Alibaba Group Holding Limited

Xiaowei Zhu

Ruiping Li

Fang Zheng

Cheng Zhu

Congnan Luo

Huaizhi Li

Luo Congnan

Hong Wu

Tieying Zhang

A method for dynamic filter pushdown for massive parallel processing databases on the cloud, including acquiring one or more filters corresponding to a query, acquiring statistics information of one or more database tables, determining a selectivity of the one or more database tables based on the statistics information, determining whether the selectivity satisfies a threshold condition, and pushing down the one or more filters to the one or more database tables based on the determination of whether the selectivity satisfies a threshold condition.

Methods and devices for dynamic filter pushdown for massive parallel processing databases on cloud

Embodiments of the present disclosure provide methods and devices for formatting a database. The method can include clustering, into each block, a set of sequence of elements from each column of a database; generating a file storing one or more blocks having the clustered set of sequences of elements; providing a first table storing file schema information that associates the elements with the file; and providing a second table for storing block schema information that associates the columns with the one or more blocks.

Row-column formatting for relational databases

The present disclosure provides systems and methods for prefetching data in databases. One method for prefetching data in a database comprises receiving a database query on the database, determining one or more sets of adjacent columns access by the database query, and for each set of adjacent columns of the one or more determined sets, prefetching data in the adjacent columns.

Method and system to prefetch data in databases

The present disclosure provides a method for generating a classification model in a database system. The method comprises generating a plurality of database tables by performing data clustering on a database, acquiring a plurality of queries associated with information from the database, generating training data by executing each of the plurality of queries on the plurality of database tables, and training a classification model using the training data, wherein the classification model is used to select a database table for a query based on corresponding performance information associated with the query on the database during executing of the query. The present disclosure also provides a method for executing a query on a database using a classification model. The method comprises obtaining the classification model, obtaining a database query, generating a target table by inputting the database query into the classification model, and executing the database query on the target table. In addition, the present disclosure provides database systems and non-transitory computer-readable media for generating a classification model or executing a query on a database using a classification model.

Adaptive data clustering for databases

Efficient large column values storage in columnar databases is disclosed, including: providing a database comprising a write storage, a read storage, and an auxiliary storage, and wherein the auxiliary storage is used to store a large column value of a row that is associated with either the write storage or the read storage, wherein the large column value comprises a column value that meets a set of large column value criteria; and operating the database using the write storage, the read storage, and the auxiliary storage.

Efficient large column values storage in columnar databases

Embodiments of the present disclosure provide methods and devices for inserting data in a database. The method can include caching one or more rows of data in a delta table in response to a request to insert the one or more rows in a destination table, wherein the destination table is one of a group of destination tables of a first database, and wherein the delta table contains requested changes of rows of the destination table, the changes being caused by a data operation; determining a priority of the destination table based on runtime information of the destination table relative to the group of destination tables; and scheduling the merging of the inserted rows from the delta table to the destination table based on the determined priority of the destination table. The destination table is in a format of a file storing one or more blocks, each block having a set of sequence of elements clustered from each column of the destination table.

Method and system for ingesting data in a database

Intelligent merging for efficient updates in columnar databases are disclosed, including: determining a current age associated with a row in a database, wherein the database comprises a write storage and a read storage, wherein the row is located in the write storage; determining a set of one or more merge criteria based at least in part on a modification age distribution, wherein the modification age distribution is dynamically determined based at least in part on previous transactions at the database; determining whether the current age meets the set of merge criteria; and in response to a determination that the current age meets the set of merge criteria, merging the row from the write storage into the read storage.

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San Mateo, CA, United States of America

Xiaowei Zhu