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Apache Spark 2.2.0 正式发布
格伯纳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

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 tojson, fromjson
  • 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

  • SPARK-21093: Multiple gapply execution occasionally failed in SparkR

相关链接

Apache Spark 2.2.0 官方发版说明:spark.apache.org/releas

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:spark.apache.org/downlo

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