The Realtime Compute development platform provides multiple features for Realtime Compute Flink SQL jobs: data storage management, job development, job debugging, job administration, monitoring and alerts, and job optimization.

This document consists of the following topics:
  • Data storage

    You can manage upstream and downstream storage systems, such as ApsaraDB for RDS, DataHub, and Tablestore, for Realtime Compute jobs on the Realtime Compute development platform. After you register resources from these systems with Realtime Compute, you can preview or sample their related data, or obtain the Data Definition Language (DDL) statements that are automatically generated to reference these resources. For more information about data storage, see Overview.

    Note For more information about how to add the IP addresses of Realtime Compute to the whitelist of an upstream or downstream storage system, see Configure whitelists for accessing storage resources.
  • Job development

    This topic describes how to develop, publish, and start a Flink SQL job. For more information, see Develop a job, Publish a job, and Start a job.

  • Job debugging

    This topic describes how to debug Flink SQL jobs in Realtime Compute. For more information, see Online debugging.

  • Job administration

    This topic describes how to view the administration information of a Realtime Compute job, such as the running information, curve charts, and failover. For more information, see Running information, Curve charts, and Failover.

  • Monitoring and alerts

    This topic describes how to create and activate alert rules. For more information, see Monitoring and alerts.

  • Job optimization

    This topic describes how to optimize Flink SQL jobs. For more information, see Skills for optimizing the Flink SQL code, Optimize performance by AutoScale, and Performance optimization by manual configuration.

  • Flink SQL

    This topic describes the syntax of Flink SQL. For more information, see Flink SQL overview.