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performance without having too much of a negative impact on data entry For applications that do a lot of data input and little querying, it is often best to not mess with indexes An example of creating a simple index follows:
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test=# create index "Customer_City_Index" on store"Customer" ("City"); CREATE INDEX test=# \di store List of relations Schema | Name | Type | Owner | Table + + + + store | CustomerKey | index | postgres | Customer store | Customer_City_Index | index | postgres | Customer store | OrderKey | index | postgres | Order store | ProductKey | index | postgres | Product (4 rows) test=# \d store"Customer_City_Index" Index "storeCustomer_City_Index" Column | Type + City | character varying btree, for table "storeCustomer" test=#
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Notice that the naming rules apply to indexes as well If you use uppercase letters in the index name, remember to use double quotes Also, notice that by default the index was created in the same schema as the data table it references You can also create indexes based on more than one column:
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test=# create index "Customer_City_State_Index" on store"Customer" test-# ("City", "State"); CREATE INDEX test=# \d store"Customer_City_State_Index" Index "storeCustomer_City_State_Index" Column | Type + City | character varying State | character(2) btree, for table "storeCustomer" test=#
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PostgreSQL will automatically use the resulting index on queries where the WHERE condition references both the City and State columns
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The TABLESPACE parameter allows you to create the index file on a tablespace different from where the original table is located By default, the index is placed on the default system tablespace, usually pg_default On a heavily loaded system, it is common for database administrators to place table indexes on a separate hard drive from the data table This helps minimize the head movement on a specific hard drive as PostgreSQL searches the index file and the table data The WHERE parameter allows you to create a partial index, which indexes only a subset of the full table data Only the data that meets the WHERE condition is placed in the index As mentioned, creating an index does have a performance cost If there is a particular subset of data that is queried more so than other data, you can create the index using only this data
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test=# create index "Customer_State_IN_Index" on store"Customer" test-# ("City", "State") where "State" = 'IN'; CREATE INDEX test=# \d store"Customer_State_IN_Index" Index "storeCustomer_State_IN_Index" Column | Type + City | character varying State | character(2) btree, for table "storeCustomer", predicate ("State" = 'IN'::bpchar) test=#
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If you determine that the created index is detrimental to your database performance, you can remove the index by using the DROP INDEX command If lots of data is added to the table, you may consider using the REINDEX command The REINDEX command is used to, obviously, reindex the index files associated with the table In very active tables, often the index can get somewhat out of wack, and reindexing can help speed up performance of the index The REINDEX command is part of the table maintenance tools provided by pgAdmin III
Determining the Index Method
I saved the most complex indexing parameter for last The USING keyword allows you to specify the indexing algorithm used to sort the index values The sorting algorithm determines how column values are placed in the index for access Different sorting algorithms are used in different situations in order to speed up the search Knowing which indexing method to use can often help improve the performance of your application The following are the indexing methods currently available in PostgreSQL: btree rtree hash gist
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The btree option is the default value used by PostgreSQL It uses the balanced-tree (B-Tree) sorting algorithm to create links between index values The B-Tree sorts the data based on the index value, then references index values with links Each index value links to values both less than and greater than the value The B-Tree index contains a root value, which is usually in the middle of the data range The B-Tree index compares the desired result value to the root value and determines if it is less than, greater than, or equal to the root value Obviously, if the desired value is equal to the root value, the search is complete If the value is less than the root value, a link to the midpoint of the lower range is given In a similar manner, if the desired value is greater than the root value a link to the midpoint of the higher range is given Each of the resulting sides is then split in half, and so on until the desired value is found in the index This is demonstrated in Figure 7-3 The B-Tree links allow PostgreSQL to quickly scan through the index looking for the appropriate values The City index starts with the middle value, Gary If the city that is being searched is greater than Gary, the right-side link is followed to Hammond If the desired value is less than Hammond (but greater than Gary), the left-side link is followed to Granger Thus, instead of having to read every record in the table, PostgreSQL was able to find the proper record in just a couple of reads in the City index file The B-Tree index method is useful for indexing numerical and string values If you are querying a table for values equal to, less than, or greater than a value, the B-Tree index can quickly produce results, as this is the core basis of the underlying data sort in the index If you are in doubt about what indexing method to use, it is safe to use the default B-Tree method The rtree option uses an R-Tree (real tree) index, and is often used for data ranges (such as television programs that span a timeframe) or multidimensional data (such as spatial data or combinations of data) It uses a recursion tree-indexing method The layout of the R-Tree index is similar to the B-Tree, but the benefit of the R-Tree is that it can group data elements together within the individual nodes An example of an R-Tree index is shown in Figure 7-4 The spatial points are first grouped into two groups, R1 and R2 The points are then further grouped into smaller groups, R3, R4, R5, and R6 The R-Tree index is organized by the individual groups, and shows which spatial groups are subsets of other spatial groups
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