Abstract: A computer-implemented database access method includes receiving, by a server, a database access statement including a target identifying parameter; parsing the database access statement to determine the target identifying parameter; determining a statement type of the database access statement; obtaining a pre-stored execution plan group corresponding to the statement type, the pre-stored execution plan group including one or more execution plans for accessing a database corresponding to the database access statement, in which each execution plan corresponds to a different respective identifying parameter; selecting a target execution plan from the pre-stored execution plan group based on the target identifying parameter; and executing the target execution plan.

Abstract: A data modification request for modifying data in a primary database is received at the primary database. Based on the data modification request, second data in the primary database is modified to first data. Based on the data modification request, a first log and a second log are generated, where the first log is a simplified log including the first data and the second log is a complete log including the second data. Persistence processing is performed on the first log. A modification result that corresponds to the data modification request is returned.

Abstract: Implementations of the present application provide a partition splitting method. Data files in a target source partition are divided into at least two data groups. A first data group from the at least two data groups is selected as a target splitting data group. A first splitting step is performed on the target splitting data group, including: splitting the target splitting data group into a plurality of data sub-groups based on a predetermined splitting rule, wherein each data sub-group of the plurality of data sub-groups corresponds to a new partition of at least two new partitions for the target source partition, and assigning each data sub-group to the new partition corresponding to the data sub-group. A second data group from the at least two data groups is selected as the target splitting data group, wherein the second data group is different from the first data group.

Abstract: A same voting time, a same vote counting time, and a same leader node tenure is configured by a host for all nodes. Time configuration information including the same configured voting time, the same vote counting time, and the same leader node tenure, is sent to all the nodes. The nodes are operable to vote during the same voting time, count the number of votes during the same vote counting time, and elect a leader node according to a vote counting result. The nodes are enabled to perform periodic node election according to the same leader node tenure.

Abstract: A data modification request for modifying data in a primary database is received at the primary database. Based on the data modification request, second data in the primary database is modified to first data. Based on the data modification request, a first log and a second log are generated, where the first log is a simplified log including the first data and the second log is a complete log including the second data. Persistence processing is performed on the first log. A modification result that corresponds to the data modification request is returned.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for writing data in one or more storage units. One of the methods includes obtaining an erasing count for each of multiple storage units, wherein the erasing count equals a total count of erasing operations that have been performed on all blocks of the storage unit. The method further includes identifying one or more of the storage units that satisfy one or more conditions associated with writing data, determining a storage unit that has the smallest erasing count among the identified storage units, and writing the data in the determined storage unit.

Abstract: This disclosure relates to bitmap index writing, dumping, merging, and querying. In one aspect, a method includes determining, based on update data, a to-be-updated row of a bitmap index that indexes data of a database using a bitmap data structure. The to-be-updated row of the bitmap index is locked. The locking includes adding a row lock to the to-be-updated row, thereby preventing the to-be-updated row from being updated by other update operations. At least one other row of the bitmap index remains unlocked for updating by other update operations. Data in the to-be-updated row of the bitmap index is updated based on the update data.

Abstract: The specification provides a system, method, and storage medium for executing an outer join command on a first table and a second table in a distributed database. The method may comprise: broadcasting the first data set to a plurality of database nodes storing the second data set; dividing the plurality of database nodes into a first subset and a second subset; obtaining, an OUTER JOIN result based on the first data set broadcasted to each database node of the first subset and the portion of the second data set stored therein; obtaining, an INNER JOIN result based on the first data set broadcasted to each database node of the second subset and the portion of the second data set stored therein; and returning a result to the OUTER JOIN command by aggregating the OUTER JOIN result and the INNER JOIN result.

Abstract: A computer-implemented database access method includes receiving, by a server, a database access statement including a target identifying parameter; parsing the database access statement to determine the target identifying parameter; determining a statement type of the database access statement; obtaining a pre-stored execution plan group corresponding to the statement type, the pre-stored execution plan group including one or more execution plans for accessing a database corresponding to the database access statement, in which each execution plan corresponds to a different respective identifying parameter; selecting a target execution plan from the pre-stored execution plan group based on the target identifying parameter; and executing the target execution plan.

Abstract: A method includes: obtaining, by an inner table node that stores a portion of an inner table, a portion of an outer table from each of outer table nodes, and wherein a size of the outer table is less than a size of the inner table; reading, by the inner table node, the outer table by table joining threads or table joining processes of the inner table node; matching, by the table joining threads or the table joining processes, the outer table with the portion of the inner table stored at the inner table node; and outputting, by the inner table node, data from the outer table and the portion of the inner table that matches with each other.

Abstract: Implementations of the present application provide a partition splitting method. Data files in a target source partition are divided into at least two data groups. A first data group from the at least two data groups is selected as a target splitting data group. A first splitting step is performed on the target splitting data group, including: splitting the target splitting data group into a plurality of data sub-groups based on a predetermined splitting rule, wherein each data sub-group of the plurality of data sub-groups corresponds to a new partition of at least two new partitions for the target source partition, and assigning each data sub-group to the new partition corresponding to the data sub-group. A second data group from the at least two data groups is selected as the target splitting data group, wherein the second data group is different from the first data group.

Abstract: Embodiments provide a data block storage method applied to a database employing an LSM tree. According to the method, data in the 0th layer of the LSM tree is organized in MacroBlocks. When the data in the LSM tree is transferred from an upper layer to a lower layer, the data is transferred to the lower layer in the form of MacroBlocks, so that all layers of the LSM tree are organized in the form of MacroBlocks. When inter-layer data merger is performed, the merger may be performed based on the MacroBlocks, which can avoid rewriting of all data on an entire layer and can effectively reduce disk IO and temporary space consumption. Moreover, as each MacroBlock comprises a plurality of variable-length MicroBlocks, the data may be stored in a storage space in a more compact manner.