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1 1 v es/SIGMOD98.asp

2 2 Homework V v V.1 Exercise 5.3: 2, 3, 6, 7, 8, 9, 10. v V.2. Exercise 5.6: 9. v (Extra Credit) Write a JDBC based program for querying some database. v Reading Exercise: –QBE (required) –S. Chakravarthy. Architectures and monitoring techniques for active databases: an evaluation. Data and Knowledge Enginnering 16(1):1--16, 1995.

3 3 Homework V v V.4 Find out parameters of a high quality disk. v V.5. Make a table of RAID configuration with D disks versus effective reading/writing bandwidth, and effective space utilization, and state any idiosyncracy. State any assumptions. v Reading Exercise: What are Reed-Solomon Codes?

4 4 Memory Mgmt Your memory is a monster: you forget - it doesn’t. It simply files things away. It keeps things for you, or hides things from you and summons them to your recall with a will of its own. You think you have a memory; but it has you! From “A prayer for owen meany” by John Irving.

5 5 Disk Space Management v Lowest layer of DBMS software manages space on disk. v Higher levels call upon this layer to: – allocate/de-allocate a page – read/write a page v Request for a sequence of pages must be satisfied by allocating the pages sequentially on disk! Higher levels don’t need to know how this is done, or how free space is managed.

6 6 Buffer Management in a DBMS v Data must be in RAM for DBMS to operate on it. v \\Table of pairs is maintained. DB MAIN MEMORY DISK disk page free frame Page Requests from Higher Levels BUFFER POOL choice of frame dictated by replacement policy

7 7 When a Page is Requested... v If requested page is not in pool: – Choose a frame for replacement – If frame is dirty, write it to disk – Read requested page into chosen frame v Pin the page and return its address. * If requests can be predicted (e.g., sequential scans) pages can be pre-fetched several pages at a time!

8 8 More on Buffer Management v Requestor of page must unpin it, and indicate whether page has been modified: – dirty bit is used for this. v Page in pool may be requested many times, – a pin count is used. A page is a candidate for replacement iff pin count = 0. v CC & recovery may entail additional I/O when a frame is chosen for replacement. ( Write-Ahead Log protocol; more later.)

9 9 Buffer Replacement Policy v Frame is chosen for replacement by a replacement policy: – Least-recently-used (LRU), Clock, MRU etc. v Policy can have big impact on # of I/O’s; depends on the access pattern. v Sequential flooding : Nasty situation caused by LRU + repeated sequential scans. – # buffer frames < # pages in file means each page request causes an I/O. MRU much better in this situation (but not in all situations, of course).

10 10 DBMS vs. OS File System OS does disk space & buffer mgmt: why not let OS manage these tasks? v Differences in OS support: portability issues v Some limitations, e.g., files can’t span disks. v Buffer management in DBMS requires ability to: – pin a page in buffer pool, force a page to disk (important for implementing CC & recovery), – adjust replacement policy, and pre-fetch pages based on access patterns in typical DB operations.

11 11 Record Formats: Fixed Length v Information about field types same for all records in a file; stored in system catalogs. v Finding i’th field requires scan of record. Base address (B) L1L2L3L4 F1F2F3F4 Address = B+L1+L2

12 12 Record Formats: Variable Length v Two alternative formats (# fields is fixed): * Second offers direct access to i’th field, efficient storage of nulls (special don’t know value); small directory overhead. 4$$$$ Field Count Fields Delimited by Special Symbols F1 F2 F3 F4 Array of Field Offsets

13 13 Page Formats: Fixed Length Records * Record id =. In first alternative, moving records for free space management changes rid; may not be acceptable. Slot 1 Slot 2 Slot N... N M1 0 M... 3 2 1 PACKED UNPACKED, BITMAP Slot 1 Slot 2 Slot N Free Space Slot M 11 number of records number of slots

14 14 Page Formats: Variable Length Records * Can move records on page without changing rid; so, attractive for fixed-length records too. Page i Rid = (i,N) Rid = (i,2) Rid = (i,1) Pointer to start of free space SLOT DIRECTORY N... 2 1 201624 N # slots

15 15 Files of Records v Page or block is OK when doing I/O, but higher levels of DBMS operate on records, and files of records. v FILE : A collection of pages, each containing a collection of records. Must support: – insert/delete/modify record – read a particular record (specified using record id ) – scan all records (possibly with some conditions on the records to be retrieved)

16 16 Unordered (Heap) Files v Simplest file structure contains records in no particular order. v As file grows and shrinks, disk pages are allocated and de-allocated. v To support record level operations, 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 v There are many alternatives for keeping track of this.

17 17 Heap File Implemented as a List v The header page id and Heap file name must be stored someplace. v Each page contains 2 `pointers’ plus data. Header Page Data Page Data Page Data Page Data Page Data Page Data Page Pages with Free Space Full Pages

18 18 Heap File Using a Page Directory v The entry for a page can include the number of free bytes on the page. v The directory is a collection of pages; linked list implementation is just one alternative. – Much smaller than linked list of all HF pages ! Data Page 1 Data Page 2 Data Page N Header Page DIRECTORY

19 19 Indexes v A Heap file allows us to retrieve records: – by specifying the rid, or – by scanning all records sequentially v Sometimes, we want to retrieve records by specifying the values in one or more fields, e.g., – Find all students in the “CS” department – Find all students with a gpa > 3 v Indexes are file structures that enable us to answer such value-based queries efficiently.

20 20 System Catalogs v For each index: – structure (e.g., B+ tree) and search key fields v For each relation: – name, file name, file structure (e.g., Heap file) – attribute name and type, for each attribute – index name, for each index – integrity constraints v For each view: – view name and definition v Plus statistics, authorization, buffer pool size, etc. * Catalogs are themselves stored as relations !

21 21 Attr_Cat(attr_name, rel_name, type, position)

22 22 Summary v Disks provide cheap, non-volatile storage. – Random access, but cost depends on location of page on disk; important to arrange data sequentially to minimize seek and rotation delays. v Buffer manager brings pages into RAM. – Page stays in RAM until released by requestor. – Written to disk when frame chosen for replacement (which is sometime after requestor releases the page). – Choice of frame to replace based on replacement policy. – Tries to pre-fetch several pages at a time.

23 23 Summary (Contd.) v DBMS vs. OS File Support – DBMS needs features not found in many OS’s, e.g., forcing a page to disk, controlling the order of page writes to disk, files spanning disks, ability to control pre-fetching and page replacement policy based on predictable access patterns, etc. v Variable length record format with field offset directory offers support for direct access to i’th field and null values. v Slotted page format supports variable length records and allows records to move on page.

24 24 Summary (Contd.) v File layer keeps track of pages in a file, and supports abstraction of a collection of records. – Pages with free space identified using linked list or directory structure (similar to how pages in file are kept track of). v Indexes support efficient retrieval of records based on the values in some fields. v Catalog relations store information about relations, indexes and views. ( Information that is common to all records in a given collection. )

25 25 Alternative File Organizations Many alternatives exist, each ideal for some situation, and not so good in others: – Heap files: Suitable when typical access is a file scan retrieving all records. – Sorted Files: Best if records must be retrieved in some order, or only a `range’ of records is needed. – Hashed Files: Good for equality selections. u File is a collection of buckets. Bucket = primary page plus zero or more overflow pages. u Hashing function h : h ( r ) = bucket in which record r belongs. h looks at only some of the fields of r, called the search fields.

26 26 Cost Model for Our Analysis We ignore CPU costs, for simplicity: – B: The number of data pages – R: Number of records per page – D: (Average) time to read or write disk page – Measuring number of page I/O’s ignores gains of pre-fetching blocks of pages; thus, even I/O cost is only approximated. – Average-case analysis; based on several simplistic assumptions. * Good enough to show the overall trends!

27 27 Assumptions in Our Analysis v Single record insert and delete. v Heap Files: – Equality selection on key; exactly one match. – Insert always at end of file. v Sorted Files: – Files compacted after deletions. – Selections on sort field(s). v Hashed Files: – No overflow buckets, 80% page occupancy.

28 28 Cost of Operations * Several assumptions underlie these (rough) estimates!

29 29 Cost of Operations * Several assumptions underlie these (rough) estimates!

30 30 Indexes v An index on a file speeds up selections on the search key fields for the index. – Any subset of the fields of a relation can be the search key for an index on the relation. – Search key is not the same as key (minimal set of fields that uniquely identify a record in a relation). v An index contains a collection of data entries, and supports efficient retrieval of all data entries k* with a given key value k.

31 31 Alternatives for Data Entry k* in Index v Three alternatives: À Data record with key value k Á Â v Choice of alternative for data entries is orthogonal to the indexing technique used to locate data entries with a given key value k. – Examples of indexing techniques: B+ trees, hash- based structures – Typically, index contains auxiliary information that directs searches to the desired data entries

32 32 Alternatives for Data Entries (Contd.) v Alternative 1: – If this is used, index structure is a file organization for data records (like Heap files or sorted files). – At most one index on a given collection of data records can use Alternative 1. (Otherwise, data records duplicated, leading to redundant storage and potential inconsistency.) – If data records very large, # of pages containing data entries is high. Implies size of auxiliary information in the index is also large, typically.

33 33 Alternatives for Data Entries (Contd.) v Alternatives 2 and 3: – Data entries typically much smaller than data records. So, better than Alternative 1 with large data records, especially if search keys are small. (Portion of index structure used to direct search is much smaller than with Alternative 1.) – If more than one index is required on a given file, at most one index can use Alternative 1; rest must use Alternatives 2 or 3. – Alternative 3 more compact than Alternative 2, but leads to variable sized data entries even if search keys are of fixed length.

34 34 Index Classification v Primary vs. secondary : If search key contains primary key, then called primary index. – Unique index: Search key contains a candidate key. v 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, but not vice-versa. – 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!

35 35 Clustered vs. Unclustered Index v 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 (with some free space on each page for future inserts). – Overflow pages may be needed for inserts. (Thus, order of data recs is `close to’, but not identical to, the sort order.) Index entries Data entries direct search for (Index File) (Data file) Data Records data entries Data entries Data Records CLUSTERED UNCLUSTERED

36 36 Index Classification (Contd.) v Dense vs. Sparse : If there is at least one data entry per search key value (in some data record), then dense. – Alternative 1 always leads to dense index. – Every sparse index is clustered! – Sparse indexes are smaller; however, some useful optimizations are based on dense indexes. Ashby, 25, 3000 Smith, 44, 3000 Ashby Cass Smith 22 25 30 40 44 50 Sparse Index on Name Data File Dense Index on Age 33 Bristow, 30, 2007 Basu, 33, 4003 Cass, 50, 5004 Tracy, 44, 5004 Daniels, 22, 6003 Jones, 40, 6003

37 37 Index Classification (Contd.) v Composite Search Keys : Search on a combination of fields. – Equality query: Every field value is equal to a constant value. E.g. wrt index: u age=20 and sal =75 – Range query: Some field value is not a constant. E.g.: u age =20; or age=20 and sal > 10 v Data entries in index sorted by search key to support range queries. – Lexicographic order sue1375 bob cal joe12 10 20 8011 12 nameagesal 12,20 12,10 11,80 13,75 20,12 10,12 75,13 80,11 11 12 13 10 20 75 80 Data records sorted by name Data entries in index sorted by Data entries sorted by Examples of composite key indexes using lexicographic order.

38 38 Summary v Many alternative file organizations exist, each appropriate in some situation. v If selection queries are frequent, sorting the file or building an index is important. – Hash-based indexes only good for equality search. – Sorted files and tree-based indexes best for range search; also good for equality search. (Files rarely kept sorted in practice; B+ tree index is better.) v Index is a collection of data entries plus a way to quickly find entries with given key values.

39 39 Summary (Contd.) v Data entries can be actual data records, pairs, or pairs. – Choice orthogonal to indexing technique used to locate data entries with a given key value. v Can have several indexes on a given file of data records, each with a different search key. v Indexes can be classified as clustered vs. unclustered, primary vs. secondary, and dense vs. sparse. Differences have important consequences for utility/performance.

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