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Empty Leaf Blocks and Statistics (Sense Of Doubt) July 8, 2008

Posted by Richard Foote in Index Access Path, Index Block Splits, Index Delete Operations, Index statistics, Oracle Cost Based Optimizer, Oracle Indexes, Performance Tuning.

I’ve recently been discussing how empty index blocks or those blocks that contain nothing but deleted index entries are placed on the index freelist and can potentially be recycled during subsequent index block split operations.

A point that’s not so well known about such empty index blocks is how Oracle considers them when calculating index related statistics and the possible implications this may have on the CBO.

Let’s set the scene with an example I’ve used previously where we load a table/index with 10000 entries and then subsequently delete the vast majority of them.

SQL> create table rich as select rownum id, ‘Bowie’ text from dual connect by level <= 10000;
Table created.
SQL> create index rich_i on rich(id);
Index created.

OK, so we now have an index with 10000 entries. Let’s just check to see how many leaf blocks we currently have:

SQL> analyze index rich_i validate structure;

Index analyzed.

SQL> select lf_rows, lf_blks, del_lf_rows from index_stats;

---------- ---------- -----------
     10000         21           0

So we currently have 10000 LF_ROWS and 21 LK_BLKS with no deleted index rows at this stage.

Let’s now deleted the vast majority of rows from the table and hence index row entries from the index:
SQL> delete rich where id <= 9990;
9990 rows deleted.
SQL> commit;
Commit complete.

OK, so now we have an index with the vast majority of the index entries having been deleted and with all but one index leaf block effectively empty.

Let’s start by looking at how the ANALYZE INDEX … VALIDATE STRUCTURE deals with empty leaf blocks and index entries:

SQL> analyze index rich_i validate structure;
Index analyzed.
SQL> select lf_rows, lf_blks, del_lf_rows from index_stats;

---------- ---------- -----------
     10000         21        9990

The first thing we notice is that the LF_ROWS statistics still has a value of 10000. It still counts index entries, even if they’ve been deleted.

We also notice that the LF_BLKS value is 21 so those leaf blocks that are effectively empty are still counted as well.

Let’s now collect statistics using DBMS_STATS as currently recommended by Oracle:

SQL> exec dbms_stats.gather_table_stats(ownname=>null, tabname=> ‘RICH’, cascade => true, estimate_percent=> null, method_opt=> ‘FOR ALL COLUMNS SIZE 1’);

If we now look at the index statistics:

SQL> select index_name, num_rows, leaf_blocks from dba_indexes where index_name = ‘RICH_I’;

---------- -------- -----------
RICH_I           10           1

We notice a couple of important differences. Firstly, the NUM_ROWS value is 10, highlighting that only non-deleted index entries are counted. We also notice that the number of LEAF_BLOCKS is only 1, highlighting that only those index leaf blocks that contain non-deleted index entries are counted. Although there are 20 other leaf blocks within the index structure, these are not counted and considered by the CBO when statistics are calculated using DBMS_STATS.

If we run the following simple little query that effectively selects all remaining rows from the table, we notice the following execution plan:

SQL> select * from rich where id between 1 and 10000;

        ID TEXT
---------- -----
      9991 Bowie
      9992 Bowie
      9993 Bowie
      9994 Bowie
      9995 Bowie
      9996 Bowie
      9997 Bowie
      9998 Bowie
      9999 Bowie
     10000 Bowie

Execution Plan
|Id | Operation                   | Name   |
| 0 | SELECT STATEMENT            |        |
|*2 |   INDEX RANGE SCAN          | RICH_I |

The index is actually used to select all the remaining 10 rows, in part because the index related costs are so low.

Let’s see what would happens if we were to use the old, ANALYZE command to calculate the index statistics:

SQL> analyze index rich_i compute statistics;

Index analyzed.

First, let’s see if the index statistics are any different …

select index_name, num_rows, leaf_blocks from dba_indexes where index_name = ‘RICH_I’;

---------- -------- -----------
RICH_I           10          21

OK, a big big difference here. Where previously, DBMS_STATS didn’t include the empty leaf blocks in it’s statistics, we now notice that using the ANALYZE command does include such empty leaf blocks. The LEAF_BLOCKS value is now 21, not 1 as it was previously. Note though that the number of NUM_ROWS is still 10, so it still doesn’t count the deleted index entries themselves, just the empty leaf blocks.

But leaf blocks is one of the key statistics used by the CBO when calculating the cost of using an index related access path. Could this all make a difference in how our previous query is costed by the CBO ?

SQL> select * from rich where id between 1 and 10000;

        ID TEXT
---------- -----
      9991 Bowie
      9992 Bowie
      9993 Bowie
      9994 Bowie
      9995 Bowie
      9996 Bowie
      9997 Bowie
      9998 Bowie
      9999 Bowie
     10000 Bowie

10 rows selected.

Execution Plan
| Id  | Operation         | Name |
|   0 | SELECT STATEMENT  |      |

Oh yes indeed. Now the CBO has decided to use a Full Table Scan, in large part because of the additional calculated costs associated with using the index.

Note these tests work the same on all supported versions of Oracle.

So empty leaf blocks can still have a large impact on not only how a query may perform but indeed on how the CBO calculates the associated costs, depending on how the statistics are generated.

Yes, there are differences between the ANALYZE command and DBMS_STATS. This is one of the more subtle differences …

So When Does An Oracle B-Tree Index Increase In Height ? (Almost Grown) April 3, 2008

Posted by Richard Foote in Index Height, Index statistics, Oracle General, Oracle Indexes, Oracle Myths.

So when does an Oracle B-Tree index actually increase in height ?

I’ve basically been asked this same question a number of times over the past few days with regard to the discussions on indexes and different block sized tablespaces, so I thought it might be worth quickly sharing the answer to a wider audience.

Imagine a new, empty table and a corresponding new, empty index. At this stage, the index structure basically consists of one, empty block. The index has a BLEVEL of 0 (from DBA_INDEXES) and a HEIGHT of 1 (from INDEX_STATS), yes it can be confusing 😉 This block is basically the Root block of the index as it’s the first (and currently only) block to be accessed during an index scan, but at this stage is used to also store the actual index entries as well (and so can kinda be viewed as being a Leaf block as well).

We now start to insert rows into the table and thus row entries into the index. These index entries basically consist of the indexed column(s) and its corresponding ROWID, and are sorted based on the indexed column values.

Eventually, this single index block will fill; Oracle simply can’t add any more index entries into it. Now comes the fun bit.

When Oracle wants to insert a new index entry but it can’t as this Root index block is full, Oracle will allocate two new index blocks. If the new index entry is the maximum value currently to be indexed, Oracle will move all the index entries from the full block and put it into one of the new index blocks and place the new index entry into the other block. This is known as a 90-10 index block split.

If the new index entry isn’t the maximum value, Oracle will place the lower 1/2 valued index entries into one new block and the other 1/2 into the other new block. This is known as a 50-50 index block split.

These two new blocks are now the new leaf blocks in the index structure.

The contents of the previously single filled block is now totally replaced with pointers to the two new blocks. This block therefore remains the Root block in the index structure. These pointers basically consist of the Relative Block Address (RBA) to the new index blocks and a value which represents the lowest indexed value found in the specific referenced leaf block. These indexed values in the Root block are now used by Oracle as the method by which it can navigate the index structure to find the specific index leaf block containing a required indexed entry.

The index has just increased in height and now has a BLEVEL of 1 and a HEIGHT of 2.

As we continue to add more rows into the table, we add more index entries into our 2 leaf blocks. Eventually they will fill again and will again perform either a 90-10 or 50-50 block split depending on the new index value to be inserted. With a non Root block split, only one additional index block is allocated and the index entries are distributed between the full and new index block. Each time a leaf block splits in a BLEVEL 1 index, a new entry is also added into the Root block to point to the new Leaf block. 

Once we have enough Leaf blocks, the Root block will again eventually fill. At this point, Oracle will again allocate two new blocks and distribute the contents of the Root block into these two new blocks, again 90-10 or 50-50 depending on the new indexed value to be inserted. The contents of the Root block is now totally replaced with pointers to these 2 new “Branch” blocks which of course in turn now contain the pointers to the Leaf blocks.

The index has again increased in height and we now have an index with a BLEVEL of 2 and a HEIGHT of 3.

As the leaf blocks continue fill and split, a new entry is added to the corresponding Branch block each time. When these Branch blocks fill and split, a new entry is added to the Root block. When the Root block eventually fills, it will again allocate 2 new blocks and so the index grows in height again.

So basically, an index increases in height whenever the index Root block splits and the two new allocated blocks result in a new level within the index structure. Note the index Root block remains the same throughout the entire life of the index, no matter the index height.

Note also a Root block split is the only time an index increases in height. Therefore, the number of levels between the Root block and any/all of the Leaf blocks is always and must always be the same. Hence, an Oracle B-Tree index is always structurally height balanced, always.

Store Indexes In A Larger Block Tablespace: The Multiblock Read Myth Part II (The Fly) March 20, 2008

Posted by Richard Foote in Index Block Size, Index statistics, Oracle Cost Based Optimizer, Oracle General, Oracle Indexes, Oracle Myths, Performance Tuning, Tablespace Management.

Thought I might begin by mentioning a lovely little story by Billy Verreynne in this OTN Forum Thread.

Basically a scientist is doing research on the behaviour of flies. He notes when he opens the jar lid of a trapped fly and claps his hands, the fly takes off and flies away. One day, he decides to pull the wings off the fly. When he claps his hands, the fly just sits there. No matter how loud he claps, other than a slight rocking motion, the fly just doesn’t budge.

Excitedly, he writes in his journal his latest discovery. When you pull the wings off a fly, it goes stone deaf !!

This is soooo funny because this sort of thing happens all the time in the Oracle world (and elsewhere). I see it time after time after time, we’ve all done it.

Hey, I just compressed a segment into one extent and performance improved. Conclusion, storing a segment in one extent improves performance.

Hey, I just separated my indexes from my tables and performance improved. Conclusion, separating indexes from tables improves performance.

Hey, I just moved my indexes into a larger block sized tablespace and my Index Fast Full Scan performance improved. Conclusion, a larger index block size improves Index Fast Full Scan performance.

Lots of flies with no wings. Lots of people thinking flies go deaf when they don’t have wings. An action appears to cause an effect, but does it really …

As Billy himself suggests, the fundamental reason why so many people think “flies with no wings go deaf” is that many don’t understand what’s actually going on. Many simply don’t understand the basic workings, the fundamental processes and mechanisms involved in how Oracle functions or how Oracle performs a specific operation. Tuning by observation, tuning by making change “A” without understanding all the implications of such a change and subsequently making suppositions on the results of such a change, ultimately means we have lots of people thinking flies without wings are deaf.

As I discussed in Part 1, Oracle performs exactly the same sized I/O during a multiblock read, regardless of the block size of the segment. Exactly the same. Without understanding this simple fact, one could very easily come to a wrong conclusion regarding the ramification of block sizes on multiblock reads. Without understanding flies don’t have ears or sound sensors in their wings, one could very well come to a wrong conclusion regarding the ramifications of removing the wings from a fly.

If we perform an Index Fast Full Scan and performance improves, it can’t be because associated multiblock I/Os are more efficient. A fly doesn’t have sound sensors in its wings. There must be another explanation. Conversely, in my example in Part I of this discussion, performance went worse with a larger index block size (as it did in Greg Rahn’s example on this OTN Forum Thread), but again not as a result of multiblock read performance.

So how could the performance of an Index Fast Full Scan change (for better or worse), if one simply rebuilds the index within a larger block size tablespace ? Well there are of course many possible reasons, with the two more obvious explanations being the following:

1) Most randomly inserted indexes have a PCT_USED value ranging between 70-75% as these indexes perform random 50-50 block splits that are subsequently in differing stages of being filled. By rebuilding an index (say back to a default PCTFREE of 10%) one might increase index compactness by say 15% and hence decrease the overall index size (note reduced block overheads may also reduce the index a little as well, depending on index size and differences in block sizes). The Fast Full Index Scan is the access path that potentially benefits most by defragmenting an index as the associated costs are proportional to the overall size of the index. Reducing the size of an index could therefore impact subsequent performance. However, rebuilding the index in the current block size would likely achieve a similar result (plus block overheads), compacting the index and resulting in potentially better performance (although once the index blocks begin to split again, the index would eventually return back to its previous state). Therefore, it’s not the bigger block size but the resultant defragmentation of the index that’s improved matters. The fly isn’t deaf, it just needs its wings to fly …

2) By storing an index in a larger block tablespace, the index must physically be stored on a different database file. This file could be on a faster disk, improving performance, this file could be on a faster part of the disk, improving performance, this file could be on a disk with far less disk contention, improving performance, etc. etc. It’s not the larger block size that’s improved (or worsened) performance, it’s the new physical characteristics of where the index is now stored. If one were to rebuild the index with the current block size and use the same physical characteristics of the larger block index, subsequent performance would likewise increase (or decrease). The fly isn’t deaf, it just needs its wings to fly …

There are many other possible reasons, the system was less busy when using the larger block index, more of the index was physically cached when using the larger block index, etc. etc.

Of course, an Index Fast Full Scan is rarely a scalability issue anyways. Do we really want our applications to perform hundreds of large, concurrent Index Fast Full Scans ? Tuning the application to avoid these overheads should be the focus rather than moving indexes into a larger block tablespace in the vain hope it will improve things dramatically. But that’s the topic of another discussion …

Can the performance of an Index Fast Full scan change after moving the index to a larger block size tablespace. Absolutely. However, it doesn’t necessarily mean such a change in performance is a direct result of the index having a larger block size and that multiblock read performance has improved.

It doesn’t mean moving indexes to larger block size tablespaces suddenly makes Oracle go deaf …


UPDATE: I’ve added this simple little demo that illustrates how performance improves when an index is rebuilt in a larger block tablespace. This will of course suggest to some folk that the larger block tablespace improved the performance but what actually improved things was rebuilding the fragmented index to be a more efficient structure. The larger block tablespace was not the fix, rebuilding the index was the important factor. In fact, by rebuilding the index in the original smaller block tablespace, not only do we also improve performance, but things are further improved as we reduce CPU overheads incurred by the larger block tablespace and as a result elapsed times are further improved.

Store Indexes In A Larger Block Tablespace: The Multiblock Read Myth (Karma Police) March 18, 2008

Posted by Richard Foote in Index Block Size, Index statistics, Oracle Cost Based Optimizer, Oracle General, Oracle Indexes, Oracle Myths, Performance Tuning, Tablespace Management.

One of the great myths surrounding the use of differing block sizes is that storing indexes in larger block sizes somehow dramatically improves the performance of index related multiblock reads.

Oracle performs index multiblock reads when performing an Index Fast Full Scan, when it basically treats the index structure as a skinny version of the table. It reads the entire index structure, multiple blocks at a time, “throwing away” any non leaf blocks as it stumbles across them.

The theory goes that by storing indexes in larger size blocks, we would obviously have fewer index related blocks. If we need to read the entire index, as we do with an Index Fast Full Scan, surely it must be more efficient if we have fewer, larger index blocks.

The evidence looks convincing. Here’s a link to an extract from a book by Robin Schumacher where he clearly shows a dramatic “improvement” by using an index tablespace with double the block size. In one query using an 8K block index, the consistent reads during an Index Fast Full Scan was 421. However, when the same index was recreated as a 16K block index, the same query only used 211 consistent gets, 1/2 of what it was previously.

Conclusive “proof” that the 16K block index improved performance wouldn’t you say ?

Well actually, it’s only conclusive proof that the number of consistent gets has dropped, whether it actually improves “performance” is another thing entirely.

There are a couple of little “details” that many don’t quite appreciate. The devil is always in the details …

The first point to note is that when Oracle performs a multiblock read, it uses the value in the db_file_multiblock_read_count parameter to determine how many blocks to read per multiblock read (with system statistics, Oracle itself can determine how best to set this value).

So if the db_file_multiblock_read_count value were set to say 16, Oracle will attempt to read as many as 16 blocks at a time during a multiblock read operation.

Note this value is based on the default block size of the database. So if the default block size is 8K and the db_file_multiblock_read_count is 16, Oracle will try and read 16 x 8K blocks at a time during a multiblock read operation.

However, if there’s a non-default block sized segment (say 16K), Oracle will adjust the number of blocks that are actually read during a multiblock read operation so that the maximum size of the overall multiblock read is identical to that of the default block size.

So if the db_file_multiblock_read_count is 16 and the default block size is 8K, a multiblock read of an object in a 16K tablespace will only read 8 blocks at a time (and not 16). A multiblock read of an object in a 2K tablespace will read 64 blocks at a time.

The actual size of a multiblock read therefore is identical regardless of the block size of an object within a database.

An easy way to highlight this is to simply trace a session and see the specific size of corresponding multiblock read operations.

A sample from a trace on an 8K block index (with the db_file_multiblock_read_count set to 16), performing an Index Fast Full Scan looks like this:

WAIT #1: nam=’db file scattered read’ ela= 1487 file#=8 block#=1050 blocks=16 obj#=78294 tim=615409554677
WAIT #1: nam=’db file scattered read’ ela= 1377 file#=8 block#=1066 blocks=16 obj#=78294 tim=615409557777
WAIT #1: nam=’db file scattered read’ ela= 1143 file#=8 block#=1082 blocks=16 obj#=78294 tim=615409561563

Note that Oracle is reading 16 x 8K blocks (128K) per multiblock read operation.

However, when the index is recreated in a 16K block size tablespace, the Fast Full Index Scan looks like this:

WAIT #1: nam=’db file scattered read’ ela= 1413 file#=6 block#=14 blocks=8 obj#=78296 tim=626802128684
WAIT #1: nam=’db file scattered read’ ela= 1447 file#=6 block#=22 blocks=8 obj#=78296 tim=626802131649
WAIT #1: nam=’db file scattered read’ ela= 2014 file#=6 block#=30 blocks=8 obj#=78296 tim=626802135222

Note that Oracle is now only reading 8 x 16K blocks (128K) per multiblock operation.

Both indexes are effectively doing exactly the same work, both are effectively reading up to 128K of data per multiblock read …

It’s like paying someone $50 per 1/2 hour of work and then deciding to make things more “efficient” by paying them $100 per hour of work instead. In the end, you’re still just paying them $800 for an 8 hour day’s work regardless …

Note a larger block size will have less associated block overheads with there being less actual blocks so the overall size of an index may reduce a little, depending on index size and differences in block sizes. Therefore any possible improvements will only be restricted to the potential savings in the overall index size. With many databases having default block sizes of 8k and a maximum block size restricted to 16k, these savings may be minimum or non-existent.

This demo on the impact of different block sizes on multiblock read operations shows how Oracle actually performs the same sized reads when performing multiblock reads from differing block sized tablespaces, with the performance of the index in the larger block size tablespace being somewhat worse in this specific example.

With Oracle effectively performing identical work behind the scenes, the performance between different block size tablespaces is likely to be similar. You’re still paying $800 a day regardless …

Although it’s often claimed that multiblock reads is one of the key areas where larger index block sizes are beneficial, a claim based generally on the simplistic fact the number of consistent reads is reduced, the reality of the situation is somewhat different …

Introduction To Reverse Key Indexes: Part IV (Cluster One) January 21, 2008

Posted by Richard Foote in Clustering Factor, Index statistics, Oracle Indexes, Oracle Myths, Reverse Key Indexes.

There’s a myth that suggests if the Clustering Factor (CF) of an index is greater than a certain ratio when compared to the number of rows in the table, the CF is poor and an index rebuild would be beneficial.

The slight problem with this advice is that the CF actually measures how well aligned the order of the column values are in the table as compared to the order of the index entries in the index. Generally, a table in which the column values are ordered in a similar manner to the index will have a CF closer to the number of blocks in the table. A table in which the column values are ordered in a random manner when compared to the index will have a CF closer to the number of rows in the table.

An index rebuild doesn’t change the ordering of the index row entries and an index rebuild has no impact on the table so therefore the comparative ordering of both remains unchanged. Therefore the CF of an index will be identical after the rebuild as it was before.

Well actually, there is one slight exception to this rule. Reverse Key Indexes.

Generally, rows with monotonically increasing column values are physically inserted in the order of the monotonically increasing columns. This may not be the case however with tables in ASSM tablespaces or tables with multiple freelists or freelist groups as concurrent inserts will be directed to differing blocks. In these cases we may actually have data that is quite well clustered but may have quite poor CF values due to the manner in which the CF is calculated.

Assuming a Non-ASSM, single freelist/freelist group table, the CF of monotonically increasing indexed values would ordinarily be quite good. As a non-reverse index must also have its values in the monotonically column order, the CF of the index is likely to be nice and low.

However, if you were to rebuild the index as a Reverse Key Index, the index values get reversed and “randomly” redistributed within the index structure, totally changing the order of the index entries within the index. As a result, the index values are no longer aligned with those of the table and the CF is likely to now be quite appalling.

Rebuilding an index generally has no impact on the CF as the index row values retain the same logically order. Rebuilding an index to be reverse (or visa-versa) is the exception to the rule as it will physically (and logically) change the index row order.

A Reverse Key Index is likely therefore to have a much worse CF than it’s non-reverse equivalent.

A Reverse Key Index will be ignored for range predicates (as already discussed in Part I) so a poor CF may not have an impact. However, as also discussed, index range scans are still viable in some scenarios so an increased CF may impact execution plans detrimentally.

See this demo on how a Reverse Index Rebuild turns a “perfect” CF into a shocker.

DBMS_STATS METHOD_OPT default behaviour changed in 10g. Be careful … January 4, 2008

Posted by Richard Foote in Index statistics, Oracle Cost Based Optimizer, Oracle General, Performance Tuning, Richard's Musings.

A question on the OTN forum has prompted me to quickly knock up a demo on the possible dangers of the default behaviour in 10g with regard to the METHOD_OPT option in DBMS_STATS.

When collecting statistics with DBMS_STATS in 9i, the default value of METHOD_OPT was ‘FOR ALL COLUMNS SIZE 1’. This basically says to Oracle please only collect basic column statistics (min, max, distinct values etc.), do not collect histograms on these columns. For columns that are evenly distributed and for columns that are not referenced in SQL statements, this is perfectly adequate. If a column was unevenly distributed and detrimentally impacted the CBO’s costings of an execution plan, then one could generate histograms for those particular columns separately.

However, this default behaviour changed in 10g and IMHO this change is possibly the most significant and problematic difference when migrating to 10g.

The new default value of METHOD_OPT with 10g is ‘FOR ALL COLUMNS SIZE AUTO’. This basically means that Oracle will automatically decide for us which columns need histograms and which columns don’t based on what it considers to be the distribution of values within a column and based on the “workload” associated with the table (basically are there any SQL statements running in the database referencing columns which might need histograms for those statements to be costed correctly).

This sounds like an ideal scenario, just let Oracle work it out for us.

However, the problem is that Oracle in many cases doesn’t do a particularly good job at determining when it should generate a histogram and when it shouldn’t. In fact, the likelihood is that Oracle will actually generate many many many unnecessary histograms while still missing out on some columns that should have them.

In environments with few tables and with few users executing few distinct SQL statements, the impact of some unnecessary histograms may be minimal. However in environments with many tables and columns (potentially many thousands) with many users executing many different SQL statements, the ramifications of potentially suddenly having thousands of additional histograms can be disastrous.

Note also that by having a histogram, Oracle changes the manner in which the DENSITY statistic for a column is calculated (as stored in DBA_TAB_COLUMNS). This is often used by Oracle to determine the selectivity of predicates so the impact of suddenly having additional unnecessary histograms can be wider and more significant than one might initially imagine.

Of course, the impact on the shared_pool and the row_cache and it’s associated latches in particular can be extremely ugly indeed if suddenly Oracle had to deal with thousands of new histograms when parsing statements.

This silly little demo, “Dangers of default METHOD_OPT behaviour in 10g“,  creates a simple little table with three columns. The first column has an outlier value and as previously discussed here, a histogram might be required to correctly cost range scans. The second column is perfectly distributed, it has 10 distinct values with 100,000 occurrences of each. The third column is also perfectly distributed but it’s a special example in that it has only 1 distinct value.

As you can see by the results of the demo, Oracle has got it wrong one way or the other in varying degrees in all three examples. It hasn’t created a histogram when it was needed and created histograms when they weren’t needed, impacting the Density column statistics as a result.

My advice. Just be very careful when using the default method_opt ‘FOR ALL COLUMNS SIZE AUTO’ behaviour in 10g.

Index Internals – Rebuilding The Truth December 11, 2007

Posted by Richard Foote in Index Coalesce, Index Height, Index Internals, Index Rebuild, Index Shrink, Index statistics, Oracle Indexes, Oracle Myths, Oracle Opinion, Richard's Musings.

The issue of when to rebuild indexes crops up again and again and again. Unfortunately, so do the same incorrect, myth-filled uninspired responses which considering how important and “key” (no pun intended) indexes are to database design and performance generally, is very puzzling indeed.

In the coming days I’ll post why I believe these index related myths simply refuse to go away …

This presentation was originally written several years ago but is still as relevant today as it’s always been.

Recently updated version: Index Internals – Rebuilding The Truth