1. Database Systems An Overview of Storage and Indexing
Chapter 8 of the textbook

2. The Database Architecture
Web Forms
App Front ends
SQL interface
Parser
Plan Executor
Optimizer
Operator Evaluator
Files & Access
Methods
Transaction
Manager
Recovery
Manager
Buffer Manager
Lock
Manager
Disk Space Manager
Index Files
Data Files
System
Catalog

3. Learning Objectives for Chapter 8
Identify the input, output and function of each component in the architecture diagram
Be able to describe the relationships between different classifications of indexes
Alternatives, clustering, uniqueness, denseness, primary
For each cell in the table on the “cost of operations” slide , explain the underlying algorithm and justify the cost.
Definitions: Index only plans, composite keys
Given a workload, choose an optimal index configuration.

4. Table of Contents: Chapter 8
8.1 Data on external storage
Database architecture
8.2 File organizations and indexing
Indexes
Alternatives for k* data entries
Index classification: clustered, dense, unique, primary
8.3 Index search data structures
Hash-based indexes
Tree indexes
8.4 Comparing File Organizations
8.5 Indexes and performance tuning

5. 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.
Hashed files: Best for quick equality access.
RDBMSs typically use heap files plus indexes
A tree index using alternative 1 is similar to a sorted file, a hash index using alternative 1 is the same as a hash file. 2

6. How the Records Fall
Heap
Sorted
Hashed
Bucket 1 2 3 16 9 22 41 62 15 2 11 2 9 11 15 16 22 41 62 16 9 41 2 22 62 11 15

7. Indexes
An index on a file speeds up selections on the search key fields for the index.
Any subset of the fields, or a prefix of a field, 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.
Given data entry k*, we can find record with key k in at most one disk I/O. (Details soon …) 7

8. Typical index architecture
Search structure: given a key, it finds the data entry for that key
Data entries k* k* k*
(Index File)
(Data file)
Data Records
Used by modern DBMSs
What are typical search structures in CS?

9. Alternatives for Data Entry k* in Index
In a data entry k* we can store:
Data record with key value k, or
In this case the previous page is not accurate
<k, rid of data record with search key value k>, or
<k, list of rids of data records with search key k>
Choice of alternative for data entries is orthogonal to the indexing technique used to locate data entries with a given key value k.
Examples of indexing techniques: B+ trees, hash-based structures
Typically, index contains auxiliary information that directs searches to the desired data entries 8

10. Alternative 1: k* holds the data
INDEX
10 Blue
14 Red
21 Blue
42 Red
24 Green
30 Blue .

11. Alternative 2: One pointer per k*
10 Blue
42 Red
10 Green
30 Blue
21 Red
24 Blue
INDEX
10*
21*
24*
30*
42*

12. Alternative 3: 1 or more pointers per k*
10 Blue
INDEX
10*
18*
21*
24*
30*
21 Red
24 Blue
10 Green
30 Blue
18 Red

13. Alternatives for Data Entries (Contd.)
If this is used, index structure is a file organization for data records (instead of a Heap file or sorted file).
At most one single-attribute index on a given collection of data records can use Alternative 1. (Otherwise, data records are duplicated, leading to redundant storage and potential inconsistency.)
Alternatives 2 and 3:
Alternative 3 more compact than Alternative 2, but leads to variable sized data entries even if search keys are of fixed length. 9

14. Clustered vs. Un-clustered Index
Suppose that Alternative (2) is used for data entries, and that the data records are stored in a Heap file.
To build clustered index, first sort the Heap file (with some free space on each page for future inserts).
Overflow pages may be needed for inserts. (Thus, order of data recs is `close to’, but not identical to, the sort order.)
(Data file)
Data entries
Data Records
UNCLUSTERED
Index entries
CLUSTERED
direct search for
data entries
Data entries
(Index File)
Data Records 12

15. Index Classification
Primary vs. secondary: If search key contains primary key, then called primary index.
In practice, primary means alternative 1
Unique index: Search key contains a candidate key.
Clustered vs. un-clustered: If order of data records is the same as, or `close to’, order of data entries, then called clustered index.
Alternative 1 implies clustered.
A file can be clustered on at most one single-attributesearch key.
Cost of retrieving data records through index varies greatly based on whether index is clustered or not! 11

16. Index Classification (Cont.)
Dense vs. Sparse: If there is at least one data entry per search key value (in some data record), then it’s dense.
Alternative 1 always leads to dense index.
Every sparse index is clustered!
Sparse indexes are smaller; however, some useful optimizations are based on dense indexes.
Ashby, 25, 3000 22
Basu, 33, 4003 25
Bristow, 30, 2007 30
Ashby 33
Cass
Cass, 50, 5004
Smith
Daniels, 22, 6003 40
Jones, 40, 6003 44 44
Smith, 44, 3000 50
Tracy, 44, 5004
Sparse Index
Dense Index on on
Data File
Name
Age

17. Index Classification
Clustered, sparse indexes are smaller; they work well for range searches and sorting.
But…data blocks are difficult to keep sorted and some useful optimizations are based on dense indexes.
Note: one file can have at most one clustered index - all of the additional indices must be unclustered.
sparse dense
clustered YES YES
unclustered NO! YES 13

18. Hash-based Index Examples
Smith,44,3000
Jones,40,6003
Tracy,44,5004
Ashby,25,3000
Basu,33,4003
Bristow,29,2007
Cass,50,5004
Daniels,22,6003
3000
5004
h(age)=00
h(sal)=00
age h
h(age)=01
sal h
4003
2007
6003
h(sal)=11
h(age)=10
Employees file hashed on age
Index on salary

19. 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. 2

20. B+ Tree Indexes
Non-leaf
Pages
Leaf
Pages
(Sorted by search key)
Leaf pages contain data entries, and are chained (prev & next)
Non-leaf pages have index entries; only used to direct searches:
index entry P K P K 1 2 P K P 1 2 m m 4

21. Example B+ Tree Note how data entries in leaf level are sorted
Root 17
Entries <= 17
Entries > 17 5 13 27 30 2* 3* 5* 7* 8*
14*
16*
22*
24*
27*
29*
33*
34*
38*
39*
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 15

22. Comparing File Organizations
Assume an employee file is organized by the search key <age, salary>.
We will consider various file organizations, and operations like scan, equality, range, insert, delete.
For each file organization, we will compute the cost of each operation.

23. Comparing File Organizations
Heap files (random order; insert at eof)
Sorted files, sorted on <age, sal>
Clustered B+ tree file, Alternative (1), search key <age, sal>
Heap file with unclustered B + tree index on search key <age, sal>
Heap file with unclustered hash index on search key <age, sal>

24. Operations to Compare Scan: Fetch all records from disk
Equality search
Range selection
Insert a record
Delete a record

25. Cost Model for Our Analysis
We ignore CPU costs, for simplicity:
B: The number of data pages
R: Number of data records per page
F: Fanout, number of index records per page
D: (Average) time to read or write disk page
Measuring number of page I/O’s ignores gains of pre-fetching a sequence of pages; thus, even I/O cost is only approximated.
Average-case analysis; based on several simplistic assumptions.
Good enough to show the overall trends! 3

26. Assumptions in Our Analysis
Heap Files:
Equality selection on key; exactly one match.
Sorted Files:
Files compacted after deletions.
Indexes:
Alt (2), (3): data entry size = 10% size of record
Hash: No overflow buckets.
80% page occupancy => File size = 1.25 data size
Tree: 67% occupancy (this is typical).
Implies file size = 1.5 data size 4

27. Assumptions (contd.) Scans: Range searches:
Leaf levels of a tree-index are chained.
Index data-entries plus actual file scanned for unclustered indexes.
Range searches:
We use tree indexes to restrict the set of data records fetched, but ignore hash indexes.

28. Cost of Operations
Several assumptions underlie these (rough) estimates! 5

29. Indexes and Performance Tuning
One of a DBA’s most important duties is to deal with performance problems
One of the most effective methods for improving performance is to choose the best indexes for the given workload
Why not index everything?
What are the two costs of an index?
Part of a DBA’s job: choose indexes so the database will “run fast”.
What information does the DBA need, to do this?

30. Understanding the Workload
For each query in the workload:
Which relations does it access?
Which attributes are retrieved?
Which attributes are involved in selection/join conditions? How selective are these conditions likely to be?
For each update in the workload:
The type of update (INSERT/DELETE/UPDATE), and the attributes that are affected. 11

31. 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?
What kinds of indexes should we create?
Clustered? Hash/tree?
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.
Also consider dropping indexes 12

32. 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!
Second approach: Use an Index Tuning Wizard [163].
Inputs: A workload (it can also capture a workload) and an amount of disk storage for indexes
Output: an index configuration 13

33. Index Selection Guidelines
Attributes in WHERE clause are candidates for index keys.
Exact match condition suggests hash index.
Range query suggests tree index.
Clustering is especially useful for range queries; can also help on equality queries if there are many duplicates.
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.
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. 14

34. Index-Only Plans <E.dno> <E.dno,E.sal> Tree index May help
SELECT E.dno, COUNT(*)
FROM Emp E
GROUP BY E.dno
A number of queries can be answered without retrieving any tuples from one or more of the relations involved if a suitable index is available.
For index-only strategies, clustering is not important!
<E.dno>
SELECT E.dno, MIN(E.sal)
FROM Emp E
GROUP BY E.dno
<E.dno,E.sal>
Tree index
May help
<E. age,E.sal> or
<E.sal, E.age>
SELECT AVG(E.sal)
FROM Emp E
WHERE E.age=25 AND
E.sal BETWEEN 3000 AND 5000
Tree index! 21

35. Examples of Clustered Indexes
SELECT E.dno
FROM Emp E
WHERE E.age>40
B+ tree index on E.age can be used to get qualifying tuples.
How selective is the condition?
Is the index clustered?
Consider the GROUP BY query.
If many tuples have E.age > 10, using E.age index and sorting the retrieved tuples may be costly.
Clustered E.dno index may be better!
Equality queries and duplicates:
Clustering on E.hobby helps!
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 18

36. 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 <sal,age> 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.
Examples of composite key
indexes using lexicographic order.
11,80 11
12,10 12
name
age
sal
12,20 12
13,75
bob 12 10 13
<age, sal>
cal 11 80
<age>
joe 12 20
10,12
sue 13 75 10
20,12
Data records
sorted by name 20
75,13 75
80,11 80
<sal, age>
<sal>
Data entries in index
sorted by <sal,age>
Data entries
sorted by <sal> 13

37. Composite Search Keys
To retrieve Emp records with age=30 AND sal=4000, an index on <age,sal> 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 <age,sal> or <sal,age> is best.
If condition is: age=30 AND 3000<sal<5000:
Clustered <age,sal> index much better than <sal,age> index!
Composite indexes are larger, updated more often. 20

38. Indexes in the real world
Types of indexes supported
Oracle, SQLServer and DB2 support only B+Tree indexes. Postgres supports hash indexes but does not recommend using them.
MySQL?
Everyone uses hash indexes for hash joins, but they are constructed on the fly.

39. Clustering in the real world
Oracle, SQL Server:
Declares a clustered index on any primary key.
It uses alternative 1.
DB2, Postgres
The user can define an index to be clustered.
The DBMS uses alternative 2.
At first the index will be clustered.
If you also specify a percentage of free space, it will place subsequent inserts/updates (Postgres:updates only) in sorted order so the index should stay clustered for a while.
It’s up to you to recluster the index if overflow chains get to be long.
MySQL
Oracle
page 5-34 to 5-48
Index-organized table
SQL Server
DB2
ftp://ftp.software.ibm.com/ps/products/db2/info/vr8/pdf/letter/db2d2e80.pdf, pg 146 and ftp://ftp.software.ibm.com/ps/products/db2/info/vr8/pdf/letter/db2d3e80.pdf, pg 300 etc.
Postgres

40. Slides From Chapter 9 Relevant to Buffer Management
9.3 Disk Space Management
9.4 Buffer Management

41. Disk Space Management
The lowest layer of DBMS software manages space on disk.
Higher levels call upon this layer to:
allocate/de-allocate a disk page
read/write a disk page
A request for a sequence of disk 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 on the disk is managed. 24

42. Buffer Management in a DBMS
Disk Page Requests from Higher Levels
BUFFER POOL
disk page
free frame
MAIN MEMORY
DISK DB
choice of frame dictated
by replacement policy
Data must be in RAM for DBMS to operate on it!
Table of <frame#, pageid> pairs is maintained. 4

43. When a Disk Page is Requested ...
If requested disk page is not in buffer pool:
Choose a frame for replacement
If frame is dirty, write it to disk
Read requested page into chosen frame
Pin the page and return its address. 5

44. More on Buffer Management
Requestor of page must unpin it, and indicate whether page has been modified:
Dirty bit is used for this
What if requestor does not unpin the page?
Page in pool may be requested many times,
a pin count is used. A page is a candidate for replacement iff pin count = 0. 6

45. Buffer Replacement Policy
Frame is chosen for replacement by a replacement policy:
Least-recently-used (LRU), Clock, MRU etc.
Policy can have big impact on # of I/O’s; depends on the access pattern. 7