Database Transmission Service
Database Transmission Service
Database Transmission Service
Document
What's new
Overview
Constraints and limits
Billing
Pre-check items
Permission management
Getting started
Quick start guide for data migration
Quick start guide for data subscription
Quick start guide for data synchronization
Data migration
Creating a data migration task for MySQL
Volcano Engine RDS for MySQL as the source
Volcano Engine veDB for MySQL as the source
MySQL connected over public network as the source
MySQL connected over express network as the source
Creating a data migration task for PostgreSQL
Volcano Engine RDS for PostgreSQL as the source
PostgreSQL connected over public network as the source
PostgreSQL connected over express network as the source
Creating a data migration task for SQL Server
Volcano Engine SQL Server as the source
SQL Server connected over public network as the source
SQL Server connected over express network as the source
Creating a data migration task for Redis
Volcano Engine Cache for Redis as the source
Redis connected over public network as the source
Redis connected over express network as the source
Creating a data migration task for MongDB
Volcano Engine Document Database for MongoDB as the source
MongoDB connected over public network as the source
MongoDB connected over express network as the source
Creating a data migration task for Supabase
Supabase connected over public network as the source
Supabase connected over express network as the source
Data subscription
Creating a data subscription task for MySQL
Creating a data subscription task for PostgreSQL
Consuming subscription data
Data synchronization
Creating a data synchronization task for MySQL
Volcano Engine RDS for MySQL as the source
Volcano Engine veDB for MySQL as the source
MySQL connected over public network as the source
MySQL connected over express network as the source
Creating a data synchronization task for Redis
Volcano Engine Cache for Redis as the source
Redis connected over public network as the source
Redis connected over express network as the source
Creating a data synchronization task for PostgreSQL
Volcano Engine RDS for PostgreSQL as the source
PostgreSQL connected over public network as the source
PostgreSQL connected over express network as the source
Creating a data synchronization task for MongoDB
Volcano Engine Database for MongoDB as the source
MongoDB connected over public network as the source
MongoDB connected over express network as the source
Creating a data synchronization task for Milvus
ETL task management
(Beta) Data verification
(Beta) Migration evaluation
Monitoring and alerts
Best practices
Data migration
Data subscription
API reference
Billing
Data transmission
Data subscription (consumer group management)
Data verification
Migration evaluation
Pre-check
Appendix
SDK reference
FAQs
Common general questions
Pre-check
Troubleshooting
- Documentation
Database Transmission ServiceFAQsCommon general questionsCommon issues with data verification tasks
Copy page
Download PDFCommon general questions
Common issues with data verification tasks
Copy pageDownload PDFCommon issues with data verification tasks
This topic applies to scenarios where the state or progress of a created DTS data verification task does not match expectations. Common scenarios are shown as follows:
- Scenario 1: why does the progress of the full data verification always show as 0%?
- Cause analysis: before formal verification, the full data verification task may need to split the data to be verified into blocks. If the full data to be verified is large or the data structure is complex, the splitting process may take a long time. During this phase, the <task progress> may show 0%.
- The purpose of splitting is to divide the full data into segments better suited for subsequent verification, improving verification efficiency. Therefore, 0% progress does not necessarily indicate task abnormality but may indicate the task is still in the data splitting phase before verification.
- Handling suggestion: it is recommended to observe whether the task is still running. If the validation object has a large data volume or complex structure, it is appropriate to wait. If the task shows no changes for a long time, it is recommended to check the task log or task details to confirm whether there are any errors.
- Scenario 2: why do some data verification tasks remain in execution and do not automatically complete?
- Cause analysis: If a data migration task or data synchronization task is associated with a data verification task, and the incremental data migration or incremental data synchronization task has not ended, the data verification task will continue running and will not automatically complete. This is because the incremental task continuously processes changes from the source end, and the data verification task needs to constantly follow the data changes on both the source end and the destination for verification. With incremental migration or incremental synchronization tasks still running, it is expected that the verification task remains "in progress."
- Handling suggestion: If you need to continuously verify data between the source end and the destination, you can let the verification task keep running. If verification is no longer needed, you can manually terminate the verification task. For more information, refer to Data verification task management.
Last updated: 2026.08.13 11:47:38
All rights reserved © Beijing Volcano Engine Technology Co., Ltd2024
