The High-Stakes Problem

At CodingClave, we recently audited a fintech platform processing $50M in daily transaction volume. Their core infrastructure relied on a monolithic MySQL 8.0 cluster. The dataset had grown to 10TB, heavily comprised of JSON payloads stored in TEXT fields and complex geospatial data.

The system was hitting hard limits. Complex analytical queries were locking tables, the lack of native JSONB indexing was causing latency spikes in the 500ms+ range, and write throughput was saturating the primary node during peak trading hours.

The mandate was clear: Migrate to PostgreSQL to leverage GIN indexing, PostGIS, and better vacuum management.

The constraint: Zero downtime.

A standard maintenance window for a 10TB dump-and-restore operation would require approximately 48 to 72 hours depending on network bandwidth and disk I/O. In the fintech world, 72 hours of downtime is effectively a company-ending event. We needed a strategy to migrate the data while the plane was still flying.

Technical Deep Dive: The Solution

We architected a migration pipeline utilizing the Snapshot + CDC (Change Data Capture) pattern. This approach decouples the bulk data load from the real-time synchronization.

Phase 1: The Architecture

We utilized Debezium, running on top of Apache Kafka, to capture row-level changes from the MySQL binary logs.

  1. Source: MySQL 8.0 (Binlog enabled, row-based logging).
  2. Transport: Kafka Connect with Debezium MySQL Connector.
  3. Sink: PostgreSQL 16 (optimized for bulk writes).
  4. Verification: A custom Go service using row-hashing.

Phase 2: Schema Translation & Optimization

A direct schema port is rarely sufficient. MySQL's TINYINT(1) must become Postgres BOOLEAN. MySQL's DATETIME needs to be standardized to TIMESTAMPTZ.

Crucially, we deferred index creation on the target PostgreSQL instance. Loading 10TB of data into a fully indexed table creates massive write amplification. We implemented a "Load First, Index Later" strategy.

Phase 3: The Snapshot and Catch-Up

We initiated the Debezium connector to start reading the binlog before starting the bulk snapshot. This ensures we capture every write that occurs during the long-running initial load.

Debezium Connector Configuration:

{
  "name": "production-inventory-connector",
  "config": {
    "connector.class": "io.debezium.connector.mysql.MySqlConnector",
    "tasks.max": "1",
    "database.hostname": "primary-db.internal",
    "database.port": "3306",
    "database.user": "migration_user",
    "database.password": "${VAULT_SECRET}",
    "database.server.id": "184054",
    "database.server.name": "dbserver1",
    "database.include.list": "core.transactions, core.users, core.audit_logs",
    "database.history.kafka.bootstrap.servers": "kafka:9092",
    "database.history.kafka.topic": "schema-changes.inventory",
    "snapshot.mode": "initial"
  }
}

Once the snapshot was complete (taking roughly 36 hours), the system automatically transitioned to streaming binlog events.

Phase 4: Data Verification

Trusting the pipe is not an option at this scale. We ran a parallel verification service. We implemented a hashing strategy where we computed a CRC32 hash of row batches on both Source and Target.

// Simplified Verification Logic
func VerifyBatch(sourceBatch []Row, targetBatch []Row) bool {
    sourceHash := CalculateHash(sourceBatch)
    targetHash := CalculateHash(targetBatch)
    
    if sourceHash != targetHash {
        LogDiscrepancy(sourceBatch[0].ID)
        return false
    }
    return true
}

We sampled 5% of historical data and 100% of live data during the catch-up phase.

Phase 5: The Cutover

Once the replication lag dropped to 0ms:

  1. Lock Source: We placed the application in read-only mode (preventing new writes to MySQL).
  2. Drain Queue: We waited for the final offsets in Kafka to be committed to Postgres.
  3. Sequence Reset: We executed a script to reset PostgreSQL sequences (SELECT setval...) to MAX(id) + 1 to prevent primary key collisions.
  4. Switch Traffic: DNS and connection strings were updated to point to the Postgres cluster.

Total Write Downtime: 14 seconds.

Architecture & Performance Benefits

Post-migration, the metrics validated the engineering effort:

  1. Query Latency: The migration of JSON blobs to JSONB with GIN indexing reduced complex search query latency from 500ms to 12ms.
  2. Storage Efficiency: PostgreSQL's robust data types reduced the total disk footprint from 10TB to 8.2TB due to better page compression and elimination of redundant padding.
  3. Concurrent Writes: MVCC implementation in Postgres handled the high-concurrency write loads significantly better, eliminating the table-level locking issues we saw with specific MySQL ISAM-legacy behaviors.
  4. Geospatial Processing: Moving logic from the application layer into PostGIS queries reduced CPU load on the application servers by 30%.

How CodingClave Can Help

While the benefits outlined above are compelling, the execution of Case Study: Migrating a 10TB MySQL Database to PostgreSQL with Zero Downtime is fraught with risk.

Data corruption, replication lag spirals, character set mismatches, and sequence divergence can turn a migration into a catastrophic data loss event. For internal teams balancing feature development with infrastructure maintenance, this level of system re-engineering is often a distraction that carries unacceptable liability.

This is what CodingClave does.

We specialize in high-stakes, high-scale architecture. We do not just run scripts; we build the safety harness, the verification engines, and the rollback protocols that guarantee data integrity.

If your organization is hitting the ceiling of your current database architecture, do not attempt a blind migration.

Book a Technical Audit with CodingClave. Let’s map out a migration strategy that protects your data and accelerates your performance.