Distributed Query Processing. Banking Example

Transcription

1 Distributed Query Processing Advanced Topics in Database Management (INFSCI 2711) Some materials are from Database System Concepts, Siberschatz, Korth and Sudarshan Vladimir Zadorozhny, DINS, University of Pittsburgh 1 Banking Example branch (branch_name, branch_city, assets) customer (customer_name, customer_street, customer_city) account (account_number, branch_name, balance) loan (loan_number, branch_name, amount) depositor (customer_name, account_number) borrower (customer_name, loan_number) 2 1

2 - Basic Query Processing Architecture Find the names of customers having accounts in Brooklyn select customer-name from branch, account, depositor where branch-name = Brooklyn and branch.branch-id = account.branch-id and account.customer-id = depositor.customer-id) query parser internal representation optimizer P customer-name( (s branch-city = Brooklyn (branch (account depositor))) Catalog (metadata repository) output evaluator execution plan Statistics about data data branch(branch-id, branch-name) account(acc-number,branch-id,customer-id) depositor(customer-id,customer-name,customer-addr) 3 Relational Algebra s p 4 2

3 Sailors Database 5 6 3

4 7 Basic Steps in Query Processing Parsing and translation translate the query into its internal form. This is then translated into relational algebra. Parser checks syntax, verifies relations Evaluation The query-execution engine takes a query evaluation plan, executes that plan, and returns the answers to the query. 8 4

5 Evaluation Plan An evaluation plan defines exactly what algorithm is used for each operation, and how the execution of the operations is coordinated. 9 Measures of Query Cost Cost is generally measured as total elapsed time for answering query Many factors contribute to time cost 4 disk accesses, CPU, or network communication Typically disk access is the predominant cost in centralized system, and is also relatively easy to estimate. Measured by taking into account Number of seeks * average-seek-cost Number of blocks read * average-block-read-cost Number of blocks written * average-block-write-cost 4 Cost to write a block is greater than cost to read a block data is read back after being written to ensure that the write was successful For simplicity we just use the number of block transfers from disk 10 5

6 Selection Operation File scan search algorithms that locate and retrieve records that fulfill a selection condition. Algorithm A1 (linear search). Scan each file block and test all records to see whether they satisfy the selection condition. Cost estimate = b r block transfers 4 b r denotes number of blocks containing records from relation r If selection is on a key attribute, can stop on finding record 4 cost = (b r /2) block transfers Linear search can be applied regardless of 4 selection condition or 4 ordering of records in the file, or 4 availability of indices 11 Selection Operation (Cont.) A2 (binary search). Applicable if selection is an equality comparison on the attribute on which file is ordered. Assume that the blocks of a relation are stored contiguously Cost estimate (number of disk blocks to be scanned): 4 cost of locating the first tuple by a binary search on the blocks élog 2 (b r )ù 4 If there are multiple records satisfying selection Add transfer cost of the number of blocks containing records that satisfy selection condition Index scan search algorithms that use an index selection condition must be on search-key of index. A3 (primary index on candidate key, equality). Retrieve a single record that satisfies the corresponding equality condition Cost = (h i + 1) 12 6

7 Join Operation Several different algorithms to implement joins Nested-loop join Merge-join Choice based on cost estimate Examples use the following information Number of records of customer: 10,000 depositor: 5000 Number of blocks of customer: 400 depositor: Nested-Loop Join (Cont.) In the worst case, if there is enough memory only to hold one block of each relation, the estimated cost is n r * b s + b r block transfers If the smaller relation fits entirely in memory, use that as the inner relation. Reduces cost to b r + b s block transfers Assuming worst case memory availability cost estimate is with depositor as outer relation: * = 2,000,100 block transfers, with customer as the outer relation * = 1,000,400 block transfers If smaller relation (depositor) fits entirely in memory, the cost estimate will be 500 block transfers. 14 7

8 Merge-Join 1. Sort both relations on their join attribute (if not already sorted on the join attributes). 2. Merge the sorted relations to join them 1. Join step is similar to the merge stage of the sort-merge algorithm. 2. Main difference is handling of duplicate values in join attribute every pair with same value on join attribute must be matched 15 Merge-Join (Cont.) Can be used only for equi-joins and natural joins Each block needs to be read only once (assuming all tuples for any given value of the join attributes fit in memory Thus the cost of merge join is: b r + b s block transfers + the cost of sorting if relations are unsorted. 16 8

9 Query Optimization A relational algebra expression may have many equivalent expressions E.g., s balance<2500 (Õ balance (account)) is equivalent to Õ balance (s balance<2500 (account)) Each relational algebra operation can be evaluated using one of several different algorithms Correspondingly, a relational-algebra expression can be evaluated in many ways. Annotated expression specifying detailed evaluation strategy is called an evaluationplan. E.g., can use an index on balance to find accounts with balance < 2500, or can perform complete relation scan and discard accounts with balance ³ 2500 Query Optimization: Amongst all equivalent evaluation plans choose the one with lowest cost. Cost is estimated using statistical information from the database catalog 4 e.g. number of tuples in each relation, size of tuples, etc. 17 Pictorial Depiction of Equivalence Rules 18 9

10 Multiple Transformations (Cont.) 19 Join Ordering Example For all relations r 1, r 2, and r 3, (r 1 r 2 ) r 3 = r 1 (r 2 r 3 ) (Join Associativity) If r 2 r 3 is quite large and r 1 r 2 is small, we choose (r 1 r 2 ) r 3 so that we compute and store a smaller temporary relation

11 Join Ordering Example (Cont.) Consider the expression P customer_name ((s branch_city = Brooklyn (branch)) (account depositor)) Could compute account depositor first, and join result with s branch_city = Brooklyn (branch) but account depositor is likely to be a large relation. Only a small fraction of the bank s customers are likely to have accounts in branches located in Brooklyn it is better to compute first. s branch_city = Brooklyn (branch) account 21 Cost Estimation Cost of each operator computed using statistics of input relations 4 E.g. number of tuples, sizes of tuples Inputs can be results of sub-expressions Need to estimate statistics of expression results To do so, we require additional statistics 4 E.g. number of distinct values for an attribute 22 11

12 Statistical Information for Cost Estimation n r : number of tuples in a relation r. b r : number of blocks containing tuples of r. l r : size of a tuple of r. f r : blocking factor of r i.e., the number of tuples of r that fit into one block. V(A, r): number of distinct values that appear in r for attribute A; same as the size of Õ A (r). If tuples of r are stored together physically in a file, then: $ n b = # # f r r # r "!!! 23 Histograms Histogram on attribute age of relation person 24 12

13 Evaluation of Expressions So far: we have seen algorithms for individual operations Alternatives for evaluating an entire expression tree Materialization: generate results of an expression whose inputs are relations or are already computed, materialize (store) it on disk. Repeat. Pipelining: pass on tuples to parent operations even as an operation is being executed 25 Materialization Materialized evaluation: evaluate one operation at a time, starting at the lowest-level. Use intermediate results materialized into temporary relations to evaluate next-level operations. E.g., in figure below, compute and store s balance<2500 ( account) then compute and store its join with customer, and finally compute the projections on customer-name

14 Materialization (Cont.) Materialized evaluation is always applicable Cost of writing results to disk and reading them back can be quite high Our cost formulas for operations ignore cost of writing results to disk, so 4 Overall cost = Sum of costs of individual operations + cost of writing intermediate results to disk 27 Pipelining Pipelined evaluation : evaluate several operations simultaneously, passing the results of one operation on to the next. E.g., in previous expression tree, don t store result of s balance<2500 ( account ) instead, pass tuples directly to the join.. Similarly, don t store result of join, pass tuples directly to projection. Much cheaper than materialization: no need to store a temporary relation to disk. Pipelining may not always be possible e.g., sort. For pipelining to be effective, evaluation algorithms should generate output tuples even as tuples are received for inputs to the operation. Pipelines can be executed in two ways: demand driven (lazy, pull) and producer driven (eager, push)

15 Distributed Database System A distributed database system consists of loosely coupled sites that share no physical component Database systems that run on each site are independent of each other Transactions may access data at one or more sites 29 Distributed Query Processing For centralized systems, the primary criterion for measuring the cost of a particular strategy is the number of disk accesses. In a distributed system, other issues must be taken into account: The cost of a data transmission over the network. The potential gain in performance from having several sites process parts of the query in parallel

16 Distributed Data Storage Assume relational data model Replication System maintains multiple copies of data, stored in different sites, for faster retrieval and fault tolerance. Fragmentation Relation is partitioned into several fragments stored in distinct sites Replication and fragmentation can be combined Relation is partitioned into several fragments: system maintains several identical replicas of each such fragment. 31 Data Replication A relation or fragment of a relation is replicated if it is stored redundantly in two or more sites. Full replication of a relation is the case where the relation is stored at all sites. Fully redundant databases are those in which every site contains a copy of the entire database

17 Data Replication (Cont.) Advantages of Replication Availability: failure of site containing relation r does not result in unavailability of r is replicas exist. Parallelism: queries on r may be processed by several nodes in parallel. Reduced data transfer: relation r is available locally at each site containing a replica of r. Disadvantages of Replication Increased cost of updates: each replica of relation r must be updated. Increased complexity of concurrency control: concurrent updates to distinct replicas may lead to inconsistent data unless special concurrency control mechanisms are implemented. 4 One solution: choose one copy as primary copy and apply concurrency control operations on primary copy 33 Data Fragmentation Division of relation r into fragments r 1, r 2,, r n which contain sufficient information to reconstruct relation r. Horizontal fragmentation: each tuple of r is assigned to one or more fragments Vertical fragmentation: the schema for relation r is split into several smaller schemas All schemas must contain a common candidate key (or superkey) to ensure lossless join property. A special attribute, the tuple-id attribute may be added to each schema to serve as a candidate key. Example : relation account with following schema Account = (branch_name, account_number, balance ) 34 17

18 Horizontal Fragmentation of account Relation branch_name account_number balance Hillside Hillside Hillside A-305 A-226 A account 1 = s branch_name= Hillside (account ) branch_name account_number balance Valleyview Valleyview Valleyview Valleyview A-177 A-402 A-408 A account 2 = s branch_name= Valleyview (account ) 35 Vertical Fragmentation of employee_info Relation branch_name customer_name tuple_id Hillside Hillside Valleyview Valleyview Hillside Valleyview Valleyview Lowman Camp Camp Kahn Kahn Kahn Green deposit 1 = P branch_name, customer_name, tuple_id (employee_info ) account_number balance tuple_id A A A A A A A deposit 2 = P account_number, balance, tuple_id (employee_info )

19 Advantages of Fragmentation Horizontal: allows parallel processing on fragments of a relation allows a relation to be split so that tuples are located where they are most frequently accessed Vertical: allows tuples to be split so that each part of the tuple is stored where it is most frequently accessed tuple-id attribute allows efficient joining of vertical fragments allows parallel processing on a relation Vertical and horizontal fragmentation can be mixed. Fragments may be successively fragmented to an arbitrary depth. 37 Data Transparency Data transparency: Degree to which system user may remain unaware of the details of how and where the data items are stored in a distributed system Consider transparency issues in relation to: Fragmentation transparency Replication transparency Location transparency 38 19

20 Query Transformation Translating algebraic queries on fragments. It must be possible to construct relation r from its fragments Replace relation r by the expression to construct relation r from its fragments Consider the horizontal fragmentation of the account relation into account 1 = s branch_name = Hillside (account ) account 2 = s branch_name = Valleyview (account ) The query s branch_name = Hillside (account ) becomes s branch_name = Hillside (account 1 È account 2 ) which is optimized into s branch_name = Hillside (account 1 ) È s branch_name = Hillside (account 2 ) 39 Example Query (Cont.) Since account 1 has only tuples pertaining to the Hillside branch, we can eliminate the selection operation. Apply the definition of account 2 to obtain s branch_name = Hillside (s branch_name = Valleyview (account ) This expression is the empty set regardless of the contents of the account relation. Final strategy is for the Hillside site to return account 1 as the result of the query

21 Simple Join Processing Consider the following relational algebra expression in which the three relations are neither replicated nor fragmented account depositor branch account is stored at site S 1 depositor at S 2 branch at S 3 For a query issued at site S I, the system needs to produce the result at site S I 41 Possible Query Processing Strategies Ship copies of all three relations to site S I and choose a strategy for processing the entire result locally at site S I. Ship a copy of the account relation to site S 2 and compute temp 1 = account depositor at S 2. Ship temp 1 from S 2 to S 3, and compute temp 2 = temp 1 branch at S 3. Ship the result temp 2 to S I. Devise similar strategies, exchanging the roles S 1, S 2, S 3 Must consider following factors: amount of data being shipped cost of transmitting a data block between sites relative processing speed at each site 42 21

22 Semijoin Strategy Let r 1 be a relation with schema R 1 stores at site S 1 Let r 2 be a relation with schema R 2 stores at site S 2 Evaluate the expression r 1 r 2 and obtain the result at S Compute temp 1 Õ R1 Ç R2 (r1) at S1. 2. Ship temp 1 from S 1 to S Compute temp 2 r 2 temp1 at S 2 4. Ship temp 2 from S 2 to S Compute r 1 temp 2 at S 1. This is the same as r 1 r Formal Definition The semijoin of r 1 with r 2, is denoted by: it is defined by: Õ R1 (r 1 r 2 ) r 1 r 2 Thus, r 1 r 2 selects those tuples of r 1 that contributed to r 1 r 2. In step 3 above, temp 2 =r 2 r 1. For joins of several relations, the above strategy can be extended to a series of semijoin steps

23 Join Strategies that Exploit Parallelism Consider r 1 r 2 r 3 r 4 where relation ri is stored at site S i. The result must be presented at site S 1. r 1 is shipped to S 2 and r 1 r 2 is computed at S 2 : simultaneously r 3 is shipped to S 4 and r 3 r 4 is computed at S 4 S 2 ships tuples of (r 1 r 2 ) to S 1 as they produced; S 4 ships tuples of (r 3 r 4 ) to S 1 Once tuples of (r 1 r 2 ) and (r 3 r 4 ) arrive at S 1 (r 1 r 2 ) (r 3 r 4 ) is computed in parallel with the computation of (r 1 r 2 ) at S 2 and the computation of (r 3 r 4 ) at S Heterogeneous Distributed Databases Many database applications require data from a variety of preexisting databases located in a heterogeneous collection of hardware and software platforms Data models may differ (hierarchical, relational, etc.) Transaction commit protocols may be incompatible Concurrency control may be based on different techniques (locking, timestamping, etc.) System-level details almost certainly are totally incompatible. A multidatabase system is a software layer on top of existing database systems, which is designed to manipulate information in heterogeneous databases Creates an illusion of logical database integration without any physical database integration 46 23

24 Advantages Preservation of investment in existing hardware system software applications Local autonomy and administrative control Allows use of special-purpose DBMSs Step towards a unified homogeneous DBMS Full integration into a homogeneous DBMS faces 4 Technical difficulties and cost of conversion 4 Organizational/political difficulties Organizations do not want to give up control on their data Local databases wish to retain a great deal of autonomy 47 Unified View of Data Agreement on a common data model Typically the relational model Agreement on a common conceptual schema Different names for same relation/attribute Same relation/attribute name means different things Agreement on a single representation of shared data E.g. data types, precision, Character sets 4 ASCII vs EBCDIC 4 Sort order variations Agreement on units of measure Variations in names E.g. Köln vs Cologne, Mumbai vs Bombay 48 24

25 Query Processing Several issues in query processing in a heterogeneous database Schema translation Write a wrapper for each data source to translate data to a global schema Wrappers must also translate updates on global schema to updates on local schema Limited query capabilities Some data sources allow only restricted forms of selections 4 E.g. web forms, flat file data sources Queries have to be broken up and processed partly at the source and partly at a different site Removal of duplicate information when sites have overlapping information Decide which sites to execute query Global query optimization 49 Mediator Systems Mediator systems are systems that integrate multiple heterogeneous data sources by providing an integrated global view, and providing query facilities on global view Unlike full fledged multidatabase systems, mediators generally do not bother about transaction processing But the terms mediator and multidatabase are sometimes used interchangeably The term virtual database is also used to refer to mediator/multidatabase systems 50 25

26 Querying Web Data Manual navigation over multilevel links: inefficient Find the top selling book on C++ at Amazon? Objective: database-like declarative queries: select booktitle from Amazon where booktopic = C++ and booksalesrank > all ( select booksalesrank from Amazon where booktopic = C++ ) 51 Approximate Joins Name:&autogeneratedQ1: Descrip3on:&based&on&column&name relate Precipitation and Population Average&Confidence:&1 in different states South&Carolina Florida SC FL ! 26

27 Approximate Joins (cont.) City Year Precipitation Pittsburgh Philadelphia Area Year Population Temperature Allegheny County , Pennsylvania ,548, Q1: relate Precipitation and Population in different areas City Year Precipitation Area Year Population Score Pittsburgh Allegheny County ,211 s1 Philadelphia Pennsylvania ,548,000 s2 Pittsburgh Pennsylvania ,211 s3 Q2: relate Precipitation and Temperature in different areas City Year Precipitation Area Year Temperature Score Pittsburgh Pennsylvania s1 Philadelphia Pennsylvania s2 Pittsburgh Allegheny County s3 53 Approximate Joins (cont.) City From To Precipitation Pittsburgh Philadelphia Area Start End Population Temperature Allegheny County , Allegheny County , Pennsylvania ,348, Pennsylvania ,548, Q1: relate Precipitation and Population in different areas? Q2: relate Precipitation and Temperature in different areas? We need more advanced data linkage methods 54 27