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File Organizations

File Organizations. Chapter 8. “How index-learning turns no student pale Yet holds the eel of science by the tail.” -- Alexander Pope (1688-1744). Files of Records. Page or block is OK when doing I/O, but higher levels of DBMS operate on records , and files of records .

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File Organizations

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  1. File Organizations Chapter 8 “How index-learning turns no student pale Yet holds the eel of science by the tail.” -- Alexander Pope (1688-1744)

  2. Files of Records • Page or block is OK when doing I/O, but higher levels of DBMS operate on records, and files of records. • FILE: A collection of pages, each containing a collection of records. Must support: • insert/delete/modify record • read a particular record (specified using record id) • scan all records (possibly with some conditions on the records to be retrieved)

  3. Unordered (Heap) Files • Simplest file structure contains records in no particular order. • 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.

  4. Heap File Implemented as a List • The header page id and Heap file name must be stored someplace. • Each page contains 2 `pointers’ plus data. Data Page Data Page Data Page Full Pages Header Page Data Page Data Page Data Page Pages with Free Space

  5. Data Page 1 Header Page Data Page 2 Data Page N DIRECTORY Heap File Using a Page Directory • The entry for a page can include the number of free bytes on the page. • The directory is a collection of pages; linked list implementation is just one alternative. • Much smaller than linked list of all HF pages!

  6. 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 some of the fields of r, called the search fields.

  7. Example: Stored Database index structure Index on B# Block#= B# mod 10 Index on Author Relation Book 1 11 51 20 30 0 W, … (1, C,W) (20, Y,W) (51, C, B) 1 (11, Y,W) (30, Z, B) Relation Checkout (101,…) (200,…) (500,…) 0 storage structure Index on since Relation Person Block#= ssn mod 10 1

  8. Cost Model for Our Analysis We 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. • Count read and writes as a single access each • Good enough to show the overall trends!

  9. Assumptions 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.

  10. Cost of Operations • Several assumptions underlie these (rough) estimates!

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