大数据开发之Hive优化篇6-Hive on spark
备注:
Hive 版本 2.1.1
一.Hive on Spark介绍
Hive是基于Hadoop平台的数据仓库，最初由Facebook开发，在经过多年发展之后，已经成为Hadoop事实上的SQL引擎标准。相较于其他诸如Impala、Shark（SparkSQL的前身）等引擎而言，Hive拥有更为广泛的用户基础以及对SQL语法更全面的支持。Hive最初的计算引擎为MapReduce，受限于其自身的Map+Reduce计算模式，以及不够充分的内存利用，MapReduce的性能难以得到提升。
Hortonworks于2013年提出将Tez作为另一个计算引擎以提高Hive的性能。Spark则是最初由加州大学伯克利分校开发的分布式计算引擎，借助于其灵活的DAG执行模式、对内存的充分利用，以及RDD所能表达的丰富语义，Spark受到了Hadoop社区的广泛关注。在成为Apache顶级项目之后，Spark更是集成了流处理、图计算、机器学习等功能，是业界公认最具潜力的下一代通用计算框架。鉴于此，Hive社区于2014年推出了Hive on Spark项目（HIVE-7292），将Spark作为继MapReduce和Tez之后Hive的第三个计算引擎。该项目由Cloudera、Intel和MapR等几家公司共同开发，并受到了来自Hive和Spark两个社区的共同关注。目前Hive on Spark的功能开发已基本完成，并于2015年1月初合并回trunk，预计会在Hive下一个版本中发布。本文将介绍Hive on Spark的设计架构，包括如何在Spark上执行Hive查询，以及如何借助Spark来提高Hive的性能等。另外本文还将介绍Hive on Spark的进度和计划，以及初步的性能测试数据。
我们建议修改Hive，增加Spark作为第三执行后端(Hive -7292)，与MapReduce和Tez并行。
Spark是一个开源的数据分析集群计算框架，它建立在Hadoop的两阶段MapReduce范式之外，但建立在HDFS之上。Spark的主要抽象是一个分布式项目集合，称为弹性分布式数据集(Resilient distributed Dataset, RDD)。rdd可以通过Hadoop inputformat(例如HDFS文件)创建，也可以通过转换其他rdd创建。通过一系列的转换(如groupBy和filter)，或者Spark提供的count和save等操作来应用rdd，可以处理和分析rdd，从而实现MapReduce作业的功能，而不需要中间阶段。
SQL查询可以很容易地转换为Spark转换和操作，正如在Shark和Spark SQL中演示的那样。事实上，许多原始转换和操作都是面向sql的，比如join和count。
1.1 Hive on spark 动机
下面是Hive在Spark上运行的主要动机:
Spark用户的好处:对于已经在使用Spark进行其他数据处理和机器学习需求的用户来说，这个特性非常有价值。在一个执行后端进行标准化对于操作管理来说是很方便的，并且可以更容易地开发专门技术来调试问题和进行改进。
更广泛地采用Hive:遵循前面的观点，这将Hive作为SQL on Hadoop选项引入Spark用户群，进一步增加了Hive的采用。
性能:Hive查询，特别是涉及多个减速阶段的查询，运行速度会更快，从而像Tez一样提高用户体验。
Spark执行后端并不是为了取代Tez或MapReduce。Hive项目的多个后端并存是正常的。用户可以选择使用Tez、Spark或MapReduce。根据用例的不同，每种方法都有不同的优点。而Hive的成功并不完全依赖于Tez或Spark的成功。
1.2 设计原则
主要的设计原则是不影响或限制Hive现有的代码路径，因此不影响功能或性能。也就是说，用户选择在MapReduce或Tez上运行Hive，就会像现在一样拥有现有的功能和代码路径。此外，在执行层插入Spark可以最大限度地保持代码共享，并降低维护成本，因此Hive community不需要专门为Spark进行投资。
同时，选择Spark作为执行引擎的用户将自动拥有Hive提供的所有丰富功能特性。未来添加到Hive的特性(如新数据类型、udf、逻辑优化等)应该会自动提供给那些用户，而无需在Hive的Spark执行引擎中进行任何定制工作。
1.3 与Shark和Spark SQL的比较
Spark生态系统中有两个相关项目对Spark提供Hive QL支持:Shark和Spark SQL。
Shark项目将Hive生成的查询计划转换为自己的表示，并在Spark上执行。
Spark SQL是Spark中的一个特性。它使用Hive的解析器作为前台提供Hive QL支持。Spark应用程序开发人员可以在代码中轻松地用SQL以及其他Spark操作符表示数据处理逻辑。Spark SQL支持与Hive不同的用例。
与Shark和Spark SQL相比，我们的设计方法支持所有现有的Hive特性，包括Hive QL(以及任何未来的扩展)，以及Hive与授权、监视、审计和其他操作工具的集成。
1.4 其它考虑
我们知道一个新的执行后端是一个重要的任务。它不可避免地增加了复杂性和维护成本，即使设计避免触及现有的代码路径。Hive现在可以在MapReduce、Tez和Spark上运行单元测试。我们认为利大于弊。从基础设施的角度来看，我们可以赞助更多的硬件来进行持续集成。
最后，Hive on Tez已经奠定了一些重要的基础，这对支持一个新的执行引擎(如Spark)非常有帮助。这里的项目肯定会从中受益。另一方面，Spark是一个非常不同于MapReduce或Tez的框架。因此，在集成过程中很可能会发现漏洞和小问题。预计Hive社区将与Spark社区紧密合作，以确保集成的成功。
二.Hive on Spark 性能测试
代码:
set hive.execution.engine=mr;
select count(*) from ods_fact_sale;
set hive.execution.engine=spark;
select count(*) from ods_fact_sale;
测试记录:
hive> > set hive.execution.engine=mr;hive> select count(*) from ods_fact_sale;
Query ID = root_20210106155340_8c89f5f6-c599-49e6-9cec-d73d278a1df6
Total jobs = 1
Launching Job 1 out of 1
Number of reduce tasks determined at compile time: 1
In order to change the average load for a reducer (in bytes):set hive.exec.reducers.bytes.per.reducer=<number>
In order to limit the maximum number of reducers:set hive.exec.reducers.max=<number>
In order to set a constant number of reducers:set mapreduce.job.reduces=<number>
21/01/06 15:53:40 INFO client.ConfiguredRMFailoverProxyProvider: Failing over to rm69
Starting Job = job_1609141291605_0037, Tracking URL = http://hp3:8088/proxy/application_1609141291605_0037/
Kill Command = /opt/cloudera/parcels/CDH-6.3.1-1.cdh6.3.1.p0.1470567/lib/hadoop/bin/hadoop job -kill job_1609141291605_0037
Hadoop job information for Stage-1: number of mappers: 117; number of reducers: 1
2021-01-06 15:53:48,454 Stage-1 map = 0%, reduce = 0%
2021-01-06 15:53:57,802 Stage-1 map = 2%, reduce = 0%, Cumulative CPU 12.98 sec
2021-01-06 15:54:03,965 Stage-1 map = 3%, reduce = 0%, Cumulative CPU 24.23 sec
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MapReduce Total cumulative CPU time: 11 minutes 35 seconds 500 msec
Ended Job = job_1609141291605_0037
MapReduce Jobs Launched:
Stage-Stage-1: Map: 117 Reduce: 1 Cumulative CPU: 695.5 sec HDFS Read: 31436910990 HDFS Write: 109 HDFS EC Read: 0 SUCCESS
Total MapReduce CPU Time Spent: 11 minutes 35 seconds 500 msec
OK
767830000
Time taken: 447.145 seconds, Fetched: 1 row(s)
hive> > set hive.execution.engine=spark;
hive> select count(*) from ods_fact_sale;
Query ID = root_20210106160132_8d81e192-ceb7-46a3-bc60-70a5eeabce87
Total jobs = 1
Launching Job 1 out of 1
In order to change the average load for a reducer (in bytes):set hive.exec.reducers.bytes.per.reducer=<number>
In order to limit the maximum number of reducers:set hive.exec.reducers.max=<number>
In order to set a constant number of reducers:set mapreduce.job.reduces=<number>
Running with YARN Application = application_1609141291605_0038
Kill Command = /opt/cloudera/parcels/CDH-6.3.1-1.cdh6.3.1.p0.1470567/lib/hadoop/bin/yarn application -kill application_1609141291605_0038
Hive on Spark Session Web UI URL: http://hp3:44667Query Hive on Spark job[0] stages: [0, 1]
Spark job[0] status = RUNNING
--------------------------------------------------------------------------------------STAGES ATTEMPT STATUS TOTAL COMPLETED RUNNING PENDING FAILED
--------------------------------------------------------------------------------------
Stage-0 ........ 0 FINISHED 117 117 0 0 0
Stage-1 ........ 0 FINISHED 1 1 0 0 0
--------------------------------------------------------------------------------------
STAGES: 02/02 [==========================>>] 100% ELAPSED TIME: 51.30 s
--------------------------------------------------------------------------------------
Spark job[0] finished successfully in 51.30 second(s)
Spark Job[0] Metrics: TaskDurationTime: 316285, ExecutorCpuTime: 247267, JvmGCTime: 5415, BytesRead / RecordsRead: 31436921640 / 767830000, BytesReadEC: 0, ShuffleTotalBytesRead / ShuffleRecordsRead: 6669 / 117, ShuffleBytesWritten / ShuffleRecordsWritten: 6669 / 117
OK
767830000
Time taken: 71.384 seconds, Fetched: 1 row(s)
hive>
从测试记录可以看出，执行速度从mr的8分钟比哪位了71秒，性能大幅提升
参考
1.https://cwiki.apache.org/confluence/display/Hive/Hive+on+Spark
2.http://lxw1234.com/archives/2015/05/200.htm
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