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## What changes were proposed in this pull request? In the PR, I propose to use `uuuu` for years instead of `yyyy` in date/timestamp patterns without the era pattern `G` (https://docs.oracle.com/javase/8/docs/api/java/time/format/DateTimeFormatter.html). **Parsing/formatting of positive years (current era) will be the same.** The difference is in formatting negative years belong to previous era - BC (Before Christ). I replaced the `yyyy` pattern by `uuuu` everywhere except: 1. Test, Suite & Benchmark. Existing tests must work as is. 2. `SimpleDateFormat` because it doesn't support the `uuuu` pattern. 3. Comments and examples (except comments related to already replaced patterns). Before the changes, the year of common era `100` and the year of BC era `-99`, showed similarly as `100`. After the changes negative years will be formatted with the `-` sign. Before: ```Scala scala> Seq(java.time.LocalDate.of(-99, 1, 1)).toDF().show +----------+ | value| +----------+ |0100-01-01| +----------+ ``` After: ```Scala scala> Seq(java.time.LocalDate.of(-99, 1, 1)).toDF().show +-----------+ | value| +-----------+ |-0099-01-01| +-----------+ ``` ## How was this patch tested? By existing test suites, and added tests for negative years to `DateFormatterSuite` and `TimestampFormatterSuite`. Closes #25230 from MaxGekk/year-pattern-uuuu. Authored-by: Maxim Gekk <max.gekk@gmail.com> Signed-off-by: Dongjoon Hyun <dhyun@apple.com> |
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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, MLlib for machine learning, GraphX for graph processing, and Structured 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 set up 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 0.10.8.1), but some additional sub-packages have their own extra requirements for some features (including numpy, pandas, and pyarrow).