Doris: Unifying SQL Dialects for a Seamless Data Query Ecosystem

In the field of big data, different database systems often use different SQL dialects. This is similar to people from different regions speaking different languages, which brings great trouble to data analysts and developers. When an enterprise needs to integrate multiple data sources for analysis, it may have to spend a great deal of time and effort switching between different SQL syntaxes. 

However, Apache Doris breaks this barrier with its powerful SQL dialect compatibility and builds a unified data query ecosystem for users.

SQL Dialect Compatibility: The "Universal Language" in a Complex Data Environment

In today's enterprise data architecture, it is a common phenomenon that data is scattered across multiple database systems. These database systems have their own characteristics. For example, MySQL is often used for online transaction processing (OLTP) and excels in high-concurrency writing and transaction processing. Hive, on the other hand, is a leading player in big data offline analysis and can handle massive amounts of data. Different database systems use different SQL dialects, which makes it extremely difficult for data analysts and developers to query and integrate data across systems.

The SQL dialect compatibility function of Apache Doris is like a master translator, helping users communicate freely between different database systems. Doris not only supports standard SQL syntax but is also compatible with the SQL dialects of multiple mainstream databases, greatly reducing the learning and usage costs. Users no longer need to worry about the syntax differences of different database systems and can easily query and analyze data from multiple data sources through Doris.

How Doris Achieves SQL Dialect Compatibility

1. The "Smart Collaboration" of the Parser and Optimizer

Doris achieves support for multiple SQL dialects through its unique parser and optimizer design. When a user submits an SQL query, the parser will first perform lexical and syntactic analysis on the query statement and convert it into an abstract syntax tree (AST). During this process, the parser can recognize the syntax structures of different dialects and handle them accordingly.

Subsequently, the optimizer will optimize the abstract syntax tree. It will generate an efficient execution plan based on the semantics of the query and the data distribution. During this process, the optimizer fully considers the characteristics of different data sources and selects the optimal query strategy, thus ensuring that the query can be executed efficiently on different data sources.

2. The "Seamless Docking" of Metadata Management

In order to achieve a unified query of different data sources, Doris has established a complete metadata management mechanism. It can automatically discover and synchronize the metadata information of multiple data sources, including table structures, field types, indexes, etc. In this way, when users query data in Doris, it is as convenient as querying local tables, and they do not need to care about the actual storage location of the data.

At the same time, Doris's metadata management mechanism also supports real-time updates to ensure that users can always obtain the latest data source information. This provides great convenience for users and enables them to respond to business changes in a timely manner.

Analysis of Practical Application Scenarios

1. Replacing the Original OLAP System With Doris

For example, if the original system is Trino or ClickHouse, and it is switched to Doris. There are a large number of existing SQL business logics in the upstream business. If it is required for the business side to change the SQL dialect, the cost will be very high. The business hopes to be able to use the original SQL dialect to query in Doris.

2. Unified SQL Entrance

Doris serves as a unified entrance for OLAP. Users may query Hive tables through Doris and hope to use the SQL dialects of Hive or Spark.

3. Query Degradation

Users use Doris as a high-speed query engine, but if some queries are not supported or fail (such as insufficient memory), the SQL needs to be downgraded and routed to, for example, a Spark cluster for execution. In this case, users hope to use the Spark dialect uniformly, send it to Doris first, and if it fails, send it directly to Spark.

Advantages of Achieving SQL Dialect Compatibility with Doris

1. Reducing the Technical Threshold

For data analysts and developers, the SQL dialect compatibility function of Doris reduces the learning and usage costs. They do not need to spend a lot of time learning the SQL syntax of different database systems and can easily query and analyze data from multiple data sources through Doris. This enables them to focus more on business analysis and improve work efficiency.

2. Improving Data Integration Efficiency

Doris breaks down the barriers between different database systems and achieves rapid data integration and analysis. Enterprises can establish a unified data query platform through Doris, enabling personnel from different departments to conveniently obtain the required data, promoting data sharing and utilization, and providing strong support for enterprise decision-making.

3. Ensuring Business Continuity

In the process of continuous evolution of the enterprise's data architecture, the SQL dialect compatibility function of Doris provides a guarantee for business continuity. Even if the enterprise replaces or adds new data sources, Doris can still dock seamlessly to ensure that data queries and analysis are not affected.

Conclusion

The SQL dialect compatibility function of Apache Doris provides an efficient and convenient data query solution for enterprises in a complex data environment. It breaks down the barriers of SQL dialects, allowing data to flow freely and injecting strong impetus into the digital transformation of enterprises. It is believed that in the future, with the continuous development and improvement of Doris, it will play an important role in more fields and help enterprises maximize the value of data.

If you are interested in the SQL dialect compatibility function of Doris, you might as well give it a try and experience the convenience and efficiency it brings!