Lemon Inc.

Douyin Vision Co., Ltd.

Beijing Volcano Engine Technology Co., Ltd.

Beijing Vocano Engine Technology Co., Ltd.

Wei Ding

Li Feng Zhang

Jianjun Chen

Yuanjin Lin

Jianfeng Qian

Rui Shi

Yonghua Ding

Fangshi Li

Ye Liu

Mingyi Zhang

Kai Wu

Kui Wei

Jason Yang Sun

Shicai Zeng

This disclosure provides a query task execution method, apparatus, computer device, storage medium. The method includes: receiving a query task for a target data table; determining at least one candidate degree of parallelism for executing the query task based on a first number of child tables obtained by pre-partitioning the target data table; selecting a target degree of parallelism within a preset range of degrees of parallelism from the at least one candidate degree of parallelism; and determining required computing units according to the target degree of parallelism, and evenly allocating the child tables to the computing units which concurrently execute the query task based on the allocated child tables.

Query task execution method, apparatus, computer device and storage medium

A hybrid transactional/analytical processing (HTAP) database includes an online transaction processing (OLTP) engine, an online analytical processing (OLAP) engine provided separately from the OLTP engine, and a shared storage holding data of multiple tenants. The shared storage is configured to receive a request from the OLAP engine for evaluation of a predicate against the data stored on the shared storage. The shared storage is further configured to determine that a CPU utilization of the shared storage exceeds a predicate refusal threshold. Upon determining that the CPU utilization of the shared storage exceeds the predicate refusal threshold, the shared storage is further configured to refrain from evaluating at least a portion of the predicate.

Predicate evaluation based on CPU utilization of shared storage

The present disclosure provides a query method for a data table in a database, an apparatus, a computer device and a storage medium. The method includes: acquiring a data table query instruction, the data table query instruction is used to perform a Hash Join query operation on target join columns of a plurality of data tables in the database; constructing, in a build phase of the Hash Join, a hash data table according to tabular data corresponding to a target join column of a first data table among the plurality of data tables; determining, based on table statistical information of the hash data table, whether to construct a runtime filter for filtering a second data table in a probe phase of the Hash Join; and if so, determining a data query result with the constructed runtime filter based on the hash data table and the second data table.

Query method for data table in database, apparatus, computer device and storage medium

The present disclosure relates to a database-based data processing method, device, medium and electronic apparatus, the method including: receiving a query request task to be executed, and determining a plurality of coroutine tasks corresponding to the query request task; in each thread, determining a target coroutine task to be executed according to time information of each coroutine task in the local task queue of the thread; interrupting the target coroutine task and adding the target coroutine task to the global task queue when execution of the target coroutine task is not completed and the target coroutine task has been executed in the thread for a current time slice; according to the global task queue, and the local task queue of the thread, determining a new target coroutine task for the thread, and executing the new target coroutine task in a next time slice.

Database-based data processing method, device, medium and electronic apparatus

The present disclosure describes storage techniques for hybrid transactional and analytical processing. Data captured by a first processing engine may be received. The first processing engine may be configured to perform online transactional processing). Multiple replicas of logical logs generated based on the data may be distributed to a Delta Store by applying a quorum protocol on the multiple replicas. Data in the Delta Store are stored in a row format and are visible to a query for online analytical processing performed by a second processing engine. Data may be flushed from the Delta Store to a Base Store based on one or more predetermined rules. Data in the Base Store are stored in a columnar format and may be accessible by the second processing engine.

Storage engine for hybrid data processing

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Los Angeles, CA, United States of America

Wei Ding