[Hadoop Wiki] Update of "Hive/JoinOptimization" by Kirk True

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The "Hive/JoinOptimization" page has been changed by Kirk True.
The comment on this change is: Fixed what appears to be a copy-and-paste error with the Jira links.
http://wiki.apache.org/hadoop/Hive/JoinOptimization?action=diff&rev1=12&rev2=13

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  In Fig 1 above, the previous map join implementation does not scale well when the larger table is huge because each Mapper will directly read the small table data from HDFS. If the larger table is huge, there will be thousands of Mapper launched to read different records of the larger table. And those thousands of Mappers will read this small table data from HDFS into their memory, which can make access to the small table become the performance bottleneck; or, sometimes Mappers will get lots of time-outs for reading this small file, which may cause the task to fail.
  
- 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 below.
+ Hive-1641 ([[http://issues.apache.org/jira/browse/HIVE-1641|http://issues.apache.org/jira/browse/HIVE-1641]]) has solved this problem, as shown in Fig2 below.
  
  {{attachment:fig2.jpg||height="881px",width="1184px"}}
  
@@ -23, +23 @@

  Obviously, the Local Task is a very memory intensive. So the query processor will launch this task in a child jvm, which has the same heap size as the Mapper's. Since the Local Task may run out of memory, the query processor will measure the memory usage of the local task very carefully. Once the memory usage of the Local Task is higher than a threshold number. This Local Task will abort itself and tells the user that this table is too large to hold in the memory. User can change this threshold by '''''set hive.mapjoin.localtask.max.memory.usage = 0.999;'''''
  
  == 1.2 Removing JDBM ==
- 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 profiling the Map Join, we found out this JDBM component takes more than 70 % CPU time as shown in Fig3. Also the persistent file JDBM generated is too large to put into the Distributed Cache. For example, if users put 67,000 simple integer 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 component 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 a persistent hashtable any more. Hive-1754 ([[http://issues.apache.org/jira/browse/HIVE-1293|http://issues.apache.org/jira/browse/HIVE-1754]])
+ 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 profiling the Map Join, we found out this JDBM component takes more than 70 % CPU time as shown in Fig3. Also the persistent file JDBM generated is too large to put into the Distributed Cache. For example, if users put 67,000 simple integer 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 component 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 a persistent hashtable any more. Hive-1754 ([[http://issues.apache.org/jira/browse/HIVE-1754|http://issues.apache.org/jira/browse/HIVE-1754]])
  
  {{attachment:fig3.jpg}}
  
@@ -42, +42 @@

  == 2.1 New Join Execution Flow ==
  Since map join is faster than the common join, it would be better to run the map join whenever possible. Previously, Hive users need to give a hint in the query to assign which table the small table is. For example, '''''select /*+mapjoin(a)*/ * from src1 x  join src2y on x.key=y.key''''';   It is not a good way for user experience and query performance, because sometimes user may give a wrong hint and also users may not give any hints. It would be much better to convert the Common Join into Map Join without users' hint.
  
- Hive-1642 ([[http://issues.apache.org/jira/browse/HIVE-1293|http://issues.apache.org/jira/browse/HIVE-1642]]) has solved the problem by converting the Common Join into Map Join automatically. For the Map Join, the query processor should know which input table the big table is. The other input tables will be recognize as the small tables during the execution stage and these tables need to be held in the memory. However, in general, the query processor has no idea of input file size during compiling time (even with statistics) because some of the table may be intermediate tables generated from sub queries. So the query processor can only figure out the input file size during the execution time.
+ Hive-1642 ([[http://issues.apache.org/jira/browse/HIVE-1642|http://issues.apache.org/jira/browse/HIVE-1642]]) has solved the problem by converting the Common Join into Map Join automatically. For the Map Join, the query processor should know which input table the big table is. The other input tables will be recognize as the small tables during the execution stage and these tables need to be held in the memory. However, in general, the query processor has no idea of input file size during compiling time (even with statistics) because some of the table may be intermediate tables generated from sub queries. So the query processor can only figure out the input file size during the execution time.
  
  Right now, users need to enable this feature by''''' set hive.auto.convert.join = true;'''''