Abstract: The disclosure provides a read-write separation and automatic scaling-based cloud arrangement system and method. Thus, a read operation and a write operation are separated. Capacity expansion and capacity reduction are performed on a cluster. All index data is stored in corresponding object storage. A pre-computing module submits resources (memory and CPU) required by a computing task to an automatic scaling module. Resource creation is performed by invoking API corresponding to an infrastructure automated arrangement tool Terraform, to guarantee the operation of a construction task. The automatic scaling module provides two resource expansion strategies, which are based on time and the maximum number of waiting tasks. According to the disclosure, the utilization rate of cloud resources is increased, costs are lowered, and manual intervention is reduced.

Abstract: An embodiment of the present invention discloses a computing resource configuration method and device for enterprise cloudification. The method comprises the following steps: obtaining parameters configured by a user through a configuration page, wherein the parameters include a proportion of a bidding instance to an on-demand instance; determining a total amount of computing resources required by a computing task after the computing task is obtained; determining the computing resources corresponding to the bidding instance and the computing resources corresponding to the on-demand instance based on existing computing resources, the total amount of the computing resources and the proportion; and applying for the computing resources from a cloud provider based on the computing resources corresponding to the bidding instance and the computing resources corresponding to the on-demand instance. Big data is computed in a bidding instance mode so that the total cost of the computing resources is greatly reduced.

Abstract: This application discloses a precomputation method and apparatus for continuous iterative optimization. The precomputation method for continuous iterative optimization includes: determining a query task corresponding to each of a plurality of time periods; and continuously performing multiple rounds of optimization on a precomputation model according to the query task corresponding to each time period. According to this application, the precomputation model is continuously optimized, so that the performance of the precomputation model is improved. Therefore, the technical problem of poor performance caused by long-term non-tuning of the precomputation model can be avoided.

Abstract: The present invention discloses a data query method, device and equipment and a storage medium. A data query method comprises the following steps: receiving an MDX query statement; acquiring related information of measurement and dimension in the MDX query statement; and performing query according to the related information of measurement and dimension to obtain a query result. According to the present invention, an MDX expression is computed by extracting the related information of measurement and dimension and utilizing a distributed computing framework, so that the data analysis efficiency is greatly improved.

Abstract: This application discloses a method and apparatus for scoring a precomputation model, a device, and a storage medium. The method includes: calculating, in a plurality of precomputation models, a score when each precomputation model executes a same query load; determining the precomputation model with the largest score as a target precomputation model according to the score of each precomputation model; and using the target precomputation model for query calculation. According to this application, the precomputation model may be quantitatively scored, so that horizontal comparison is conveniently performed when different precomputation models execute query tasks of the same load. Therefore, a user can conveniently select different precomputation models for query calculation.

Abstract: The present application discloses a dynamic routing method and apparatus for a query engine in a pre-computing system. The method includes: pre-obtaining cube data under a preset dimensional combination in a pre-computing system; determining a degree of aggregation of the cube data selected as expected under the preset dimensional combination after a query request is received; executing query processing on the query request in a first distributed query engine when the degree of aggregation of the cube data under the preset dimensional combination is high; and switching to a second distributed query engine to execute query processing on the query request when the degree of aggregation of the cube data under the preset dimensional combination is low. The present application solves the technical problem that the query response speed of the pre-computing query system is not ideal. Through the present application, the sub-second high-performance query response can be achieved.

Abstract: The present application discloses a data configuration query method and device. The method includes: determining at least two target data sets required to be queried by a query instruction and an ordered association between the target data sets, wherein the ordered association at least includes a unidirectional association and/or a bidirectional association; generating, on the basis of the at least two target data sets and the ordered association between the target data sets, a target association path; and outputting an OLAP model conforming to the target association path in a database. According to the present application, the technical problems of a large demand quantity of OLAP models included in an OLAP query system and a low utilization rate of the OLAP models in the related art can be solved.