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1
Intro
2
ANSI SOL Compliance
3
Fail Earlier for Invalid Data
4
Forbid Confusing CAST
5
ANSI Mode GA in Spark 3.2
6
Unified CREATE TABLE SOL Syntax
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CHAR/VARCHAR Support
8
More ANSI Features Coming in Spark 3.2!
9
Node Decommissioning
10
Summary
11
SOL Performance
12
Shuffle Hash Join Improvement
13
Partition Pruning Improvement
14
Predicate Pushdown Improvement
15
Reduce Query Compiling Latency (3.2)
16
Stream-stream Join
17
State Store for Structured Streaming
18
Rocks DB State Store
19
Add the type hints PEP 484 to PySpark!
20
Static Error Detection
21
Python Dependency Management
22
Visualization and Plotting
23
Usability Enhancements
24
New Utility Functions for Unix Time
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New Utility Functions for Time Zone
26
EXPLAIN FORMMATTED
27
Ignore Hints
28
Documentation and Environments
29
New Doc for PySpark
30
Deprecations and Removals
Description:
Explore the latest advancements in Apache Spark 3.1 through this comprehensive 49-minute Databricks video. Dive deep into over 1500 resolved JIRAs, focusing on key improvements that make Spark faster, easier, and smarter. Learn about crucial SQL features for ANSI compliance, innovative streaming capabilities, and Python usability enhancements. Discover performance optimizations and new tuning techniques in the query compiler. Gain insights into upcoming major initiatives and future developments. Through examples and demos, understand important changes such as ANSI SQL mode, unified CREATE TABLE syntax, CHAR/VARCHAR support, node decommissioning, shuffle hash join improvements, partition pruning, predicate pushdown, and reduced query compiling latency. Explore advancements in stream-stream joins, state store for Structured Streaming, PySpark type hints, static error detection, Python dependency management, and new utility functions for Unix time and time zones. Familiarize yourself with usability enhancements, documentation updates, and important deprecations and removals in this essential update for Spark developers and data professionals. Read more

Deep Dive into New Features of Apache Spark 3.1

Databricks
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