What Is DAS?

As we all know, cloud databases free developers from basic O&M operations such as resource elasticity, high availability, backup, and monitoring, and greatly improve database performance.

Database Autonomy Service DAS was designed to solve pressing problems related to databases. It lets us optimize databases to improve stability and maximize performance, and also allows us to quickly troubleshoot unexpected database performance issues. Moreover, it enables us to meet business needs at the lowest resource cost.

DAS is a cloud service that implements database auto-perception, auto-troubleshooting, auto-optimization, auto-O&M, and auto-security based on machine learning and expert experience. It provides six autonomous features, covering anomaly detection, root cause analysis, decision execution, tracking, and evaluation. DAS implements a full closed-loop autonomous process without manual intervention, evaluates the effect of autonomous optimization in real time, and enables continuous feedback and optimization.

The preceding figure shows the evolution of DAS. DAS originated as a 2016 upgrade to the CloudDBA SQL diagnostic engine. At the time, it was only an auxiliary diagnostic tool. In 2017, we decided to give it "autonomous driving" capabilities, and the service was incubated and tested in various business scenarios of the Alibaba Group. In 2019, Alibaba Cloud officially released DAS for external users.

Understanding DAS

Unlike Oracle, whose autonomous capability is built into the database engine, DAS is an "autonomous driving" platform for databases. DAS can work as a single database engine or can support all databases in its database product family, including ApsaraDB PolarDB, AnalyticDB, ApsaraDB for RDS (RDS), and ApsaraDB for Redis. This means it supports online transaction processing (OLTP), online analytical processing (OLAP), and NoSQL databases.

Next, we need to consider the level of DAS in autonomous driving tools, and how it stands out from similar products. For databases with autonomous driving features, different levels correspond to very different capabilities. Currently, the autonomous driving capabilities of databases are classified into the following levels:

Level 0: Only manual operations are allowed, without any supporting tools or products.

Level 1: Basic monitoring and alerting information is provided, but no suggestions are output.

Level 2: In some scenarios, diagnosis or optimization suggestions can be output. However, you need to decide whether to apply the suggestions. The SQL diagnosis engine is an example of a level 2 database engine.

Level 3: In some scenarios, autonomy is implemented without human intervention, such as automatic SQL throttling, automatic SQL optimization, and auto-scaling.

Level 4: Full autonomous driving for databases is implemented.

Autonomy Service

DAS detects anomalies 24/7 based on machine learning and fine-grained data monitoring. DAS provides features such as automatic SQL throttling, anomaly snapshot capture, automatic SQL review and optimization, elastic capacity evaluation, automatic storage expansion, and automatic computing resource expansion. You can use DAS to perform diagnostics and deliver measurable optimization results.

Enterprise-class Database Service

Supports database management from the global perspective, at the application group level, and the instance level, and provides enterprise-level features such as custom performance dashboards, batch management, and inspection. DAS can also be integrated with the original database management systems of enterprises.

Centralized Management

DAS supports multiple environments and engines, such as mainstream relational, NoSQL, and NewSQL databases. DAS also supports Alibaba Cloud databases, user-created databases, and databases from other cloud service providers.

Security Audit

DAS provides features such as high-risk SQL identification, SQL injection detection, new access source identification, and sensitive data access identification. DAS can identify abnormal access to databases and database breaches to ensure database security in a fast and effective manner.