Apache Spark 2.2.0 正式发布

Apache Spark 2.2.0是 2.x 中的第三个发行版，原计划3月底发布，距离上个发行版本（2.1.0）的发布已有6个多月的时间，就Spark的常规发版节奏而言，2.2.0的发版可谓是长了不少。

这个版本中Structured Streaming移除了实验性标记，可用于生产环境。该版本共处理了1100多个Issue，侧重于可用性、Bug修复及稳定性改进，建议所有2.x的用户更新到此版本。

## Core and Spark SQL

Spark2.2.0版本中SQL的[CBO（Cost-Based Optimizer）优化器](https://issues.apache.org/jira/browse/SPARK-16026)基本完成。

- API updates
  - SPARK-19107: Support creating hive table with DataFrameWriter and Catalog
  - SPARK-13721: Add support for LATERAL VIEW OUTER explode()
  - SPARK-18885: Unify CREATE TABLE syntax for data source and hive serde tables
  - SPARK-16475: Added Broadcast Hints BROADCAST, BROADCASTJOIN, and MAPJOIN, for SQL Queries
  - SPARK-18350: Support session local timezone
  - SPARK-19261: Support ALTER TABLE table_name ADD COLUMNS
  - SPARK-20420: Add events to the external catalog
  - SPARK-18127: Add hooks and extension points to Spark
  - SPARK-20576: Support generic hint function in Dataset/DataFrame
  - SPARK-17203: Data source options should always be case insensitive
  - SPARK-19139: AES-based authentication mechanism for Spark
- Performance and stability
  - Cost-Based Optimizer
    - SPARK-17075 SPARK-17076 SPARK-19020 SPARK-17077 SPARK-19350: Cardinality estimation for filter, join, aggregate, project and limit/sample operators
    - SPARK-17080: Cost-based join re-ordering
    - SPARK-17626: TPC-DS performance improvements using star-schema heuristics
  - SPARK-17949: Introduce a JVM object based aggregate operator
  - SPARK-18186: Partial aggregation support of HiveUDAFFunction
  - SPARK-18362 SPARK-19918: File listing/IO improvements for CSV and JSON
  - SPARK-18775: Limit the max number of records written per file
  - SPARK-18761: Uncancellable / unkillable tasks shouldn’t starve jobs of resources
  - SPARK-15352: Topology aware block replication
- Other notable changes
  - SPARK-18352: Support for parsing multi-line JSON files
  - SPARK-19610: Support for parsing multi-line CSV files
  - SPARK-21079: Analyze Table Command on partitioned tables
  - SPARK-18703: Drop Staging Directories and Data Files after completion of Insertion/CTAS against Hive-serde Tables
  - SPARK-18209: More robust view canonicalization without full SQL expansion
  - SPARK-13446: [SPARK-18112] Support reading data from Hive metastore 2.0/2.1
  - SPARK-18191: Port RDD API to use commit protocol
  - SPARK-8425:Add blacklist mechanism for task scheduling
  - SPARK-19464: Remove support for Hadoop 2.5 and earlier
  - SPARK-19493: Remove Java 7 support

## Structured Streaming

- General Availablity
  - SPARK-20844: The Structured Streaming APIs are now GA and is no longer labeled experimental
- Kafka Improvements
  - SPARK-19719: Support for reading and writing data in streaming or batch to/from Apache Kafka
  - SPARK-19968: Cached producer for lower latency kafka to kafka streams.
- API updates
  - SPARK-19067: Support for complex stateful processing and timeouts using [flat]MapGroupsWithState
  - SPARK-19876: Support for one time triggers
- Other notable changes
  - SPARK-20979: Rate source for testing and benchmarks

## MLlib

- New algorithms in DataFrame-based API
  - SPARK-14709: LinearSVC (Linear SVM Classifier) (Scala/Java/Python/R)
  - SPARK-19635: ChiSquare test in DataFrame-based API (Scala/Java/Python)
  - SPARK-19636: Correlation in DataFrame-based API (Scala/Java/Python)
  - SPARK-13568: Imputer feature transformer for imputing missing values (Scala/Java/Python)
  - SPARK-18929: Add Tweedie distribution for GLMs (Scala/Java/Python/R)
  - SPARK-14503: FPGrowth frequent pattern mining and AssociationRules (Scala/Java/Python/R)
- Existing algorithms added to Python and R APIs
  - SPARK-18239: Gradient Boosted Trees ®
  - SPARK-18821: Bisecting K-Means ®
  - SPARK-18080: Locality Sensitive Hashing (LSH) (Python)
  - SPARK-6227: Distributed PCA and SVD for PySpark (in RDD-based API)
- Major bug fixes
  - SPARK-19110: DistributedLDAModel.logPrior correctness fix
  - SPARK-17975: EMLDAOptimizer fails with ClassCastException (caused by GraphX checkpointing bug)
  - SPARK-18715: Fix wrong AIC calculation in Binomial GLM
  - SPARK-16473: BisectingKMeans failing during training with “java.util.NoSuchElementException: key not found” for certain inputs
  - SPARK-19348: pyspark.ml.Pipeline gets corrupted under multi-threaded use
  - SPARK-20047: Box-constrained Logistic Regression

## SparkR

SparkR在2.2.0中的主要变化是添加了诸多Spark SQL的特性。

- Major features
  - SPARK-19654: Structured Streaming API for R
  - SPARK-20159: Support complete Catalog API in R
  - SPARK-19795: column functions to_json, from_json
  - SPARK-19399: Coalesce on DataFrame and coalesce on column
  - SPARK-20020: Support DataFrame checkpointing
  - SPARK-18285: Multi-column approxQuantile in R

## GraphX

- Bug fixes
  - SPARK-18847: PageRank gives incorrect results for graphs with sinks
  - SPARK-14804: Graph vertexRDD/EdgeRDD checkpoint results ClassCastException
- Optimizations
  - SPARK-18845: PageRank initial value improvement for faster convergence
  - SPARK-5484: Pregel should checkpoint periodically to avoid StackOverflowError

## 过期的特性

- MLlib
  - SPARK-18613: spark.ml LDA classes should not expose spark.mllib in APIs. In spark.ml.LDAModel, deprecated oldLocalModel and getModel.
- SparkR
  - SPARK-20195: deprecate createExternalTable

## 行为变化

- MLlib
  - SPARK-19787: DeveloperApi ALS.train() uses default regParam value 0.1 instead of 1.0, in order to match regular ALS API’s default regParam setting.
- SparkR
  - SPARK-19291: This added log-likelihood for SparkR Gaussian Mixture Models, but doing so introduced a SparkR model persistence incompatibility: Gaussian Mixture Models saved from SparkR 2.1 may not be loaded into SparkR 2.2. We plan to put in place backwards compatibility guarantees for SparkR in the future.

## 已知问题

无。

# 相关链接

Apache Spark 2.2.0 官方发版说明：<http://spark.apache.org/releases/spark-release-2-2-0.html>

JIRA变更记录：<https://issues.apache.org/jira/secure/ReleaseNote.jspa?projectId=12315420&version=12338275>

Apache Spark 2.2.0 下载地址：<http://spark.apache.org/downloads.html>

Apache Spark 2.2.0 正式发布

请我喝咖啡