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Database Management Systems, R. Ramakrishnan and J. Gehrke 1File Organizations and IndexingDatabase Management Systems, R. Ramakrishnan and J. Gehrke 2Alternative File OrganizationsMany alternatives exist, each ideal for some situation , and not so good in others:– Heap files: Suitable when typical access is a file scan retrieving all records.– Sorted Files: Best if records must be retrieved in some order, or only a `range’ of records is needed.– Hashed Files: Good for equality selections. File is a collection of buckets. Bucket = primarypage plus zero or more overflow pages. Hashing function h: h(r) = bucket in which record r belongs. h looks at only some of the fields of r, called the search fields.Database Management Systems, R. Ramakrishnan and J. Gehrke 3Desired Operations Scan records Equality search Range search Insert record Delete recordDatabase Management Systems, R. Ramakrishnan and J. Gehrke 4Cost Model for Our AnalysisWe ignore CPU costs, for simplicity:– B: The number of data pages– R: Number of records per page– D: (Average) time to read or write disk page– Measuring number of page I/O’s ignores gains of pre-fetching blocks of pages; thus, even I/O cost is only approximated. – Average-case analysis; based on several simplistic assumptions. Good enough to show the overall trends!Database Management Systems, R. Ramakrishnan and J. Gehrke 5Assumptions in Our Analysis Single record insert and delete. Heap Files:– Equality selection on key; exactly one match.– Insert always at end of file. Sorted Files:– Files compacted after deletions.– Selections on sort field(s). Hashed Files:– No overflow buckets, 80% page occupancy. Database Management Systems, R. Ramakrishnan and J. Gehrke 6Cost of Operations HeapFileSorted FileHashedFileScan all recsBD BD 1.25 BDEquality Search0.5 BD D log2B DRange SearchBD D (log2B + # ofpages withmatches)1.25 BDInsert2D Search + BD 2DDeleteSearch + D Search + BD 2D Several assumptions underlie these (rough) estimates!Database Management Systems, R. Ramakrishnan and J. Gehrke 7Indexes An index on a file speeds up selections on the search key fields for the index.– Any subset of the fields of a relation can be the search key for an index on the relation.– Search key is not the same as key (minimal set of fields that uniquely identify a record in a relation). An index contains a collection of data entries, and supports efficient retrieval of all data entries k* with a given key value k.Database Management Systems, R. Ramakrishnan and J. Gehrke 8Alternatives for Data Entry k* in Index Three alternatives: Data record with key value k <k, rid of data record with search key value k> <k, list of rids of data records with search key k> Choice of alternative for data entries is orthogonal to the indexing technique used– Examples of indexing techniques: B+ trees, hash-based structures– Typically, index contains auxiliary information that directs searches to the desired data entriesDatabase Management Systems, R. Ramakrishnan and J. Gehrke 9Alternatives for Data Entries (Contd.) Alternative 1:– If this is used, index structure is a file organization for data records (like Heap files or sorted files).– At most one index on a given collection of data records can use Alternative 1. (Otherwise, data records duplicated, leading to redundant storage and potential inconsistency.)– If data records very large, # of pages containing data entries is high. Implies size of auxiliary information in the index is also large, typically. Database Management Systems, R. Ramakrishnan and J. Gehrke 10Alternatives for Data Entries (Contd.) Alternatives 2 and 3:– Data entries typically much smaller than data records. So, better than Alternative 1 with large data records– If more than one index is required on a given file, at most one index can use Alternative 1; rest must use Alternatives 2 or 3.– Alternative 3 more compact than Alternative 2, but leads to variable sized data entries even if search keys are of fixed length.Database Management Systems, R. Ramakrishnan and J. Gehrke 11Index Classification Primary vs. secondary: If search key contains primary key, then called primary index.– Unique index: Search key contains a candidate key. Clustered vs. unclustered: If order of data records is the same as, or `close to’, order of data entries, then called clustered index.– Alternative 1 implies clustered, but not vice-versa.– A file can be clustered on at most one search key.– Cost of retrieving data records through index varies greatly based on whether index is clustered or not!Database Management Systems, R. Ramakrishnan and J. Gehrke 12Clustered vs. Unclustered Index Suppose that Alternative (2) is used for data entries, and that the data records are stored in a Heap file.– To build clustered index, first sort the Heap file (with some free space on each page for future inserts). – Overflow pages may be needed for inserts. (Thus, order of data recs is `close to’, but not identical to, the sort order.)Index entriesData entriesdirect search for (Index File)(Data file)Data Recordsdata entriesData entriesData RecordsCLUSTEREDUNCLUSTEREDDatabase Management Systems, R. Ramakrishnan and J. Gehrke 13Index Classification (Contd.) Dense vs. Sparse: If there is at least one data entry per search key value (in some data record), then dense.– Alternative 1 always leads to dense index.– Every sparse index is clustered!– Sparse indexes are smaller; however, some useful optimizations are based on dense indexes.Ashby, 25, 3000Smith, 44, 3000AshbyCassSmith22253040444450Sparse IndexonNameData FileDense IndexonAge33Bristow, 30, 2007Basu, 33, 4003Cass, 50, 5004Tracy, 44, 5004Daniels, 22, 6003Jones, 40, 6003Database Management Systems, R. Ramakrishnan and J. Gehrke 14Index Classification (Contd.) Composite Search Keys: Search on a combination of fields.– Equality query: Every field value is equal to a constant value. E.g. wrt <sal,age> index: age=20 and sal =75– Range query: Some field value is not a constant. E.g.: age =20; or age=20 and sal > 10 Data entries in index sorted by search key to support range queries.– Lexicographic order, or– Spatial order.sue 13 75bobcaljoe 121020801112name age sal<sal, age><age, sal> <age><sal>12,2012,1011,8013,7520,1210,1275,1380,111112121310207580Data recordssorted by nameData


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