CN117216099A - Multimode database-oriented acceleration query method and system - Google Patents
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Abstract
The invention discloses an acceleration query method and system for a multimode database, which belong to the technical field of multimode databases, and a specific calculation is pushed down to a specific modulus database by utilizing an optimization rule and optimizing an operator plan of a query plan part; the implementation of the method comprises the steps of customizing an optimization rule and optimizing a query plan according to the optimization rule; the self-defined optimization rule is set and read through system parameters; and comparing according to the operations contained in each layer of plan when the query plan is generated, and if the operations are the operations contained in the optimization rules and accord with the parameters defined in the optimization rules, pushing the operations to the corresponding module database plans. The invention can improve the advantage utilization rate of each mode database and reduce the time loss and network bandwidth occupation in the data transmission process.
Description
Technical Field
The invention relates to the technical field of multimode databases, in particular to an acceleration query method and system for multimode databases.
Background
With the increasing data volume and data types, various types of databases are generated in the market, and the different types of databases can fully exert their own performances and advantages for certain application scenarios, but cannot fully cover all fields. At present, the multi-mode database is generated, the multi-mode database centrally manages the databases of different modes, the advantages of the databases of each mode can be exerted to the maximum extent, and how to manage the data of each mode becomes the core problem of the multi-mode database. In the process of data circulation, if each database processes own data, and then the processed data is summarized to a management node for processing, the quantity of the circulated data is extremely large, and the difficulty is increased for data transmission and calculation in the second stage. Thus requiring an optimization process for the characteristics of the different modes.
Disclosure of Invention
The technical task of the invention is to provide the multi-mode database oriented acceleration query method and the multi-mode database oriented acceleration query system aiming at the defects, so that the advantage utilization rate of each mode database can be improved, and the time loss and the network bandwidth occupation in the data transmission process can be reduced.
The technical scheme adopted for solving the technical problems is as follows:
the method for accelerating the query of the multimode database utilizes an optimization rule, pushes specific calculation down to the specific modulus database by optimizing an operator plan of a query plan part, so that the calculation can be calculated on the modulus database with the fastest calculation, thereby saving the data quantity transmitted by a network and accelerating the query of the CPU utilization rate; the implementation of the method comprises the steps of customizing an optimization rule and optimizing a query plan according to the optimization rule;
the user-defined optimization rule enables a user or DBA to release calculation and operation of processing of different module databases through the rule; the optimization rule is set and read through system parameters; comparing operations contained in each layer of plans when generating the query plans, and if the operations are the operations contained in the optimization rules and accord with parameters defined in the optimization rules, pushing the operations into the corresponding module database plans;
the query plan is optimized according to the optimization rule, each layer of planning operator layer is traversed to judge, if the layer can be completely pushed to a specific module database to calculate, a corresponding plan is constructed, and meanwhile, the modification of columns output by the layer is recorded; if the upper layer cannot be completely pushed to the specific module database, making corresponding update according to the modification; after traversing all the planning layers, it can determine which operations can be pushed to the specific module database, and then update the column ids used by the remaining planning layers according to the new result columns output by the operations.
For the inquiry of the multimode database, if the speed is to be increased, the database performance of each mode needs to be exerted to the greatest extent, the data transmission and network occupation are comprehensively considered, the calculation speed of different types of data is increased, for example, the calculation speed of the time sequence database for time sequence data is better than that of the relation database, more time sequence data calculation needs to be put on the time sequence database for calculation, the calculation speed is increased, and sometimes the transmitted result set is reduced, so that the inquiry speed is increased.
The method comprises the steps that processing rules for calculating advantages of different modulus databases are recorded into the multimode databases, when a user inquires about the multimode databases, the user inquires about the multimode databases, plans for inquiring different models are arranged through structural analysis and then optimization through predefined rules, the different modulus databases are executed according to the plans, and finally the user results are presented. The predefined rules can be changed through user setting, so that the DBA can conveniently define the rules manually, the optimized query efficiency is accelerated, and the later database maintenance is also facilitated.
After the query sentence of the user passes through the analyzer to generate a corresponding structure, if the query contains database data capable of accelerating the query, operators capable of accelerating the calculation are required to be pushed down to the database capable of accelerating, so that a more efficient database system can do more things, the calculation of the database is improved, and the query of the user is accelerated.
Preferably, the optimization rule is stored in a metadata table of the multimode database.
Preferably, all operations supported by the SQL language element are recorded in the optimization rule, including functions, comparison operations and binary operations, and the number of parameters, the type of each parameter, the used position and the effective mode are recorded for the operations.
Preferably, the specific implementation of the optimization rule matching is as follows: performing hash calculation by using the name of the expression and the parameters of the operation or the types of the left and right operation variables of the comparison operation, comparing with partial hash values recorded in metadata, and if the hash values are equal, comparing with the current use position of the expression; if so, success is returned.
Further, the push judgment for different layers includes:
direct push against scan layer; this layer pulls the data directly from the table, without other calculations;
traversing the expression of the filter aiming at the filter layer, checking whether all element operations have the and conditions which can meet the rule, if so, updating the push operator layer, and updating the output column id;
aiming at the project layer, the optimization judgment can be carried out only by inputting all the push, the optimization judgment needs to traverse the projection expression, whether all element operations are in accordance with rules is checked, if the projection expression in accordance with the rules is in the push, the push operator layer is updated, and the output column id is updated;
aiming at the group by layer, the optimization judgment can be carried out only by pushing all input, the optimization judgment needs to traverse the group by expression and all the ag function expression, the group by expression and the ag function expression all accord with the rule to push the layer, the push operator layer is updated, and the output column id is updated;
aiming at the order by layer, the optimization judgment can be carried out only by pushing all input, the optimization judgment needs to traverse all order by expressions, if all the order by expressions accord with rules, the order by layer can be pushed, the push operator layer is updated, and the output column id is updated;
aiming at a limit layer, all inputs need to be pushed to push;
and finally updating column ids used by the unpulsed layer to the lower layer.
Preferably, the flow for performing the filter layer optimization is as follows:
s1, judging whether optimization is performed according to a result after input optimization is completed;
s2, processing rule matching of all filter expressions;
s2.1, recording a rule-capable and condition;
s2.2, recording the and condition that the rule cannot be passed;
s3, merging the pass rule and conditions and merging the fail rule and conditions;
s4.1, if the irregular and condition exists, updating the condition of the layer;
s4.2, if the whole pushing is possible, the layer is removed;
s5, updating the output column id of the push layer.
Preferably, the process for performing project layer optimization is as follows:
s1, judging whether optimization is performed according to a result after input optimization is completed;
s2, processing rule matching of all project expressions;
s2.1, recording an expression capable of passing rules;
s2.2, recording an expression which cannot pass the rule;
s3, pushing down the expression capable of passing the rule;
s4.1, if an expression which does not pass through the rule exists, updating the expression of the layer;
s4.2, if the whole pushing is possible, the layer is removed;
s5, updating the output column id of the push layer.
Preferably, the flow of the group by layer optimization is as follows:
s1, judging whether optimization is performed according to a result after input optimization is completed;
s2, processing rule matching of all group by expressions, and returning that the layer cannot be pushed down if the rule cannot be passed;
s3, processing rule matching of all the agg functions, and returning that the layer cannot be pushed down if the rule cannot be passed;
s4, removing the layer;
s5, updating the output column id of the push layer.
Preferably, which operations of the different databases are computation capable of acceleration is controlled by means of a white list dictionary.
The invention also claims an acceleration query system facing the multimode database, which comprises a self-defined optimization rule module and an optimization query plan module according to the optimization rule;
the system realizes the data management and query of the multimode database by the acceleration query method facing the multimode database.
Compared with the prior art, the method and the system for accelerating query of the multimode database have the following beneficial effects:
based on the plan optimized by the optimization rule, the partial plan with calculation advantages is distributed to the database of the corresponding module, so that the query efficiency is accelerated, the network and CPU resources are saved, and the query time of the user is reduced.
Drawings
FIG. 1 is a diagram of custom optimization rule determination logic provided by one embodiment of the present invention;
FIG. 2 is an overall flow diagram of plan optimization provided by one embodiment of the present invention;
FIG. 3 is a flow chart of a filter layer push down determination provided by one embodiment of the present invention;
FIG. 4 is a flow diagram of project layer push-down provided by one embodiment of the present invention;
fig. 5 is a flowchart of a group by layer push-down according to an embodiment of the present invention.
Detailed Description
The invention will be further illustrated with reference to specific examples.
The embodiment of the invention provides an acceleration query method for a multimode database, which utilizes an optimization rule recorded in metadata, pushes specific calculation down to the specific modulus database by optimizing an operator plan of a query plan part, so that the calculation can be calculated on the fastest calculation modulus database, thereby saving the data volume transmitted by a network and accelerating the query of CPU utilization rate.
All operations supported by the SQL language elements, including functions, comparison operations and binary operations, are recorded in the optimization rules, and the number of parameters, the type of each parameter, the used position and the effective mode are recorded for the operations.
An optimization rule matching section: performing hash calculation by using the name of the expression and the parameters of the operation or the types of the left and right operation variables of the comparison operation, comparing with partial hash values recorded in metadata, and if the hash values are equal, comparing with the current use position of the expression; if so, success is returned. Reference is made to figure 1.
After the query sentence of the user passes through the analyzer to generate a corresponding structure, if the query contains database data capable of accelerating the query, operators capable of accelerating the calculation are required to be pushed down to the database capable of accelerating, so that a more efficient database system can do more things, the calculation of the database is improved, and the query of the user is accelerated. Which operations of the different databases are computation capable of acceleration is controlled by means of a white list dictionary.
The implementation of the method includes customizing an optimization rule and optimizing a query plan according to the optimization rule.
1. The implementation of the custom optimization rule(s),
the databases of different modes are all on the optimized and upgraded road, and some characteristics are possibly imperfect at present and need to be continuously updated, so that in order to facilitate the later expansion and optimization, an optimized rule needs to be constructed, and a user or DBA can release the calculation and operation of the processing of the databases of different modes through the rule; the optimization rule is stored in a metadata table of the multimode database, is set and read through system parameters, and supports all operations supported by the SQL language elements: a function, a comparison operation, a binary operation, and a mode for which the number of parameters, the type of each parameter, the used position and the effect are required to be recorded; and comparing according to the operations contained in each layer of plan when the query plan is generated, and if the operations are the operations contained in the optimization rules and accord with the parameters defined in the optimization rules, pushing the operations to the corresponding module database plans.
2. The query plan is optimized according to the optimization rules,
the general query plan is a layered operator plan, such as scan table to obtain scan layer of projection column, filter layer to perform table filtering, project layer to perform projection operation, group by layer to perform aggregation calculation, order by layer to perform sorting, limit layer to perform paging, each layer has its own input and output, the input is operator needed, and the output is column provided for the previous layer. For example, the scan layer input is the original table, and the output is which columns are taken from the table; for example, the filter layer may be a scan layer or a group by layer, if the input is a scan layer, it represents a where conditional filter, if the input is a group by layer, it represents a hang filter, and the output is a column provided to an upper layer after the filter. The columns of the Filter layer output and the scan layer output may be different because some columns may be needed in the Filter layer filtering conditions, but the upper layer plan of the present layer plan is not. The columns mentioned above are generally the IDs of the recorded columns, and then all the column information is recorded to a unified memory address for storage.
The method is suitable for the situation that the old system is limited by some problems and cannot be reconstructed, and based on the situation, each layer of planning operator layer needs to be traversed to judge, if the layer can be completely pushed to a specific modulus database to calculate, a corresponding plan is constructed, and meanwhile, the modification of the columns output by the layer is recorded; if the upper layer cannot be completely pushed to the specific module database, the upper layer makes corresponding update according to the modification. After traversing all the planning layers, the method can determine which operations can be pushed to a specific module database, and then update column ids used by the rest of planning layers according to new result columns output by the operations.
Push judgment for different layers:
direct push against scan layer; this layer pulls the data directly from the table with no other calculations.
Aiming at the filter layer, traversing the expression of the filter, checking whether all element operations have the and conditions which can meet the rule, if so, pushing part or all of the and conditions which can meet the rule, updating the push operator layer, and updating the output column id;
aiming at the project layer, the optimization judgment can be carried out only by pushing all input, the optimization judgment needs to traverse the projection expression, whether all element operations are in accordance with rules is checked, if the projection expression in accordance with the rules is in contact with the projection, the push operator layer needs to be updated, and the output column id is updated;
aiming at the group by layer, the optimization judgment can be carried out only by pushing all input, the optimization judgment needs to traverse the group by expression and all the ag function expression, the group by expression and the ag function expression all accord with the rule and can be pushed to the layer, the push operator layer needs to be updated, and the output column id is updated;
aiming at the order by layer, the optimization judgment can be carried out only by pushing all input, the optimization judgment needs to traverse all order by expressions, if all the order by expressions accord with rules, the order by layer can be pushed, the push operator layer needs to be updated, and the output column id needs to be updated;
aiming at a limit layer, all inputs need to be pushed to push;
and finally, updating the column id used by the unpulsed layer to the lower layer.
As shown in fig. 3, the flow of the filter layer optimization is as follows:
s1, judging whether optimization is performed according to a result after input optimization is completed;
s2, processing rule matching of all filter expressions;
s2.1, recording a rule-capable and condition;
s2.2, recording the and condition that the rule cannot be passed;
s3, merging the pass rule and conditions and merging the fail rule and conditions;
s4.1, if the irregular and condition exists, updating the condition of the layer;
s4.2, if the whole pushing is possible, the layer is removed;
s5, updating the output column id of the push layer.
As shown in fig. 4, the process flow for project layer optimization is as follows:
s1, judging whether optimization is performed according to a result after input optimization is completed;
s2, processing rule matching of all project expressions;
s2.1, recording an expression capable of passing rules;
s2.2, recording an expression which cannot pass the rule;
s3, pushing down the expression capable of passing the rule;
s4.1, if an expression which does not pass through the rule exists, updating the expression of the layer;
s4.2, if the whole pushing is possible, the layer is removed;
s5, updating the output column id of the push layer.
As shown in fig. 5, the flow of the group by layer optimization is as follows:
s1, judging whether optimization is performed according to a result after input optimization is completed;
s2, processing rule matching of all group by expressions, and returning that the layer cannot be pushed down if the rule cannot be passed;
s3, processing rule matching of all the agg functions, and returning that the layer cannot be pushed down if the rule cannot be passed;
s4, removing the layer;
s5, updating the output column id of the push layer.
The embodiment of the invention also provides an acceleration query system facing the multimode database, which comprises a self-defined optimization rule module and an optimization query plan module according to the optimization rule; the system realizes the data management and query of the multimode database by the acceleration query method for the multimode database.
The user-defined optimization rule module enables a user or DBA to release calculation and operation of processing of different module databases through rules; the optimization rules are stored in a metadata table of the multimode database, are set and read through system parameters, and support all operations supported by the SQL language elements in the rules: a function, a comparison operation, a binary operation, and a mode for which the number of parameters, the type of each parameter, the used position and the effect are required to be recorded; comparing operations contained in each layer of plans when generating the query plans, and if the operations are the operations contained in the optimization rules and accord with parameters defined in the optimization rules, pushing the operations into the corresponding module database plans;
optimizing a query plan module according to an optimization rule, traversing each layer of plan operator layers to judge, constructing a corresponding plan if the layer can be completely pushed to a specific module database to calculate, and recording the modification of columns output by the layer; if the upper layer cannot be completely pushed to the specific module database, making corresponding update according to the modification; after traversing all the planning layers, it can determine which operations can be pushed to the specific module database, and then update the column ids used by the remaining planning layers according to the new result columns output by the operations.
The push decisions for the different layers are as follows:
direct push against scan layer;
aiming at the filter layer, traversing the expression of the filter, checking whether all element operations have the and conditions which can meet the rule, if so, pushing part or all of the and conditions which can meet the rule, updating the push operator layer, and updating the output column id;
aiming at the project layer, the optimization judgment can be carried out only by pushing all input, the optimization judgment needs to traverse the projection expression, whether all element operations are in accordance with rules is checked, if the projection expression in accordance with the rules is in contact with the projection, the push operator layer needs to be updated, and the output column id is updated;
aiming at the group by layer, the optimization judgment can be carried out only by pushing all input, the optimization judgment needs to traverse the group by expression and all the ag function expression, the group by expression and the ag function expression all accord with the rule and can be pushed to the layer, the push operator layer needs to be updated, and the output column id is updated;
aiming at the order by layer, the optimization judgment can be carried out only by pushing all input, the optimization judgment needs to traverse all order by expressions, if all the order by expressions accord with rules, the order by layer can be pushed, the push operator layer needs to be updated, and the output column id needs to be updated;
aiming at a limit layer, all inputs need to be pushed to push;
and finally, updating the column id used by the unpulsed layer to the lower layer. The present invention can be easily implemented by those skilled in the art through the above specific embodiments. It should be understood that the invention is not limited to the particular embodiments described above. Based on the disclosed embodiments, a person skilled in the art may combine different technical features at will, so as to implement different technical solutions.
Other than the technical features described in the specification, all are known to those skilled in the art.
Claims (10)
1. An acceleration query method for a multimode database is characterized in that a specific calculation is pushed down to a specific modulus database by optimizing an operator plan of a query plan part by utilizing an optimization rule; the implementation of the method comprises the steps of customizing an optimization rule and optimizing a query plan according to the optimization rule;
the self-defined optimization rule is set and read through system parameters; comparing operations contained in each layer of plans when generating the query plans, and if the operations are the operations contained in the optimization rules and accord with parameters defined in the optimization rules, pushing the operations into the corresponding module database plans;
the query plan is optimized according to the optimization rule, each layer of planning operator layer is traversed to judge, if the layer can be completely pushed to a specific module database to calculate, a corresponding plan is constructed, and meanwhile, the modification of columns output by the layer is recorded; if the upper layer cannot be completely pushed to the specific module database, making corresponding update according to the modification; after traversing all the planning layers, it can determine which operations can be pushed to the specific module database, and then update the column ids used by the remaining planning layers according to the new result columns output by the operations.
2. The method for accelerating query against a multimodal database as recited in claim 1, wherein the optimization rules are stored in a metadata table of the multimodal database.
3. The method for accelerating query against multimode database according to claim 1 or 2, wherein all operations supported by the SQL language element, including functions, comparison operations, binary operations, are recorded in the optimization rule, and the number of parameters, the type of each parameter, the location of use, and the effective mode are recorded for these operations.
4. The method for accelerating query of a multimode database according to claim 3, wherein the specific implementation of optimizing rule matching is as follows: performing hash calculation by using the name of the expression and the parameters of the operation or the types of the left and right operation variables of the comparison operation, comparing with partial hash values recorded in metadata, and if the hash values are equal, comparing with the current use position of the expression; if so, success is returned.
5. The method for accelerating query of a multimode database according to claim 1, 2, 3 or 4, wherein the push judgment for different layers comprises:
direct push against scan layer;
traversing the expression of the filter aiming at the filter layer, checking whether all element operations have the and conditions which can meet the rule, and updating the push operator layer and updating the output column id if the element operations have the and conditions which can meet the push part or all;
aiming at the project layer, the optimization judgment can be carried out only by pushing all input, the optimization judgment needs to traverse the projection expression, whether all element operations are in accordance with rules is checked, if the projection expression in accordance with the rules is in contact with the projection, the push operator layer needs to be updated, and the output column id is updated;
aiming at the group by layer, the optimization judgment can be carried out only by pushing all input, the optimization judgment needs to traverse the group by expression and all the ag function expression, the group by expression and the ag function expression all accord with the rule and can be pushed to the layer, the push operator layer needs to be updated, and the output column id is updated;
aiming at the order by layer, the optimization judgment can be carried out only by pushing all input, the optimization judgment needs to traverse all order by expressions, if all the order by expressions accord with rules, the order by layer can be pushed, the push operator layer needs to be updated, and the output column id needs to be updated;
aiming at a limit layer, all inputs need to be pushed to push;
and finally updating column ids used by the unpulsed layer to the lower layer.
6. The method for accelerating query of a multimode database according to claim 5, wherein the process of performing the filter layer optimization is as follows:
s1, judging whether optimization is performed according to a result after input optimization is completed;
s2, processing rule matching of all filter expressions;
s2.1, recording an AND condition capable of passing a rule;
s2.2, recording an AND condition that the rule cannot pass;
s3, merging the AND conditions passing the rule AND merging the AND conditions not passing the rule;
s4.1, if conditions of the non-passing rule exist, updating the conditions of the layer;
s4.2, if the whole pushing is possible, the layer is removed;
s5, updating the output column id of the push layer.
7. The method for accelerating query of a multimode database according to claim 5, wherein project layer optimization is performed, and the flow is as follows:
s1, judging whether optimization is performed according to a result after input optimization is completed;
s2, processing rule matching of all project expressions;
s2.1, recording an expression capable of passing rules;
s2.2, recording an expression which cannot pass the rule;
s3, pushing down the expression capable of passing the rule;
s4.1, if an expression which does not pass through the rule exists, updating the expression of the layer;
s4.2, if the whole pushing is possible, the layer is removed;
s5, updating the output column id of the push layer.
8. The method for accelerating query of a multimode database according to claim 5, wherein the group by layer optimization is performed, and the flow is as follows:
s1, judging whether optimization is performed according to a result after input optimization is completed;
s2, processing rule matching of all group by expressions, and returning that the layer cannot be pushed down if the rule cannot be passed;
s3, processing rule matching of all the agg functions, and returning that the layer cannot be pushed down if the rule cannot be passed;
s4, removing the layer;
s5, updating the output column id of the push layer.
9. The method for accelerating query of multimode database according to claim 1, wherein the operations of different databases are accelerated calculations are controlled by means of a white list dictionary.
10. The multi-mode database-oriented acceleration query system is characterized by comprising a custom optimization rule module and an optimization query plan module according to an optimization rule;
the system realizes the data management and query of the multimode database by the acceleration query method facing the multimode database according to any one of claims 1 to 9.
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