Ecosyste.ms: Awesome

An open API service indexing awesome lists of open source software.

Awesome Lists | Featured Topics | Projects

https://github.com/maxgekk/spark

Mirror of Apache Spark
https://github.com/maxgekk/spark

Last synced: about 1 month ago
JSON representation

Mirror of Apache Spark

Awesome Lists containing this project

README 

        
# Apache Spark



Spark is a unified analytics engine for large-scale data processing. It provides

high-level APIs in Scala, Java, Python, and R, and an optimized engine that

supports general computation graphs for data analysis. It also supports a

rich set of higher-level tools including Spark SQL for SQL and DataFrames,

pandas API on Spark for pandas workloads, MLlib for machine learning, GraphX for graph processing,

and Structured Streaming for stream processing.



[![GitHub Actions Build](https://github.com/apache/spark/actions/workflows/build_main.yml/badge.svg)](https://github.com/apache/spark/actions/workflows/build_main.yml)

[![PySpark Coverage](https://codecov.io/gh/apache/spark/branch/master/graph/badge.svg)](https://codecov.io/gh/apache/spark)

[![PyPI Downloads](https://static.pepy.tech/personalized-badge/pyspark?period=month&units=international_system&left_color=black&right_color=orange&left_text=PyPI%20downloads)](https://pypi.org/project/pyspark/)



## Online Documentation



You can find the latest Spark documentation, including a programming

guide, on the [project web page](https://spark.apache.org/documentation.html).

This README file only contains basic setup instructions.



## Building Spark



Spark is built using [Apache Maven](https://maven.apache.org/).

To build Spark and its example programs, run:



```bash

./build/mvn -DskipTests clean package

```



(You do not need to do this if you downloaded a pre-built package.)



More detailed documentation is available from the project site, at

["Building Spark"](https://spark.apache.org/docs/latest/building-spark.html).



For general development tips, including info on developing Spark using an IDE, see ["Useful Developer Tools"](https://spark.apache.org/developer-tools.html).



## Interactive Scala Shell



The easiest way to start using Spark is through the Scala shell:



```bash

./bin/spark-shell

```



Try the following command, which should return 1,000,000,000:



```scala

scala> spark.range(1000 * 1000 * 1000).count()

```



## Interactive Python Shell



Alternatively, if you prefer Python, you can use the Python shell:



```bash

./bin/pyspark

```



And run the following command, which should also return 1,000,000,000:



```python

>>> spark.range(1000 * 1000 * 1000).count()

```



## Example Programs



Spark also comes with several sample programs in the `examples` directory.

To run one of them, use `./bin/run-example  [params]`. For example:



```bash

./bin/run-example SparkPi

```



will run the Pi example locally.



You can set the MASTER environment variable when running examples to submit

examples to a cluster. This can be spark:// URL,

"yarn" to run on YARN, and "local" to run

locally with one thread, or "local[N]" to run locally with N threads. You

can also use an abbreviated class name if the class is in the `examples`

package. For instance:



```bash

MASTER=spark://host:7077 ./bin/run-example SparkPi

```



Many of the example programs print usage help if no params are given.



## Running Tests



Testing first requires [building Spark](#building-spark). Once Spark is built, tests

can be run using:



```bash

./dev/run-tests

```



Please see the guidance on how to

[run tests for a module, or individual tests](https://spark.apache.org/developer-tools.html#individual-tests).



There is also a Kubernetes integration test, see resource-managers/kubernetes/integration-tests/README.md



## A Note About Hadoop Versions



Spark uses the Hadoop core library to talk to HDFS and other Hadoop-supported

storage systems. Because the protocols have changed in different versions of

Hadoop, you must build Spark against the same version that your cluster runs.



Please refer to the build documentation at

["Specifying the Hadoop Version and Enabling YARN"](https://spark.apache.org/docs/latest/building-spark.html#specifying-the-hadoop-version-and-enabling-yarn)

for detailed guidance on building for a particular distribution of Hadoop, including

building for particular Hive and Hive Thriftserver distributions.



## Configuration



Please refer to the [Configuration Guide](https://spark.apache.org/docs/latest/configuration.html)

in the online documentation for an overview on how to configure Spark.



## Contributing



Please review the [Contribution to Spark guide](https://spark.apache.org/contributing.html)

for information on how to get started contributing to the project.