1. CS522 Advanced database Systems
5/26/2018
CS522 Advanced database Systems
2. Overview of data storage and indexing
Huiping Guo
Department of Computer Science
California State University, Los Angeles

2. We are here … DBMS DBMS 2. Overview CS522_S16 5/26/2018 Plan Executor
Parser
Operator Evaluator
Optimizer
Query evaluation
engine
Transaction
Manager
Lock
Concurrency control
File and Access Methods
Buffer Manager
Disk Space Manager
Recovery
Manager
Storage & indexing
DBMS
2. Overview CS522_S16

3. Outline Data on external storage File organization Indices Hash index
B+ tree index
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4. A file
field
record
page
“File”
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5. External storage Access unit: page
The unit of information read from or written to disk
The size of a page is a DBMS parameter
Cost unit: Page I/O
Read a page into memory
Write a page to disk
Cost of database operations
Dominated by page I/Os
Our purpose: minimize page I/Os
2. Overview CS522_S16

6. External storage Disks Tapes Random access devices
Can retrieve any page at fixed cost
Reading consecutive pages is much cheaper than reading them in random order
Tapes
Sequential access devices
Can only read pages in sequence
Cheaper than disks, used for archival storage
2. Overview CS522_S16

7. Files and access methods layer
How is a relation stored?
A relation is stored as a file of records
Each record has a record id (rid) which is used to locate a record (page number)
Implemented by the component: files and access methods layer
Files and access methods layer
Operations supported
creation, insertion, deletion, scan,…
Keeps track of pages allocated to each file
Tracks available space within pages allocated to the file
Supports fast file access to desired subsets of queries
2. Overview CS522_S16

8. File organization How the records are organized? What is it? Target
a method of arranging the records in a file when the file is stored on disk
Sorted or unsorted
Target
Minimize the cost of page I/O (input from disk to main memory and output from memory to disk)
2. Overview CS522_S16

9. Three file organization
5/26/2018
Three file organization
Heap (random order) files
Suitable when typical access is a file scan that retrieves all records or a particular record specified by its rid
Sorted files
Best if records mush be retrieved by some order, or a ‘range’ of records is needed
Updates are expensive
Index
Data structures that organize records via trees or hashes
Speed up search for a subset of records
Updates are much faster than in sorted files
Many alternatives exist, each ideal for some situations, and not so good in others:
2. Overview CS522_S16

10. Indices
An index speeds up selections on the search key fields for the index
An index file contains a collection of data entries
A data entry is a record stored in a index file
A data entry with search key k is denoted as k*
A data entry is used to obtain a data record (if they are different)
Index supports efficient retrieval of all data entries k* with a given key value k
2. Overview CS522_S16

11. Data entry Employees Data records sorted by name Data entries
age
sal
Data records
sorted by name 75 13
Sue 20 15
Ioe 80 11
Cal 10 12
Bob
Data entries
Index on age
Index on sal
2. Overview CS522_S16

12. Alternatives for data entries K*
1: A data entry k* is an actual data record (with search key value k)
At most one index on a given collection of data records can use alternative 1. (?)
If data records are very large, #of pages containing data entries is high.
It’s a special file organization, called indexed file organization
At most one of the indices should use alternative 1.
2. Overview CS522_S16

13. Alternatives for data entries K* (cont.)
2. a data entry is a <k, rid> pair
rid is the record id of a data record with search key value k
3. a data entry is <k, rid-list>
Rid-list is a list of record ids of data records with search key value k
Advantages of alternative 2 & 3
Contain data entries that point to data records
Are independent of the file organization that is used for the index file
Alternative 3 offers better space utilization than Alternative 2.
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14. Index data structures How to organize data entries in an index?
Hash index
B+ Tree index
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15. Hash index Hash data entries on the search key
Index is a collection of buckets.
A bucket consists of a primary page and overflow pages linked in a chain
How to determine which bucket a record belongs to?
Hashing function h is applied to the search key
Good for equality selections
How to handle updates and search?
2. Overview CS522_S16

16. Example hash index H(age)=00 H(sal)=00 H(age)=01 h sal H(age)=10
Smith, 44, 3000
Jones, 40, 6003
Tracy, 44, 5004
Ashyby,25,4000
Basu, 23, 4003
Bristow, 29,2007
Cass, 50, 5004
Daniels,22, 6003
3000
------
4000
5004
4003
2007
6003 h
sal
H(sal)=00
H(sal)=01
H(age)=00
H(age)=01
H(age)=10
h:2 Least Significant Bits of search key
Files of <sal,rid> pairs
Hashed on sal
Alternative 2 used
Employee files
Hashed on age
Alternative 1 used
2. Overview CS522_S16

17. B+ Tree index
Data entries are arranged in sorted order by search key value
A hierarchical search data structure is maintained that directs searches to the correct page of data entries
Allows to efficiently direct locate all data entries with search key values in a desired range
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18. B+ Tree index Index entry Data entry Non-leaf Pages Leaf
(Sorted by search key)
Leaf
Index entry
Data entry
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19. B+ Tree index Root Non-leaf pages Leaf pages
Topmost node where all searches begin
Non-leaf pages
contain index entries
directed searches to the correct leaf page
Non-leaf pages contain node pointers separated by search key values
The node pointer to the left of a key vale k points to a subtree that contains only data entries less than k
The node pointer to the right of a key vale k points to a subtree that contains only data entries greater than k
Leaf pages
contain data entries and are chained
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20. Example B+ Tree Entries < 17 Entries >= 17 age 47
Root
age 17 47
Entries < 17
Entries >= 17 5 13 27 30 2* 3* 5* 7* 8*
14*
16*
22*
24*
27*
29*
33*
34*
38*
Why leaf pages are chained by a doubly linked list?
Find 28*, 29*, All > 15* and < 30*
Insert/delete
Cost?
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21. More on B+ tree index The number of page I/Os of a search is equal to
the length of a path from the root to a leaf plus
the number of leaf pages with qualifying data entries
B+ tree ensures that all paths from the root to a leaf in a given tree are of the same length.
The tree is always balanced in height
The height of a balanced tree is the length of a path from root to leaf
fan-out
The average number of children for a non-leaf node
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22. Differentiate the three concepts
Data records
Actual data
Data entries
Records in a index file
Content of data entries
Alternative 1: data entries are data records
Alternative 2 & 3: data entries contain search keys and rids
Index entries
Specific to B+ tree index file
Are on the non-leaf nodes(pages)
Contain search keys and pointers
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23. Index classification Primary vs. secondary Clustered vs. unclustered
If search key contains primary key, then called primary index
Unique index: Search key contain a candidate key
Clustered vs. unclustered
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 search key
Cost of retrieving data records through index varies greatly based on whether index is clustered or not
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24. Clustered vs. unclustered 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
Overflow pages may be needed for inserts
Index entries
UNCLUSTERED
CLUSTERED
direct search for
data entries
Data entries
Data entries
(Index File)
(Data file)
Data Records
Data Records
2. Overview CS522_S16