# US 2013/0159353 A1 · Generating a test workload for a database > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 2013/0159353 A1 - Document ID: US20130159353A1 - Status: Published application - Kind: A1 - Application number: 13/647,122 - Filed: 2012-10-08 - Published: 2013-06-20 - Earliest claimed: 2011-12-20 - Assignees: International Business Machines Corporation - Inventors: YOU-CHIN FUH; SHUO LI; HENG LIU; MICHAEL SCHENKER; KE WEI WEI; XIN YING YANG - Web page: https://patentgrep.com/patent/US20130159353A1 ## CPC - G06F 21/6254: {by anonymising data, e.g. decorrelating personal data from the owner's identification} - G06F 11/3414: {Workload generation, e.g. scripts, playback} - G06F 11/3672: {Test management} - G06F 2201/80: Database-specific techniques - G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N) ## Abstract A method of transferring files in a data-processing network using a current node within the network includes reading an outbound content and outbound characteristics of an outbound file. An outbound message is created having outbound strings including a first set of the outbound strings representing the outbound characteristics and a second set of the outbound strings representing the outbound content. The outbound message is sent to a receiver node within the network. An inbound message is received from a sender node within the network. The inbound message has inbound strings including a first set of the inbound strings representing inbound characteristics and a second set of the inbound strings representing inbound content. An inbound file having the inbound content is stored, and the inbound characteristics are applied to the inbound file. ## Claims ### Claim 1 A system for generating a test workload for a database, comprising: a computer hardware system having a processor, wherein the computer hardware system includes a workload capturing module configured to capture a workload comprising database statements sent to a source database on a source system; a meta information collecting module configured to collect the data schema and data distribution statistics in the source database; a data generating module configured to, based on the data schema collected by the meta information collecting module, create data objects with the same data schema in a target database, and based on the data distribution statistics collected by the meta information collecting module, generate data with similar data distribution characteristics in the target database; and a statement mutating module configured to mutate at least some original database statements in the workload captured by the workload capturing module, wherein execution of the mutated database statements on the target database will have similar performance characteristics to those of execution of the original database statements on the source database to obtain a test workload. ### Claim 2 (depends on 1) The system as recited in claim 1, further comprising: a test workload execution module configured to execute the test workload on the target database to perform testing on the target database. ### Claim 3 (depends on 1) The system as recited in claim 1, wherein the meta information collecting module is configured to only collect the data schema and data distribution statistics in the source database involved by original database statements included in the workload. ### Claim 4 (depends on 1) The system as recited in claim 1, wherein the similar performance characteristics refer to similar predicate filter factors. ### Claim 5 (depends on 1) The system as recited in claim 1, wherein the data distribution statistics include the maximum, minimum, cardinality and size of the data in a column of a database table, or the respective occurrence frequencies of a number of values with the highest occurrence frequencies in the data of a column of a database table and the maximum, minimum, cardinality and size of each data section divided by the values. ### Claim 6 (depends on 1) The system as recited in claim 1, further comprising: a constraint obtaining module configured to obtain constrains related to the selectivity of the data involved by original database statements on the source system, wherein the data generating module is further configured to generate data in the target database that satisfies or substantially satisfies the constraints related to the selectivity and with the similar data distribution statistics. ### Claim 7 (depends on 6) The system as recited in claim 6, wherein the constrains related to the selectivity of the data involved by original database statements comprise constraints related to the filter factors of a plurality of predicates in an original database statement involving a plurality of columns of a same database table; and the data generating module is further configured to: first generate data satisfying or substantially satisfying the filter factors of predicates or combination thereof involving more columns, and then generate data satisfying or substantially satisfying the filter factors of predicates or combination thereof involving less columns. ### Claim 8 (depends on 6) The system as recited in claim 6, wherein the constrains related to the selectivity of the data involved by original database statements comprise constrains related to the filter factors of the predicates in an original database statement involving a plurality of columns of a plurality of database tables having a join relationship therebetween; and the data generating module is further configured to: make the data generated in the target database satisfy or substantially satisfy the filter factors of the predicates. ### Claim 9 (depends on 6) The system as recited in claim 6, further comprising: a predicate classification module configured to classify predicates and combinations thereof in original database statements into different groups based on the involved columns of database tables; and an occurrence frequency calculating module configured to calculate the occurrence frequencies of the predicates and combinations thereof in different groups in database statements, wherein the constraint obtaining module is further configured to only obtain the filter factors of the predicates and combinations thereof in groups with higher frequencies. ### Claim 10 (depends on 1) The system as recited in claim 1, wherein the statement mutating module further comprises: a statement mutating sub-module configured to, for an original database statement comprising a join relationship, delete the local predicates therein, and add the columns of database tables involved in the local predicates into the SELECT clause, to obtain a modified database statement; a result set obtaining sub-module configured to execute the modified database statement on the target database to obtain a result set; a column value finding sub-module configured to find in the result set column values the selectivity of which are similar to the individual or combined filter factors of the local predicates in the source database; and a literal substituting sub-module configured to substitute the column values for the literals in the local predicates in the database statement comprising a join relationship. ### Claim 11 A computer-implemented method for generating a test workload for a database, comprising: capturing a workload comprising database statements sent to a source database on a source system; collecting the data schema and data distribution statistics in the source database; creating, based on the data schema collected, data objects with the same data schema in a target database; generating, based on the data distribution statistics collected, data with similar data distribution characteristics in the target database; and mutating at least some original database statements in the workload captured by a workload capturing module, wherein execution of the mutated database statements on the target database will have similar performance characteristics to those of execution of the original database statements on the source database to obtain a test workload. ### Claim 12 (depends on 11) The method as recited in claim 11, further comprising: executing the test workload on the target database to perform testing on the target database. ### Claim 13 (depends on 11) The method as recited in claim 11, wherein the collecting the data schema and data distribution statistics in the source database comprises only collecting the data schema and data distribution statistics in the source database involved by original database statements included in the workload. ### Claim 14 (depends on 11) The method as recited in claim 11, wherein the similar performance characteristics refer to similar predicate filter factors. ### Claim 15 (depends on 11) The method as recited in claim 11, wherein the data distribution statistics include the maximum, minimum, cardinality and size of the data in a column of a database table, or the respective occurrence frequencies of a number of values with the highest occurrence frequencies in the data of a column of a database table and the maximum, minimum, cardinality and size of each data section divided by the values. ### Claim 16 (depends on 11) The method as recited in claim 11, further comprising: obtaining constrains related to the selectivity of the data involved by original database statements on the source system, wherein the generating data with similar data distribution characteristics in the target database further comprises generating data in the target database that satisfies or substantially satisfies the constraints related to the selectivity and with the similar data distribution statistics. ### Claim 17 (depends on 16) The method as recited in claim 16, wherein the constrains related to the selectivity of the data involved by original database statements comprise constraints related to the filter factors of a plurality of predicates in an original database statement involving a plurality of columns of a same database table; and the generating data in the target database that satisfies or substantially satisfies the constraints related to the selectivity and with the similar data distribution statistics further comprises: first generating data satisfying or substantially satisfying the filter factors of predicates or combination thereof involving more columns, and then generating data satisfying or substantially satisfying the filter factors of predicates or combination thereof involving less columns. ### Claim 18 (depends on 16) The method as recited in claim 16, wherein the constrains related to the selectivity of the data involved by original database statements comprise constrains related to the filter factors of the predicates in an original database statement involving a plurality of columns of a plurality of database tables having a join relationship therebetween; and the generating data in the target database that satisfies or substantially satisfies the constraints related to the selectivity and with the similar data distribution statistics further comprises: make the data generated in the target database satisfy or substantially satisfy the filter factors of the predicates. ### Claim 19 (depends on 6) The method as recited in claim 6, further comprising: classifying predicates and combinations thereof in original database statements into different groups based on the involved columns of database tables; and calculating the occurrence frequencies of the predicates and combinations thereof in different groups in database statements, wherein the obtaining constrains related to the selectivity of the data involved by original database statements on the source system comprises only obtaining the filter factors of the predicates and combinations thereof in groups with higher frequencies. ### Claim 20 (depends on 11) The method as recited in claim 11, wherein the mutating at least some original database statements in the workload captured further comprises: for an original database statement comprising a join relationship, deleting the local predicates therein, and adding the columns of database tables involved in the local predicates into the SELECT clause, to obtain a modified database statement; executing the modified database statement on the target database to obtain a result set; finding in the result set column values the selectivity of which are similar to the individual or combined filter factors of the local predicates in the source database; and substituting the column values for the literals in the local predicates in the database statement comprising a join relationship.