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  • Welcome!

    - by mannamal
    Welcome to the Oracle Big Data Connectors blog, which will focus on posts related to integrating data on a Hadoop cluster with Oracle Database. In particular the blog will focus on best practices, usage notes, and performance tips for using Oracle Loader for Hadoop and Oracle Direct Connector for HDFS, which are part of Oracle Big Data Connectors. Oracle Big Data Connectors 1.0 also includes Oracle R Connector for Hadoop and Oracle Data Integrator Application Adapters for Hadoop. Oracle Loader for Hadoop: Oracle Loader for Hadoop loads data from Hadoop to Oracle Database. It runs as a MapReduce job on Hadoop to partition, sort, and convert the data into an Oracle-ready format, offloading to Hadoop the processing that is typically done using database CPUs. The data is thenloaded to the database by the Oracle Loader for Hadoop job (online load) or written out as Oracle Data Pump files for load and access later (offline load) with Oracle Direct Connector for HDFS. Oracle Direct Connector for HDFS: Oracle Direct Connector for HDFS is a connector for high speed access of data on HDFS from Oracle Database. With this connector Oracle SQL can be used to directly query data on HDFS. The data can be Oracle Data Pump files generated by Oracle Loader for Hadoop or delimited text files. The connector can also be used to load data into the database using SQL.

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  • Using a "local" S3 emulation layer as a replacement for HDFS?

    - by user183394
    I have been testing out the most recent Cloudera CDH4 hadoop-conf-pseudo (i.e. MRv2 or YARN) on a notebook, which has 4 cores, 8GB RAM, an Intel X25MG2 SSD, and runs a S3 emulation layer my colleagues and I wrote in C++. The OS is Ubuntu 12.04LTS 64bit. So far so good. Looking at Setting up hadoop to use S3 as a replacement for HDFS, I would like to do it on my notebook. Nevertheless, I can't find where I can change the jets3t.properties for setting the end point to localhost. I downloaded the hadoop-2.0.1-alpha.tar.gz and searched the source without finding out a clue. There is a similar Q on SO Using s3 as fs.default.name or HDFS?, but I want to use our own lightweight and fast S3 emulation layer, instead of AWS S3, for our experiments. I would appreciate a hint as to how I can change the end point to a different hostname. Regards, --Zack

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  • Building Simple Workflows in Oozie

    - by dan.mcclary
    Introduction More often than not, data doesn't come packaged exactly as we'd like it for analysis. Transformation, match-merge operations, and a host of data munging tasks are usually needed before we can extract insights from our Big Data sources. Few people find data munging exciting, but it has to be done. Once we've suffered that boredom, we should take steps to automate the process. We want codify our work into repeatable units and create workflows which we can leverage over and over again without having to write new code. In this article, we'll look at how to use Oozie to create a workflow for the parallel machine learning task I described on Cloudera's site. Hive Actions: Prepping for Pig In my parallel machine learning article, I use data from the National Climatic Data Center to build weather models on a state-by-state basis. NCDC makes the data freely available as gzipped files of day-over-day observations stretching from the 1930s to today. In reading that post, one might get the impression that the data came in a handy, ready-to-model files with convenient delimiters. The truth of it is that I need to perform some parsing and projection on the dataset before it can be modeled. If I get more observations, I'll want to retrain and test those models, which will require more parsing and projection. This is a good opportunity to start building up a workflow with Oozie. I store the data from the NCDC in HDFS and create an external Hive table partitioned by year. This gives me flexibility of Hive's query language when I want it, but let's me put the dataset in a directory of my choosing in case I want to treat the same data with Pig or MapReduce code. CREATE EXTERNAL TABLE IF NOT EXISTS historic_weather(column 1, column2) PARTITIONED BY (yr string) STORED AS ... LOCATION '/user/oracle/weather/historic'; As new weather data comes in from NCDC, I'll need to add partitions to my table. That's an action I should put in the workflow. Similarly, the weather data requires parsing in order to be useful as a set of columns. Because of their long history, the weather data is broken up into fields of specific byte lengths: x bytes for the station ID, y bytes for the dew point, and so on. The delimiting is consistent from year to year, so writing SerDe or a parser for transformation is simple. Once that's done, I want to select columns on which to train, classify certain features, and place the training data in an HDFS directory for my Pig script to access. ALTER TABLE historic_weather ADD IF NOT EXISTS PARTITION (yr='2010') LOCATION '/user/oracle/weather/historic/yr=2011'; INSERT OVERWRITE DIRECTORY '/user/oracle/weather/cleaned_history' SELECT w.stn, w.wban, w.weather_year, w.weather_month, w.weather_day, w.temp, w.dewp, w.weather FROM ( FROM historic_weather SELECT TRANSFORM(...) USING '/path/to/hive/filters/ncdc_parser.py' as stn, wban, weather_year, weather_month, weather_day, temp, dewp, weather ) w; Since I'm going to prepare training directories with at least the same frequency that I add partitions, I should also add that to my workflow. Oozie is going to invoke these Hive actions using what's somewhat obviously referred to as a Hive action. Hive actions amount to Oozie running a script file containing our query language statements, so we can place them in a file called weather_train.hql. Starting Our Workflow Oozie offers two types of jobs: workflows and coordinator jobs. Workflows are straightforward: they define a set of actions to perform as a sequence or directed acyclic graph. Coordinator jobs can take all the same actions of Workflow jobs, but they can be automatically started either periodically or when new data arrives in a specified location. To keep things simple we'll make a workflow job; coordinator jobs simply require another XML file for scheduling. The bare minimum for workflow XML defines a name, a starting point, and an end point: <workflow-app name="WeatherMan" xmlns="uri:oozie:workflow:0.1"> <start to="ParseNCDCData"/> <end name="end"/> </workflow-app> To this we need to add an action, and within that we'll specify the hive parameters Also, keep in mind that actions require <ok> and <error> tags to direct the next action on success or failure. <action name="ParseNCDCData"> <hive xmlns="uri:oozie:hive-action:0.2"> <job-tracker>localhost:8021</job-tracker> <name-node>localhost:8020</name-node> <configuration> <property> <name>oozie.hive.defaults</name> <value>/user/oracle/weather_ooze/hive-default.xml</value> </property> </configuration> <script>ncdc_parse.hql</script> </hive> <ok to="WeatherMan"/> <error to="end"/> </action> There are a couple of things to note here: I have to give the FQDN (or IP) and port of my JobTracker and NameNode. I have to include a hive-default.xml file. I have to include a script file. The hive-default.xml and script file must be stored in HDFS That last point is particularly important. Oozie doesn't make assumptions about where a given workflow is being run. You might submit workflows against different clusters, or have different hive-defaults.xml on different clusters (e.g. MySQL or Postgres-backed metastores). A quick way to ensure that all the assets end up in the right place in HDFS is just to make a working directory locally, build your workflow.xml in it, and copy the assets you'll need to it as you add actions to workflow.xml. At this point, our local directory should contain: workflow.xml hive-defaults.xml (make sure this file contains your metastore connection data) ncdc_parse.hql Adding Pig to the Ooze Adding our Pig script as an action is slightly simpler from an XML standpoint. All we do is add an action to workflow.xml as follows: <action name="WeatherMan"> <pig> <job-tracker>localhost:8021</job-tracker> <name-node>localhost:8020</name-node> <script>weather_train.pig</script> </pig> <ok to="end"/> <error to="end"/> </action> Once we've done this, we'll copy weather_train.pig to our working directory. However, there's a bit of a "gotcha" here. My pig script registers the Weka Jar and a chunk of jython. If those aren't also in HDFS, our action will fail from the outset -- but where do we put them? The Jython script goes into the working directory at the same level as the pig script, because pig attempts to load Jython files in the directory from which the script executes. However, that's not where our Weka jar goes. While Oozie doesn't assume much, it does make an assumption about the Pig classpath. Anything under working_directory/lib gets automatically added to the Pig classpath and no longer requires a REGISTER statement in the script. Anything that uses a REGISTER statement cannot be in the working_directory/lib directory. Instead, it needs to be in a different HDFS directory and attached to the pig action with an <archive> tag. Yes, that's as confusing as you think it is. You can get the exact rules for adding Jars to the distributed cache from Oozie's Pig Cookbook. Making the Workflow Work We've got a workflow defined and have collected all the components we'll need to run. But we can't run anything yet, because we still have to define some properties about the job and submit it to Oozie. We need to start with the job properties, as this is essentially the "request" we'll submit to the Oozie server. In the same working directory, we'll make a file called job.properties as follows: nameNode=hdfs://localhost:8020 jobTracker=localhost:8021 queueName=default weatherRoot=weather_ooze mapreduce.jobtracker.kerberos.principal=foo dfs.namenode.kerberos.principal=foo oozie.libpath=${nameNode}/user/oozie/share/lib oozie.wf.application.path=${nameNode}/user/${user.name}/${weatherRoot} outputDir=weather-ooze While some of the pieces of the properties file are familiar (e.g., JobTracker address), others take a bit of explaining. The first is weatherRoot: this is essentially an environment variable for the script (as are jobTracker and queueName). We're simply using them to simplify the directives for the Oozie job. The oozie.libpath pieces is extremely important. This is a directory in HDFS which holds Oozie's shared libraries: a collection of Jars necessary for invoking Hive, Pig, and other actions. It's a good idea to make sure this has been installed and copied up to HDFS. The last two lines are straightforward: run the application defined by workflow.xml at the application path listed and write the output to the output directory. We're finally ready to submit our job! After all that work we only need to do a few more things: Validate our workflow.xml Copy our working directory to HDFS Submit our job to the Oozie server Run our workflow Let's do them in order. First validate the workflow: oozie validate workflow.xml Next, copy the working directory up to HDFS: hadoop fs -put working_dir /user/oracle/working_dir Now we submit the job to the Oozie server. We need to ensure that we've got the correct URL for the Oozie server, and we need to specify our job.properties file as an argument. oozie job -oozie http://url.to.oozie.server:port_number/ -config /path/to/working_dir/job.properties -submit We've submitted the job, but we don't see any activity on the JobTracker? All I got was this funny bit of output: 14-20120525161321-oozie-oracle This is because submitting a job to Oozie creates an entry for the job and places it in PREP status. What we got back, in essence, is a ticket for our workflow to ride the Oozie train. We're responsible for redeeming our ticket and running the job. oozie -oozie http://url.to.oozie.server:port_number/ -start 14-20120525161321-oozie-oracle Of course, if we really want to run the job from the outset, we can change the "-submit" argument above to "-run." This will prep and run the workflow immediately. Takeaway So, there you have it: the somewhat laborious process of building an Oozie workflow. It's a bit tedious the first time out, but it does present a pair of real benefits to those of us who spend a great deal of time data munging. First, when new data arrives that requires the same processing, we already have the workflow defined and ready to run. Second, as we build up a set of useful action definitions over time, creating new workflows becomes quicker and quicker.

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  • Any good method for mounting Hadoop HDFS from another system?

    - by Beel
    I want to mount the Cloudera Hadoop as a Linux file system over the LAN. As a setup, I already have the hadoop cluster running on a set of Ubuntu machines. But now I need to be able to use it as a normal file system from a Fedora system over the LAN. I tried FUSe but two things: 1. Cloudera says FUSE loses data (click here for that comment by a Cloudera employee on the official Cloudera support site) 2. I've had no success making it work the way we want As a point of clarification, I am using Hadoop ONLY for the file system, not for its other capabilities.

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  • New Feature in ODI 11.1.1.6: ODI for Big Data

    - by Julien Testut
    Normal 0 false false false EN-US X-NONE X-NONE /* 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";} By Ananth Tirupattur Starting with Oracle Data Integrator 11.1.1.6.0, ODI is offering a solution to process Big Data. This post provides an overview of this feature. With all the buzz around Big Data and before getting into the details of ODI for Big Data, I will provide a brief introduction to Big Data and Oracle Solution for Big Data. So, what is Big Data? Big data includes: structured data (this includes data from relation data stores, xml data stores), semi-structured data (this includes data from weblogs) unstructured data (this includes data from text blob, images) Traditionally, business decisions are based on the information gathered from transactional data. For example, transactional Data from CRM applications is fed to a decision system for analysis and decision making. Products such as ODI play a key role in enabling decision systems. However, with the emergence of massive amounts of semi-structured and unstructured data it is important for decision system to include them in the analysis to achieve better decision making capability. While there is an abundance of opportunities for business for gaining competitive advantages, process of Big Data has challenges. The challenges of processing Big Data include: Volume of data Velocity of data - The high Rate at which data is generated Variety of data In order to address these challenges and convert them into opportunities, we would need an appropriate framework, platform and the right set of tools. Hadoop is an open source framework which is highly scalable, fault tolerant system, for storage and processing large amounts of data. Hadoop provides 2 key services, distributed and reliable storage called Hadoop Distributed File System or HDFS and a framework for parallel data processing called Map-Reduce. Innovations in Hadoop and its related technology continue to rapidly evolve, hence therefore, it is highly recommended to follow information on the web to keep up with latest information. Oracle's vision is to provide a comprehensive solution to address the challenges faced by Big Data. Oracle is providing the necessary Hardware, software and tools for processing Big Data Oracle solution includes: Big Data Appliance Oracle NoSQL Database Cloudera distribution for Hadoop Oracle R Enterprise- R is a statistical package which is very popular among data scientists. ODI solution for Big Data Oracle Loader for Hadoop for loading data from Hadoop to Oracle. Further details can be found here: http://www.oracle.com/us/products/database/big-data-appliance/overview/index.html ODI Solution for Big Data: ODI’s goal is to minimize the need to understand the complexity of Hadoop framework and simplify the adoption of processing Big Data seamlessly in an enterprise. ODI is providing the capabilities for an integrated architecture for processing Big Data. This includes capability to load data in to Hadoop, process data in Hadoop and load data from Hadoop into Oracle. ODI is expanding its support for Big Data by providing the following out of the box Knowledge Modules (KMs). IKM File to Hive (LOAD DATA).Load unstructured data from File (Local file system or HDFS ) into Hive IKM Hive Control AppendTransform and validate structured data on Hive IKM Hive TransformTransform unstructured data on Hive IKM File/Hive to Oracle (OLH)Load processed data in Hive to Oracle RKM HiveReverse engineer Hive tables to generate models Using the Loading KM you can map files (local and HDFS files) to the corresponding Hive tables. For example, you can map weblog files categorized by date into a corresponding partitioned Hive table schema. Normal 0 false false false EN-US X-NONE X-NONE /* 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";} Using the Hive control Append KM you can validate and transform data in Hive. In the below example, two source Hive tables are joined and mapped to a target Hive table. Normal 0 false false false EN-US X-NONE X-NONE /* 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";} The Hive Transform KM facilitates processing of semi-structured data in Hive. In the below example, the data from weblog is processed using a Perl script and mapped to target Hive table. Normal 0 false false false EN-US X-NONE X-NONE /* 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";} Using the Oracle Loader for Hadoop (OLH) KM you can load data from Hive table or HDFS to a corresponding table in Oracle. OLH is available as a standalone product. ODI greatly enhances OLH capability by generating the configuration and mapping files for OLH based on the configuration provided in the interface and KM options. ODI seamlessly invokes OLH when executing the scenario. In the below example, a HDFS file is mapped to a table in Oracle. Development and Deployment:The following diagram illustrates the development and deployment of ODI solution for Big Data. Using the ODI Studio on your development machine create and develop ODI solution for processing Big Data by connecting to a MySQL DB or Oracle database on a BDA machine or Hadoop cluster. Schedule the ODI scenarios to be executed on the ODI agent deployed on the BDA machine or Hadoop cluster. ODI Solution for Big Data provides several exciting new capabilities to facilitate the adoption of Big Data in an enterprise. You can find more information about the Oracle Big Data connectors on OTN. You can find an overview of all the new features introduced in ODI 11.1.1.6 in the following document: ODI 11.1.1.6 New Features Overview

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  • Big Data Matters with ODI12c

    - by Madhu Nair
    contributed by Mike Eisterer On October 17th, 2013, Oracle announced the release of Oracle Data Integrator 12c (ODI12c).  This release signifies improvements to Oracle’s Data Integration portfolio of solutions, particularly Big Data integration. Why Big Data = Big Business Organizations are gaining greater insights and actionability through increased storage, processing and analytical benefits offered by Big Data solutions.  New technologies and frameworks like HDFS, NoSQL, Hive and MapReduce support these benefits now. As further data is collected, analytical requirements increase and the complexity of managing transformations and aggregations of data compounds and organizations are in need for scalable Data Integration solutions. ODI12c provides enterprise solutions for the movement, translation and transformation of information and data heterogeneously and in Big Data Environments through: The ability for existing ODI and SQL developers to leverage new Big Data technologies. A metadata focused approach for cataloging, defining and reusing Big Data technologies, mappings and process executions. Integration between many heterogeneous environments and technologies such as HDFS and Hive. Generation of Hive Query Language. Working with Big Data using Knowledge Modules  ODI12c provides developers with the ability to define sources and targets and visually develop mappings to effect the movement and transformation of data.  As the mappings are created, ODI12c leverages a rich library of prebuilt integrations, known as Knowledge Modules (KMs).  These KMs are contextual to the technologies and platforms to be integrated.  Steps and actions needed to manage the data integration are pre-built and configured within the KMs.  The Oracle Data Integrator Application Adapter for Hadoop provides a series of KMs, specifically designed to integrate with Big Data Technologies.  The Big Data KMs include: Check Knowledge Module Reverse Engineer Knowledge Module Hive Transform Knowledge Module Hive Control Append Knowledge Module File to Hive (LOAD DATA) Knowledge Module File-Hive to Oracle (OLH-OSCH) Knowledge Module  Nothing to beat an Example: To demonstrate the use of the KMs which are part of the ODI Application Adapter for Hadoop, a mapping may be defined to move data between files and Hive targets.  The mapping is defined by dragging the source and target into the mapping, performing the attribute (column) mapping (see Figure 1) and then selecting the KM which will govern the process.  In this mapping example, movie data is being moved from an HDFS source into a Hive table.  Some of the attributes, such as “CUSTID to custid”, have been mapped over. Figure 1  Defining the Mapping Before the proper KM can be assigned to define the technology for the mapping, it needs to be added to the ODI project.  The Big Data KMs have been made available to the project through the KM import process.   Generally, this is done prior to defining the mapping. Figure 2  Importing the Big Data Knowledge Modules Following the import, the KMs are available in the Designer Navigator. v\:* {behavior:url(#default#VML);} o\:* {behavior:url(#default#VML);} w\:* {behavior:url(#default#VML);} .shape {behavior:url(#default#VML);} Normal 0 false false false EN-US ZH-TW X-NONE MicrosoftInternetExplorer4 /* 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";} Figure 3  The Project View in Designer, Showing Installed IKMs Once the KM is imported, it may be assigned to the mapping target.  This is done by selecting the Physical View of the mapping and examining the Properties of the Target.  In this case MOVIAPP_LOG_STAGE is the target of our mapping. Figure 4  Physical View of the Mapping and Assigning the Big Data Knowledge Module to the Target Alternative KMs may have been selected as well, providing flexibility and abstracting the logical mapping from the physical implementation.  Our mapping may be applied to other technologies as well. The mapping is now complete and is ready to run.  We will see more in a future blog about running a mapping to load Hive. To complete the quick ODI for Big Data Overview, let us take a closer look at what the IKM File to Hive is doing for us.  ODI provides differentiated capabilities by defining the process and steps which normally would have to be manually developed, tested and implemented into the KM.  As shown in figure 5, the KM is preparing the Hive session, managing the Hive tables, performing the initial load from HDFS and then performing the insert into Hive.  HDFS and Hive options are selected graphically, as shown in the properties in Figure 4. Figure 5  Process and Steps Managed by the KM What’s Next Big Data being the shape shifting business challenge it is is fast evolving into the deciding factor between market leaders and others. Now that an introduction to ODI and Big Data has been provided, look for additional blogs coming soon using the Knowledge Modules which make up the Oracle Data Integrator Application Adapter for Hadoop: Importing Big Data Metadata into ODI, Testing Data Stores and Loading Hive Targets Generating Transformations using Hive Query language Loading Oracle from Hadoop Sources For more information now, please visit the Oracle Data Integrator Application Adapter for Hadoop web site, http://www.oracle.com/us/products/middleware/data-integration/hadoop/overview/index.html Do not forget to tune in to the ODI12c Executive Launch webcast on the 12th to hear more about ODI12c and GG12c. Normal 0 false false false EN-US ZH-TW X-NONE MicrosoftInternetExplorer4 /* 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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  • file to map/reduce program

    - by vana
    Hi , I am working on extracting Parts Of speech (POS) using xml documents and I have a englishPCFG.ser.gz file which is used in extracting POS on xml files. I cannot send this .gz file as input in HDFS directory, but my program uses it for parsing xml files. The file is in my local directory. I am getting "File Not Found" error when I run my mapreduce program. How can i make it available to mapper? I tried placing the file in HDFS where my xml files are present. I also tried adding it in .jar along with class files but not luck. I tried to change the hdfs-default.xml with entry to local directory, still doesnt work. Please let me know how to make mapper read this file? Thank you,

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  • Hadoop write directory

    - by FaultyJuggler
    Simple question but reading through documentation and configuration I can't quite seem to figure it out. How do I A) know where hadoop is writing to on the local disk and B) change that For initial testing I setup HDFS on a 20gb linux VM - to it we've added a 500gb networked drive for moving towards prototyping the full system. So now how do I point HDFS at that drive, or do I simply move the home directory/install with some slight change in setup and restart the process?

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  • Big Data – Data Mining with Hive – What is Hive? – What is HiveQL (HQL)? – Day 15 of 21

    - by Pinal Dave
    In yesterday’s blog post we learned the importance of the operational database in Big Data Story. In this article we will understand what is Hive and HQL in Big Data Story. Yahoo started working on PIG (we will understand that in the next blog post) for their application deployment on Hadoop. The goal of Yahoo to manage their unstructured data. Similarly Facebook started deploying their warehouse solutions on Hadoop which has resulted in HIVE. The reason for going with HIVE is because the traditional warehousing solutions are getting very expensive. What is HIVE? Hive is a datawarehouseing infrastructure for Hadoop. The primary responsibility is to provide data summarization, query and analysis. It  supports analysis of large datasets stored in Hadoop’s HDFS as well as on the Amazon S3 filesystem. The best part of HIVE is that it supports SQL-Like access to structured data which is known as HiveQL (or HQL) as well as big data analysis with the help of MapReduce. Hive is not built to get a quick response to queries but it it is built for data mining applications. Data mining applications can take from several minutes to several hours to analysis the data and HIVE is primarily used there. HIVE Organization The data are organized in three different formats in HIVE. Tables: They are very similar to RDBMS tables and contains rows and tables. Hive is just layered over the Hadoop File System (HDFS), hence tables are directly mapped to directories of the filesystems. It also supports tables stored in other native file systems. Partitions: Hive tables can have more than one partition. They are mapped to subdirectories and file systems as well. Buckets: In Hive data may be divided into buckets. Buckets are stored as files in partition in the underlying file system. Hive also has metastore which stores all the metadata. It is a relational database containing various information related to Hive Schema (column types, owners, key-value data, statistics etc.). We can use MySQL database over here. What is HiveSQL (HQL)? Hive query language provides the basic SQL like operations. Here are few of the tasks which HQL can do easily. Create and manage tables and partitions Support various Relational, Arithmetic and Logical Operators Evaluate functions Download the contents of a table to a local directory or result of queries to HDFS directory Here is the example of the HQL Query: SELECT upper(name), salesprice FROM sales; SELECT category, count(1) FROM products GROUP BY category; When you look at the above query, you can see they are very similar to SQL like queries. Tomorrow In tomorrow’s blog post we will discuss about very important components of the Big Data Ecosystem – Pig. 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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  • Is there any way to use arrays in a puppet module (not in template)?

    - by KARASZI István
    I want to use puppet to manage a hadoop cluster. On the machines we have several directories which must be created and set permissions. But i'm unable to add array values for defined methods. define hdfs_site( $dirs ) { file { $dirs: ensure => directory, owner => "hadoop", group => "hadoop", mode => 755; } file { "/opt/hadoop/conf/hdfs-site.xml": content => template("hdfs-site.xml.erb"), owner => "root", group => "root", mode => 644; } } define hadoop_slave( $mem, $cpu, $dirs ) { hadoop_base { mem => $mem, cpu => $cpu, } hdfs_site { dirs => $dirs, } } hadoop_base is similar to hdfs_site. Thanks!

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  • New Big Data Appliance Security Features

    - by mgubar
    The Oracle Big Data Appliance (BDA) is an engineered system for big data processing.  It greatly simplifies the deployment of an optimized Hadoop Cluster – whether that cluster is used for batch or real-time processing.  The vast majority of BDA customers are integrating the appliance with their Oracle Databases and they have certain expectations – especially around security.  Oracle Database customers have benefited from a rich set of security features:  encryption, redaction, data masking, database firewall, label based access control – and much, much more.  They want similar capabilities with their Hadoop cluster.    Unfortunately, Hadoop wasn’t developed with security in mind.  By default, a Hadoop cluster is insecure – the antithesis of an Oracle Database.  Some critical security features have been implemented – but even those capabilities are arduous to setup and configure.  Oracle believes that a key element of an optimized appliance is that its data should be secure.  Therefore, by default the BDA delivers the “AAA of security”: authentication, authorization and auditing. Security Starts at Authentication A successful security strategy is predicated on strong authentication – for both users and software services.  Consider the default configuration for a newly installed Oracle Database; it’s been a long time since you had a legitimate chance at accessing the database using the credentials “system/manager” or “scott/tiger”.  The default Oracle Database policy is to lock accounts thereby restricting access; administrators must consciously grant access to users. Default Authentication in Hadoop By default, a Hadoop cluster fails the authentication test. For example, it is easy for a malicious user to masquerade as any other user on the system.  Consider the following scenario that illustrates how a user can access any data on a Hadoop cluster by masquerading as a more privileged user.  In our scenario, the Hadoop cluster contains sensitive salary information in the file /user/hrdata/salaries.txt.  When logged in as the hr user, you can see the following files.  Notice, we’re using the Hadoop command line utilities for accessing the data: $ hadoop fs -ls /user/hrdataFound 1 items-rw-r--r--   1 oracle supergroup         70 2013-10-31 10:38 /user/hrdata/salaries.txt$ hadoop fs -cat /user/hrdata/salaries.txtTom Brady,11000000Tom Hanks,5000000Bob Smith,250000Oprah,300000000 User DrEvil has access to the cluster – and can see that there is an interesting folder called “hrdata”.  $ hadoop fs -ls /user Found 1 items drwx------   - hr supergroup          0 2013-10-31 10:38 /user/hrdata However, DrEvil cannot view the contents of the folder due to lack of access privileges: $ hadoop fs -ls /user/hrdata ls: Permission denied: user=drevil, access=READ_EXECUTE, inode="/user/hrdata":oracle:supergroup:drwx------ Accessing this data will not be a problem for DrEvil. He knows that the hr user owns the data by looking at the folder’s ACLs. To overcome this challenge, he will simply masquerade as the hr user. On his local machine, he adds the hr user, assigns that user a password, and then accesses the data on the Hadoop cluster: $ sudo useradd hr $ sudo passwd $ su hr $ hadoop fs -cat /user/hrdata/salaries.txt Tom Brady,11000000 Tom Hanks,5000000 Bob Smith,250000 Oprah,300000000 Hadoop has not authenticated the user; it trusts that the identity that has been presented is indeed the hr user. Therefore, sensitive data has been easily compromised. Clearly, the default security policy is inappropriate and dangerous to many organizations storing critical data in HDFS. Big Data Appliance Provides Secure Authentication The BDA provides secure authentication to the Hadoop cluster by default – preventing the type of masquerading described above. It accomplishes this thru Kerberos integration. Figure 1: Kerberos Integration The Key Distribution Center (KDC) is a server that has two components: an authentication server and a ticket granting service. The authentication server validates the identity of the user and service. Once authenticated, a client must request a ticket from the ticket granting service – allowing it to access the BDA’s NameNode, JobTracker, etc. At installation, you simply point the BDA to an external KDC or automatically install a highly available KDC on the BDA itself. Kerberos will then provide strong authentication for not just the end user – but also for important Hadoop services running on the appliance. You can now guarantee that users are who they claim to be – and rogue services (like fake data nodes) are not added to the system. It is common for organizations to want to leverage existing LDAP servers for common user and group management. Kerberos integrates with LDAP servers – allowing the principals and encryption keys to be stored in the common repository. This simplifies the deployment and administration of the secure environment. Authorize Access to Sensitive Data Kerberos-based authentication ensures secure access to the system and the establishment of a trusted identity – a prerequisite for any authorization scheme. Once this identity is established, you need to authorize access to the data. HDFS will authorize access to files using ACLs with the authorization specification applied using classic Linux-style commands like chmod and chown (e.g. hadoop fs -chown oracle:oracle /user/hrdata changes the ownership of the /user/hrdata folder to oracle). Authorization is applied at the user or group level – utilizing group membership found in the Linux environment (i.e. /etc/group) or in the LDAP server. For SQL-based data stores – like Hive and Impala – finer grained access control is required. Access to databases, tables, columns, etc. must be controlled. And, you want to leverage roles to facilitate administration. Apache Sentry is a new project that delivers fine grained access control; both Cloudera and Oracle are the project’s founding members. Sentry satisfies the following three authorization requirements: Secure Authorization:  the ability to control access to data and/or privileges on data for authenticated users. Fine-Grained Authorization:  the ability to give users access to a subset of the data (e.g. column) in a database Role-Based Authorization:  the ability to create/apply template-based privileges based on functional roles. With Sentry, “all”, “select” or “insert” privileges are granted to an object. The descendants of that object automatically inherit that privilege. A collection of privileges across many objects may be aggregated into a role – and users/groups are then assigned that role. This leads to simplified administration of security across the system. Figure 2: Object Hierarchy – granting a privilege on the database object will be inherited by its tables and views. Sentry is currently used by both Hive and Impala – but it is a framework that other data sources can leverage when offering fine-grained authorization. For example, one can expect Sentry to deliver authorization capabilities to Cloudera Search in the near future. Audit Hadoop Cluster Activity Auditing is a critical component to a secure system and is oftentimes required for SOX, PCI and other regulations. The BDA integrates with Oracle Audit Vault and Database Firewall – tracking different types of activity taking place on the cluster: Figure 3: Monitored Hadoop services. At the lowest level, every operation that accesses data in HDFS is captured. The HDFS audit log identifies the user who accessed the file, the time that file was accessed, the type of access (read, write, delete, list, etc.) and whether or not that file access was successful. The other auditing features include: MapReduce:  correlate the MapReduce job that accessed the file Oozie:  describes who ran what as part of a workflow Hive:  captures changes were made to the Hive metadata The audit data is captured in the Audit Vault Server – which integrates audit activity from a variety of sources, adding databases (Oracle, DB2, SQL Server) and operating systems to activity from the BDA. Figure 4: Consolidated audit data across the enterprise.  Once the data is in the Audit Vault server, you can leverage a rich set of prebuilt and custom reports to monitor all the activity in the enterprise. In addition, alerts may be defined to trigger violations of audit policies. Conclusion Security cannot be considered an afterthought in big data deployments. Across most organizations, Hadoop is managing sensitive data that must be protected; it is not simply crunching publicly available information used for search applications. The BDA provides a strong security foundation – ensuring users are only allowed to view authorized data and that data access is audited in a consolidated framework.

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  • How to Set Up a Hadoop Cluster Using Oracle Solaris (Hands-On Lab)

    - by Orgad Kimchi
    Oracle Technology Network (OTN) published the "How to Set Up a Hadoop Cluster Using Oracle Solaris" OOW 2013 Hands-On Lab. This hands-on lab presents exercises that demonstrate how to set up an Apache Hadoop cluster using Oracle Solaris 11 technologies such as Oracle Solaris Zones, ZFS, and network virtualization. Key topics include the Hadoop Distributed File System (HDFS) and the Hadoop MapReduce programming model. We will also cover the Hadoop installation process and the cluster building blocks: NameNode, a secondary NameNode, and DataNodes. In addition, you will see how you can combine the Oracle Solaris 11 technologies for better scalability and data security, and you will learn how to load data into the Hadoop cluster and run a MapReduce job. Summary of Lab Exercises This hands-on lab consists of 13 exercises covering various Oracle Solaris and Apache Hadoop technologies:     Install Hadoop.     Edit the Hadoop configuration files.     Configure the Network Time Protocol.     Create the virtual network interfaces (VNICs).     Create the NameNode and the secondary NameNode zones.     Set up the DataNode zones.     Configure the NameNode.     Set up SSH.     Format HDFS from the NameNode.     Start the Hadoop cluster.     Run a MapReduce job.     Secure data at rest using ZFS encryption.     Use Oracle Solaris DTrace for performance monitoring.  Read it now

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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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  • Hadoop streaming job on EC2 stays in "pending" state

    - by liamf
    Trying to experiment with Hadoop and Streaming using cloudera distribution CDH3 on Ubuntu. Have valid data in hdfs:// ready for processing. Wrote little streaming mapper in python. When I launch a mapper only job using: hadoop jar /usr/lib/hadoop/contrib/streaming/hadoop-streaming*.jar -file /usr/src/mystuff/mapper.py -mapper /usr/src/mystuff/mapper.py -input /incoming/STBFlow/* -output testOP hadoop duly decides it will use 66 mappers on the cluster to process the data. The testOP directory is created on HDFS. A job_conf.xml file is created. But the job tracker UI at port 50030 never shows the job moving out of "pending" state and nothing else happens. CPU usage stays at zero. (the job is created though) If I give it a single file (instead of the entire directory) as input, same result (except Hadoop decides it needs 2 mappers instead of 66). I also tried using the "dumbo" Python utility and launching jobs using that: same result: permanently pending. So I am missing something basic: could someone help me out with what I should look for? The cluster is on Amazon EC2. Firewall issues maybe: ports are enabled explicitly, case by case, in the cluster security group.

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  • SQLAuthority News – Amazon Gift Card Raffle for Beta Tester Feedback for NuoDB

    - by pinaldave
    As regular readers know I’ve been spending some time working with the NuoDB beta software. They contacted me last week and asked if I would give you a chance to try their new web-based console for their scalable, SQL-compliant database. They have just put out their final beta release, Beta 9.  It contains a preview of a new web-based “NuoConsole” that will replace and extend the functionality of their current desktop version.  I haven’t spent any time with the new console yet but a really quick look tells me it should make it easier to do deeper monitoring than the older one. It also looks like they have added query-level reporting through the console. I will try to play with it soon. NuoDB is doing a last, big push to get some more feedback from developers before they release their 1.0 product sometime in the next several weeks. Since the console is new, they are especially interested in some quick feedback on it before general availability. For SQLAuthority readers only, NuoDB will raffle off three $50 Amazon gift cards in exchange for your feedback on the NuoConsole preview. Here’s how to Enter Download NuoDBeta 9 here You must build a domain before you can start the console. Launch the Web Console. Windows Code: start java -jar jarnuodbwebconsole.jar Mac, Linux, Solaris, Unix Code: java -jar jar/nuodbwebconsole.jar Access the Web Console: Code: http://localhost:8080 When you have tried it out, go to a short (8 question) survey to enter the raffle Click here for the survey You must complete the survey before midnight EDT on October 17, 2012. Here’s what else they are saying about this last beta before general availability: Beta 9 now supports the Zend PHP framework so that PHP developers can directly integrate web applications with NuoDB. Multi-threaded HDFS support – NuoDB Storage Managers can now be configured to persist data to the high performance Hadoop distributed file system (HDFS). Beta 9 optimizes for multi-thread I/O streams at maximum performance. This enhancement allows users to make Hadoop their core storage with no extra effort which is a pretty cool idea. Improved Performance –On a single transaction node, Beta 9 offers performance comparable with MySQL and MariaDB. As additional nodes are added, NuoDB performance improves significantly at near linear scale. Query & Explain Plan Logging – Beta 9 introduces SQL explain plans for your queries. Qualify queries with the word “EXPLAIN” and NuoDB will respond with the details of the execution plan allowing performance optimization to SQL. Through the NuoConsole, you can now kill hung or long running queries. Java App Server Support – Beta 9 now supports leading Web JEE app servers including JBoss, Tomcat, and ColdFusion. They’ve also reported: Improved PHP/PDO drivers Support for Drupal Faster Ruby on Rails driver The Hibernate Dialect supports version 4.1 And good news for my readers: numerous SQL enhancements They will share the results of the web console feedback with me.  I’ll let you know how it goes. Also the winner of their last contest was Jaime Martínez Lafargue!  Do leave a comment here once you complete the survey.  Reference: Pinal Dave (http://blog.sqlauthority.com) Filed under: SQL Authority Tagged: NuoDB

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  • Big Data – Interacting with Hadoop – What is Sqoop? – What is Zookeeper? – Day 17 of 21

    - by Pinal Dave
    In yesterday’s blog post we learned the importance of the Pig and Pig Latin in Big Data Story. In this article we will understand what is Sqoop and Zookeeper in Big Data Story. There are two most important components one should learn when learning about interacting with Hadoop – Sqoop and Zookper. What is Sqoop? Most of the business stores their data in RDBMS as well as other data warehouse solutions. They need a way to move data to the Hadoop system to do various processing and return it back to RDBMS from Hadoop system. The data movement can happen in real time or at various intervals in bulk. We need a tool which can help us move this data from SQL to Hadoop and from Hadoop to SQL. Sqoop (SQL to Hadoop) is such a tool which extract data from non-Hadoop data sources and transform them into the format which Hadoop can use it and later it loads them into HDFS. Essentially it is ETL tool where it Extracts, Transform and Load from SQL to Hadoop. The best part is that it also does extract data from Hadoop and loads them to Non-SQL (or RDBMS) data stores. Essentially, Sqoop is a command line tool which does SQL to Hadoop and Hadoop to SQL. It is a command line interpreter. It creates MapReduce job behinds the scene to import data from an external database to HDFS. It is very effective and easy to learn tool for nonprogrammers. What is Zookeeper? ZooKeeper is a centralized service for maintaining configuration information, naming, providing distributed synchronization, and providing group services. In other words Zookeeper is a replicated synchronization service with eventual consistency. In simpler words – in Hadoop cluster there are many different nodes and one node is master. Let us assume that master node fails due to any reason. In this case, the role of the master node has to be transferred to a different node. The main role of the master node is managing the writers as that task requires persistence in order of writing. In this kind of scenario Zookeeper will assign new master node and make sure that Hadoop cluster performs without any glitch. Zookeeper is the Hadoop’s method of coordinating all the elements of these distributed systems. Here are few of the tasks which Zookeepr is responsible for. Zookeeper manages the entire workflow of starting and stopping various nodes in the Hadoop’s cluster. In Hadoop cluster when any processes need certain configuration to complete the task. Zookeeper makes sure that certain node gets necessary configuration consistently. In case of the master node fails, Zookeepr can assign new master node and make sure cluster works as expected. There many other tasks Zookeeper performance when it is about Hadoop cluster and communication. Basically without the help of Zookeeper it is not possible to design any new fault tolerant distributed application. Tomorrow In tomorrow’s blog post we will discuss about very important components of the Big Data Ecosystem – Big Data Analytics. 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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  • Hadoop reduce task gets hung

    - by user806098
    I set up a hadoop cluster with 4 nodes, When running a map-reduce task, the map task finishes quickly, while the reduce task hangs at 27% percent. I checked the log, it's that the reduce task fails to fetch map output from map nodes. The job tracker log of master shows messages like this: 2011-06-27 19:55:14,748 INFO org.apache.hadoop.mapred.JobTracker: Adding task (REDUCE) 'attempt_201106271953_0001_r_000000_0' to tip task_201106271953_0001_r_000000, for tracker 'tracker_web30.bbn.com.cn:localhost/127.0.0.1:56476' And the name node log of master shows messages like this: 2011-06-27 14:00:52,898 INFO org.apache.hadoop.ipc.Server: IPC Server handler 4 on 54310, call register(DatanodeRegistration(202.106.199.39:50010, storageID=DS-1989397900-202.106.199.39-50010-1308723051262, infoPort=50075, ipcPort=50020)) from 192.168.225.19:16129: error: java.io.IOException: verifyNodeRegistration: unknown datanode 202.106.199.3 9:50010 However, neither the "web30.bbn.com.cn" or 202.106.199.39, 202.106.199.3 is the slave node. I think such ip/domains appear because hadoop fails to resolve a node(first in the Intranet DNS server), then it goes to a higher-level DNS server, later to the top, still fails, then the "junk" ip/domains are returned. But I checked my config, it goes like this: /etc/hosts: 127.0.0.1 localhost.localdomain localhost ::1 localhost6.localdomain6 localhost6 192.168.225.16 master 192.168.225.66 slave1 192.168.225.20 slave5 192.168.225.17 slave17 conf/core-site.xml: hadoop.tmp.dir /root/hadoop_tmp/hadoop_${user.name} fs.default.name hdfs://master:54310 io.sort.mb 1024 hdfs-site.xml: dfs.replication 3 masters: master slaves: master slave1 slave5 slave17 Also, all firewalls(iptables) are turned off, and ssh between each 2 nodes is ok. so I don't know where exact the error comes from. Please help. Thanks a lot.

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  • Big Data Accelerator

    - by Jean-Pierre Dijcks
    For everyone who does not regularly listen to earnings calls, Oracle's Q4 call was interesting (as it mostly is). One of the announcements in the call was the Big Data Accelerator from Oracle (Seeking Alpha link here - slightly tweaked for correctness shown below):  "The big data accelerator includes some of the standard open source software, HDFS, the file system and a number of other pieces, but also some Oracle components that we think can dramatically speed up the entire map-reduce process. And will be particularly attractive to Java programmers [...]. There are some interesting applications they do, ETL is one. Log processing is another. We're going to have a lot of those features, functions and pre-built applications in our big data accelerator."  Not much else we can say right now, more on this (and Big Data in general) at Openworld!

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  • Big Data – Buzz Words: What is Hadoop – Day 6 of 21

    - by Pinal Dave
    In yesterday’s blog post we learned what is NoSQL. In this article we will take a quick look at one of the four most important buzz words which goes around Big Data – Hadoop. What is Hadoop? Apache Hadoop is an open-source, free and Java based software framework offers a powerful distributed platform to store and manage Big Data. It is licensed under an Apache V2 license. It runs applications on large clusters of commodity hardware and it processes thousands of terabytes of data on thousands of the nodes. Hadoop is inspired from Google’s MapReduce and Google File System (GFS) papers. The major advantage of Hadoop framework is that it provides reliability and high availability. What are the core components of Hadoop? There are two major components of the Hadoop framework and both fo them does two of the important task for it. Hadoop MapReduce is the method to split a larger data problem into smaller chunk and distribute it to many different commodity servers. Each server have their own set of resources and they have processed them locally. Once the commodity server has processed the data they send it back collectively to main server. This is effectively a process where we process large data effectively and efficiently. (We will understand this in tomorrow’s blog post). Hadoop Distributed File System (HDFS) is a virtual file system. There is a big difference between any other file system and Hadoop. When we move a file on HDFS, it is automatically split into many small pieces. These small chunks of the file are replicated and stored on other servers (usually 3) for the fault tolerance or high availability. (We will understand this in the day after tomorrow’s blog post). Besides above two core components Hadoop project also contains following modules as well. Hadoop Common: Common utilities for the other Hadoop modules Hadoop Yarn: A framework for job scheduling and cluster resource management There are a few other projects (like Pig, Hive) related to above Hadoop as well which we will gradually explore in later blog posts. A Multi-node Hadoop Cluster Architecture Now let us quickly see the architecture of the a multi-node Hadoop cluster. A small Hadoop cluster includes a single master node and multiple worker or slave node. As discussed earlier, the entire cluster contains two layers. One of the layer of MapReduce Layer and another is of HDFC Layer. Each of these layer have its own relevant component. The master node consists of a JobTracker, TaskTracker, NameNode and DataNode. A slave or worker node consists of a DataNode and TaskTracker. It is also possible that slave node or worker node is only data or compute node. The matter of the fact that is the key feature of the Hadoop. In this introductory blog post we will stop here while describing the architecture of Hadoop. In a future blog post of this 31 day series we will explore various components of Hadoop Architecture in Detail. Why Use Hadoop? There are many advantages of using Hadoop. Let me quickly list them over here: Robust and Scalable – We can add new nodes as needed as well modify them. Affordable and Cost Effective – We do not need any special hardware for running Hadoop. We can just use commodity server. Adaptive and Flexible – Hadoop is built keeping in mind that it will handle structured and unstructured data. Highly Available and Fault Tolerant – When a node fails, the Hadoop framework automatically fails over to another node. Why Hadoop is named as Hadoop? In year 2005 Hadoop was created by Doug Cutting and Mike Cafarella while working at Yahoo. Doug Cutting named Hadoop after his son’s toy elephant. Tomorrow In tomorrow’s blog post we will discuss Buzz Word – MapReduce. 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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  • Scalable distributed file system for blobs like images and other documents

    - by Pinnacle
    Cassandra & HBase both do not efficiently support storage of blobs like images. Storing directly on HDFS stresses the Namenode because of huge number of files. Facebook uses Haystack for images and attachments storage, but this is not open source. So is Lustre a good choice for distributed blob storage? I have read that Amazon S3 is used by many, but this would cost money and personally, I would not like to rely on third party system. What are other suggestions?

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  • how to convince other we should move to hadoop?

    - by Ramy
    Everything I've read about Hadoop seems like exactly the technology we need to make our enterprise more scalable. We have terabytes of raw data that is in non-relational form (text files of some kind). We're quickly approaching the upper limits of what our centralized file server can handle and everyone is aware of this. Most people on the tech team, especially the more junior members of the tech team are all in favor of moving from the central file system to HDFS. The problem is, there is one key (most senior, etc.) member of the team who is resisting this change and every time Hadoop comes up, he tells us that we could simply add another file server and be in the clear. So, my question (and yes, it's really subjective, but I need more help with this than any of my other questions) is what steps can we take to get upper management to move forward with Hadoop despite the hesitation of one member of the team?

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  • how to convince other we should move to hadoop?

    - by Ramy
    Everything I've read about Hadoop seems like exactly the technology we need to make our enterprise more scalable. We have terabytes of raw data that is in non-relational form (text files of some kind). We're quickly approaching the upper limits of what our centralized file server can handle and everyone is aware of this. Most people on the tech team, especially the more junior members of the tech team are all in favor of moving from the central file system to HDFS. The problem is, there is one key (most senior, etc.) member of the team who is resisting this change and every time Hadoop comes up, he tells us that we could simply add another file server and be in the clear. So, my question (and yes, it's really subjective, but I need more help with this than any of my other questions) is what steps can we take to get upper management to move forward with Hadoop despite the hesitation of one member of the team?

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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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  • Big Data&rsquo;s Killer App&hellip;

    - by jean-pierre.dijcks
    Recently Keith spent  some time talking about the cloud on this blog and I will spare you my thoughts on the whole thing. What I do want to write down is something about the Big Data movement and what I think is the killer app for Big Data... Where is this coming from, ok, I confess... I spent 3 days in cloud land at the Cloud Connect conference in Santa Clara and it was quite a lot of fun. One of the nice things at Cloud Connect was that there was a track dedicated to Big Data, which prompted me to some extend to write this post. What is Big Data anyways? The most valuable point made in the Big Data track was that Big Data in itself is not very cool. Doing something with Big Data is what makes all of this cool and interesting to a business user! The other good insight I got was that a lot of people think Big Data means a single gigantic monolithic system holding gazillions of bytes or documents or log files. Well turns out that most people in the Big Data track are talking about a lot of collections of smaller data sets. So rather than thinking "big = monolithic" you should be thinking "big = many data sets". This is more than just theoretical, it is actually relevant when thinking about big data and how to process it. It is important because it means that the platform that stores data will most likely consist out of multiple solutions. You may be storing logs on something like HDFS, you may store your customer information in Oracle and you may store distilled clickstream information in some distilled form in MySQL. The big question you will need to solve is not what lives where, but how to get it all together and get some value out of all that data. NoSQL and MapReduce Nope, sorry, this is not the killer app... and no I'm not saying this because my business card says Oracle and I'm therefore biased. I think language is important, but as with storage I think pragmatic is better. In other words, some questions can be answered with SQL very efficiently, others can be answered with PERL or TCL others with MR. History should teach us that anyone trying to solve a problem will use any and all tools around. For example, most data warehouses (Big Data 1.0?) get a lot of data in flat files. Everyone then runs a bunch of shell scripts to massage or verify those files and then shoves those files into the database. We've even built shell script support into external tables to allow for this. I think the Big Data projects will do the same. Some people will use MapReduce, although I would argue that things like Cascading are more interesting, some people will use Java. Some data is stored on HDFS making Cascading the way to go, some data is stored in Oracle and SQL does do a good job there. As with storage and with history, be pragmatic and use what fits and neither NoSQL nor MR will be the one and only. Also, a language, while important, does in itself not deliver business value. So while cool it is not a killer app... Vertical Behavioral Analytics This is the killer app! And you are now thinking: "what does that mean?" Let's decompose that heading. First of all, analytics. I would think you had guessed by now that this is really what I'm after, and of course you are right. But not just analytics, which has a very large scope and means many things to many people. I'm not just after Business Intelligence (analytics 1.0?) or data mining (analytics 2.0?) but I'm after something more interesting that you can only do after collecting large volumes of specific data. That all important data is about behavior. What do my customers do? More importantly why do they behave like that? If you can figure that out, you can tailor web sites, stores, products etc. to that behavior and figure out how to be successful. Today's behavior that is somewhat easily tracked is web site clicks, search patterns and all of those things that a web site or web server tracks. that is where the Big Data lives and where these patters are now emerging. Other examples however are emerging, and one of the examples used at the conference was about prediction churn for a telco based on the social network its members are a part of. That social network is not about LinkedIn or Facebook, but about who calls whom. I call you a lot, you switch provider, and I might/will switch too. And that just naturally brings me to the next word, vertical. Vertical in this context means per industry, e.g. communications or retail or government or any other vertical. The reason for being more specific than just behavioral analytics is that each industry has its own data sources, has its own quirky logic and has its own demands and priorities. Of course, the methods and some of the software will be common and some will have both retail and service industry analytics in place (your corner coffee store for example). But the gist of it all is that analytics that can predict customer behavior for a specific focused group of people in a specific industry is what makes Big Data interesting. Building a Vertical Behavioral Analysis System Well, that is going to be interesting. I have not seen much going on in that space and if I had to have some criticism on the cloud connect conference it would be the lack of concrete user cases on big data. The telco example, while a step into the vertical behavioral part is not really on big data. It used a sample of data from the customers' data warehouse. One thing I do think, and this is where I think parts of the NoSQL stuff come from, is that we will be doing this analysis where the data is. Over the past 10 years we at Oracle have called this in-database analytics. I guess we were (too) early? Now the entire market is going there including companies like SAS. In-place btw does not mean "no data movement at all", what it means that you will do this on data's permanent home. For SAS that is kind of the current problem. Most of the inputs live in a data warehouse. So why move it into SAS and back? That all worked with 1 TB data warehouses, but when we are looking at 100TB to 500 TB of distilled data... Comments? As it is still early days with these systems, I'm very interested in seeing reactions and thoughts to some of these thoughts...

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