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InformIT: MySQL Query Optimization > Using Indexing

  • Match index types to the type of comparisons you perform.
    When you create an index, most storage engines choose the index implementation
    they Match index types to the type of comparisons you perform. When you
    create an index, most storage engines choose the index implementation they will
    use. For example, InnoDB always uses B-tree indexes. MySQL also uses B-tree indexes,
    except that it uses R-tree indexes for spatial data types. However, the MEMORY
    storage engine supports hash indexes and B-tree indexes, and allows you to select
    which one you want. To choose an index type, consider what kind of comparison
    operations you plan to perform on the indexed column:
  • If you use a MEMORY table only for exact-value lookups, a hash index is a
    good choice. This is the default index type for MEMORY tables, so you need
    do nothing special. If you need to perform range-based comparisons with a MEMORY
    table, you should use a B-tree index instead. To specify this type of index,
    add USING BTREE to your index definition. For example:
10 May 09

Index (search engine) - Wikipedia, the free encyclopedia

  • Major factors in designing a search engine's architecture include:
04 May 09

Inverted index - Wikipedia, the free encyclopedia

  • The inverted index data structure is a central component of a typical search engine indexing algorithm. A goal of a search engine implementation is to optimize the speed of the query: find the documents where word X occurs. Once a forward index is developed, which stores lists of words per document, it is next inverted to develop an inverted index. Querying the forward index would require sequential iteration through each document and to each word to verify a matching document. The time, memory, and processing resources to perform such a query are not always technically realistic. Instead of listing the words per document in the forward index, the inverted index data structure is developed which lists the documents per word.


    With the inverted index created, the query can now be resolved by jumping to the word id (via random access) in the inverted index. Random access is generally regarded as being faster than sequential access.


    In pre-computer times, concordances to important books were manually assembled. These were effectively inverted indexes with a small amount of accompanying commentary, that required a tremendous amount of effort to produce.

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