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File Organizations and Indices

File Organizations and Indices. Unordered (Heap) Files. Simplest file structure: contains records in no particular order; default in many systems. As file grows and shrinks, disk pages are allocated and de-allocated. To support record-level operations, we must:

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File Organizations and Indices

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  1. File Organizations and Indices

  2. Unordered (Heap) Files • Simplest file structure: contains records in no particular order; default in many systems. • As file grows and shrinks, disk pages are allocated and de-allocated. • To support record-level operations, we must: • keep track of the pages in a file • keep track of free space on pages • keep track of the records on a page • There are many alternatives for keeping track of this.

  3. Alternative File Organizations Many 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 = primary page plus zero or moreoverflow pages. • Hashing functionh: h(r) = bucket in which record r belongs. h looks at only the search fields of r. • Clustered Files:Tuples from > 1 relation stored together to speed up joins (later….)

  4. Index Motivation • A Heap file allows us to retrieve records: • by specifying the rid, or • by scanning all records sequentially • Sometimes, we want to retrieve records by specifying the values in one or more fields, e.g., • Find all students in the “CS” department • Find all students with a gpa > 3 • Indexes are structured files that enable us to answer such value-based queries efficiently.

  5. Index Definitions • 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(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 search key value k.

  6. Alternatives 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 to locate data entries with a given key value k. • Examples of indexing techniques: B+ trees, hash-based structures • Typically, index contains auxiliary information that directs searches to the desired data entries

  7. Index 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. • A relation 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!

  8. Clustered vs. Unclustered Index • Suppose 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 entries UNCLUSTERED CLUSTERED direct search for data entries Data entries Data entries (Index File) (Data file) Data Records Data Records

  9. Index Classification (Contd.) • Dense vs. Sparse: If there is at least one data entry per search key value (in some data record), then dense. • Every sparse index is clustered! • Sparse indexes are smaller; however, some useful optimizations are based on dense indexes. Ashby, 25, 3000 22 Basu, 33, 4003 25 Bristow, 30, 2007 30 Ashby 33 Cass, 50, 5004 Cass Smith Daniels, 22, 6003 40 Jones, 40, 6003 44 44 Smith, 44, 3000 50 Tracy, 44, 5004 Sparse Index Dense Index on on Data File Name Age

  10. Summary • Many alternative file organizations exist, each appropriate in some situation. • Index is a collection of data entries plus a way to quickly find entries with given search key values. • Can have several indexes on a given file of data records, each with a different search key. • Indexes can be classified as clustered vs. unclustered, primary vs. secondary, and dense vs. sparse. Differences have important consequences for utility/performance.

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