Select Operation Strategies And Indexing (Chapter 8)

Disk access DBs traditionally stored on disk Cheaper to store on disk than in memory Seek time, latency, data transfer time disk access is page oriented KB page size

Access time A to randomly access a page – ms – I/O's per secondaccess a page large disparity between disk access and memory access ( ns) hash disk page address and look in lookaside table to see if page in memory buffer In memory DBs the future?

Table scan Linear search - all data rows read in – I/O parallelism can be used multiple I/O read requests satisfied at the same time stripe the data across different disks –Problems with parallelism? must balance disk arm load to gain maximum parallelism requires the same total number of random I/O's, but using devices for a shorter time

Sequential prefetch I/O retrieve one disk page after another (on same track) - typically 32 seek time no longer a problem must know in advance to read 32 successive pages speed up of I/O by a factor of 10 (500 I/O's per second vs. 70)

Access time Seek time – 10-15ms Latency time – 2-5 ms Data transfer time – ns

Access time for fast I/O RIO Seq. Prefetch Seek - disk arm to cylinder Latency - platter to sector Data transfer - Page page vs. 32 pages.43 seconds.060 seconds for 32 pages for both

Textbook access time RIO Seq. Prefetch Seek - disk arm to cylinder Latency - platter to sector Data transfer - Page page vs. 32 pages.40 seconds.028 seconds for 32 pages for both

Disk allocation Disk Resource Allocation for Databases (control DBA has) No standard SQL approach, but general way to deal with allocation Some OS allow specification of size of file and disk device contiguous sectors on disk - want close together as possible to minimize seek time

Tablespace Allocation medium for tables and indexes for ORACLE, DB2 usually files (relations) cannot span disk devices can put >1 table in table space if accessed together corresponds to 1 or more OS files and can span disk devices

Query Language ORACLE DB's contain several tablespaces, including one called system - data description + indexes + user-defined tables Create tablespace tspace1 datafile 'fname1', 'fname2'; default tablespace given to each user if multiple tablespaces - better control over load balancing can take some disk space off-line

Extent extent - contiguous storage on disk when data segment or index segment first created, given an initial extent from tablespace 10KB (5 pages) if need more space given next contiguous extent can increase the size by a positive % (cannot decrease) initial n - size of initial extent next n - size of next max extents - maximum number of extents min extents - number of extents initially allocated pct increase n - % by which next extent grows over previous one

Create table Create table statement - can specify tablespace, no. of extents can override parameters for extent allocation pctfree - determine how much space can be used for inserts of new rows if 10%, inserts stop when page is 90% full pctused - where new inserts start again if fall below certain percentage of total, default = 40% pctfree + pctused < 100

Rows Row layout on each disk page (see figure) Row directory - page byte offset can have rows from multiple tables on same page, more info in index, point to or RID – page #, slot # RID can be retrieved in ORACLE but not DB2 (violates relational model rule) – in ORACLE, rows can be slit between pages (row record fragmentation) – in DB2, entire row moved to new page, need forwarding pointer

Binary Search Binary search on disk – optimal for comparisons - not optimal for disk-based look-up – must keep data in order – may be reading values from same page at different times Instead use B+-tree index

Indexing Keyed access retrieval method index is a sorted file - sorted by index key index entries: index key pointer (RID) pointer is RID index resides on disk, partially memory resident when accessed

B+-tree Most commonly used index structure type in DBs today Based on B-tree Used to minimize disk I/O available in DB2, ORACLE also has hash cluster, Ingres has heap structure, B-tree, isam (chain together new nodes) Example Example

B+-tree leaf level pointers to data (RID) the remaining are directory (index) nodes that point to other index nodes assume number of entries in each index node fits on one page - one node is one page if tree with depth of 3, 3 I/Os to get pointer to data

B+-tree B+-tree structured to get most out of every disk page read Read in index node, can make multiple probes to same page if remains in memory likely since frequent access to upper -level nodes of actively used B+-trees search for leftmost index entry S i such that X <= S i

B+-tree Index has a directory structure that allows retrieval of a range of values efficiently Index entries always placed in sequence by value - can use sequential prefetch on index Index entries shorter than data rows and require proportionately less I/O

B+-tree Balancing of B+-trees - insert, delete nodes usually not full utilities to reorganize to lower disk I/O most systems allow nodes to become depopulated- no automatic algorithm to balance average node below root level 71% full in active growing B+-trees

Duplicate key values Duplicate key values in index leaf nodes have sibling pointers but a delete of a row that has a heavily duplicated key entails a long search through the leaf-level of the B+-tree Index compression - with multiple duplicates | header info | PrX keyval RID RID... RID | PrX keyval RID…RID| where PrX is count of RID values

Create Index Options: multiple columns tablespace storage - initial extents, etc. percent free default = 10 % of each page left unfilled free page (1 free page for every n index pages) Can control % of B+-tree node pages left unfilled when index created, refers to initial creation

Clustering Placing rows on disk in order by some common index key value (remember the index itself is always sorted) clustered (clustering) index - index with rows in the same order as the key values efficiency advantage read in a page, get all of the rows with the same value clustering is useful for range queries e.g. between keyval1 and keyval2

Clustering can only cluster table by 1 clustering index at a time In DB2 – –if the table is empty, rows sorted as placed on disk –subsequent insertions not clustered, must use REORG

Indexes vs. table scan To illustrate the difference between table scan, secondary index (non clustered) and clustered index Assume 10 M customers, 200 cities 2KB/page, row = 100 bytes, 20 rows/page Select * From Customers Where city = Birmingham 1/200 * 10M if assume selectivity = 1/200 50,000 customers in a city

Tables Scan Table Scan - read entire table 10,000,000/20 = 500,000 pages If use prefetch? /32 *.? =

Secondary Index Secondary Index– In the worst case 1 entry for B'ham per page 50,000 pages (10M/200) 3 upper nodes of the tree Assume 1000 index entries per leaf node, read 50000/1000 index pages ( ,000)*?=

Clustering Index Clustering Index – All entries for B'ham clustered on same pages 50,000/20 = 2500 pages (with 20 rows per page) ( )*?=

% Free Redo the previous calculations assuming relations created with 50% free option specified.

Multiple Indexes More than one index on a relation –e.g. class - one index, gender - one index

Composite Index One index based on more than one attribute Create Index index_name on Table (col1, col2,... coln) Composite index entry - values for each attribute class, gender entry in index is: C1, C2, RID What would B+ tree look like?

Creating Indexes When determining what indexes to create consider: workload - mix of queries and frequencies of requests 20% of requests are updates, etc. can create lots of indexes but: cost to create insertions initial load time high if a large table index entries can become longer and longer as multiple columns included