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ALTER TABLE MOVE: A table may be moved in parallel. ALTER TABLE SPLIT|COALESCE PARTITION: The individual table partitions may be split or coalesced in parallel. ALTER INDEX SPLIT PARTITION: An index partition may be split in parallel. The first four of these commands work for individual table/index partitions as well that is, you may MOVE an individual partition of a table in parallel. To me, parallel DDL is where the parallel execution in Oracle is of greatest measurable benefit. Sure, it can be used with parallel query to speed up certain long-running operations, but from a maintenance standpoint, and from an administration standpoint, parallel DDL is where the parallel operations affect us, DBAs and developers, the most. If you think of parallel query as being designed for the end user for the most part, then parallel DDL is designed for the DBA/developer.
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One of my favorite new features in Oracle 9i is external tables, which are especially useful in the area of data loading. We ll cover data loading and external tables in some detail in the next chapter but, as a quick introduction, we ll take a brief look at these topics here to study the effects of parallel DDL on extent sizing and extent trimming. External tables allow us to easily perform parallel direct path loads without thinking too hard about it. Oracle 7.1 gave us the ability to perform parallel direct path loads, whereby multiple sessions could write directly to the Oracle data files, bypassing the buffer cache entirely, bypassing undo for the table data, and perhaps even bypassing redo generation. This was accomplished via SQL*Loader. The DBA would have to script multiple SQL*Loader sessions, split the input data files to be loaded manually, determine the degree of parallelism, and coordinate all of the SQL*Loader processes. In short, it could be done, but it was hard. With parallel DDL plus external tables, we have a parallel direct path load that is implemented via a simple CREATE TABLE AS SELECT or INSERT /*+ APPEND */. No more scripting, no more splitting of files, and no more coordinating the N number of scripts that would be running. In short, this combination provides pure ease of use, without a loss of performance. Let s take a look at a simple example of this in action. We ll see shortly how to create an external table. We ll look at data loading with external tables in much more detail in the next chapter. For now, we ll use a real table to load another table from, much like many people do with staging tables in their data warehouse. The technique in short is as follows: 1. Use some extract, transform, load (ETL) tool to create input files. 2. Load these input files into staging tables. 3. Load a new table using queries against these staging tables. We ll use the same BIG_TABLE from earlier, which is parallel-enabled and contains 10 million records. We re going to join this table to a second table, USER_INFO, which contains OWNERrelated information from the ALL_USERS dictionary view. The goal is to denormalize this information into a flat structure.
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We ll start by creating the USER_INFO table, enabling it for parallel operations, and then gathering statistics on it: big_table@ORA10GR1> create table user_info as select * from all_users; Table created. big_table@ORA10GR1> alter table user_info parallel; Table altered. big_table@ORA10GR1> exec dbms_stats.gather_table_stats( user, 'USER_INFO' ); PL/SQL procedure successfully completed. Now, we would like to parallel direct path load a new table with this information. The query we ll use is simply create as select from where table new_table parallel a.*, b.user_id, b.created user_created big_table a, user_info b a.owner = b.username
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The plan for that particular CREATE TABLE AS SELECT looked like this in Oracle 10g: --------------------------------------------------------------------------| Id | Operation | Name | TQ |IN-OUT| PQ Distrib | --------------------------------------------------------------------------| 0 | CREATE TABLE STATEMENT | | | | | | 1 | PX COORDINATOR | | | | | | 2 | PX SEND QC (RANDOM) | :TQ10001 | Q1,01 | P->S | QC (RAND) | | 3 | LOAD AS SELECT | | Q1,01 | PCWP | | |* 4 | HASH JOIN | | Q1,01 | PCWP | | | 5 | PX RECEIVE | | Q1,01 | PCWP | | | 6 | PX SEND BROADCAST | :TQ10000 | Q1,00 | P->P | BROADCAST | | 7 | PX BLOCK ITERATOR | | Q1,00 | PCWC | | | 8 | TABLE ACCESS FULL| USER_INFO | Q1,00 | PCWP | | | 9 | PX BLOCK ITERATOR | | Q1,01 | PCWC | | | 10 | TABLE ACCESS FULL | BIG_TABLE | Q1,01 | PCWP | | --------------------------------------------------------------------------If you look at the steps from 4 on down, that is the query (SELECT) component. The scan of BIG_TABLE and hash join to USER_INFO was performed in parallel, and each of the subresults was loaded into a portion of the table (step 3, the LOAD AS SELECT). After each of the parallel execution servers finishes its part of the join and load, it sends its results up to the query coordinator. In this case, the results simply indicated success or failure, as the work had already been performed. And that is all there is to it parallel direct path loads made easy. The most important thing to consider with these operations is how space is used (or not used). Of particular importance is a side effect called extent trimming. I d like to spend some time investigating that now.
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