Related
I need to know the number of rows in a table to calculate a percentage. If the total count is greater than some predefined constant, I will use the constant value. Otherwise, I will use the actual number of rows.
I can use SELECT count(*) FROM table. But if my constant value is 500,000 and I have 5,000,000,000 rows in my table, counting all rows will waste a lot of time.
Is it possible to stop counting as soon as my constant value is surpassed?
I need the exact number of rows only as long as it's below the given limit. Otherwise, if the count is above the limit, I use the limit value instead and want the answer as fast as possible.
Something like this:
SELECT text,count(*), percentual_calculus()
FROM token
GROUP BY text
ORDER BY count DESC;
Counting rows in big tables is known to be slow in PostgreSQL. The MVCC model requires a full count of live rows for a precise number. There are workarounds to speed this up dramatically if the count does not have to be exact like it seems to be in your case.
(Remember that even an "exact" count is potentially dead on arrival under concurrent write load.)
Exact count
Slow for big tables.
With concurrent write operations, it may be outdated the moment you get it.
SELECT count(*) AS exact_count FROM myschema.mytable;
Estimate
Extremely fast:
SELECT reltuples AS estimate FROM pg_class where relname = 'mytable';
Typically, the estimate is very close. How close, depends on whether ANALYZE or VACUUM are run enough - where "enough" is defined by the level of write activity to your table.
Safer estimate
The above ignores the possibility of multiple tables with the same name in one database - in different schemas. To account for that:
SELECT c.reltuples::bigint AS estimate
FROM pg_class c
JOIN pg_namespace n ON n.oid = c.relnamespace
WHERE c.relname = 'mytable'
AND n.nspname = 'myschema';
The cast to bigint formats the real number nicely, especially for big counts.
Better estimate
SELECT reltuples::bigint AS estimate
FROM pg_class
WHERE oid = 'myschema.mytable'::regclass;
Faster, simpler, safer, more elegant. See the manual on Object Identifier Types.
Replace 'myschema.mytable'::regclass with to_regclass('myschema.mytable') in Postgres 9.4+ to get nothing instead of an exception for invalid table names. See:
How to check if a table exists in a given schema
Better estimate yet (for very little added cost)
This does not work for partitioned tables because relpages is always -1 for the parent table (while reltuples contains an actual estimate covering all partitions) - tested in Postgres 14.
You have to add up estimates for all partitions instead.
We can do what the Postgres planner does. Quoting the Row Estimation Examples in the manual:
These numbers are current as of the last VACUUM or ANALYZE on the
table. The planner then fetches the actual current number of pages in
the table (this is a cheap operation, not requiring a table scan). If
that is different from relpages then reltuples is scaled
accordingly to arrive at a current number-of-rows estimate.
Postgres uses estimate_rel_size defined in src/backend/utils/adt/plancat.c, which also covers the corner case of no data in pg_class because the relation was never vacuumed. We can do something similar in SQL:
Minimal form
SELECT (reltuples / relpages * (pg_relation_size(oid) / 8192))::bigint
FROM pg_class
WHERE oid = 'mytable'::regclass; -- your table here
Safe and explicit
SELECT (CASE WHEN c.reltuples < 0 THEN NULL -- never vacuumed
WHEN c.relpages = 0 THEN float8 '0' -- empty table
ELSE c.reltuples / c.relpages END
* (pg_catalog.pg_relation_size(c.oid)
/ pg_catalog.current_setting('block_size')::int)
)::bigint
FROM pg_catalog.pg_class c
WHERE c.oid = 'myschema.mytable'::regclass; -- schema-qualified table here
Doesn't break with empty tables and tables that have never seen VACUUM or ANALYZE. The manual on pg_class:
If the table has never yet been vacuumed or analyzed, reltuples contains -1 indicating that the row count is unknown.
If this query returns NULL, run ANALYZE or VACUUM for the table and repeat. (Alternatively, you could estimate row width based on column types like Postgres does, but that's tedious and error-prone.)
If this query returns 0, the table seems to be empty. But I would ANALYZE to make sure. (And maybe check your autovacuum settings.)
Typically, block_size is 8192. current_setting('block_size')::int covers rare exceptions.
Table and schema qualifications make it immune to any search_path and scope.
Either way, the query consistently takes < 0.1 ms for me.
More Web resources:
The Postgres Wiki FAQ
The Postgres wiki pages for count estimates and count(*) performance
TABLESAMPLE SYSTEM (n) in Postgres 9.5+
SELECT 100 * count(*) AS estimate FROM mytable TABLESAMPLE SYSTEM (1);
Like #a_horse commented, the added clause for the SELECT command can be useful if statistics in pg_class are not current enough for some reason. For example:
No autovacuum running.
Immediately after a large INSERT / UPDATE / DELETE.
TEMPORARY tables (which are not covered by autovacuum).
This only looks at a random n % (1 in the example) selection of blocks and counts rows in it. A bigger sample increases the cost and reduces the error, your pick. Accuracy depends on more factors:
Distribution of row size. If a given block happens to hold wider than usual rows, the count is lower than usual etc.
Dead tuples or a FILLFACTOR occupy space per block. If unevenly distributed across the table, the estimate may be off.
General rounding errors.
Typically, the estimate from pg_class will be faster and more accurate.
Answer to actual question
First, I need to know the number of rows in that table, if the total
count is greater than some predefined constant,
And whether it ...
... is possible at the moment the count pass my constant value, it will
stop the counting (and not wait to finish the counting to inform the
row count is greater).
Yes. You can use a subquery with LIMIT:
SELECT count(*) FROM (SELECT 1 FROM token LIMIT 500000) t;
Postgres actually stops counting beyond the given limit, you get an exact and current count for up to n rows (500000 in the example), and n otherwise. Not nearly as fast as the estimate in pg_class, though.
I did this once in a postgres app by running:
EXPLAIN SELECT * FROM foo;
Then examining the output with a regex, or similar logic. For a simple SELECT *, the first line of output should look something like this:
Seq Scan on uids (cost=0.00..1.21 rows=8 width=75)
You can use the rows=(\d+) value as a rough estimate of the number of rows that would be returned, then only do the actual SELECT COUNT(*) if the estimate is, say, less than 1.5x your threshold (or whatever number you deem makes sense for your application).
Depending on the complexity of your query, this number may become less and less accurate. In fact, in my application, as we added joins and complex conditions, it became so inaccurate it was completely worthless, even to know how within a power of 100 how many rows we'd have returned, so we had to abandon that strategy.
But if your query is simple enough that Pg can predict within some reasonable margin of error how many rows it will return, it may work for you.
Reference taken from this Blog.
You can use below to query to find row count.
Using pg_class:
SELECT reltuples::bigint AS EstimatedCount
FROM pg_class
WHERE oid = 'public.TableName'::regclass;
Using pg_stat_user_tables:
SELECT
schemaname
,relname
,n_live_tup AS EstimatedCount
FROM pg_stat_user_tables
ORDER BY n_live_tup DESC;
How wide is the text column?
With a GROUP BY there's not much you can do to avoid a data scan (at least an index scan).
I'd recommend:
If possible, changing the schema to remove duplication of text data. This way the count will happen on a narrow foreign key field in the 'many' table.
Alternatively, creating a generated column with a HASH of the text, then GROUP BY the hash column.
Again, this is to decrease the workload (scan through a narrow column index)
Edit:
Your original question did not quite match your edit. I'm not sure if you're aware that the COUNT, when used with a GROUP BY, will return the count of items per group and not the count of items in the entire table.
You can also just SELECT MAX(id) FROM <table_name>; change id to whatever the PK of the table is
In Oracle, you could use rownum to limit the number of rows returned. I am guessing similar construct exists in other SQLs as well. So, for the example you gave, you could limit the number of rows returned to 500001 and apply a count(*) then:
SELECT (case when cnt > 500000 then 500000 else cnt end) myCnt
FROM (SELECT count(*) cnt FROM table WHERE rownum<=500001)
For SQL Server (2005 or above) a quick and reliable method is:
SELECT SUM (row_count)
FROM sys.dm_db_partition_stats
WHERE object_id=OBJECT_ID('MyTableName')
AND (index_id=0 or index_id=1);
Details about sys.dm_db_partition_stats are explained in MSDN
The query adds rows from all parts of a (possibly) partitioned table.
index_id=0 is an unordered table (Heap) and index_id=1 is an ordered table (clustered index)
Even faster (but unreliable) methods are detailed here.
We have a system that loads data and then conducts data QC in PostgreSQL. The QC function's performance fluctuates drastically in one of our environments with no apparent pattern. I was able to track down the performance of the following simple query in the QC function:
WITH foo AS (SELECT full_address, jsonb_agg (gad_rec_id) gad_rec_ids
FROM azgiv.v_full_addresses
WHERE gad_gly_id = 495
GROUP BY full_address
HAVING count(1) > 1)
SELECT gad_nguid, gad_rec_id, foo.full_address
FROM azgiv.v_full_addresses JOIN foo
ON foo.full_address = v_full_addresses.full_address
AND v_full_addresses.gad_gly_id = 495;
When I ran into slow-performance situation (Fig 2), I had to ANALYZE the table behind the view before the query plan changes to fast (Fig 1). The v_full_addresses is a simple view of a partitioned table with bunch of columns concatenated.
Here are two images of the query plans for the above query. I am newbie when comes to understanding query optimization and any help is greatly appreciated.
&
If performance improves after you ANALYZE a table, that means that the database's knowledge about the distribution of the data is outdated.
The best remedy is to tell PostgreSQL to collect these statistics more often:
ALTER TABLE some_table SET (autovacuum_analyze_scale_factor = 0.02);
0.02 is five times lower than the default 0.1, so statistics will be gathered five times more often.
If the bad query plans are generated right after a bulk load, you must choose a different strategy. In this case the problem is that it takes up to a minute for auto-analyze to kick in and calculate new statistics.
In that case you should run an explicit ANALYZE at the end of the bulk load.
According to AWS doc:
Amazon Redshift does not automatically reclaim and reuse space that is freed when you delete rows and update rows.
Before running VACUUM, is there a way to know or evaluate how much space will be free from disk by the VACUUM?
Thx
References:
http://docs.aws.amazon.com/redshift/latest/dg/t_Reclaiming_storage_space202.html
http://docs.aws.amazon.com/redshift/latest/dg/r_VACUUM_command.html
You can calculate the amount of storage that will be freed up from a vacuum command by looking up the tbl_rows column in the svv_table_info view. This includes rows that are marked for deletion. Compare that to a select count(*) from the same table and you'll have a ratio. Something like this on a theoretical table named factsales.
select (select cast(count(*) as numeric(12,0)) from factsales) /
cast(tbl_rows as numeric(12,0))
as "percentage of non deleted rows"
from svv_table_info where "table" = 'factsales'
There doesn't appear to be a straightforward way to execute dynamic SQL and cursors so to get this same ratio across all tables you'd have to execute the code from an external source or programming language i.e. python.
Its not an extremely accurate way, but you can query svv_table_info and look for the column deleted_pct. This will give you a rough idea, in percentage terms, about what fraction of the table needs to be rebuilt using vacuum.
You can run it for all the tables in your system to get this estimate for the whole system.
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'm using Sybase 12.5.3 (ASE); I'm new to Sybase though I've worked with MSSQL pretty extensively. I'm running into a scenario where a stored procedure is really very slow. I've traced the issue to a single SELECT stmt for a relatively large table. Modifying that statement dramatically improves the performance of the procedure (and reverting it drastically slows it down; i.e., the SELECT stmt is definitely the culprit).
-- Sybase optimizes and uses multi-column index... fast!<br>
SELECT ID,status,dateTime
FROM myTable
WHERE status in ('NEW','SENT')
ORDER BY ID
-- Sybase does not use index and does very slow table scan<br>
SELECT ID,status,dateTime
FROM myTable
WHERE status in (select status from allowableStatusValues)
ORDER BY ID
The code above is an adapted/simplified version of the actual code. Note that I've already tried recompiling the procedure, updating statistics, etc.
I have no idea why Sybase ASE would choose an index only when strings are hard-coded and choose a table scan when choosing from another table. Someone please give me a clue, and thank you in advance.
1.The issue here is poor coding. In your release, poor code and poor table design are the main reasons (98%) the optimiser makes incorrect decisions (the two go hand-in-hand, I have not figured out the proportion of each). Both:
WHERE status IN ('NEW','SENT')
and
WHERE status IN (SELECT status FROM allowableStatusValues)
are substandard, because in both cases they cause ASE to create a worktable for the contents between the brackets, which can easily be avoided (and all consequential issues avoided with it). There is no possibility of statistics on a worktable, since the statistics on either t.status or s.status is missing (AdamH is correct re that point), it correctly chooses a table scan.
Subqueries have their place, but never as a substitute for a pure (the tables are related) join. The corrections are:
WHERE status = "NEW" OR status = "SENT"
and
FROM myTable t,
allowableStatusValues s
WHERE t.status = s.status
2.The statement
|Now you don't have to add an index to get statistics on a column, but it's probably the best way.
is incorrect. Never create Indices that you will not use. If you want statistics updated on a column, simply
UPDATE STATISTICS myTable (status)
3.It is important to ensure that you have current statistics on (a) all indexed columns and (b) all join columns.
4.Yes, there is no substitute for SHOWPLAN on every code segment that is intended for release, doubly so for any code with questionable performance. You can also SET NOEXEC ON, to avoid execution, eg. for large result sets.
An index hint will work around it, but is probably not the solution.
Firstly I'd like to know if there is an index on allowableStatusValues.status, if there is then sybase will have stats on it and will have a good idea on the number of values in there.
If not then the optimiser probably won't have a good idea how many different values Status may take. It's then having to make the assumption that you're going to be extracting almost all of the rows from myTable, and the best way of doing this is a table scan (if no covering index).
Now you don't have to add an index to get statistics on a column, but it's probably the best way.
If you do have an index on allowableStatusValues.status, then i'd wonder how good your stats are. Get yourself a copy of sp__optdiag. You probably also need to tune the values of "histogram tuning factor" and "number of histogram steps", increasing these slightly from the defaults will give you more detailed statistics which always helps the optimiser.
Does it still do a table scan if you replace the subquery with a join:
SELECT m.ID,m.status,m.dateTime
FROM myTable m
JOIN allowableStatusValues a on m.status = a.status
ORDER BY ID
Rather than relying on experimental observations of how long a query takes to run, I would highly recommend getting Sybase to show you the execution plans for each query, for example:
SET showplan ON
GO
-- query/procedure call goes here
SELECT id, status, datetime
FROM myTable
WHERE status IN('NEW','SENT')
ORDER BY id
GO
SET showplan OFF
GO
With SET showplan ON, Sybase generates execution plans for every statement it executes. These can be invaluable in helping to identify where queries are not making use of appropriate indexes. For stored procedures in Sybase, the execution plan for the entire procedure is generated when the stored procedure is first executed after being compiled.
If you post the plans for each of your queries we might be able to shed more light on the problem.
Amazingly, using an index hint resolves the issue (see the (index myIndexName) line below - re-written/simplififed code below:
-- using INDEX HINT
SELECT ID,status,dateTime
FROM myTable (index myIndexName)
WHERE status in (select status from allowableStatusValues)
ORDER BY ID
Weird that I have to use this technique to avoid a table scan, but there ya go.
Garrett, by showing only the simplified code, you have likely stripped out exactly the information that would illuminate the source of the problem.
My first guess would be a type mismatch between allowableStatusValues.status and myTable.status. However, that is not the only possibility. As ninesided stated, the complete query plans (using showplan and fmtonly flags), as well as the actual table definitions and stored procedure source, is much more likely to produce a useful answer.