Ok Well...i am getting hundred of files daily which all need to process thats why i am using hadoop so it manage distribution of processing itself. Yes, one record has millions of fields
Thanks for comments. On Mon, Nov 8, 2010 at 8:50 PM, Michael Segel <[email protected]>wrote: > > Switch out the JDOM for a Stax parser. > > Ok, having said that... > You said you have a single record per file. Ok that means you have a lot of > fields. > Because you have 1 record, this isn't a map/reduce problem. You're better > off writing a single threaded app > to read the file, parse the file using Stax, and then write the fields to > HBase. > > I'm not sure why you have millions of put()s. > Do you have millions of fields in this one record? > > Writing a good stax parser and then mapping the fields to your hbase > column(s) will help. > > HTH > > -Mike > PS. A good stax implementation would be a recursive/re-entrant piece of > code. > While the code may look simple, it takes a skilled developer to write and > maintain. > > > > Date: Mon, 8 Nov 2010 14:36:34 +0500 > > Subject: Re: Best Way to Insert data into Hbase using Map Reduce > > From: [email protected] > > To: [email protected] > > > > HI > > > > I have used JDOM library to parse the xml in mapper and in my case, one > > single file consist of 1 record so i give one complete file to map > process > > and extract the information from it which i need. I have only 2 column > > families in my schema and bottleneck was the put statements which run > > millions of time for each file. when i comment this put statement then > job > > complete within minutes but with put statement, it was taking about 7 > hours > > to complete the same job. Anyhow I have changed the code according to > > suggestion given by Michael and now using java api to dump data instead > of > > table output format and used the list of puts and then flush them at each > > 1000 records and it reduces the time significantly. Now the whole job > > process by 1 hour and 45 min approx but still not in minutes. So is there > > anything left which i might apply and performance increase? > > > > Thanks > > > > On Fri, Nov 5, 2010 at 10:46 PM, Buttler, David <[email protected]> > wrote: > > > > > Good points. > > > Before we can make any rational suggestion, we need to know where the > > > bottleneck is, so we can make suggestions to move it elsewhere. I > > > personally favor Michael's suggestion to split the ingest and the > parsing > > > parts of your job, and to switch to a parser that is faster than a DOM > > > parser (SAX or Stax). But, without knowing what the bottleneck actually > is, > > > all of these suggestions are shots in the dark. > > > > > > What is the network load, the CPU load, the disk load? Have you at > least > > > installed Ganglia or some equivalent so that you can see what the load > is > > > across the cluster? > > > > > > Dave > > > > > > > > > -----Original Message----- > > > From: Michael Segel [mailto:[email protected]] > > > Sent: Friday, November 05, 2010 9:49 AM > > > To: [email protected] > > > Subject: RE: Best Way to Insert data into Hbase using Map Reduce > > > > > > > > > I don't think using the buffered client is going to help a lot w > > > performance. > > > > > > I'm a little confused because it doesn't sound like Shuja is using a > > > map/reduce job to parse the file. > > > That is... he says he parses the file in to a dom tree. Usually your > map > > > job parses each record and then in the mapper you parse out the record. > > > Within the m/r job we don't parse out the fields in the records because > we > > > do additional processing which 'dedupes' the data so we don't have to > > > further process the data. > > > The second job only has to parse a portion of the original records. > > > > > > So assuming that Shuja is actually using a map reduce job, and each xml > > > record is being parsed within the mapper() there are a couple of > things... > > > 1) Reduce the number of column families that you are using. (Each > column > > > family is written to a separate file) > > > 2) Set up the HTable instance in Mapper.setup() > > > 3) Switch to a different dom class (not all java classes are equal) or > > > switch to Stax. > > > > > > > > > > > > > > > > From: [email protected] > > > > To: [email protected] > > > > Date: Fri, 5 Nov 2010 08:28:07 -0700 > > > > Subject: RE: Best Way to Insert data into Hbase using Map Reduce > > > > > > > > Have you tried turning off auto flush, and managing the flush in your > own > > > code (say every 1000 puts?) > > > > Dave > > > > > > > > > > > > -----Original Message----- > > > > From: Shuja Rehman [mailto:[email protected]] > > > > Sent: Friday, November 05, 2010 8:04 AM > > > > To: [email protected] > > > > Subject: Re: Best Way to Insert data into Hbase using Map Reduce > > > > > > > > Michael > > > > > > > > hum....so u are storing xml record in the hbase and in second job, u > r > > > > parsing. but in my case i am parsing it also in first phase. what i > do, i > > > > get xml file and i parse it using jdom and then putting data in > hbase. so > > > > parsing+putting both operations are in 1 phase and in mapper code. > > > > > > > > My actual problem is that after parsing file, i need to use put > statement > > > > millions of times and i think for each statement it connects to hbase > and > > > > then insert it and this might be the reason of slow processing. So i > am > > > > trying to figure out some way we i can first buffer data and then > insert > > > in > > > > batch fashion. it means in one put statement, i can insert many > records > > > and > > > > i think if i do in this way then the process will be very fast. > > > > > > > > secondly what does it means? "we write the raw record in via a single > > > put() > > > > so the map() method is a null writable." > > > > > > > > can u explain it more? > > > > > > > > Thanks > > > > > > > > > > > > On Fri, Nov 5, 2010 at 5:05 PM, Michael Segel < > [email protected] > > > >wrote: > > > > > > > > > > > > > > Suja, > > > > > > > > > > Just did a quick glance. > > > > > > > > > > What is it that you want to do exactly? > > > > > > > > > > Here's how we do it... (at a high level.) > > > > > > > > > > Input is an XML file where we want to store the raw XML records in > > > hbase, > > > > > one record per row. > > > > > > > > > > Instead of using the output of the map() method, we write the raw > > > record in > > > > > via a single put() so the map() method is a null writable. > > > > > > > > > > Its pretty fast. However fast is relative. > > > > > > > > > > Another thing... we store the xml record as a string (converted to > > > > > bytecode) rather than a serialized object. > > > > > > > > > > Then you can break it down in to individual fields in a second > batch > > > job. > > > > > (You can start with a DOM parser, and later move to a Stax parser. > > > > > Depending on which DOM parser you have and the size of the record, > it > > > should > > > > > be 'fast enough'. A good implementation of Stax tends to be > > > > > recursive/re-entrant code which is harder to maintain.) > > > > > > > > > > HTH > > > > > > > > > > -Mike > > > > > > > > > > > > > > > > Date: Fri, 5 Nov 2010 16:13:02 +0500 > > > > > > Subject: Best Way to Insert data into Hbase using Map Reduce > > > > > > From: [email protected] > > > > > > To: [email protected] > > > > > > > > > > > > Hi > > > > > > > > > > > > I am reading data from raw xml files and inserting data into > hbase > > > using > > > > > > TableOutputFormat in a map reduce job. but due to heavy put > > > statements, > > > > > it > > > > > > takes many hours to process the data. here is my sample code. > > > > > > > > > > > > conf.set(TableOutputFormat.OUTPUT_TABLE, "mytable"); > > > > > > conf.set("xmlinput.start", "<adc>"); > > > > > > conf.set("xmlinput.end", "</adc>"); > > > > > > conf > > > > > > .set( > > > > > > "io.serializations", > > > > > > > > > > > > > > > > > > > > > "org.apache.hadoop.io.serializer.JavaSerialization,org.apache.hadoop.io.serializer.WritableSerialization"); > > > > > > > > > > > > Job job = new Job(conf, "Populate Table with Data"); > > > > > > > > > > > > FileInputFormat.setInputPaths(job, input); > > > > > > job.setJarByClass(ParserDriver.class); > > > > > > job.setMapperClass(MyParserMapper.class); > > > > > > job.setNumReduceTasks(0); > > > > > > job.setInputFormatClass(XmlInputFormat.class); > > > > > > job.setOutputFormatClass(TableOutputFormat.class); > > > > > > > > > > > > > > > > > > *and mapper code* > > > > > > > > > > > > public class MyParserMapper extends > > > > > > Mapper<LongWritable, Text, NullWritable, Writable> { > > > > > > > > > > > > @Override > > > > > > public void map(LongWritable key, Text value1,Context > context) > > > > > > > > > > > > throws IOException, InterruptedException { > > > > > > *//doing some processing* > > > > > > while(rItr.hasNext()) > > > > > > { > > > > > > * //and this put statement runs for 132,622,560 > > > times > > > > > to > > > > > > insert the data.* > > > > > > context.write(NullWritable.get(), new > > > > > > Put(rowId).add(Bytes.toBytes("CounterValues"), > > > > > > Bytes.toBytes(counter.toString()), > > > > > Bytes.toBytes(rElement.getTextTrim()))); > > > > > > > > > > > > } > > > > > > > > > > > > }} > > > > > > > > > > > > Is there any other way of doing this task so i can improve the > > > > > performance? > > > > > > > > > > > > > > > > > > -- > > > > > > Regards > > > > > > Shuja-ur-Rehman Baig > > > > > > <http://BLOCKEDBLOCKEDpk.linkedin.com/in/shujamughal> > > > > > > > > > > > > > > > > > > > > > > > > > -- > > > > Regards > > > > Shuja-ur-Rehman Baig > > > > <http://BLOCKEDBLOCKEDpk.linkedin.com/in/shujamughal> > > > > > > > > > > > > -- > > Regards > > Shuja-ur-Rehman Baig > > <http://pk.linkedin.com/in/shujamughal> > > -- Regards Shuja-ur-Rehman Baig <http://pk.linkedin.com/in/shujamughal>
