When uploading a file by the hdfs dfs -put command line in HDFS, how to set a replication factor instead of the global one for that file? For example, HDFS’s global replication factor is 3. For some temporary files, I would like to save just one copy for faster uploading and saving disk space. The
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Tag: MapReduce
how to skip mapper function in hadoop
Posted onIn hadoop I need to skip mapper function and directly execute the reducer function. We doing this to improve hadoop performance, if the hadoop framework is used to analyze same data sets, then mapper’s output will be same for different kind of jobs. To save the redundant computation for same results, I am planning to
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Making Hadoop Java process heap larger?
Posted onIn Hadoop 2.5.0, I use ‘ps -aux’ and find the Java process has options: -Xmx1000m However, my nodes have 32GB memory. How to make Hadoop Java process heap larger? In yarn-env.sh, you can find: # For setting YARN specific HEAP sizes please use this # Parameter and set appropriately # YARN_HEAPSIZE=1000 In hadoop-env.sh, you can
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How to set the number of mappers and reducers of Hadoop in command line?
Posted onHow to set the number of mappers and reducers of Hadoop in command line? Number of mappers and reducers can be set like (5 mappers, 2 reducers): -D mapred.map.tasks=5 -D mapred.reduce.tasks=2 in the command line. In the code, one can configure JobConf variables. job.setNumMapTasks(5); // 5 mappers job.setNumReduceTasks(2); // 2 reducers Note that on Hadoop
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Hadoop 2 (YARN) default configuration values
Posted onWhere to check the default Hadoop 2 (YARN) configuration values for: HDFS: hdfs-site.xml YARN: yarn-site.xml MapReduce: mapred-site.xml Default Hadoop 2 (YARN) configuration values for Hadoop 2.2.0 from Apache Hadoop website: HDFS: http://hadoop.apache.org/docs/r2.2.0/hadoop-project-dist/hadoop-hdfs/hdfs-default.xml YARN: https://hadoop.apache.org/docs/r2.2.0/hadoop-yarn/hadoop-yarn-common/yarn-default.xml MapReduce: https://hadoop.apache.org/docs/r2.2.0/hadoop-mapreduce-client/hadoop-mapreduce-client-core/mapred-default.xml
Good introductions to Hadoop 2.0 (YARN)?
Posted onWhich ones are recommended introductions to Hadoop 2.0 (YARN)? Pointers to webpages are good. Those are good ones that I find: The SoCC13 paper “Apache Hadoop YARN: Yet Another Resource Negotiator” by Vinod Kumar Vavilapalli et al.: http://www.socc2013.org/home/program/a5-vavilapalli.pdf The introduction from Hortonworks by Arun Murthy:http://hortonworks.com/blog/apache-hadoop-yarn-concepts-and-applications/ The “Official” one from Apache Hadoop website (very brief):https://hadoop.apache.org/docs/r2.2.0/hadoop-yarn/hadoop-yarn-site/YARN.html
Classpath for compiling MapReduce jobs on Hadoop 2.2.0
Posted onHow to get the correct classpath for compiling MapReduce jobs on Hadoop 2.2.0 (YARN)? The yarn command from Hadoop 2 can find it out for you: yarn classpath You may add the full path to yarn which is under bin directory of the Hadoop distribution pachage, if it is not in your $PATH.
How to choose the number of mappers and reducers in Hadoop
Posted onHow to choose the number of mappers and reducers in Hadoop to get good job performance? The Hadoop Wiki gives a discussion on this: http://wiki.apache.org/hadoop/HowManyMapsAndReduces Some valuable points: About the number of Maps: The number of maps is usually driven by the number of DFS blocks in the input files. Although that causes people to
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SQL layers on NoSQL databases
Posted onWhat are the SQL layer solution over NoSQL databases such as key/value stores? Phoenix: A SQL layer on HBase: https://github.com/forcedotcom/phoenix They also show some performance results: https://github.com/forcedotcom/phoenix/wiki/Performance F1 – The Fault-Tolerant Distributed RDBMS Supporting Google’s Ad Business: http://research.google.com/pubs/pub38125.html With F1, we have built a novel hybrid system that combines the scalability, fault tolerance, transparent sharding,
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Hadoop Installation Tutorial (Hadoop 2.x)
Posted onHadoop 2 or YARN is the new version of Hadoop. It adds the yarn resource manager in addition to the HDFS and MapReduce components. Hadoop MapReduce is a programming model and software framework for writing applications, which is an open-source variant of MapReduce designed and implemented by Google initially for processing and generating large data
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Big Data Benchmark from AMPLab of UC Berkeley
Posted onBenchmarks are important to understand the performance and quantitative and qualitative comparison of different systems. Many analytic frameworks, such as Hive, Impala and Shark, are designed and implemented these years and become fundamental software for processing big data. How to benchmark these big data analytic systems is an interesting problem. The Big Data Benchmark The
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Hadoop MapReduce Tutorials
Posted onHere is a list of tutorials for learning how to write MapReduce programs on Hadoop, the opensource MapReduce implementation with HDFS. MapReduce Tutorials The official tutorial on Hadoop MapReduce framework: http://hadoop.apache.org/docs/r1.0.4/mapred_tutorial.html. Yahoo! Hadoop Tutorial A comprehensive tutorial on Hadoop from Yahoo! Developer Network: http://developer.yahoo.com/hadoop/tutorial/. More about MapReduce To better understand the design behind MapReduce, it
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PUMA: A MapReduce Benchmark Suite
Posted onMapReduce is a well-known programming model designed for generating and processing large data. There are various MapReduce implementations. One widely known and used one may be Hadoop. Benchmarking MapReduce frameworks gets to be important. Faraz Ahmad et al. developed a benchmark suite: PUMA MapReduce Benchmark. During our work on MapReduce, we developed a benchmark suite
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Hadoop TeraSort Benchmark
Posted onTeraSort is one of Hadoop’s widely used benchmarks. Hadoop’s distribution contains both the input generator and sorting implementations: the TeraGen generates the input and TeraSort conducts the sorting. Here, we provide a short tutorial for using the Hadoop TeraSort benchmark. TeraGen generates random data that can be used as input data for a subsequent running
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Large-scale Data Storage and Processing System in Datacenters
Posted onResearch on Cloud Computing has made big progresses and many excellent large-scale systems have been designed in recent years. I compiled a list of some large-scale data storage and processing systems in datacenters as follows. Storage systems Google File System (GFS): http://research.google.com/archive/gfs.html HDFS implementation: https://hadoop.apache.org/docs/r2.7.2/hadoop-project-dist/hadoop-hdfs/HdfsDesign.html Colossus (GFS2): Colossus: Successor to the Google File System (GFS)
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Microsofts Cosmos Service
Posted onCosmos is “Microsoft’s internal data storage/query system for analyzing enormous amounts (as in petabytes) of data”. There is no paper/technical report about Cosmos published yet. I compiled a list of information about Cosmos on the Web as follows. What is Microsoft’s Cosmos service? by Yaron Y. Goland. Microsoft Cosmos: Petabytes perfectly processed perfunctorily by Seth
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Hadoop Installation Tutorial (Hadoop 1.x)
Posted onUpdate: If you are new to Hadoop and trying to install one. Please check the newer version: Hadoop Installation Tutorial (Hadoop 2.x). Hadoop mainly consists of two parts: Hadoop MapReduce and HDFS. Hadoop MapReduce is a programming model and software framework for writing applications, which is an open-source variant of MapReduce that is initially designed
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Reading List for Distributed Systems and Cloud Computing
Posted onUnderstanding the literature is usually the first step to do research, which is the same for systems research on cloud computing. A reading list may help a lot to those that just start in cloud computing research. Prof. Lin Gu, my PhD supervisor, compiled a reading list for system research on cloud computing. The reading
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Hadoop Default Ports
Posted onHadoop’s namenode and datanodes expose a bunch of TCP ports used by Hadoop’s daemons to communicate to each other or listen directly to users’ requests. These ports information are needed by both the Hadoop users and cluster administrators to write programs or configure firewalls/gateways accordingly. A post written by Philip Zeyliger from Cloudera’s blog summarizes the
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A Simple Sort Benchmark on Hadoop
Posted onAfter [[hadoop-installation-tutorial|installing Hadoop]], we usually run some benchmark programs to test whether the system works well. In the post of the Hadoop install tutorial, we show a very simple to grep strings from a simple sets of files. In this post, we introduce the Sort for testing and benchmarking Hadoop. The Sort program is also
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