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  • MapReduce results seem limited to 100?

    - by user1813867
    I'm playing around with Map Reduce in MongoDB and python and I've run into a strange limitation. I'm just trying to count the number of "book" records. It works when there are less than 100 records but when it goes over 100 records the count resets for some reason. Here is my MR code and some sample outputs: var M = function () { book = this.book; emit(book, {count : 1}); } var R = function (key, values) { var sum = 0; values.forEach(function(x) { sum += 1; }); var result = { count : sum }; return result; } MR output when record count is 99: {u'_id': u'superiors', u'value': {u'count': 99}} MR output when record count is 101: {u'_id': u'superiors', u'value': {u'count': 2.0}} Any ideas?

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  • Help me build a CouchDB mapreduce

    - by mit
    There are CouchDB documents that are list elements: { "type" : "el", "id" : "1", "content" : "first" } { "type" : "el", "id" : "2", "content" : "second" } { "type" : "el", "id" : "3", "content" : "third" } There is one document that defines the list: { "type" : "list", "elements" : ["2","1"] , "id" : "abc123" } As you can see the third element was deleted, it is no longer part of the list. So it must not be part of the result. Now I want a view that returns the content elements including the right order. The result could be: { "content" : ["second", "first"] } In this case the order of the elements is already as it should be. Another possible result: { "content" : [{"content" : "first", "order" : 2},{"content" : "second", "order" : 1}] } I started writing the map function: map = function (doc) { if (doc.type === 'el') { emit(doc.id, {"content" : doc.content}); //emit the id and the content exit; } if (doc.type === 'list') { for ( var i=0, l=doc.elements.length; i<l; ++i ){ emit(doc.elements[i], { "order" : i }); //emit the id and the order } } } This is as far as I can get. Can you correct my mistakes and write a reduce function? Remember that the third document must not be part of the result.

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  • Is it possible to write map/reduce jobs for Amazon Elastic MapReduce using .NET?

    - by Chris
    Is it possible to write map/reduce jobs for Amazon Elastic MapReduce (http://aws.amazon.com/elasticmapreduce/) using .NET languages? In particular I would like to use C#. Preliminary research suggests not. The above URL's marketing text suggests you have a "choice of Java, Ruby, Perl, Python, PHP, R, or C++", without mentioning .NET languages. This Amazon thread (http://developer.amazonwebservices.com/connect/thread.jspa?messageID=136051 -- "Support for C# / F# map/reducers") explicitly says that "currently Amazon Elastic MapReduce does not support Mono platform or languages such as C# or F#." The above suggests that it can't be done. I'm wondering if there are any workarounds, though. For example, can I modify the Elastic MapReduce machine image for my account, and install Mono on there? An alternative, suggested by Amazon FAQs "Using Other Software Required by Your Jar" (http://docs.amazonwebservices.com/ElasticMapReduce/latest/DeveloperGuide/index.html?CHAP_AdvancedTopics.html) and "How to Use Additional Files and Libraries With the Mapper or Reducer" (http://docs.amazonwebservices.com/ElasticMapReduce/latest/DeveloperGuide/index.html?addl_files.html), is to make the first step of the Map/Reduce job be to install Mono on the local instance. That sounds kind of inefficient, but maybe it could work? Maybe a saner alternative would be to try to forgo the convenience of Elastic MapReduce, and manually set up my own Hadoop cluster on EC2. Then I assume I could install Mono without difficulty.

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  • Reverse and Forward DNS set up correctly but sometimes MapReduce job fails

    - by phodamentals
    Ever since we switched over our cluster to communicate via private interfaces and created a DNS server with correct forward and reverse lookup zones, we get this message before the M/R job runs: ERROR org.apache.hadoop.hbase.mapreduce.TableInputFormatBase - Cannot resolve the host name for /192.168.3.9 because of javax.naming.NameNotFoundException: DNS name not found [response code 3]; remaining name '9.3.168.192.in-addr.arpa' A dig and nslookup both show that the reverse and forward look-ups both get good responses with no errors from within the cluster. Shortly after these messages, the job runs...but every once in awhile we get a NPE: Exception in thread "main" java.lang.NullPointerException INFO app.insights.search.SearchIndexUpdater - at org.apache.hadoop.net.DNS.reverseDns(DNS.java:93) INFO app.insights.search.SearchIndexUpdater - at org.apache.hadoop.hbase.mapreduce.TableInputFormatBase.reverseDNS(TableInputFormatBase.java:219) INFO app.insights.search.SearchIndexUpdater - at org.apache.hadoop.hbase.mapreduce.TableInputFormatBase.getSplits(TableInputFormatBase.java:184) INFO app.insights.search.SearchIndexUpdater - at org.apache.hadoop.mapred.JobClient.writeNewSplits(JobClient.java:1063) INFO app.insights.search.SearchIndexUpdater - at org.apache.hadoop.mapred.JobClient.writeSplits(JobClient.java:1080) INFO app.insights.search.SearchIndexUpdater - at org.apache.hadoop.mapred.JobClient.access$600(JobClient.java:174) INFO app.insights.search.SearchIndexUpdater - at org.apache.hadoop.mapred.JobClient$2.run(JobClient.java:992) INFO app.insights.search.SearchIndexUpdater - at org.apache.hadoop.mapred.JobClient$2.run(JobClient.java:945) INFO app.insights.search.SearchIndexUpdater - at java.security.AccessController.doPrivileged(Native Method) INFO app.insights.search.SearchIndexUpdater - at javax.security.auth.Subject.doAs(Subject.java:415) INFO app.insights.search.SearchIndexUpdater - at org.apache.hadoop.security.UserGroupInformation.doAs(UserGroupInformation.java:1408) INFO app.insights.search.SearchIndexUpdater - at org.apache.hadoop.mapred.JobClient.submitJobInternal(JobClient.java:945) INFO app.insights.search.SearchIndexUpdater - at org.apache.hadoop.mapreduce.Job.submit(Job.java:566) INFO app.insights.search.SearchIndexUpdater - at org.apache.hadoop.mapreduce.Job.waitForCompletion(Job.java:596) INFO app.insights.search.SearchIndexUpdater - at app.insights.search.correlator.comments.CommentCorrelator.main(CommentCorrelator.java:72 Does anyone else who has set-up a CDH Hadoop cluster on a private network w/DNS server get this? CDH 4.3.1 with MR1 2.0.0 and HBase 0.94.6

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  • MapReduce

    - by kaleidoscope
    MapReduce is a programming model and an associated implementation for processing and generating large data sets. Users specify a map function that processes a key/value pair to generate a set of  intermediate key/value pairs, and a reduce function that merges all intermediate values associated with the same intermediate key. Many real world tasks are expressible in this model, as shown in the paper. Programs written in this functional style are automatically parallelized and executed on a large cluster of commodity machines. The run-time system takes care of the details of partitioning the input data,  scheduling the program's execution across a set of machines, handling machine failures, and managing the required inter-machine communication. This allows programmers without any experience with parallel and distributed systems to easily utilize the resources of a large distributed system. Example: A process to count the appearances of each different word in a set of documents void map(String name, String document):   // name: document name   // document: document contents   for each word w in document:     EmitIntermediate(w, 1); void reduce(String word, Iterator partialCounts):   // word: a word   // partialCounts: a list of aggregated partial counts   int result = 0;   for each pc in partialCounts:     result += ParseInt(pc);   Emit(result); Here, each document is split in words, and each word is counted initially with a "1" value by the Map function, using the word as the result key. The framework puts together all the pairs with the same key and feeds them to the same call to Reduce, thus this function just needs to sum all of its input values to find the total appearances of that word.   Sarang, K

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  • Is MapReduce one form of Continuation-Passing Style (CPS)?

    - by Jeffrey
    As the title says. I was reading Yet Another Language Geek: Continuation-Passing Style and I was sort of wondering if MapReduce can be categorized as one form of Continuation-Passing Style aka CPS. I am also wondering how can CPS utilise more than one computer to perform complex computation. Maybe CPS makes it easier to work with Actor model.

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  • Network bandwidth bottleneck for sorting of mapreduce intermediate keys?

    - by Zubair
    I have been learning the mapreduce algorithm and how it can potentially scale to millions of machines, but I don't understand how the sorting of the intermediate keys after the map phase can scale, as there will be: 1,000,000 x 1,000,000 : potential machines communicating small key / value pairs of the intermediate results with each other? Isn't this a bottleneck?

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  • Twitter Storm VS. Google's MapReduce

    - by Edward J. Yoon
    IMO, the era of Information Retrieval is dead with the advent of SNS. And the question type is changed from "How many backlinks your site has?" to "How many people have clicked URL you've shared on SNS?". So many people who newbie in Big Data Analytics often asks me "How can I analyze stream data time-series pattern mining methods using Map/Reduce?", "How can I mining the valuable insights using Map/Reduce?", "blah~ blah~ using Map/Reduce?". The answer is No Map/Reduce.

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  • How is intermediate data organized in MapReduce?

    - by Pedro Cattori
    From what I understand, each mapper outputs an intermediate file. The intermediate data (data contained in each intermediate file) is then sorted by key. Then, a reducer is assigned a key by the master. The reducer reads from the intermediate file containing the key and then calls reduce using the data it has read. But in detail, how is the intermediate data organized? Can a data corresponding to a key be held in multiple intermediate files? What happens when there is too much data corresponding to one key to be held by a single file? In short, how do intermediate partitions differ from intermediate files and how are these differences dealt with in the implementation?

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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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  • Is Using Python to MapReduce for Cassandra Dumb?

    - by UltimateBrent
    Since Cassandra doesn't have MapReduce built in yet (I think it's coming in 0.7), is it dumb to try and MapReduce with my Python client or should I just use CouchDB or Mongo or something? The application is stats collection, so I need to be able to sum values with grouping to increment counters. I'm not, but pretend I'm making Google analytics so I want to keep track of which browsers appear, which pages they went to, and visits vs. pageviews. I would just atomically update my counters on write, but Cassandra isn't very good at counters either. May Cassandra just isn't the right choice for this? Thanks!

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  • Apply limit in mapreduce function in php?

    - by Rohan Kumar
    How to apply limit in php, mongodb when using mapreduce function? I tried this $cmd=array(// codition array "mapreduce" => "user", "map" => $map, "reduce" => $reduce, "out" => array("inline" => 1), "limit"=>2 ); $db=connect(); $query = $db->command($cmd);// run command But its not working it gives 2 documents.I can't use limit on sub documents. If I have 100's of sub documents and then I want paging in sub documents.Then it fails.Is it possible to apply limit on sub documents?

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  • Need help implementing this algorithm with map reduce(hadoop)

    - by Julia
    Hi all! i have algorithm that will go through a large data set read some text files and search for specific terms in those lines. I have it implemented in Java, but I didnt want to post code so that it doesnt look i am searching for someone to implement it for me, but it is true i really need a lot of help!!! This was not planned for my project, but data set turned out to be huge, so teacher told me I have to do it like this. I was reading about MapReduce and thaught that i first do the standard implementation and then it will be more/less easier to do it with mapreduce. But didnt happen, since algorithm is quite stupid and nothing special, and map reduce...i cant wrap my mind around it. So here is shortly pseudo code of my algorithm LIST termList (there is method that creates this list from lucene index) FOLDER topFolder INPUT topFolder IF it is folder and not empty list files (there are 30 sub folders inside) FOR EACH sub folder GET file "CheckedFile.txt" analyze(CheckedFile) ENDFOR END IF Method ANALYZE(CheckedFile) read CheckedFile WHILE CheckedFile has next line GET line FOR(loops through termList) GET third word from line IF third word = term from list append whole line to string buffer ENDIF ENDFOR END WHILE OUTPUT string buffer to file Also, as you can see, each time when "analyze" is called, new file has to be created, i understood that map reduce is difficult to write to many outputs??? I understand mapreduce intuition, and my example seems perfectly suited for mapreduce, but when it comes to do this, obviously I do not know enough and i am STUCK! Please please help.

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  • OpenStreetMap and Hadoop

    - by portoalet
    Hi, I need some ideas for a weekend project about Hadoop and OpenStreetMap. I have access to AWS EC2 instance with OpenStreetMap snapshot in my EBS volume. The OpenStreetMap data is in a PostgreSQL database. What kind of MapReduce function can be run on the OpenStreetMap data, assuming I can export them into xml format, and then place into HDFS ? In other words, I am having a brain cramp at the moment, and cannot think what kind of MapReduce operation that can extract valuable insight from the OpenStreetMap xml? (i.e. extract all the places designated as park or golf course. But this needs to be done once only, not continuously) Many Thanks

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  • Hadoop 0.2: How to read outputs from TextOutputFormat?

    - by S.N
    My reducer class produces outputs with TextOutputFormat (the default OutputFormat given by Job). I like to consume this outputs after the MapReduce job complete to aggregate the outputs. In addition to this, I like to write out the aggregated information with TextInputFormat so that the output from this process can be consumed by the next iteration of MapReduce task. Can anyone give me an example on how to write & read with TextFormat? By the way, the reason why I am using TextFormat, rather Sequence, is the interoperability. The outputs should be consumed by any software.

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  • Hive NR map progress inconsistent and regurlarly restart from 0%

    - by user92471
    I have a Yarn MR (with two ec2 instances to mapreduce) job on a dataset of approximately a thousand avro records, and the map phase is behaving erratically. See the progress below. Of course i checked the logs on resourcemanager and nodemanagers and saw nothing suspicious, but these logs are too verbose What is going on there ? hive> select * from nikon where qs_cs_s_aid='VIEW' limit 10; Total MapReduce jobs = 1 Launching Job 1 out of 1 Number of reduce tasks is set to 0 since there's no reduce operator Starting Job = job_1352281315350_0020, Tracking URL = http://blabla.ec2.internal:8088/proxy/application_1352281315350_0020/ Kill Command = /usr/lib/hadoop/bin/hadoop job -Dmapred.job.tracker=blabla.com:8032 -kill job_1352281315350_0020 Hadoop job information for Stage-1: number of mappers: 4; number of reducers: 0 2012-11-07 11:14:40,976 Stage-1 map = 0%, reduce = 0% 2012-11-07 11:15:06,136 Stage-1 map = 1%, reduce = 0%, Cumulative CPU 10.38 sec 2012-11-07 11:15:07,253 Stage-1 map = 1%, reduce = 0%, Cumulative CPU 12.18 sec 2012-11-07 11:15:08,371 Stage-1 map = 1%, reduce = 0%, Cumulative CPU 12.18 sec 2012-11-07 11:15:09,491 Stage-1 map = 1%, reduce = 0%, Cumulative CPU 12.18 sec 2012-11-07 11:15:10,643 Stage-1 map = 2%, reduce = 0%, Cumulative CPU 15.42 sec (...) 2012-11-07 11:15:35,441 Stage-1 map = 28%, reduce = 0%, Cumulative CPU 37.77 sec 2012-11-07 11:15:36,486 Stage-1 map = 28%, reduce = 0%, Cumulative CPU 37.77 sec here restart at 16% ? 2012-11-07 11:15:37,692 Stage-1 map = 16%, reduce = 0%, Cumulative CPU 21.15 sec 2012-11-07 11:15:38,815 Stage-1 map = 16%, reduce = 0%, Cumulative CPU 21.15 sec 2012-11-07 11:15:39,865 Stage-1 map = 16%, reduce = 0%, Cumulative CPU 21.15 sec 2012-11-07 11:15:41,064 Stage-1 map = 18%, reduce = 0%, Cumulative CPU 22.4 sec 2012-11-07 11:15:42,181 Stage-1 map = 18%, reduce = 0%, Cumulative CPU 22.4 sec 2012-11-07 11:15:43,299 Stage-1 map = 18%, reduce = 0%, Cumulative CPU 22.4 sec here restart at 0% ? 2012-11-07 11:15:44,418 Stage-1 map = 0%, reduce = 0% 2012-11-07 11:16:02,076 Stage-1 map = 1%, reduce = 0%, Cumulative CPU 6.86 sec 2012-11-07 11:16:03,193 Stage-1 map = 1%, reduce = 0%, Cumulative CPU 6.86 sec 2012-11-07 11:16:04,259 Stage-1 map = 2%, reduce = 0%, Cumulative CPU 8.45 sec (...) 2012-11-07 11:16:31,291 Stage-1 map = 22%, reduce = 0%, Cumulative CPU 35.34 sec 2012-11-07 11:16:32,414 Stage-1 map = 26%, reduce = 0%, Cumulative CPU 37.93 sec here restart at 11% ? 2012-11-07 11:16:33,459 Stage-1 map = 11%, reduce = 0%, Cumulative CPU 19.53 sec 2012-11-07 11:16:34,507 Stage-1 map = 11%, reduce = 0%, Cumulative CPU 19.53 sec 2012-11-07 11:16:35,731 Stage-1 map = 13%, reduce = 0%, Cumulative CPU 21.47 sec (...) 2012-11-07 11:16:46,839 Stage-1 map = 17%, reduce = 0%, Cumulative CPU 24.14 sec here restart at 0% ? 2012-11-07 11:16:47,939 Stage-1 map = 0%, reduce = 0% 2012-11-07 11:16:56,653 Stage-1 map = 1%, reduce = 0%, Cumulative CPU 7.54 sec 2012-11-07 11:16:57,814 Stage-1 map = 1%, reduce = 0%, Cumulative CPU 7.54 sec (...) Needless to say the job crashes after some time with an Error: java.io.IOException: java.io.IOException: java.lang.ArrayIndexOutOfBoundsException: -56

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  • Where are Riak Post-Commit Hooks run?

    - by pixelcort
    I'm trying to evaluate using Riak's Post-Commit Hooks to build a distributed, incremental MapReduce-based index, but was wondering which Riak nodes the Post-Commit Hooks actually run on. Are they run on the nodes the client used to put the commits, or on the primary nodes where the data is persisted? If it's the latter, I'm thinking I can from there efficiently do a map or reduce and put additional records from the output.

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  • kmeans based on mapreduce by python

    - by user3616059
    I am going to write a mapper and reducer for the kmeans algorithm, I think the best course of action to do is putting the distance calculator in mapper and sending to reducer with the cluster id as key and coordinates of row as value. In reducer, updating the centroids would be performed. I am writing this by python. As you know, I have to use Hadoop streaming to transfer data between STDIN and STOUT. according to my knowledge, when we print (key + "\t"+value), it will be sent to reducer. Reducer will receive data and it calculates the new centroids but when we print new centroids, I think it does not send them to mapper to calculate new clusters and it just send it to STDOUT and as you know, kmeans is a iterative program. So, my questions is whether Hadoop streaming suffers of doing iterative programs and we should employ MRJOB for iterative programs?

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  • Hadoop Rolling Small files

    - by Arenstar
    I am running Hadoop on a project and need a suggestion. Generally by default Hadoop has a "block size" of around 64mb.. There is also a suggestion to not use many/small files.. I am currently having very very very small files being put into HDFS due to the application design of flume.. The problem is, that Hadoop <= 0.20 cannot append to files, whereby i have too many files for my map-reduce to function efficiently.. There must be a correct way to simply roll/merge roughly 100 files into one.. Therefore Hadoop is effectively reading 1 large file instead of 10 Any Suggestions??

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  • Best practice for administering a (hadoop) cluster

    - by Alex
    Dear all, I've recently been playing with Hadoop. I have a six node cluster up and running - with HDFS, and having run a number of MapRed jobs. So far, so good. However I'm now looking to do this more systematically and with a larger number of nodes. Our base system is Ubuntu and the current setup has been administered using apt (to install the correct java runtime) and ssh/scp (to propagate out the various conf files). This is clearly not scalable over time. Does anyone have any experience of good systems for administering (possibly slightly heterogenous: different disk sizes, different numbers of cpus on each node) hadoop clusters automagically? I would consider diskless boot - but imagine that with a large cluster, getting the cluster up and running might be bottle-necked on the machine serving the OS. Or some form of distributed debian apt to keep the machines native environment synchronised? And how do people successfully manage the conf files over a number of (potentially heterogenous) machines? Thanks very much in advance, Alex

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