![]() ![]() ( SPARK-34863)Īllows users to query the metadata of the input files for all file formats, expose them as built-in hidden columns, meaning users can only see them when they explicitly reference them. In addition, this implementation can help improve the non-nested column performance when reading non-nested and nested columns together in one query. It impacts performance improvements compared to a nonvectorized reader when reading nested columns. The Apache Spark 3.3 contains an implementation of nested column vectorized reader for FB-ORC in our internal fork of Spark. Previously, Parquet vectorized reader didn't support nested column types like struct, array, and map. Support complex types for Parquet vectorized reader. ( SPARK-38860)Įrror message improvements to identify problems faster and take the necessary steps to resolve them. Improve the compatibility of Spark with the SQL standard: ANSI enhancements. Row-level filtering: improve the performance of joins by prefiltering one side, as long as there are no deprecation or regression impacts on using a Bloom filter and IN predicate generated from the values from the other side of the join. The following extended summary describes key new features related to Apache Spark version 3.3.0 and 3.3.1: New features and improvements - Apache Spark 3.3.1 ![]() If you are currently using Runtime 1.1, you can upgrade to Runtime 1.2 by navigating to Workspace Settings > Data Engineering / Science > Spark Settings. ![]() Ensuring you stay up to date with these updates guarantees optimal performance and reliability for your data processing tasks. Microsoft Fabric periodically releases maintenance updates for Runtime 1.1, delivering bug fixes, performance enhancements, and security patches. Refer to the documentation for a complete list of libraries. These libraries are automatically included when using notebooks or jobs in the Microsoft Fabric platform. Microsoft Fabric Runtime 1.1 comes with a collection of default level packages, including a full Anaconda installation and commonly used libraries for Java/Scala, Python, and R. Microsoft Fabric Runtime 1.1 is one of the runtimes offered within the Microsoft Fabric platform. This document covers the Fabric Runtime 1.1 components and versions. Microsoft Fabric Runtime is an Azure-integrated platform based on Apache Spark that enables the execution and management of the Data Engineering and Data Science experiences in Fabric. ![]()
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |