International Business Machines Corporation

Xiao Xiao Chen

Shuo Li

Xiaobo Wang

Peng Hui Jiang

Sheng Yan Sun

ShengYan Sun

Jing Wang

Hong Mei Zhang

Bing Jiang Sun

Heng Liu

Xiao Yi Tian

Liu Wang

Ye Tao

Xiao Hui Wang

Xiao Hui Xh Wang

Shen Yan Sun

Access usage of one or more shards of a set of shards of a database is determined. The set of shards includes a plurality of shards including a primary shard and one or more replica shards of the primary shard. A shard of the plurality of shards is selected based on the access usage, and a set of indexes is built for the shard that is selected. The set of indexes built for the shard that is selected is used for at least multiple shards of the plurality of shards.

Building and using scalable database indexes

In several aspects, a computing device analyzes data to determine its characteristics. The computing device selects at least one compression process based on the characteristics. A compression switch and a compression level are dynamically adjusted based on multiple factors including available system resources, desired storage savings and performance requirement. The compression level dynamically varies depending on the data being processed and a workload on a system. Performance of compression and decompression operations are continuously monitored for dynamically adjusting compression parameters to optimize performance.

Adaptive compression/decompression in distributed database application

Computer implemented methods, systems, and computer program products include program code executing on a processor(s) obtains a given query referencing a LOB. The program code obtains statistics and activities performed in the database system, establishes performance benchmarks, and analyzes the given query to determine how the LOB is utilized. The program code determines that meeting a benchmark requires replacing a reference to the LOB with a reference to implicit column(s). The program code executes the given query by referencing the implicit column(s) to obtain query results. The program code returns the query results.

High-performance large object operations

A computer-implemented method, including receiving, by a processor set, a query including a query string for a system catalog; identifying, by the processor set, a default index structure of the system catalog; executing, by the processor set, the query based on the default index structure of each index in the system catalog; ranking, by the processor set, a performance of each execution of the query by each index; mapping, by the processor set, a query pattern to a corresponding index of the system catalog; selecting, by the processor set, the index to perform the query using a machine learning (ML) model trained with a knowledge base that includes the ranking and the mapping; executing, by the processor set, the query on the selected index; and in response to executing the query on the selected index, returning, by the processor set, a result of the query.

Hybrid query and index for high performance in a cloud

Monitor stand

A computer-implemented method dynamically switches access plans for a query during concurrent query execution. The method includes receiving a first query configured to be processed by a database system. The method also includes generating, for the first query, an access plan for each of identified resource sets. The method includes determining a first set of available resources that represent an available capacity for the database system. The method further includes selecting a first resource set of the one or more resource sets, where the selecting is based on the first set of available resources being closest to the first resource set. The method also includes selecting, based on the first set of available resources, a first access plan of the one or more access plans. The method includes executing the first query and returning results of the first query to a source of the first query.

Maintaining query performance during high concurrency operations

Playpen

A computer-implemented method to estimate an execution time for large query batches. The method includes identifying a batch of queries, where the batch of queries where each query is configured to retrieve data from a database. Selecting a first query and generating an access path for the first query including one or more mini plans. Building a learning model for the one or more mini plans. Estimating an estimated execution time for each of the one or more mini plans. Adding each of the estimated execution times for each of the one or more mini plans to determine a first total estimated time for the first query. Displaying the first total estimated time.

Estimating execution time for batch queries

A computer-implemented method for access path optimization is provided according to embodiments of the present disclosure. In the method, a plurality of real values of an access path factor may be collected during a specified time period. One of the real values may be generated when a query is executed on a first access path. Then, at least one second access path may be generated for the query based on the plurality of real values of the access path factor. Moreover, an optimal access path for the query may be identified from the first access path and the at least one second access path.

Access path optimization

An approach is provided for evaluating a performance of a query. A risk of selecting a low performance access path for a query is determined. The risk is determined to exceed a risk threshold. Based on the risk exceeding the risk threshold and using a machine learning optimizer, first costs of access paths for the query are determined. Using a cost-based database optimizer, second costs of the access paths are determined. Using a strong classifier operating on the first costs and the second costs, a final access path for the query is selected from the access paths.

Evaluating query performance

Functional verification of a new database feature is provided. A set of SQL query seeds are evolved using information contained in a knowledge map that was generated based on a real SQL query workload of an entity corresponding to a database. Real SQL queries are identified in the database that are similar to the evolved set of sample SQL query seeds. A new feature to be implemented in the database is injected into the real SQL queries that are similar to the evolved set of sample SQL query seeds. Multiple runtime access paths are generated for each of the real SQL queries injected with the new feature for output cross validation. Each of the real SQL queries injected with the new feature is run in a test environment using each of the multiple runtime access paths corresponding to a real SQL query injected with the new feature for functional verification.

Structured query language runtime access path generation for deep test in a database

Embodiments are disclosed for a method for dynamic access paths. The method includes generating real-time statistics (RTS) estimates based on a log of a database. Further, the method includes generating access paths based on a structured query language command and the RTS estimates. The method also includes training a machine learning model to map the RTS estimates to the access paths.

Dynamic access paths

A query including at least a first part and a second part can be received by an electronic system. The first part of the query can indicate to access at least a first database table and a second database table to generate an intermediate result. The second part of the query can indicate to perform a sort operation on the intermediate result with respect to columns across the first database table and the second database table to produce a final result. The method further includes building execution plans for generating the intermediate result according to the first part, where the intermediate result is to be used as the final result without building an execution plan for performing the sort operation.

Optimization of database execution planning

A method, system and computer program for analysis of patch impact. The method comprises obtaining first data manipulating statement (DMS) from description of a patch to be installed to a database system; extracting first parse tree and first access path of first DMS; obtaining second DMS from an running database application; extracting second parse tree of second DMS; comparing first parse tree with second parse tree and first access path with second access path; and installing the patch to a database system in response to a determination that first parse tree is a subset of second parse tree and first access path is a subset of second access path.

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Beijing, China

Xiao Xiao Chen