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Murali Mani Overview of Storage and Indexing (based on slides from Wisconsin)

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1 Murali Mani Overview of Storage and Indexing (based on slides from Wisconsin)

2 Murali Mani Data on External Storage Disks: Can retrieve random page at fixed cost But reading several consecutive pages is much cheaper than reading them in random order Tapes: Can only read pages in sequence Cheaper than disks; used for archival storage File organization: Method of arranging a file of records on external storage. Record id (rid) is sufficient to physically locate record Indexes are data structures that allow us to find the record ids of records with given values in index search key fields Architecture: Buffer manager stages pages from external storage to main memory buffer pool. File and index layers make calls to the buffer manager.

3 Murali Mani Why Not Store Everything in Main Memory? Cost. Disks always cheaper than memory. Main memory is volatile. We want data to be saved between runs !! Typical storage hierarchy: Main memory (RAM) for currently used data. Disk for the main database (secondary storage). Tapes for archiving older versions of the data (tertiary storage).

4 Murali Mani Disks Secondary storage device of choice. Main advantage over tapes: random access vs. sequential. Data is stored and retrieved in units called disk blocks or pages. Unlike RAM, time to retrieve a disk page varies depending upon location on disk. Therefore, relative placement of pages on disk has major impact on DBMS performance!

5 Murali Mani Components of a Disk  The platters spin (say, 90rps).  The arm assembly is moved in or out to position a head on a desired track. Tracks under heads make a cylinder (imaginary!).  Only one head reads/writes at any one time. Platters Spindle Disk head Arm movement Arm assembly Tracks Sector  Block size is a multiple of sector size (which is fixed).

6 Murali Mani Accessing a Disk Page Time to access (read/write) a disk block: seek time ( moving arms to position disk head on track ) rotational delay ( waiting for block to rotate under head ) transfer time ( actually moving data to/from disk surface ) Seek time and rotational delay dominate. Seek time varies from about 1 to 20msec Rotational delay varies from 0 to 10msec Transfer rate is about 1msec per 4KB page Key to lower I/O cost: reduce seek/rotation delays! Hardware vs. software solutions?

7 Murali Mani Arranging Pages on Disk `Next’ block concept: blocks on same track, followed by blocks on same cylinder, followed by blocks on adjacent cylinder Blocks in a file should be arranged sequentially on disk (by `next’), to minimize seek and rotational delay. For a sequential scan, pre-fetching several pages at a time is a big win!

8 Murali Mani RAID Disk Array: Arrangement of several disks that gives abstraction of a single, large disk. Goals: Increase performance and reliability. Two main techniques: Data striping: Data is partitioned; size of a partition is called the striping unit. Partitions are distributed over several disks. Redundancy: More disks => can handle more failures. Redundant information allows reconstruction of data if a disk fails.

9 Murali Mani Disk Space Management Lowest layer of DBMS software manages space on disk. Higher levels call upon this layer to: allocate/de-allocate a page read/write a page Request for a sequence of pages must be satisfied by allocating the pages sequentially on disk! Higher levels don’t need to know how this is done, or how free space is managed.

10 Murali Mani Buffer Management in a DBMS Data must be in RAM for DBMS to operate on it! Table of pairs is maintained. DB MAIN MEMORY DISK disk page free frame Page Requests from Higher Levels BUFFER POOL choice of frame dictated by replacement policy

11 Murali Mani DBMS vs. OS File System OS does disk space & buffer mgmt: why not let OS manage these tasks? Differences in OS support: portability issues Some limitations, e.g., files can’t span disks.

12 Murali Mani Record Formats: Fixed Length Information about field types same for all records in a file; stored in system catalogs. Finding i’th field does not require scan of record. Base address (B) L1L2L3L4 F1F2F3F4 Address = B+L1+L2

13 Murali Mani 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. 4$$$$ Field Count Fields Delimited by Special Symbols F1 F2 F3 F4 Array of Field Offsets

14 Murali Mani 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... N M1 0 M... 3 2 1 PACKED UNPACKED, BITMAP Slot 1 Slot 2 Slot N Free Space Slot M 11 number of records number of slots

15 Murali Mani Page Formats: Variable Length Records * Can move records on page without changing rid; so, attractive for fixed-length records too. Page i Rid = (i,N) Rid = (i,2) Rid = (i,1) Pointer to start of free space SLOT DIRECTORY N... 2 1 201624 N # slots

16 Murali Mani 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)

17 Murali Mani Alternative File Organizations Many alternatives exist, each ideal for some situations, and not so good in others: Heap (random order) 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. Indexes: Data structures to organize records via trees or hashing. Like sorted files, they speed up searches for a subset of records, based on values in certain (“search key”) fields Updates are much faster than in sorted files.

18 Murali Mani Indexes 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.

19 Murali Mani B+ Tree Indexes  Leaf pages contain data entries, and are chained (prev & next)  Non-leaf pages have index entries; only used to direct searches: P 0 K 1 P 1 K 2 P 2 K m P m index entry Non-leaf Pages (Sorted by search key) Leaf

20 Murali Mani Example B+ Tree Find 28*? 29*? All > 15* and < 30* Insert/delete: Find data entry in leaf, then change it. Need to adjust parent sometimes. And change sometimes bubbles up the tree 2*3* Root 17 30 14*16* 33*34* 38* 39* 135 7*5*8*22*24* 27 27*29* Entries <= 17Entries > 17 Note how data entries in leaf level are sorted

21 Murali Mani Hash-Based Indexes Good for equality selections. Index is a collection of buckets. Bucket = primary page plus zero or more overflow pages. Buckets contain data entries. Hashing function h: h(r) = bucket in which (data entry for) record r belongs. h looks at the search key fields of r. No need for “index entries” in this scheme.

22 Murali Mani Choice of Indexes What indexes should we create? Which relations should have indexes? What field(s) should be the search key? Should we build several indexes? For each index, what kind of an index should it be? Hash/tree?

23 Murali Mani Choice of Indexes (Contd.) One approach: Consider the most important queries in turn. Consider the best plan using the current indexes, and see if a better plan is possible with an additional index. If so, create it. Obviously, this implies that we must understand how a DBMS evaluates queries and creates query evaluation plans! For now, we discuss simple 1-table queries. Before creating an index, must also consider the impact on updates in the workload! Trade-off: Indexes can make queries go faster, updates slower. Require disk space, too.

24 Murali Mani Index Selection Guidelines Attributes in WHERE clause are candidates for index keys. Exact match condition suggests hash index. Range query suggests tree index. Multi-attribute search keys should be considered when a WHERE clause contains several conditions. Order of attributes is important for range queries. Such indexes can sometimes enable index-only strategies for important queries. Try to choose indexes that benefit as many queries as possible. Since only one index can be clustered per relation, choose it based on important queries that would benefit the most from clustering.

25 Murali Mani Examples of Indexes B+ tree index on E.age can be used to get qualifying tuples. How selective is the condition? Consider the GROUP BY query. If many tuples have E.age > 10, using E.age index and sorting the retrieved tuples may be costly. Equality queries and duplicates: SELECT E.dno FROM Emp E WHERE E.age>40 SELECT E.dno, COUNT (*) FROM Emp E WHERE E.age>10 GROUP BY E.dno SELECT E.dno FROM Emp E WHERE E.hobby=Stamps

26 Murali Mani Indexes with Composite Search Keys Composite Search Keys: Search on a combination of fields. Equality query: Every field value is equal to a constant value. E.g. wrt 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. sue1375 bob cal joe12 10 20 8011 12 nameagesal 12,20 12,10 11,80 13,75 20,12 10,12 75,13 80,11 11 12 13 10 20 75 80 Data records sorted by name Data entries in index sorted by Data entries sorted by Examples of composite key indexes using lexicographic order.

27 Murali Mani Composite Search Keys To retrieve Emp records with age=30 AND sal=4000, an index on would be better than an index on age or an index on sal. Choice of index key orthogonal to clustering etc. If condition is: 20<age<30 AND 3000<sal<5000: Clustered tree index on or is best. If condition is: age=30 AND 3000<sal<5000: Clustered index much better than index! Composite indexes are larger, updated more often.

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