/ 中存储网

Apache Spark 1.4 发布,开源集群计算系统

2015-07-06 23:51:16 来源:中存储网

Apache Spark 1.4 发布,该版本将 R API 引入 Spark,同时提升了 Spark 的核心引擎和 MLlib ,以及 Spark Streaming 的可用性。部分重要更新如下:

Spark Core

Spark core 有多各方面的改进,主要集中在操作,性能和兼容性上:

  • SPARK-6942: Visualization for Spark DAGs and operational monitoring

  • SPARK-4897: Python 3 support

  • SPARK-3644: A REST API for application information

  • SPARK-4550: Serialized shuffle outputs for improved performance

  • SPARK-7081: Initial performance improvements in project Tungsten

  • SPARK-3074: External spilling for Python groupByKey operations

  • SPARK-3674: YARN support for Spark EC2 and SPARK-5342: Security for long running YARN applications

  • SPARK-2691: Docker support in Mesos and SPARK-6338: Cluster mode in Mesos

DataFrame API and Spark SQL

The DataFrame API 在 Spark 1.4 有重要扩展 (see this link for a full list),主要集中在分析和数学函数。 Spark SQL 引入新的实用工具,并且支持 ORCFile.

  • SPARK-2883: Support for ORCFile format

  • SPARK-2213: Sort-merge joins to optimize very large joins

  • SPARK-5100: Dedicated UI for the SQL JDBC server

  • SPARK-6829: Mathematical functions in DataFrames

  • SPARK-8299: Improved error message reporting for DataFrame and SQL

  • SPARK-1442: Window functions in Spark SQL and DataFrames

  • SPARK-6231 / SPARK-7059: Improved API support for self joins

  • SPARK-5947: Partitioning support in Spark’s data source API

  • SPARK-7320: Rollup and cube functions

  • SPARK-6117: Summary and descriptive statistics

Spark ML/MLlib

Spark’s ML pipelines API graduates from alpha in this release, with new transformers and improved Python coverage. MLlib 增加了几种新算法。

  • SPARK-5884: A variety of feature transformers for ML pipelines

  • SPARK-7381: Python API for ML pipelines

  • SPARK-5854: Personalized PageRank for GraphX

  • SPARK-6113: Stabilize DecisionTree and ensembles APIs

  • SPARK-7262: Binary LogisticRegression with L1/L2 (elastic net)

  • SPARK-7015: OneVsRest multiclass to binary reduction

  • SPARK-4588: Add API for feature attributes

  • SPARK-1406: PMML model evaluation support via MLib

  • SPARK-5995: Make ML Prediction Developer APIs public

  • SPARK-3066: Support recommendAll in matrix factorization model

  • SPARK-4894: Bernoulli naive Bayes

  • SPARK-5563: LDA with online variational inference to the release note

Spark Streaming

Spark streaming 添加了新的视觉仪表图形,并大大提高了 UI 调试器的信息,同时增强了对 Kafka 和 Kinesis 的支持。

  • SPARK-7602: Visualization and monitoring in the streaming UI including batch drill down (SPARK-6796, SPARK-6862)

  • SPARK-7621: Better error reporting for Kafka

  • SPARK-2808: Support for Kafka 0.8.2.1 and Kafka with Scala 2.11

  • SPARK-5946: Python API for Kafka direct mode

  • SPARK-7111: Input rate tracking for Kafka

  • SPARK-5960: Support for transferring AWS credentials to Kinesis

  • SPARK-7056 A pluggable interface for write ahead logs

更多内容请查看 发行说明。

Spark 1.4下载请点这里: downloads 。

Apache Spark 是一种与 Hadoop 相似的开源集群计算环境,但是两者之间还存在一些不同之处,这些有用的不同之处使 Spark 在某些工作负载方面表现得更加优越,换句话说,Spark 启用了内存分布数据集,除了能够提供交互式查询外,它还可以优化迭代工作负载。

Spark 是在 Scala 语言中实现的,它将 Scala 用作其应用程序框架。与 Hadoop 不同,Spark 和 Scala 能够紧密集成,其中的 Scala 可以像操作本地集合对象一样轻松地操作分布式数据集。

尽 管创建 Spark 是为了支持分布式数据集上的迭代作业,但是实际上它是对 Hadoop 的补充,可以在 Hadoo 文件系统中并行运行。通过名为 Mesos 的第三方集群框架可以支持此行为。Spark 由加州大学伯克利分校 AMP 实验室 (Algorithms, Machines, and People Lab) 开发,可用来构建大型的、低延迟的数据分析应用程序。