Future Wei Technologies, Inc.

Huawei Technologies Co., Limited

Mingyi Zhang

Yu Dong

Huaizhi Li

Lei Liu

Ron-Chung Hu

Le Cai

Demai Ni

Ahmad Ghazal

Yantao Qiao

A query processing device includes a communication interface accessing a database and database catalog, a memory storing instructions, and a processor coupled to the memory and the communication interface. The processor executes the instructions to parse a query and generate first and second execution plans for the query, retrieve respective previously determined cardinality values for previously executed logical steps of the first and second execution plans from the database catalog, select an execution plan from the first execution plan or the second execution plan, the selected execution plan having a lower cost based on the previously determined cardinality values, and execute the selected execution plan on data accessed from the database. The query processing system stores actual cardinality values determined during the execution of the logical steps in the database catalog for use by subsequent queries. The query processing device, therefore, re-uses previously determined cardinality values.

Query processing using logical query steps having canonical forms

Methods and apparatus are provided for using machine learning to estimate query resource consumption in a massively parallel processing database (MPPDB). In various embodiments, the machine learning may jointly perform query resource consumption estimation for a query and resource extreme events detection together, utilize an adaptive kernel that is configured to learn most optimal similarity relation metric for data from each system settings, and utilize multi-level stacking technology configured to leverage outputs of diverse base classifier models. Advantages and benefits of the disclosed embodiments include providing faster and more reliable system performance and avoiding resource issues such as out of memory (OOM) occurrences.

Using machine learning to estimate query resource consumption in MPPDB

Embodiments of the present technology relate managing database query concurrency. A method of the present technology can include receiving a query, generating a first query plan that can be used to execute the query in system memory without any system memory constraints, and estimating a system memory cost for executing the query in the system memory using the first query plan. The method can also include placing the query in a queue if available system memory does not satisfy the estimated system memory cost. The method can further include conditionally selecting the query from the queue, conditionally generating a second query plan for the query that can be used to execute the query in the system memory in compliance with a system memory constraint, and conditionally executing the query in the system memory.

Memory-aware plan negotiation in query concurrency control

A system (and associated method) includes a processor which determines a performance metric ratio of a performance metric of a second type of server relative to a performance metric of a first server type for each of multiple sizes of multiple benchmark datasets to thereby determine a plurality of performance metric ratio values. The processor also determines an interpolation function for the plurality of performance metric ratio values usable to compute interpolation performance metric ratios of the second type of server relative to the first type of server for dataset sizes other than the first plurality of sizes. Given a second dataset, the processor determines an amount of the second dataset to provide to each of the respective server types using the interpolation function. The processor configures a load balancer based on the amount of the second dataset determined for each type of server.

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Santa Clara, CA, United States of America

Mingyi Zhang