Query evaluation and optimization
- 1. QUERY EVALUATION AND OPTIMIZATION Presented by, R.RAMADEVI MSc(CS&IT) Nadar Saraswathi college of arts & science vadapudupatti ,Theni.
- 2. QUERY PROCESSING • Query processing refers to the range of activities involved in extracting the data from a database • These activities include translation of queries expressed in high-level database languages into expressions that can be implemented at the physical level of the file system, a variety of query-optimizing transformations and actual evaluation of queries.
- 3. Selection operation • The file scan is the lowest level operator to access data . • File scans are search algorithm that locate and retrieve records that fulfill a selection condition. • Algorithm A1 (linear search). Scan each file block and test all records to see whether they satisfy the selection condition. – Cost estimate = br block transfers + 1 seek • br denotes number of blocks containing records from relation r – If selection is on a key attribute, can stop on finding record • cost = (br /2) block transfers + 1 seek – Linear search can be applied regardless of • selection condition or • ordering of records in the file, or • availability of indices
- 4. Selections Using Indices • Index scan – search algorithms that use an index – selection condition must be on search-key of index. • A2 (primary index, equality on key). Retrieve a single record that satisfies the corresponding equality condition – Cost = (hi + 1) * (tT + tS) • A3 (primary index, equality on nonkey) Retrieve multiple records. – Records will be on consecutive blocks • Let b = number of blocks containing matching records – Cost = hi * (tT + tS) + tS + tT * b
- 5. Selections Using Indices • A4 (secondary index, equality on nonkey). – Retrieve a single record if the search-key is a candidate key • Cost = (hi + 1) * (tT + tS) – Retrieve multiple records if search-key is not a candidate key • each of n matching records may be on a different block • Cost = (hi + n) * (tT + tS) – Can be very expensive!
- 6. Selections Involving Comparisons • Can implement selections of the form AV (r) or A V(r) by using – a linear file scan, – or by using indices in the following ways: • A5 (primary index, comparison). (Relation is sorted on A) • For A V(r) use index to find first tuple v and scan relation sequentially from there • For AV (r) just scan relation sequentially till first tuple > v; do not use index • A6 (secondary index, comparison). • In either case, retrieve records that are pointed to – requires an I/O for each record – Linear file scan may be cheaper
- 7. Implementation of Complex Selections • Conjunction: • A7 (conjunctive selection using one index). – Select a combination of i and algorithms A1 through A7 that results in the least cost for i (r). – Test other conditions on tuple after fetching it into memory buffer. • A8 (conjunctive selection using composite index). – Use appropriate composite (multiple-key) index if available. • A9 (conjunctive selection by intersection of identifiers). – Requires indices with record pointers. – Use corresponding index for each condition, and take intersection of all the obtained sets of record pointers.
- 8. Sorting • We may build an index on the relation, and then use the index to read the relation in sorted order. May lead to one disk block access for each tuple . • For relations that fit in memory, techniques like quick sort can be used. For relations that don’t fit in memory, external sort-merge is a good choice.
- 9. Join Operation • Several different algorithms to implement joins – Nested-loop join – Block nested-loop join – Indexed nested-loop join – Merge-join – Hash-join • Choice based on cost estimate • Examples use the following information – Number of records of student: 5,000 takes: 10,000 – Number of blocks of student: 100 takes: 400
- 10. Evaluation of Expressions • Alternatives for evaluating an entire expression tree – Materialization: generate results of an expression whose inputs are relations or are already computed, materialize (store) it on disk. Repeat. – Pipelining: pass on tuples to parent operations even as an operation is being executed
- 11. Query Optimization • Alternative ways of evaluating a given query – Equivalent expressions – Different algorithms for each operation
- 12. An evaluation plan defines exactly what algorithm is used for each operation, and how the execution of the operations is coordinated.
- 13. THANK YOU