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This produces two sets of values The actual time shown in the output is still defined using relative units and not actual time The real time the query took to complete is shown at the end of the statistics Although the command actually runs the query to produce the time results, the output from the query is suppressed and not displayed This example showed that to produce the result set for this SELECT command, the database engine performed a sequential scan of the Customer table, looking for records that match the query The entire query was processed in a single step Things get more interesting with more complicated queries:
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test=> explain analyze select * from store"Customer" order by "LastName"; QUERY PLAN -------------------------------------------------------------------------Sort (cost=108109 rows=4 width=232) (actual time=01480150 rows=5 loops=1) Sort Key: "LastName" -> Seq Scan on "Customer" (cost=000104 rows=4 width=232) (actual time=00100015 rows=5 loops=1) Total runtime: 0258 ms (4 rows) test=>
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By using the ORDER BY clause, we force the database engine to perform two steps One thing that you need to get used to when analyzing query explain plans is that you must work from the bottom up The first step the database engine performs is the last item in the list Just as with the first example, the first step in this query was to scan the Customer table for the records that match the query This process did not take all that long (relatively speaking) The second step the database engine took was to sort the data records in the result set from the scan This component is what slowed down the query While the query explain plan estimated that it would not take much time, the actual numbers show that it took over 0150 time units to complete sorting the data Compared to the 0015 time units for the scan, you can see how sorting your result set consumes more database engine time Now we can compare this query to a query that sorts the data based on a table key:
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test=> explain analyze select * from store"Customer" order by "CustomerID"; QUERY PLAN --------------------------------------------------------------------------Sort (cost=108109 rows=4 width=232) (actual time=00320034 rows=5 loops=1) Sort Key: "CustomerID" -> Seq Scan on "Customer" (cost=000104 rows=4 width=232) (actual time=00060011 rows=5 loops=1) Total runtime: 0077 ms (4 rows) test=>
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By examining the query explain plan for this query, you can see the benefit of using indexes in queries The query on the CustomerID column only took 0077 millisecond, compared to the 0258 millisecond for the query sorting the result set on a non-key value While these values do not look all too much different, remember this example was for a table with only five records If instead this table had five million records, these values would be multiplied many times over, creating quite a performance hit As you use the EXPLAIN command, you may notice that sometimes the database engine does not do things you expect it to do Usually, the database engine keeps track of new database objects, such as table indexes, and utilizes them in the query explain plan Sometimes, however, you must force the database engine to re-evaluate how to execute a query The ANALYZE SQL command (different from the ANALYZE parameter in the EXPLAIN command) tells the database engine to re-examine all database objects The table owner can use the ANALYZE command along with the table name to force PostgreSQL to re-examine the table and reset any query explain plans set for the table:
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test=# analyze store"Customer"; ANALYZE test=#
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The normal output of the ANALYZE command is not too exciting To get some information on what is happening, you can use the VERBOSE keyword:
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test=# analyze verbose store"Product"; INFO: analyzing "storeProduct" INFO: "Product": scanned 1 of 1 pages, containing 2 live rows and 6 dead rows; 2 rows in sample, 2 estimated total rows ANALYZE test=#
The output now shows some information about the table In this example, there are two records that contain live data, and six records that had been either deleted or updated By performing a VACUUM command on the table, you can free up six records worth of storage space If your application performs lots of record updates and deletions, it is a good idea to regularly analyze the status of the table, and vacuum when necessary This is the basic idea behind using the EXPLAIN command As your customers start using more complex queries, and your application starts containing more data, query performance can become a problem Using the EXPLAIN command gives you (and the database programmers) a simple tool for examining how individual queries affect application performance
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