Hi Chris, The overall elapsed time to run a sort depends on many factors other than the sort algorithm. If you follow the data flow in MR from the point where sorting starts in Map phase to the point where <Key, Value> pairs are available for reduction in Reduce phase there are CPU and IO intensive activities happening. You are right, passing data to an external process adds CPU cycles. However, a well engineered implementation of the overall process can cut down the elapsed time. From some of my experiments with a prototype implementation, I was able to cut down the elapsed time by about 40% to run some huge sorts(500 GB) on a modest cluster of 6 nodes.
Besides, an external sorter can provide additional functionalities to Hadoop. For example, on the Map side, an external sorter process can support filtering, reformatting, and aggregation in a single process with performance optimized for a multicore system. With the current MR framework, filtering and reformatting happen before sorting and all these operations are very sequential in nature. On the Reduce side, an external sorter can offer even exotic solution like Join since the external sorter implementation on the Reduce side is free to work on more than one stream(one from Hadoop MR shuffled data and the other from HDFS for example.) Thank you very much for your feedback. If you any more questions, please let me know. -- Asokan On 04/26/2011 11:41 AM, Christopher Smith wrote: Aren't you worried that the overhead of shoving all that data through an external sort facility would outweigh any benefits from the algo? --Chris On Apr 26, 2011, at 8:34 AM, "Asokan, M" <maso...@syncsort.com><mailto:maso...@syncsort.com> wrote: Hi All, I am submitting this notice of intent to contribute to the Hadoop community on behalf of Syncsort, Inc. (www.syncsort.com<http://www.syncsort.com><http://www.syncsort.com><http://www.syncsort.com>) an interface for an external sorter. Although Hadoop MR (Map/Reduce) provides users with pluggable InputFormat, Mapper, Partitioner, Combiner, Reducer, and OutputFormat it does not provide a plug-in for an external sorter. There is limited support to plug in a sorter class in the Map phase. The merge logic in the Reduce phase cannot be changed. Also, the sorting process is tightly coupled to the framework. The goal of our project is to decouple the sorting process and contribute a defined clean interface to allow developers to easily plug in external sorters through this interface. THIS INTERFACE WILL BE INDEPENDENT FROM SYNCSORT’S PROPRIETARY SOFTWARE PRODUCTS WHICH ARE NOT INTENDED TO BE CONTRIBUTED. The following are some of the motivating factors for this project (not in any order of significance): · An external sort plug-in will promote innovative implementations by developers who have expertise in sort algorithms. · Hadoop developers can experiment with different sort implementations (in both the Map and Reduce phases) without modifying the framework code. · An external implementation of sort can be very well optimized to take advantage of OS and hardware architecture compared to the pure Java implementation in Hadoop. · The Hadoop implementation of sort is not self tuning. Users may be overwhelmed by so many parameters to be specified to tune the performance of sort. · One of the top memory consumers in the MR child JVMs is the sort. Users are advised to set a reasonably high value for -mx argument to JVM. Failure to do so will result in job termination. If the external sorter is implemented as a subprocess, it can adjust its memory usage automatically and make sure that it does not fail. Besides, the memory needed by the MR child JVM can be reduced to a meager 128 MB. · The performance of Hadoop sort may be at the mercy of JVM. See LUCENE-2504 in Hadoop Jira for a related performance regression issue. An external sorter implemented in C or C++ and run as a subprocess will not suffer from these types of problems. · ETL tool vendors can complement Hadoop's strengths namely HDFS, job scheduling, restartability, etc. with their sort technologies. This will enable Hadoop to make inroads into IT shops that use traditional ETL tools. The goals of this project are: · The primary goal of this project is to allow users to seamlessly plug in the external sorter to their existing MR applications. This is in contrast to the approach taken by HCE (see MAPREDUCE-1270 in Hadoop Jira) which requires users to code their MR applications in C++. · A secondary goal is to enable users of existing ETL tools to exploit Hadoop's distributed processing framework. We are confident there will be interest in this contribution to the code to the Hadoop community. I intend to provide a reference implementation of the interfaces defined in the design. This reference implementation uses GNU sort command to do the sorting of text data. -- Asokan M. 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