Index management in shallow depth

Index managementin shallow depth

Andrea Giuliano@bit_shark

Architecture of a DBMS

Andrea Giuliano @bit_shark

Disk managerThe disk manager provides the following commands for a page

• allocate a page • deallocate a page • read a page • write a page

The size of a page is chosen to be the size of a disk block and pages are stored as disk blocks so that reading or writing a page can be done in one disk I/O


Buffer manageris the software layer responsible for bringing pages from disk to main memory as needed and manages the available main memory by partitioning it into a collection of pages

the buffer pool


Mysql indexes are stored!in B-trees

B-tree structure

data entry: record stored in an index file data record: record stored in a database file


Clustered indexdata entries and data records have the same (or close) order


Unclustered indexdata entries and data records have the different order criteria


Dense indexAn index is dense if every value of the search key that appears in the data file appears also in at least one data entry of the index


An index is sparse if every value of the search key that appears in the data entry points to a page of the data record

Sparse index


Search in a b-tree

Cost: logF n

fan-out

n leaves


Insert and delete

..too deep

Insert and delete operations must keep the tree balanced towards split, redistribution and coalesce techniques.


How can I compute I/O accesses?


ask for code, author, publisher af all books with a given cost

how many page accesses do we need to answer to the query?

Book case

Query:

• 2.000.000 records (tuples) • 200.000 pages • 10 data record in a page • 200 records with the same value of the attribute cost (on

average) • dense non-clustering B+-tree index with search key cost

BOOK

codeauthorcostpublisher


Let’s build the index structure !• we know that each tuple has 4 field so in each page there are 40 fields • we can infer that 20 data entries fit in one leaf page of the index • so we have a fan-out of 20

Book case• 2.000.000 records (tuples) • 200.000 pages • 10 data record in a page • 200 records with the same value of the attribute cost (on

average) • dense non-clustering B+-tree index with search key cost

BOOK



We know there is an occupancy factor of 67% we have 13 data entries in the leaves (each of which can contain 20 data entries)

How many leaves are there in the tree? 2.000.000/13 = 153.846 leaves

Book case

…

fan-out: 20


There are 153.846 leavesIn order to go to the leaves we need

Book case

log20 (153.846) = 4 I/O page accesses

…

fan-out: 20


Book case

Remember, we have on average 200 records with the same value of the attribute cost therefore 200/13 = 15 pages (on average) We need to visit these leaves because the index is dense and for each tuple we have to access the 200 data record in order to obtain the other attributes

…

fan-out: 20


The total cost is: 4 + 15 + 200 = 219 I/O accesses

Book case

~ 3 sec

…

fan-out: 20


• 2.000.000 records (tuples) • 200.000 pages • 10 data record in a page • 200 records with the same value of the attribute cost (on

average) • sparse clustering B+-tree index with search key cost

ask for code, author, publisher af all books with a given cost

how many page accesses do we need to answer to the query?

BOOK


Book case

Query:


Let’s build the index structure !• we know that each tuple has 4 field so in each page there are 40 fields • we can infer that 20 data entries fit in one leaf page of the index • so we have a fan-out of 20

Book case• 2.000.000 records (tuples) • 200.000 pages • 10 data record in a page • 200 records with the same value of the attribute cost (on

average) • sparse clustering B+-tree index with search key cost

BOOK



We know there is an occupancy factor of 67% we have 13 data entries in the leaves (each of which can contain 20 data entries)BUT each data entry points to a data record page (and not to a tuple)

How many data record pages do we have? 2.000.000/10 = 200.000 data record pages

Book case

…

fan-out: 20


We know there is an occupancy factor of 67% we have 13 data entries in the leaves (each of which can contain 20 data entries) BUT each data entry points to a data record (and not to a tuple)

How many leaves there are in the tree? 200.000/13 = 15.384 leaves

Book case

…

fan-out: 20


There are 15.384 leavesIn order to go to the leaves we need

Book case

log20 (15.384) = 3 I/O pages accessesRemember, we have on average 200 data records with the same value of the attribute cost therefore 200/10 = 20 data record pages to visit

…

fan-out: 20


The total cost is: 3 + 20 = 23 I/O accesses

Book case

~ 0.3 sec

…

fan-out: 20


And what if the attributes we wantwere part of the search key?

Book case


In the worst case we have to visit all the 2.000.000 tuples

Book casewithout index

~ 50 min


Ο λογος δηλοι οτι

Think before doing

?Thanks!


References:

https://www.flickr.com/photos/james_wheeler/9340597900/sizes/o/in/photolist-fep1ko-bQByHk-duQ4Qr-82aKA9-82aL6y-8Tn6uc-iPzADZ-99etoQ-cZy6e9-jyqdnW-bxHjLf-8gP59X-cZDq3h-cZDq9d-cZDq8N-cZDq7d-cZDqwy-cZDqym-cZDqCf-cZDqAo-cZDqsS-cZDqnQ-cZDqey-cZDqkA-cZDqkJ-

cZDqLh-7Dg8pp-a7f1QC-a7c8rK-7Dg7n6-gCbBVr-9FZ4J1-e6XCpX-aZnsGv-ecTv5D-atFACM-gjXozL-9LBjtC-knoEf8-8LGGqw-a8Hw3M-gvL3bp-a7gmG6-aju6p2-

brQ76S-7Ckbm1-85XaXe-8JBcwN-9oYU3p-a3VsvR-atFAup/ http://www.woking.gov.uk/images/instances/00004A290FD4.C0A801BA.000079A7.0015.jpg

http://assets.20bits.com/20080513/b-tree.png, http://dblab.cs.toronto.edu/courses/443/2014/basic-index/dense-index.png http://dblab.cs.toronto.edu/courses/443/2014/basic-index/sparse-index.png

http://www.geeky-gadgets.com/wp-content/uploads/2008/10/insert-delete_cufflinks.jpg, http://www.geeky-gadgets.com/wp-content/uploads/2008/10/insert-delete_cufflinks.jpg http://webhostinggeeks.com/blog/wp-content/uploads/2012/07/611157_small.jpg

https://farm7.staticflickr.com/6237/6230474283_50d1f0f4ac_b.jpg, https://www.flickr.com/photos/javiercosio/6230474283/sizes/l/in/photolist-

auyNWp-9GRjnM-9GRjmZ-9GRjFz-9GRjvV-9GUcPq-9GRjz8-9GRjm4-9GUcTb-9GRjCt-9GUcQC-9GUcYm-9GRjZD-9GRk1Z-9GUcMU-9GUcGh-9GRjsg-9GRjYZ-9GRjTF-9GRjGe-9GRjNe-9GRjBz-9GUcNs-9GU

d25-9GUcKS-9GRjPn-9GRjRg-9GUcBh-9GUcVf-9GUcxj-9GUcuu-brLe7G-e8s4Cw-fyi4Rj-83LyYW-83HuFg-83LyLm-83LzUY-83Htrv-83Hv2H-83LBBb-83LAg9-83LBhQ-83Hw8t-83HtKD-83H

sYk-afT6uk-cwVhL1-ceVgGC-8tFezr-8SeW9d/

Index management in shallow depth

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