CA2468614A1 - System and method of query transformation - Google Patents
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- CA2468614A1 CA2468614A1 CA002468614A CA2468614A CA2468614A1 CA 2468614 A1 CA2468614 A1 CA 2468614A1 CA 002468614 A CA002468614 A CA 002468614A CA 2468614 A CA2468614 A CA 2468614A CA 2468614 A1 CA2468614 A1 CA 2468614A1
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/24—Querying
- G06F16/245—Query processing
- G06F16/2453—Query optimisation
- G06F16/24534—Query rewriting; Transformation
- G06F16/24537—Query rewriting; Transformation of operators
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/28—Databases characterised by their database models, e.g. relational or object models
- G06F16/283—Multi-dimensional databases or data warehouses, e.g. MOLAP or ROLAP
Abstract
A system for summary filter transformation is provided. The system comprises a summary filter analysis module for analysing a multidimensional query that is not supported by a target database system, and a summary filter transformation module for transforming the multidimensional query into a semantically equivalent query that is supported by the target database system.
Description
System and Method of Query Transformation FIELD OF THE INVENTION
The invention relates generally to data access middleware, and in particular to a system and method of query transformation.
BACKGROUND OF THE INVENTION
A typical data access environment has a multi-tier architecture. For description purposes, it can be separated into three distinct tiers:
~ Web server ~ Applications ~ Data The tiers are based on business function, and are typically separated by firewalls. Client software, such as a browser or a report-authoring tool, sits above the tiers.
The web server contains a firewall and one or more gateways.. All web communication is performed through a gateway. A gateway is responsible for passing on requests to the application server, in tier 2, for execution.
The applications tier contains one or more application servers. The application server runs requests, such as reports and queries that are forwarded by a gateway running on the web server. Typically, one of the components of the applications tier is a query engine, which is data access middleware that provides universal data access to a variety of heterogeneous database systems. The query engine formulates queries (typically SQL) and passes them on to the data tier, through a native database API (such as ODBC) for execution.
The data tier contains database management systems (DBMS), which manage raw data stored in a database. Examples of such systems include Oracle, DB2, and Microsoft SQL Server.
Although a multi-tier architecture can be configured in several different ways, a typical configuration places each tier on a separate computer (server). A
database server is typically a "high end" server, and thus can process queries at a relatively fast speed.
An application server cannot generally process queries as quickly as a database server.
In order to solve many business questions, a query engine may generate SQL
queries that utilize the SQL/OLAP technology introduced in the SQL-99 standard.
However, many database systems do not support this technology. Thus, the SQL
queries would have to be performed on the report server that is generally slower than the database server. It is desirable to have as much processing performed on the database server.
There is a need to prevent or reduce the amount of local (application server) processing required to process a summary filter.
One way of overcoming this problem is for the query engine to generate a basic query to retrieve the data required to process the filter and all post-filter aggregates.
Unfortunately, this solution requires processing time on the report server. It is desirable to have a way of transferring the SQL queries to the database server with minimal processing on the report server.
SUMMARY OF THE INVENTION
It is an object of the present invention to provide a method of summary filter transformation in a database system that does not support SQL-99 standard.
In accordance with an embodiment of the present invention, there is provided a system for summary filter transformation. The system comprises a summary filter analysis module for analysing a multidimensional query that is not supported by a target database system, and a summary filter transformation module for transforming the multidimensional query into a semantically equivalent query that is supported by the target database system.
In accordance with another embodiment of the present invention, there is provided a method of summary filter transformation. The method comprises the steps of analysing a multidimensional query that is not supported by a target database system, and transforming the multidimensional query into a semantically equivalent query that is supported by the target database system.
In accordance with an embodiment of the present invention, there is provided a method of summary filter transformation. The method comprises the steps of analysing a summary filter transformation to determine an overall filter grouping level, analysing a transformation select list to determine if a transformation is to be performed, creating a derived table, traversing the transformation select list to move PREFILTER
aggregates
The invention relates generally to data access middleware, and in particular to a system and method of query transformation.
BACKGROUND OF THE INVENTION
A typical data access environment has a multi-tier architecture. For description purposes, it can be separated into three distinct tiers:
~ Web server ~ Applications ~ Data The tiers are based on business function, and are typically separated by firewalls. Client software, such as a browser or a report-authoring tool, sits above the tiers.
The web server contains a firewall and one or more gateways.. All web communication is performed through a gateway. A gateway is responsible for passing on requests to the application server, in tier 2, for execution.
The applications tier contains one or more application servers. The application server runs requests, such as reports and queries that are forwarded by a gateway running on the web server. Typically, one of the components of the applications tier is a query engine, which is data access middleware that provides universal data access to a variety of heterogeneous database systems. The query engine formulates queries (typically SQL) and passes them on to the data tier, through a native database API (such as ODBC) for execution.
The data tier contains database management systems (DBMS), which manage raw data stored in a database. Examples of such systems include Oracle, DB2, and Microsoft SQL Server.
Although a multi-tier architecture can be configured in several different ways, a typical configuration places each tier on a separate computer (server). A
database server is typically a "high end" server, and thus can process queries at a relatively fast speed.
An application server cannot generally process queries as quickly as a database server.
In order to solve many business questions, a query engine may generate SQL
queries that utilize the SQL/OLAP technology introduced in the SQL-99 standard.
However, many database systems do not support this technology. Thus, the SQL
queries would have to be performed on the report server that is generally slower than the database server. It is desirable to have as much processing performed on the database server.
There is a need to prevent or reduce the amount of local (application server) processing required to process a summary filter.
One way of overcoming this problem is for the query engine to generate a basic query to retrieve the data required to process the filter and all post-filter aggregates.
Unfortunately, this solution requires processing time on the report server. It is desirable to have a way of transferring the SQL queries to the database server with minimal processing on the report server.
SUMMARY OF THE INVENTION
It is an object of the present invention to provide a method of summary filter transformation in a database system that does not support SQL-99 standard.
In accordance with an embodiment of the present invention, there is provided a system for summary filter transformation. The system comprises a summary filter analysis module for analysing a multidimensional query that is not supported by a target database system, and a summary filter transformation module for transforming the multidimensional query into a semantically equivalent query that is supported by the target database system.
In accordance with another embodiment of the present invention, there is provided a method of summary filter transformation. The method comprises the steps of analysing a multidimensional query that is not supported by a target database system, and transforming the multidimensional query into a semantically equivalent query that is supported by the target database system.
In accordance with an embodiment of the present invention, there is provided a method of summary filter transformation. The method comprises the steps of analysing a summary filter transformation to determine an overall filter grouping level, analysing a transformation select list to determine if a transformation is to be performed, creating a derived table, traversing the transformation select list to move PREFILTER
aggregates
-2-and aggregates computed at the filter grouping level into the derived table, and extracting and moving aggregates from the summary filter into a derived table select list.
In accordance with an embodiment of the present invention, there is provided a computer data signal embodied in a earner wave and representing sequences of instructions which, when executed by a processor, cause the processor to perform a method of summary filter transformation. The method comprises the steps of analysing a multidimensional query that is not supported by a target database system, and transforming the multidimensional query into a semantically equivalent query that is supported by the target database system.
In accordance with an embodiment of the present invention, there is provided a computer-readable medium having computer readable code embodied therein for use in the execution in a computer of a method of summary filter transformation. The method comprises the steps of analysing a multidimensional query that is not supported by a target database system, and transforming the multidimensional query into a semantically equivalent query that is supported by the target database system.
In accordance with an embodiment of the present invention, there is provided a computer program product for use in the execution in a computer of a l;roup query transformation system for summary filter transformation. The computer program product comprises a summary filter analysis module for analysing a multidimensional query that is not supported by a target database system, and a summary filter transformation module for transforming the multidimensional query into a semantically equivalent query that is supported by the target database system.
BRIEF DESCRIPTION OF THE DRAWINGS
Figure 1 shows a typical data access environment.
Figure 2 shows a summary filter transformation system, in accordance with an embodiment of the present invention.
Figure 3 shows in a flowchart an example of a method of summary filter transformation, in accordance with the summary filter transformation system.
Figure 4 shows in a flowchart another example of a method of summary filter transformation, in accordance with the summary filter transformation system.
DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS
In accordance with an embodiment of the present invention, there is provided a computer data signal embodied in a earner wave and representing sequences of instructions which, when executed by a processor, cause the processor to perform a method of summary filter transformation. The method comprises the steps of analysing a multidimensional query that is not supported by a target database system, and transforming the multidimensional query into a semantically equivalent query that is supported by the target database system.
In accordance with an embodiment of the present invention, there is provided a computer-readable medium having computer readable code embodied therein for use in the execution in a computer of a method of summary filter transformation. The method comprises the steps of analysing a multidimensional query that is not supported by a target database system, and transforming the multidimensional query into a semantically equivalent query that is supported by the target database system.
In accordance with an embodiment of the present invention, there is provided a computer program product for use in the execution in a computer of a l;roup query transformation system for summary filter transformation. The computer program product comprises a summary filter analysis module for analysing a multidimensional query that is not supported by a target database system, and a summary filter transformation module for transforming the multidimensional query into a semantically equivalent query that is supported by the target database system.
BRIEF DESCRIPTION OF THE DRAWINGS
Figure 1 shows a typical data access environment.
Figure 2 shows a summary filter transformation system, in accordance with an embodiment of the present invention.
Figure 3 shows in a flowchart an example of a method of summary filter transformation, in accordance with the summary filter transformation system.
Figure 4 shows in a flowchart another example of a method of summary filter transformation, in accordance with the summary filter transformation system.
DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS
-3-Figure 1 shows a typical data access environment 10 for processing data.
Typically, data is stored in a database 11. A DBMS running on a database server 12 accesses the raw data stored in the database 11. A query engine 15, running on a report server (or application server) 13 is used to generate reports on the raw data and instruct the DBMS on the database server 12 to obtain information pertaining to the raw data in the database 11. The query engine 15 provides universal data access to a variety of heterogeneous database systems. An end user uses a client application 14, running on a client workstation, to facilitate application server 13 operations.
In order to solve many business questions, a query engine 1 S generates SQL
queries that utilize the SQL/OLAP (Online Analytical Programming) technology introduced in the SQL-99 standard. These SQL queries include SQL/OLAP
functions (windowed aggregates). However, many database systems 12 do not support this technology. In order to prevent or reduce the amount of local (application server) processing required to process these types of queries, the query engine 15 attempts to generate semantically equivalent queries that can be processed on the database server 12 by the target database system. These semantically equivalent queries include standard aggregate functions and the GROUP BY operator.
Figure 2 shows a summary filter transformation system 20, in accordance with an embodiment of the present invention. The summary filter transformation system comprises a summary filter analysis module 21 for analysing SQL/OLAP queries that are not supported by a target database system, and a summary filter transformation module 22 for transforming the SQL/OLAP queries into semantically equivalent queries that are supported by the target database system.
The summary filter transformation system 20 is implemented as a sub-system of the query engine 15 in the data access environment 10. This transformation 20 may generate queries that can be processed in their entirety on the database server 12, or queries that require processing on both the application server 13 and the database server 12.
Advantageously, the summary filter transformation system 20 reduces processing that might otherwise be required on an application server, thereby improving performance in many cases. Furthermore, the summary filter transformation system 20 takes advantage of functionality provided by a target database system.
Typically, data is stored in a database 11. A DBMS running on a database server 12 accesses the raw data stored in the database 11. A query engine 15, running on a report server (or application server) 13 is used to generate reports on the raw data and instruct the DBMS on the database server 12 to obtain information pertaining to the raw data in the database 11. The query engine 15 provides universal data access to a variety of heterogeneous database systems. An end user uses a client application 14, running on a client workstation, to facilitate application server 13 operations.
In order to solve many business questions, a query engine 1 S generates SQL
queries that utilize the SQL/OLAP (Online Analytical Programming) technology introduced in the SQL-99 standard. These SQL queries include SQL/OLAP
functions (windowed aggregates). However, many database systems 12 do not support this technology. In order to prevent or reduce the amount of local (application server) processing required to process these types of queries, the query engine 15 attempts to generate semantically equivalent queries that can be processed on the database server 12 by the target database system. These semantically equivalent queries include standard aggregate functions and the GROUP BY operator.
Figure 2 shows a summary filter transformation system 20, in accordance with an embodiment of the present invention. The summary filter transformation system comprises a summary filter analysis module 21 for analysing SQL/OLAP queries that are not supported by a target database system, and a summary filter transformation module 22 for transforming the SQL/OLAP queries into semantically equivalent queries that are supported by the target database system.
The summary filter transformation system 20 is implemented as a sub-system of the query engine 15 in the data access environment 10. This transformation 20 may generate queries that can be processed in their entirety on the database server 12, or queries that require processing on both the application server 13 and the database server 12.
Advantageously, the summary filter transformation system 20 reduces processing that might otherwise be required on an application server, thereby improving performance in many cases. Furthermore, the summary filter transformation system 20 takes advantage of functionality provided by a target database system.
-4-There are two types of OLAP functions: framed functions and report functions.
Framed OLAP functions contain a window frame specification (ROWS or RANGE) and an ORDER BY clause. Through window frames, capabilities such as cumulative (running) sums and moving averages can be supported. Report functions do not contain a S window frame specification, and produce the same value for each row in a partition.
The SQL language is extended to include a FILTER clause that allows the specification of a summary filter (note that this clause is not part of the current SQL
standard). Unlike the WHERE clause, which is applied before any OLAP functions in the select list are computed, the FILTER clause is applied before some OLAP
functions are computed, and after others are computed.
The SQL language is also extended to include a PREFILTER keyword in an OLAP function specification to allow control of when the function is computed in the presence of a FILTER clause. Any OLAP function with PREFILTER specified is computed before the FILTER clause is applied, while all others are computed after.
The summary filter transformation generates a derived table and standard WHERE clause to apply the filter condition. Before describing this transformation, a couple of definitions are provided:
~ A group is a list of expressions over which an aggregate is computed, and is specified by either the FOR clause or AT clause, depending on the type of aggregate. For instance, given the aggregate SUM( QTY ) OVER ( PARTITION BY SNO, PNO ), the group is (SNO, PNO).
~ Two groups C 1 and C2 are compatible if C 1 and C2 are identical, or C 1 is a subset/superset of C2. For instance, the groups (SNO, PNO) and (SNO) are compatible, but the groups (SNO) and (PNO) are not.
Figure 3 shows in a flowchart an example of a method of SQL group transformation (30), in accordance with an embodiment of the group query transformation system 20. The method (30) begins with analysing a query containing a group query that is not supported by a target database system (31). Next, the query is transformed into a semantically equivalent query that is supported by the target database system (32). The method (30) is done (33).
Figure 4 shows in a flowchart another example of a method of summary filter transformation (40), in accordance with the summary filter transformation system 20.
Framed OLAP functions contain a window frame specification (ROWS or RANGE) and an ORDER BY clause. Through window frames, capabilities such as cumulative (running) sums and moving averages can be supported. Report functions do not contain a S window frame specification, and produce the same value for each row in a partition.
The SQL language is extended to include a FILTER clause that allows the specification of a summary filter (note that this clause is not part of the current SQL
standard). Unlike the WHERE clause, which is applied before any OLAP functions in the select list are computed, the FILTER clause is applied before some OLAP
functions are computed, and after others are computed.
The SQL language is also extended to include a PREFILTER keyword in an OLAP function specification to allow control of when the function is computed in the presence of a FILTER clause. Any OLAP function with PREFILTER specified is computed before the FILTER clause is applied, while all others are computed after.
The summary filter transformation generates a derived table and standard WHERE clause to apply the filter condition. Before describing this transformation, a couple of definitions are provided:
~ A group is a list of expressions over which an aggregate is computed, and is specified by either the FOR clause or AT clause, depending on the type of aggregate. For instance, given the aggregate SUM( QTY ) OVER ( PARTITION BY SNO, PNO ), the group is (SNO, PNO).
~ Two groups C 1 and C2 are compatible if C 1 and C2 are identical, or C 1 is a subset/superset of C2. For instance, the groups (SNO, PNO) and (SNO) are compatible, but the groups (SNO) and (PNO) are not.
Figure 3 shows in a flowchart an example of a method of SQL group transformation (30), in accordance with an embodiment of the group query transformation system 20. The method (30) begins with analysing a query containing a group query that is not supported by a target database system (31). Next, the query is transformed into a semantically equivalent query that is supported by the target database system (32). The method (30) is done (33).
Figure 4 shows in a flowchart another example of a method of summary filter transformation (40), in accordance with the summary filter transformation system 20.
-5-The method (40) begins with analyzing a summary filter condition to determine an overall filter grouping level (41 ). Next, a select list is analyzed to determine how the transformation should be performed (42). The first step in performing the transformation (43) is to create a derived table (44). Next, the select list is traversed, moving PREFILTER aggregates and aggregates computed at the filter grouping level into the derived table, and performing the appropriate conversion on all other aggregates (45).
Next, aggregates are extracted from the summary filter and moved into a select list of the derived table (46). The method (40) is now done (47).
As described above, the first step in performing the summary filter transformation (40) is to analyze the summary filter condition to determine an overall filter grouping level (if any) (41). Preferably, step (41) is accomplished by first enumerating all groups using the following rules:
~ A specific group is derived from each aggregate appearing in the filter condition.
~ For report aggregates having a standard aggregate counterpart (MIN, MAX, SUM, AVG, COUNT, and COUNT(*)), the group is derived from the FOR clause.
~ For all other aggregates, the group is derived from the AT clause.
~ For non-aggregate filter conditions, the group is derived from the detail column references.
To determine how to perform the transformation (42), all enumerated groups are compared to determine an overall grouping level. If all groups are compatible, the group with the lowest level of granularity (group with the most columns) is chosen as the overall filter group. For instance, if the enumerated groups are (SNO), and (SNO, PNO), the filter group is (SNO, PNO). If the groups are not compatible, the filter group is NULL, and no optimization can be performed.
Some examples are given in the following table:
FfLTER ~o~adiho~i ~~ FTI~'~'ER Group SUM( QTY ) OVER ( PARTITION BY SNO ) > 100 (SNO) SUM( QTY ) OVER ( PARTITION BY SNO, PNO (SNO, PNO) ) >
AVG( QTY ) OVER ( PARTITION BY SNO ) RANK() OVER ( PARTITION BY SNO ORDER BY NULL
QTY ) *
Next, aggregates are extracted from the summary filter and moved into a select list of the derived table (46). The method (40) is now done (47).
As described above, the first step in performing the summary filter transformation (40) is to analyze the summary filter condition to determine an overall filter grouping level (if any) (41). Preferably, step (41) is accomplished by first enumerating all groups using the following rules:
~ A specific group is derived from each aggregate appearing in the filter condition.
~ For report aggregates having a standard aggregate counterpart (MIN, MAX, SUM, AVG, COUNT, and COUNT(*)), the group is derived from the FOR clause.
~ For all other aggregates, the group is derived from the AT clause.
~ For non-aggregate filter conditions, the group is derived from the detail column references.
To determine how to perform the transformation (42), all enumerated groups are compared to determine an overall grouping level. If all groups are compatible, the group with the lowest level of granularity (group with the most columns) is chosen as the overall filter group. For instance, if the enumerated groups are (SNO), and (SNO, PNO), the filter group is (SNO, PNO). If the groups are not compatible, the filter group is NULL, and no optimization can be performed.
Some examples are given in the following table:
FfLTER ~o~adiho~i ~~ FTI~'~'ER Group SUM( QTY ) OVER ( PARTITION BY SNO ) > 100 (SNO) SUM( QTY ) OVER ( PARTITION BY SNO, PNO (SNO, PNO) ) >
AVG( QTY ) OVER ( PARTITION BY SNO ) RANK() OVER ( PARTITION BY SNO ORDER BY NULL
QTY ) *
-6-AVG( QTY ) OVER ( PARTITION BY SNO ) > 100 RANK() OVER ( AT SNO, PNO, JNO ORDER BY QTY ) * ~ (SNO, PNO, JNO) SUM( QTY ) OVER ( PARTITION BY SNO ) > 100 SUM( QTY ) OVER ( PARTITION BY SNO ) > ~ NULL
SUM( QTY ) OVER ( PARTITION BY PNO ) SNO > 'S2' I (SNO) If no optimization can be performed, a simple transformation is performed.
Otherwise, aggregates in the select list are analyzed and replaced with equivalent expressions in an effort to avoid introducing detail information into the inner select. 'This might involve replacing the aggregate all together, or replacing the aggregate operand with another aggregate (a nested aggregate) computed at the same level as the FILTER
group.
The basic steps in performing the transformation are as follows:
1. Construct a derived table (44).
2. Traverse the select list of the original query, performing the following actions (45):
a. Move PREFILTER aggregates and aggregates with a grouping level that matches the grouping level of the FILTER condition into the derived table.
b. For aggregates MTN, MAX, and SUM, the replace the operand with an aggregate computed at the same level as the FILTER group, and an AT
clause is introduced to eliminate duplicates values from the computation.
c. Replace AVG with an equivalent expression involving SUM and COUNT.
d. Replace COUNT and COUNT(*)with equivalent SUM aggregate expressions.
3. Traverse the FILTER condition, moving detail columns and aggregates into the select list of the derived table (46).
Assuming the FILTER group is (SNO, PNO), the action taken for various aggregates is described below:
o SUM( QTY ) OVER ( PARTITION BY SNO ) Replace with XSUM( C 1 AT SNO, PNO FOR SNO ), where C 1 =~ XSUM( QTY
FOR SNO, PNO ) and add C 1 to the inner select.
o AVG( QTY ) OVER Q
Replace with XSUM( C 1 AT SNO, PNO ) / XSUM( C2 AT SNO, PNO ), where C 1 =
SUM( QTY ) OVER ( PARTITION BY SNO, PNO ), C2 = COUNT( QTY ) OVER
PARTITION BY SNO, PNO ), and add C 1 and C2 to the inner select.
o MAX( QTY ) OVER ( PARTITION BY SNO, PNO ) Move the aggregate into inner select, since it is computed at the same level as the FILTER group.
a COUNT( QTY ) OVER ( PARTITION BY SNO ) Replace with SUM( C1 ) OVER ( AT SNO, PNO PARTITION BY SNO ), where C1 = COUNT( QTY ) OVER ( PARTITION BY SNO, PNO ), and add C1 to the inner select.
The following examples are provided to illustrate the functionality of the summary filter transformation system (20) and methods (30), (40):
Example 1 In this example, a simple summary filter is illustrated.
Original Query SELECT SNO, PNO, SUM( QTY ) OVER ( PARTITION BY SNO ), SUM( QTY ) OVER ( PARTITION BY SNO, PNO ) FROM SUPPLY
FILTER SUM( QTY ) OVER ( PARTITION BY SNO, PNO ) > 100 Transformed Query SELECT T1.C0, TI.CI, SUM( T1.C2 ) OVER ( AT T1.C0, T1.C1 PARTITION BY'T1.C0 ), T1.C2 _g_ FROM ( SELECT SNO C0, PNO C1, SUM( QTY ) OVER ( PARTITION BY SNO, PNO ) C2 FROM SUPPLY ) T1 WHERE T1.C2 > 100 Explanation The FILTER condition is first analyzed, and the group is determined to be (SNO, PNO). A derived table is then constructed whose select list contains the required detail information (SNO, PNO) and the aggregate appearing in the condition. The first SUM in the main select list is computed based on the SUM in the derived table. Since it's group is (SNO), an AT clause is added to its specification to eliminated double counting. The second SUM is identical to the SUM in the derived table, so it is replaced accordingly.
Example 2 In this example, use of the PREFILTER keyword in an OLAP function is illustrated.
Original Query SELECT SNO, PNO, SUM( QTY ) OVER ( PARTITION BY SNO ), SUM( QTY ) OVER ( PARTITION BY PNO PREFILTER ) FROM SUPPLY
FILTER SUM( QTY ) OVER ( PARTITION BY SNO, PNO ) > 100 Transformed Query SELECT T1.C0, T1.C1, SUM( T1.C2 ) OVER ( AT T1.C0, T1.C1 PARTITION BY T1.C0 ), T1.C3 FROM ( SELECT SNO C0, PNO C1, SUM( QTY ) OVER ( PARTITION BY SNO, PI'JO ) C2, SUM( QTY ) OVER ( PARTITION BY PNO ) C3 FROM SUPPLY ) T1 WHERE T1.C2 > 100 After applying the GROUP query transformation on the derived table, the query becomes:
SELECT T1.C0, T1.C1, SUM( Tl.C2 ) OVER ( AT T1.C0, T1.C1 PARTITION BY T1.C0 ), TO.C1 FROM ( SELECT T2.C0 C0, T2.C1 C1, T1.C2 C2, TO.C1 C3 FROM ( SELECT PNO C0, SUM( QTY ) C 1 FROM SUPPLY
GROUP BY PNO ) T0, ( SELECT SNO C0, PNO C 1, SUM( QTY ) C2 FROM SUPPLY
GROUP BY SNO, PNO ) T1, ( SELECT SNO C0, PNO C1 FROM SUPPLY ) T2 WHERE T2.C0 = T1.C0 OR ( T2.C0 IS NULL AND T1.C0 IS NULL ) AND T2.C1 = T1.C1 OR ( T2.C1 IS NULL AND T1.C1 IS NULL ) AND T2.C1 = TO.CO OR ( T2.C1 IS NULL AND TO.CO IS NULL ) ) T1 WHERE T1.C3 > 100 Explanation The FILTER condition is first analyzed, and the group is determined to be (SNO, PNO). A derived table is then constructed whose select list contains the required detail information (SNO, PNO) and the aggregate appearing in the condition. The first SUM in the main select list is computed based on the SUM in the derived table. Since it's group is (SNO), an AT clause is added to its specification to eliminated double counting. The second SUM has a group of (PNO), which does not match the group of the FILTER
condition, but the PREFILTER keyword is specified, so it is moved into the derived table.
Example 3 In this example, the effect the presence of the AVG function has on the transformation is illustrated.
Original Query SELECT SNO, PNO, MAX( QTY ) OVER ( PARTITION BY SNO, PNO ), AVG( QTY ) OVER Q
FROM SUPPLY
FILTER SUM( QTY ) OVER ( PARTITION BY SNO, PNO ) > 100 Transformed Query SELECT T1.C0, T1.C1, T1.C2, SUM( T1.C3 ) OVER ( AT Tl.CO, T1.C1 ) /
SUM( T1.C4 ) OVER ( AT T1.C0, TI.CI ) FROM ( SELECT SNO C0, PNO C 1, MAX( QTY ) OVER ( PARTITION BY SNO, PNO ) C2, SUM( QTY ) OVER ( PARTITION BY SNO, PNO ) C3, COUNT( QTY ) OVER ( PARTITION BY SNO, PNO ) C4 FROM SUPPLY ) T1 WHERE T1.C3 > 100 After applying the GROUP query transformation on the derived table, the query becomes:
SELECT T1.C0, T1.C1, T1.C2, SUM( T1.C3 ) OVER ( AT T1.C0, T1.C1 ) /
SUM( T1.C4 ) OVER ( AT T1.C0, T1.C1 ) FROM ( SELECT T1.C0 C0, T1.C1 C1, T0.C2 C2, TO.C3 C3, TO.C4 C4 FROM ( SELECT SNO C0, PNO C1, MAX( QTY ) C2, SUM( QTY ) C3, COUNT( QTY ) C4 FROM SUPPLY
GROUP BY SNO, PNO ) T0, ( SELECT SNO C0, PNO C1 FROM SUPPLY ) T1 WHERE T1.C0 = TO.CO OR ( T1.C0 IS NULL AND TO.CO IS NULL ) AND T1.C1 = TO.C1 OR ( T1.C1 IS NULL AND TO.C1 IS NULL ) ) T1 WHERE T1.C3 > 100 Explanation The FILTER condition is first analyzed, and the group is determined to be (SNO, PNO). A derived table is then constructed whose select list contains the required detail information (SNO, PNO) and the aggregate appearing in the condition. The MAX
function has a group of (SNO, PNO) which matches the group of the FILTER
condition, so it is added to the derived table. The AVG function has a group of (), which does not match the group of the FILTER condition, so it must be replaced by an expression that involves aggregates computed at the same grouping level as the FILTER
condition.
Hence, a SUM and COUNT aggregate are added to the derived table, and the AVG
function is replaced. 'The AT clauses in the two SUM function in the outer select eliminate double counting.
Example 4 In this example, the effect the presence of the DISTINCT keyword has on the transformation is illustrated.
Original Query SELECT DISTINCT SNO, PNO, SUM( QTY ) OVER ( PARTITION BY SNO ), SUM( QTY ) OVER ( PARTITION BY SNO, PNO ) FROM SUPPLY
FILTER SUM( QTY ) OVER ( PARTITION BY SNO, PNO ) > 100 Transformed Query SELECT T1.C0, TI.CI, SUM( TI.C2 ) OVER ( PARTITION BY TI.CO ), T1.C2 FROM ( SELECT SNO C0, PNO C1, SUM( QTY ) C2 FROM SUPPLY
GROUP BY SNO, PNO ) T1 WHERE T1.C2 > 100 The query above can then be reformulated as follows:
SELECT T1.C0, T1.C1, SUM( T1.C2 ) OVER ( PARTITION BY T1.C0 ), T1.C2 FROM ( SELECT SNO C0, PNO C 1, SUM( QTY ) C2 FROM SUPPLY
GROUP BY SNO, PNO
HAVING SUM( QTY ) > 100 ) T1 Exulanation 'The FILTER condition is first analyzed, and the group is determined to be (SNO, PNO). A derived table is then constructed whose select list contains the required detail information (SNO, PNO) and the aggregate appearing in the condition. Because of the presence of the DISTINCT keyword, and the fact that the detail information required are columns in the FILTER condition group; a GROUP BY can be introduced into the derived table. The first SUM can be computed based on the SUM in the derived table -no AT clause is required since the GROUP BY eliminates the possibility of duplicates.
The second SUM is the same as the SUM in the derived table, so it is replaced accordingly. Finally, the DISTINCT keyword can be eliminated since the GROUP
BY
inside the derived table ensures that there will be no duplicate rows.
The systems and methods according to the present invention may be implemented by any hardware, software or a combination of hardware and software having the functions described above. The software code, either in its entixety or a part thereof, may be stored in a computer readable memory. Further, a computer data signal representing the software code that may be embedded in a carrier wave may be transmitted via a communication network. Such a computer readable memory and a computer data signal are also within the scope of the present invention, as well as the hardware, software and the combination thereof.
While particular embodiments of the present invention have been shown and described, changes and modifications may be made to such embodiments without departing from the true scope of the invention.
SUM( QTY ) OVER ( PARTITION BY PNO ) SNO > 'S2' I (SNO) If no optimization can be performed, a simple transformation is performed.
Otherwise, aggregates in the select list are analyzed and replaced with equivalent expressions in an effort to avoid introducing detail information into the inner select. 'This might involve replacing the aggregate all together, or replacing the aggregate operand with another aggregate (a nested aggregate) computed at the same level as the FILTER
group.
The basic steps in performing the transformation are as follows:
1. Construct a derived table (44).
2. Traverse the select list of the original query, performing the following actions (45):
a. Move PREFILTER aggregates and aggregates with a grouping level that matches the grouping level of the FILTER condition into the derived table.
b. For aggregates MTN, MAX, and SUM, the replace the operand with an aggregate computed at the same level as the FILTER group, and an AT
clause is introduced to eliminate duplicates values from the computation.
c. Replace AVG with an equivalent expression involving SUM and COUNT.
d. Replace COUNT and COUNT(*)with equivalent SUM aggregate expressions.
3. Traverse the FILTER condition, moving detail columns and aggregates into the select list of the derived table (46).
Assuming the FILTER group is (SNO, PNO), the action taken for various aggregates is described below:
o SUM( QTY ) OVER ( PARTITION BY SNO ) Replace with XSUM( C 1 AT SNO, PNO FOR SNO ), where C 1 =~ XSUM( QTY
FOR SNO, PNO ) and add C 1 to the inner select.
o AVG( QTY ) OVER Q
Replace with XSUM( C 1 AT SNO, PNO ) / XSUM( C2 AT SNO, PNO ), where C 1 =
SUM( QTY ) OVER ( PARTITION BY SNO, PNO ), C2 = COUNT( QTY ) OVER
PARTITION BY SNO, PNO ), and add C 1 and C2 to the inner select.
o MAX( QTY ) OVER ( PARTITION BY SNO, PNO ) Move the aggregate into inner select, since it is computed at the same level as the FILTER group.
a COUNT( QTY ) OVER ( PARTITION BY SNO ) Replace with SUM( C1 ) OVER ( AT SNO, PNO PARTITION BY SNO ), where C1 = COUNT( QTY ) OVER ( PARTITION BY SNO, PNO ), and add C1 to the inner select.
The following examples are provided to illustrate the functionality of the summary filter transformation system (20) and methods (30), (40):
Example 1 In this example, a simple summary filter is illustrated.
Original Query SELECT SNO, PNO, SUM( QTY ) OVER ( PARTITION BY SNO ), SUM( QTY ) OVER ( PARTITION BY SNO, PNO ) FROM SUPPLY
FILTER SUM( QTY ) OVER ( PARTITION BY SNO, PNO ) > 100 Transformed Query SELECT T1.C0, TI.CI, SUM( T1.C2 ) OVER ( AT T1.C0, T1.C1 PARTITION BY'T1.C0 ), T1.C2 _g_ FROM ( SELECT SNO C0, PNO C1, SUM( QTY ) OVER ( PARTITION BY SNO, PNO ) C2 FROM SUPPLY ) T1 WHERE T1.C2 > 100 Explanation The FILTER condition is first analyzed, and the group is determined to be (SNO, PNO). A derived table is then constructed whose select list contains the required detail information (SNO, PNO) and the aggregate appearing in the condition. The first SUM in the main select list is computed based on the SUM in the derived table. Since it's group is (SNO), an AT clause is added to its specification to eliminated double counting. The second SUM is identical to the SUM in the derived table, so it is replaced accordingly.
Example 2 In this example, use of the PREFILTER keyword in an OLAP function is illustrated.
Original Query SELECT SNO, PNO, SUM( QTY ) OVER ( PARTITION BY SNO ), SUM( QTY ) OVER ( PARTITION BY PNO PREFILTER ) FROM SUPPLY
FILTER SUM( QTY ) OVER ( PARTITION BY SNO, PNO ) > 100 Transformed Query SELECT T1.C0, T1.C1, SUM( T1.C2 ) OVER ( AT T1.C0, T1.C1 PARTITION BY T1.C0 ), T1.C3 FROM ( SELECT SNO C0, PNO C1, SUM( QTY ) OVER ( PARTITION BY SNO, PI'JO ) C2, SUM( QTY ) OVER ( PARTITION BY PNO ) C3 FROM SUPPLY ) T1 WHERE T1.C2 > 100 After applying the GROUP query transformation on the derived table, the query becomes:
SELECT T1.C0, T1.C1, SUM( Tl.C2 ) OVER ( AT T1.C0, T1.C1 PARTITION BY T1.C0 ), TO.C1 FROM ( SELECT T2.C0 C0, T2.C1 C1, T1.C2 C2, TO.C1 C3 FROM ( SELECT PNO C0, SUM( QTY ) C 1 FROM SUPPLY
GROUP BY PNO ) T0, ( SELECT SNO C0, PNO C 1, SUM( QTY ) C2 FROM SUPPLY
GROUP BY SNO, PNO ) T1, ( SELECT SNO C0, PNO C1 FROM SUPPLY ) T2 WHERE T2.C0 = T1.C0 OR ( T2.C0 IS NULL AND T1.C0 IS NULL ) AND T2.C1 = T1.C1 OR ( T2.C1 IS NULL AND T1.C1 IS NULL ) AND T2.C1 = TO.CO OR ( T2.C1 IS NULL AND TO.CO IS NULL ) ) T1 WHERE T1.C3 > 100 Explanation The FILTER condition is first analyzed, and the group is determined to be (SNO, PNO). A derived table is then constructed whose select list contains the required detail information (SNO, PNO) and the aggregate appearing in the condition. The first SUM in the main select list is computed based on the SUM in the derived table. Since it's group is (SNO), an AT clause is added to its specification to eliminated double counting. The second SUM has a group of (PNO), which does not match the group of the FILTER
condition, but the PREFILTER keyword is specified, so it is moved into the derived table.
Example 3 In this example, the effect the presence of the AVG function has on the transformation is illustrated.
Original Query SELECT SNO, PNO, MAX( QTY ) OVER ( PARTITION BY SNO, PNO ), AVG( QTY ) OVER Q
FROM SUPPLY
FILTER SUM( QTY ) OVER ( PARTITION BY SNO, PNO ) > 100 Transformed Query SELECT T1.C0, T1.C1, T1.C2, SUM( T1.C3 ) OVER ( AT Tl.CO, T1.C1 ) /
SUM( T1.C4 ) OVER ( AT T1.C0, TI.CI ) FROM ( SELECT SNO C0, PNO C 1, MAX( QTY ) OVER ( PARTITION BY SNO, PNO ) C2, SUM( QTY ) OVER ( PARTITION BY SNO, PNO ) C3, COUNT( QTY ) OVER ( PARTITION BY SNO, PNO ) C4 FROM SUPPLY ) T1 WHERE T1.C3 > 100 After applying the GROUP query transformation on the derived table, the query becomes:
SELECT T1.C0, T1.C1, T1.C2, SUM( T1.C3 ) OVER ( AT T1.C0, T1.C1 ) /
SUM( T1.C4 ) OVER ( AT T1.C0, T1.C1 ) FROM ( SELECT T1.C0 C0, T1.C1 C1, T0.C2 C2, TO.C3 C3, TO.C4 C4 FROM ( SELECT SNO C0, PNO C1, MAX( QTY ) C2, SUM( QTY ) C3, COUNT( QTY ) C4 FROM SUPPLY
GROUP BY SNO, PNO ) T0, ( SELECT SNO C0, PNO C1 FROM SUPPLY ) T1 WHERE T1.C0 = TO.CO OR ( T1.C0 IS NULL AND TO.CO IS NULL ) AND T1.C1 = TO.C1 OR ( T1.C1 IS NULL AND TO.C1 IS NULL ) ) T1 WHERE T1.C3 > 100 Explanation The FILTER condition is first analyzed, and the group is determined to be (SNO, PNO). A derived table is then constructed whose select list contains the required detail information (SNO, PNO) and the aggregate appearing in the condition. The MAX
function has a group of (SNO, PNO) which matches the group of the FILTER
condition, so it is added to the derived table. The AVG function has a group of (), which does not match the group of the FILTER condition, so it must be replaced by an expression that involves aggregates computed at the same grouping level as the FILTER
condition.
Hence, a SUM and COUNT aggregate are added to the derived table, and the AVG
function is replaced. 'The AT clauses in the two SUM function in the outer select eliminate double counting.
Example 4 In this example, the effect the presence of the DISTINCT keyword has on the transformation is illustrated.
Original Query SELECT DISTINCT SNO, PNO, SUM( QTY ) OVER ( PARTITION BY SNO ), SUM( QTY ) OVER ( PARTITION BY SNO, PNO ) FROM SUPPLY
FILTER SUM( QTY ) OVER ( PARTITION BY SNO, PNO ) > 100 Transformed Query SELECT T1.C0, TI.CI, SUM( TI.C2 ) OVER ( PARTITION BY TI.CO ), T1.C2 FROM ( SELECT SNO C0, PNO C1, SUM( QTY ) C2 FROM SUPPLY
GROUP BY SNO, PNO ) T1 WHERE T1.C2 > 100 The query above can then be reformulated as follows:
SELECT T1.C0, T1.C1, SUM( T1.C2 ) OVER ( PARTITION BY T1.C0 ), T1.C2 FROM ( SELECT SNO C0, PNO C 1, SUM( QTY ) C2 FROM SUPPLY
GROUP BY SNO, PNO
HAVING SUM( QTY ) > 100 ) T1 Exulanation 'The FILTER condition is first analyzed, and the group is determined to be (SNO, PNO). A derived table is then constructed whose select list contains the required detail information (SNO, PNO) and the aggregate appearing in the condition. Because of the presence of the DISTINCT keyword, and the fact that the detail information required are columns in the FILTER condition group; a GROUP BY can be introduced into the derived table. The first SUM can be computed based on the SUM in the derived table -no AT clause is required since the GROUP BY eliminates the possibility of duplicates.
The second SUM is the same as the SUM in the derived table, so it is replaced accordingly. Finally, the DISTINCT keyword can be eliminated since the GROUP
BY
inside the derived table ensures that there will be no duplicate rows.
The systems and methods according to the present invention may be implemented by any hardware, software or a combination of hardware and software having the functions described above. The software code, either in its entixety or a part thereof, may be stored in a computer readable memory. Further, a computer data signal representing the software code that may be embedded in a carrier wave may be transmitted via a communication network. Such a computer readable memory and a computer data signal are also within the scope of the present invention, as well as the hardware, software and the combination thereof.
While particular embodiments of the present invention have been shown and described, changes and modifications may be made to such embodiments without departing from the true scope of the invention.
Claims (15)
1. A system for summary filter transformation, the system comprising:
a summary filter analysis module for analysing a multidimensional query that is not supported by a target database system; and a summary filter transformation module for transforming the multidimensional query into a semantically equivalent query that is supported by the target database system.
a summary filter analysis module for analysing a multidimensional query that is not supported by a target database system; and a summary filter transformation module for transforming the multidimensional query into a semantically equivalent query that is supported by the target database system.
2. A method of summary filter transformation, the method comprising the steps of:
analysing a multidimensional query that is not supported by a target database system; and transforming the multidimensional query into a semantically equivalent query that is supported by the target database system.
analysing a multidimensional query that is not supported by a target database system; and transforming the multidimensional query into a semantically equivalent query that is supported by the target database system.
3. The method as claimed in claim 2, further comprising the steps of:
analysing a summary filter condition to determine an overall filter grouping level, comprising the steps of:
enumerating group levels using rules;
comparing enumerated groups to determine the overall grouping; and selecting the overall grouping; and analysing and replacing aggregates with equivalent expressions.
analysing a summary filter condition to determine an overall filter grouping level, comprising the steps of:
enumerating group levels using rules;
comparing enumerated groups to determine the overall grouping; and selecting the overall grouping; and analysing and replacing aggregates with equivalent expressions.
4. The method as claimed in claim 3, wherein the step of enumerating group levels includes the steps of:
deriving a specific group from each aggregate appearing in the filter condition;
for extended aggregates having a standard aggregate counterpart (MIN, MAX, SUM, AVG, COUNT, and COUNT(*)), deriving the group from the FOR clause;
for all other aggregates, deriving the group from the AT clause; and for non-aggregate filter conditions, deriving the group from the detail column references.
deriving a specific group from each aggregate appearing in the filter condition;
for extended aggregates having a standard aggregate counterpart (MIN, MAX, SUM, AVG, COUNT, and COUNT(*)), deriving the group from the FOR clause;
for all other aggregates, deriving the group from the AT clause; and for non-aggregate filter conditions, deriving the group from the detail column references.
5. The method as claimed in claim 3, wherein the step of analysing a summary filter condition to determine an overall filter grouping level comprises the step selecting the filter group (SNO) for a filter condition SUM(QTY)OVER(PARTITION BY SNO)>
100.
100.
6. The method as claimed in claim 3, wherein the step of analysing a summary filter condition to determine an overall filter grouping level comprises the step selecting the filter group (SNO, PNO) for a filter condition SUM(QTY)OVER(PARTITION BY
SNO,PNO)>AVG(QTY)OVER(PARTITION BY SNO).
SNO,PNO)>AVG(QTY)OVER(PARTITION BY SNO).
7. The method as claimed in claim 3, wherein the step of analysing a summary filter condition to determine an overall filter grouping level comprises the step selecting the filter group NULL for a filter condition RANK()OVER(PARTITION BY SNO
ORDER BY QTY)*AVG(QTY)OVER(PARTITION BY SNO)>100.
ORDER BY QTY)*AVG(QTY)OVER(PARTITION BY SNO)>100.
8. The method as claimed in claim 3, wherein the step of analysing a summary filter condition to determine an overall filter grouping level comprises the step selecting the filter group (SNO,PNO,JNO) for a filter condition RANK()OVER(AT SNO,PNO, JNO ORDER BY QTY)*SUM(QTY)OVER(PARTITION BY SNO)>100.
9. The method as claimed in claim 3, wherein the step of analysing a summary filter condition to determine an overall filter grouping level comprises the step selecting the filter group NULL for a filter condition SUM(QTY)OVER(PARTITION BY SNO)>
SUM(QTY)OVER(PARTITION BY PNO).
SUM(QTY)OVER(PARTITION BY PNO).
10. The method as claimed in claim 3, wherein the step of analysing a summary filter condition to determine an overall filter grouping level comprises the step selecting the filter group (SNO) for a filter condition SNO>'S2'.
11. The method as claimed in claim 3, wherein the step of analysing and replacing aggregates with equivalent expressions comprises the rules of:
for aggregates MIN, MAX, and SUM, the operand is replaced with an aggregate computed at the same level as the FILTER group, and an AT clause is introduced to eliminate duplicates values from the computation;
AVG is replaced with an equivalent expression involving SUM and COUNT; and COUNT and COUNT(*) are replaced with equivalent SUM aggregate expressions.
for aggregates MIN, MAX, and SUM, the operand is replaced with an aggregate computed at the same level as the FILTER group, and an AT clause is introduced to eliminate duplicates values from the computation;
AVG is replaced with an equivalent expression involving SUM and COUNT; and COUNT and COUNT(*) are replaced with equivalent SUM aggregate expressions.
12. A method of summary filter transformation, the method comprising the steps of:
analysing a summary filter transformation to determine an overall filter grouping level;
analysing a transformation select list to determine if a transformation is to be performed;
creating a derived table;
traversing the transformation select list to move PREFILTER aggregates and aggregates computed at the filter grouping level into the derived table; and extracting and moving aggregates from the summary filter into a derived table select list.
analysing a summary filter transformation to determine an overall filter grouping level;
analysing a transformation select list to determine if a transformation is to be performed;
creating a derived table;
traversing the transformation select list to move PREFILTER aggregates and aggregates computed at the filter grouping level into the derived table; and extracting and moving aggregates from the summary filter into a derived table select list.
13. A computer data signal embodied in a carrier wave and representing sequences of instructions which, when executed by a processor, cause the processor to perform a method of summary filter transformation, the method comprising the steps of:
analysing a multidimensional query that is not supported by a target database system; and transforming the multidimensional query into a semantically equivalent query that is supported by the target database system.
analysing a multidimensional query that is not supported by a target database system; and transforming the multidimensional query into a semantically equivalent query that is supported by the target database system.
14. A computer-readable medium having computer readable code embodied therein for use in the execution in a computer of a method of summary filter transformation, the method comprising the steps of:
analysing a multidimensional query that is not supported by a target database system; and transforming the multidimensional query into a semantically equivalent query that is supported by the target database system.
analysing a multidimensional query that is not supported by a target database system; and transforming the multidimensional query into a semantically equivalent query that is supported by the target database system.
15. A computer program product for use in the execution in a computer of a group query transformation system for summary filter transformation, the computer program product comprising:
a summary filter analysis module for analysing a multidimensional query that is not supported by a target database system; and a summary filter transformation module for transforming the multidimensional query into a semantically equivalent query that is supported by the target database system.
a summary filter analysis module for analysing a multidimensional query that is not supported by a target database system; and a summary filter transformation module for transforming the multidimensional query into a semantically equivalent query that is supported by the target database system.
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|---|---|---|---|
| CA002468614A CA2468614A1 (en) | 2003-05-27 | 2004-05-27 | System and method of query transformation |
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| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CA002429910A CA2429910A1 (en) | 2003-05-27 | 2003-05-27 | System and method of query transformation |
| CA2,429,910 | 2003-05-27 | ||
| CA002468614A CA2468614A1 (en) | 2003-05-27 | 2004-05-27 | System and method of query transformation |
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ID=33453247
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