I'm trying to copy a Postgres table1 to another table2 while changing the value of one of the columns. To make the transfer faster I run 10 different processes each having different offsets in the table1 to start from, e.g., 1st process: SELECT * FROM table OFFSET offset1 LIMIT x; then copy to table2, 2nd process: SELECT * FROM table OFFSET offset2 LIMIT x then copy to table2.
But even though I don't have duplicate rows in my table1 I do get duplicate rows in my table2 (x is smaller than offset2-offset1). Is it possible that the same offset value is not pointing to the same row in the table across different processes? If yes, what would be better way to copy a table while modifying a column in Postgres? Thanks!
Without an order by statement, limit and offset are seldom meaningful... SQL offers no guarantee on row order unless you make it explicit. So add an order by clause.
Also, if copying a table wholesale is what you want, it's better to simply:
insert into table2 select * from table1
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With concurrent write operations, it may be outdated the moment you get it.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.
my table contains 1 billion records. It is also partitioned by month.Id and datetime is the primary key for the table. When I select
select col1,col2,..col8
from mytable t
inner join cte on t.Id=cte.id and dtime>'2020-01-01' and dtime<'2020-10-01'
It uses index scan, but takes more than 5 minutes to select.
Please suggest me.
Note: I have set work_mem to 1GB. cte table results comes with in 3 seconds.
Well it's the nature of join and it is usually known as a time consuming operation.
First of all, I recommend to use in rather than join. Of course they have got different meanings, but in some cases technically you can use them interchangeably. Check this question out.
Secondly, according to the relation algebra whenever you use join each rows of mytable table is combined with each rows from the second table, and DBMS needs to make a huge temporary table, and finally igonre unsuitable rows. Undoubtedly all the steps and the result would take much time. Before using the Join opeation, it's better to filter your tables (for example mytable based date) and make them smaller, and then use the join operations.
I am running Postgres 9.1.3 32-bit on Windows 7 x64. (Have to use 32 bit because there is no Windows PostGIS release compatible with 64 bit Postgres.) (EDIT: As of PostGIS 2.0, it is compatible with Postgres 64 bit on windows.)
I have a query that left joins a table (consistent.master) with a temporary table, then inserts the resulting data into a third table (consistent.masternew).
Since this is a left join, the resulting table should have the same number of rows as the left table in the query. However, if I run this:
SELECT count(*)
FROM consistent.master
I get 2085343. But if I run this:
SELECT count(*)
FROM consistent.masternew
I get 2085703.
How can masternew have more rows than master? Shouldn't masternew have the same number of rows as master, the left table in the query?
Below is the query. The master and masternew tables should be identically-structured.
--temporary table created here
--I am trying to locate where multiple tickets were written on
--a single traffic stop
WITH stops AS (
SELECT citation_id,
rank() OVER (ORDER BY offense_timestamp,
defendant_dl,
offense_street_number,
offense_street_name) AS stop
FROM consistent.master
WHERE citing_jurisdiction=1
)
--Here's the insert statement. Below you'll see it's
--pulling data from a select query
INSERT INTO consistent.masternew (arrest_id,
citation_id,
defendant_dl,
defendant_dl_state,
defendant_zip,
defendant_race,
defendant_sex,
defendant_dob,
vehicle_licenseplate,
vehicle_licenseplate_state,
vehicle_registration_expiration_date,
vehicle_year,
vehicle_make,
vehicle_model,
vehicle_color,
offense_timestamp,
offense_street_number,
offense_street_name,
offense_crossstreet_number,
offense_crossstreet_name,
offense_county,
officer_id,
offense_code,
speed_alleged,
speed_limit,
work_zone,
school_zone,
offense_location,
source,
citing_jurisdiction,
the_geom)
--Here's the select query that the insert statement is using.
SELECT stops.stop,
master.citation_id,
defendant_dl,
defendant_dl_state,
defendant_zip,
defendant_race,
defendant_sex,
defendant_dob,
vehicle_licenseplate,
vehicle_licenseplate_state,
vehicle_registration_expiration_date,
vehicle_year,
vehicle_make,
vehicle_model,
vehicle_color,
offense_timestamp,
offense_street_number,
offense_street_name,
offense_crossstreet_number,
offense_crossstreet_name,
offense_county,
officer_id,
offense_code,
speed_alleged,
speed_limit,
work_zone,
school_zone,
offense_location,
source,
citing_jurisdiction,
the_geom
FROM consistent.master LEFT JOIN stops
ON stops.citation_id = master.citation_id
In case it matters, I have run a VACUUM FULL ANALYZE and reindexed both tables. (Not sure of exact commands; did it through pgAdmin III.)
A left join does not necessarily have the same number of rows as the number of rows in the left table. Basically, it is like a normal join, except rows of the left table that would not appear in the normal join are also added. So, if you have more than one row in the right table that matches one row in the left table, you can have more rows in your results than the number of rows of the left table.
In order to do what you want to do, you should use a group by, and a count to detect multiples.
select citation_id
from stops join master on stops.citation_id = master.citation_id
group by citation_id
having count(*) > 1
Sometimes you know there are multiples, but don't care. You just want to take the first or top entry.
If so, you can use SELECT DISTINCT ON:
FROM consistent.master LEFT JOIN (SELECT DISTINCT ON (citation_id) * FROM stops) s
ON s.citation_id = master.citation_id
Where citation_id is the column that you want to take the first (any) row for each match.
You might want to ensure this is deterministic and use ORDER BY with some other orderable column:
SELECT DISTINCT ON (citation_id) * FROM stops ORDER BY citation_id, created_at
I have two tables, table1 and table2, both of which contain columns that store postgis geometries. What I want to do is see where the geometry stored in any row of table2 geometrically intersects with the geometry stored in any row of table1 and update a count column in table1 with the number of intersections. Therefore, if I have a geometry in row 1 of table1 that intersects with the geometries stored in 5 rows in table2, I want to store a count of 5 in a separate column in table one. The tricky part for me is that I want to do this for every row of column 1 at the same time.
I have the following:
UPDATE circles SET intersectCount = intersectCount + 1 FROM rectangles
WHERE ST_INTERSECTS(cirlces.geom, rectangles.geom);
...which doesn't seem to be working. I'm not too familiar with postgres (or sql in general) and I'm wondering if I can do this all in one statement or if I need a few. I have some ideas for how I would do this with multiple statements (or using for loop) but I'm really looking for a concise solution. Any help would be much appreciated.
Thanks!
something like:
update t1 set ctr=helper.ctr
from (
select t1.id, count(*) as cnt
from t1, t2
where st_intersects(t1.col, t2.col)
group by t1.id
) helper
where helper.id=t1.id
?
btw: Your version does not work, because a row can get updated only once in a single update statement.
I have a table in my database and I want for each row in my table to have an unique id and to have the rows named sequently.
For example: I have 10 rows, each has an id - starting from 0, ending at 9. When I remove a row from a table, lets say - row number 5, there occurs a "hole". And afterwards I add more data, but the "hole" is still there.
It is important for me to know exact number of rows and to have at every row data in order to access my table arbitrarily.
There is a way in sqlite to do it? Or do I have to manually manage removing and adding of data?
Thank you in advance,
Ilya.
It may be worth considering whether you really want to do this. Primary keys usually should not change through the lifetime of the row, and you can always find the total number of rows by running:
SELECT COUNT(*) FROM table_name;
That said, the following trigger should "roll down" every ID number whenever a delete creates a hole:
CREATE TRIGGER sequentialize_ids AFTER DELETE ON table_name FOR EACH ROW
BEGIN
UPDATE table_name SET id=id-1 WHERE id > OLD.id;
END;
I tested this on a sample database and it appears to work as advertised. If you have the following table:
id name
1 First
2 Second
3 Third
4 Fourth
And delete where id=2, afterwards the table will be:
id name
1 First
2 Third
3 Fourth
This trigger can take a long time and has very poor scaling properties (it takes longer for each row you delete and each remaining row in the table). On my computer, deleting 15 rows at the beginning of a 1000 row table took 0.26 seconds, but this will certainly be longer on an iPhone.
I strongly suggest that you re-think your design. In my opinion your asking yourself for troubles in the future (e.g. if you create another table and want to have some relations between the tables).
If you want to know the number of rows just use:
SELECT count(*) FROM table_name;
If you want to access rows in the order of id, just define this field using PRIMARY KEY constraint:
CREATE TABLE test (
id INTEGER PRIMARY KEY,
...
);
and get rows using ORDER BY clause with ASC or DESC:
SELECT * FROM table_name ORDER BY id ASC;
Sqlite creates an index for the primary key field, so this query is fast.
I think that you would be interested in reading about LIMIT and OFFSET clauses.
The best source of information is the SQLite documentation.
If you don't want to take Stephen Jennings's very clever but performance-killing approach, just query a little differently. Instead of:
SELECT * FROM mytable WHERE id = ?
Do:
SELECT * FROM mytable ORDER BY id LIMIT 1 OFFSET ?
Note that OFFSET is zero-based, so you may need to subtract 1 from the variable you're indexing in with.
If you want to reclaim deleted row ids the VACUUM command or pragma may be what you seek,
http://www.sqlite.org/faq.html#q12
http://www.sqlite.org/lang_vacuum.html
http://www.sqlite.org/pragma.html#pragma_auto_vacuum