File Organizations and Indexing Lecture 4 R&G Chapter 8 "If you don't find it in the index, look very carefully through the entire catalogue." -- Sears, Roebuck, and Co., Consumer's Guide, 1897

Review: Memory, Disks, & Buffer Mgt Everything won’t fit in RAM (usually) Hierarchy of storage, RAM, disk, tape “Block” - unit of storage on disk “Frame” – a block-sized chunk of memory Allocate space on disk for fast access Buffer pool management –Frames in RAM to hold blocks –Policy to move blocks between RAM & disk

Context Query Optimization and Execution Relational Operators Files and Access Methods Buffer Management Disk Space Management DB

Files of Records Blocks interface for 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 –fetch a particular record (specified using record id) –scan all records (possibly with some conditions on the records to be retrieved) Note: typically page size = block size = frame size.

Record Formats: Fixed Length Information about field types same for all records in a file; stored in system catalogs. Finding i’th field done via arithmetic. Base address (B) L1L2 L3L4 F1F2 F3F4 Address = B+L1+L2

Record Formats: Variable Length Two alternative formats (# fields is fixed): * Second offers direct access to i’th field, efficient storage of nulls (special don’t know value); small directory overhead. $$ $$ Fields Delimited by Special Symbols F1 F2 F3 F4 Array of Field Offsets

Page Formats: Fixed Length Records *Record id =. In first alternative, moving records for free space management changes rid; may not be acceptable. Slot 1 Slot 2 Slot N... NM1 0 M PACKED UNPACKED, BITMAP Slot 1 Slot 2 Slot N Free Space Slot M 11 number of records number of slots

“Slotted Page” for Variable Length Records Record id = Can move records on page without changing rid; so, attractive for fixed-length records too. Page is full when data space and slot array meet. Page i Rid = (i,N) Rid = (i,2) Rid = (i,1) Pointer to start of free space SLOT DIRECTORY N N # slots Slot Array Data Q: What if a record grows too big to fit in the page???

System Catalogs For each relation: –name, file location, file structure (e.g., Heap file) –attribute name and type, for each attribute –index name, for each index –integrity constraints For each index: –structure (e.g., B+ tree) and search key fields For each view: –view name and definition Plus statistics, authorization, buffer pool size, etc. * Catalogs are themselves stored as relations !

Attr_Cat(attr_name, rel_name, type, position)

Alternative File Organizations Many alternatives exist, each good for some situations, and not so good in others: –Heap files: Suitable when typical access is a file scan retrieving all records. –Sorted Files: Best for retrieval in search key order, or only a `range’ of records is needed. –Clustered Files (with Indexes): A compromise between the above two extremes.

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. –We’ll consider 2

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

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! Data Page 1 Data Page 2 Data Page N Header Page DIRECTORY Q: How to find a particular record in a Heap file???

Cost Model for Analysis We ignore CPU costs, for simplicity: –B: The number of data blocks –R: Number of records per block –D: (Average) time to read or write disk block Measuring number of block I/O’s ignores gains of pre-fetching and sequential access; thus, even I/O cost is only loosely approximated. Average-case analysis; based on several simplistic assumptions. –Often called a “back of the envelope” calculation. * Good enough to show the overall trends!

Some Assumptions in the Analysis Single record insert and delete. Equality selection - exactly one match (what if more or less???). Heap Files: –Insert always appends to end of file. –Delete just leaves free space in the page. Sorted Files: –Files compacted after deletions. –Selections on search key.

Cost of Operations B: The number of data pages R: Number of records per page D: (Average) time to read or write disk page Heap FileSorted FileClustered File Scan all records Equality Search (unique key) Range Search Insert Delete BD 0.5 BD BD 2D (0.5B+1)D

Sorted Files Q: When do Heap files perform well? When don’t they? Heap files are lazy on update - you end up paying on searches. Sorted files eagerly maintain the file on update. –The opposite choice in the trade-off Let’s consider an extreme version –No gaps allowed, pages fully packed always –Q: How might you relax these assumptions?

Cost of Operations B: The number of data pages R: Number of records per page D: (Average) time to read or write disk page Heap FileSorted FileClustered File Scan all records Equality Search (unique key) Range Search Insert Delete BD (log 2 B) * D [(log 2 B) + #match pg]*D ((log 2 B)+B)D (because rd,w0.5 File) Same cost as Insert BD 0.5 BD BD 2D (0.5B+1)D

Indexes: avoiding the extremes Hash files are great for lots of updates and scans. Sorted files are great for lots of “rifle-shot” look ups on one particular search key. –Q: How do they do on look ups on other fields?? “goldilocks”Q: Is there a “goldilocks” solution???? Clustered files are “sort of sorted”. –Need additional structure to help find things. A Primary Index provides such structure.

Index Overview An Index is a collection of “data entries” plus a way to quickly find entries with given key values. Two main families of indexes: Hash and Tree –Hash-based indexes only good for equality search. –Tree-based indexes best for range search; also good for equality search. (Files rarely kept sorted in practice; B+ tree index is better.) Primary index is associated with file structure. –can have at most one per file Can have any number of additional Secondary Indexes. –These can speed up other “Access Paths”.

File Structure Summary File Layer manages access to records in pages. –Record and page formats depend on fixed vs. variable- length. –Free space management is an important issue. –Slotted page format supports variable length records and allows records to move on page. Many alternative file organizations exist, each appropriate in some situation. –We looked at Heap and Sorted so far. –If selection queries are frequent, sorting the file or building an index is important. Back of the envelope calculations are imprecise, but can expose fundamental systems tradeoffs. –A technique that you should become comfortable with! Next up: Indexes.