Renmin University of China

Yan-Song Zhang

Shan Wang

Xuan Zhou

Zhan-Wei Wang

Min Jiao

An OLAP query processing method oriented to a database and Hadoop hybrid platform is described. When OLAP query processing is performed, the processing is executed first on a main working copy, and a query processing result is recorded in an aggregate result table of a local database; when a working node is faulty, node information of a fault-tolerant copy corresponding to the main working copy is searched for through namenode, and a MapReduce task is invoked to complete the OLAP query processing task on the fault-tolerant copy. The database technology and the Hadoop technology are combined, and the storage performance of the database and the high expandability and high availability of the Hadoop are combined; the database query processing and the MapReduce query processing are integrated in a loosely-coupled mode, thereby ensuring the high query processing performance, and ensuring the high fault-tolerance performance.

OLAP query processing method oriented to database and HADOOP hybrid platform

An access optimization method for a main memory database based on page-coloring is described. An access sequence of all data pages of a weak locality dataset is ordered by page-color, and all the data pages are grouped by page-color, and then all the data pages of the weak locality dataset are scanned in a sequence of page-color grouping. Further, a number of memory pages having the same page-color are preset as a page-color queue, in which the page-color queue serves as a memory cache before a memory page is loaded into a CPU cache; the data page of the weak locality dataset first enters the page-color queue in an asynchronous mode, and is then loaded into the CPU cache to complete data processing. Accordingly, cache conflicts between datasets with different data locality strengths can be effectively reduced.

Access optimization method for main memory database based on page-coloring

A concurrent on-line analytical processing (OLAP)-oriented database query processing method is described, for performing, on the basis of predicate vector-based memory OLAP star-join optimization, concurrent OLAP query processing based on a batch query predicate vector bit operation. The concurrent query processing optimization technology is implemented for I/O performance and parallel OLAP processing performance in a database management system, and setting of concurrent OLAP processing load in an optimized way catering to the I/O performance is supported, thereby improving predictable processing performance oriented to diversified OLAP queries and implementing concurrent query star-join bitmap filtering processing based on predicate vector arrays.

Concurrent OLAP-oriented database query processing method

A multi-dimensional OLAP query processing method oriented to a column store data warehouse is described. With this method, an OLAP query is divided into a bitmap filtering operation, a group-by operation and an aggregate operation. In the bitmap filtering operation, a predicate is first executed on a dimension table to generate a predicate vector bitmap, and a join operation is converted, through address mapping of a surrogate key, into a direct dimension table tuple access operation; in the group-by operation, a fact table tuple satisfying a filtering condition is pre-generated into a group-by unit according to a group-by attribute in an SQL command and is allocated with an increasing ID; and in the aggregate operation, group-by aggregate calculation is performed according to a group item of a fact table filtering group-by vector through one-pass column scan on a fact table measure attribute.

Growing community of inventors

Beijing, China

Yan-Song Zhang