54 lines
1.7 KiB
Markdown
54 lines
1.7 KiB
Markdown
# What is Apache Spark™?
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Apache Spark™ is a multi-language engine for executing data engineering, data science, and machine learning on single-node machines or clusters. 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.
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%%LOGO%%
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## Online Documentation
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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.
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## Interactive Scala Shell
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The easiest way to start using Spark is through the Scala shell:
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```console
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docker run -it %%IMAGE%% /opt/spark/bin/spark-shell
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```
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Try the following command, which should return 1,000,000,000:
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```scala
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scala> spark.range(1000 * 1000 * 1000).count()
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```
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## Interactive Python Shell
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The easiest way to start using PySpark is through the Python shell:
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```console
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docker run -it %%IMAGE%%:python3 /opt/spark/bin/pyspark
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```
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And run the following command, which should also return 1,000,000,000:
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```python
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>>> spark.range(1000 * 1000 * 1000).count()
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```
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## Interactive R Shell
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The easiest way to start using R on Spark is through the R shell:
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```console
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docker run -it %%IMAGE%%:r /opt/spark/bin/sparkR
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```
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## Running Spark on Kubernetes
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https://spark.apache.org/docs/latest/running-on-kubernetes.html
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## Configuration and environment variables
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See more in https://github.com/apache/spark-docker/blob/master/OVERVIEW.md#environment-variable
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