Since I only recently learned about the concept of “killed index tuples”, I thought there might be some others who are not yet familiar with this interesting PostgreSQL concept.
This may give you an explanation the next time you encounter wildly varying execution times for the same execution plan of the same PostgreSQL query.
Before we look more closely at the index, let’s review the life cycle of a table row version (“heap tuple”).
Life, death and visibility in the table heap
It is widely known that the visibility of heap tuples is determined by the system columns
xmax (though there is more to
xmax than meets the eye). A heap tuple is “dead” if its
xmax is less than the
xmin of all active transactions.
xmax are only valid if the respective transactions have been marked committed in the “commit log”. Consequently, any transaction that needs to know if it can see a tuple has to consult the commit log. To save future readers that extra work, the first one that consults the commit log will save the information in the tuple’s “hint bits”.
Dead tuples are eventually reclaimed by
This is all fairly well known, but how is the situation with index entries?
Life, death and visibility in the index
To avoid redundancy and to keep index tuples small, the visibility information is not stored in the index.
The status of an index tuple is determined by the heap tuple it points to, and both are removed by
VACUUM at the same time.
As a consequence, an index scan has to inspect the heap tuple to determine if it can “see” an entry. This is the case even if all the columns needed are in the index tuple itself. Even worse, this “heap access” will result in random I/O, which is not very efficient on spinning disks.
This makes index scans in PostgreSQL more expensive than in other database management systems that use a different architecture. To mitigate that, several features have been introduced over the years:
- PostgreSQL 8.1 introduced the “bitmp index scan”. This scan method first creates a list of heap blocks to visit and then scans them sequentially. This not only reduces the random I/O, but also avoids that the same block is visited several times during an index scan.
- PostgreSQL 9.2 introduced the “index-only scan”, which avoids fetching the heap tuple. This requires that all the required columns are in the index and the “visibility map” shows that all tuples in the table block are visible to everybody.
But I want to focus on another feature that was also introduced in 8.1, yet never made it into the release notes.
Killed index tuples
The feature was introduced with this commit:
commit 3f4d48802271126b1343289a9d2267ff1ed3788a Author: Tom Lane <email@example.com> Date: Fri May 24 18:57:57 2002 +0000 Mark index entries "killed" when they are no longer visible to any transaction, so as to avoid returning them out of the index AM. Saves repeated heap_fetch operations on frequently-updated rows. Also detect queries on unique keys (equality to all columns of a unique index), and don't bother continuing scan once we have found first match. Killing is implemented in the btree and hash AMs, but not yet in rtree or gist, because there isn't an equally convenient place to do it in those AMs (the outer amgetnext routine can't do it without re-pinning the index page). Did some small cleanup on APIs of HeapTupleSatisfies, heap_fetch, and index_insert to make this a little easier.
Whenever an index scan fetches a heap tuple only to find that it is dead (that the entire “HOT chain” of tuples is dead, to be more precise), it marks the index tuple as “killed”. Then future index scans can simply ignore it.
This “hint bit for indexes” can speed up future index scans considerably.
Let’s create a table to demonstrate killed index tuples. We have to disable autovacuum so that it does not spoil the effect by removing the dead tuples.
CREATE UNLOGGED TABLE hole ( id integer PRIMARY KEY, val text NOT NULL ) WITH (autovacuum_enabled = off); INSERT INTO hole SELECT i, 'text number ' || i FROM generate_series(1, 1000000) AS i; ANALYZE hole;
We create a number of dead tuples by deleting rows from the table:
DELETE FROM hole WHERE id BETWEEN 501 AND 799500; ANALYZE hole;
Spot the differences!
Now we run the same query twice:
EXPLAIN (ANALYZE, BUFFERS, COSTS OFF, TIMIMG OFF) SELECT * FROM hole WHERE id BETWEEN 1 AND 800000; QUERY PLAN --------------------------------------------------------------- Index Scan using hole_pkey on hole (actual rows=1000 loops=1) Index Cond: ((id >= 1) AND (id <= 800000)) Buffers: shared hit=7284 Planning Time: 0.346 ms Execution Time: 222.129 ms (5 rows) EXPLAIN (ANALYZE, BUFFERS, COSTS OFF, TIMIMG OFF) SELECT * FROM hole WHERE id BETWEEN 1 AND 800000; QUERY PLAN --------------------------------------------------------------- Index Scan using hole_pkey on hole (actual rows=1000 loops=1) Index Cond: ((id >= 1) AND (id <= 800000)) Buffers: shared hit=2196 Planning Time: 0.217 ms Execution Time: 14.540 ms (5 rows)
Discussion of the example
There are a number of things that cannot account for the difference:
- Often the second execution is faster because the first run has to read more data from secondary storage, which are already cached on the second run. But you can observe from the execution plan that in both cases, all blocks were already in the cache (“shared hit”) and none had to be read from disk.
- No hint bits had to be written during the first execution (no buffers were “dirtied”), because that was already done during the sequential scan from the
What happened is that the first execution had to visit all the table blocks and killed the 799000 index tuples that pointed to dead tuples.
The second execution didn’t have to do that, which is the reason why it was ten times faster.
Note that the difference in blocks touched by the two queries is not as big as one might suspect, since each table block contains many dead tuples (there is a high correlation).
It is also worth noting that even though they are not shown in the
EXPLAIN output, some index pages must have been dirtied in the process, which will cause some disk writes from this read-only query.
Next time you encounter mysterious query run-time differences that cannot be explained by caching effects, consider killed index tuples as the cause. This may be an indication to configure autovacuum to run more often on the affected table, since that will get rid of the dead tuples and speed up the first query execution.