- Query Processing In Dbms Ppt
- Query Processing In Dbms Pdf
- Query Processing In Dbms Slideshare
- Query Processing In Dbms Ppt
- What is query processing and optimization in DBMS? DBMS Database Big Data Analytics The activities involved in parsing, validating, execution and optimizing a query is called Query Processing.
- In this paper, we give an overview of query processing in multistore systems. We start by introducing the recent cloud data management solutions and query processing in multidatabase systems. Then, we describe and analyze some representative multistore systems, based on their architecture, data model, query languages and query processing.
Chapter 13: Query Processing Overview Measures of Query Cost Selection Operation Sorting Join Operation Other Operations Evaluation of Expressions Slideshare uses cookies to improve functionality and performance, and to provide you with relevant advertising. Query Processing Query Processing would mean the entire process or activity which involves query translation into low level instructions, query optimization to save resources, cost estimation or evaluation of query, and extraction of data from the database. What is Query Decomposition in DBMS? Query decomposition is the first phase of query processing. The primary targets of query decomposition are to transform a high-level query into a relational algebra query and to check that the query is syntactically and semantically correct. The typical stages of query decomposition are analysis.
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The APPROX_COUNT_DISTINCT
function was added, but not documented, in Oracle 11g to improve the speed of calculating the number of distinct values (NDV) when gathering statistics using the DBMS_STATS
package. Oracle Database 12c Release 1 (12.1) documented it for the first time, making it a supported feature. Oracle Database 12c Release 2 (12.2) extends the concept of approximate query processing by the addition of new functions and transparent conversion to approximate query processing.
Related articles.
Approximate Functions
In Oracle Database 12c Release 2 (12.2) the following functions provide approximate results.
The documentation states they 'obtain approximate results with negligible deviation from the exact result'. If you are writing a new query or refactoring an existing query and approximate results are acceptable, you can use them explicitly.
Convert Exact to Approximate
Having the new approximate query processing is great, but what do you do about all the existing code you have that uses the original calls? You could refactor your code, or you could ask Oracle to convert your exact calls to approximate calls instead.
Query Processing In Dbms Ppt
Oracle Database 12c Release 2 (12.2) includes three new parameters that control approximate query processing, which can be set at the system or session level.
APPROX_FOR_AGGREGATION
: Setting this toTRUE
is the equivalent of settingAPPROX_FOR_COUNT_DISTINCT
toTRUE
andAPPROX_FOR_PERCENTILE
toALL
.APPROX_FOR_COUNT_DISTINCT
: Setting this toTRUE
convertsCOUNT(DISTINCT ...)
calls toAPPROX_COUNT_DISTINCT
calls.APPROX_FOR_PERCENTILE
: This can be set toNONE
PERCENTILE_CONT
,PERCENTILE_CONT DETERMINISTIC
,PERCENTILE_DISC
,PERCENTILE_DISC DETERMINISTIC
,ALL
,ALL DETERMINISTIC
.
We can do a simple test to prove to ourselves this works. Remember, estimated execution plans are not always representative of the actual plans used by a query.
Build a large table.
Turn on the approximate query processing and get the estimated execution plan for a regular COUNT(DISTINCT ...)
query.
We can see from the output the estimated plan includes a SORT AGGREGATE APPROX
operation.
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Create a new session and run the same test without enabling the approximate query processing.
We can see from the output below the estimated execution plan no longer contains the approximate query processing.
Here are some examples of setting the parameters at the session and system level.
Query Transformation
If you want to see the associated query transformation you can perform a 10053 trace and look at the resulting trace file. As an example, run the following.
Open the trace file displayed by the v$DIAG_INFO query and search for the term 'Final query after transformations'. You will see something like this.
Approximate Query Processing and Materialized Views
You can use approximate query processing functions in materialized views, which can subsequently be used for query rewrites.
Create a materialized view based on the test table we used in the previous section, using a query containing the APPROX_COUNT_DISTINCT
function.
We check the approximate query processing and query rewrite parameters for the session, then check the estimated execution plan for a query against the base table using the APPROX_COUNT_DISTINCT
function.
We can see a query rewrite was done to use the materialized view.
Let's see what happens if we use a conventional COUNT(DISTINCT ...)
query against the base table.
We can see the estimated execution plan used the base table, rather than performing a rewrite to use the materialized view.
This time we will enable approximate query processing and try again.
We can see the estimated execution plan used the materialized view, rather than the base table.
There are some restrictions associated with fast refreshes of materialized views containing approximate query processing functions listed here.
For more information see:
Hope this helps. Regards Tim...
Part 8
Query Processing andOptimization, andDatabase Tuning
Chapter 19
Algorithms for Query Processing and Optimization
In this chapter we discuss the techniques used internally by a DBMS to process, optimize, and execute high-level queries. A query expressed in a high-level query language such as SQL must first be scanned, parsed, and validated. The scanner identifies the query tokens—such as SQL keywords, attribute names, and relation names—that appear in the text of the query, whereas the parser checks the query syntax to determine whether it is formulated according to the syntax rules (rules of grammar) of the query language. The query must also be validated by checking that all attribute and relation names are valid and semantically meaningful names in the schema of the particular database being queried. An internal representation of the query is then created, usually as a tree data structure called a query tree. It is also possible to rep-resent the query using a graph data structure called a query graph. The DBMS must then devise an execution strategy or query plan for retrieving the results of the query from the database files. A query typically has many possible execution strategies, and the process of choosing a suitable one for processing a query is known as query optimization.
Figure 19.1 shows the different steps of processing a high-level query. The queryoptimizer module has the task of producing a good execution plan, and the code generator generates the code to execute that plan. The runtime database processor has the task of running (executing) the query code, whether in compiled or interpreted mode, to produce the query result. If a runtime error results, an error mes-sage is generated by the runtime database processor.
Query Processing In Dbms Pdf
The term optimization is actually a misnomer because in some cases the chosen execution plan is not the optimal (or absolute best) strategy—it is just a reasonably efficient strategy for executing the query. Finding the optimal strategy is usually tootime-consuming—except for the simplest of queries. In addition, trying to find the optimal query execution strategy may require detailed information on how the files are implemented and even on the contents of the files—information that may not be fully available in the DBMS catalog. Hence, planning of a good execution strategy may be a more accurate description than query optimization.
Query Processing In Dbms Slideshare
For lower-level navigational database languages in legacy systems—such as the net-work DML or the hierarchical DL/1 (see Section 2.6)—the programmer must choose the query execution strategy while writing a database program. If a DBMS provides only a navigational language, there is limited need or opportunity for extensive query optimization by the DBMS; instead, the programmer is given the capability to choose the query execution strategy. On the other hand, a high-level query language—such as SQL for relational DBMSs (RDBMSs) or OQL (see Chapter 11) for object DBMSs (ODBMSs)—is more declarative in nature because it specifies what the intended results of the query are, rather than identifying the details of how the result should be obtained. Query optimization is thus necessary for queries that are specified in a high-level query language.
We will concentrate on describing query optimization in the context of an RDBMS because many of the techniques we describe have also been adapted for other types of database management systems, such as ODBMSs. A relational DBMS must systematically evaluate alternative query execution strategies and choose a reasonably efficient or near-optimal strategy. Each DBMS typically has a number of general database access algorithms that implement relational algebra operations such as SELECT or JOIN (see Chapter 6) or combinations of these operations. Only execution strategies that can be implemented by the DBMS access algorithms and that apply to the particular query, as well as to the particular physical database design, can be considered by the query optimization module.
Query Processing In Dbms Ppt
This chapter starts with a general discussion of how SQL queries are typically translated into relational algebra queries and then optimized in Section 19.1. Then we discuss algorithms for implementing relational algebra operations in Sections 19.2 through 19.6. Following this, we give an overview of query optimization strategies. There are two main techniques that are employed during query optimization. The first technique is based on heuristic rules for ordering the operations in a query execution strategy. A heuristic is a rule that works well in most cases but is not guaranteed to work well in every case. The rules typically reorder the operations in a query tree. The second technique involves systematically estimating the cost of different execution strategies and choosing the execution plan with the lowest cost estimate. These techniques are usually combined in a query optimizer. We discuss heuristic optimization in Section 19.7 and cost estimation in Section 19.8. Then we provide a brief overview of the factors considered during query optimization in the Oracle commercial RDBMS in Section 19.9. Section 19.10 introduces the topic of semantic query optimization, in which known constraints are used as an aid to devising efficient query execution strategies.
The topics covered in this chapter require that the reader be familiar with the mate-rial presented in several earlier chapters. In particular, the chapters on SQL (Chapters 4 and 5), relational algebra (Chapter 6), and file structures and indexing (Chapters 17 and 18) are a prerequisite to this chapter. Also, it is important to note that the topic of query processing and optimization is vast, and we can only give an introduction to the basic principles and techniques in this chapter.