CPS216: Advanced Database Systems

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Presentation transcript:

CPS216: Advanced Database Systems Notes 06: Operators for Data Access (contd.) Shivnath Babu

Insertion in a B-Tree n = 2 49 15 36 49 Insert: 62

Insertion in a B-Tree n = 2 49 15 36 49 62 Insert: 62

Insertion in a B-Tree n = 2 49 15 36 49 62 Insert: 50

Insertion in a B-Tree n = 2 49 62 15 36 49 50 62 Insert: 50

Insertion in a B-Tree n = 2 49 62 15 36 49 50 62 Insert: 75

Insertion in a B-Tree n = 2 49 62 15 36 49 50 62 75 Insert: 75

Insertion

Insertion

Insertion

Insertion

Insertion

Insertion

Insertion

Insertion

Insertion

Insertion

Insertion

Insertion: Primitives Inserting into a leaf node Splitting a leaf node Splitting an internal node Splitting root node

Inserting into a Leaf Node 58 54 57 60 62

Inserting into a Leaf Node 58 54 57 60 62

Inserting into a Leaf Node 58 54 57 58 60 62

Splitting a Leaf Node 61 54 66 54 57 58 60 62

Splitting a Leaf Node 61 54 66 54 57 58 60 62

Splitting a Leaf Node 61 54 66 54 57 58 60 61 62

Splitting a Leaf Node 59 61 54 66 54 57 58 60 61 62

Splitting a Leaf Node 61 54 59 66 54 57 58 60 61 62

Splitting an Internal Node … 21 99 … 59 40 54 66 74 84 [54, 59) [ 59, 66) [66,74)

Splitting an Internal Node … 21 99 … 59 40 54 66 74 84 [54, 59) [ 59, 66) [66,74)

Splitting an Internal Node 66 … 21 99 … [66, 99) [21,66) 40 54 59 74 84 [54, 59) [ 59, 66) [66,74)

Splitting the Root 59 40 54 66 74 84 [54, 59) [ 59, 66) [66,74)

Splitting the Root 59 40 54 66 74 84 [54, 59) [ 59, 66) [66,74)

Splitting the Root 66 40 54 59 74 84 [54, 59) [ 59, 66) [66,74)

Deletion

Deletion redistribute

Deletion

Deletion - II

Deletion - II merge

Deletion - II

Deletion - II

Deletion - II

Deletion - II Not needed merge

Deletion - II

Deletion: Primitives Delete key from a leaf Redistribute keys between sibling leaves Merge a leaf into its sibling Redistribute keys between two sibling internal nodes Merge an internal node into its sibling

Merge Leaf into Sibling 72 … 67 85 54 58 64 68 72 75

Merge Leaf into Sibling 72 … 67 85 54 58 64 68 75

Merge Leaf into Sibling 72 … 67 85 54 58 64 68 75

Merge Leaf into Sibling 72 … 85 54 58 64 68 75

Merge Internal Node into Sibling … 59 … 41 48 52 63 74 [52, 59) [59,63)

Merge Internal Node into Sibling … 59 … 41 48 52 59 63 [52, 59) [59,63)

B-Tree Roadmap B-Tree B-Tree variants Hash-based Indexes Recap Insertion (recap) Deletion Construction Efficiency B-Tree variants Hash-based Indexes

How does insertion-based construction perform? Question How does insertion-based construction perform?

B-Tree Construction Sort 48 57 41 15 75 21 62 34 81 11 97 13

B-Tree Construction 11 13 15 21 34 41 48 57 62 75 81 97 11 13 15 21 34 41 48 57 62 75 81 97 Scan

B-Tree Construction 21 48 75 11 13 15 21 34 41 48 57 62 75 81 97 Scan

Why is sort-based construction better than insertion-based one? B-Tree Construction Why is sort-based construction better than insertion-based one?

Cost of B-Tree Operations Height of B-Tree: H Assume no duplicates Question: what is the random I/O cost of: Insertion: Deletion: Equality search: Range Search:

Height of B-Tree Number of keys: N B-Tree parameter: n log N Height ≈ log N = n log n In practice: 2-3 levels

Question: How do you pick parameter n? Ignore inserts and deletes Optimize for equality searches Assume no duplicates

Roadmap B-Tree B-Tree variants Hash-based Indexes Sparse Index Duplicate Keys Hash-based Indexes

Roadmap B-Tree B-Tree variants Hash-based Indexes Static Hash Table Extensible Hash Table Linear Hash Table

Hash-Based Indexes Adaptations of main memory hash tables Support equality searches No range searches

Indexing Problem (recap) Index Keys record pointers a 1 2 i n A = val

Main Memory Hash Table buckets 32 48 (null) 1 key 2 10 (null) h (key) 48 (null) 1 key 2 10 (null) h (key) 3 27 75 (null) 4 h (key) = key % 8 5 21 (null) 6 7 55 (null)

Adapting to disk 1 Hash Bucket = 1 Block All keys that hash to bucket stored in the block Intuition: keys in a bucket usually accessed together No need for linked lists of keys …

Adapting to Disk How do we handle this?

Adapting to disk 1 Hash Bucket = 1 Block All keys that hash to bucket stored in the block Intuition: keys in a bucket usually accessed together No need for linked lists of keys … … but need linked list of blocks (overflow blocks)

Adapting to Disk

Adapting to disk Bucket Id  Disk Address mapping Contiguous blocks Store mapping in main memory Too large? Dynamic  Linear and Extensible hash tables

Beware of claims that assume 1 I/O for hash tables and 3 I/Os for B-Tree!!