tachyon与hdfs,以及spark整合

本文介绍如何在Tachyon0.7.1中整合HDFS并进行伪分布式集群安装与测试。通过配置Tachyon环境变量指向HDFS地址,实现从HDFS读取文件并加载到Tachyon内存中。尽管遇到读取失败的问题,但通过检查发现文件已成功加载。

Tachyon 0.7.1伪分布式集群安装与测试:
http://blog.youkuaiyun.com/stark_summer/article/details/48321605
从官方文档得知,Spark 1.4.x和Tachyon 0.6.4版本兼容,而最新版的Tachyon 0.7.1和Spark 1.5.x兼容,目前所用的Spark为1.4.1,tachyon为 0.7.1

tachyon 与 hdfs整合

修改tachyon-env.sh

export TACHYON_UNDERFS_ADDRESS=hdfs://master:8020
Dtachyon.data.folder=$TACHYON_UNDERFS_ADDRESS/tmp/tachyon/data

上传文件到hdfs

 hadoop fs -put /home/cluster/data/test/bank/ /data/spark/

 hadoop fs -ls /data/spark/bank/
Found 3 items
-rw-r--r--   3 wangyue supergroup    4610348 2015-09-11 20:02 /data/spark/bank/bank-full.csv
-rw-r--r--   3 wangyue supergroup       3864 2015-09-11 20:02 /data/spark/bank/bank-names.txt
-rw-r--r--   3 wangyue supergroup     461474 2015-09-11 20:02 /data/spark/bank/bank.csv

通过tachyon 读取/data/spark/bank/bank-full.csv文件

val bankFullFile = sc.textFile("tachyon://master:19998/data/spark/bank/bank-full.csv/bank-full.csv")
2015-09-11 20:08:20,136 INFO  [main] storage.MemoryStore (Logging.scala:logInfo(59)) - ensureFreeSpace(177384) called with curMem=630803, maxMem=257918238
2015-09-11 20:08:20,137 INFO  [main] storage.MemoryStore (Logging.scala:logInfo(59)) - Block broadcast_3 stored as values in memory (estimated size 173.2 KB, free 245.2 MB)
2015-09-11 20:08:20,154 INFO  [main] storage.MemoryStore (Logging.scala:logInfo(59)) - ensureFreeSpace(17665) called with curMem=808187, maxMem=257918238
2015-09-11 20:08:20,155 INFO  [main] storage.MemoryStore (Logging.scala:logInfo(59)) - Block broadcast_3_piece0 stored as bytes in memory (estimated size 17.3 KB, free 245.2 MB)
2015-09-11 20:08:20,156 INFO  [sparkDriver-akka.actor.default-dispatcher-2] storage.BlockManagerInfo (Logging.scala:logInfo(59)) - Added broadcast_3_piece0 in memory on localhost:41040 (size: 17.3 KB, free: 245.9 MB)
2015-09-11 20:08:20,157 INFO  [main] spark.SparkContext (Logging.scala:logInfo(59)) - Created broadcast 3 from textFile at <console>:21
bankFullFile: org.apache.spark.rdd.RDD[String] = MapPartitionsRDD[7] at textFile at <console>:21

count

bankFullFile.count()
但是发现报错如下:
2015-09-11 21:34:31,494 WARN  [Executor task launch worker-6]  (RemoteBlockInStream.java:retrieveByteBufferFromRemoteMachine(320)) - Read nothing
2015-09-11 21:34:31,495 WARN  [Executor task launch worker-6]  (RemoteBlockInStream.java:retrieveByteBufferFromRemoteMachine(320)) - Read nothing
2015-09-11 21:34:31,489 WARN  [Executor task launch worker-7]  (RemoteBlockInStream.java:retrieveByteBufferFromRemoteMachine(320)) - Read nothing
2015-09-11 21:34:31,495 WARN  [Executor task launch worker-7]  (RemoteBlockInStream.java:retrieveByteBufferFromRemoteMachine(320)) - Read nothing
2015-09-11 21:34:31,495 WARN  [Executor task launch worker-7]  (RemoteBlockInStream.java:retrieveByteBufferFromRemoteMachine(320)) - Read nothing
2015-09-11 21:34:31,495 WARN  [Executor task launch worker-7]  (RemoteBlockInStream.java:retrieveByteBufferFromRemoteMachine(320)) - Read nothing
2015-09-11 21:34:31,495 WARN  [Executor task launch worker-7]  (RemoteBlockInStream.java:retrieveByteBufferFromRemoteMachine(320)) - Read nothing
2015-09-11 21:34:31,495 WARN  [Executor task launch worker-7]  (RemoteBlockInStream.java:retrieveByteBufferFromRemoteMachine(320)) - Read nothing
2015-09-11 21:34:31,496 WARN  [Executor task launch worker-7]  (RemoteBlockInStream.java:retrieveByteBufferFromRemoteMachine(320)) - Read nothing
2015-09-11 21:34:31,496 WARN  [Executor task launch worker-7]  (RemoteBlockInStream.java:retrieveByteBufferFromRemoteMachine(320)) - Read nothing
2015-09-11 21:34:31,496 WARN  [Executor task launch worker-7]  (RemoteBlockInStream.java:retrieveByteBufferFromRemoteMachine(320)) - Read nothing
2015-09-11 21:34:31,496 WARN  [Executor task launch worker-7]  (RemoteBlockInStream.java:retrieveByteBufferFromRemoteMachine(320)) - Read nothing
2015-09-11 21:34:31,496 WARN  [Executor task launch worker-7]  (RemoteBlockInStream.java:retrieveByteBufferFromRemoteMachine(320)) - Read nothing
2015-09-11 21:34:31,496 WARN  [Executor task launch worker-7]  (RemoteBlockInStream.java:retrieveByteBufferFromRemoteMachine(320)) - Read nothing
2015-09-11 21:34:31,496 WARN  [Executor task launch worker-7]  (RemoteBlockInStream.java:retrieveByteBufferFromRemoteMachine(320)) - Read nothing

感觉错误很诡异,有人知道这是什么原因?tell me why?

但是 我在tachyon 文件系统中可以看到如下内容:

./bin/tachyon tfs ls /data/spark/bank/bank-full.csv/
4502.29 KB09-11-2015 20:09:02:078  Not In Memory  /data/spark/bank/bank-full.csv/bank-full.csv

而bank-full.csv在hdfs文件是

hadoop fs -ls /data/spark/bank/
Found 3 items
-rw-r--r--   3 wangyue supergroup    4610348 2015-09-11 20:02 /data/spark/bank/bank-full.csv
-rw-r--r--   3 wangyue supergroup       3864 2015-09-11 20:02 /data/spark/bank/bank-names.txt
-rw-r--r--   3 wangyue supergroup     461474 2015-09-11 20:02 /data/spark/bank/bank.csv

其实Tachyon本身将bank-full.csv文件加载到了内存,并存放到自身的文件系统里面:tachyon://master:19998/data/spark/bank/bank-full.csv/bank-full.csv”
Tachyon的conf/tachyon-env.sh文件里面配置的,通过export TACHYON_UNDERFS_ADDRESS=hdfs://master:8020配置,这样tachyon://localhost:19998就可以获取hdfs文件指定路径文件

好吧,那我就先通过hdfs方式读取文件然后 保存到tachyon

scala> val bankfullfile =  sc.textFile("/data/spark/bank/bank-full.csv")
scala> bankfullfile.count
res0: Long = 45212

scala> bankfullfile.saveAsTextFile("tachyon://master:19998/data/spark/bank/newbankfullfile")

未完成,待续~

Tachyon是一个分布式内存文件系统,可以在集群里以访问内存的速度来访问存在tachyon里的文件。把Tachyon是架构在最底层的分布式文件存储和上层的各种计算框架之间的一种中间件。主要职责是将那些不需要落地到DFS里的文件,落地到分布式内存文件系统中,来达到共享内存,从而提高效率。同时可以减少内存冗余,GC时间等。        特性:类 Java 的文件 API兼容性:实现 Hadoop 文件系统接口可插入式的底层文件系统内建 Raw 原生表的支持基于 Web 的 UI 提供命令行接口Tachyon 架构: HDFS 的比较:        Hadoop足够快吗?美国加州大学伯克利分校的AMPLab基于Hadoop的核心组件开发出一个更快的版本Tachyon。AMPLab从底层重建了Hadoop平台,“没有最快,只有更快”。        AMPLab在大数据领域最知名的产品是Spark,它是一个内存中并行处理的框架,Spark的创造者声称:使用Shark运行并行处理Job速度要比MapReduce快100倍。又因为Spark是在内存运行,所以Shark可Druid或者SAP's HANA系统一较高下。Spark也为ClearStory下一代分析和可视化服务提供处理引擎。如果你喜欢用Hive作为Hadoop的数据仓库,那么你一定会喜欢Shark,因为它代表了“Hive on Spark”。       AMPLab的最新目标就是Hadoop分布式文件系统(HDFS),不过HDFS在可用性和速度方面一直受人诟病,所以AMPLab创建了Tachyon( 在High Scalability上非常夺目,引起了Derrick Harris的注意)。       当然,AMPLab并不是第一个对HDFS提出质疑的组织,同时也有很多商业版本可供选择,像Quantcast就自己开发了开源文件系统,声称其在运行大规模文件系统时速度更快、更高效。诚然,AMPLab所做的工作就是打破现有商业软件的瓶颈限制。如果碰巧破坏了现状,那么就顺其自然吧!不过,对于用户来说,AMPLab只是为那些寻找合适工具的人员提供了一种新的选择,AMPLab的合作伙伴和赞助商包括谷歌,Facebook,微软和亚马逊网络服务,它们当然非常乐意看到这些新技术,如果很有必要的话。       AMPLab的其他项目包括PIQL,类似于一种基于键/值存储的SQL查询语言;MLBase,基于分布式系统的机器学习系统;Akaros,一个多核和大型SMP系统的操作系统;Sparrow,一个低延迟计算集群调度系统。Tachyon可运行在如下任意平台上: 标签:分布式  文件系统
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