The Interpretation of Database Statistics
By Ann Harrison
With InterBase and Firebird there is a program called GSTAT that will produce statistics about your database. The interpretation of those statistics has sometimes been somewhat of a mystery.
Here are the "example" databases and the statistics
CPTVAL (39) Primary pointer page: 293, Index root page: 294 Data pages: 1385, data page slots: 1385, average fill: 69% Fill distribution: 60 - 79% = 1385 Index RDB$FOREIGN14 (3) Depth: 2, leaf buckets: 109, nodes: 73373 Average data length: 0.00, total dup: 73372, max dup: 32351 Fill distribution: 80 - 99% = 109 Index RDB$FOREIGN146 (1) Depth: 2, leaf buckets: 109, nodes: 73373 Average data length: 0.00, total dup: 73303, max dup: 7905 Fill distribution: 80 - 99% = 109 Index RDB$FOREIGN147 (2) Depth: 2, leaf buckets: 112, nodes: 73373 Average data length: 0.00, total dup: 65220, max dup: 23 Fill distribution: 80 - 99% = 112 Index RDB$PRIMARY10 (0) Depth: 3, leaf buckets: 298, nodes: 73373 Average data length: 10.00, total dup: 0, max dup: 0 Fill distribution: 0 - 19% = 1 80 - 99% = 297
WIPQUE (149) Primary pointer page: 515, Index root page: 516 Data pages: 61, data page slots: 63, average fill: 15% Fill distribution: 0 - 19% = 49 20 - 39% = 12 Index RDB$FOREIGN263 (1) Depth: 1, leaf buckets: 10, nodes: 481 Average data length: 0.00, total dup: 480, max dup: 480 Fill distribution: 0 - 19% = 10 Index RDB$FOREIGN280 (2) Depth: 2, leaf buckets: 9, nodes: 481 Average data length: 0.00, total dup: 474, max dup: 117 Fill distribution: 0 - 19% = 9 Index RDB$PRIMARY131 (0) Depth: 2, leaf buckets: 12, nodes: 481 Average data length: 1.00, total dup: 0, max dup: 0 Fill distribution: 0 - 19% = 12
Here is the commentary
The table name is CPTVAL, its relation_id is 39. You probably don't care at all about the relation_id, but the internal code generator does and this report is created from the same sources used for code generation.:
Primary pointer page: 293, Index root page: 294
These are table attributes. Rows in a table are located through a three level index. The first part indicates a page of pointers to pages, the second the offset on that page of the data page, and the third the index slot on the data page that points to the actual row. When a table is created, it immediately gets the initial pointer page. If you were trying to put a broken database back together - a process I used to call "making hamburger into cows" knowing the initial pointer page location can help. From your point of view, it doesn't help at all.
The index root page is also created when the table is defined. That's why it happens to be adjacent to the pointer page. You'll find that the primary pointer page and the index root page are almost always adjacent. The index root page points to the apex of each index. Again, its location doesn't help you at all, but the internals use it a lot.:
Data pages: 1385, data page slots: 1385, average fill: 69%
The table fills 1385 data pages. The pointer pages have used a total of 1385 page slots, indicating that the table is about as large as it has ever been. Page slots are the pointers on pointer pages. If a page empties and is released, the page slot it occupied is set to null until another page is allocated for the table.
The average fill is the amount of space used on each data page. By default, space is left on every page to hold one record header plus a bit for each row on the page. That is done to increase the chances that a back version can be stored on the same page with the primary record version. Keeping them together saves LOTS of swapping back and forth when back versions are created and garbage collected. Your average fill looks fine, perhaps a little low for a stable table - indicating that your row size is small compared with the page size. Nothing to worry about though.:
Fill distribution: 60 - 79% = 1385
This is a degenerate histogram - all the values for fill distribution fall in a single bar of the histogram - meaning that all pages are evenly filled, probably to about 69%. That's a good sign, and agrees with your description of the table as quite stable.:
Index RDB$FOREIGN14 (3)
Ooooh, I just love numbers sorted alphabetically. Anyway, this is the first index on the CPTVAL table in alphabetical order by index name. It's a system generated index (starts with "RDB$") supporting a foreign key (next token is "FOREIGN") and was the 14th such index created in the DDL. Oddly, its internal identifier is three, indicating that it was the fourth (InterBase was a C shop, so everything starts at 0) index actually created on the table. That's inobvious, but makes sense to me. When the DDL is processed, the actual work is deferred until commit time. The list of deferred work is built from the front (LIFO) then sorted for dependencies. Indexes are created LIFO.:
Depth: 2, leaf buckets: 109, nodes: 73373
An index is a tree - upside down if you must be terribly literal - with the root at the top and the leaves at the bottom. This index has only two levels. That's good. The bottom level has only 109 pages in it. That begins to seem suspicious. Those 109 pages point to 73,373 individual rows, which seems like a lot. Let's look further.:
Average data length: 0.00, total dup: 73372, max dup: 32351
The average data length is the average length of the key as stored. This has very little to do with the length as defined. Index keys actually hold only three types of data - four if you want to be very fussy and look at an obscure case. The third,the obscure one, is a byte array. That's a string in which bytes with a value of zero and 32 (aka space) are significant. Nobody uses those. The second most obscure is a null - I've forgotten how they're represented, but its the same for all datatypes.
The third is a string. Varchar and char are both represented the same way; both have trailing spaces suppressed. If you're using a multi-byte collating sequence (French, for sure, but also the other European languages) the key is translated into its multi-byte format. The result is a string of bytes that produces the desired order (collating sequence) when sorted bytewise (fast).
The fourth type is a double precision floating point number. All numerics and all dates are stored in indexes as double precision numbers, with trailing zeros suppressed. The number is also mushed around so it sorts bytewise (sign byte at the front, exponent, then mantissa).
OK, so all that suggests that the key will be expanded if its a string and uses a multi-byte collating sequence, or if its a float, integer, or small int. Then it shrinks when trailing spaces and zeros are removed from the end of each segment. It expands again as segment boundaries are introduced - that's slightly obscure, but it means that segmented keys can't be quite as long (taken alltogether) as single column keys.
Now here comes the good part. The keys are sorted byte wise and prefix compressed. If the first key is AAA123A, and the next is AAA124B, then the second is stored as (5)4B, meaning "first five bytes are the same as the previous, then append "4B".
How does all this lead to an average key length of zero. Well, prefix compression reduces the key length of a duplicate zero. And look there, its says: total dup: 73372, max dup: 32351. So of 73374 rows, all but one is a duplicate. Probably all but two, but some of us are better at boundary conditions than others. The largest number of duplicates on a single chain is 32351. The numbers don't quite add up, but I suspect that you've got a two-valued field here that you've indexed to support a foriegn key. That's not the end of the world in a stable table.
Part of the discrepency (if the longest chain is 32351 and there are only two chains, then there are only 64702 rows accounted for), may be in the way the first (and last? don't remember) nodes in an index page are represented. To reduce the cost of index walking, they're not compressed. Could the others be nulls? Don't know.
Here's a statistic to remember - you've got 109 leaf pages holding pointers to 73,000 rows or approximately 673 nodes per page. Each chain is about 32,000 nodes long, so each duplicate chain is about 48 pages long.:
Fill distribution: 80 - 99% = 109
The fill distribution for an index is the amount of space in each page of the index that's used for data and pointers. 80-99% is good. Lower fill distributions are the strongest clue that you should rebuild the index.
And on to the next index. This one will be quicker, I promise.:
Index RDB$FOREIGN146 (1)
Also an automatically generated index to support a foreign key constraint.:
Depth: 2, leaf buckets: 109, nodes: 73373
Depth, buckets, and nodes, all the same. Did I mention that 'bucket' is old-geek-speak for a page in an index? Did I mention that old geeks get forgetful?:
Average data length: 0.00, total dup: 73303, max dup: 7905
This one is bad, but not nearly so terrible. The problem with the previous index was not so much the number of duplicates, but the length of each chain. The chains in this index are less than a quarter of the length of the previous chains - on the order of 10 or twelve pages long.:
Fill distribution: 80 - 99% = 109
Fill is find.:
Index RDB$FOREIGN147 (2) Depth: 2, leaf buckets: 112, nodes: 73373 Average data length: 0.00, total dup: 65220, max dup: 23 Fill distribution: 80 - 99% = 112
This automatically generated index still reports the average data length as zero, but it must be a bigger zero than the other zeros because the number of leaf buckets is up by three. Each duplicate chain fits with a single bucket...:
Index RDB$PRIMARY10 (0) Depth: 3, leaf buckets: 298, nodes: 73373 * Average data length: 10.00, total dup: 0, max dup: 0 Fill distribution: 0 - 19% = 1 80 - 99% = 297
Also a system generated index, this time to support the primary key constraint. The key length of 10 indicates that some compression is done - which is normal and good. The fact that one bucket is under filled doesn't mean anything - except that the number of nodes didn't fit evenly into the buckets. That's completely normal.
Parenthetical Lecture on the Evils of Duplicates.
Here, I'm going to give a brief summary of the problem with long duplicate chains for those who were asleep or off-list during previous descriptions.
There's no problem with storing duplicates. New entries are stored at the front of the chain.
In later versions, there's a good chance that an index with lousy distribution (i.e. long duplicate chains - a two-valued index) won't ruin the performance of a query. In pre V5 versions, InterBase was completely capable of looking at two access strategies - find a row based on primary key and find the same row based on an index with thousands of duplicates. It would use the primary key in computing a join order but add on the terrible foreign key in the final pass - so it spent lots of time building a huge bitmap of qualified rows from the lousy index then threw out all then entries but one when it combined that bit map with the one it created from the unique index. [Not bright? Maybe not, but we really couldn't imagine anyone defining an index that was not selective enough to be used. Why bother?]
However, an update of the key value, or the deletion of a row is really expensive when you've got lots of duplicates. One of the worst things you can do to an InterBase database is define a table with a million rows, all with the same key value for a secondary index, then delete all those rows. The last duplicate stored appears first in the list, and the first stored is last. The deletion ordinarily takes the first row stored first, then the second, and so on. So the index walking code has to run through the whole duplicate chain for each deletion, always finding the entry it wants in the very last position. Churns the cache like nothing you ever saw.
One not terribly obvious factor in all this is that the cost of all that churning and roiling is NEVER borne by the transaction that deletes all the rows in a table. Updating a key value or deleting a row affects the index ONLY when the old back version is garbage collected. That means that cost goes to the next transaction that touches the row AFTER all the transactions that were active when the update or delete happend have exited. Let me say that again: nodes are deleted from an index when the record version is garbage collected, not when it is created!
End of Parenthetical Lecture on the Evils of Duplicates.
On to the table that holds data temporarily while it's being validated and is flushed and reloaded periodically.:
WIPQUE (149) Primary pointer page: 515, Index root page: 516 Data pages: 61, data page slots: 63, average fill: 15% Fill distribution: 0 - 19% = 49 20 - 39% = 12
A smaller table, certainly, and more volitile. It's been bigger - there are empty slots. The fill level suggests that rows have been removed selectively. That's OK.:
Index RDB$FOREIGN263 (1) Depth: 1, leaf buckets: 10, nodes: 481 Average data length: 0.00, total dup: 480, max dup: 480 Fill distribution: 0 - 19% = 10
Every row has the same value. Not good. Terrible fill level, suggesting spotty deletions.... The whole thing is very small, which is good, because if it were large it would be dreadful.:
Index RDB$FOREIGN280 (2) Depth: 2, leaf buckets: 9, nodes: 481 Average data length: 0.00, total dup: 474, max dup: 117 Fill distribution: 0 - 19% = 9
Again, as with the previous table, this index is bad, but only a quarter as bad as the previous one.:
Index RDB$PRIMARY131 (0) Depth: 2, leaf buckets: 12, nodes: 481 Average data length: 1.00, total dup: 0, max dup: 0 Fill distribution: 0 - 19% = 12
Now, finally, on to suggestions
This table WIPQue is very dynamic. It is a WorkInProcessQue where external data (500-1000 records/day) are loaded until it can be validated. Once validated the data is moved to production tables and the WIPQue records are deleted. After several days of loading the system slows down and the customer has to do a backup and restore for several hours.
Don't do a backup and restore, instead, drop and recreate the foriegn key constraints on the volitile table whenever you can get exclusive access to it. You could watch the index fill level on those indexes and when it gets down below 40%, then drop and recreate the constraints. If you can possibly drop the constraint that creates index RDB$FOREIGN263, do so. If your master tables are stable, then you could use triggers to validate input data rather than foreign key constraints - triggers can be less than reliable if the master table is volitile, but they're fine for a stable master, and they avoid the automatic creation of really terrible indexes.