International Business Machines Corporation

DongJie Wei

Mi Wan Shum

Xin Yang

Brian K Caufield

Ron E Liu

Xiang Sean Zhou

Sriram Kolinjivadi Padmanabhan

Zhen Li

Xian Wu

Chun H Sun

Samuel H K Wong

Samuel H Wong

Ke Wei Wei

Miao Zheng

Embodiments of the present invention provide systems and methods for dynamically allocating data to multiple nodes. The method includes determining the usage of multiple buffers and the capability factors of multiple servers. Data is then allocated to multiple buffers associated with multiple active servers, based on the determined usage and capability factors, in order to keep the processing load on the multiple servers balanced.

Dynamic load balancing for data allocation to servers

Parsing XML (extensible markup language) data by performing the following operations: (i) dividing the piece of markup language into a plurality of pre-parsing segments; (ii) assigning the pre-parsing segment to a pre-parsing processor thread of a plurality of pre-parsing processor threads; (iii) determining any parsing division point(s) occurring in the pre-parsing segment so that data corresponding to a single tabular record is between each consecutive pair of parsing division points; (iv) dividing the piece of language into a plurality of parsing segments defined by the parsing division points so that each parsing segment corresponds to a single tabular record; (v) assigning the parsing segment to a parsing processor thread of a plurality of parsing processor threads; and (vi) parsing to generate a parsed tabular record corresponding to the parsing segment.

Parallel parsing of markup language data

Optimizing query statements in relational databases including determining filtering performance for each of at least two complex predicates in a predetermined layer of the query statement based on a result of a query performed on predetermined data records by using only the complex predicate. The results are ranked based on the filtering performance and re-ordered so that a complex predicate with a higher filtering performance is ranked before a complex predicate with a lower filtering performance. These techniques produce shortened data query response times and improved execution efficiency.

Optimizing query statements in relational databases

Provided are techniques for multiple stage workload management. A staging queue and a run queue are provided. A workload is received. In response to determining that application resources are not available and that the workload has not been previously semi-started, the workload is added to the staging queue. In response to determining that the application resources are not available and that the workload has been semi-started, and, in response to determining that run resources are available, the workload is started. In response to determining that the application resources are not available and that the workload has been semi-started, and, in response to determining that the run resources are not available, adding the workload to the run queue.

Multiple stage workload management system

A system identifies a performance bottleneck status in a parallel data processing environment by examining data flow associated with the parallel data processing environment to identify at least one operator, where an operator type is associated with at least one operator, at least one buffer, and a relationship that the buffer has with the operator, where the relationship is associated with the operator type. The system monitors the buffer to determine a buffer status associated with the buffer. The system applies a set of rules to identify an operator bottleneck status associated with the operator. The set of rules is applied to the operator, based on the operator type, the buffer status, and relationship that the buffer has with the operator. The system then determines a performance bottleneck status associated with the parallel data processing environment, based on the operator bottleneck status.

Apparatus and method for identifying performance bottlenecks in pipeline parallel processing environment

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Beijing, China

DongJie Wei