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Distributed Query Processing –An Overview

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1 Distributed Query Processing –An Overview
Univ.-Prof. Dr. Peter Brezany Institut für Scientific Computing Universität Wien Tel Sprechstunde: Di, LV-Portal:

2 Introduction To construct the answer to the query, the user does not precisely specify the procedure to follow  this procedure is devised by a DBMS module called query processor, which performs query optimization. The query processing problem is much more difficult in distributed environments  a larger number of parameters affect the performance: relations involved in a distributed query may be fragmented and/or replicated, thereby inducing communication costs. Furthermore, with many sites to access, query response time may become very high. The role of a distributed query processor is to map a high-level query (expressed in relational calculus) on a distributed DB (i.e., a set of global relations) into a sequence of DB operations (of relational algebra) on relation fragments. The calculus query must be decomposed into a sequence of relational operations called an algebraic query. The data accessed by the query must be localized so that the operations on relations are translated to bear on local data (fragments). The algebraic query on fragments must be extended with communication operations and optimized with respect to a cost function (based on features of disk I/Os, CPUs, and communication networks) to be minimized.

3 Query Processing Problem
Example 1: Consider EMP(ENO, ENAME, TITLE) ASG (ENO, PNO, RESP, DUR) and the following simple user query: „Find the names of employees who are managing a project“ In relational calculus using SQL: SELECT ENAME FROM EMP, ASG WHERE EMP.ENO = ASG.ENO AND ASG.RESP = “Manager“ Two equivalent rel. algebra queries: ENAME (ASG.RESP=“Manager“  EMP.ENO=ASG.ENO (EMP  ASG) and ENAME (ENAME ⋈ENO (ASG.RESP=“Manager“(ASG))) consumes less comp. resources – it is intuitively obvious

4 Query Processing Problem (cont.)
In a distributed system, rel. algebra is not enough to express execution strategy. It must be suplemented with operations for exchanging data between sites. The best sites to process data must also be selected. This increases the solution space. Example 2: ENAME (ENAME ⋈ENO (RESP=“Manager“(ASG))) We assume that EMP and ASG are horizontally fragmented: EMP1 = ENO  “E3“ (EMP) EMP2 = ENO  “E3“ (EMP) ASG1 = ENO  “E3“ (ASG) ASG2 = ENO “E3“ (ASG) Fragments ASG1, ASG2, EMP1, and EMP2 are stored at sites 1, 2, 3, and 4, respectively, and the result is expected at site 5.

5 Equivalent Distributed Execution Strategies
For the sake of pedagogical simplicity, the project operation is ignored. Strategy A exploits the fact that EMP and ASG are fragmented the same way in order to perform the select and join in parallel. Strategy B centralizes all the operand data at the result site before processing the query. (b) Strategy B

6 Simple Cost Model and Statistics
tuple access: 1 unit (which we leave unspecified) tuple transfer: tuptrans: 10 units relations EMP and ASG have 400 and 1000 tuples, respectively. there are 20 managers in relation ASG. data is is uniformly distributed (fragmentation + allocation) among the sites. relations ASG and EMP are locally clustered on attributes RESP and ENO, respectively. Therefore, there is direct access to tuples of ASG (respectively, EMP) based on the value of attribute RESP (respectively, ENO)

7 Cost Estimation

8 Complexity of Relational Algebra Operations
n denotes the relation cardinality

9 Layers of Query Processing

10 Query Decomposition – An Overview
The distributed calculus query is decomposed into an algebraic query on global relations. The information about data distribution is not used.  The techniques used by this layer are those of a centralized DBMS. Query is rewritten in a normalized form that is suitable for subsequent manipulation. The normalized query is analyzed semantically so that incorrect queries are rejected as soon as possible. The correct query is simplified (e.g., eliminating redundant predicates). The calculus query is restructured as an algebraic query.  Several alg. queries can be derived from the same calc. query, but some alg. queries are “better“ than others. The quality is defined in terms of expected performance.

11 Data Localization – An Overview
The main role is to localize the query‘s data using data distribution information. Repetition: Relations are fragmented; each being stored at a different site. Fragmentation is defined through fragmentation rules (fragmentation scheme). This layer determines which fragments are involved in the query and transforms the distributed query into a fragment query. The distributed query is mapped into a fragment query by substituting each distributed relation by its recontructing program (materialization program). The fragment query is simplified and restructured to produce another “good“ query (applying the same rules used in the decomposition layer).

12 Global and Local Query Optimization
Global optimization: the goal is to find an execution strategy for the query which is close to optimal Local optimization: it is performed by all the sites having fragments involved in the query. Each subquery executing at one site, called a local query, is then optimized using the local schema of the site. At this time, the algorithms to perform the relational operations may be chosen. Local optimization uses the algorithms of centralized systems.

13 Query Decomposition – 1. Normalization
The most important transformation is that of the query qualification (the WHERE clause), which may be arbitrarily complex, quantifier-free predicate or preceded by all necessary quantifiers ( or ). Conjunctive and disjunctive normal forms. Rules for the transformation of the quantifier-free predicates.

14 Normalization (cont.) „Find the names of employees who have been working on project P1 for 12 or 24 months“ SELECT ENAME FROM EMP, ASG WHERE EMP.ENO = ASG.ENO AND ASG.PNO = “P1“ AND DUR = 12 OR DUR = 24 The qualification in conjunctive normal form is EMP.ENO = ASG.ENO  ASG.PNO = “P1“  (DUR = 12  DUR = 24) and in disjunctive normal form: (EMP.ENO = ASG.ENO  ASG.PNO = “P1“  DUR = 12)  (EMP.ENO = ASG.ENO  ASG.PNO = “P1“  DUR = 24) In the latter form, treating the two conjunctions may lead to redundant work if common subexpressions are not eliminated.

15 Query Decomposition - 2.Analysis
The main reasons for query rejection (The query is simply returned to the user with an explanation.) are that the query is type incorrect or semantically incorrect. Example 1: The following query is type incorrect SELECT E# FROM EMP WHERE ENAME > 200 for 2 reasons: (1) Attribute E# is not declared in the schema; and (2) Operation “>200“ is incompatible with the type string of ENAME.

16 Analysis (cont.) A query is semantically incorrect if components of it do not contribute in any way to the generation of the result. In the context of relational calculus, it is not possible to determine the semantic correctness of general queries. However, it is possible to do so for large class of relational queries, those which do not contain disjunction and negation. This is based on the representation of the query as a query graph or connection graph – one node indicates the result relation, and any other node indicates an operand relation. An edge between two nodes that are not results represents a join, whereas an edge whose destination node is the result represents a project. Furthermore, a nonresult node may be labeled by a select or a self-join predicate. An important subgraph of the relation connection graph is the join graph, in which only the joins are considered. The join graph is particularly useful in the query optimization phase.

17 Analysis (cont.) Example 1: “Find the names and responsibility of programmers who have been working on the CAD/CAM project for more than 3 years“ SELECT ENAME, RESP FROM EMP, ASG, PROJ WHERE EMP.ENO = ASG.ENO AND ASG.PNO = PROJ.PNO AND PNAME = “CAD/CAM“ AND DUR >= 36 AND TITLE = “Programmer“ The query graph and the corresponding join graph are shown in the next slide.

18 Analysis (cont.) Fig. 8.1

19 Analysis (cont.) The query graph is useful to determine the semantic
correctness of a conjunctive multivariable query without negation. Such a query is semantically incorrect if its query graph is not connected. In this case one or more subgraphs (corresponding to subqueries) are disconnected from the graph that contains the result relation. The query could be considered correct (which some systems do) by considering the missing connection as a Cartesian product. In general, the problem is that join predicates are missing and the query should be rejected.

20 Analysis (cont.) Example 2: Let us consider the following query:
SELECT ENAME, RESP FROM EMP, ASG, PROJ WHERE EMP.ENO = ASG.PROJ AND PNAME = “CAD/CAM“ AND DUR >= 36 AND TITLE = “Programmer“ Its query graph is disconnected, which tells us that the query is semantically incorrect.There are basically 3 solutions to the problem: (1) reject the query (2) assume that there is an implicit Cartesian product between relations ASG and PROJ, or (3) infer (using the schema) the missing join predicate ASG.PNO = PROJ.PNO which transforms the query into that of Example 1.

21 Query Decomposition - 3.Elimination of Redundancy
The query qualification may contain redundant predicates.  A naive evaluation can well lead to duplicated work.  This can be eliminated by simplifying the qualification with the following well-known idempotency rules: Example:

22 Elimination of Redundancy (cont.)
Example (cont.) Slide 12 Slide 12

23 Query Decomposition - 4.Rewriting
The last step of query decomposition rewrites the query in relational algebra – in two substeps: (1) straighforward transformation of the query from relational calculus into relational algebra (2) restructuring of the relational algebra query to improve performance. It is customary to represent the relational algebra query graphically by an operator tree. Example 1: The query can be mapped in a straightforward way in the tree in the following slide.

24 Rewriting (cont.) By applying transformation rules, many different equivalent trees may be found  Vorlesung Datenbanksysteme (Prof. Schikuta)

25 Rewriting (cont.) Example 2: The restructuring of the tree in previous slide leads to the tree below. The resulting tree is good in the sense that repeated access to the same relation (as in the previous figure is avoided and that the most selective operations are done first. However, this tree is far from optimal.  The select operation on EMP is not very useful before the join because it does not greatly reduce the size of the operand relation.

26 Localization of Distributed Data
This layer translates an algebraic query on global relations into an algebraic query expressed on physical fragments. Localization uses information stored in the fragment schema. Fragmentation is defined through fragmentation rules. Reduction techniques are a way how to localize a distributed query.

27 Reduction for Primary Horizontal Fragmentation
The following example is used in subsequent discussions. Example: Relation EMP(ENO, ENAME, TITLE) can be split into three horizontal fragments EMP1, EMP2, and EMP3, defined as follows: EMP1 = ENO  “E3“(EMP) EMP2 = “E3“ < ENO  “E6“(EMP) EMP3 = ENO > “E6“(EMP) The localization program for an horizontally fragmented relation is the union of the fragments. EMP = EMP1  EMP2  EMP3 Thus the generic form of any query specified on EMP is obtained by replacing it by (EMP1  EMP2  EMP3). The reduction of queries consists primarily of detecting those subtrees that will produce empty relations, and removing them.

28 Reduction with Selection
Selections on fragments that have a qualification contradicting the qualifications of the fragmentation rule generate empty relations. Given a relation R that has been horizontally fragmented as R1, R2, ..., Rw, where Rj = pj (R), the rule can be stated formally as follows: Rule: pi (Rj) =  if x in R:  (pi(x)  pj(x)) where pi and pj are selection predicates, x denotes a tuple, and p(x) denotes “predicate p holds for x.“ Determining the contradicting predicates requires theorem-proving techniques if the predicates are quite general. However, DBMSs generally simplify predicate comparison by supporting only simple predicates for defining fragmentation rules (by the DB administrator).

29 Reduction with Selection (cont.)
Example: SELECT * FROM EMP WHERE ENO = “E5“ Applying the naive approach to localize EMP from EMP1, EMP2, and EMP3 gives the generic query of Figure (a) below. It is easy to detect that the selection predicate contradicts the predicate of EMP1 and EMP3, thereby producing empty relations. The reduced query is simply applied to EMP2 as shown in Figure (b).

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