emr

I've successfully completed mahout vectorizing job on Amazon EMR (using Mahout on Elastic MapReduce as reference). Now I want to copy results from HDFS to S3 (to use it in future clustering). For that I've used hadoop distcp: den@aws:~$ elastic-mapreduce --jar s3://elasticmapreduce/samples/distcp/distcp.jar \ > --arg hdfs://my.bucket/prj1/seqfiles \ > --arg s3n://ACCESS_KEY:SECRET_KEY@my.bucket/prj1/seqfiles \ > -j $JOBID Failed. Found that suggestion: Use s3distcp Tried it also: elastic-mapreduce --jobflow $JOBID \ > --jar --arg s3://eu-west-1.elasticmapreduce/libs/s3distcp/1.latest/s3distcp

I am getting the following excpetion in my reducers: EMFILE: Too many open files at org.apache.hadoop.io.nativeio.NativeIO.open(Native Method) at org.apache.hadoop.io.SecureIOUtils.createForWrite(SecureIOUtils.java:161) at org.apache.hadoop.mapred.TaskLog.writeToIndexFile(TaskLog.java:296) at org.apache.hadoop.mapred.TaskLog.syncLogs(TaskLog.java:369) at org.apache.hadoop.mapred.Child$4.run(Child.java:257) at java.security.AccessController.doPrivileged(Native Method) at javax.security.auth.Subject.doAs(Subject.java:396) at org.apache.hadoop.security.UserGroupInformation.doAs

I am running some machine learning algorithms on EMR Spark cluster. I am curious about which kind of instance to use so I can get the optimal cost/performance gain? For the same level of prices, I can choose among: vCPU ECU Memory(GiB) m3.xlarge 4 13 15 c4.xlarge 4 16 7.5 r3.xlarge 4 13 30.5 Which kind of instance should be used in EMR Spark cluster? Generally speaking, it depends on your use case, needs, etc... But I can suggest a minimum configuration considering the information that you have shared. You seem to be trying to train an ALS factorization or SVD on matrices between 2 ~ 4 GBs of

I'm running a job on Apache Spark on Amazon Elastic Map Reduce (EMR). Currently I'm running on emr-4.1.0 which includes Amazon Hadoop 2.6.0 and Spark 1.5.0. When I start the job, YARN correctly has allocated all the worker nodes to the spark job (with one for the driver, of course). I have the magic "maximizeResourceAllocation" property set to "true", and the spark property "spark.dynamicAllocation.enabled" also set to "true". However, if I resize the emr cluster by adding nodes to the CORE pool of worker machines, YARN only adds some of the new nodes to the spark job. For example, this

I'm running a 5 node Spark cluster on AWS EMR each sized m3.xlarge (1 master 4 slaves). I successfully ran through a 146Mb bzip2 compressed CSV file and ended up with a perfectly aggregated result. Now I'm trying to process a ~5GB bzip2 CSV file on this cluster but I'm receiving this error: 16/11/23 17:29:53 WARN TaskSetManager: Lost task 49.2 in stage 6.0 (TID xxx, xxx.xxx.xxx.compute.internal): ExecutorLostFailure (executor 16 exited caused by one of the running tasks) Reason: Container killed by YARN for exceeding memory limits. 10.4 GB of 10.4 GB physical memory used. Consider boosting

I want to do something really basic, simply fire up a Spark cluster through the EMR console and run a Spark script that depends on a Python package (for example, Arrow ). What is the most straightforward way of doing this? The most straightforward way would be to create a bash script containing your installation commands, copy it to S3, and set a bootstrap action from the console to point to your script. Here's an example I'm using in production: s3://mybucket/bootstrap/install_python_modules.sh #!/bin/bash -xe # Non-standard and non-Amazon Machine Image Python modules: sudo pip install -U \

Amazon Kinesis 概念 处理AWS上大量流数据的数据平台 Kinesis Streams 用于搜集数据，Client Library 用于分析后的展示 构建用于处理或分析流数据的自定义应用程序 可以支持从数十万中来源捕获和存储TB级的数据，如网站点击流、财务交易、媒体馈送、IT日志等 使用IAM限制用户和角色对Kinesis的访问，使用角色的临时安全凭证可以提高安全性 Kiesis只能使用SSL加密进行访问 Kinesis组件 Kinesis Data Firehose 加载大量流数据到AWS服务中 数据默认被存储在S3中，从S3还可以再被进一步转存到Redshift 数据也可以被写入到ElaticSearch中，并且同时备份到S3 Kinesis Data Streams: 自定义构建应用程序，实时分析流数据 利用AWS开发工具包，可以实现数据在流中移动时仍然能被处理，从而接近实时 为了接近实时，处理的复杂度通常较轻 创建者 Producer 持续将数据推送进Data Streams 数据在DataStream 由一组组分片（Shards）组成，每个分片就是一条记录，通过不断分片实现几乎无限的扩展能力 使用者 Comsumer 会实时对Data Steams的内容进行处理，并且将结果推送到不同的AWS服务 数据在Stream中是临时的，默认存储24小时