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CHAPTER 12 HASH JOINS
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was too high the cost when hinted was 542. Similarly, we might have seen a merge join, but again this would have been too expensive the cost when hinted was 445. To get a picture of how Oracle performs the join, you can imagine it starts by splitting the SQL into two completely separate components: select bu.id, bu.build_vc, bu.id_probe from build_tab where bu.id between 1 and 500 ; select pb.probe_vc, pb.probe_padding, pb.id from probe_tab ; The two components select the join columns and all the referenced columns in their select lists, using any available predicates from the main query to restrict the row selection. In this case, this mechanism allows the pseudo-query against build_tab to be reasonably selective, but the pseudo-query against probe_tab has no row restrictions at all. This lack of restrictions on the probe table explains why partition elimination often fails to occur when the second table in a hash join is a partitioned table. Unless the optimizer decides to execute a preliminary query (known as a pruning subquery) against the build table, or the decomposed query against the probe table includes a reference to the partitioning column(s), then there is nothing to tell Oracle how to eliminate partitions. You will notice that I ve included the ID column in the select list for build_tab. This seems to be unnecessary, but the optimizer allows for it in the calculations, so I ve put it there. After generating the two pseudo-queries, the optimizer estimates the number of rows and row size (hence total volume of data) of the two data sets. The cost and cardinality of the two queries comes from the standard calculations. The bytes figure is derived from the cardinality of the queries and (usually) the values from the column avg_col_len in user_tab_columns. pb bu
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HOW BIG IS A ROW
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As a general rule, the figures for bytes in execution plans are derived from the avg_col_len columns of user_tab_columns. The deprecated analyze command excludes the length byte(s) for the column, but the call to dbms_stats.gather_table_stats includes the length byte(s). Since the choice of build table in a hash join is affected by the size of the data sets involved, a switch from analyze to dbms_stats could (in principle) change the order of a hash join, or even cause the optimizer to use a different join mechanism.
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CHAPTER 12 HASH JOINS
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For the special case of select * from table, the optimizer seems to use the avg_row_len from user_tables as the row size if the statistics have been generated by dbms_stats, but sum(avg_col_len) if the statistics have been generated by analyze. (The optimizer can identify how the statistics were generated by checking the global_stats column of user_tables or user_tab_columns.)
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If we check the column names and column lengths from the view user_tab_columns, we get the results shown in Table 12-1.
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Table 12-1. Calculating the Row Size
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Table
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Build_tab Build_tab Build_tab Probe_tab Probe_tab Probe_tab
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Column
Build_vc Id Id_probe Id Probe_padding Probe_vc
avg_col_len (dbms_stats)
21 4 5 5 501 21
Total for Table (dbms_stats)
avg_col_len (analyze)
20 3
Total for Table (analyze)
4 4 500
So for build_tab, we have 500 rows at 30 bytes for a total of 15,000 bytes; for probe_tab, we have 5,000 rows at 527 bytes for a total of 2,635,000 bytes as we saw in the execution plan. If you look closely at the execution plan, you will also infer that the output row size that the optimizer is using is 557 (from 278,500 / 500) which means it has simply added the lengths of the input rows, and has not allowed for double-counting of the join columns. The figures for bytes would change to 13,500 bytes, 2,620,000 bytes, and 275,500 bytes if we switch from the dbms_stats package to the old analyze command. After working out the data size, the optimizer identifies the smaller set as the build table, and starts working on building a hash table. Since we have a hash_area_size of 1MB, and only 15,000 bytes of data, we can be confident that the hash table will fit in memory, and we won t have to spill to disk. This is what gives us a definition of a small data set it is one that fits entirely within the hash_area_size, even after allowing for some overheads.
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