Which of the following are new features in Spark 3 x?
Table of Contents
Which of the following are new features in Spark 3 x?
Here are the feature highlights in Spark 3.0: adaptive query execution; dynamic partition pruning; ANSI SQL compliance; significant improvements in pandas APIs; new UI for structured streaming; up to 40x speedups for calling R user-defined functions; accelerator-aware scheduler; and SQL reference documentation.
What changed in Spark 3?
Here are the biggest new features in Spark 3.0: 2x performance improvement on TPC-DS over Spark 2.4, enabled by adaptive query execution, dynamic partition pruning and other optimizations. ANSI SQL compliance. Significant improvements in pandas APIs, including Python type hints and additional pandas UDFs.
When did spark 3.0 release?
10th of June 2020
5 of the most exciting features of the new release of Apache Spark 3.0. A new major release was made available on the 10th of June 2020 for Apache Spark. Version 3.0 — a result of more than 3,400 tickets — builds on top of version 2.
What is partition pruning in spark?
Partition pruning in Spark is a performance optimization that limits the number of files and partitions that Spark reads when querying. After partitioning the data, queries that match certain partition filter criteria improve performance by allowing Spark to only read a subset of the directories and files.
What is the latest spark version?
Apache Spark
| Original author(s) | Matei Zaharia |
|---|---|
| Developer(s) | Apache Spark |
| Initial release | May 26, 2014 |
| Stable release | 3.2.0 / October 13, 2021 |
| Repository | Spark Repository |
What is adaptive query execution?
Adaptive query execution (AQE) is query re-optimization that occurs during query execution. The motivation for runtime re-optimization is that Azure Databricks has the most up-to-date accurate statistics at the end of a shuffle and broadcast exchange (referred to as a query stage in AQE).
What is predicate pushdown in spark?
A predicate push down filters the data in the database query, reducing the number of entries retrieved from the database and improving query performance. By default the Spark Dataset API will automatically push down valid WHERE clauses to the database.
What is Apache PySpark?
PySpark is an interface for Apache Spark in Python. It not only allows you to write Spark applications using Python APIs, but also provides the PySpark shell for interactively analyzing your data in a distributed environment.