File Processing : Index and Hash

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File Processing Index and Hash

• 2005, Spring
• Pusan National University
• Ki-Joune Li

2
What is index ?

• Index in a book
• Index Keyword ? Pages
• Without Index
• Exhaustive search Too Expensive
• Index for a file or database
• A function or mechanism
• FIndex SPredicate ? B (block numbers on hard
  disk)
• e.g. find student records where student.GPA gt 4.0

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Data Retrieval Time

• Data retrieval on disk Two phases
• 1st phase Search with a condition (Predicate)
• 2nd phase Data access

Data Access Time- File Structure- Disk
Placement- Clustering, etc..
Search Condition
Block
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Blocking Factor Bf

• Blocking Factor
• Number of Records in a Block
• Blocking Number and Number of Disk Accesses
• ND Nrecord / Bf

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How to Accelerate Phase 1 ?

• Of course, we could accelerate the phase 1
• by index or by hash
• Index vs. Hash
• Index a type of data structures
• Needs additional data structures
• Hash a type of mechanism
• May not need any additional data structure (not
  exactly true)

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A Simple Idea on Index

• Mapping Table from keywords to block numbers
• Inverted File
• Why inverted file is better than nothing ?
• If the table is too large (to fit in main memory)
• It has to be stored on disk
• Disk Access for Index Access

Keyword
Block
Juliet
Romeo
B26
Hamlet
B22


Carmen
B212
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Searching Algorithms and Index

• A good way to accelerate searching
• Tree O( logn )
• Reorganize Inverted File to Tree
• Binary Search Tree Branching Factor 2
• Tree in memory space vs. in disk space
• Memory space Number of Comparisons
• Disk space Number of Block Accesses

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Paged Tree m-way search tree

• How to determine m ?
• One Node One Disk Page
• e.g. When 1 disk page is 4 K bytes
• 44m8(m-1) 4096 ? m 341
• Very fat tree

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Problem of m-Way search tree

• m-way search tree
• Search Performance determined by the height
• Not balanced
• Average O(log n)
• Worst case n / Bf ? O(n)
• Height determined by insertion order
• e.g insertion by ascending order
• How to make it balanced ?
• Balanced m-Way search tree B-tree

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B-tree

• B-tree Balanced m-way search Tree
• Root Node no child node or more than one child
  nodes
• Internal Node ?m/2? m child nodes (block
  number)
• External Node data block number instead of
  child node
• Balanced
• Upward split
• instead of downward split Binary Tree

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Downward Split
Suppose m3
Insert 10, 20
10
20
Insert 30
Insert 40
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Downward Split
40
Insert 50
60
Insert 70
70
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Meaning of Downward Split

• Always Balanced
• Not so much influenced by the order of insertions
• Internal Nodes ?m/2? m child nodes (block
  number)

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Search by B-tree
? 45
45
40
45
20
60
45
50
10
30
70
Not Found
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Performance of B-tree

• Number of Comparison within a node Trivial
• Number of Nodes to visit Depth

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Problem of B-tree

• Types of Search
• Exact Match Search
• Range Search
• E.g. find students where 25ltstudent.GPAlt50
• B-tree
• Good for Exact match search
• Bad for range search

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B-tree

• A Variant of B-tree
• Duplicate all elements at leaf nodes (external
  nodes)
• Linked List of Leaf Nodes
• Performance
• Exact Match Search and Insertion
• A small fraction of performance sacrifice
• Range Search much more powerful than B-tree

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B-tree Example
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Range Search with B-tree

• Find students where GPAgt3.5

35
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Performance of B-tree

• Performance
• Determined by the Depth
• Exact Match Search and Insertion (without split)
• d node (page) accesses
• Range Search
• node accesses ( nq number of
  records to retrieve)