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## What changes were proposed in this pull request? Adding date_trunc() as a built-in function. `date_trunc` is common in other databases, but Spark or Hive does not have support for this. `date_trunc` is commonly used by data scientists and business intelligence application such as Superset (https://github.com/apache/incubator-superset). We do have `trunc` but this only works with 'MONTH' and 'YEAR' level on the DateType input. date_trunc() in other databases: AWS Redshift: http://docs.aws.amazon.com/redshift/latest/dg/r_DATE_TRUNC.html PostgreSQL: https://www.postgresql.org/docs/9.1/static/functions-datetime.html Presto: https://prestodb.io/docs/current/functions/datetime.html ## How was this patch tested? Unit tests (Please explain how this patch was tested. E.g. unit tests, integration tests, manual tests) (If this patch involves UI changes, please attach a screenshot; otherwise, remove this) Please review http://spark.apache.org/contributing.html before opening a pull request. Author: Youngbin Kim <ykim828@hotmail.com> Closes #20015 from youngbink/date_trunc. |
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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 0.10.6), but additional sub-packages have their own requirements (including numpy and pandas).