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Apache Spark is an open-source, distributed processing system used for big data workloads. It utilizes in-memory caching, and optimized query execution for fast analytic queries against data of any size.
Apache Spark ™ is a multi-language engine for executing data engineering, data science, and machine learning on single-node machines or clusters. Simple. Fast. Scalable. Unified. Key features. Batch/streaming data. Unify the processing of your data in batches and real-time streaming, using your preferred language: Python, SQL, Scala, Java or R.
- Reducing Build Times
- Running Individual Tests
- Testing with GitHub Actions Workflow
- ScalaTest Issues
- Checking Out Pull Requests
- Organizing Imports
- Formatting Code
- IDE Setup
- Nightly Builds
SBT: Avoiding re-creating the assembly JAR
Spark’s default build strategy is to assemble a jar including all of its dependencies. This can be cumbersome when doing iterative development. When developing locally, it is possible to create an assembly jar including all of Spark’s dependencies and then re-package only Spark itself when making changes.
When developing locally, it’s often convenient to run a single test or a few tests, rather than running the entire test suite.
Apache Spark leverages GitHub Actions that enables continuous integration and a wide range of automation. Apache Spark repository provides several GitHub Actions workflows for developers to run before creating a pull request.
If the following error occurs when running ScalaTest It is due to an incorrect Scala library in the classpath. To fix it: 1. Right click on project 2. Select Build Path | Configure Build Path 3. Add Library | Scala Library 4. Remove scala-library-2.10.4.jar - lib_managed\jars In the event of “Could not find resource path for Web UI: org/apache/spar...
Git provides a mechanism for fetching remote pull requests into your own local repository. This is useful when reviewing code or testing patches locally. If you haven’t yet cloned the Spark Git repository, use the following command: To enable this feature you’ll need to configure the git remote repository to fetch pull request data. Do this by modi...
You can use a IntelliJ Imports Organizerfrom Aaron Davidson to help you organize the imports in your code. It can be configured to match the import ordering from the style guide.
To format Scala code, run the following command prior to submitting a PR: By default, this script will format files that differ from git master. For more information, see scalafmt documentation, but use the existing script not a locally installed version of scalafmt.
IntelliJ
While many of the Spark developers use SBT or Maven on the command line, the most common IDE we use is IntelliJ IDEA. You can get the community edition for free (Apache committers can get free IntelliJ Ultimate Edition licenses) and install the JetBrains Scala plugin from Preferences > Plugins. To create a Spark project for IntelliJ: 1. Download IntelliJ and install the Scala plug-in for IntelliJ. 2. Go to File -> Import Project, locate the spark source directory, and select “Maven Project”....
Debug Spark remotely
This part will show you how to debug Spark remotely with IntelliJ. Follow Run > Edit Configurations > + > Remoteto open a default Remote Configuration template: Normally, the default values should be good enough to use. Make sure that you choose Listen to remote JVMas Debugger mode and select the right JDK version to generate proper Command line arguments for remote JVM. Once you finish configuration and save it. You can follow Run > Run > Your_Remote_Debug_Name > Debugto start remote debugpr...
Eclipse
Eclipse can be used to develop and test Spark. The following configuration is known to work: 1. Eclipse Juno 2. Scala IDE 4.0 3. Scala Test The easiest way is to download the Scala IDE bundle from the Scala IDE download page. It comes pre-installed with ScalaTest. Alternatively, use the Scala IDE update site or Eclipse Marketplace. SBT can create Eclipse .project and .classpathfiles. To create these files for each Spark sub project, use this command: To import a specific project, e.g. spark-c...
Spark publishes SNAPSHOT releases of its Maven artifacts for both master and maintenance branches on a nightly basis. To link to a SNAPSHOT you need to add the ASF snapshot repository to your build. Note that SNAPSHOT artifacts are ephemeral and may change orbe removed. To use these you must add the ASF snapshot repository at https://repository.apa...
What is Apache Spark? An Introduction. Spark is an Apache project advertised as “lightning fast cluster computing”. It has a thriving open-source community and is the most active Apache project at the moment. Spark provides a faster and more general data processing platform.
- Radek Ostrowski
Feb 24, 2019 · Apache Spark — it’s a lightning-fast cluster computing tool. Spark runs applications up to 100x faster in memory and 10x faster on disk than Hadoop by reducing the number of read-write cycles to disk and storing intermediate data in-memory.
- Dilyan Kovachev
Apache Spark is an open-source data-processing engine for large data sets, designed to deliver the speed, scalability and programmability required for big data.
Apache Spark is a unified analytics engine for large-scale data processing with built-in modules for SQL, streaming, machine learning, and graph processing. Spark can run on Apache...
