e4065376d2
- Add dependency on aws-java-sdk-sts - Replace SerializableAWSCredentials with new SerializableCredentialsProvider interface - Make KinesisReceiver take SerializableCredentialsProvider as argument and pass credential provider to KCL - Add new implementations of KinesisUtils.createStream() that take STS arguments - Make JavaKinesisStreamSuite test the entire KinesisUtils Java API - Update KCL/AWS SDK dependencies to 1.7.x/1.11.x ## What changes were proposed in this pull request? [JIRA link with detailed description.](https://issues.apache.org/jira/browse/SPARK-19405) * Replace SerializableAWSCredentials with new SerializableKCLAuthProvider class that takes 5 optional config params for configuring AWS auth and returns the appropriate credential provider object * Add new public createStream() APIs for specifying these parameters in KinesisUtils ## How was this patch tested? * Manually tested using explicit keypair and instance profile to read data from Kinesis stream in separate account (difficult to write a test orchestrating creation and assumption of IAM roles across separate accounts) * Expanded JavaKinesisStreamSuite to test the entire Java API in KinesisUtils ## License acknowledgement This contribution is my original work and that I license the work to the project under the project’s open source license. Author: Budde <budde@amazon.com> Closes #16744 from budde/master. |
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Apache Spark
Spark is a fast and general cluster computing system for Big Data. 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, MLlib for machine learning, GraphX for graph processing, and Spark Streaming for stream processing.
Online Documentation
You can find the latest Spark documentation, including a programming guide, on the project web page
Python Packaging
This README file only contains basic information related to pip installed PySpark. This packaging is currently experimental and may change in future versions (although we will do our best to keep compatibility). Using PySpark requires the Spark JARs, and if you are building this from source please see the builder instructions at "Building Spark".
The Python packaging for Spark is not intended to replace all of the other use cases. This Python packaged version of Spark is suitable for interacting with an existing cluster (be it Spark standalone, YARN, or Mesos) - but does not contain the tools required to setup your own standalone Spark cluster. You can download the full version of Spark from the Apache Spark downloads page.
NOTE: If you are using this with a Spark standalone cluster you must ensure that the version (including minor version) matches or you may experience odd errors.
Python Requirements
At its core PySpark depends on Py4J (currently version 0.10.4), but additional sub-packages have their own requirements (including numpy and pandas).