Abstract: The present application discloses a data query method. The method can be applied to a columnar storage system. The columnar storage system stores target data that is stored in a row storage form, target data that is stored in a columnar storage form, and a row storage index constructed based on the target data that is stored in a row storage form. The columnar storage system can receive a data query request and matches the data query request with the row storage index corresponding to the columnar storage system. If the matching is successful, data matched with the data query request is queried based on the row storage index. Therefore, by use of the scheme, the target data can be stored in the row storage form into the columnar storage system, and the row storage index can be constructed.

Abstract: The present application discloses a data query method, comprising: receiving a data query request, the data query request being used for querying data from a columnar storage system which applies object storage technology, the columnar storage system comprising an index file and a data file which are stored individually. After receiving the data query request, the index file is obtained based on the data query request, i.e., the whole index file may be obtained when receiving the data query request. Afterwards, a storage range in the data file of target data which matches the data query request determined based on the index file, and further, a network input and output (IO) is called to read the target data stored in the storage range. As such, it can reduce the number of calls to the network IO and further improves the efficiency of querying data from the columnar storage system.

Abstract: A data processing method based on online analytical processing, an electronic device and a storage medium are provided. The method includes: receiving a data operation request, wherein the data operation request includes a first field; determining a target associated data pair from a set of associated data pairs stored in a row-based manner based on the data operation request, and determining a second field associated with the first field according to the determined target associated data pair; taking a field value corresponding to the second field in a fact table of a first database as a target field value corresponding to the first field, and executing an operation indicated by the data operation request for the target field value; wherein fields belonging to the second data type among the associated data pairs are stored in the fact table.

Abstract: Embodiments of the present disclosure provide a data query method based on on-line analytical processing, an electronic device and a storage medium. The method includes: receiving a first query request, and determining a request type of the first query request; executing a query in a row-based storage portion of an event table of a database in response to the request type being a preset type; and generating a query result based on retrieved target data, wherein the event table includes a column-based storage portion and the row-based storage portion, and the column-based storage portion includes a plurality of data rows; and the row-based storage portion includes a coding column, and each piece of coded information in the coding column includes row data belonging to a same data row in the column-based storage portion.

Abstract: Embodiments of the present application provide a data processing method and apparatus, an electronic device, and a medium. The method includes: receiving a data query request for a first data processing engine; selecting a target data processing engine from a data processing engine candidate set in a front-end node based on the data query request; if the target data processing engine is the first data processing engine, parsing the data query request in the front-end node, and sending a parsing result to a back-end node, so that the back-end node performs data query; if the target data processing engine is a second data processing engine, generating target information in the front-end node based on the data query request, and sending the target information to the second data processing engine, so that the second data processing engine obtains data from the first data processing engine for data query.

Abstract: A querier sends a SparkSQL query instruction to a connector, the SparkSQL query instruction contains a table identifier of an HBase table, metadata of the HBase table, and an attribute value corresponding to the metadata. When the connector receives the SparkSQL query instruction, if an index table corresponding to the table identifier and the metadata exists in a storage device, the connector generates a first Phoenix query instruction and sends the first Phoenix query instruction to the storage device, the index table is used to record a correspondence between an attribute value of the metadata of the HBase table and a RowKey of the HBase table, and the first Phoenix query instruction contains the table identifier, the metadata, and the attribute value. When receiving the first Phoenix query instruction, the storage device obtains the RowKey and the metadata, obtains the data row, and sends the data row to the connector.

Abstract: A querier sends a SparkSQL query instruction to a connector, wherein the SparkSQL query instruction contains a table identifier of an HBase table, metadata of the HBase table, and an attribute value corresponding to the metadata. When the connector receives the SparkSQL query instruction, if an index table corresponding to the table identifier and the metadata exists in a storage device, the connector generates a first Phoenix query instruction and sends the first Phoenix query instruction to the storage device, wherein the index table is used to record a correspondence between an attribute value of the metadata of the HBase table and a RowKey of the HBase table, and the first Phoenix query instruction contains the table identifier, the metadata, and the attribute value.