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  • hadoop - large database query

    - by Mastergeek
    Situation: I have a Postgres DB that contains a table with several million rows and I'm trying to query all of those rows for a MapReduce job. From the research I've done on DBInputFormat, Hadoop might try and use the same query again for a new mapper and since these queries take a considerable amount of time I'd like to prevent this in one of two ways that I've thought up: 1) Limit the job to only run 1 mapper that queries the whole table and call it good. or 2) Somehow incorporate an offset in the query so that if Hadoop does try to use a new mapper it won't grab the same stuff. I feel like option (1) seems more promising, but I don't know if such a configuration is possible. Option(2) sounds nice in theory but I have no idea how I would keep track of the mappers being made and if it is at all possible to detect that and reconfigure. Help is appreciated and I'm namely looking for a way to pull all of the DB table data and not have several of the same query running because that would be a waste of time.

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  • Knowledge mining using Hadoop.

    - by Anurag
    Hello there, I want to do a project Hadoop and map reduce and present it as my graduation project. To this, I've given some thought,searched over the internet and came up with the idea of implementing some basic knowledge mining algorithms say on a social websites like Facebook or may stckoverflow, Quora etc and draw some statistical graphs, comparisons frequency distributions and other sort of important values.For searching purpose would it be wise to use Apache Solr ? I want know If such thing is feasible using the above mentioned tools, if so how should I build up on this little idea? Where can I learn about knowledge mining algorithms which are easy to implement using java and map reduce techniques? In case this is a wrong idea please suggest what else can otherwise be done on using Hadoop and other related sub-projects? Thank you

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  • Microsoft lance Hadoop pour Windows Server et Windows Azure, première version Beta du framework "HDInsight"

    Microsoft lance Hadoop pour Windows Server et Windows Azure Première version Beta du framework HDInsight. Microsoft lance une version bêta publique du Framework Hadoop pour Windows Server et Windows Azure. Les deux nouveaux produits portent les noms officiels de Windows Azure HDInsight Service et Microsoft HDInsight Server pour Windows. Ces produits sont nés d'un partenariat entre Microsoft et Hortonworks, éditeur de logiciels et fournisseur de solutions Hadoop commerciales. Un mois après l'annonce du partenariat en automne 2011, Microsoft a renoncé à faire sa propre solution Big-Data intitulée Dryad

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  • Découvrir Hadoop, avec un tutoriel traduit par Stéphane Dupont

    Bonjour,Je vous présente ce tutoriel traduit par Stéphane Dupont intitulé : Tutoriel Hadoop Hadoop est un système distribué, tolérant aux pannes, pour le stockage de données et qui est hautement scalable. Cette capacité de monter en charge est le résultat d'un stockage en cluster à haute bande passante et répliqué, connu sous l'acronyme de HDFS (Hadoop Distributed File System) et d'un traitement distribué...

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  • If Nvidia Shield can stream a game via WiFi (~150-300Mbps), where is the 1-10Gbps wired streaming?

    - by Enigma
    Facts: It is surprising and uncharacteristic that a wireless game streaming solution is the *first to hit the market when a 1000mbps+ Ethernet connection would accomplish the same feat with roughly 6x the available bandwidth. 150-300mbps WiFi is in no way superior to a 1000mbps+ LAN connection aside from well wireless mobility. Throughout time, (since the internet was created) wired services have **always come first yet in this particular case, the opposite seems to be true. We had wired internet first, wired audio streaming, and wired video streaming all before their wireless counterparts. Why? Largely because the wireless bandwidth was and is inferior. Even today despite being significantly better and capable of a lot more, it is still inferior to a wired connection. Situation: Chief among these is that NVIDIA’s Shield handheld game console will be getting a microconsole-like mode, dubbed “Shield Console Mode”, that will allow the handheld to be converted into a more traditional TV-connected console. In console mode Shield can be controlled with a Bluetooth controller, and in accordance with the higher resolution of TVs will accept 1080p game streaming from a suitably equipped PC, versus 720p in handheld mode. With that said 1080p streaming will require additional bandwidth, and while 720p can be done over WiFi NVIDIA will be requiring a hardline GigE connection for 1080p streaming (note that Shield doesn’t have Ethernet, so this is presumably being done over USB). Streaming aside, in console mode Shield will also support its traditional local gaming/application functionality. - http://www.anandtech.com/show/7435/nvidia-consolidates-game-streaming-tech-under-gamestream-brand-announces-shield-console-mode ^ This is not acceptable to me for a number of reasons not to mention the ridiculousness of having a little screen+controller unit sitting there while using a secondary controller and screen instead. That kind of redundant absurdity exemplifies how wrong of a solution that is. They need a second product for this solution without the screen or controller for it to make sense... at which point your just buying a little computer that does what most other larger computers do better. While this secondary project will provide a wired connection, it still shouldn't be necessary to purchase a Shield to have this benefit. Not only this but Intel's WiDi claims game streaming support as well - wirelessly. Where is the wired streaming? All that is required, by my understanding, is the ability to decode H.264 video compression and transmit control/feedback so by any logical comparison, one (Nvidia especially) should have no difficulty in creating an application for PC's (win32/64 environment) that does the exact same thing their android app does. I have 2 video cards capable of streaming (encoding) H.264 so by right they must be capable of decoding it I would think. I should be able to stream to my second desktop or my laptop both of which by hardware comparison are superior to the Shield. I haven't found anything stating plans to allow non-shield owners to do this. Can a third party create this software or does it hinge on some limitation that only Nvidia can overcome? Reiteration of questions: Is there a technical reason (non marketing) for why Nvidia opted to bottleneck the streaming service with a wireless connection limiting the resolution to 720p and introducing intermittent video choppiness when on a wired connection one could achieve, presumably, 1080p with significantly less or zero choppiness? Is there anything limiting developers from creating a PC/Desktop application emulating the same H.264 decoding functionality that circumvents the need to get an Nvidia Shield altogether? (It is not a matter of being too cheap to support Nvidia - I have many Nvidia cards that aren't being used. One should not have to purchase specialty hardware when = hardware already exists) Same questions go for Intel Widi also. I am just utterly perplexed that there are wireless live streaming solution and yet no wired. How on earth can wireless be the goto transmission medium? Is there another solution that takes advantage of H.264 video compression allowing live streaming over a wired connection? (*) - Perhaps this isn't the first but afaik it is the first complete package. (**) - I cant back that up with hard evidence/links but someone probably could. Edit: Maybe this will be the solution I am looking for but I still find it hard to believe that they would be the first and after wireless solutions already exist. In-home Streaming You can play all your Windows and Mac games on your SteamOS machine, too. Just turn on your existing computer and run Steam as you always have - then your SteamOS machine can stream those games over your home network straight to your TV! - http://store.steampowered.com/livingroom/SteamOS/

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  • HDFS some datanodes of cluster are suddenly disconnected while reducers are running

    - by user1429825
    I have 8 slave computers and 1 master computer for running Hadoop (ver 0.21) some datanodes of cluster are suddenly disconnected while I was running MapReduce code on 10GB data After all mappers finished and around 80% of reducers was processed, randomly one or more datanode disconned from network. and then the other datanodes start to disappear from network even if I killed the MapReduce job when I found some datanode was disconnected. I've tried to change dfs.datanode.max.xcievers to 4096, turned off fire-walls of all computing node, disabled selinux and increased the number of file open limit to 20000 but they didn't work at all... anyone have a idea to solve this problem? followings are error log from mapreduce 12/06/01 12:31:29 INFO mapreduce.Job: Task Id : attempt_201206011227_0001_r_000006_0, Status : FAILED java.io.IOException: Bad connect ack with firstBadLink as ***.***.***.148:20010 at org.apache.hadoop.hdfs.DFSOutputStream$DataStreamer.createBlockOutputStream(DFSOutputStream.java:889) at org.apache.hadoop.hdfs.DFSOutputStream$DataStreamer.nextBlockOutputStream(DFSOutputStream.java:820) at org.apache.hadoop.hdfs.DFSOutputStream$DataStreamer.run(DFSOutputStream.java:427) and followings are logs from datanode 2012-06-01 13:01:01,118 INFO org.apache.hadoop.hdfs.server.datanode.DataNode: Receiving block blk_-5549263231281364844_3453 src: /*.*.*.147:56205 dest: /*.*.*.142:20010 2012-06-01 13:01:01,136 INFO org.apache.hadoop.hdfs.server.datanode.DataNode: DatanodeRegistration(*.*.*.142:20010, storageID=DS-1534489105-*.*.*.142-20010-1337757934836, infoPort=20075, ipcPort=20020) Starting thread to transfer block blk_-3849519151985279385_5906 to *.*.*.147:20010 2012-06-01 13:01:19,135 WARN org.apache.hadoop.hdfs.server.datanode.DataNode: DatanodeRegistration(*.*.*.142:20010, storageID=DS-1534489105-*.*.*.142-20010-1337757934836, infoPort=20075, ipcPort=20020):Failed to transfer blk_-5797481564121417802_3453 to *.*.*.146:20010 got java.net.ConnectException: > Connection timed out at sun.nio.ch.SocketChannelImpl.checkConnect(Native Method) at sun.nio.ch.SocketChannelImpl.finishConnect(SocketChannelImpl.java:701) at org.apache.hadoop.net.SocketIOWithTimeout.connect(SocketIOWithTimeout.java:206) at org.apache.hadoop.net.NetUtils.connect(NetUtils.java:373) at org.apache.hadoop.hdfs.server.datanode.DataNode$DataTransfer.run(DataNode.java:1257) at java.lang.Thread.run(Thread.java:722) 2012-06-01 13:06:20,342 INFO org.apache.hadoop.hdfs.server.datanode.DataBlockScanner: Verification succeeded for blk_6674438989226364081_3453 2012-06-01 13:09:01,781 WARN org.apache.hadoop.hdfs.server.datanode.DataNode: DatanodeRegistration(*.*.*.142:20010, storageID=DS-1534489105-*.*.*.142-20010-1337757934836, infoPort=20075, ipcPort=20020):Failed to transfer blk_-3849519151985279385_5906 to *.*.*.147:20010 got java.net.SocketTimeoutException: 480000 millis timeout while waiting for channel to be ready for write. ch : java.nio.channels.SocketChannel[connected local=/*.*.*.142:60057 remote=/*.*.*.147:20010] at org.apache.hadoop.net.SocketIOWithTimeout.waitForIO(SocketIOWithTimeout.java:246) at org.apache.hadoop.net.SocketOutputStream.waitForWritable(SocketOutputStream.java:164) at org.apache.hadoop.net.SocketOutputStream.transferToFully(SocketOutputStream.java:203) at org.apache.hadoop.hdfs.server.datanode.BlockSender.sendChunks(BlockSender.java:388) at org.apache.hadoop.hdfs.server.datanode.BlockSender.sendBlock(BlockSender.java:476) at org.apache.hadoop.hdfs.server.datanode.DataNode$DataTransfer.run(DataNode.java:1284) at java.lang.Thread.run(Thread.java:722) hdfs-site.xml <configuration> <property> <name>dfs.name.dir</name> <value>/home/hadoop/data/name</value> </property> <property> <name>dfs.data.dir</name> <value>/home/hadoop/data/hdfs1,/home/hadoop/data/hdfs2,/home/hadoop/data/hdfs3,/home/hadoop/data/hdfs4,/home/hadoop/data/hdfs5</value> </property> <property> <name>dfs.replication</name> <value>3</value> </property> <property> <name>dfs.datanode.max.xcievers</name> <value>4096</value> </property> <property> <name>dfs.http.address</name> <value>0.0.0.0:20070</value> <description>50070 The address and the base port where the dfs namenode web ui will listen on. If the port is 0 then the server will start on a free port. </description> </property> <property> <name>dfs.datanode.http.address</name> <value>0.0.0.0:20075</value> <description>50075 The datanode http server address and port. If the port is 0 then the server will start on a free port. </description> </property> <property> <name>dfs.secondary.http.address</name> <value>0.0.0.0:20090</value> <description>50090 The secondary namenode http server address and port. If the port is 0 then the server will start on a free port. </description> </property> <property> <name>dfs.datanode.address</name> <value>0.0.0.0:20010</value> <description>50010 The address where the datanode server will listen to. If the port is 0 then the server will start on a free port. </description> <property> <name>dfs.datanode.ipc.address</name> <value>0.0.0.0:20020</value> <description>50020 The datanode ipc server address and port. If the port is 0 then the server will start on a free port. </description> </property> <property> <name>dfs.datanode.https.address</name> <value>0.0.0.0:20475</value> </property> <property> <name>dfs.https.address</name> <value>0.0.0.0:20470</value> </property> </configuration> mapred-site.xml <configuration> <property> <name>mapred.job.tracker</name> <value>masternode:29001</value> </property> <property> <name>mapred.system.dir</name> <value>/home/hadoop/data/mapreduce/system</value> </property> <property> <name>mapred.local.dir</name> <value>/home/hadoop/data/mapreduce/local</value> </property> <property> <name>mapred.map.tasks</name> <value>32</value> <description> default number of map tasks per job.</description> </property> <property> <name>mapred.tasktracker.map.tasks.maximum</name> <value>4</value> </property> <property> <name>mapred.reduce.tasks</name> <value>8</value> <description> default number of reduce tasks per job.</description> </property> <property> <name>mapred.map.child.java.opts</name> <value>-Xmx2048M</value> </property> <property> <name>io.sort.mb</name> <value>500</value> </property> <property> <name>mapred.task.timeout</name> <value>1800000</value> <!-- 30 minutes --> </property> <property> <name>mapred.job.tracker.http.address</name> <value>0.0.0.0:20030</value> <description> 50030 The job tracker http server address and port the server will listen on. If the port is 0 then the server will start on a free port. </description> </property> <property> <name>mapred.task.tracker.http.address</name> <value>0.0.0.0:20060</value> <description> 50060 </property> </configuration>

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  • How do I control output files name and content of an Hadoop streaming job?

    - by Eran Kampf
    Is there a way to control the output filenames of an Hadoop Streaming job? Specifically I would like my job's output files content and name to be organized by the ket the reducer outputs - each file would only contain values for one key and its name would be the key. Update: Just found the answer - Using a Java class that derives from MultipleOutputFormat as the jobs output format allows control of the output file names. http://hadoop.apache.org/core/docs/current/api/org/apache/hadoop/mapred/lib/MultipleOutputFormat.htmlhttp://hadoop.apache.org/core/docs/current/api/org/apache/hadoop/mapred/lib/MultipleOutputFormat.html I havent seen any samples for this out there... Can anyone point out to an Hadoop Streaming sample that makes use of a custom output format Java class?

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  • UDP multicast streaming of media content over WIFI

    - by sajad
    I am using vlc to stream media content over wireless network in scenario like this (from content streamer to stream receiver client): The bandwidth of wireless network is 54 Mb/s and UDP stream's required bandwidth is only 4 Mb/s; however there is trouble in receiving media stream and quality of playing specifically in multicast mode; means I can play the stream but it has jitter and does not play smoothly. In uni-cast I can stream up to 5 media streams correctly, but in multicast mode there is problem with streaming just one media! However when I stream from client some multicast streams; the wifi access-point can receive data correctly and I can see the video in "udp streamer" side correctly even when number of multicast streams increases to 9; But as you see I want to stream from streaming server and receive media in client size. Is this a typical problem of streaming real-time contents over wireless networks? Is it necessary to change configurations of my WIFI switch or it is just a software trouble? thank you

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  • Back up of Streaming server

    - by Maxwell
    I want to take a new streaming server for my website which generally holds videos and audio files. But how do we maintain backup of the streaming server if storage size is increasing day by day. Generally on Database servers, like Sql Server, backups can be easily taken and restored very easily as they do not occupy much space for medium range applications. On the other hand how can we take backup of streaming server? If the server fails, the there should be an alternative server / solution that should decrease downtime of the server. How is the back-end architecture of YouTube built to handle this?

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  • Streaming video file to iPhone

    - by user34157
    I have a http streaming link which gives me .flv streaming feed. I want to convert that and access in my iPhone program. How can i do that? I want to have a desktop software like VLC and input this streaming feed URL and convert to iPhone supported and stream again to iPhone. I tried VLC with H.264 and Mpeg-1 audio, but seems to be it doesn't give the supported format, so as iPhone program doesn't play the video. Could someone please guide me how can i setup a desktop software which can stream iPhone supported file?

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  • VLC RTP Streaming in FC12

    - by Matt D
    I'm trying to get VLC to work streaming RTP audio/video over my office network. The goal is multicast a/v streaming. In all test cases, we are streaming from VLC to VLC. I am able to stream from Windows to Windows, and from Fedora to Windows, but not from Windows to Fedora. Additionally, I am unable to receive a LOCAL stream from one instance of VLC to another, within Fedora. I don't see any reason why this would be. The buffer indicator (where the elapsed/total time is normally displayed) never shows any connectivity, so it would appear to be a network problem, but since I am able to stream from Fedora to Windows (same IP, same port) I thought it would be something else. Does anyone know of a solution to this issue?

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  • UDP multicast streaming of media content over WIFI

    - by sajad
    I am using vlc to stream media content over wireless network in scenario like this (from content streamer to stream receiver client): The bandwidth of wireless network is 54 Mb/s and UDP stream's required bandwidth is only 4 Mb/s; however there is trouble in receiving media stream and quality of playing specifically in multicast mode; means I can play the stream but it has jitter and does not play smoothly. In uni-cast I can stream up to 5 media streams correctly, but in multicast mode there is problem with streaming just one media! However when I stream from client some multicast streams; the wifi access-point can receive data correctly and I can see the video in "udp streamer" side correctly even when number of multicast streams increases to 9; But as you see I want to stream from streaming server and receive media in client size. Is this a typical problem of streaming real-time contents over wireless networks? Is it necessary to change configurations of my WIFI switch or it is just a software trouble? thank you

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  • HTTP Live Streaming Broadcast

    - by user761389
    I'm designing an app for streaming video from a device (e.g. iPhone) via a server to one or more devices and have been researching Apples HTTP Live Streaming protocol. One thing that isn't clear is whether it is possible to stream live video (with audio) to the server and then have it streamed simultaneously in real time to the client devices. From reading the documentation and technical notes from Apple it seems like the index file needs to be created before the segmented video files can be served to a client. Is this right? If so maybe HTTP Live Streaming isn't suitable in this case, what other technologies or software should I consider? Thanks

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  • Oracle Big Data Software Downloads

    - by Mike.Hallett(at)Oracle-BI&EPM
    Companies have been making business decisions for decades based on transactional data stored in relational databases. Beyond that critical data, is a potential treasure trove of less structured data: weblogs, social media, email, sensors, and photographs that can be mined for useful information. Oracle offers a broad integrated portfolio of products to help you acquire and organize these diverse data sources and analyze them alongside your existing data to find new insights and capitalize on hidden relationships. Oracle Big Data Connectors Downloads here, includes: Oracle SQL Connector for Hadoop Distributed File System Release 2.1.0 Oracle Loader for Hadoop Release 2.1.0 Oracle Data Integrator Companion 11g Oracle R Connector for Hadoop v 2.1 Oracle Big Data Documentation The Oracle Big Data solution offers an integrated portfolio of products to help you organize and analyze your diverse data sources alongside your existing data to find new insights and capitalize on hidden relationships. Oracle Big Data, Release 2.2.0 - E41604_01 zip (27.4 MB) Integrated Software and Big Data Connectors User's Guide HTML PDF Oracle Data Integrator (ODI) Application Adapter for Hadoop Apache Hadoop is designed to handle and process data that is typically from data sources that are non-relational and data volumes that are beyond what is handled by relational databases. Typical processing in Hadoop includes data validation and transformations that are programmed as MapReduce jobs. Designing and implementing a MapReduce job usually requires expert programming knowledge. However, when you use Oracle Data Integrator with the Application Adapter for Hadoop, you do not need to write MapReduce jobs. Oracle Data Integrator uses Hive and the Hive Query Language (HiveQL), a SQL-like language for implementing MapReduce jobs. Employing familiar and easy-to-use tools and pre-configured knowledge modules (KMs), the application adapter provides the following capabilities: Loading data into Hadoop from the local file system and HDFS Performing validation and transformation of data within Hadoop Loading processed data from Hadoop to an Oracle database for further processing and generating reports Oracle Database Loader for Hadoop Oracle Loader for Hadoop is an efficient and high-performance loader for fast movement of data from a Hadoop cluster into a table in an Oracle database. It pre-partitions the data if necessary and transforms it into a database-ready format. Oracle Loader for Hadoop is a Java MapReduce application that balances the data across reducers to help maximize performance. Oracle R Connector for Hadoop Oracle R Connector for Hadoop is a collection of R packages that provide: Interfaces to work with Hive tables, the Apache Hadoop compute infrastructure, the local R environment, and Oracle database tables Predictive analytic techniques, written in R or Java as Hadoop MapReduce jobs, that can be applied to data in HDFS files You install and load this package as you would any other R package. Using simple R functions, you can perform tasks such as: Access and transform HDFS data using a Hive-enabled transparency layer Use the R language for writing mappers and reducers Copy data between R memory, the local file system, HDFS, Hive, and Oracle databases Schedule R programs to execute as Hadoop MapReduce jobs and return the results to any of those locations Oracle SQL Connector for Hadoop Distributed File System Using Oracle SQL Connector for HDFS, you can use an Oracle Database to access and analyze data residing in Hadoop in these formats: Data Pump files in HDFS Delimited text files in HDFS Hive tables For other file formats, such as JSON files, you can stage the input in Hive tables before using Oracle SQL Connector for HDFS. Oracle SQL Connector for HDFS uses external tables to provide Oracle Database with read access to Hive tables, and to delimited text files and Data Pump files in HDFS. Related Documentation Cloudera's Distribution Including Apache Hadoop Library HTML Oracle R Enterprise HTML Oracle NoSQL Database HTML Recent Blog Posts Big Data Appliance vs. DIY Price Comparison Big Data: Architecture Overview Big Data: Achieve the Impossible in Real-Time Big Data: Vertical Behavioral Analytics Big Data: In-Memory MapReduce Flume and Hive for Log Analytics Building Workflows in Oozie

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  • hadoop implementing a generic list writable

    - by Guruprasad Venkatesh
    I am working on building a map reduce pipeline of jobs(with one MR job's output feeding to another as input). The values being passed around are fairly complex, in that there are lists of different types and hash maps with values as lists. Hadoop api does not seem to have a ListWritable. Am trying to write a generic one, but it seems i can't instantiate a generic type in my readFields implementation, unless i pass in the class type itself: public class ListWritable<T extends Writable> implements Writable { private List<T> list; private Class<T> clazz; public ListWritable(Class<T> clazz) { this.clazz = clazz; list = new ArrayList<T>(); } @Override public void write(DataOutput out) throws IOException { out.writeInt(list.size()); for (T element : list) { element.write(out); } } @Override public void readFields(DataInput in) throws IOException{ int count = in.readInt(); this.list = new ArrayList<T>(); for (int i = 0; i < count; i++) { try { T obj = clazz.newInstance(); obj.readFields(in); list.add(obj); } catch (InstantiationException e) { e.printStackTrace(); } catch (IllegalAccessException e) { e.printStackTrace(); } } } } But hadoop requires all writables to have a no argument constructor to read the values back. Has anybody tried to do the same and solved this problem? TIA.

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  • Hadoop File Read

    - by user3684584
    Hadoop Distributed Cache Wordcount example in hadoop 2.2.0. Copied file into hdfs filesystem to be used inside setup of mapper class. protected void setup(Context context) throws IOException,InterruptedException { Path[] uris = DistributedCache.getLocalCacheFiles(context.getConfiguration()); cacheData=new HashMap(); for(Path urifile: uris) { try { BufferedReader readBuffer1 = new BufferedReader(new FileReader(urifile.toString())); String line; while ((line=readBuffer1.readLine())!=null) { System.out.println("**************"+line); cacheData.put(line,line); } readBuffer1.close(); } catch (Exception e) { System.out.println(e.toString()); } } } Inside Driver Main class Configuration conf = new Configuration(); String[] otherArgs = new GenericOptionsParser(conf,args).getRemainingArgs(); if (otherArgs.length != 3) { System.err.println("Usage: wordcount <in> <out>"); System.exit(2); } Job job = new Job(conf, "word_count"); job.setJarByClass(WordCount.class); job.setMapperClass(Map.class); job.setReducerClass(Reduce.class); job.setOutputKeyClass(Text.class); job.setOutputValueClass(IntWritable.class); FileInputFormat.addInputPath(job, new Path(otherArgs[0])); Path outputpath=new Path(otherArgs[1]); outputpath.getFileSystem(conf).delete(outputpath,true); FileOutputFormat.setOutputPath(job,outputpath); System.out.println("CachePath****************"+otherArgs[2]); DistributedCache.addCacheFile(new URI(otherArgs[2]),job.getConfiguration()); System.exit(job.waitForCompletion(true) ? 0 : 1); But getting exception java.io.FileNotFoundException: file:/home/user12/tmp/mapred/local/1408960542382/cache (No such file or directory) So Cache functionality not working properly. Any Idea ?

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  • An equivalent of IceCast but for Live Video Streaming ?

    - by Kedare
    Hello, I am looking for a solution to Stream live video like that : A camera/webcam/video output ---> Stream server ---> Clients And if possible multiple Stream Servers like this (like IceCast): A camera/webcam/video output --> Master Stream server +---> Slave Stream Server ---> Clients | `--> Clients | `--> Slave Stream Server ---> Clients `--> Clients The clients will be in flash, so I think RTMP should be a good protocol, I've heard of Red5, is it good for that ? Does it scale ? I would like to get statistics (Amount of clients, Bandwidth, etc), is it possible with red5 ? Do you know any other good solution to do that ? (Only free and if possible Open Source) Thank you !

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  • What's the best way to test a P2P live streaming app?

    - by hbt
    Hey guys, I've been working on a P2P live streaming app and I'm having some trouble testing it properly. At the moment, I'm testing it using: 1) Another laptop + an external server 2) Multiple instances running on different ports Problem is: this is not exactly ready for production. Is there something like a simulator OR any of you guys worked on a torrent client, p2p client, live streaming solution and had to test it? Please let me know, Thanks, -hbt

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  • Hadoop Hive web interface options

    - by Garethr
    I've been experimenting with Hive for some data mining activities and would like to make it easily available to less command line orientated colleagues. Hive does now ship with a web interface (http://wiki.apache.org/hadoop/Hive/HiveWebInterface) but it's very basic at this stage. My question is does a visually polished and fully featured interface (either desktop or preferably web based) to Hive exist yet? Are their any open source efforts outside the Hive project working on this?

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  • Very basic question about Hadoop and compressed input files

    - by Luis Sisamon
    I have started to look into Hadoop. If my understanding is right i could process a very big file and it would get split over different nodes, however if the file is compressed then the file could not be split and wold need to be processed by a single node (effectively destroying the advantage of running a mapreduce ver a cluster of parallel machines). My question is, assuming the above is correct, is it possible to split a large file manually in fixed-size chunks, or daily chunks, compress them and then pass a list of compressed input files to perform a mapreduce?

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  • Having two sets of input combined on hadoop

    - by aeolist
    I have a rather simple hadoop question which i'll try to present with an example say you have a list of strings and a large file and you want each mapper to process a piece of the file and one of the strings in a grep like program. how are you supposed to do that? I am under the impression that the number of mappers is a result of the inputsplits produced. I could run subsequent jobs, one for each string, but it seems kinda... messy?

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