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

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. data consistency When using SeaTunnel for batch and streaming data synchronization, enterprise users typically focus on these questions: batch and streaming data synchronization 🔍 "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?" 🔍 "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. 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": Read Consistency 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 Full Read: Obtaining a complete data snapshot at a specific point in time Full Read Incremental Capture: Accurately recording all data change events (CDC mode) Incremental Capture Lock-free Snapshot Consistency: Ensuring data continuity between full snapshot and incremental changes through low and high watermark mechanisms Lock-free Snapshot Consistency Write Consistency Write Consistency ensures data is reliably and correctly written to the target system, addressing "how to write safely": Write Consistency 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 Idempotent Writing: Multiple writes of the same data won't produce duplicate records Idempotent Writing Transaction Integrity: Ensuring related data is written atomically as a whole Transaction Integrity Error Handling: Ability to rollback or safely retry in exceptional cases Error Handling State Consistency State Consistency is the bridge connecting read and write ends, ensuring state tracking and recovery throughout the data synchronization process: State Consistency 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 Position Management: Recording read progress for precise incremental synchronization Position Management Checkpoint Mechanism: Periodically saving task state Checkpoint Mechanism Breakpoint Resume: Ability to recover from the last interruption point without data loss or duplication Breakpoint Resume 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. JDBC Batch Mode CDC Real-time Capture Mode 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: 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 Real-time: Millisecond-level delay in capturing data changes Real-time Low Impact: Almost zero performance impact on source database Low Impact Completeness: Captures complete events for INSERT/UPDATE/DELETE Completeness Transaction Boundaries: Preserves original transaction context Transaction Boundaries Consistency Guarantee: Consistency Guarantee Precise recording of binlog filename + position Supports multiple startup modes (Initial snapshot + incremental / Incremental only) Event order strictly consistent with source database 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: 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 Simple Development: Based on standard SQL, flexible configuration Simple Development Full Synchronization: Suitable for initializing large amounts of data Full Synchronization Filtering Capability: Supports complex WHERE condition filtering Filtering Capability Parallel Loading: Multi-shard parallel reading based on primary key or range Parallel Loading Consistency Guarantee: Consistency Guarantee Records synchronization progress of Split + position Supports breakpoint resume Table-level parallel processing 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. Initial Snapshot Binlog Position Tracking Startup Modes and Consistency Guarantee: 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 } Initial Mode: First creates full snapshot, then seamlessly switches to incremental mode MySQL-CDC { startup.mode = "initial" } Initial Mode: First creates full snapshot, then seamlessly switches to incremental mode Initial Mode MySQL-CDC { startup.mode = "initial" } MySQL-CDC { startup.mode = "initial" } Latest Mode: Only captures the latest changes after connector startup MySQL-CDC { startup.mode = "latest" } Latest Mode: Only captures the latest changes after connector startup Latest Mode MySQL-CDC { startup.mode = "latest" } 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 } Specific Mode: Starts synchronization from specified binlog position Specific Mode MySQL-CDC { startup.mode = "specific" startup.specific.offset.file = "mysql-bin.000003" startup.specific.offset.pos = 4571 } 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 earliest JDBC Mode: Shard-based Efficient Batch Reading JDBC connector achieves efficient parallel reading through smart sharding strategy: Sharding Strategy and Consistency: 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 Primary Key Sharding: Automatically splits into multiple parallel tasks based on primary key range Primary Key Sharding Range Sharding: Supports custom numeric columns as sharding basis Range Sharding Modulo Sharding: Suitable for balanced reading of hash-distributed data Modulo Sharding 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 } 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 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: Upsert Mode Example configuration for idempotent writing: Jdbc { url = "jdbc:mysql://target_mysql:3306/test" table = "users" primary_keys = ["id"] enable_upsert = true } Jdbc { url = "jdbc:mysql://target_mysql:3306/test" table = "users" primary_keys = ["id"] enable_upsert = true } Batch Commit and Optimization: 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 Dynamic Batch Size: Automatically adjusts batch size based on data volume Dynamic Batch Size Timeout Control: Prevents resource occupation from long transactions Timeout Control Retry Mechanism: Automatic transaction retry during network jitter Retry Mechanism 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 } 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: 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 Consistency: Maintains database from one consistent state to another Consistency Isolation: Concurrent transactions don't interfere with each other Isolation Durability: Once committed, changes are permanent Durability 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: 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 Position Recording: Records binlog filename and position in CDC mode, records shard and offset in JDBC mode Position Recording Checkpoint Trigger: Triggers checkpoint creation based on time or data volume Checkpoint Trigger State Persistence: Persists state information to storage system State Persistence Failure Recovery: Automatically loads most recent valid checkpoint on task restart Failure Recovery End-to-End Consistency Guarantee SeaTunnel achieves end-to-end consistency guarantee by coordinating Source and Sink states: Checkpoint Configuration Example: Checkpoint Configuration Example env { checkpoint.interval = 5000 checkpoint.timeout = 60000 } 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 = "${table_name}" schema_save_mode = "CREATE_SCHEMA_WHEN_NOT_EXIST" data_save_mode = "APPEND_DATA" # enable_upsert = false # support_upsert_by_query_primary_key_exist = true # Exactly-once semantics (optional) #is_exactly_once = true #xa_data_source_class_name = "com.mysql.cj.jdbc.MysqlXADataSource" } } env { job.mode = "STREAMING" parallelism = 3 checkpoint.interval = 60000 } source { MySQL-CDC { base-url="jdbc:mysql://xxx:3306/qa_source" username = "xxxx" password = "xxxxxx" database-names=[ "test_db" ] table-names=[ "test_db.mysqlcdc_to_mysql_table1", "test_db.mysqlcdc_to_mysql_table2", ] # Initialization mode (full + incremental) startup.mode = "initial" # Enable DDL changes schema-changes.enabled = true # Parallel read configuration snapshot.split.size = 8096 snapshot.fetch.size = 1024 } } transform { # Optional data transformation processing } sink { Jdbc { url = "jdbc:mysql://mysql_target:3306/test_db?useUnicode=true&characterEncoding=UTF-8&rewriteBatchedStatements=true" driver = "com.mysql.cj.jdbc.Driver" user = "root" password = "password" database = "test_db" table = "${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 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; Count Comparison: Most basic validation method, comparing record counts between source and target tables Count Comparison -- Source database SELECT COUNT(*) FROM source_db.users; -- Target database SELECT COUNT(*) FROM target_db.users; -- 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; Hash Comparison: Calculate hash for key fields to compare data content consistency Hash Comparison -- 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; -- 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 Sample Comparison: Randomly sample records from source table and compare with target table Sample Comparison 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) Synchronization Lag: Time difference between current time and latest processed record time Synchronization Lag Write Success Rate: Proportion of successfully written records to total records Write Success Rate Data Deviation Rate: Difference rate between source and target database data (can be implemented through DolphinScheduler 3.1.x's data quality task) Data Deviation Rate 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 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 Reliability Scenario Use CDC mode + XA transactions Configure shorter checkpoint intervals Enable idempotent writing Configure reasonable retry strategy 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 High Performance Scenario Use CDC mode + batch writing Disable XA transactions, use normal transactions Increase batch size Optimize parallelism settings 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 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 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 Unstable Network Environment: Increase connection timeout and retry counts Enable breakpoint resume Consider using smaller batch sizes Unstable Network Environment Increase connection timeout and retry counts Enable breakpoint resume Consider using smaller batch sizes 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 High Concurrency Write Scenario Adjust target database connection pool size Consider using table partitioning or batch writing 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 Resource-constrained Environment Reduce parallelism Increase checkpoint interval Optimize JVM memory configuration 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 End-to-end Consistency: Full-chain guarantee from data reading to writing End-to-end Consistency Failure Recovery Capability: Able to recover and continue synchronization even under extreme conditions Failure Recovery Capability Flexible Consistency Levels: Choose appropriate consistency strength based on business requirements Flexible Consistency Levels Verifiable Consistency: Verify data integrity through multiple mechanisms Verifiable Consistency 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. If you have more questions about SeaTunnel's data consistency mechanism, welcome to join the community discussion.