Select Operation- disk access and Indexing *Some info on slides from Dr. S. Son, U. Va
Jan 13, 2015
Select Operation- disk access
and Indexing
*Some info on slides from Dr. S. Son, U. Va
Disk access
• DBs traditionally stored on disk
• Cheaper to store on disk than in memory
• Costs for:– Seek time, latency, data transfer time
• Disk access is page (block) oriented
• 2 - 4 KB page size
Access time
• Access time is the time to randomly access a page
• System initially determines if page in memory buffer (page tables, etc.)
• Large disparity between disk access and memory access
Select operation using table scan
• If read the entire table for a select – table scan
• Improvements to table scan of disk:– Parallel access– Sequential prefetch
Parallel access
• Linear search - all data rows read in from disk – I/O parallelism can be used (Raid)
• 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 – average 8-10 ms, as low as 4 ms server
• Latency time – 2-4 ms, as low as 1 or less
• Data transfer time – .4-2 ms
Access time
RIO Seq. Prefetch .010 .010 Seek - disk arm to cylinder .002 .002 Latency - platter to sector .0015 .048 Data transfer - Page .0135 .060 1 page vs. 32 pages
.43* seconds vs .060 seconds 32 pages for both
* .00135X32=.43
Access time for fast I/O
RIO Seq. Prefetch .004 .004 Seek - disk arm to cylinder .001 .001 Latency - platter to sector .0005 .016 Data transfer - Page .0055 .021 1 page vs. 32 pages
.176* seconds .021 seconds 32 pages for both
* .0055X32=.176
Organizing disk space
• How to store data so minimize access time if read the entire table?
Disk allocation
• Disk Resource Allocation for Databases (DBA has control)
• Goal – contiguous sectors on disk - want data as close together as possible to minimize seek time
• No standard SQL approach, but general way to deal with allocation
• Some OS allow specification of size of file and disk device
Types of Files• Heap files (unordered – sequential)• Sorted files (ordered – sort key)• Hash files (hash key, hash function)
– Internal, external, file expansion– B+-trees
• Raid technology (parallelizing)• Storage area networks – ERP (enterprise resource
planning) and DW (data warehouses)– Storage devices configured as nodes in network – can
attach/detach
Tablespace
Tablespace is:• Allocation medium for tables and indexes for
ORACLE, DB2, etc.• Can put >1 table in a table space if accessed
together • Tablespace corresponds to 1 or more OS files
and can span disk devices• Usually relations cannot span disk devices
DB storage structures
DB CAP DatabaseTable- tspace 1
system
space
OS files fname1 fname2 fname3 Tables Cust agents prods orders orindx
Segments data data data data index
Extents
Tablespace
• ORACLE DB's contain several tablespaces, including one called system - data description + indexes + user-defined tables
• default tablespace given to each user • if multiple tablespaces - better control over load
balancing • can take some disk space off-line
Extent• Relation composed of 1 or more extents
• 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
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
Oracle create tablespace
• http://www.adp-gmbh.ch/ora/sql/create_tablespace.html
Create table
• Create table statement - can specify tablespace, no. of extents– When initial extent full, new extent allocated
– pctfree - determine how much space in a page can be used for inserts of new rows
• if pctfree =10%, inserts stop when page is 90% full» Uses another page
– pctused – determines when new inserts start again • if fall below certain percentage of total, default pctused = 40%
pctfree + pctused < 100
For more info: http://download-west.oracle.com/docs/cd/B19306_01/server.102/b14200/statements_7002.htm
Rows
• Row layout on each disk page
1 2 3… N Row N Row N-1 … Row 1Header info Row directory free space data rows
• Header - • Row directory – row number and page byte offset
– Row number is row number in page – book calls it slot#• Page byte offset – with varchar, row size not constant
• To identify a particular row use RID (RowID) – page #, slot # [file#]
slot# is number in row directory (logical #)
Differences in DBMSs re: rows
• ROWID can be retrieved in ORACLE but not DB2 (violates relational model rule)
• ORACLE • rows can be split between pages (row record
fragmentation) • Can have rows from multiple tables on same page,
more info
• DB2, no splitting, entire row moved to new page, need forwarding pointer
Select operation using Indexes
• Alternative to table scan
Binary Search
• “Find all students with gpa > 3.0”– If data is in sorted file, do binary search to find
first such student, then scan to find others.– Cost of binary search can be quite high.
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
Indexing
• Instead: Create an `index’ file• 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
Index File
Page 1 Page 2 Page NPage 3 Data File
k2 kNk1 Index File
Tree-based index
• B-tree – balanced tree
• Nodes point to data (RIDs) and also point to other nodes in tree
B+-tree
• Most commonly used index structure type in DBs today • Based on B-tree• Good for equality and range searches• B+ tree: dynamic, adjusts gracefully under inserts and
deletes.• 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)
Structure of B+ Trees
• leaf level pointers to data (RIDs)
• the remaining are directory (index) nodes that point to other index nodes Fig.
Index Entries
Data Entries("Sequence set")
(Direct search)
Characteristics of B+ Tree
• Insert/delete at log F N cost; keep tree height-balanced. (F = fanout, N = # leaf pages)
• Minimum 50% occupancy (except for root). Each node contains d <= m <= 2d entries. The parameter d is called the order of the tree.
• Supports equality and range-searches efficiently
Cost of I/O for B+-tree
• 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 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
B+ Trees in Practice
• Typical order: 100. • Typical fill-factor: 2/3 full (66.6%)
– average fanout = 133
• Typical capacities:– Height 4: 1334 = 312,900,700 records– Height 3: 1333 = 2,352,637 records
• Can often hold top levels in buffer pool:– Level 1 = 1 page = 8 Kbytes– Level 2 = 133 pages = 1 Mbyte– Level 3 = 17,689 pages = 133 MBytes
B+-tree
• B+ tree has a directory structure that allows retrieval of a range of values efficiently – search for leftmost index entry Si such that
X <= Si
• 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
• Insert/delete
Inserting into B+ Tree
• Find correct leaf L. • Put data entry onto L.
– If L has enough space, done!– Else, must split L (into L and a new node L2)
• Redistribute entries evenly, copy up middle key.• Insert index entry pointing to L2 into parent of L.
• This can happen recursively– To split index node, redistribute entries evenly, but
push up middle key. (Contrast with leaf splits.)• Splits “grow” tree; root split increases height.
– Tree growth: gets wider or one level taller at top.– Algorithm
Deleting from B+ tree
• Start at root, find leaf L where entry belongs.• Remove the entry.
– If L is at least half-full, done! – If L has only d-1 entries,
• Try to re-distribute, borrowing from sibling (adjacent node with same parent as L).
• If re-distribution fails, merge L and sibling.
• If merge occurred, must delete entry (pointing to L or sibling) from parent of L.
• Merge could propagate to root, decreasing height.• Algorithm
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
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Why use an index?
• If use a select (or join) on the same attribute frequently
• want a way to improve performance - use indexes– For example:
Select from Employee
where ssn = 333445555
• Instead of reading the entire file until ssn is found, it would be nice if we had a pointer to that employee
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Types of indexes (textbook)
• Primary index - key field is a candidate key (must be unique) – data file ordered by key field
• Clustering index - key field is not unique, data file is ordered – all records with same values on same pages
• Secondary index - non-clustering index – data file not ordered– First record in the data page (or block) is called the
anchor record• Non-dense index - pointer in index entry points to anchor• Dense index - pointer to every record in the file
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Non-clustered indexes
• Non-clustered index (secondary index)– key field is a non ordering field - it is not used
to physically order the data file– the index itself is still ordered– How many non-clustering indexes can a table
have?
Clustered Indexes
• Placing rows on disk in order by some common index key value (remember the index itself is always sorted)
– Clustered index - (primary and clustering)– key field is an ordering field - all the data with the
same values for the key field physically placed on the same pages on the disk.
– If primary key, data ordered on a page by key field– Usually assume disk pages themselves also clustered
on the disk – How many clustering indexes can a table have?
Clustering
• 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
Example
• http://www.dba-oracle.com/oracle_tip_hash_index_cluster_table.htm
Clustering
• Can only cluster table by 1 clustering index at a time • In SQL server
– creates clustered index on PK automatically if no other clustered index on table and PK nonclustered index not specified
• In DB2 – – if the table is empty, rows sorted as placed on disk – subsequent insertions not clustered, must use REORG
• In Oracle-– Cluster index – now available for PK in 10g– Define a cluster to create cluster index for 2 tables
Indexes vs. table scan
• To illustrate the difference between table scan, secondary index (non clustered) and clustered index Assume 10 M customers, 200 cities2KB/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
Rules of Thumb for I/O
• Random I/O – 160 pages/second, .00625
• Sequential prefetch I/O – 1600 pages/second, .000625
Will discuss later:
• List prefetch I/O – 400 pages/second, .0025
Table Scan
Table Scan - read entire table
If used an random I/O 10,000,000/20 = 500,000 pages
500,000*R = 3125
Instead, it makes more sense to use:sequential prefetch read 32 pages at a time
500,000*S = 312
Clustering Index
Clustering Index –
All entries for B'ham clustered on same pages 50,000/20 = 2500 pages (with 20 rows per page) Assume: 3 upper nodes of the tree Assume: 1000 index entries per leaf node, read
50000/1000 index pages
(3 + 50 + 2500) * ? = 2,553 * ?
If assume ?=R, then 2,553*R=16
Makes more sense to assume (3+50+2500) * S = 1.6
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
(3 + 50 + 50,000)*? = 50, 053 * ?
If assume ?=R then 50,053*R=312.8Better to assume (3+50)*S + 50,000*R=312.53
List Prefetch
Create list of data pages to access
Pages not necessarily in contiguous sequential order
system orders pages to minimize disk I/O
E.g. elevator algorithm for disk request scheduling
50, 053 * L = 125.1
Best to assume (3+50)*S+50,000*L=125.03
% Free
• Redo the previous calculations assuming relations created with 50% free option specified.
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
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?
Threads• Thread results from a fork of a computer program, usually contained
inside a process– Multiple threads inside same process, share resources, address
space and memory– Processes do not share these resources– Thread have their own stack, copy of registers, PC and local
thread storage• Some languages support multiple threads, but do not execute at the
same time– Kernel threads can run concurrently
Parallel computing• Form of computation in which many calculations carried out
simultaneously– Divide large problem into smaller ones– data, instruction level and task parallelism– SISD, SIMD, MISD, MIMD
• Dominant paradigm in the form of multicore processors• Parallel computer – shared or distributed memory• Parallel program difficult to write due to
– Software bugs, race conditions– Communication and synchronization
• Multiple processing elements working concurrently– Single computer with multiple processors, networked computers,
special hardware, etc.
• Multithreading– Model to allow multiple threads within single process– Can execute in parallel on multiprocessor system
• Process, kernel thread, user thread, fiber (cooperatively scheduled, can run in any thread in the same process)
• Subtasks in a parallel program are called threads– Lightweight version of threads – fibers– Bigger versions – processes
• Parallel computing – model of computation– Can utilize processes, multithreading to
implement