Presentation on theme: "Postgres Tips and Tricks By Lloyd Albin 5/1/2013, 6/11/2013."— Presentation transcript:
Postgres Tips and Tricks By Lloyd Albin 5/1/2013, 6/11/2013
Why does my query run slow SELECT max(l1.upload_timestamp) AS LastUploadTimestamp, lcase(l1.network) as network, l1.filename, count(l1.file_id) AS NumberUploads FROM lab_upload_log AS l1 GROUP BY lcase(l1.network), l1.filename DISTINCT and GROUP BY give the same results, but internally DISTINCT is faster. SELECT DISTINCT max(l1.upload_timestamp) AS LastUploadTimestamp, lcase(l1.network) as network, l1.filename, count(l1.file_id) AS NumberUploads FROM lab_upload_log AS l1 GROUP BY lcase(l1.network), l1.filename
Why does my query run slow SELECT max(l1.upload_timestamp) AS LastUploadTimestamp, lcase(l1.network) as network, l1.filename, count(l1.file_id) AS NumberUploads FROM lab_upload_log AS l1 GROUP BY lcase(l1.network), l1.filename CREATE INDEX lab_upload_log_idx ON lab_data_ops.lab_upload_log USING btree (network, filename); Create a functional index for the field CREATE INDEX lab_upload_log_idx2 ON lab_data_ops.lab_upload_log USING btree (lower(network), filename); Create an index for the field if you are going to use the field in a WHERE clause or a GROUP BY clause.
Why does my query run slow SELECT ucase(final_lab_upload_log.lab) AS lab, count(final_lab_upload_log.filename) AS "# Unique Files", max(x.NumUploads) AS "Total # Uploads“ FROM final_lab_upload_log LEFT JOIN ( SELECT count(lab_upload_log.filename) AS NumUploads, ucase(lab_upload_log.lab) AS lab FROM lab_upload_log WHERE lcase(lab_upload_log.network)='vtn‘ AND lab_upload_log.upload_timestamp > (curdate() - 30) GROUP BY ucase(lab_upload_log.lab)) AS x ON x.lab = final_lab_upload_log.lab WHERE final_lab_upload_log.LastUploadTimestamp > (curdate() - 30) GROUP BY ucase(final_lab_upload_log.lab) Remove doubling of the ucase. The doubling stops the index from being used. SELECT ucase(final_lab_upload_log.lab) AS lab, … GROUP BY ucase(final_lab_upload_log.lab)
How much will this really benefit me? The previous queries when used in production run in: 5 seconds (individually) But in reality, we have three of these types of queries and so they take over: 15 seconds combined After the changes on the three previous slides: 30-100 milliseconds per each individual query <200 milliseconds for the 3 queries together
Reduce Code Don’t write the same query in two different places. This causes you to have to maintain twice as much code and makes it so that when people are updating the code, that they may only catch one instance of the code. A good example of this problem is when you create a user defined query in atlas on a folder and then create a module with the same query and you attach the module to other folders. The user defined query should be removed and the module attached to that folder.
Where did my NULL’s go? SELECT * FROM table WHERE v != 398 When you do comparisons in Postgres NULL’s are automatically removed unless you specifically ask for them. vt 100 398 500 null vt 100 500 SELECT * FROM table WHERE t != ‘398’ vt 100 null SELECT * FROM table WHERE v != 398 OR v IS NULL vt 100 500 null Source Table
The normal way to get current job SELECT d.* FROM ( SELECT b.emplid, b.empl_rcd, b.effdt, max(b.effseq) AS effseq FROM ( SELECT ps_job.emplid, ps_job.empl_rcd, max(ps_job.effdt) AS effdt FROM finance_feeds.ps_job GROUP BY ps_job.emplid, ps_job.empl_rcd ) a LEFT JOIN finance_feeds.ps_job b ON a.emplid::text = b.emplid::text AND a.empl_rcd = b.empl_rcd AND a.effdt = b.effdt GROUP BY b.emplid, b.empl_rcd, b.effdt ) c LEFT JOIN finance_feeds.ps_job d ON c.emplid::text = d.emplid::text AND c.empl_rcd = d.empl_rcd AND c.effdt = d.effdt AND c.effseq = d.effseq WHERE d.empl_status::text <> 'T' ::text; -- 833 rows returned (execution time: 32 ms; total time: 93 ms) -- 14,719 rows in finance_feeds.ps_job DISTINCT ON with ORDER BY and GROUP BY with MIN or MAX can give the same results, but internally DISTINCT ON is faster and is easier to read the code.
Fast way to get current job SELECT * FROM ( SELECT DISTINCT ON (emplid, empl_rcd) * FROM finance_feeds.ps_job ORDER BY emplid, empl_rcd, effdt DESC, effseq DESC ) a WHERE a.empl_status::text <> 'T' ::text; -- 833 rows returned (execution time: 31 ms; total time: 31 ms) -- 14,719 rows in finance_feeds.ps_job Much simpler code using DISTINCT ON with ORDER BY instead of GROUP BY with MAX.
Query Plan Difference Original Query DISTINCT ON Query Query plan is almost ½ the speed and only scans the table once instead of three times. LabKey does not support DISTINCT ON at this time. https://www.labkey.o rg/wiki/home/Docu mentation/page.view ?name=labkeySql LabKey does not support DISTINCT ON at this time. https://www.labkey.o rg/wiki/home/Docu mentation/page.view ?name=labkeySql
How to compare two queries There is a great command called EXCEPT. This will compare the results of two queries and tell you what is in the first query that is not in the second query. SELECT * FROM view_a EXCEPT SELECT * FROM view_b SELECT * FROM (SELECT * FROM view_a)) a EXCEPT SELECT * FROM (SELECT * FROM view_b)) b This will show you all lines in view_a that are not in view_b. To find out all lines on view_b that are not in view_a, reverse the two queries. If you are comparing two complex query statements, wrap them in a simple SELECT statement so that the EXCEPT will not get confused.
How to compare two queries There is a great command called EXCEPT. This will compare the results of two queries and tell you what is in the first query that is not in the second query. SELECT * FROM ( SELECT 1 UNION SELECT 2 ) a EXCEPT SELECT * FROM ( SELECT 1 UNION SELECT 3 ) b ?Column? 2
How to compare two queries Normally you will also want to reverse the two queries so that you can check the results going the other direction. This way you have two sets of results, what is extra in each query. SELECT * FROM ( SELECT 1 UNION SELECT 3 ) b EXCEPT SELECT * FROM ( SELECT 1 UNION SELECT 2 ) a ?Column? 3
How to compare two queries If you want only the records that match, then you want to use INTERSECT. SELECT * FROM ( SELECT 1 UNION SELECT 3 ) b INTERSECT SELECT * FROM ( SELECT 1 UNION SELECT 2 ) a ?Column? 1
Finding the slow line in your query EXPLAIN will show you the query plan, and this by itself is helpful, but even more helpful is the EXPLAIN (ANALYZE, BUFFERS) which compares the query plan to what actually happened when the query was run. Also use http://explain.depesz.com/http://explain.depesz.com/ Nested Loop Left Join (cost=305819.49..449850.69 rows=1 width=572) (actual time=1871.328..9512784.289 rows=10983 loops=1) Filter: ((NOT (hashed SubPlan 42)) AND ((SubPlan 38) IS NOT NULL)) Rows Removed by Filter: 74 Buffers: shared hit=2412568803 read=99 1)Estimate rows=1 vers Actual rows=10983. When you have big difference between these numbers, this is a sign of a problem. This can be caused by not having enough statistics, not having an index, etc. 2)Actual time=1871.328..9912784.289. This means that this row started 1.87 seconds into the query and took the difference of the two times, 2.64 hours, to complete
Number of months between dates date_trunc(‘month’, date) -- First day of the month date + ‘1 day’::interval -- Converts last day of the month to the first day of next month age(date, date) -- The difference between two timestamps as an interval date_part(‘year’, date/interval) -- returns only the year portion of the date/interval SELECT (date_part('year', age(max(b.earnenddate)::timestamp + interval '1 day', date_trunc('month',min(b.earnenddate)::date)::timestamp))*12 + date_part('month', age(max(b.earnenddate)::timestamp + interval '1 day', date_trunc('month',min(b.earnenddate)::date)::timestamp)))::integer AS elapsed_months FROM ( SELECT '06/30/2012' AS earnenddate UNION SELECT '12/31/2013' AS earnenddate ) b; elapsed_months 19
Creating a Table from a View CREATE TABLE schema.table AS SELECT * FROM schema.view; SELECT * INTO schema.table FROM schema.view; The PostgreSQL usage of SELECT INTO to represent table creation is historical. It is best to use CREATE TABLE AS for this purpose in new code. This allows you to create a table without having to look up all the field names and types to first generate a table and then fill it with the results of the view.
Update a Table from a View BEGIN; TRUNCATE schema.table; INSERT INTO schema.table SELECT * FROM schema.view; COMMIT; This allows you to take the results of a view and append them to an existing table. You may wish to TRUNCATE schema.table before adding the new data.
What order is my data in? When you don’t use an ORDER BY clause, your data is in physical order of insert and update. CREATE TEMP TABLE test ( key SERIAL, test BOOLEAN ); INSERT INTO test VALUES (1,FALSE); INSERT INTO test VALUES (2,FALSE); INSERT INTO test VALUES (3,FALSE); SELECT * FROM test; keytest 1f 2f 3f deletedkeytest 1f 2f 3f
What order is my data in? UPDATE test SET test = TRUE WHERE key = 2; SELECT * FROM test; deletedkeytest 1f X2f 3f 2t keytest 1f 3f 2t
ORDER BY field Normally you use ORDER BY field_name, but you can also use ORDER BY field_number. I have found this to sometimes be useful when unioning one or more sets of data together. SELECT 'test5' AS test1 UNION ALL SELECT 'test2' AS test2 ORDER BY 1 Notes: ORDER BY test1 will work. ORDER BY test2 will not work. UNION ALL gives you all rows from each SELECT and runs much faster. UNION only gives you DISTINCT rows between the two tables and ordered test1 test2 test5
TRUNCATE vers DELETE TRUNCATE is normally the best way to go because it removes all the data within the table(s) quickly and by specifying more than one table, deals automatically with foreign key dependencies. Delete can take a long time depending on the foreign key dependencies, etc. TRUNCATE is not MVCC-save, so after truncation, the table will appear empty to all concurrent transactions, even if they are using a snapshot taken before the truncation occurred. DELETE does not have this issue. RESTART IDENTITY Automatically restart sequences owned by columns of the truncated table(s). test1 test2 test5
pg_stat_activity 9.1- SELECT * FROM pg_stat_activity; This will show you what queries that you currently have running on a server. As user Postgres, you will see all queries running on a server. If there is no query running, you will see. datiddatnameprocpidusesysidusernameapplication_name 487377201main1813310postgresEMS SQL Manager for PostgreSQL client_addrclient_portbackend_startxact_startquery_start 10.6.106.202642412013-06-24 15:10:13.139305- 07 2013-06-24 15:22:32.100781- 07 waitingcurrent_query FalseSELECT * FROM pg_stat_activity
pg_stat_activity 9.2+ SELECT * FROM pg_stat_activity; This will show you what queries that you currently have running on a server. As user Postgres, you will see all queries running on a server. They will also say or. The connections show you the last query executed. datiddatnameprocpidusesysidusernameapplication_name 487377201main1813310postgresEMS SQL Manager for PostgreSQL client_addrclient_portbackend_startxact_startquery_start 10.6.106.202642412013-06-24 15:10:13.139305- 07 2013-06-24 15:22:32.100781- 07 waitingcurrent_query False : SELECT * FROM pg_stat_activity
Killing your own backend’s SELECT pg_cancel_backend(pid); SELECT pg_terminate_backend(pid); This cancels your current command and closes your connection. If you are in the middle of a transaction, the transaction will be aborted instantly. This cancels your current command and leaves your connection open for your next command. If you are in the middle of a transaction, the transaction will be aborted once you try and COMMIT your transaction. It will also complain about every line failing until you try and COMMIT. If all the backends you see are gone and you are still getting the open connections when trying to drop your database, contact a dba.
Materialized Views CREATE MATERIALIZED VIEW schema.materialized_view AS SELECT * FROM schema.table; SELECT * FROM schema.materialized_view; REFRESH MATERIALIZED VIEW schema.materialized_view; This is basically a simple melding of a TABLE and VIEW into a single entity. When you create the MATERIALIZED VIEW is populates the underlying TABLE. Every time you use the MATERIALIZED VIEW it returns you the data in the TABLE. To update the data in the TABLE, you need to run the REFRESH MATERIALIZED VIEW command.
Foreign Data Tables CREATE EXTENSION postgres_fdw; CREATE SERVER db_main FOREIGN DATA WRAPPER postgres_fdw OPTIONS (host 'db.scharp.org', dbname 'main', port '5432'); CREATE USER MAPPING FOR postgres SERVER db_main OPTIONS (user 'webservices', password 'password'); CREATE FOREIGN TABLE ist.webservices_token ( "time" TIMESTAMP WITHOUT TIME ZONE DEFAULT now() NOT NULL, token TEXT NOT NULL ) SERVER db_main; SELECT * FROM ist.webservices_token; The Postgres DBA’s should take care of the EXTENSION, SERVER and USER MAPPING’s. The developer can then create the FOREIGN TABLES. For each user that wants to use the FOREIGN TABLE, there must be a USER MAPPING created by a DBA.