Memory problems with BytesWritable and huge binary files
Hi there, We have several diverse large datasets to process (one set may be as much as 27 TB), however all of the files in these datasets are binary files. We need to be able to pass each binary file to several tools running in the Map Reduce framework. We already have a working pipeline of MapReduce tasks that receives each binary file (as BytesWritable) and processes it, we have tested it with very small test datasets so far. For any particular data set, the size of the files involves varies wildly with each file being anywhere between about 2 KB and 4 GB. With that in mind we have tried to follow the advice to read the files into a Sequence File in HDFS. To create the Sequence File we have a Map Reduce Job that uses a SequenceFileOutputFormat[Text, BytesWritable]. We cannot split these files into chunks, they must be processed by our tools in our mappers and reducers as complete files. The problem we have is that BytesWritable appears to load the entire content of a file into memory, and now that we are trying to process our production size datasets, once you get a couple of large files on the go, the JVM throws the dreaded OutOfMemoryError. What we need is someway to process these binary files, by reading and writing their contents as Streams to and from the Sequence File. Or really any other mechanism that does not involve loading the entire file into RAM! Our own tools that we use in the mappers and reducers in-fact expect to work with java.io.InputStream. We have tried quite a few things now, including writing some custom Writable implementations, but we then end up buffering data in temporary files which is not exactly ideal when the data already exists in the sequence files in HDFS. Is there any hope? Thanks Adam. -- Adam Retter skype: adam.retter tweet: adamretter http://www.adamretter.org.uk
Re: Memory problems with BytesWritable and huge binary files
Is your data in any given file a bunch of key-value pairs? No. The content of each file itself is the value we are interested in, and I guess that it's filename is the key. If that isn't the case, I'm wondering how writing a single large key-value into a sequence file helps. It won't. May be you can give an example of your input data? Well from the Hadoop O'Reilly book, I rather got the impression that HDFS does not like small files due to it's 64MB block size, and it is instead recommended to place small files into a Sequence file. Is that not the case? Our input data really varies between 130 different file types, it could be Microsoft Office documents, Video Recordings, Audio, CAD diagrams etc. If indeed they are a bunch of smaller sized key-value pairs, you can write your own custom InputFormat that reads the data from your input files one k-v pair after another, and feed it to your MR job. There isn't any need for converting them to sequence-files at that point. As I mentioned in my initial email, each file cannot be split up! Thanks +Vinod Hortonworks Inc. http://hortonworks.com/ CONFIDENTIALITY NOTICE NOTICE: This message is intended for the use of the individual or entity to which it is addressed and may contain information that is confidential, privileged and exempt from disclosure under applicable law. If the reader of this message is not the intended recipient, you are hereby notified that any printing, copying, dissemination, distribution, disclosure or forwarding of this communication is strictly prohibited. If you have received this communication in error, please contact the sender immediately and delete it from your system. Thank You. -- Adam Retter skype: adam.retter tweet: adamretter http://www.adamretter.org.uk
Re: Memory problems with BytesWritable and huge binary files
So I am not sure I follow you, as we already have a custom InputFormat and RecordReader and that does not seem to help. The reason it does not seem to help is that it needs to return the data as a Writable so that the Writable can then be used in the following map operation. The map operation needs access to the entire file. The only way to do this in Hadoop by default is to use BytesWritable, but that places everything in memory. What am I missing? On 24 January 2014 22:42, Vinod Kumar Vavilapalli vino...@hortonworks.com wrote: Okay. Assuming you don't need a whole file (video) in memory for your processing, you can simply write a Inputformat/RecordReader implementation that streams through any given file and processes it. +Vinod On Jan 24, 2014, at 12:44 PM, Adam Retter adam.ret...@googlemail.com wrote: Is your data in any given file a bunch of key-value pairs? No. The content of each file itself is the value we are interested in, and I guess that it's filename is the key. If that isn't the case, I'm wondering how writing a single large key-value into a sequence file helps. It won't. May be you can give an example of your input data? Well from the Hadoop O'Reilly book, I rather got the impression that HDFS does not like small files due to it's 64MB block size, and it is instead recommended to place small files into a Sequence file. Is that not the case? Our input data really varies between 130 different file types, it could be Microsoft Office documents, Video Recordings, Audio, CAD diagrams etc. If indeed they are a bunch of smaller sized key-value pairs, you can write your own custom InputFormat that reads the data from your input files one k-v pair after another, and feed it to your MR job. There isn't any need for converting them to sequence-files at that point. As I mentioned in my initial email, each file cannot be split up! Thanks +Vinod Hortonworks Inc. http://hortonworks.com/ CONFIDENTIALITY NOTICE NOTICE: This message is intended for the use of the individual or entity to which it is addressed and may contain information that is confidential, privileged and exempt from disclosure under applicable law. If the reader of this message is not the intended recipient, you are hereby notified that any printing, copying, dissemination, distribution, disclosure or forwarding of this communication is strictly prohibited. If you have received this communication in error, please contact the sender immediately and delete it from your system. Thank You. -- Adam Retter skype: adam.retter tweet: adamretter http://www.adamretter.org.uk -- CONFIDENTIALITY NOTICE NOTICE: This message is intended for the use of the individual or entity to which it is addressed and may contain information that is confidential, privileged and exempt from disclosure under applicable law. If the reader of this message is not the intended recipient, you are hereby notified that any printing, copying, dissemination, distribution, disclosure or forwarding of this communication is strictly prohibited. If you have received this communication in error, please contact the sender immediately and delete it from your system. Thank You. -- Adam Retter skype: adam.retter tweet: adamretter http://www.adamretter.org.uk
RE: Appropriate for Hadoop?
I was more concerned that the input to our input is from SQL databases and a proprietary EMC document store. And that our output is to a different SQL database. I don't want to use any sort of file system at all. Adam Retter Software Developer Landmark Information Group T: 01392 685403 (x5403) 5-7 Abbey Court, Eagle Way, Sowton, Exeter, Devon, EX2 7HY www.landmark.co.uk -Original Message- From: Chuck Lam [mailto:chuck@gmail.com] Sent: 28 April 2009 20:25 To: core-user@hadoop.apache.org Subject: Re: Appropriate for Hadoop? HDFS is designed with Hadoop in mind, so there are certain advantages (e.g. performance, reliability, and ease of use) to using HDFS for Hadoop. However, it's not required. For example, when you run Hadoop in standalone mode, it just uses the file system on your local machine. When you run it on Amazon AWS, it can use S3 as a file system. On Tue, Apr 28, 2009 at 6:15 AM, Adam Retter adam.ret...@landmark.co.ukwrote: Each document processing is independent and can be processed parallelly, so that part could be done in a map reduce job. Now whether it suits this use case depends on rate at which new URI's are discovered for processing and acceptable delay in processing of a document. The way I see it you can batch the URI's and input that to mapreduce job. Each mapper can work on sublist of URIs. You can choose to make DB inserts from mapper itself. In that case you can set no of reducers to 0. Otherwise if batching of the queries is an option then you can consider making batch inserts in reducer. It will help in reducing load on DB. So I don't have to use HDFS at all when using Hadoop? Registered Office: 7 Abbey Court, Eagle Way, Sowton, Exeter, Devon, EX2 7HY Registered Number 2892803 Registered in England and Wales This email has been scanned by the MessageLabs Email Security System. For more information please visit http://www.messagelabs.com/email The information contained in this e-mail is confidential and may be subject to legal privilege. If you are not the intended recipient, you must not use, copy, distribute or disclose the e-mail or any part of its contents or take any action in reliance on it. If you have received this e-mail in error, please e-mail the sender by replying to this message. All reasonable precautions have been taken to ensure no viruses are present in this e-mail. Landmark Information Group Limited cannot accept responsibility for loss or damage arising from the use of this e-mail or attachments and recommend that you subject these to your virus checking procedures prior to use. Registered Office: 7 Abbey Court, Eagle Way, Sowton, Exeter, Devon, EX2 7HY Registered Number 2892803 Registered in England and Wales This email has been scanned by the MessageLabs Email Security System. For more information please visit http://www.messagelabs.com/email The information contained in this e-mail is confidential and may be subject to legal privilege. If you are not the intended recipient, you must not use, copy, distribute or disclose the e-mail or any part of its contents or take any action in reliance on it. If you have received this e-mail in error, please e-mail the sender by replying to this message. All reasonable precautions have been taken to ensure no viruses are present in this e-mail. Landmark Information Group Limited cannot accept responsibility for loss or damage arising from the use of this e-mail or attachments and recommend that you subject these to your virus checking procedures prior to use.
Appropriate for Hadoop?
If I understand correctly - Hadoop forms a general purpose cluster on which you can execute jobs? We have a Java data processing application here that follows the Producer - Consumer pattern. It has been written with threading as a concern from the start using java.util.concurrent.Callable. At present the producer is a thread that retrieves a list of document URI's from a SQL query against databaseA and adds them to a shared (synchronised) queue. Each consumer is a thread, of which there can be n, but we typically run with 16 on the current hardware. The consumer sits in a loop, processing the queue until it is empty. It removes a document URI from the shared queue, retrieves the document and performs a pipeline of transformations on the document, resulting in a series of 600 to 16000 SQL insert statements which are then executed against databaseB. I have been reading about both Terracotta and Hadoop. Hadoop appears the more general purpose solution that we could use for many applications, however I am not sure how our application would map onto Hadoop concepts. I have been studying the Map/Reduce Hadoop approach but our application does not produce any intermediate files that would be the input/output to the Map/Reduce processes. Any guidance would be appreciated, it may well be that our application is not an appropriate use of Hadoop? Thanks Adam. Adam Retter Software Developer Landmark Information Group T: 01392 685403 (x5403) 5-7 Abbey Court, Eagle Way, Sowton, Exeter, Devon, EX2 7HY www.landmark.co.uk Registered Office: 7 Abbey Court, Eagle Way, Sowton, Exeter, Devon, EX2 7HY Registered Number 2892803 Registered in England and Wales This email has been scanned by the MessageLabs Email Security System. For more information please visit http://www.messagelabs.com/email The information contained in this e-mail is confidential and may be subject to legal privilege. If you are not the intended recipient, you must not use, copy, distribute or disclose the e-mail or any part of its contents or take any action in reliance on it. If you have received this e-mail in error, please e-mail the sender by replying to this message. All reasonable precautions have been taken to ensure no viruses are present in this e-mail. Landmark Information Group Limited cannot accept responsibility for loss or damage arising from the use of this e-mail or attachments and recommend that you subject these to your virus checking procedures prior to use.
RE: General purpose processing on Hadoop
Are you interested in building such a system? I would be interested in using such a system, but otherwise I am afraid that I do not have the time resources available to be involved in such a project. Sorry. Registered Office: 7 Abbey Court, Eagle Way, Sowton, Exeter, Devon, EX2 7HY Registered Number 2892803 Registered in England and Wales This email has been scanned by the MessageLabs Email Security System. For more information please visit http://www.messagelabs.com/email The information contained in this e-mail is confidential and may be subject to legal privilege. If you are not the intended recipient, you must not use, copy, distribute or disclose the e-mail or any part of its contents or take any action in reliance on it. If you have received this e-mail in error, please e-mail the sender by replying to this message. All reasonable precautions have been taken to ensure no viruses are present in this e-mail. Landmark Information Group Limited cannot accept responsibility for loss or damage arising from the use of this e-mail or attachments and recommend that you subject these to your virus checking procedures prior to use.
RE: Appropriate for Hadoop?
Each document processing is independent and can be processed parallelly, so that part could be done in a map reduce job. Now whether it suits this use case depends on rate at which new URI's are discovered for processing and acceptable delay in processing of a document. The way I see it you can batch the URI's and input that to mapreduce job. Each mapper can work on sublist of URIs. You can choose to make DB inserts from mapper itself. In that case you can set no of reducers to 0. Otherwise if batching of the queries is an option then you can consider making batch inserts in reducer. It will help in reducing load on DB. So I don't have to use HDFS at all when using Hadoop? Registered Office: 7 Abbey Court, Eagle Way, Sowton, Exeter, Devon, EX2 7HY Registered Number 2892803 Registered in England and Wales This email has been scanned by the MessageLabs Email Security System. For more information please visit http://www.messagelabs.com/email The information contained in this e-mail is confidential and may be subject to legal privilege. If you are not the intended recipient, you must not use, copy, distribute or disclose the e-mail or any part of its contents or take any action in reliance on it. If you have received this e-mail in error, please e-mail the sender by replying to this message. All reasonable precautions have been taken to ensure no viruses are present in this e-mail. Landmark Information Group Limited cannot accept responsibility for loss or damage arising from the use of this e-mail or attachments and recommend that you subject these to your virus checking procedures prior to use.
