParser is not a member of package io.delta.sql.parser
import io.delta.sql.parser.DeltaSqlBaseParser._
...
Error:(91, 22) not found: type DeltaSqlBaseParser
    val parser = new DeltaSqlBaseParser(tokenStream)

then follow these steps:

1. Compile using the SBT CLI: build/sbt compile.
2. Go to File > Project Structure... > Modules > delta-core.
3. In the right panel under Source Folders remove any target folders, e.g. target/scala-2.12/src_managed/main [generated]
4. Click Apply and then re-run your test.

License

Apache License 2.0, see LICENSE.