I have a Postgres GIN index on a table, t1, over a JSONB column a_col.
CREATE INDEX i_1 ON t1 USING gin (t1.a_col)
This takes a query runtime of ~2s down to ~50ms.
This table is somewhat active, taking ~1k new rows of data daily with no updates/deletes.
The problem I am seeing is the performance of this index dramatically falls off after a few hundred writes - query performance falls back to ~2s.
The issue is resolved after running a reindex operation.
REINDEX INDEX i_1
But this leaves the question what is driving the index to run so poorly?
While running
select * from pg_stat_all_tables where relname = 't1'
I was seeing that n_mod_since_analyze had >2k rows and last_autoanalyze was several days out.
By dropping the value of autovacuum_analyze_scale_factor from 0.05 to 0.002, autoanalyze began to run move frequently and started to see more consistent performance.
Related
I have a bloated table, its name is "role_info".
There are about 20K insert operations and a lot of update operations per day, there are no delete operations.
The table is about 4063GB now.
We have migrated the table to another database using dump, and the new table is about 62GB, so the table on the old database is bloated very seriously.
PostgreSQL version: 9.5.4
The table schema is below:
CREATE TABLE "role_info" (
"roleId" bigint NOT NULL,
"playerId" bigint NOT NULL,
"serverId" int NOT NULL,
"status" int NOT NULL,
"baseData" bytea NOT NULL,
"detailData" bytea NOT NULL,
PRIMARY KEY ("roleId")
);
CREATE INDEX "idx_role_info_serverId_playerId_roleId" ON "role_info" ("serverId", "playerId", "roleId");
The average size of field 'detailData' is about 13KB each line.
There are some SQL execution results below:
1)
SELECT
relname AS name,
pg_stat_get_live_tuples(c.oid) AS lives,
pg_stat_get_dead_tuples(c.oid) AS deads
FROM pg_class c
ORDER BY deads DESC;
Execution Result:
2)
SELECT *,
Pg_size_pretty(total_bytes) AS total,
Pg_size_pretty(index_bytes) AS INDEX,
Pg_size_pretty(toast_bytes) AS toast,
Pg_size_pretty(table_bytes) AS TABLE
FROM (SELECT *,
total_bytes - index_bytes - Coalesce(toast_bytes, 0) AS
table_bytes
FROM (SELECT c.oid,
nspname AS table_schema,
relname AS TABLE_NAME,
c.reltuples AS row_estimate,
Pg_total_relation_size(c.oid) AS total_bytes,
Pg_indexes_size(c.oid) AS index_bytes,
Pg_total_relation_size(reltoastrelid) AS toast_bytes
FROM pg_class c
LEFT JOIN pg_namespace n
ON n.oid = c.relnamespace
WHERE relkind = 'r') a
WHERE table_schema = 'public'
ORDER BY total_bytes DESC) a;
Execution Result:
3)
I have tried to vacuum full the table "role_info", but it seemed blocked by some other process, and didn't execute at all.
select * from pg_stat_activity where query like '%VACUUM%' and query not like '%pg_stat_activity%';
Execution Result:
select * from pg_locks;
Execution Result:
There are parameters of vacuum:
I have two questions:
How to deal with table bloating? autovacuum seems not working.
Why did the vacuum full blocked?
With your autovacuum settings, it will sleep for 20ms once for every 10 pages (200 cost_limit / 20 cost_dirty) it dirties. Even more because there will also be cost_hit and cost_miss as well. At that rate is would take over 12 days to autovacuum a 4063GB table which is mostly in need of dirtying pages. That is just the throttling time, not counting the actual work-time, nor the repeated scanning of the indexes. So it the actual run time could be months. The chances of autovacuum getting to run to completion in one sitting without being interrupted by something could be pretty low. Does your database get restarted often? Do you build and drop indexes on this table a lot, or add and drop partitions, or run ALTER TABLE?
Note that in v12, the default setting of autovacuum_vacuum_cost_delay was lowered by a factor of 10. This is not just because of some change to the code in v12, it was because we realized the default setting was just not sensible on modern hardware. So it would probably make sense to backport this change your existing database, if not go even further. Before 12, you can't lower to less than 1 ms, but you could lower it to 1 ms and also either increase autovacuum_vacuum_cost_delay or lower vacuum_cost_page_* setting.
Now this analysis is based on the table already being extremely bloated. Why didn't autovacuum prevent it from getting this bloated in the first place, back when the table was small enough to be autovacuumed in a reasonable time? That is hard to say. We really have no evidence as to what happened back then. Maybe your settings were even more throttled than they are now (although unlikely as it looks like you just accepted the defaults), maybe it was constantly interrupted by something. What is the "autovacuum_count" from pg_stat_all_tables for the table and its toast table?
Why did the vacuum full blocked?
Because that is how it works, as documented. That is why it is important to avoid getting into this situation in the first place. VACUUM FULL needs to swap around filenodes at the end, and needs an AccessExclusive lock to do that. It could take a weaker lock at first and then try to upgrade to AccessExclusive later, but lock upgrades have a strong deadlock risk, so it takes the strongest lock it needs up front.
You need a maintenance window where no one else is using the table. If you think you are already in such window, then you should look at the query text for the process doing the blocking. Because the lock already held is ShareUpdateExclusive, the thing holding it is not a normal query/DML, but some kind of DDL or maintenance operation.
If you can't take a maintenance window now, then you can at least do a manual VACUUM without the FULL. This takes a much weaker lock. It probably won't shrink the table dramatically, but should at least free up space for internal reuse so it stops getting even bigger while you figure out when you can schedule a maintenance window or what your other next steps are.
I want find if the row is part of an indexed column in postgresql.
For example:
When I open the table object I see my indexes and the cardinality of the each index.
my total rows in the table is 1,45,454 but the cardinality of the all the indexes are 1,45,300. Some 150 odd rows are not indexed in any of the indexes that I have created.
I ran the below query to find the cardinality,
SELECT relname,
relkind,
reltuples AS cardinality,
relpages
FROM pg_class
WHERE relname LIKE '%table_name%';
Could someone please explain why some rows are left as part of indexing and how to find the rows the 150 rows that are not indexed in my original table.
my total rows in the table is 1,45,454 but the cardinality of the all the indexes are 1,45,300
that means you have 154 duplicated index entries thus some of those 154 index entries(or less) points to more than 1 row(or more).
From the postgres documentation on planner statistics:
For efficiency reasons, reltuples and relpages are not updated on-the-fly, and so they usually contain somewhat out-of-date values. They are updated by VACUUM, ANALYZE, and a few DDL commands such as CREATE INDEX. A VACUUM or ANALYZE operation that does not scan the entire table (which is commonly the case) will incrementally update the reltuples count on the basis of the part of the table it did scan, resulting in an approximate value. In any case, the planner will scale the values it finds in pg_class to match the current physical table size, thus obtaining a closer approximation.
In other words, as long as that number is approximately correct, there's nothing wrong and nothing to worry about. If it were wildly off (say "203" instead of its current value), then it would be time to issue a VACUUM or ANALYZE job on the table.
Also worth checking the value of default_statistics_target. If that's set too low, your statistics will end up less and less accurate.
I have a very simple query that is taking way too long to run.
SELECT DISTINCT col1,col2,col3,col4 FROM tbl1;
What indexes do I need to add to speed up? I ran a simple vacuum; command and added the following index but neither helped.
CREATE INDEX tbl_idx ON tbl1(col1,col2,col3,col4);
The table has 400k rows. In fact counting them is taking extremely long as well. Running a simple
SELECT count(*) from tbl1;
is taking 8 seconds. So it's possible my problems are with vacuuming or reindexing or something I'm not sure.
Here is the explain command
EXPLAIN SELECT DISTINCT col1,col2,col3,col4 FROM tbl1;
QUERY PLAN
---------------------------------------------------------------------------------
Unique (cost=3259846.80..3449267.51 rows=137830 width=25)
-> Sort (cost=3259846.80..3297730.94 rows=15153657 width=25)
Sort Key: col1, col2, col3, col4
-> Seq Scan on tbl1 (cost=0.00..727403.57 rows=15153657 width=25)
(4 rows)
Edit: I'm currently running vacuum full; which hopefully fixes the issue and then maybe someone can give me some pointers on how to fix where I went wrong. It is several hours in and still going as far as I can tell. I did run
select relname, last_autoanalyze, last_autovacuum, last_vacuum, n_dead_tup from pg_stat_all_tables where n_dead_tup >0;
and the table has nearly 16 million n_dead_tup rows.
My data doesn't change that frequently so I ended up creating a materialized view
CREATE MATERIALIZED VIEW tbl1_distinct_view AS SELECT DISTINCT col1,col2,col3,col4 FROM tbl1;
that I refresh with a cronjob once a day at 6am
0 6 * * * psql -U mydb mydb -c 'REFRESH MATERIALIZED VIEW tbl1_distinct_view;
try force database to use your index
set enable_seqscan=off ;
SELECT DISTINCT col1,col2,col3,col4 FROM tbl1;
set enable_seqscan=on ;
VACUUM and VACUUM FULL are two commands that sound the same but have very different effects.
VACUUM scans a table for tuples that it no longer needs, so that it can overwrite that space during INSERT or UPDATE statements. This command only looks at deleted rows, and does not "defragment" the table - it leaves the space usage the same, but simply marks some space as "dead" in order that it can be reused.
VACUUM FULL looks at every row, and reclaims the space left by deleted rows and dead tuples, essentially "defragmenting" the table. If this is done on a live table, it can take a very long time, and can result in heavy weight locks, increased IO, and index bloat.
I imagine what you need is a VACUUM followed by an ANALYZE, which will rebuild your statistics for each table, improving index performance. These should be performed reasonably regularly in low-usage times for a database. Only if you have a lot of space to reclaim (due to lots of DELETE statements) should you use VACUUM FULL.
Anyhow, since you've run a VACUUM FULL, once that it complete you should run an ANALYZE on the database, followed by a REINDEX (on the database), and then an EXPLAIN on your query again, you should notice an improvement.
I have a query that feels like it is taking more time then it should be. This only applies on the first query for a given set of parameters, so when cached there is no issue.
I am not sure what to expect, however, given the setup and settings I was hoping someone could shed some light on a few questions and give some insight into what can be done to speed up the query. The table in question is fairly large and Postgres estimates around 155963000 in it (14 GB).
Query
select ts, sum(amp) as total_amp, sum(230 * factor) as wh
from data_cbm_aggregation_15_min
where virtual_id in (1818) and ts between '2015-02-01 00:00:00' and '2015-03-31 23:59:59'
and deleted is null
group by ts
order by ts
When I started looking into this the query it took around 15 seconds, after some changes I have gotten it to around 10 seconds which still seems long for a simply query like this. Here are the results from explain analyze: http://explain.depesz.com/s/97V1. Note the reason why GroupAggregate returns the same amount of rows is this example only has one virtual_id being used, but there can be more.
Table and index
Table being queried, it has values inserted into it every 15 minutes
CREATE TABLE data_cbm_aggregation_15_min (
virtual_id integer NOT NULL,
ts timestamp without time zone NOT NULL,
amp real,
recs smallint,
min_amp real,
max_amp real,
deleted boolean,
factor real DEFAULT 0.25,
min_amp_ts timestamp without time zone,
max_amp_ts timestamp without time zone
)
ALTER TABLE data_cbm_aggregation_15_min ALTER COLUMN virtual_id SET STATISTICS 1000;
ALTER TABLE data_cbm_aggregation_15_min ALTER COLUMN ts SET STATISTICS 1000;
The index that is used in the query
CREATE UNIQUE INDEX idx_data_cbm_aggregation_15_min_virtual_id_ts
ON data_cbm_aggregation_15_min USING btree (virtual_id, ts DESC);
ALTER TABLE data_cbm_aggregation_15_min
CLUSTER ON idx_data_cbm_aggregation_15_min_virtual_id_ts;
Postgres settings
Other settings are default.
default_statistics_target = 100
maintenance_work_mem = 2GB
effective_cache_size = 11GB
work_mem = 256MB
shared_buffers = 3840MB
random_page_cost = 1
What I have tried
I have been following the Things to try before you post in https://wiki.postgresql.org/wiki/Slow_Query_Questions and the results in a bit more detail were as follows:
Fiddling with the Postgres settings, mostly lowering random_page_cost since the index scan, while it seems not too special is miles ahead of the bitmap heap scan it tried doing instead when the random_page_cost was higher.
Adding increased statistics to the virtual_id and ts columns which the index and WHERE conditions are based on. The query planner's estimated row count was much closer to the actual row count after changing this.
Clustering on the idx_data_cbm_aggregation_15_min_virtual_id_ts index did not seem to change much, not that I noticed.
Running VACUUM manually did not change much, I am already running autovacuum so this was no surprise.
Running REINDEX on the index shrunk it considerably (by almost 50%!) but it did not improve the speed by much.
A couple of small improvements
SELECT ts, sum(amp) AS total_amp, sum(factor) * 230 AS wh
FROM data_cbm_aggregation_15_min
WHERE virtual_id = 1818
AND ts >= '2015-02-01 00:00'
AND ts < '2015-04-01 00:00'
AND deleted IS NULL
GROUP BY ts
ORDER BY ts;
sum(230 * factor) - it's cheaper to multiply the sum once instead of multiplying each element: sum(factor) * 230 The result is the same, even with NULL values.
ts between '2015-02-01 00:00:00' and '2015-03-31 23:59:59' is potentially incorrect. To include all of March 2015, use the presented alternative. BETWEEN is translated to ts >= lower AND ts <= upper anyway. It is always slightly faster to spell it out.
virtual_id in (1818) is just a needlessly convoluted way to say virtual_id = 1818.
Better index, potentially bigger improvement
CREATE INDEX data_cbm_aggregation_15_min_special_idx
ON data_cbm_aggregation_15_min (virtual_id, ts, amp, factor)
WHERE deleted IS NULL;
I see nothing in your question that would suggest DESC in your original index. While Index Scan Backward is almost as fast as a plain Index Scan, it's still better to drop the modifier.
Most importantly, there are index-only scans since Postgres 9.2. The two index columns I appended (amp, factor) only make sense if you get index-only scans out of it.
Since you obviously are not interested in deleted rows, make it a partial index. Only pays if you have more than a few deleted rows in the table.
If you have other large parts of the table that can be excluded, add more conditions - and remember to repeat the condition in the query (even if it seems redundant) so Postgres understands that the index is applicable.
Table definition
Reordering table columns like this would save 8 bytes per row:
CREATE TABLE data_cbm_aggregation_15_min (
virtual_id integer NOT NULL,
recs smallint,
deleted boolean,
ts timestamp NOT NULL,
amp real,
min_amp real,
max_amp real,
factor real DEFAULT 0.25,
min_amp_ts timestamp,
max_amp_ts timestamp
);
Related:
Configuring PostgreSQL for read performance
Most important information for last
The first query call can be substantially more expensive for very big tables, since the whole table cannot be cached. Subsequent calls profit from the populated cache. Postgres caches blocks, not necessarily whole tables.
One more thing that can be important for the first call. Due to the MVCC model of Postgres it has to maintain visibility information. When reading pages of a table the first time since the last write operation, Postgres opportunistically updates visibility information, which can impose some extra cost for the first access (and help a lot for subsequent calls). More in the manual here. Related answer on dba.SE:
Why does a SELECT statement dirty cache buffers in Postgres?
About what you've tried so far
SET STATISTICS 1000 for ts and virtual_id was an excellent idea, but the effect was largely nullified by setting random_page_cost = 1, which basically forces an index scan for this query either way.
random_page_cost = 1 is telling Postgres that random access is just as cheap as sequential access. This makes sense for a DB that (almost) completely resides in cache. For a DB with huge tables like yours, this setting seems too extreme (even if it gets Postgres to favor the desired index scan). Set it to random_page_cost = 1.1 or probably higher.
A bitmap index scan is typically a good plan for the first call of the query you presented - for data distributed randomly across the table. Since you clustered the table just like you need it for this query, an index scan is more efficient. The question is: will your table stay clustered?
Your settings for work_mem and other resources depend on how much RAM you have, the speed of your disks, on access pattern, how many concurrent connections you typically have, what other programs on the server compete for resources, etc. work_mem = 256MB seems too high. You don't need nearly as much for the presented query. Setting it that high may actually harm performance, because it reduces RAM available to cache.
REINDEX is not redundant immediately after CLUSTER, since that recreates all indexes anyway. You must have run REINDEX before cluster, or you have heavy write access on the table to get so much bloat again already.
Various
Upgrade to Postgres 9.4 (or the upcoming 9.5, currently alpha). Version 9.2 is 3 years old now, the latest version has received many improvements.
The query plan suggests that nothing is actually aggregated. rows=4,117 are read from the index and rows=4,117 remain after GroupAggregate. Looks like rows are unique on ts already? Then you can remove the aggregation completely and make it a simple SELECT ...
If that's just a misleading EXPLAIN output and you typically output much fewer rows than are read, a MATERIALIZED VIEW with index on ts would be another option. Especially in combination with Postgres 9.4, which introduces REFRESH MATERIALIZED VIEW CONCURRENTLY.
I have a table briefly structured like this:
tn( id integer NOT NULL primary key DEFAULT nextval('tn_sequence'),
create_dt TIMESTAMP NOT NULL DEFAULT NOW(),
...............
deleted boolean );
create_dt is the timestamp when the row is inserted into the database.
deleted indicates that the row is or no longer useful.
And I have the following queries:
select * from tn where create_dt > ( NOW() - interval '150 seconds ) and deleted = FALSE;
select * from tn where create_dt < ( NOW() - interval '150 seconds ) and deleted = FALSE;
My question is how these query will slow down when the number of rows increase? For instance, when the number of rows exceeds 10K, 20K, or 100K, will it make a big impact on the speed? Is there any way I can optimize these queries? Note that every 5 seconds I will turn the column 'deleted' of rows which are older than 150 seconds into 'TRUE'.
The effect of table growth on performance will depend on the query plan chosen, available indexes, the selectivity of the query, and lots of other factors. EXPLAIN ANALYZE on the query might help. In short, if your query only selects a few rows and can use a simple b-tree index then it won't usually slow down tons, only a little as the index grows. On the other hand queries using complex non-indexed conditions or returning lots of rows could perform very badly indeed.
Your issue appears to mirror that in the question How should we handle rows which won't be queried once they are old in PostgreSQL?
The advice given there should apply:
Use a partial index with the condition WHERE (not deleted); or
partition on 'deleted' with constraint exclusion enabled.
For example, you might:
CREATE INDEX create_dt_when_not_deleted_idx
ON tn (create_dt)
WHERE (NOT deleted);
This includes only rows where deleted = 'f' (assuming deleted is `not null) in the index. This isn't the same as having them gone from the table completely.
Nothing changes with full table sequential scans, the deleted='t' rows must still be scanned; and
There's more I/O than if the deleted = 't' rows weren't there because any given heap page is likely to contain a mix of deleted = 't' and deleted = 'f' rows.
You can reduce the impact of the latter by CLUSTERing on an index that includes deleted. Again, this will have no effect on sequential scans. To help with sequential scans you would have to partition the table on deleted.
Pg 9.2's index only scans should (I think, haven't tested) use the partial index. When an index only scan is possible the partial index should be as fast as an index on a table containing only the deleted = 'f' rows.
Note that you'll need to keep table and index bloat under control. Ensure autovaccum runs very frequently and use a current version of PostgreSQL that doesn't need things like manually-managed free space map and has the latest, best-behaved autovacuum. I'd recommend 9.0 or above, preferably 9.1 or 9.2. Tune autovacuum to run aggressively.
When tuning and testing performance - test your queries with EXPLAIN ANALYZE, don't just guess.