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No index The optimizer must use a full table scan since no index exists. Small number of rows The cost of full table scan is less than index range scan due to small table. When query processed SELECT COUNT(*), nulls existed in the column The query is counting the number of null columns in a typical index. However, SELECT COUNT(*) can't count ...
To reduce such index size, some systems allow including non-key fields in the index. Non-key fields are not themselves part of the index ordering but only included at the leaf level, allowing for a covering index with less overall index size. This can be done in SQL with CREATE INDEX my_index ON my_table (id) INCLUDE (name);. [8] [9]
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The use of efficient indexes on both primary and foreign keys can dramatically improve query performance. This is because B-tree indexes result in query times proportional to log(n) where n is the number of rows in a table and hash indexes result in constant time queries (no size dependency as long as the relevant part of the index fits into ...
For example, PostgreSQL versions 8.1 and later implement a "bitmap index scan" optimization to speed up arbitrarily complex logical operations between available indexes on a single table. For tables with many columns, the total number of distinct indexes to satisfy all possible queries (with equality filtering conditions on either of the fields ...
The same may not be true of B-tree: B-tree requires a tree node for every approximately N rows in the table, where N is the capacity of a single node, thus the index size is large. As BRIN only requires a tuple for each block (of many rows), the index becomes sufficiently small to make the difference between disk and memory.
The performance of a query plan is determined largely by the order in which the tables are joined. For example, when joining 3 tables A, B, C of size 10 rows, 10,000 rows, and 1,000,000 rows, respectively, a query plan that joins B and C first can take several orders-of-magnitude more time to execute than one that joins A and C first.
Creating complex queries using query-by-example (QBE) Searching globally in a database or a subset of it; Transforming stored data into any format using a set of predefined functions, like displaying BLOB-data as image or download-link; Live charts to monitor MySQL server activity like connections, processes, CPU/memory usage, etc.