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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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• Running a simple integration scenario using the Oracle Big Data Connectors on Hadoop/HDFS cluster

  - by hamsun

  Between the elephant ( the tradional image of the Hadoop framework) and the Oracle Iron Man (Big Data..) an english setter could be seen as the link to the right data Data, Data, Data, we are living in a world where data technology based on popular applications , search engines, Webservers, rich sms messages, email clients, weather forecasts and so on, have a predominant role in our life. More and more technologies are used to analyze/track our behavior, try to detect patterns, to propose us "the best/right user experience" from the Google Ad services, to Telco companies or large consumer sites (like Amazon:) ). The more we use all these technologies, the more we generate data, and thus there is a need of huge data marts and specific hardware/software servers (as the Exadata servers) in order to treat/analyze/understand the trends and offer new services to the users. Some of these "data feeds" are raw, unstructured data, and cannot be processed effectively by normal SQL queries. Large scale distributed processing was an emerging infrastructure need and the solution seemed to be the "collocation of compute nodes with the data", which in turn leaded to MapReduce parallel patterns and the development of the Hadoop framework, which is based on MapReduce and a distributed file system (HDFS) that runs on larger clusters of rather inexpensive servers. Several Oracle products are using the distributed / aggregation pattern for data calculation ( Coherence, NoSql, times ten ) so once that you are familiar with one of these technologies, lets says with coherence aggregators, you will find the whole Hadoop, MapReduce concept very similar. Oracle Big Data Appliance is based on the Cloudera Distribution (CDH), and the Oracle Big Data Connectors can be plugged on a Hadoop cluster running the CDH distribution or equivalent Hadoop clusters. In this paper, a "lab like" implementation of this concept is done on a single Linux X64 server, running an Oracle Database 11g Enterprise Edition Release 11.2.0.4.0, and a single node Apache hadoop-1.2.1 HDFS cluster, using the SQL connector for HDFS. The whole setup is fairly simple: Install on a Linux x64 server ( or virtual box appliance) an Oracle Database 11g Enterprise Edition Release 11.2.0.4.0 server Get the Apache Hadoop distribution from: http://mir2.ovh.net/ftp.apache.org/dist/hadoop/common/hadoop-1.2.1. Get the Oracle Big Data Connectors from: http://www.oracle.com/technetwork/bdc/big-data-connectors/downloads/index.html?ssSourceSiteId=ocomen. Check the java version of your Linux server with the command: java -version java version "1.7.0_40" Java(TM) SE Runtime Environment (build 1.7.0_40-b43) Java HotSpot(TM) 64-Bit Server VM (build 24.0-b56, mixed mode) Decompress the hadoop hadoop-1.2.1.tar.gz file to /u01/hadoop-1.2.1 Modify your .bash_profile export HADOOP_HOME=/u01/hadoop-1.2.1 export PATH=$PATH:$HADOOP_HOME/bin export HIVE_HOME=/u01/hive-0.11.0 export PATH=$PATH:$HADOOP_HOME/bin:$HIVE_HOME/bin (also see my sample .bash_profile) Set up ssh trust for Hadoop process, this is a mandatory step, in our case we have to establish a "local trust" as will are using a single node configuration copy the new public keys to the list of authorized keys connect and test the ssh setup to your localhost: We will run a "pseudo-Hadoop cluster", in what is called "local standalone mode", all the Hadoop java components are running in one Java process, this is enough for our demo purposes. We need to "fine tune" some Hadoop configuration files, we have to go at our $HADOOP_HOME/conf, and modify the files: core-site.xml hdfs-site.xml mapred-site.xml check that the hadoop binaries are referenced correctly from the command line by executing: hadoop -version As Hadoop is managing our "clustered HDFS" file system we have to create "the mount point" and format it , the mount point will be declared to core-site.xml as: The layout under the /u01/hadoop-1.2.1/data will be created and used by other hadoop components (MapReduce = /mapred/...) HDFS is using the /dfs/... layout structure format the HDFS hadoop file system: Start the java components for the HDFS system As an additional check, you can use the GUI Hadoop browsers to check the content of your HDFS configurations: Once our HDFS Hadoop setup is done you can use the HDFS file system to store data ( big data : )), and plug them back and forth to Oracle Databases by the means of the Big Data Connectors ( which is the next configuration step). You can create / use a Hive db, but in our case we will make a simple integration of "raw data" , through the creation of an External Table to a local Oracle instance ( on the same Linux box, we run the Hadoop HDFS one node cluster and one Oracle DB). Download some public "big data", I use the site: http://france.meteofrance.com/france/observations, from where I can get *.csv files for my big data simulations :). Here is the data layout of my example file: Download the Big Data Connector from the OTN (oraosch-2.2.0.zip), unzip it to your local file system (see picture below) Modify your environment in order to access the connector libraries , and make the following test: [oracle@dg1 bin]$./hdfs_stream Usage: hdfs_stream locationFile [oracle@dg1 bin]$ Load the data to the Hadoop hdfs file system: hadoop fs -mkdir bgtest_data hadoop fs -put obsFrance.txt bgtest_data/obsFrance.txt hadoop fs -ls /user/oracle/bgtest_data/obsFrance.txt [oracle@dg1 bg-data-raw]$ hadoop fs -ls /user/oracle/bgtest_data/obsFrance.txt Found 1 items -rw-r--r-- 1 oracle supergroup 54103 2013-10-22 06:10 /user/oracle/bgtest_data/obsFrance.txt [oracle@dg1 bg-data-raw]$hadoop fs -ls hdfs:///user/oracle/bgtest_data/obsFrance.txt Found 1 items -rw-r--r-- 1 oracle supergroup 54103 2013-10-22 06:10 /user/oracle/bgtest_data/obsFrance.txt Check the content of the HDFS with the browser UI: Start the Oracle database, and run the following script in order to create the Oracle database user, the Oracle directories for the Oracle Big Data Connector (dg1 it’s my own db id replace accordingly yours): #!/bin/bash export ORAENV_ASK=NO export ORACLE_SID=dg1 . oraenv sqlplus /nolog <<EOF CONNECT / AS sysdba; CREATE OR REPLACE DIRECTORY osch_bin_path AS '/u01/orahdfs-2.2.0/bin'; CREATE USER BGUSER IDENTIFIED BY oracle; GRANT CREATE SESSION, CREATE TABLE TO BGUSER; GRANT EXECUTE ON sys.utl_file TO BGUSER; GRANT READ, EXECUTE ON DIRECTORY osch_bin_path TO BGUSER; CREATE OR REPLACE DIRECTORY BGT_LOG_DIR as '/u01/BG_TEST/logs'; GRANT READ, WRITE ON DIRECTORY BGT_LOG_DIR to BGUSER; CREATE OR REPLACE DIRECTORY BGT_DATA_DIR as '/u01/BG_TEST/data'; GRANT READ, WRITE ON DIRECTORY BGT_DATA_DIR to BGUSER; EOF Put the following in a file named t3.sh and make it executable, hadoop jar $OSCH_HOME/jlib/orahdfs.jar \ oracle.hadoop.exttab.ExternalTable \ -D oracle.hadoop.exttab.tableName=BGTEST_DP_XTAB \ -D oracle.hadoop.exttab.defaultDirectory=BGT_DATA_DIR \ -D oracle.hadoop.exttab.dataPaths="hdfs:///user/oracle/bgtest_data/obsFrance.txt" \ -D oracle.hadoop.exttab.columnCount=7 \ -D oracle.hadoop.connection.url=jdbc:oracle:thin:@//localhost:1521/dg1 \ -D oracle.hadoop.connection.user=BGUSER \ -D oracle.hadoop.exttab.printStackTrace=true \ -createTable --noexecute then test the creation fo the external table with it: [oracle@dg1 samples]$ ./t3.sh ./t3.sh: line 2: /u01/orahdfs-2.2.0: Is a directory Oracle SQL Connector for HDFS Release 2.2.0 - Production Copyright (c) 2011, 2013, Oracle and/or its affiliates. All rights reserved. Enter Database Password:] The create table command was not executed. The following table would be created. CREATE TABLE "BGUSER"."BGTEST_DP_XTAB" ( "C1" VARCHAR2(4000), "C2" VARCHAR2(4000), "C3" VARCHAR2(4000), "C4" VARCHAR2(4000), "C5" VARCHAR2(4000), "C6" VARCHAR2(4000), "C7" VARCHAR2(4000) ) ORGANIZATION EXTERNAL ( TYPE ORACLE_LOADER DEFAULT DIRECTORY "BGT_DATA_DIR" ACCESS PARAMETERS ( RECORDS DELIMITED BY 0X'0A' CHARACTERSET AL32UTF8 STRING SIZES ARE IN CHARACTERS PREPROCESSOR "OSCH_BIN_PATH":'hdfs_stream' FIELDS TERMINATED BY 0X'2C' MISSING FIELD VALUES ARE NULL ( "C1" CHAR(4000), "C2" CHAR(4000), "C3" CHAR(4000), "C4" CHAR(4000), "C5" CHAR(4000), "C6" CHAR(4000), "C7" CHAR(4000) ) ) LOCATION ( 'osch-20131022081035-74-1' ) ) PARALLEL REJECT LIMIT UNLIMITED; The following location files would be created. osch-20131022081035-74-1 contains 1 URI, 54103 bytes 54103 hdfs://localhost:19000/user/oracle/bgtest_data/obsFrance.txt Then remove the --noexecute flag and create the external Oracle table for the Hadoop data. Check the results: The create table command succeeded. CREATE TABLE "BGUSER"."BGTEST_DP_XTAB" ( "C1" VARCHAR2(4000), "C2" VARCHAR2(4000), "C3" VARCHAR2(4000), "C4" VARCHAR2(4000), "C5" VARCHAR2(4000), "C6" VARCHAR2(4000), "C7" VARCHAR2(4000) ) ORGANIZATION EXTERNAL ( TYPE ORACLE_LOADER DEFAULT DIRECTORY "BGT_DATA_DIR" ACCESS PARAMETERS ( RECORDS DELIMITED BY 0X'0A' CHARACTERSET AL32UTF8 STRING SIZES ARE IN CHARACTERS PREPROCESSOR "OSCH_BIN_PATH":'hdfs_stream' FIELDS TERMINATED BY 0X'2C' MISSING FIELD VALUES ARE NULL ( "C1" CHAR(4000), "C2" CHAR(4000), "C3" CHAR(4000), "C4" CHAR(4000), "C5" CHAR(4000), "C6" CHAR(4000), "C7" CHAR(4000) ) ) LOCATION ( 'osch-20131022081719-3239-1' ) ) PARALLEL REJECT LIMIT UNLIMITED; The following location files were created. osch-20131022081719-3239-1 contains 1 URI, 54103 bytes 54103 hdfs://localhost:19000/user/oracle/bgtest_data/obsFrance.txt This is the view from the SQL Developer: and finally the number of lines in the oracle table, imported from our Hadoop HDFS cluster SQL select count(*) from "BGUSER"."BGTEST_DP_XTAB"; COUNT(*) ---------- 1151 In a next post we will integrate data from a Hive database, and try some ODI integrations with the ODI Big Data connector. Our simplistic approach is just a step to show you how these unstructured data world can be integrated to Oracle infrastructure. Hadoop, BigData, NoSql are great technologies, they are widely used and Oracle is offering a large integration infrastructure based on these services. Oracle University presents a complete curriculum on all the Oracle related technologies: NoSQL: Introduction to Oracle NoSQL Database Using Oracle NoSQL Database Big Data: Introduction to Big Data Oracle Big Data Essentials Oracle Big Data Overview Oracle Data Integrator: Oracle Data Integrator 12c: New Features Oracle Data Integrator 11g: Integration and Administration Oracle Data Integrator: Administration and Development Oracle Data Integrator 11g: Advanced Integration and Development Oracle Coherence 12c: Oracle Coherence 12c: New Features Oracle Coherence 12c: Share and Manage Data in Clusters Oracle Coherence 12c: Oracle GoldenGate 11g: Fundamentals for Oracle Oracle GoldenGate 11g: Fundamentals for SQL Server Oracle GoldenGate 11g Fundamentals for Oracle Oracle GoldenGate 11g Fundamentals for DB2 Oracle GoldenGate 11g Fundamentals for Teradata Oracle GoldenGate 11g Fundamentals for HP NonStop Oracle GoldenGate 11g Management Pack: Overview Oracle GoldenGate 11g Troubleshooting and Tuning Oracle GoldenGate 11g: Advanced Configuration for Oracle Other Resources: Apache Hadoop : http://hadoop.apache.org/ is the homepage for these technologies. "Hadoop Definitive Guide 3rdEdition" by Tom White is a classical lecture for people who want to know more about Hadoop , and some active "googling " will also give you some more references. About the author: Eugene Simos is based in France and joined Oracle through the BEA-Weblogic Acquisition, where he worked for the Professional Service, Support, end Education for major accounts across the EMEA Region. He worked in the banking sector, ATT, Telco companies giving him extensive experience on production environments. Eugen currently specializes in Oracle Fusion Middleware teaching an array of courses on Weblogic/Webcenter, Content,BPM /SOA/Identity-Security/GoldenGate/Virtualisation/Unified Comm Suite) throughout the EMEA region.

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• ClassNotFoundException error in implementing Bayesian algorithm in Apache Mahout on Hadoop

  - by Shweta

  Hi, I have a problem in executing the Bayesian algorithm in Mahout. I built it with Maven and the job file is in target directory. When run from terminal using hadoop, I'm getting the ClassNotFoundException error. What should be done? $HADOOP_HOME/bin/hadoop jar mahout-core-0.3-SNAPSHOT.job org.apache.mahout.classifier.bayes.mapreduce.bayes.bayesdriver -i test -o output Exception in thread "main" java.lang.ClassNotFoundException: org.apache.mahout.classifier.bayes.mapreduce.bayes.bayesdriver at java.net.URLClassLoader$1.run(URLClassLoader.java:200) at java.security.AccessController.doPrivileged(Native Method) at java.net.URLClassLoader.findClass(URLClassLoader.java:188) at java.lang.ClassLoader.loadClass(ClassLoader.java:307) at java.lang.ClassLoader.loadClass(ClassLoader.java:252) at java.lang.ClassLoader.loadClassInternal(ClassLoader.java:320) at java.lang.Class.forName0(Native Method) at java.lang.Class.forName(Class.java:247) at org.apache.hadoop.util.RunJar.main(RunJar.java:149)

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• Fast Data: Go Big. Go Fast.

  - by J Swaroop

  Cross-posting Dain Hansen's excellent recap of the Big Data/Fast Data announcement during OOW: For those of you who may have missed it, today’s second full day of Oracle OpenWorld 2012 started with a rumpus. Joe Tucci, from EMC outlined the human face of big data with real examples of how big data is transforming our world. And no not the usual tried-and-true weblog examples, but real stories about taxi cab drivers in Singapore using big data to better optimize their routes as well as folks just trying to get a better hair cut. Next we heard from Thomas Kurian who talked at length about the important platform characteristics of Oracle’s Cloud and more specifically Oracle’s expanded Cloud Services portfolio. Especially interesting to our integration customers are the messaging support for Oracle’s Cloud applications. What this means is that now Oracle’s Cloud applications have a lightweight integration fabric that on-premise applications can communicate to it via REST-APIs using Oracle SOA Suite. It’s an important element to our strategy at Oracle that supports this idea that whether your requirements are for private or public, Oracle has a solution in the Cloud for all of your applications and we give you more deployment choice than any vendor. If this wasn’t enough to get the juices flowing, later that morning we heard from Hasan Rizvi who outlined in his Fusion Middleware session the four most important enterprise imperatives: Social, Mobile, Cloud, and a brand new one: Fast Data. Today, Rizvi made an important step in the definition of this term to explain that he believes it’s a convergence of four essential technology elements: Event Processing for event filtering, business rules – with Oracle Event Processing Data Transformation and Loading - with Oracle Data Integrator Real-time replication and integration – with Oracle GoldenGate Analytics and data discovery – with Oracle Business Intelligence Each of these four elements can be considered (and architect-ed) together on a single integrated platform that can help customers integrate any type of data (structured, semi-structured) leveraging new styles of big data technologies (MapReduce, HDFS, Hive, NoSQL) to process more volume and variety of data at a faster velocity with greater results.  Fast data processing (and especially real-time) has always been our credo at Oracle with each one of these products in Fusion Middleware. For example, Oracle GoldenGate continues to be made even faster with the recent 11g R2 Release of Oracle GoldenGate which gives us some even greater optimization to Oracle Database with Integrated Capture, as well as some new heterogeneity capabilities. With Oracle Data Integrator with Big Data Connectors, we’re seeing much improved performance by running MapReduce transformations natively on Hadoop systems. And with Oracle Event Processing we’re seeing some remarkable performance with customers like NTT Docomo. Check out their upcoming session at Oracle OpenWorld on Wednesday to hear more how this customer is using Event processing and Big Data together. If you missed any of these sessions and keynotes, not to worry. There's on-demand versions available on the Oracle OpenWorld website. You can also checkout our upcoming webcast where we will outline some of these new breakthroughs in Data Integration technologies for Big Data, Cloud, and Real-time in more details.

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• Building massively scalable systems, where to start? [closed]

  - by Mahmoud Hossam

  Recently, I've been seeing these job postings about building scalable systems using Java, and some of the technologies mentioned were: Cassandra Thrift Hadoop MapReduce Among others. How can I get started with these technologies? Is there something else I need to know before actually learning any of these technologies? Maybe some general concepts about building highly available and scalable systems? I already know Java SE, so I won't be starting from scratch.

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• Training a Toy Elephant with Google Summer of Code

  Google has redefined many many things. It has redefined scalability. When the entire world was racing towards high performance computing, Google came up with MapReduce and the Google...

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• Facebook sort Presto, son moteur de requêtes open source pour le big data, qui serait dix fois plus performant que celui de Hadoop

  Facebook sort Presto, son moteur de requêtes open source pour le big data qui serait dix fois plus performant que celui de HadoopDe nombreuses entreprises comme Facebook dépendent du Big data. Dans le domaine, on compte la paire Hadoop/Hive parmi les références. Pour rappel, Hive c'est le moteur de requêtes populaire pour Hadoop. Cependant, il se pourrait que le MapReduce élément essentiel sur lequel repose Hive ne soit pas optimisé pour des situations ou la quantité de données excède un certain...

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• ORDER BY job failed in the Pig script while running EmbeddedPig using Java

  - by C.c. Huang

  I have this following pig script, which works perfectly using grunt shell (stored the results to HDFS without any issues); however, the last job (ORDER BY) failed if I ran the same script using Java EmbeddedPig. If I replace the ORDER BY job by others, such as GROUP or FOREACH GENERATE, the whole script then succeeded in Java EmbeddedPig. So I think it's the ORDER BY which causes the issue. Anyone has any experience with this? Any help would be appreciated! The Pig script: REGISTER pig-udf-0.0.1-SNAPSHOT.jar; user_similarity = LOAD '/tmp/sample-sim-score-results-31/part-r-00000' USING PigStorage('\t') AS (user_id: chararray, sim_user_id: chararray, basic_sim_score: float, alt_sim_score: float); simplified_user_similarity = FOREACH user_similarity GENERATE $0 AS user_id, $1 AS sim_user_id, $2 AS sim_score; grouped_user_similarity = GROUP simplified_user_similarity BY user_id; ordered_user_similarity = FOREACH grouped_user_similarity { sorted = ORDER simplified_user_similarity BY sim_score DESC; top = LIMIT sorted 10; GENERATE group, top; }; top_influencers = FOREACH ordered_user_similarity GENERATE com.aol.grapevine.similarity.pig.udf.AssignPointsToTopInfluencer($1, 10); all_influence_scores = FOREACH top_influencers GENERATE FLATTEN($0); grouped_influence_scores = GROUP all_influence_scores BY bag_of_topSimUserTuples::user_id; influence_scores = FOREACH grouped_influence_scores GENERATE group AS user_id, SUM(all_influence_scores.bag_of_topSimUserTuples::points) AS influence_score; ordered_influence_scores = ORDER influence_scores BY influence_score DESC; STORE ordered_influence_scores INTO '/tmp/cc-test-results-1' USING PigStorage(); The error log from Pig: 12/04/05 10:00:56 INFO pigstats.ScriptState: Pig script settings are added to the job 12/04/05 10:00:56 INFO mapReduceLayer.JobControlCompiler: mapred.job.reduce.markreset.buffer.percent is not set, set to default 0.3 12/04/05 10:00:58 INFO mapReduceLayer.JobControlCompiler: Setting up single store job 12/04/05 10:00:58 INFO jvm.JvmMetrics: Cannot initialize JVM Metrics with processName=JobTracker, sessionId= - already initialized 12/04/05 10:00:58 INFO mapReduceLayer.MapReduceLauncher: 1 map-reduce job(s) waiting for submission. 12/04/05 10:00:58 WARN mapred.JobClient: Use GenericOptionsParser for parsing the arguments. Applications should implement Tool for the same. 12/04/05 10:00:58 INFO input.FileInputFormat: Total input paths to process : 1 12/04/05 10:00:58 INFO util.MapRedUtil: Total input paths to process : 1 12/04/05 10:00:58 INFO util.MapRedUtil: Total input paths (combined) to process : 1 12/04/05 10:00:58 INFO filecache.TrackerDistributedCacheManager: Creating tmp-1546565755 in /var/lib/hadoop-0.20/cache/cchuang/mapred/local/archive/4334795313006396107_361978491_57907159/localhost/tmp/temp1725960134-work-6955502337234509704 with rwxr-xr-x 12/04/05 10:00:58 INFO filecache.TrackerDistributedCacheManager: Cached hdfs://localhost/tmp/temp1725960134/tmp-1546565755#pigsample_854728855_1333645258470 as /var/lib/hadoop-0.20/cache/cchuang/mapred/local/archive/4334795313006396107_361978491_57907159/localhost/tmp/temp1725960134/tmp-1546565755 12/04/05 10:00:58 INFO filecache.TrackerDistributedCacheManager: Cached hdfs://localhost/tmp/temp1725960134/tmp-1546565755#pigsample_854728855_1333645258470 as /var/lib/hadoop-0.20/cache/cchuang/mapred/local/archive/4334795313006396107_361978491_57907159/localhost/tmp/temp1725960134/tmp-1546565755 12/04/05 10:00:58 WARN mapred.LocalJobRunner: LocalJobRunner does not support symlinking into current working dir. 12/04/05 10:00:58 INFO mapred.TaskRunner: Creating symlink: /var/lib/hadoop-0.20/cache/cchuang/mapred/local/archive/4334795313006396107_361978491_57907159/localhost/tmp/temp1725960134/tmp-1546565755 <- /var/lib/hadoop-0.20/cache/cchuang/mapred/local/localRunner/pigsample_854728855_1333645258470 12/04/05 10:00:58 INFO filecache.TrackerDistributedCacheManager: Creating symlink: /var/lib/hadoop-0.20/cache/cchuang/mapred/staging/cchuang402164468/.staging/job_local_0004/.job.jar.crc <- /var/lib/hadoop-0.20/cache/cchuang/mapred/local/localRunner/.job.jar.crc 12/04/05 10:00:58 INFO filecache.TrackerDistributedCacheManager: Creating symlink: /var/lib/hadoop-0.20/cache/cchuang/mapred/staging/cchuang402164468/.staging/job_local_0004/.job.split.crc <- /var/lib/hadoop-0.20/cache/cchuang/mapred/local/localRunner/.job.split.crc 12/04/05 10:00:59 INFO filecache.TrackerDistributedCacheManager: Creating symlink: /var/lib/hadoop-0.20/cache/cchuang/mapred/staging/cchuang402164468/.staging/job_local_0004/.job.splitmetainfo.crc <- /var/lib/hadoop-0.20/cache/cchuang/mapred/local/localRunner/.job.splitmetainfo.crc 12/04/05 10:00:59 INFO filecache.TrackerDistributedCacheManager: Creating symlink: /var/lib/hadoop-0.20/cache/cchuang/mapred/staging/cchuang402164468/.staging/job_local_0004/.job.xml.crc <- /var/lib/hadoop-0.20/cache/cchuang/mapred/local/localRunner/.job.xml.crc 12/04/05 10:00:59 INFO filecache.TrackerDistributedCacheManager: Creating symlink: /var/lib/hadoop-0.20/cache/cchuang/mapred/staging/cchuang402164468/.staging/job_local_0004/job.jar <- /var/lib/hadoop-0.20/cache/cchuang/mapred/local/localRunner/job.jar 12/04/05 10:00:59 INFO filecache.TrackerDistributedCacheManager: Creating symlink: /var/lib/hadoop-0.20/cache/cchuang/mapred/staging/cchuang402164468/.staging/job_local_0004/job.split <- /var/lib/hadoop-0.20/cache/cchuang/mapred/local/localRunner/job.split 12/04/05 10:00:59 INFO filecache.TrackerDistributedCacheManager: Creating symlink: /var/lib/hadoop-0.20/cache/cchuang/mapred/staging/cchuang402164468/.staging/job_local_0004/job.splitmetainfo <- /var/lib/hadoop-0.20/cache/cchuang/mapred/local/localRunner/job.splitmetainfo 12/04/05 10:00:59 INFO filecache.TrackerDistributedCacheManager: Creating symlink: /var/lib/hadoop-0.20/cache/cchuang/mapred/staging/cchuang402164468/.staging/job_local_0004/job.xml <- /var/lib/hadoop-0.20/cache/cchuang/mapred/local/localRunner/job.xml 12/04/05 10:00:59 INFO mapred.Task: Using ResourceCalculatorPlugin : null 12/04/05 10:00:59 INFO mapred.MapTask: io.sort.mb = 100 12/04/05 10:00:59 INFO mapred.MapTask: data buffer = 79691776/99614720 12/04/05 10:00:59 INFO mapred.MapTask: record buffer = 262144/327680 12/04/05 10:00:59 WARN mapred.LocalJobRunner: job_local_0004 java.lang.RuntimeException: org.apache.hadoop.mapreduce.lib.input.InvalidInputException: Input path does not exist: file:/Users/cchuang/workspace/grapevine-rec/pigsample_854728855_1333645258470 at org.apache.pig.backend.hadoop.executionengine.mapReduceLayer.partitioners.WeightedRangePartitioner.setConf(WeightedRangePartitioner.java:139) at org.apache.hadoop.util.ReflectionUtils.setConf(ReflectionUtils.java:62) at org.apache.hadoop.util.ReflectionUtils.newInstance(ReflectionUtils.java:117) at org.apache.hadoop.mapred.MapTask$NewOutputCollector.<init>(MapTask.java:560) at org.apache.hadoop.mapred.MapTask.runNewMapper(MapTask.java:639) at org.apache.hadoop.mapred.MapTask.run(MapTask.java:323) at org.apache.hadoop.mapred.LocalJobRunner$Job.run(LocalJobRunner.java:210) Caused by: org.apache.hadoop.mapreduce.lib.input.InvalidInputException: Input path does not exist: file:/Users/cchuang/workspace/grapevine-rec/pigsample_854728855_1333645258470 at org.apache.hadoop.mapreduce.lib.input.FileInputFormat.listStatus(FileInputFormat.java:231) at org.apache.pig.backend.hadoop.executionengine.mapReduceLayer.PigFileInputFormat.listStatus(PigFileInputFormat.java:37) at org.apache.hadoop.mapreduce.lib.input.FileInputFormat.getSplits(FileInputFormat.java:248) at org.apache.pig.impl.io.ReadToEndLoader.init(ReadToEndLoader.java:153) at org.apache.pig.impl.io.ReadToEndLoader.<init>(ReadToEndLoader.java:115) at org.apache.pig.backend.hadoop.executionengine.mapReduceLayer.partitioners.WeightedRangePartitioner.setConf(WeightedRangePartitioner.java:112) ... 6 more 12/04/05 10:00:59 INFO filecache.TrackerDistributedCacheManager: Deleted path /var/lib/hadoop-0.20/cache/cchuang/mapred/local/archive/4334795313006396107_361978491_57907159/localhost/tmp/temp1725960134/tmp-1546565755 12/04/05 10:00:59 INFO mapReduceLayer.MapReduceLauncher: HadoopJobId: job_local_0004 12/04/05 10:01:04 INFO mapReduceLayer.MapReduceLauncher: job job_local_0004 has failed! Stop running all dependent jobs 12/04/05 10:01:04 INFO mapReduceLayer.MapReduceLauncher: 100% complete 12/04/05 10:01:04 ERROR pigstats.PigStatsUtil: 1 map reduce job(s) failed! 12/04/05 10:01:04 INFO pigstats.PigStats: Script Statistics: HadoopVersion PigVersion UserId StartedAt FinishedAt Features 0.20.2-cdh3u3 0.8.1-cdh3u3 cchuang 2012-04-05 10:00:34 2012-04-05 10:01:04 GROUP_BY,ORDER_BY Some jobs have failed! Stop running all dependent jobs Job Stats (time in seconds): JobId Maps Reduces MaxMapTime MinMapTIme AvgMapTime MaxReduceTime MinReduceTime AvgReduceTime Alias Feature Outputs job_local_0001 0 0 0 0 0 0 0 0 all_influence_scores,grouped_user_similarity,simplified_user_similarity,user_similarity GROUP_BY job_local_0002 0 0 0 0 0 0 0 0 grouped_influence_scores,influence_scores GROUP_BY,COMBINER job_local_0003 0 0 0 0 0 0 0 0 ordered_influence_scores SAMPLER Failed Jobs: JobId Alias Feature Message Outputs job_local_0004 ordered_influence_scores ORDER_BY Message: Job failed! Error - NA /tmp/cc-test-results-1, Input(s): Successfully read 0 records from: "/tmp/sample-sim-score-results-31/part-r-00000" Output(s): Failed to produce result in "/tmp/cc-test-results-1" Counters: Total records written : 0 Total bytes written : 0 Spillable Memory Manager spill count : 0 Total bags proactively spilled: 0 Total records proactively spilled: 0 Job DAG: job_local_0001 -> job_local_0002, job_local_0002 -> job_local_0003, job_local_0003 -> job_local_0004, job_local_0004 12/04/05 10:01:04 INFO mapReduceLayer.MapReduceLauncher: Some jobs have failed! Stop running all dependent jobs

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• CodePlex Daily Summary for Monday, January 31, 2011

  CodePlex Daily Summary for Monday, January 31, 2011Popular ReleasesMVC Controls Toolkit: Mvc Controls Toolkit 0.8: Fixed the following bugs: *Variable name error in the jvascript file that prevented the use of the deleted item template of the Datagrid *Now after the changes applied to an item of the DataGrid are cancelled all input fields are reset to the very initial value they had. *Other minor bugs. Added: *This version is available both for MVC2, and MVC 3. The MVC 3 version has a release number of 0.85. This way one can install both version. *Client Validation support has been added to all control...Office Web.UI: Beta preview (Source): This is the first Beta. it includes full source code and all available controls. Some designers are not ready, and some features are not finalized allready (missing properties, draft styles) ThanksASP.net Ribbon: Version 2.2: This release brings some new controls (part of Office Web.UI). A few bugs are fixed and it includes the "auto resize" feature as you resize the window. (It can cause an infinite loop when the window is too reduced, it's why this release is not marked as "stable"). I will release more versions 2.3, 2.4... until V3 which will be the official launch of Office Web.UI. Both products will evolve at the same speed. Thanks.Barcode Rendering Framework: 2.1.1.0: Final release for VS2008 Finally fixed bugs with code 128 symbology.HERB.IQ: HERB.IQ.UPGRADE.0.5.3.exe: HERB.IQ.UPGRADE.0.5.3.exexUnit.net - Unit Testing for .NET: xUnit.net 1.7: xUnit.net release 1.7Build #1540 Important notes for Resharper users: Resharper support has been moved to the xUnit.net Contrib project. Important note for TestDriven.net users: If you are having issues running xUnit.net tests in TestDriven.net, especially on 64-bit Windows, we strongly recommend you upgrade to TD.NET version 3.0 or later. This release adds the following new features: Added support for ASP.NET MVC 3 Added Assert.Equal(double expected, double actual, int precision) Ad...DoddleReport - Automatic HTML/Excel/PDF Reporting: DoddleReport 1.0: DoddleReport will add automatic tabular-based reporting (HTML/PDF/Excel/etc) for any LINQ Query, IEnumerable, DataTable or SharePoint List For SharePoint integration please click Here PDF Reporting has been placed into a separate assembly because it requies AbcPdf http://www.websupergoo.com/download.htmSpark View Engine: Spark v1.5: Release Notes There have been a lot of minor changes going on since version 1.1, but most important to note are the major changes which include: Support for HTML5 "section" tag. Spark has now renamed its own section tag to "segment" instead to avoid clashes. You can still use "section" in a Spark sense for legacy support by specifying ParseSectionAsSegment = true if needed while you transition Bindings - this is a massive feature that further simplifies your views by giving you a powerful ...Marr DataMapper: Marr DataMapper 1.0.0 beta: First release.WPF Application Framework (WAF): WPF Application Framework (WAF) 2.0.0.3: Version: 2.0.0.3 (Milestone 3): This release contains the source code of the WPF Application Framework (WAF) and the sample applications. Requirements .NET Framework 4.0 (The package contains a solution file for Visual Studio 2010) The unit test projects require Visual Studio 2010 Professional Remark The sample applications are using Microsoft’s IoC container MEF. However, the WPF Application Framework (WAF) doesn’t force you to use the same IoC container in your application. You can use ...Rawr: Rawr 4.0.17 Beta: Rawr is now web-based. The link to use Rawr4 is: http://elitistjerks.com/rawr.phpThis is the Cataclysm Beta Release. More details can be found at the following link http://rawr.codeplex.com/Thread/View.aspx?ThreadId=237262 and on the Version Notes page: http://rawr.codeplex.com/wikipage?title=VersionNotes As of the 4.0.16 release, you can now also begin using the new Downloadable WPF version of Rawr!This is a pre-alpha release of the WPF version, there are likely to be a lot of issues. If you...Squiggle - A Free open source LAN Messenger: Squiggle 2.5 Beta: In this release following are the new features: Localization: Support for Arabic, French, German and Chinese (Simplified) Bridge: Connect two Squiggle nets across the WAN or different subnets Aliases: Special codes with special meaning can be embedded in message like (version),(datetime),(time),(date),(you),(me) Commands: cls, /exit, /offline, /online, /busy, /away, /main Sound notifications: Get audio alerts on contact online, message received, buzz Broadcast for group: You can ri...VivoSocial: VivoSocial 7.4.2: Version 7.4.2 of VivoSocial has been released. If you experienced any issues with the previous version, please update your modules to the 7.4.2 release and see if they persist. If you have any questions about this release, please post them in our Support forums. If you are experiencing a bug or would like to request a new feature, please submit it to our issue tracker. Web Controls * Updated Business Objects and added a new SQL Data Provider File. Groups * Fixed a security issue whe...PHP Manager for IIS: PHP Manager 1.1.1 for IIS 7: This is a minor release of PHP Manager for IIS 7. It contains all the functionality available in 56962 plus several bug fixes (see change list for more details). Also, this release includes Russian language support. SHA1 codes for the downloads are: PHPManagerForIIS-1.1.0-x86.msi - 6570B4A8AC8B5B776171C2BA0572C190F0900DE2 PHPManagerForIIS-1.1.0-x64.msi - 12EDE004EFEE57282EF11A8BAD1DC1ADFD66A654mojoPortal: 2.3.6.1: see release notes on mojoportal.com http://www.mojoportal.com/mojoportal-2361-released.aspx Note that we have separate deployment packages for .NET 3.5 and .NET 4.0 The deployment package downloads on this page are pre-compiled and ready for production deployment, they contain no C# source code. To download the source code see the Source Code Tab I recommend getting the latest source code using TortoiseHG, you can get the source code corresponding to this release here.Parallel Programming with Microsoft Visual C++: Drop 6 - Chapters 4 and 5: This is Drop 6. It includes: Drafts of the Preface, Introduction, Chapters 2-7, Appendix B & C and the glossary Sample code for chapters 2-7 and Appendix A & B. The new material we'd like feedback on is: Chapter 4 - Parallel Aggregation Chapter 5 - Futures The source code requires Visual Studio 2010 in order to run. There is a known bug in the A-Dash sample when the user attempts to cancel a parallel calculation. We are working to fix this.NodeXL: Network Overview, Discovery and Exploration for Excel: NodeXL Excel Template, version 1.0.1.160: The NodeXL Excel template displays a network graph using edge and vertex lists stored in an Excel 2007 or Excel 2010 workbook. What's NewThis release improves NodeXL's Twitter and Pajek features. See the Complete NodeXL Release History for details. Installation StepsFollow these steps to install and use the template: Download the Zip file. Unzip it into any folder. Use WinZip or a similar program, or just right-click the Zip file in Windows Explorer and select "Extract All." Close Ex...Kooboo CMS: Kooboo CMS 3.0 CTP: Files in this downloadkooboo_CMS.zip: The kooboo application files Content_DBProvider.zip: Additional content database implementation of MSSQL, RavenDB and SQLCE. Default is XML based database. To use them, copy the related dlls into web root bin folder and remove old content provider dlls. Content provider has the name like "Kooboo.CMS.Content.Persistence.SQLServer.dll" View_Engines.zip: Supports of Razor, webform and NVelocity view engine. Copy the dlls into web root bin folder to enable...UOB & ME: UOB ME 2.6: UOB ME 2.6????: ???? V1.0: ???? V1.0 ??New ProjectsAuto Complete Control for ASP.NET: Autocomplete Control is a fully functional ASP.NET control for word suggestions and autocomplete. We had been using Ajax Control Toolkit AutoComplete Extender in our projects before, but we have needed some extra features and functionalities.Cours ESIEE: MAJ des cours ESIEE depuis la plateforme Icampus et autres documentsEngineering World Expenses: Demo expenses application for Engineering World 2011Entity Framework / Linq to Sql Poco Code Generator: Poco Orm data access layer (Dto) code generator for Entity Framework and Linq to Sql. Customizable code generation via simple templating system. Utilizes Managed Extensibility Framework (MEF) in order for application parts to dynamically composed and plug-able.linqish.py: Python module for manipulating iterables. An implementation of the .Net Framework's Linq to Objects for Python.Machinekey setter: This code sample is Windows Azure SDK 1.3 custom plugin. This sample do working at set custom key to machinekey of web.config file in your WebRole.MapReduce.NET: MapReduce.NET intends to implement the original paper proposed by Google on MapReduce.Marr DataMapper: Marr DataMapper provides a fast and easy to use wrapper around ADO.NET that enables you to focus more on your data access queries without having to write plumbing code. Load one-to-one, one-to-many, and hierarchical entity models with ease. No special base class required.Orchard Silverlight: Orchard module enabling embedding Silverlight applications and creating Silverlight-based content.RouteMagic: Library of useful routing helpers and classes.Smart Skelta Utilites: Smart Skelta Utilies will provide utilties like Visual Studio 2008 Skelta Starter Kit(Project Templates and Project Item Templates),Code Snippets for Skelta Components,Skleta Attachment Extracter Web based Logger,Skelta Server utility and others for skelta based development.Solfix: Solfix is a programming language tbat is work-in-progress, but it has a lot of functionality! You can make applications for console to windows applications. The main point of Solfix is to make coding easier and less time than before.SQLite Manager: A minimal manage for sqlite databases.State Search: StateSearch provides state search algoritms such as A*, IDA*, BestFirst, etc to solve problems such as puzzles and/or path searchingTable Check Custom Field Type: SharePoint Custom Field Type for displaying a list of values with checkboxes and people editors.testsgb: testWindows Phone 7 Extension Framework: An extension method framework for Windows Phone 7 to make your code more fluent and adding a lot of common functions you don't need to reproduce.

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• Fast Data: Go Big. Go Fast.

  - by Dain C. Hansen

  Normal 0 false false false EN-US X-NONE X-NONE MicrosoftInternetExplorer4 For those of you who may have missed it, today’s second full day of Oracle OpenWorld 2012 started with a rumpus. Joe Tucci, from EMC outlined the human face of big data with real examples of how big data is transforming our world. And no not the usual tried-and-true weblog examples, but real stories about taxi cab drivers in Singapore using big data to better optimize their routes as well as folks just trying to get a better hair cut. Next we heard from Thomas Kurian who talked at length about the important platform characteristics of Oracle’s Cloud and more specifically Oracle’s expanded Cloud Services portfolio. Especially interesting to our integration customers are the messaging support for Oracle’s Cloud applications. What this means is that now Oracle’s Cloud applications have a lightweight integration fabric that on-premise applications can communicate to it via REST-APIs using Oracle SOA Suite. It’s an important element to our strategy at Oracle that supports this idea that whether your requirements are for private or public, Oracle has a solution in the Cloud for all of your applications and we give you more deployment choice than any vendor. If this wasn’t enough to get the juices flowing, later that morning we heard from Hasan Rizvi who outlined in his Fusion Middleware session the four most important enterprise imperatives: Social, Mobile, Cloud, and a brand new one: Fast Data. Today, Rizvi made an important step in the definition of this term to explain that he believes it’s a convergence of four essential technology elements: Event Processing for event filtering, business rules – with Oracle Event Processing Data Transformation and Loading - with Oracle Data Integrator Real-time replication and integration – with Oracle GoldenGate Analytics and data discovery – with Oracle Business Intelligence Each of these four elements can be considered (and architect-ed) together on a single integrated platform that can help customers integrate any type of data (structured, semi-structured) leveraging new styles of big data technologies (MapReduce, HDFS, Hive, NoSQL) to process more volume and variety of data at a faster velocity with greater results.  Fast data processing (and especially real-time) has always been our credo at Oracle with each one of these products in Fusion Middleware. For example, Oracle GoldenGate continues to be made even faster with the recent 11g R2 Release of Oracle GoldenGate which gives us some even greater optimization to Oracle Database with Integrated Capture, as well as some new heterogeneity capabilities. With Oracle Data Integrator with Big Data Connectors, we’re seeing much improved performance by running MapReduce transformations natively on Hadoop systems. And with Oracle Event Processing we’re seeing some remarkable performance with customers like NTT Docomo. Check out their upcoming session at Oracle OpenWorld on Wednesday to hear more how this customer is using Event processing and Big Data together. If you missed any of these sessions and keynotes, not to worry. There's on-demand versions available on the Oracle OpenWorld website. You can also checkout our upcoming webcast where we will outline some of these new breakthroughs in Data Integration technologies for Big Data, Cloud, and Real-time in more details. /* Style Definitions */ table.MsoNormalTable {mso-style-name:"Table Normal"; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-priority:99; mso-style-qformat:yes; mso-style-parent:""; mso-padding-alt:0in 5.4pt 0in 5.4pt; mso-para-margin:0in; mso-para-margin-bottom:.0001pt; mso-pagination:widow-orphan; font-size:10.0pt; font-family:"Calibri","sans-serif"; mso-bidi-font-family:"Times New Roman";}

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• Big Data – Various Learning Resources – How to Start with Big Data? – Day 20 of 21

  - by Pinal Dave

  In yesterday’s blog post we learned how to become a Data Scientist for Big Data. In this article we will go over various learning resources related to Big Data. In this series we have covered many of the most essential details about Big Data. At the beginning of this series, I have encouraged readers to send me questions. One of the most popular questions is - “I want to learn more about Big Data. Where can I learn it?” This is indeed a great question as there are plenty of resources out to learn about Big Data and it is indeed difficult to select on one resource to learn Big Data. Hence I decided to write here a few of the very important resources which are related to Big Data. Learn from Pluralsight Pluralsight is a global leader in high-quality online training for hardcore developers.  It has fantastic Big Data Courses and I started to learn about Big Data with the help of Pluralsight. Here are few of the courses which are directly related to Big Data. Big Data: The Big Picture Big Data Analytics with Tableau NoSQL: The Big Picture Understanding NoSQL Data Analysis Fundamentals with Tableau I encourage all of you start with this video course as they are fantastic fundamentals to learn Big Data. Learn from Apache Resources at Apache are single point the most authentic learning resources. If you want to learn fundamentals and go deep about every aspect of the Big Data, I believe you must understand various concepts in Apache’s library. I am pretty impressed with the documentation and I am personally referencing it every single day when I work with Big Data. I strongly encourage all of you to bookmark following all the links for authentic big data learning. Haddop - The Apache Hadoop® project develops open-source software for reliable, scalable, distributed computing. Ambari: A web-based tool for provisioning, managing, and monitoring Apache Hadoop clusters which include support for Hadoop HDFS, Hadoop MapReduce, Hive, HCatalog, HBase, ZooKeeper, Oozie, Pig and Sqoop. Ambari also provides a dashboard for viewing cluster health such as heat maps and ability to view MapReduce, Pig and Hive applications visually along with features to diagnose their performance characteristics in a user-friendly manner. Avro: A data serialization system. Cassandra: A scalable multi-master database with no single points of failure. Chukwa: A data collection system for managing large distributed systems. HBase: A scalable, distributed database that supports structured data storage for large tables. Hive: A data warehouse infrastructure that provides data summarization and ad hoc querying. Mahout: A Scalable machine learning and data mining library. Pig: A high-level data-flow language and execution framework for parallel computation. ZooKeeper: A high-performance coordination service for distributed applications. Learn from Vendors One of the biggest issues with about learning Big Data is setting up the environment. Every Big Data vendor has different environment request and there are lots of things require to set up Big Data framework. Many of the users do not start with Big Data as they are afraid about the resources required to set up framework as well as a time commitment. Here Hortonworks have created fantastic learning environment. They have created Sandbox with everything one person needs to learn Big Data and also have provided excellent tutoring along with it. Sandbox comes with a dozen hands-on tutorial that will guide you through the basics of Hadoop as well it contains the Hortonworks Data Platform. I think Hortonworks did a fantastic job building this Sandbox and Tutorial. Though there are plenty of different Big Data Vendors I have decided to list only Hortonworks due to their unique setup. Please leave a comment if there are any other such platform to learn Big Data. I will include them over here as well. Learn from Books There are indeed few good books out there which one can refer to learn Big Data. Here are few good books which I have read. I will update the list as I will learn more. Ethics of Big Data Balancing Risk and Innovation Big Data for Dummies Head First Data Analysis: A Learner’s Guide to Big Numbers, Statistics, and Good Decisions If you search on Amazon there are millions of the books but I think above three books are a great set of books and it will give you great ideas about Big Data. Once you go through above books, you will have a clear idea about what is the next step you should follow in this series. You will be capable enough to make the right decision for yourself. Tomorrow In tomorrow’s blog post we will wrap up this series of Big Data. Reference: Pinal Dave (http://blog.sqlauthority.com) Filed under: Big Data, PostADay, SQL, SQL Authority, SQL Query, SQL Server, SQL Tips and Tricks, T SQL

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• How should calculations be handled in a document database

  - by Morten

  Ok, so I have a program that basically logs errors into a nosql database. Right now there is just a single model for an error and its stored as a document in the nosql database. Basically I want to summarize across different errors and produce a summary of the "types" of errors that occured. Traditionally in a SQL database the this normalization would work with groupings, sums and averages but in a NoSQL database I assume I need to use mapreduce. My current model seems unfit for the task, how should I change the way I store "models" in order to make statistical analysis easy? Would a NoSQL database even be the right tool for this type of problem? I'm storing things in Google AppEngine's BigTable, so there are some limitations to think of as well.

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• Interview question: How would you implement Google Search?

  - by ripper234

  Supposed you were asked in an interview "How would you implement Google Search?" How would you answer such a question? There might be resources out there that explain how some pieces in Google are implemented (BigTable, MapReduce, PageRank, ...), but that doesn't exactly fit in an interview. What overall architecture would you use, and how would you explain this in a 15-30 minute time span? I would start with explaining how to build a search engine that handles ~ 100k documents, then expand this via sharding to around 50M docs, then perhaps another architectural/technical leap. This is the 20,000 feet view. What I'd like is the details - how you would actually answer that in an interview. Which data structures would you use. What services/machines is your architecture composed of. What would a typical query latency be? What about failover / split brain issues? Etc...

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• io Exception error in wordcount example

  - by Anitha

  I have installed Hadoop 1.0.3 in Ubuntu 12.04 version (64bit) based on michael-noll.com/tutorials/running-hadoop-on-ubuntu-linux-single-node-cluster/ . I am trying to run a mapreduce job using the wordcount example. Running the command hduser@ubuntu: $/usr/local/hadoop/bin/hadoop jar hadoop-examples-1.0.3.jar wordcount /user/hduser/gutenberg /user/hduser/gutenberg-output gives the following error: Warning: $HADOOP_HOME is deprecated. Exception in thread "main" java.io.IOException: Error opening job jar: hadoop-examples-1.0.3.jar at org.apache.hadoop.util.RunJar.main(RunJar.java:90) Caused by: java.util.zip.ZipException: error in opening zip file at java.util.zip.ZipFile.open(Native Method) at java.util.zip.ZipFile.<init>(ZipFile.java:131) at java.util.jar.JarFile.<init>(JarFile.java:150) at java.util.jar.JarFile.<init>(JarFile.java:87) at org.apache.hadoop.util.RunJar.main(RunJar.java:88) Thanks in advance.

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• Cloudera Hadoop Certification Value in IT Industry for freshers

  - by Saumitra

  I am a software developer with 8 months of experience in IT industry working on development of tools for BIG DATA analytics. I have learned Hadoop basics on my own and I am pretty comfortable with writing MapReduce Jobs, PIG, HIVE, Flume and other related projects. I am thinking of appearing for Cloudera Hadoop Certification. My question is whether it will benefit me in any way, considering that I am a fresher with not even 1 year of experience. Most of the jobs posting which I have seen related to Hadoop requires at least 3 years of experience. I currently work in India but I can relocate. Please help me in deciding whether I should invest my time in perfecting my Hadoop skills for certification?

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• Azure Futures - Distributed Computing and Number Crunching

  - by JoshReuben

  "the biggest Azure customers today are the ones using HPC on-premises at the current time" - http://www.zdnet.com/blog/microsoft/windows-azure-futures-turning-the-cloud-into-a-supercomputer/8592?utm_source=feedburner&utm_medium=feed&utm_campaign=Feed%3A+zdnet%2Fmicrosoft+%28ZDNet+All+About+Microsoft%29&utm_content=Google+Reader   Orleans Framework for cloud computing - http://research.microsoft.com/en-us/projects/orleans     HPC on Azure - http://www.zdnet.com/blog/microsoft/microsoft-finalizes-its-latest-supercomputing-operating-system-release/7414   Dryad is Microsoft’s competitor to Google MapReduce and Apache Hadoop  - http://www.zdnet.com/blog/microsoft/microsoft-takes-a-step-toward-commercializing-its-dryad-distributed-computing-technologies/8255?tag=mantle_skin;content   SQL Server Analysis Services DataMining in the cloud - http://www.sqlmag.com/article/reporting2/azure-data-mining-in-the-cloud.aspx

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• What is the value of the Cloudera Hadoop Certification for people new to the IT industry?

  - by Saumitra

  I am a software developer with 8 months of experience in the IT industry, currently working on the development of tools for BIG DATA analytics. I have learned Hadoop basics on my own and I am pretty comfortable with writing MapReduce Jobs, PIG, HIVE, Flume and other related projects. I am thinking of taking the exam for the Cloudera Hadoop Certification. Will this certification add value, considering that I have less than 1 year of experience? Many of the jobs I've seen relating to Hadoop require at least 3 years of experience. Should I invest more time in learning Hadoop and improving my skills to take this certification?

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• Google BigQuery - Best Practices for Loading your Data and open Office Hours

  Google BigQuery - Best Practices for Loading your Data and open Office Hours Michael Manoochehri and Ryan Boyd from the DevRel team for cloud data services will be streaming to you live! They'll be discussing how to load your data into BigQuery and the various options available -- from commercial ETL tools to App Engine's Pipeline API and MapReduce frameworks, to simple UNIX command-line tools. They'll then open it up for a general office hours on ingestion and other topics. Please use the moderator link to ask your questions. From: GoogleDevelopers Views: 0 0 ratings Time: 00:00 More in Science & Technology

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• ??1???????????????!???????????????|Oracle Coherence|??????

  - by ???02

  ????????????3????????????????????????????3??????????????????????????(?????????1?????????????????)????????????????????????RDBMS1?????????RDBMS??????????????????????????RDBMS????????????????RDBMS????????????????????????????????????SQL ????????????????????????????????????????RDBMS?????????????????????????????·???????????·??????????????????·???????????????????????????????(?????)????????????????????????????????????????????????????????????????????????????2??????????????????????????????????·??????????2?????????????????????????????·??????????????????????????????????????(?????????)???????????????????????????(????????)???????????????????·????????????????JBoss Cache??????????1????Java Map API????put/get?????????????????Java ?????????????????·??????????????????????????????????????????????1:JBoss Cache 3.0???????????????????????(Java Map API???????Java???????????????????????????)// ??????????Person????????????????// CacheFactory?DefaultCacheFactory?Cache?Fqn?Node?JBoss Cache????CacheFactory factory = new DefaultCacheFactory();// ????????Cache cache = factory.createCache();Fqn personData = Fqn.fromString("/person");// ???????????(?????·???)???Person???Node personNode = cache.getRoot().addChild(personData);// ??Person?????????????Person p1 = new Person(1234, "??", "??", "?????");// ?????·???????????personNode.put(1234, p1);?????·???·?????????·????????????????????????????????????·???·??????????????????????????·??????????????????2?????????????????????????????????????????????????????????????????????????????????????????????????????1????????RDBMS??????????????????????????2??????????????????Java API???????????????????????????????????????????????????????????·???·????????????????????????????·?????????·????????????????????????????????????????????·???·?????????????????????MapReduce???????????????????????????????????????????(?????)????????????????????·???·????????????????????????????????????????????????????????????·???????????????????????????????????????IT??????????????Web 2.0??????????????????????????????????????????????????????????????????????????????????????????????????????????????????2:??????????·???·???????Oracle Coherence?????????????????????(??????·?????JBoss Cache?????)// ??????????Person????????????????// CacheFactory?Oracle Coherence????// Person????????Map personCache = CacheFactory.getCache("person");// ??Person?????????????Person p1 = new Person(1234, "??", "??", "?????");// ?????·??????????personCache.put(1234, p1);??????????????????????3 ???????????????????????????????????·???????????????????????????·???·??????????????????????????????????·???????????????????12

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• Distributed Computing Framework (.NET) - Specifically for CPU Instensive operations

  - by StevenH

  I am currently researching the options that are available (both Open Source and Commercial) for developing a distributed application. "A distributed system consists of multiple autonomous computers that communicate through a computer network." Wikipedia The application is focused on distributing highly cpu intensive operations (as opposed to data intensive) so I'm sure MapReduce solutions don't fit the bill. Any framework that you can recommend ( + give a brief summary of any experience or comparison to other frameworks ) would be greatly appreciated. Thanks.

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• writing hbase reports

  - by sammy

  Hello i've just started exploring hbase i've run 2 samples : SampleUploader from examples and PerformanceEvaluation as given in hadoop wiki: http://wiki.apache.org/hadoop/Hbase/MapReduce Well, my application involves updating huge amount of data once a day.. i need samples that store and retrieve rows based on timestamp and make an analysis over the data could u please provide r poinnters as to how to continue as i dont find many tutorials on samples using TIMESTAMP thank u a lot sammy

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• Definition of a simple MAP Reduce API

  - by Zubair

  I am developing a distributed processing API in Java, Erlang and Ruby. What basic commands can I include from which I can build mapreduce, pipelining, and all the most used parallell algorithms on top of it.

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• Recommendations for distributed processing/distributed storage systems

  - by Eddie

  At my organization we have a processing and storage system spread across two dozen linux machines that handles over a petabyte of data. The system right now is very ad-hoc; processing automation and data management is handled by a collection of large perl programs on independent machines. I am looking at distributed processing and storage systems to make it easier to maintain, evenly distribute load and data with replication, and grow in disk space and compute power. The system needs to be able to handle millions of files, varying in size between 50 megabytes to 50 gigabytes. Once created, the files will not be appended to, only replaced completely if need be. The files need to be accessible via HTTP for customer download. Right now, processing is automated by perl scripts (that I have complete control over) which call a series of other programs (that I don't have control over because they are closed source) that essentially transforms one data set into another. No data mining happening here. Here is a quick list of things I am looking for: Reliability: These data must be accessible over HTTP about 99% of the time so I need something that does data replication across the cluster. Scalability: I want to be able to add more processing power and storage easily and rebalance the data on across the cluster. Distributed processing: Easy and automatic job scheduling and load balancing that fits with processing workflow I briefly described above. Data location awareness: Not strictly required but desirable. Since data and processing will be on the same set of nodes I would like the job scheduler to schedule jobs on or close to the node that the data is actually on to cut down on network traffic. Here is what I've looked at so far: Storage Management: GlusterFS: Looks really nice and easy to use but doesn't seem to have a way to figure out what node(s) a file actually resides on to supply as a hint to the job scheduler. GPFS: Seems like the gold standard of clustered filesystems. Meets most of my requirements except, like glusterfs, data location awareness. Ceph: Seems way to immature right now. Distributed processing: Sun Grid Engine: I have a lot of experience with this and it's relatively easy to use (once it is configured properly that is). But Oracle got its icy grip around it and it no longer seems very desirable. Both: Hadoop/HDFS: At first glance it looked like hadoop was perfect for my situation. Distributed storage and job scheduling and it was the only thing I found that would give me the data location awareness that I wanted. But I don't like the namename being a single point of failure. Also, I'm not really sure if the MapReduce paradigm fits the type of processing workflow that I have. It seems like you need to write all your software specifically for MapReduce instead of just using Hadoop as a generic job scheduler. OpenStack: I've done some reading on this but I'm having trouble deciding if it fits well with my problem or not. Does anyone have opinions or recommendations for technologies that would fit my problem well? Any suggestions or advise would be greatly appreciated. Thanks!

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• Big Data – Learning Basics of Big Data in 21 Days – Bookmark

  - by Pinal Dave

  Earlier this month I had a great time to write Bascis of Big Data series. This series received great response and lots of good comments I have received, I am going to follow up this basics series with further in-depth series in near future. Here is the consolidated blog post where you can find all the 21 days blog posts together. Bookmark this page for future reference. Big Data – Beginning Big Data – Day 1 of 21 Big Data – What is Big Data – 3 Vs of Big Data – Volume, Velocity and Variety – Day 2 of 21 Big Data – Evolution of Big Data – Day 3 of 21 Big Data – Basics of Big Data Architecture – Day 4 of 21 Big Data – Buzz Words: What is NoSQL – Day 5 of 21 Big Data – Buzz Words: What is Hadoop – Day 6 of 21 Big Data – Buzz Words: What is MapReduce – Day 7 of 21 Big Data – Buzz Words: What is HDFS – Day 8 of 21 Big Data – Buzz Words: Importance of Relational Database in Big Data World – Day 9 of 21 Big Data – Buzz Words: What is NewSQL – Day 10 of 21 Big Data – Role of Cloud Computing in Big Data – Day 11 of 21 Big Data – Operational Databases Supporting Big Data – RDBMS and NoSQL – Day 12 of 21 Big Data – Operational Databases Supporting Big Data – Key-Value Pair Databases and Document Databases – Day 13 of 21 Big Data – Operational Databases Supporting Big Data – Columnar, Graph and Spatial Database – Day 14 of 21 Big Data – Data Mining with Hive – What is Hive? – What is HiveQL (HQL)? – Day 15 of 21 Big Data – Interacting with Hadoop – What is PIG? – What is PIG Latin? – Day 16 of 21 Big Data – Interacting with Hadoop – What is Sqoop? – What is Zookeeper? – Day 17 of 21 Big Data – Basics of Big Data Analytics – Day 18 of 21 Big Data – How to become a Data Scientist and Learn Data Science? – Day 19 of 21 Big Data – Various Learning Resources – How to Start with Big Data? – Day 20 of 21 Big Data – Final Wrap and What Next – Day 21 of 21 Reference: Pinal Dave (http://blog.sqlauthority.com) Filed under: Big Data, PostADay, SQL, SQL Authority, SQL Query, SQL Server, SQL Tips and Tricks, T SQL

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• Dryad and DryadLINQ from MSR

  - by Daniel Moth

  Microsoft Research (MSR) researches technologies, incubates projects which many times result in technology that looks like a ready-to-use product (but it is important to understand that these are not the same as products built by the various… actual product teams here at Microsoft). A very popular MSR project has been DryadLINQ, which itself builds on Dryad. To learn more follow the project pages I just linked to and I also recommend this 1-hour channel 9 video. If you only have 3 minutes, watch this great elevator pitch instead. You can also stay tuned on the official blog, which includes a post that refers to internal adoption e.g by Bing, a quick DryadLINQ code example, and some history on how DryadLINQ generalizes the MapReduce pattern and makes it accessible to regular programmers (see this post and that post). Essentially, the DryadLINQ framework (building on the Dryad runtime) allows developers to re-use their LINQ skills for creating/generating programs that process large multi-gigabyte/terabyte datasets across 100s-1000s of machines. One way to think about it is that just as Parallel LINQ allows LINQ developers to seamlessly use multiple cores from a single process on a single machine, DryadLINQ allows LINQ developers to seamlessly use multiple machines for their data parallel algorithms. In the former scenario the motivation was speed of execution, in the latter it is speed of execution AND processing large datasets that simply don't fit on a single machine. Whenever I hear about execution of parallel code on multiple machines on the Microsoft platform, I immediately think of Windows HPC Server. Indeed Dryad and DryadLINQ were made available for Windows HPC Server and I encourage you to watch the PDC session on this topic: Data-Intensive Computing on Windows HPC Server with the DryadLINQ Framework. Watch this space… Comments about this post welcome at the original blog.

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