Database design and implementation CMPSCI 645. Lecture 08: Storage and Indexing

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1 Database design and implementation CMPSCI 645 Lecture 08: Storage and Indexing 1

2 Where is the data and how to get to it? DB 2

3 DBMS architecture Query Parser Query Rewriter Query Op=mizer Query Executor Lock Manager Access Methods Buffer Manager Log Manager Disk Space Manager DB 3

4 Memory hierarchy main memory magne+c disk sequen=al scan non-vola=le long archiving random access fast vola=le random access rela=vely slow non-vola=le tape 5

5 Disks and DBMS design RAM read write expensive opera=ons DB Databases are stored on disks 6

6 Why not store everything in memory? vola=lity cost 7

7 Basics of disks PlaIers spin under the head Only one head reads and writes PlaIers have tracks, forming an (imaginary) cylinder Each track has sectors. Blocks (pages) are mul=ple of sectors Arm assembly Disk head Arm movement Spindle Pla4ers Retrieval =me varies: Seek =me + rota=on delay + transfer =me 8

8 Accessing a disk page } Time to access (read/write) a disk block: 1. seek 'me (moving arms to posi=on a disk head on a track) 2. rota'onal delay (wai=ng for a block to rotate under the head) 3. transfer 'me (actually moving data to/from disk surface) } Seek =me and rota=onal delay dominate. } Placement of pages on disk has major impact on DBMS performance. 9

9 Arranging pages on disk } Sequen=al page storage: } blocks on the same track, followed by } blocks on the same cylinder, followed by } blocks on an adjacent cylinder } Pages in a file should be arranged sequen=ally on disk, to minimize seek and rota=onal delay. } Scan of the file is a sequen'al scan. 10

10 Files of records Fields are organized in a record A collec=on of records are organized in a page A collec=on of pages makes a file 11

11 Unordered (Heap) Files } Simplest file structure contains records in no par=cular order. } As file grows and shrinks, disk pages are allocated and de-allocated. } To support record level opera=ons, we must: } keep track of the pages in a file } keep track of free space on pages } keep track of the records on a page 12

12 Heap File Using a Page Directory Header Page Data Page 1 Data Page 2 DIRECTORY Data Page N } Page entry can include the number of free bytes on the page. } The directory is a collec=on of pages; linked list implementa=on is just one alterna=ve. 13

13 Page format } How to store records on a page } Consider a page as a collec=on of slots, one for each record } A record is iden=fied by rid = <page id, slot #> } Record ids (rids) are used in indexes 14

14 Page formats: fixed length records Slot 1 Slot 2 Slot N Free Space Slot 1 Slot Slot N Slot M N M PACKED number of records M UNPACKED, BITMAP number of slots Moving records for free space management changes rid; may not be acceptable. 15

15 Page formats: variable length records Rid = (i,n) Page i Rid = (i,2) Rid = (i,1) N N # slots SLOT DIRECTORY Can move records on page without changing rid; so, airac=ve for fixed-length records too. Pointer to start of free space 16

16 Record formats: fixed length F1 F2 F3 F4 L1 L2 L3 L4 Base address (B) Address = B+L1+L2 Number of fields and type stored in system catalogs. Finding i th field does not require scan of record. 17

17 Record formats: variable length Fields Delimited by Special Symbols F1 F2 F3 F4 $ $ $ $ Array of Field Offsets Scan S1 S2 S3 S4 E4 F1 F2 F3 F4 2 nd choice offers direct access to i th field with small directory overhead. 18

18 Question } Consider the following query: SELECT S1.temp, S2.pressure! FROM! TempSensor S1, PressureSensor S2! WHERE! S1.location = S2.location!! AND S1.time = S2.time! } How can the DBMS execute this query given } 1 GB of memory } 100 GB TempSensor and 10 GB PressureSensor 19

19 Buffer manager READ/WRITE Disk page Free frame Page requests from higher-level code Buffer pool dirty pin count Files and access methods Buffer pool manager Main memory INPUT/OUTPUT Disk = collec=on of blocks Disk Data must be in RAM for DBMS to operate on it! Buffer pool = table of <frame#, pageid> pairs choice of frame dictated by replacement policy Disk space manager 1 page corresponds to 1 disk block 20

20 When a page is requested... } If requested page is not in pool (and buffer is full): } 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. If requests can be predicted (e.g., sequen=al scans) pages can be pre-fetched several pages at a =me! 23

21 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 pa>ern. } Sequen'al flooding: Nasty situa=on caused by LRU + repeated sequen=al scans. } # buffer frames < # pages in file means each page request causes an I/O. MRU much beier in this situa=on (but not in all situa=ons, of course). 24

22 DBMS vs. OS file system OS does disk space & buffer mgmt: why not let it manage these tasks? } Reason 1: Correctness } DBMS needs fine grained control for transac=ons } Needs to force pages to disk for recovery purposes } Reason 2: Performance } DBMS may be able to an=cipate access paierns } Hence, may also be able to perform prefetching } May select beier page replacement policy 25

23 Database file types The data file can be one of: } Heap file } Set of records, par==oned into blocks } Unsorted } Sequen=al file } Sorted according to some airibute(s) called (sort) key different from key"! 26

24 Index } A (possibly separate) file, that allows fast access to records in the data file given a search key } The index contains (key, value) pairs: } The key = an airibute value } The value = either a pointer to the record, or the record itself again different from key"! 27

25 High-level overview: Indexes id age salary other k k k k k k k... id age salary other k k k k k k k... data file = index file clustered (primary) index index file unclustered (secondary) index 28

26 Index classification } Clustered/unclustered } Clustered = records close in index are close in data } Unclustered = records close in index may be far in data } Primary/secondary } Primary = is over aiributes that include the primary key } Secondary = otherwise } Organiza=on: B+ tree or Hash table 29

27 Clustered/Unclustered } Clustered } Index determines the loca=on of indexed records } Typically, clustered index is one where values are data records (but not necessary) } Unclustered } Index cannot reorder data, does not determine data loca=on } In these indexes: value = pointer to data record 30

28 Clustered index } File is sorted on the index airibute } Only one per table Index File Data File

29 Unclustered index } Several per table Index File Data File

30 Clustered vs. unclustered index B+ Tree B+ Tree Data entries (Index File) (Data file) Data entries CLUSTERED Data Records Data Records UNCLUSTERED 33

31 Alternatives for data entry k* in index } In a data entry k*, we can store: } Alterna=ve 1: <k, data record with search key value k> } Alterna=ve 2: <k, rid of a record with search key value k> } Alterna=ve 3: <k, list of rids of records with search key k> } Choice of an alterna've for data entries is orthogonal to an indexing technique used. } Indexing techniques: B+ tree, hashing, 34

32 Cost model We ignore CPU costs, for simplicity: } B: The number of data pages } R: Number of records per page } D: (Average) =me to read or write disk page } Measuring number of page I/Os ignores gains of pre-fetching a sequence of pages; thus, even I/O cost is only approximated. } Average-case analysis; based on several simplis=c assump=ons. 35

33 Comparing file organizations } Heap files (random order) } Sorted files, sorted on <age, sal> } Clustered B+ tree file, Alterna=ve (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> 36

34 Operations to compare } Scan: Fetch all records from disk } Equality search } Range selec=on } Insert a record } Delete a record 37

35 Assumptions } Heap Files: } Equality selec=on on key; exactly one match. } Sorted Files: } Files compacted aver dele=ons. } 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 38

36 Assumptions (contd.) } Scans: } 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. 39

37 Cost of operations Scan Equality Range Heap file BD 0.5 BD BD Sorted file BD D log 2 B D (log 2 B + #match recs) Clustered tree index 1.5 BD D log F 1.5B D (log F 1.5B + #pages with matched recs) Unclustered tree index BD (R+0.15) D(1 + log F 0.15B) D (log F 0.15B + #pages with matched recs) Unclustered hash index BD (R ) 2D BD Several assump=ons underlie these (rough) es=mates! 40

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