DEV Community: Life in openGauss

Ustore, Rebuilding the 'Soul' of openGauss Data Storage

Life in openGauss — Tue, 26 Jul 2022 06:20:16 +0000

On August 20, 2021, HUAWEI CLOUD GaussDB (for openGauss) officially launched a new kernel feature, Ustore, a storage engine that provides high-performance database services for enterprise-level users and further injects energy into enterprise digital transformation. The openGauss community will also release this feature soon to explore the cutting-edge theories and best practices of databases with many database kernel developers.

The Ustore storage engine, also called in-place update storage engine, is a new storage mode added to the openGauss Kernel. The row storage engine used by the earlier openGauss Kernel versions is in append update mode. The append update mode has good performance in addition, deletion, and HOT (Heap Only Tuple) update (that is, update on the same page) in the service. However, in a non-HOT UPDATE scenario across data pages, garbage collection is not efficient. Ustore can solve this problem.

Cluster Manage SIG Was Created in the openGauss Community

Life in openGauss — Tue, 26 Jul 2022 06:17:12 +0000

The technical committee of the openGauss community approved the application for creating the Cluster Manage SIG in the openGauss community.

Cluster Manage (CM) monitors the primary/standby status, detecting network communication faults and file system faults, and manages automatic primary/standby switchover for openGauss. It also provides various cluster management capabilities, such as starting and stopping clusters, nodes, and instances, querying cluster status, performing primary/standby switchover, and managing logs. CM SIG is dedicated to building the openGauss cluster reliability.

Segment-Page Feature of openGauss for Solving File Storage Problems

Life in openGauss — Wed, 20 Jul 2022 06:12:05 +0000

In modern society, data is growing explosively, and service requirements in the industry are complex. The amount of data to be stored and the number of tables to be created keep increasing. Each common data table of openGauss corresponds to a logical large file (maximum size: 32 TB). The logical file is divided into multiple actual files based on the fixed size and stored in the corresponding database directory. Therefore, as the data volume of each data table increases, the number of files required for underlying data storage increases gradually. In addition, openGauss provides features such as hash bucket tables and large partitioned tables. Each data table is split into several sub-tables, and the number of files required at the bottom layer increases exponentially.

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openGauss Supports SM3 and SM4 Algorithms

Life in openGauss — Wed, 20 Jul 2022 01:35:13 +0000

Chinese cryptographic algorithms are Chinese algorithms issued by the State Cryptography Administration Office of Security Commercial Code Administration (OSCCA). Common algorithms include SM1, SM2, SM3, and SM4. The key length and block length are both 128 bits. To meet bank customers’ requirements for database security capabilities, openGauss 2.0.0 and later versions support Chinese cryptographic algorithms to enhance enterprise-level security capabilities of databases and improve product security competitiveness, including the SM3 algorithm for user authentication, and the SM4 algorithm for data encryption and decryption.

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New Feature of openGauss 3.0.0: Parallel Decoding

Life in openGauss — Tue, 19 Jul 2022 02:20:23 +0000

With the rapid development of information technology, various types of databases emerge one after another. Logical replication is increasingly important, with which data can be synchronized between heterogeneous databases. Currently, the average serial decoding performance of logical replication in openGauss is only 3 to 5 Mbit/s, which cannot meet the requirements of real-time synchronization in heavy service pressure scenarios. As a result, logs are stacked, affecting services in the production cluster. Therefore, the parallel decoding feature is designed to enable multiple threads to perform decoding in parallel, improving the decoding performance. In basic scenarios, the decoding performance can reach 100 Mbit/s.
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