Photo by Goran Ivos on Unsplash We're excited to announce that Scala 2.13 is now in Public Preview (PuPr) on Snowpark for Scala client, UDxF and Stored Procedures! This release brings the massive collections overhaul, performance improvements, and powerful language enhancements of Scala 2.13 to the Snowflake AI Data Cloud. Where Can You Use Scala 2.13 in Snowflake? Snowflake SQL Snowpark Scala client library Why Upgrade?
The spark-sql-perf toolkit doesn't work for Spark 4.0+ currently, and this guide shows you how to get it running(with a custom patch). While many developers have their own complex Spark setup, this workflow is designed to be simple and reproducible. It only requires an AWS account to provision a cluster and run a full benchmark from scratch. We'll focus on the patch, the build process, and how a tool like FlintRock makes deploying custom Spark clusters incredibly simple.
Scala 3.7.4 is now available! Notable changes Align coverage support with Scala 2, fixing multiple issues #23722 Deprecate scala_legacy /MainGenericRunner/scalac -run/scalac -repl for removal #24267. These were deprecated since 3.5.0 and would be removed in 3.8.0. Bump Scala CLI to v1.9.1 (was v1.9.0) #23962: See the Scala CLI release notes for additional details For a full list of changes and contributor credits, please refer to the release notes.
Dispatchers in Akka Dispatchers are used to control the flow of execution in Akka - they can control how messages are being sent, delivered, received and processed. Based on the dispatching policy, dispatchers will route the incoming message to the respective destination. Dispatchers are what make Akka 'tick'. There are a number of different dispatchers that Akka provides readily, and also lets you write your own implementation, should you need. Here are a few dispatchers that come out of the box with Akka
