This Open-Source Tool Promises Bulletproof Data Syncing

In enterprise-level data integration, data consistency is one of the core concerns for technical decision-makers. However, behind this seemingly simple requirement lies complex technical challenges and architectural designs.

When using SeaTunnel for batch and streaming data synchronization, enterprise users typically focus on these questions:

🔍 "How to ensure data integrity between source and target databases?"🔄 "Can data duplication or loss be avoided after task interruption or recovery?"⚙️ "How to guarantee consistency during full and incremental data synchronization?"

Based on the latest version of SeaTunnel, this article will analyze in detail how SeaTunnel achieves end-to-end consistency guarantee through its advanced three-dimensional architecture of Read Consistency, Write Consistency, and State Consistency.

I. Understanding the Three Dimensions of Data Consistency

In data integration, "consistency" is not a single concept but a systematic guarantee covering multiple dimensions. Based on years of practical experience, SeaTunnel breaks down data consistency into three key dimensions:

Read Consistency

Read Consistency ensures that data obtained from the source system maintains logical integrity at a specific point in time or event sequence. This dimension addresses the question of "what data to capture":

Full Read: Obtaining a complete data snapshot at a specific point in time
Incremental Capture: Accurately recording all data change events (CDC mode)
Lock-free Snapshot Consistency: Ensuring data continuity between full snapshot and incremental changes through low and high watermark mechanisms

Write Consistency

Write Consistency ensures data is reliably and correctly written to the target system, addressing "how to write safely":

Idempotent Writing: Multiple writes of the same data won't produce duplicate records
Transaction Integrity: Ensuring related data is written atomically as a whole
Error Handling: Ability to rollback or safely retry in exceptional cases

State Consistency

State Consistency is the bridge connecting read and write ends, ensuring state tracking and recovery throughout the data synchronization process:

Position Management: Recording read progress for precise incremental synchronization
Checkpoint Mechanism: Periodically saving task state
Breakpoint Resume: Ability to recover from the last interruption point without data loss or duplication

II. MySQL Synchronization Architecture: CDC vs. JDBC Mode Comparison

SeaTunnel provides two mainstream MySQL data synchronization modes: JDBC Batch Mode and CDC Real-time Capture Mode. These two modes are suitable for different business scenarios and have their own characteristics in consistency guarantee.

CDC Mode: Binlog-based High Real-time Solution

The MySQL-CDC connector is based on embedded Debezium framework, directly reading and parsing MySQL's binlog change stream:

Core Advantages:

Real-time: Millisecond-level delay in capturing data changes
Low Impact: Almost zero performance impact on source database
Completeness: Captures complete events for INSERT/UPDATE/DELETE
Transaction Boundaries: Preserves original transaction context

Consistency Guarantee:

Precise recording of binlog filename + position
Supports multiple startup modes (Initial snapshot + incremental / Incremental only)
Event order strictly consistent with source database

JDBC Mode: SQL-based Batch Synchronization Solution

The JDBC connector reads data from MySQL through SQL queries, suitable for periodic full synchronization or low-frequency change scenarios:

Core Advantages:

Simple Development: Based on standard SQL, flexible configuration
Full Synchronization: Suitable for initializing large amounts of data
Filtering Capability: Supports complex WHERE condition filtering
Parallel Loading: Multi-shard parallel reading based on primary key or range

Consistency Guarantee:

Records synchronization progress of Split + position
Supports breakpoint resume
Table-level parallel processing

III. Read Consistency: How to Ensure Complete Source Data Capture

CDC Mode: Binlog-based Precise Incremental Reading

MySQL-CDC connector's read consistency is based on two core mechanisms: Initial Snapshot and Binlog Position Tracking.

Startup Modes and Consistency Guarantee:

SeaTunnel's MySQL-CDC provides multiple startup modes to meet consistency requirements for different scenarios:

Initial Mode: First creates full snapshot, then seamlessly switches to incremental mode
```
MySQL-CDC {
  startup.mode = "initial"
}
```
Latest Mode: Only captures the latest changes after connector startup
```
MySQL-CDC {
  startup.mode = "latest"
}
```

Specific Mode: Starts synchronization from specified binlog position

MySQL-CDC {
  startup.mode = "specific"
  startup.specific.offset.file = "mysql-bin.000003"
  startup.specific.offset.pos = 4571
}

There's also an earliest startup mode: starts from the earliest offset found, though this mode is less common

JDBC Mode: Shard-based Efficient Batch Reading

JDBC connector achieves efficient parallel reading through smart sharding strategy:

Sharding Strategy and Consistency:

Primary Key Sharding: Automatically splits into multiple parallel tasks based on primary key range
Range Sharding: Supports custom numeric columns as sharding basis
Modulo Sharding: Suitable for balanced reading of hash-distributed data

Example configuration for SeaTunnel JDBC reading shards:

Jdbc {
  url = "jdbc:mysql://source_mysql:3306/test"
  table = "users"
  split.size = 10000
  split.even-distribution.factor.upper-bound = 100
  split.even-distribution.factor.lower-bound = 0.05
  split.sample-sharding.threshold = 1000
}

Through this approach, SeaTunnel achieves:

Maximum parallelism for data reading
Position recording for each shard
Precise recovery of failed tasks

IV. Write Consistency: How to Ensure Target Data Accuracy

In the data writing phase, SeaTunnel provides multiple guarantee mechanisms to ensure consistency and completeness of target MySQL data.

Idempotent Writing: Ensuring No Data Duplication

SeaTunnel's JDBC Sink connector implements idempotent writing through multiple strategies:

Upsert Mode:

Example configuration for idempotent writing:

Jdbc {
  url = "jdbc:mysql://target_mysql:3306/test"
  table = "users"
  primary_keys = ["id"]
  enable_upsert = true
}

Batch Commit and Optimization:

SeaTunnel optimizes JDBC Sink's batch processing performance while ensuring transaction safety:

Dynamic Batch Size: Automatically adjusts batch size based on data volume
Timeout Control: Prevents resource occupation from long transactions
Retry Mechanism: Automatic transaction retry during network jitter

Distributed Transaction: XA Guarantee and Two-Phase Commit

For business scenarios requiring extremely high consistency, SeaTunnel provides distributed transaction support based on XA protocol:

Example configuration for enabling XA distributed transactions:

Jdbc {
  url = "jdbc:mysql://target_mysql:3306/test"
  is_exactly_once = true
  xa_data_source_class_name = "com.mysql.cj.jdbc.MysqlXADataSource"
  max_commit_attempts = 3
  transaction_timeout_sec = 300
}

XA Transaction Consistency Guarantee:

Consistency: Maintains database from one consistent state to another
Isolation: Concurrent transactions don't interfere with each other
Durability: Once committed, changes are permanent

This mechanism is particularly suitable for data synchronization scenarios across multiple tables and databases, ensuring business data relationship consistency.

V. State Consistency: Breakpoint Resume and Failure Recovery

SeaTunnel's state consistency mechanism is key to ensuring end-to-end data synchronization reliability. Through carefully designed state management and checkpoint mechanisms, it achieves reliable failure recovery capability.

Distributed Checkpoint Mechanism

SeaTunnel implements state consistency checkpoints in distributed environments:

Core Implementation Principles:

Position Recording: Records binlog filename and position in CDC mode, records shard and offset in JDBC mode
Checkpoint Trigger: Triggers checkpoint creation based on time or data volume
State Persistence: Persists state information to storage system
Failure Recovery: Automatically loads most recent valid checkpoint on task restart

End-to-End Consistency Guarantee

SeaTunnel achieves end-to-end consistency guarantee by coordinating Source and Sink states:

Checkpoint Configuration Example:

env {
  checkpoint.interval = 5000
  checkpoint.timeout = 60000
}

VI. Practical Configuration: MySQL CDC to MySQL Full + Incremental Sync

Let's demonstrate how to configure SeaTunnel for reliable MySQL to MySQL data synchronization through a practical example.

Classic CDC Mode Configuration

The following configuration implements a MySQL CDC to MySQL synchronization task with complete consistency guarantee:

env {
  job.mode = "STREAMING"
  parallelism = 3
  checkpoint.interval = 60000
}

source {
  MySQL-CDC {
    base-url="jdbc:mysql://xxx:3306/qa_source"
    username = "xxxx"
    password = "xxxxxx"
    database-names=[
        "test_db"
    ]
    table-names=[
        "test_db.mysqlcdc_to_mysql_table1",
        "test_db.mysqlcdc_to_mysql_table2",
     ]
    
    # Initialization mode (full + incremental)
    startup.mode = "initial"
    
    # Enable DDL changes
    schema-changes.enabled = true
    
    # Parallel read configuration
    snapshot.split.size = 8096
    snapshot.fetch.size = 1024
  }
}

transform {
  # Optional data transformation processing
}

sink {
  Jdbc {
    url = "jdbc:mysql://mysql_target:3306/test_db?useUnicode=true&characterEncoding=UTF-8&rewriteBatchedStatements=true"
    driver = "com.mysql.cj.jdbc.Driver"
    user = "root"
    password = "password"
    database = "test_db"
    table = "${table_name}"
    schema_save_mode = "CREATE_SCHEMA_WHEN_NOT_EXIST"
    data_save_mode = "APPEND_DATA"
    # enable_upsert = false
    # support_upsert_by_query_primary_key_exist = true
    
    # Exactly-once semantics (optional)
    #is_exactly_once = true
    #xa_data_source_class_name = "com.mysql.cj.jdbc.MysqlXADataSource"
  }
}

VII. Consistency Validation and Monitoring

After deploying data synchronization tasks in production environment, validating and monitoring consistency is crucial. SeaTunnel provides multiple methods for data consistency validation and monitoring.

Data Consistency Validation Methods

Count Comparison: Most basic validation method, comparing record counts between source and target tables

-- Source database
SELECT COUNT(*) FROM source_db.users;

-- Target database
SELECT COUNT(*) FROM target_db.users;

Hash Comparison: Calculate hash for key fields to compare data content consistency

-- Source database
SELECT SUM(CRC32(CONCAT_WS('|', id, name, updated_at))) FROM source_db.users;

-- Target database
SELECT SUM(CRC32(CONCAT_WS('|', id, name, updated_at))) FROM target_db.users;

Sample Comparison: Randomly sample records from source table and compare with target table

Consistency Monitoring Metrics

During SeaTunnel task execution, the following key metrics can be monitored to evaluate synchronization consistency status:

Synchronization Lag: Time difference between current time and latest processed record time
Write Success Rate: Proportion of successfully written records to total records
Data Deviation Rate: Difference rate between source and target database data (can be implemented through DolphinScheduler 3.1.x's data quality task)

VIII. Best Practices and Performance Optimization

Based on deployment experience from hundreds of production environments, we summarize the following best practices for MySQL to MySQL synchronization:

Consistency Scenario Configuration Recommendations

High Reliability Scenario (e.g., core business data):
- Use CDC mode + XA transactions
- Configure shorter checkpoint intervals
- Enable idempotent writing
- Configure reasonable retry strategy
High Performance Scenario (e.g., analytical applications):
- Use CDC mode + batch writing
- Disable XA transactions, use normal transactions
- Increase batch size
- Optimize parallelism settings
Large-scale Initialization Scenario:
- Use JDBC mode for initialization
- Configure appropriate shard size
- Adjust parallelism to match server resources
- Switch to CDC mode after completion

Common Issues and Solutions

Unstable Network Environment:
- Increase connection timeout and retry counts
- Enable breakpoint resume
- Consider using smaller batch sizes
High Concurrency Write Scenario:
- Adjust target database connection pool size
- Consider using table partitioning or batch writing
Resource-constrained Environment:
- Reduce parallelism
- Increase checkpoint interval
- Optimize JVM memory configuration

IX. Conclusion: SeaTunnel's Path to Consistency Guarantee

Through its carefully designed three-dimensional consistency architecture, SeaTunnel successfully solves the critical data consistency issues in enterprise-level data synchronization. This design supports both high-throughput batch data processing and ensures precision in real-time incremental synchronization, providing a solid foundation for enterprise data architecture.

SeaTunnel's consistency guarantee philosophy can be summarized as:

End-to-end Consistency: Full-chain guarantee from data reading to writing
Failure Recovery Capability: Able to recover and continue synchronization even under extreme conditions
Flexible Consistency Levels: Choose appropriate consistency strength based on business requirements
Verifiable Consistency: Verify data integrity through multiple mechanisms

These features make SeaTunnel an ideal choice for building enterprise-level data integration platforms, capable of handling data synchronization challenges from TB to PB scale while ensuring enterprise data integrity and accuracy.

If you have more questions about SeaTunnel's data consistency mechanism, welcome to join the community discussion.

This Open-Source Tool Promises Bulletproof Data Syncing—Even When Everything Breaks