1. AOBD07/08H. Galhardas Index Tuning

2. AOBD2007/08 H. Galhardas Index An index is a data structure that supports efficient access to data Set of Records index Condition on attribute value Matching records (search key)

3. AOBD2007/08 H. Galhardas Performance Issues Type of Query Index Data Structure Organization of data on disk Index Overhead Data Distribution Covering

4. AOBD2007/08 H. Galhardas Types of Queries 1. Point Query SELECT balance FROM accounts WHERE number = 1023; 2. Multipoint Query SELECT balance FROM accounts WHERE branchnum = 100; 3. Range Query SELECT number FROM accounts WHERE balance > 10000; 4. Prefix Match Query SELECT * FROM employees WHERE name = ‘Jensen’ and firstname = ‘Carl’ and age < 30;

5. AOBD2007/08 H. Galhardas Types of Queries 5. Extremal Query SELECT * FROM accounts WHERE balance = max(select balance from accounts) 5. Ordering Query SELECT * FROM accounts ORDER BY balance; 7. Grouping Query SELECT branchnum, avg(balance) FROM accounts GROUP BY branchnum; 8. Join Query SELECT distinct branch.adresse FROM accounts, branch WHERE accounts.branchnum = branch.number and accounts.balance > 10000;

6. AOBD2007/08 H. Galhardas 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)

7. AOBD2007/08 H. Galhardas 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.

8. AOBD2007/08 H. Galhardas B+-Tree A B+-Tree is a balanced tree whose leaves contain a sequence of key-pointer pairs. 96 75 83107 96 98 103107 110 12083 92 9575 80 8133 48 69

9. AOBD2007/08 H. Galhardas 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.

10. AOBD2007/08 H. Galhardas Hash Index A hash index stores key-value pairs based on a pseudo-randomizing function called a hash function. Hashed key values 0 1 n R1 R5 R3 R6 R9R14 R17 R21R25 Hash function key 2341 The length of these chains impacts performance

11. AOBD2007/08 H. Galhardas 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

12. AOBD2007/08 H. Galhardas 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

13. AOBD2007/08 H. Galhardas Dense / Sparse Index Sparse index  Pointers are associated to pages Dense index  Pointers are associated to records  Non clustered indexes are dense P1PiP2 record

14. AOBD2007/08 H. Galhardas 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

15. AOBD2007/08 H. Galhardas Index Tuning Knobs Index data structure Search key Size of key Clustered/Non-clustered/No index Covering

16. AOBD2007/08 H. Galhardas Types of Queries 1. Point Query SELECT balance FROM accounts WHERE number = 1023; 2. Multipoint Query SELECT balance FROM accounts WHERE branchnum = 100; 3. Range Query SELECT number FROM accounts WHERE balance > 10000; 4. Prefix Match Query SELECT * FROM employees WHERE name = ‘Jensen’ and firstname = ‘Carl’ and age < 30;

17. AOBD2007/08 H. Galhardas Types of Queries 5. Extremal Query SELECT * FROM accounts WHERE balance = max(select balance from accounts) 5. Ordering Query SELECT * FROM accounts ORDER BY balance; 7. Grouping Query SELECT branchnum, avg(balance) FROM accounts GROUP BY branchnum; 8. Join Query SELECT distinct branch.adresse FROM accounts, branch WHERE accounts.branchnum = branch.number and accounts.balance > 10000;

18. AOBD2007/08 H. Galhardas Benefits of a clustered index 1. 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 dense 2. A clustered index is good for multipoint queries  White pages in a paper telephone book 3. A clustered index based on a B-Tree supports range, prefix, extremal and ordering queries well 4. 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

19. 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.

20. AOBD2007/08 H. Galhardas 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)

21. AOBD2007/08 H. Galhardas 21 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.

22. AOBD2007/08 H. Galhardas Evaluation of clustered indexes with insertions (2) 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.

23. AOBD2007/08 H. Galhardas 23 Evaluation of clustered indexes with insertions (3) 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

24. AOBD2007/08 H. Galhardas 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

25. AOBD2007/08 H. Galhardas 25 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

26. AOBD2007/08 H. Galhardas 26 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.

27. AOBD2007/08 H. Galhardas Benefits of non-clustered indexes 1. 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

28. AOBD2007/08 H. Galhardas 28 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.

29. AOBD2007/08 H. Galhardas Joins, Foreign Keys and Indexes R |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

30. AOBD2007/08 H. Galhardas 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

31. AOBD2007/08 H. Galhardas 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

32. AOBD2007/08 H. Galhardas 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

33. AOBD2007/08 H. Galhardas 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. 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

34. AOBD2007/08 H. Galhardas 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 queries 4. Don’t use an index if the time lost when inserting and updating overwhelms the time saved when querying

35. AOBD2007/08 H. Galhardas 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