Oracle Scratchpad

September 25, 2013

Extended Stats

Filed under: CBO,Oracle,Statistics — Jonathan Lewis @ 5:06 pm GMT Sep 25,2013

Here’s a little demo cut-n-pasted from a session running Oracle (it works on 11g, too). All it does is create a table by copying from a well-known table, gather extended stats on a column group, then show you the resulting column names by querying view user_tab_cols.


September 7, 2013

Hash Joins

Filed under: CBO,Execution plans,Hints,Oracle,Tuning — Jonathan Lewis @ 12:53 pm GMT Sep 7,2013

I’ve written notes about the different join mechanisms in the past – but such things are always worth revisiting, so here’s an accumulated bundle of comments about hash joins.

A hash join takes two inputs that (in most of the Oracle literature) are referred to as the “build table” and the “probe table”. These rowsources may be extracts from real tables or indexes, or might be result sets from previous joins. Oracle uses the “build table” to build a hash table in memory, consuming and using the rowsource in a single call; it then consumes the “probe table” one row at a time, probing the in-memory hash table to find a match.

Access to the hash table is made efficient by use of a hashing function applied to the join columns – rows with the same value on the join column end up hashing to the same place in the hash table. It is possible for different input values to produce the same hash value (a hash collision) so Oracle still has to check the actual values once it has identified “probable” joins in the hash table. Because the comparison is based on a hashing mechanism, hash joins can only be used for join predicates that are equality predicates.

August 15, 2013

MV Refresh

Filed under: Bugs,CBO,Infrastructure,Oracle,Statistics — Jonathan Lewis @ 6:12 pm GMT Aug 15,2013

Here’s a funny little problem I came across some time ago when setting up some materialized views. I have two tables, orders and order_lines, and I’ve set up materialized view logs for them that allow a join materialized view (called orders_join) to be fast refreshable. Watch what happens if I refresh this view just before gathering stats on the order_lines table.


August 13, 2013


Filed under: CBO,Conditional SQL,Execution plans,NULL,Oracle — Jonathan Lewis @ 7:14 am GMT Aug 13,2013

Here’s a little detail about how the optimizer can handle the nvl() function that I hadn’t noticed before (and it goes back to at least 8i). This is running on, and table t1 is just all_objects where rownum <= 20000:


August 5, 2013

Bloom Filter

Filed under: 12c,CBO,Execution plans,Oracle — Jonathan Lewis @ 9:22 pm GMT Aug 5,2013

I’ve posted this note as a quick way of passing on an example prompted by a twitter conversation with Timur and Maria about Bloom filters:

The Bloom filter (capital B because it’s named after a person) is not supposed to appear in Oracle plans unless the query is executing in parallel but here’s an example which seems to use a serial Bloom filter.  Running in and (the results shown are the latter – the numbers are slightly different between versions):


July 27, 2013


Filed under: CBO,Indexing,Oracle — Jonathan Lewis @ 7:21 am GMT Jul 27,2013

The clustering_factor is one of the most important numbers (if not the most important number) affecting the optimizer’s choice of execution plan – it’s the thing that has the most significant effect on the optimizer’s decision on whether to choose a table scan or an index, and on which index to choose.


July 24, 2013

Linear Decay

Filed under: CBO,Oracle,Statistics — Jonathan Lewis @ 6:11 pm GMT Jul 24,2013

I’ve mentioned “linear decay” in several posts when explaining a problem that someone has seen with an execution path – but I’ve recently realised that I don’t have a post describing what it is and how it works – although it’s in Cost Based Oracle – Fundamentals, of course, if you want some detail – so here’s a brief introduction (based on simple stats with no histograms).


July 12, 2013

Wrong Index 2

Filed under: CBO,Oracle,trace files,Troubleshooting — Jonathan Lewis @ 5:17 pm GMT Jul 12,2013

A couple of days ago I wrote an article about Oracle picking the “wrong index” after an index rebuild, and I mentioned that the sample data I had generated looked a little odd because it came from a script I had been using to investigate a completely different problem. This note describes that other problem, which appeared on the Oracle-L mailing list last month.

Stripped to a bare minimum, here’s the issue: we have a simple query against a single table with two indexes IDX2_AUFTRAG(arsAuftragsNr, dategAuftragsNr) and IDX7_AUFTRAG(arsAuftragsNr), and a predicate “arsAuftragsNr = {constant}”. Since the second column in the two-column index is irrelevant (we can’t use it to avoid visiting the table, and it’s not part of a group by or order by clause), and since adding a column to an index is likely to increase the clustering_factor and leaf block count of the index, we would probably expect to see Oracle pick the single column index as the path to the table – but it doesn’t, it picks the two-column index.


July 7, 2013

Cursor Sharing

Filed under: CBO,Oracle,Statistics,trace files — Jonathan Lewis @ 5:49 pm GMT Jul 7,2013

Here’s a couple of extracts from a trace file after I’ve set optimizer_dynamic_sampling to level 3. I’ve run two, very similar, SQL statements that both require dynamic sampling according to the rules for the parameter – but take a look at the different ways that sampling has happened, and ask yourself what’s going on:


July 3, 2013

maxthr – 3

Filed under: CBO,Oracle,Parallel Execution,Statistics,System Stats — Jonathan Lewis @ 6:29 pm GMT Jul 3,2013

In part 1 of this mini-series we looked at the effects of costing a tablescan serially and then parallel when the maxthr and slavethr statistics had not been set.

In part 2 we looked at the effect of setting just the maxthr - and this can happen if you don’t happen to do any parallel execution while the stats collection is going on.

In part 3 we’re going to look at the two variations the optimizer displays when both statistics have been set. So here are the starting system stats:

	dbms_stats.set_system_stats('MBRC',        64);
	dbms_stats.set_system_stats('MREADTIM',    10);
	dbms_stats.set_system_stats('SREADTIM',     5);
	dbms_stats.set_system_stats('CPUSPEED',  2000);
	dbms_stats.set_system_stats('MAXTHR',  262144);
	dbms_stats.set_system_stats('SLAVETHR', 65536);
	dbms_stats.set_system_stats('SLAVETHR', 47000);
	dbms_stats.set_system_stats('SLAVETHR', 16384);

You’ll notice that I’ve shown three options for slavethr so, when running the tests, I will be commenting out two of them. The middle value is the important one as I’ve set it just below a critical breakpoint. You’ll recall that the optimizer is programmed to behave as if a parallel slave will operate at 90% of the speed of a serial process. If we take the 64 block read, at 8KB per block, completed in 10 ms, this represents 52428.8 bytes per ms. 90% of that is 47,186 bytes per ms – hence the choice for slavethr in the second of the tests.

Remember that a serial tablescan of my data had an I/O cost of 1,251 (or 1,250 is you ignore the “tablescan cost plus 1″ effect) and that we could investigate the parallel costs by reference to the original serial cost compared to the degree of parallelism. We’re going to do that again, but in this case I’m going to run my tablescan just once (at parallel degree 5) for each of the three values of slavethr (lowest to highest) in turn.

Here are the resulting execution plans:

| Id  | Operation              | Name     | Rows  | Bytes | Cost (%CPU)| Time     |    TQ  |IN-OUT| PQ Distrib |
|   0 | SELECT STATEMENT       |          |     1 |     5 |   800   (0)| 00:00:05 |        |      |            |
|   1 |  SORT AGGREGATE        |          |     1 |     5 |            |          |        |      |            |
|   2 |   PX COORDINATOR       |          |       |       |            |          |        |      |            |
|   3 |    PX SEND QC (RANDOM) | :TQ10000 |     1 |     5 |            |          |  Q1,00 | P->S | QC (RAND)  |
|   4 |     SORT AGGREGATE     |          |     1 |     5 |            |          |  Q1,00 | PCWP |            |
|   5 |      PX BLOCK ITERATOR |          | 40000 |   195K|   800   (0)| 00:00:05 |  Q1,00 | PCWC |            |
|   6 |       TABLE ACCESS FULL| T1       | 40000 |   195K|   800   (0)| 00:00:05 |  Q1,00 | PCWP |            |

---------- ---------- ----------
       800    1333333        800

| Id  | Operation              | Name     | Rows  | Bytes | Cost (%CPU)| Time     |    TQ  |IN-OUT| PQ Distrib |
|   0 | SELECT STATEMENT       |          |     1 |     5 |   279   (0)| 00:00:02 |        |      |            |
|   1 |  SORT AGGREGATE        |          |     1 |     5 |            |          |        |      |            |
|   2 |   PX COORDINATOR       |          |       |       |            |          |        |      |            |
|   3 |    PX SEND QC (RANDOM) | :TQ10000 |     1 |     5 |            |          |  Q1,00 | P->S | QC (RAND)  |
|   4 |     SORT AGGREGATE     |          |     1 |     5 |            |          |  Q1,00 | PCWP |            |
|   5 |      PX BLOCK ITERATOR |          | 40000 |   195K|   279   (0)| 00:00:02 |  Q1,00 | PCWC |            |
|   6 |       TABLE ACCESS FULL| T1       | 40000 |   195K|   279   (0)| 00:00:02 |  Q1,00 | PCWP |            |

---------- ---------- ----------
       279    1333333        279

| Id  | Operation              | Name     | Rows  | Bytes | Cost (%CPU)| Time     |    TQ  |IN-OUT| PQ Distrib |
|   0 | SELECT STATEMENT       |          |     1 |     5 |   278   (0)| 00:00:02 |        |      |            |
|   1 |  SORT AGGREGATE        |          |     1 |     5 |            |          |        |      |            |
|   2 |   PX COORDINATOR       |          |       |       |            |          |        |      |            |
|   3 |    PX SEND QC (RANDOM) | :TQ10000 |     1 |     5 |            |          |  Q1,00 | P->S | QC (RAND)  |
|   4 |     SORT AGGREGATE     |          |     1 |     5 |            |          |  Q1,00 | PCWP |            |
|   5 |      PX BLOCK ITERATOR |          | 40000 |   195K|   278   (0)| 00:00:02 |  Q1,00 | PCWC |            |
|   6 |       TABLE ACCESS FULL| T1       | 40000 |   195K|   278   (0)| 00:00:02 |  Q1,00 | PCWP |            |

13 rows selected.

---------- ---------- ----------
       278    1333333        278

As a starting point, we can say that the modified cost is always going to be: 1250 * serial throughput rate / parallel throughput rate where, in this test suite, the serial throughput rate in bytes per ms is 64 * 8K / 10 = 52428.8

Working from the top down:
When slavethr = 16384 the aggregate throughput rate is 5 * 16384 = 81920, so the I/O cost should be 1250 * 52428.8/81920 = 800 (Q.E.D)

When slavethr = 41000 the aggregate throughput rate is 5 * 47000 = 235,000 so the I/O cost should be 1250 * 52428.8/205000 = 279 (Q.E.D) You’ll notice that this is very close to the figure I had from the first test when I didn’t have maxthr or slavethr set and the optimizer used its “90% of serial” trick.

When slavethr = 65536, something odd has happened – instead of a significant change in I/O cost, the result actually matches the figure we got slavethr wasn’t set. The rule is simple – if slavethr is larger than the throughput implied by mbrc (etc.) the optimizer ignores it and falls back to the “90% of serial” model.


I’ve been showing you how Oracle does the arithmetic with the statistics it has. It’s very important to remember that this is just arithmetic – it’s Oracle trying to work out the best (likely) execution plan given some assumptions about what ought to be the limiting factors when the query runs. In effect the arithmetic can have the effect of saying: “if we assume (based on the statistics) that we can’t do better than parallel 6 then the best plan is P” – but if the hint actually says /*+ parallel(t1 42) */ then at run time Oracle will take the plan that’s appropriate for running parallel 6 and try to run it at parallel 42 – and that may be a big mistake.

Warning: The manuals say that maxthr and slavethr are stored as bytes per second; it seems that they’re really bytes per millisecond in (at least) 10g and 11g, but change to bytes per second in 12c. If you upgrade to 12c, make sure you check your system statistics before and after the upgrade to make sure that you have allowed for this change otherwise you may find that Oracle becomes very unenthusiastic about running parallel queries.

June 28, 2013

Illogical Tuning

Filed under: CBO,Execution plans,Oracle,subqueries — Jonathan Lewis @ 6:55 pm GMT Jun 28,2013

The title is a bit of a joke, really. It’s mirroring a title I used a little over a year ago “Logical Tuning” and reflects my surprise that a silly little trick that I tried actually worked.

If you don’t want to read the original article, here’s a quick précis – I started with the first query, which the optimizer executed as a filter subquery, and rewrote it as the second query, which the optimizer executed as two anti-joins (reducing the execution time from 95 seconds to 27 seconds):


June 27, 2013

maxthr – 2

Filed under: CBO,Oracle,Parallel Execution,Statistics,System Stats — Jonathan Lewis @ 5:08 pm GMT Jun 27,2013

Actually, there hasn’t been a “maxthr – 1″, I called the first part of this series“System Stats”. If you look back at it you’ll see that I set up some system statistics, excluding the maxthr and slavethr values, and described how the optimizer would calculate the cost of a serial tablescan, then I followed this up with a brief description of how the calculations changed if I hinted the optimizer into a parallel tablescan.


June 25, 2013

System Stats

Filed under: CBO,Oracle,Parallel Execution,Statistics,System Stats — Jonathan Lewis @ 5:27 pm GMT Jun 25,2013

Several years ago I wrote the following in “Cost Based Oracle – Fundamentals” (p.47):

The maxthr and slavethr figures relate to throughput for parallel execution slaves. I believe that the figures somehow control the maximum degree of parallelism that any given query may operate at by recording the maximum rate at which slaves have historically been able to operate—but I have not been able to verify this.

Browsing the internet recently, I discovered that that no-one else seems to have published anything to verify my comment, so I decided it was about time I did so myself.  I’m going to work up to it in two blog notes , so if you do happen to know of any document that describes the impact of maxthr and slavethr on the optimizer’s costing algorithms please give me a reference in the comments – that way I might not have to write the second note.


June 23, 2013

Index Hints

Filed under: CBO,Hints,Indexing,Oracle,trace files — Jonathan Lewis @ 6:04 pm GMT Jun 23,2013

In my last post I made a comment about how the optimizer will use the new format of the index hint to identify an index that is an exact match if it can, and any index that starts with the same columns (in the right order) if it can’t find an exact match. It’s fairly easy to demonstrate the behaviour in 11g by examining the 10053 (CBO) trace file generated by a simple, single table, query – in fact, this is probably a case that Doug Burns might want to cite as an example of how, sometimes, the 10053 is easy to interpret (in little patches):


June 14, 2013

Hints again

Filed under: CBO,Hints,Ignoring Hints,Oracle — Jonathan Lewis @ 6:17 pm GMT Jun 14,2013

A recent posting on OTN came up with a potentially interesting problem – it started roughly like this:

I have two queries like this:

select * from emp where dept_id=10 and emp_id=15;
select * from emp where dept_id=10 and emp_id=16;

When I run them separately I get the execution plan I want, but when I run a union of the two the plans change.

This, of course, is extremely unlikely – even if we assume that the two queries are more complex than the text shown. On the other hand you might, after a little thought, come up with the idea that perhaps the optimizer had done something really clever like join factorization (moving a join that’s common to the two parts of the UNION from inside to outside the UNION), or maybe there’s some really new trick the optimizer had played because a UNION ultimately requires a SORT UNIQUE, and the optimizer had chosen a different path that returned the data from each part of the UNION in sorted order to decrease the cost of that final sort.

In fact it turned out to be a lot simpler than that. The query looked more like this:


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