spark-alchemy

Spark Alchemy is a collection of open-source Spark tools & frameworks that have made the data engineering and data science teams at Swoop highly productive in our demanding petabyte-scale environment with rich data (thousands of columns).

Supported languages

While spark-alchemy, like Spark itself, is written in Scala, much of its functionality, such as interoperable HyperLogLog functions, can be used from other Spark-supported languages such as SparkSQL and Python.

Installation

Add the following to your libraryDependencies in SBT:

libraryDependencies += "com.swoop" %% "spark-alchemy" % "1.0.1"

You can find all released versions here.

Some use cases such as interoperability with PySpark may require the assembly of a fat JAR of spark-alchemy. To assemble, run sbt assembly. To skip tests during assembly, run sbt 'set sbt.Keys.test in assembly := {}' assembly instead.

For Spark users

Native HyperLogLog functions that offer reaggregatable fast approximate distinct counting capabilities far beyond those in OSS Spark with interoperability to Postgres and even JavaScript. Just as Spark's own native functions, once the functions are registered with Spark, they can be used from SparkSQL, Python, etc.

For Spark framework developers

Helpers for native function registration
Look at SparkSessionSpec as an example of how to reuse advanced Spark testing functionality from OSS Spark.

For Python developers

See HyperLogLog functions for an example of how spark-alchemy HLL functions can be registered for use through PySpark.

What we hope to open source in the future, if we have the bandwidth

Configuration Addressable Production (CAP), Automatic Lifecycle Management (ALM) and Just-in-time Dependency Resolution (JDR) as outlined in our Spark+AI Summit talk Unafraid of Change: Optimizing ETL, ML, and AI in Fast-Paced Environments.
Utilities that make Delta Lake development substantially more productive.
Hundreds of productivity-enhancing extensions to the core user-level data types: Column, Dataset, SparkSession, etc.
Data discovery and cleansing tools we use to ingest and clean up large amounts of dirty data from third parties.
Cross-cluster named lock manager, which simplifies data production by removing the need for workflow servers much of the time.
case class code generation from Spark schema, with easy implementation customization.
Tools for deploying Spark ML pipelines to production.

Development

Build docs microsite

sbt "project docs" makeMicrosite

Run docs microsite locally (run under docs/target/site folder)

jekyll serve -b /spark-alchemy

More details

Community & contributing

Contributions and feedback of any kind are welcome. Please, create an issue and/or pull request.

Spark Alchemy is maintained by the team at Swoop. If you'd like to contribute to our open-source efforts, by joining our team or from your company, let us know at spark-interest at swoop dot com.

swoop-inc / spark-alchemy

readme