AOBD07/08H. Galhardas Index Tuning. AOBD2007/08 H. Galhardas Index An index is a data structure that...

AOBD07/08 H. Galhardas

Index Tuning


Index

An index is a data structure that supports efficient access to data

Set ofRecords

indexCondition

onattribute

value

Matchingrecords

(search key)


Performance Issues

Type of Query Index Data Structure Organization of data on disk Index Overhead Data Distribution Covering


Types of Queries

1. Point Query

SELECT balanceFROM accountsWHERE number = 1023;

2. Multipoint Query

SELECT balanceFROM accountsWHERE branchnum = 100;

3. Range Query

SELECT numberFROM accountsWHERE balance > 10000;

4. Prefix Match Query

SELECT *FROM employeesWHERE name = ‘Jensen’

and firstname = ‘Carl’

and age < 30;


Types of Queries

5. Extremal Query

SELECT *FROM accountsWHERE balance = max(select balance from accounts)

5. Ordering Query

SELECT *FROM accountsORDER BY balance;

7. Grouping Query

SELECT branchnum, avg(balance)FROM accountsGROUP BY branchnum;

8. Join Query

SELECT distinct branch.adresseFROM accounts, branchWHERE accounts.branchnum = branch.numberand accounts.balance > 10000;


Search Keys

A (search) key is a sequence of attributes.create index i1 on accounts(branchnum, balance);

Types of keys Sequential: the value of the key is monotonic with

the insertion order (e.g., counter or timestamp) Non sequential: the value of the key is unrelated

to the insertion order (e.g., social security number)


Data Structures

Most index data structures can be viewed as trees. In general, the root of this tree will always be in main

memory, while the leaves will be located on disk. The performance of a data structure depends on the

number of nodes in the average path from the root to the leaf.

Data structure with high fan-out (maximum number of children of an internal node) are thus preferred.


B+-Tree

A B+-Tree is a balanced tree whose leaves contain a sequence of key-pointer pairs.

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75 83 107

96 98 103 107 110 12083 92 9575 80 8133 48 69


B+-Tree Performance

Key length influences fanout Choose small key when creating an index Key compression

Prefix compression (Oracle 8, MySQL): only store that part of the key that is needed to distinguish it from its neighbors: Smi, Smo, Smy for Smith, Smoot, Smythe.

Front compression (Oracle 5): adjacent keys have their front portion factored out: Smi, (2)o, (2)y. There are problems with this approach: Processor overhead for maintenance Locking Smoot requires locking Smith too.


Hash Index

A hash index stores key-value pairs based on a pseudo-randomizing function called a hash function.

Hashed key values

01

n

R1 R5

R3 R6 R9 R14 R17 R21 R25Hash

function

key

2341

The length ofthese chains impacts performance


Hash Index Performance

The best for answering point queries, provided there are no overflow chains

Good for multipoint queries Useless for range, prefix or extremal queries

Must be reorganized (drop/add or use reorganize function) if there is a significant amount of overflow chaining Avoiding overflow may require underutilize the hash space

Size of hash structure is not related to the size of a key, because hash functions return keys to locations or page identifiers Hash functions take longer to execute on a long key


Clustered / Non clustered index Clustered index

(primary index) A clustered index on

attribute X co-locates records whose X values are near to one another.

Non-clustered index (secondary index) A non clustered index

does not constrain table organization.

There might be several non-clustered indexes per table.

Records Records


Dense / Sparse Index

Sparse index Pointers are associated to

pages

Dense index Pointers are associated

to records Non clustered indexes

are dense

P1 PiP2 record

recordrecord


Index Implementations in some major DBMS SQL Server

B+-Tree data structure Clustered indexes are

sparse Indexes maintained as

updates/insertions/deletes are performed

DB2 B+-Tree data structure,

spatial extender for R-tree Clustered indexes are

dense Explicit command for index

reorganization

Oracle B+-tree, hash, bitmap,

spatial extender for R-Tree No clustered index until 10g

Index organized table (unique/clustered)

Clusters used when creating tables.

MySQL B+-Tree, R-Tree (geometry

and pairs of integers) Indexes maintained as

updates/insertions/deletes are performed


Index Tuning Knobs

Index data structure Search key Size of key Clustered/Non-clustered/No index Covering


Types of Queries

1. Point Query

SELECT balanceFROM accountsWHERE number = 1023;

2. Multipoint Query

SELECT balanceFROM accountsWHERE branchnum = 100;

3. Range Query

SELECT numberFROM accountsWHERE balance > 10000;

4. Prefix Match Query

SELECT *FROM employeesWHERE name = ‘Jensen’

and firstname = ‘Carl’

and age < 30;


Types of Queries

5. Extremal Query

SELECT *FROM accountsWHERE balance = max(select balance from accounts)

5. Ordering Query

SELECT *FROM accountsORDER BY balance;

7. Grouping Query

SELECT branchnum, avg(balance)FROM accountsGROUP BY branchnum;

8. Join Query

SELECT distinct branch.adresseFROM accounts, branchWHERE accounts.branchnum = branch.numberand accounts.balance > 10000;


Benefits of a clustered index1. A sparse clustered index stores fewer pointers than a dense

index. This might save up to one level in the B-tree index. Nb pointers dense index = nb pointers sparse index * nb

records per page If records small compared to pages, there will be many

records per page, so sparse has one level less than dense2. A clustered index is good for multipoint queries

White pages in a paper telephone book3. A clustered index based on a B-Tree supports range, prefix,

extremal and ordering queries well4. A clustered index (on attribute X) can reduce lock contention:

Retrieval of records or update operations using an equality, a prefix match or a range condition based on X will access and lock only a few consecutive pages of data

AOBD2007/08 H. Galhardas 19

Evaluation of Clustered Index

Multipoint query that returns 100 records out of 1000000.

Cold buffer Clustered index is twice

as fast as non-clustered index and orders of magnitude faster than a scan.

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SQLServer Oracle DB2

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clustered nonclustered no index


Inconvenient of a clustered index Benefits can diminish if there is a large number of

overflow data pages Accessing those pages will usually entail a disk seek

Overflow pages can result from two kinds of updates:

Inserts may cause data pages to overflow Record replacements that increase the size of a record

(e.g., the replacement of a NULL values by a long string)


Evaluation of clustered indexes with insertions (1)

Index is created with fillfactor = 100.

Insertions cause page splits and extra I/O for each query

Maintenance consists in dropping and recreating the index

With maintenance performance is constant while performance degrades significantly if no maintenance is performed.

SQLServer

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% Increase in Table Size

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No maintenance

Maintenance



Index is created with pctfree = 0

Insertions cause records to be appended at the end of the table

Each query thus traverses the index structure and scans the tail of the table.

Performances degrade slowly when no maintenance is performed.

DB2

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50

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No maintenance

Maintenance



In Oracle, clustered index are approximated by an index defined on a clustered table

No automatic physical reorganization

Index defined with pctfree = 0 Overflow pages cause

performance degradation

Oracle

0 20 40 60 80 100


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Nomaintenance


Redundant tables

Because there is only one clustered index per table, it might be a good idea to replicate a table in order to use a clustered index on two different attributes• Yellow and white pages in a paper telephone

book• Works well if low insertion/update rate


Covering Index - definition A nonclustering index can eliminate the need to access

the underlying table through covering (composite index) For the query:

Select name from employee where department = “marketing”

Good covering index would be on (department, name) Index on (name, department) less useful. Index on department alone moderately useful.

Inconvenients (of composite indexes): Tend to have a large key size Update to one of the attributes causes index to be modified


Covering Index - impact

Covering index performs better than clustering index when first attributes of index are in the where clause and last attributes in the select.

When attributes are not in order then performance is much worse.

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cov e ring

cov e ring - notorde re d

non cluste ring

cluste ring


Benefits of non-clustered indexes1. A dense index can eliminate the need to access

the underlying table through covering.• It might be worth creating several indexes to

increase the likelihood that the optimizer can find a covering index

2. A non-clustered index is good if each query retrieves significantly fewer records than there are pages in the table.

• Point queries always useful• Multipoint queries useful if:

number of distinct key values > c * number of records per page

Where c is the number of pages retrieved in each prefetch


Table Scan Can Sometimes Win

IBM DB2 v7.1 on Windows 2000

Range Query If a query retrieves 10% of

the records or more, scanning is often better than using a non-clustering non-covering index.

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% of se le cte d re cords

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scan

non clustering


Joins, Foreign Keys and IndexesR |X| R.A = S.B S can be executed through:

(NR = nb tuples R, NS = nb tuples S, BR = nb pages R, BS = nb pages S)

Nested Loop (NL) Cost I/O: NR *BS + BR, can be reduced to BR + BS if the

smaller relation fits entirely in memory Indexed Nested Loop (INL)

Cost: BR + c * NR, c: cost of traversing index and fetching all matching s tuples for one tuple or r

Hash Join (HJ) Cost I/O: 3(BR + BS), can be reduced to BR + BS if the

entire build input can be kept in main memory


Choice of join algorithms (1) If there is no index present, the system will choose to use a

hash join If there is an index present:

An INL based on an index on S.B works better than a HJ if the nb of distinct values in S.B is almost equal to the nb of rows of S Common case because most joins are FK joins

The same, regardless the nb of distinct values of S.B, if the index covers the join The only accesses to S data occur within the index

The same, regardless the nb of distinct values of S.B, if S is clustered based on B All S rows having equal B values will be colocated

Otherwise, the hash join may be better


Choice of join algorithms (2)

An index may be particularly useful in two other situations:

In a non-equi-join (R.A > S.B), an index (using a Btree) on the join attribute avoids a full table scan in the NL.

To support the FK constraint when R.A is a subset of R.B, so A is a FK in R; B is a PK in S An index in S speeds up insertions on R: for every record

inserted in R, check the foreign constraint on S Similarly, an index on R.A speeds up deletions in S


Index on Small Tables Tuning manuals suggest to avoid indexes on small

tables (containing fewer than 200 records) This number depends on the size of records

compared with the size of the index key If all data from a relation fits in one page (or in a single disk

track and can be read into memory through a single physical read by prefetching) an index page adds at least an I/O

If each record fits in a page, then 200 records may require 200 disk accesses or more. an index helps performance

If many inserts execute on a table with a small index, the index itself may become a concurrency control bottleneck Lock conflicts near the root


Index on Small Tables and Updates

Small table: 100 records Two concurrent processes

perform updates (each process works for 10ms before it commits)

No index: the table is scanned for each update. No concurrent updates.

A clustered index allow to take advantage of row locking.

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If transactions update a single record, without an index, each transaction scans through many records before it locks the relevant record, thus reducing update concurrency


Table organization and index selection: basic rules (1)

1. Use a hash index for point queries only. Use a B-tree if multipoint queries or range queries are used

2. Use clustering• if your queries need all or most of the fields of each

records returned, but the records are too large for a composite index on all fields

• if multipoint or range queries are asked

3. Use a dense index to cover critical queries4. Don’t use an index if the time lost when inserting

and updating overwhelms the time saved when querying


Table organization and index selection: basic rules (2) Use key compression

If you are using a B-tree Compressing the key will reduce the number of

levels in the tree The system is disk-bound but not CPU-bound Updates are relatively rare

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