Apache Spark 2.2.0 正式发布

2020-05-12 12:18:45

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

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

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在2.2.0中的主要变化是添加了诸多Spark SQL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

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.