Further stabilize the codegen framework to avoid hitting the 64KB JVM bytecode limit on the Java method and Java compiler constant pool limit.Support for read ahead input stream to amortize disk I/O cost in the spill reader.Support for ZStandard compression codec.Better support for predicate pushdown for Hive partition pruning.Histogram support in cost-based optimizer.PySpark Performance Enhancements: Significant improvements in python performance and interoperability by fast data serialization and vectorized execution.Note that this API is still undergoing active development and breaking changes should be expected. The new API attempts to address several limitations of the V1 API and aims to facilitate development of high performant, easy-to-maintain, and extensible external data sources. Data source API V2: An experimental API for plugging in new data sources in Spark.Spark History Server V2: A new spark history server (SHS) backend that provides better scalability for large scale applications with a more efficient event storage mechanism.To enable the reader, users can set to native. Vectorized ORC Reader: Adds support for new ORC reader that substantially improves the ORC scan throughput through vectorization (2-5x).Note that this support is currently experimental and behavioral changes around configurations, container images and entrypoints should be expected. Spark on Kubernetes: A new kubernetes scheduler backend that supports native submission of spark jobs to a cluster managed by kubernetes.We have curated a list of high level changes here, grouped by major modules. You can consult JIRA for the detailed changes. To download Apache Spark 2.3.0, visit the downloads page. In addition, this release continues to focus on usability, stability, and polish while resolving around 1400 tickets. Other major updates include the new DataSource and Structured Streaming v2 APIs, and a number of PySpark performance enhancements. This release adds support for Continuous Processing in Structured Streaming along with a brand new Kubernetes Scheduler backend. Apache Spark 2.3.0 is the fourth release in the 2.x line.

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