Doris

Introduction to Doris

Cluster Management Functions

• Creating a cluster: You can create a cluster on the CloudTable console. You can select the compute and storage specifications of Frontends and Backends when creating a Doris cluster.
• Viewing a cluster: You can view cluster details on the CloudTable cluster management page.
• Managing a cluster: You can manage a created cluster.
  • Viewing monitoring metrics of a cluster: After interconnecting with CES, you can view monitoring metrics of Doris clusters and the cluster running status is displayed in graphics. When a metric is spotted as abnormal, a message is sent for notification so that users and administrators can handle this problem in a timely manner.
  • Restarting a cluster: You need to restart a cluster if the system runs slowly due to long-time running. Restart may cause data loss in running services. If you have to restart a cluster, ensure that there is no running service and all data has been saved.
  • Deleting a cluster: You can delete a cluster that is no longer needed. This is a high-risk operation. Deleting a cluster may cause data loss. Therefore, before deleting a cluster, ensure that no service is running and all data has been saved.
  • Expanding a cluster: You can perform capacity expansion on the console if you need more resources. There are two methods for cluster capacity expansion, that is, adding nodes (node scale-out), expanding disk capacity (vertical expansion), or expanding specifications.

Advantages

• High performance: Doris is equipped with an efficient column storage engine, which not only reduces the amount of data scanning, but also implements an ultra-high data compression ratio. At the same time, Doris also uses various index technologies to speed up data reading and filtering. Using the partition and bucket pruning function, Doris can support ultra-high concurrency of online service business, and a single node can support up to thousands of QPS. Further, Doris combines the vectorized execution engine to give full play to the modern CPU parallel computing power. Doris supports materialized view to accelerate pre-aggregation, and uses the query optimizer to optimize queries based on planning and costs.
• Ease of use: CloudTable Doris adheres to standard ANSI SQL syntax, encompassing single-table aggregation, sorting, filtering, multi-table joins, subqueries, and advanced SQL constructs like window functions and GROUPING SETS. In addition, it is also compatible with MySQL protocol, which allows users access Doris through various BI tools.
• Simple architecture: Doris has only two types of processes, that is, Frontend (FE) and Backend (BE). The FE node is responsible for user request access, query plan parsing, metadata storage, and cluster management. The BE node is used to store data and execute query plans. Doris can function as a complete distributed database management system and users can run the Doris cluster without installing any third-party management and control components. In addition, both FE and BE nodes support horizontal expansion. A cluster can be expanded to hundreds of nodes and can store more than 10 petabytes of data.
• Stability and reliability: Data can be stored in multiple copies and Doris clusters are capable of self-healing. Its distributed management framework can automatically manage the distribution, repair, and balancing of data copies. When a data backup is damaged, the system can automatically detect the damage and repair it.
• Rich ecosystem: Doris provides rich data ingest methods, supports fast loading of data from localhost, Hadoop, Flink, Spark, Kafka, SeaTunnel and other systems, and can also directly access data in MySQL, PostgreSQL, Oracle, S3, Hive, Iceberg, Elasticsearch and other systems without data replication. At the same time, the data stored in Doris can also be read by Spark and Flink, and can be output to the upstream data application for display and analysis.