Sphinx can complement a MySQL-based application in many ways, bolstering performance where MySQL is not a good solution and adding functionality MySQL can’t provide. Typical usage scenarios include:
• Fast, efficient, scalable, relevant full-text searches
• Optimizing WHERE conditions on low-selectivity indexes or columns without indexes
• Optimizing ORDER BY ... LIMIT N queries and GROUP BY queries
• Generating result sets in parallel
• Scaling up and scaling out
• Aggregating partitioned data
We explore each of these scenarios in the following sections. This list is not exhaustive, though, and Sphinx users find new applications regularly. For example, one of Sphinx’s most important uses—scanning and filtering records quickly—was a user innovation, not one of Sphinx’s original design goals.
Source of Information : OReIlly High Performance MySQL Second Edition
• Fast, efficient, scalable, relevant full-text searches
• Optimizing WHERE conditions on low-selectivity indexes or columns without indexes
• Optimizing ORDER BY ... LIMIT N queries and GROUP BY queries
• Generating result sets in parallel
• Scaling up and scaling out
• Aggregating partitioned data
We explore each of these scenarios in the following sections. This list is not exhaustive, though, and Sphinx users find new applications regularly. For example, one of Sphinx’s most important uses—scanning and filtering records quickly—was a user innovation, not one of Sphinx’s original design goals.
Source of Information : OReIlly High Performance MySQL Second Edition
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