A review of Files and Indexing

Transcription

1 1 From DBMS to Records to Files A review of Files and Indexing Chapters 8, 10 and 11 (A Very Short Review) To understand query processing and optimization sid 74 A DBMS is a collection of records, or files, and each file consists of one or more pages bid day 10/10/06 10/10/06 10/1/06 0/03/06 Sid Sname Salary Age Dustin 7, Brutus 1, Lubber 8, Any 8, Horatio 7, Zorba 10, Horatio 7, File access method: e.g. index Outline Data on External Persistent Storage Data on External Storage Alternative File Organizations Heap files Sorted files Indexes (and the 3 alternatives) Clustered versus unclustered primary versus secondary Index data structures: B+ trees; Hashing Disk Disk space manager I/O Buffer manager Disk page size: 4KB to 3KB (Alternative TAPES) - Archival Main memory 3 I/O cost dominate typical DB operation DBMS are optimized to minimize this cost 4 Data on External Storage Data on External Storage: Rid File organization: Method of arranging a file of records on external storage. Record id (rid) is sufficient to physically locate record Indexes are data structures that allow us to find the record ids of records with given values in index search key fields Architecture: Buffer manager stages pages from external storage to main memory buffer pool. File and index layers make calls to the buffer manager. Rid AC1000 AF1001 AC100 AC1005 AC340 AC103 AC40 Sid Sname Dustin Brutus Lubber Any Horatio Zorba Horatio Salary 7,000 1,500 8,00 8,300 7,300 10,000 7,800 Age

2 7 Alternative File Organizations Many alternatives exist, each ideal for some situations, and not so good in others: Heap (random order) 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. Indexes: Data structures to organize records via trees or hashing. Like sorted files, they speed up searches for a subset of records, based on values in certain ( search key ) fields Updates are much faster than in sorted files. More about Indexes 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). An index contains a collection of data entries, and supports efficient retrieval of all data entries k* with a given key value k. 8 Data Entry k* with Search key k Three alternatives for storing k*: Data entry k* is record with key value k <k, rid of data record with search key value k> <k, list of rids of data records with search key k> Typically, index contains auxiliary information that directs searches to the desired data entries Index Classification: Primary versus Secondary Primary vs. secondary: If search key contains primary key, then called primary index. Unique index: Search key contains a candidate key. Question: How is this enforced in a DBMS? 9 10 Index Classification: Clustered versus Unclustered If order of data records is the same as, or `close to, order of data entries, then called clustered index. A file can be clustered on at most one search key. (WHY?) Cost of retrieving data records through index varies greatly based on whether index is clustered or not! Clustered vs. Unclustered Index Suppose that Alternative () 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. CLUSTERED Index entries direct search for data entries Data entries (Index File) (Data file) Data entries UNCLUSTERED 11 Data Records Data Records 1

3 13 Index Data Structures: (Recall CSI110/114) Hashing-based Indexes Hash function Buckets (Search) Tree-based Indexes Multi-way balanced search tree (e.g. AVLtype) B+ tree Indexing options B+ trees or Hashing Ranges versus Equality Range Searches ``Find all students with gpa > 3.0 If data is in sorted file, do binary search to find first such student, then scan to find others. Cost of binary search can be quite high. Simple idea: Create an `index file. k1 k kn Index File B+ Tree: Most Widely Used Index Insert/delete at log F N cost; keep tree heightbalanced. (F = fanout, N = # leaf pages) Minimum 50% occupancy (except for root). Each node contains d <= m <= d entries. The parameter d is called the order of the tree. Supports equality and range-searches efficiently. Page 1 Page Page 3 Page N Data File Index Entries (Direct search) * Can do binary search on (smaller) index file! Data Entries ("Sequence set") B+ Tree Indexes Example B+ Tree Non-leaf Pages Search begins at root, and key comparisons direct it to a leaf (as in ISAM). Search for 5*, 15*, all data entries >= 4*... Leaf Pages Leaf pages contain data entries, and are chained (prev & next) Non-leaf pages contain index entries and direct searches: index entry Root P 0 K 1 P 1 K P K m P m 17 * 3* 5* 7* 14* 16* 19* 0* * 4* 7* 9* 33* 34* 38* 39* * Based on the search for 15*, we know it is not in the tree!

4 19 How is it loaded? (p of text book) Bulk loading Create a sorted list of page entries Load them one by one Hash-Based Indexes Hash-Based Indexes Good for equality selections. Index is a collection of buckets. Bucket = primary page plus zero or more overflow pages. Hashing function h: h(r) = bucket in which record r belongs. h looks at the search key fields of r. If Alternative (1) is used, the buckets contain the data records Otherwise, they contain <key, rid> or <key, rid-list> pairs. 1 Introduction As for any index, 3 alternatives for data entries k*: Data record with key value k <k, rid of data record with search key value k> <k, list of rids of data records with search key k> Choice orthogonal to the indexing technique Hash-based indexes are best for equality selections. Cannot support range searches. Static and dynamic hashing techniques exist; trade-offs similar to ISAM vs. B+ trees. Static Hashing # primary pages fixed, allocated sequentially, never de-allocated; overflow pages if needed. h(k) mod M = bucket to which data entry with key k belongs. (M = # of buckets) h(key) mod N key h 0 Static Hashing (Contd.) Buckets contain data entries. Hash fn works on search key field of record r. Must distribute values over range 0... M-1. h(key) = (a * key + b) usually works well. a and b are constants; lots known about how to tune h. Long overflow chains can develop and degrade performance. Extendible and Linear Hashing: Dynamic techniques to fix this problem. N-1 Primary bucket pages Overflow pages

5 Extendible Hashing Situation: Bucket (primary page) becomes full. Why not reorganize file by doubling # of buckets? Reading and writing all pages is expensive! Idea: Use directory of pointers to buckets, double # of buckets by doubling the directory, splitting just the bucket that overflowed! Directory much smaller than file, so doubling it is much cheaper. Only one page of data entries is split. No overflow page! Trick lies in how hash function is adjusted! Example Directory is array of size 4. To find bucket for r, take last `global depth # bits of h(r); we denote r by h(r). If h(r) = 5 = binary 101, it is in bucket pointed to by 01. LOCAL DEPTH GLOBAL DEPTH * 5* 1* 13* Insert: If bucket is full, split it (allocate new page, re-distribute). If necessary, double the directory. (As we will see, splitting a bucket does not always require doubling; we can tell by comparing global depth with local depth for the split bucket.) DIRECTORY 4* 1* 3* 16* 10* 15* 7* 19* DATA PAGES Bucket A Bucket B Bucket C Bucket D Insert h(r)=0 (Causes Doubling) LOCAL DEPTH GLOBAL DEPTH DIRECTORY 4* 1* 0* Bucket A 3*16* 1* 5* 1*13* Bucket B 10* 15* 7* 19* Bucket C Bucket D Bucket A (`split image' of Bucket A) LOCAL DEPTH GLOBAL DEPTH DIRECTORY 3 15* 7* 19* 3 3* 16* Bucket A 1* 5* 1*13* Bucket B 10* 4* 1* 0* Bucket C Bucket D Bucket A (`split image' of Bucket A) Indexes and performance tuning (why do we need to study this) More later when we talk about query processing (next week) BUT 8 Indexes and Performance Tuning Understanding the Workload Choice of Indexes has HUGE impact on Performance Performance Tuning done by Database designer DBA Approach- Understand the workload For each query in the workload: Which relations does it access? Which attributes are retrieved? Which attributes are involved in selection/join conditions? How selective are these conditions likely to be? For each update in the workload: Which attributes are involved in selection/join conditions? How selective are these conditions likely to be? The type of update (INSERT/DELETE/UPDATE), and the attributes that are affected. 9 30

6 31 Choice of Indexes What indexes should we create? Which relations should have indexes? What field(s) should be the search key? Should we build several indexes? For each index, what kind of an index should it be? Clustered? Hash/tree? Choice of Indexes: Our Approach Consider the most important queries in turn. Consider the best plan using the current indexes, and see if a better plan is possible with an additional index. If so, create it. Obviously, this implies that we must understand how a DBMS evaluates queries and creates query evaluation plans! For now, we discuss simple 1-table queries. Before creating an index, must also consider the impact on updates in the workload! Trade-off: Indexes can make queries go faster, updates slower. Require disk space, too. Try to choose indexes that benefit as many queries as 3 possible Index Selection Guidelines Attributes in WHERE clause are candidates for index keys. Exact match condition suggests hash index. Range query suggests tree index. Clustering is especially useful for range queries; can also help on equality queries if there are many duplicates. Only one index can be clustered per relation choose it based on important queries that would benefit the most from clustering. SELECT E.Name, E.Age FROM Employees E WHERE E.Age > 35; 33 Index Selection Guidelines Multi-attribute search keys should be considered when a WHERE clause contains several conditions. Order of attributes is important for range queries. Such indexes can sometimes enable index-only strategies for important queries. For index-only strategies, clustering is not important! SELECT E.Name FROM Employees E WHERE E.Age > 35 and E.Sal < 10,000; 34 Examples of Clustered Indexes Some Key Points B+ tree index on E.age can be used to get qualifying tuples. Equality queries and duplicates: Clustering on E.hobby helps! SELECT E.dno FROM Emp E WHERE E.hobby=Stamps SELECT E.dno FROM Emp E WHERE E.age>40 Many alternative file organizations exist, each appropriate in some situation. Index is a collection of data entries plus a way to quickly find entries with given key values. 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.) 35 36

7 37 Some Key Points Understanding the nature of the workload for the application, and the performance goals, is essential to developing a good design. What are the important queries and updates? What attributes/relations are involved? Essential to understand physical design when talking about query processing and optimization Storing Data: Disks and Files Chapter 9 38 Buffer Management in a DBMS Next Page Requests from Higher Levels BUFFER POOL disk page free frame A word about Buffer Management, from the Workload perspective MAIN MEMORY DISK DB choice of frame dictated by replacement policy 39 Data must be in RAM for DBMS to operate on it! Table of <frame#, pageid> pairs is maintained. 40 When a Page is Requested... 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 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! More on Buffer Management Requestor of page must unpin it, and indicate whether page has been modified: dirty bit is used for this. Page in pool may be requested many times, a pin count is used. A page is a candidate for replacement iff pin count = 0. CC & recovery may entail additional I/O when a frame is chosen for replacement. (Write- Ahead Log protocol; recall...) 41 4

8 43 Buffer Replacement Policy Frame is chosen for replacement by a replacement policy: Next Least-recently-used (LRU), Clock, MRU etc. Policy can have big impact on # of I/O s; depends on the access pattern. Sequential flooding: Nasty situation caused by LRU + repeated sequential scans. Query processing # 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). 44