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The "Hive/JoinOptimization" page has been changed by LiyinTang. http://wiki.apache.org/hadoop/Hive/JoinOptimization?action=diff&rev1=4&rev2=5 -------------------------------------------------- == 1.1 Using Distributed Cache to Propagate Hashtable File == Previously, when 2 large data tables need to do a join, there will be 2 different Mappers to sort these tables based on the join key and emit an intermediate file, and the Reducer will take the intermediate file as input file and do the real join work. This join mechanism is perfect with two large data size. But if one the join table is small enough to fit into the Mapper’s memory, then there is no need to launch the Reducer. Actually, the Reducer stage is very expensive for the performance because the Map/Reduce framework needs to sort and merge the intermediate files. - {{attachment:fig1.jpg||height="764px",width="911px"}} + {{attachment:fig1.jpg||height="763px",width="909px"}} '''Fig 1. The Previous Map Join''' So the basic idea of map join is to hold the data of small table in Mapper’s memory and do the join work in Map stage, which saves the Reduce stage. As shown in Fig 1, the previous map join operation is not scale for large data because each Mapper will directly read the small table data from HDFS. If the large data file is large enough, there will be thousands of Mapper launched to read different record of this large data file. And thousands of Mapper will read this small table data from HDFS into the memory, which makes the small table to be the performance bottleneck, or sometimes Mapper will get lots of time-out for reading this small file, which may cause the task failed. - {{attachment:fig2.jpg}} + {{attachment:fig2.jpg||height="881px",width="1184px"}} Hive-1641 ([[http://issues.apache.org/jira/browse/HIVE-1293|http://issues.apache.org/jira/browse/HIVE-1641]]) has solved this problem. As shown in Fig2, the basic idea is to create a new task, MapReduce Local Task, before the orginal Join Map/Reduce Task. This new task will read the small table data from HDFS to in-memory hashtable. After reading, it will serialize the in-memory hashtable into files on disk and compress the hashtable file into a tar file. In next stage, when the MapReduce task is launching, it will put this tar file to Hadoop Distributed Cache, which will populate the tar file to each Mapper’s local disk and decompress the file. So all the Mappers can deserialize the hashtable file back into memory and do the join work as before. == 1.2 Removing JDBM == - When profiing the Map Join, + Previously, Hive uses JDBM ([[http://issues.apache.org/jira/browse/HIVE-1293|http://jdbm.sourceforge.net/]]) as a persistent hashtable. Whenever the in-memory hashtable cannot hold data any more, it will swap the key/value into the JDBM table. However when profiing the Map Join, we found out this JDBM component takes more than 70 % CPU time as shown in Fig3. Also the persistent file JDBM genreated is too large to put into the Distributed Cache. For example, if users put 67,000 simple interger key/value pairs into the JDBM, it will generate more 22M hashtable file. So the JDBM is too heavy weight for Map Join and it would better to remove this componet from Hive. Map Join is designed for holding the small table's data into memory. If the table is too large to hold, just run as a Common Join. There is no need to use persistent hashtable any more. == 1.3 Performance Evaluation == = 2. Converting Join into Map Join dyanmically =
