The High-Stakes Problem

At CodingClave, we often inherit architectures where MongoDB was chosen for velocity during the MVP phase. It works well—until it doesn't. Recently, we oversaw a high-volume fintech platform handling peer-to-peer payments and currency swaps. The system was originally built on MongoDB to leverage its schema flexibility and rapid write speeds.

However, as the platform scaled to millions of transactions per day, the document model began to crack under the specific demands of financial rigor.

We encountered three critical architectural failures:

  1. Multi-Document ACID Complexity: While MongoDB supports multi-document transactions (since v4.0), the performance penalty at scale is significant compared to a native RDBMS. We saw write latency spikes during high-concurrency ledger updates.
  2. Aggregation Pipeline Overhead: Financial reporting requires joining Transactions, Wallets, Users, and CurrencyRates. In MongoDB, complex $lookup stages resulted in memory-intensive aggregation pipelines that stalled the analytical replicas.
  3. Floating Point Drift: The previous implementation relied on application-side handling of decimal precision, stored as strings or doubles in BSON. This is a non-starter for auditable banking standards.

We made the decision to migrate the core ledger to PostgreSQL. This wasn't a preference war; it was an engineering necessity for data correctness.

Technical Deep Dive: The Solution & Code

The migration wasn't a simple ETL job. We had to fundamentally restructure the data model from a document-centric view to a normalized, double-entry bookkeeping system.

The Double-Entry Schema

In the Mongo implementation, a transfer was a single document. In Postgres, we moved to an immutable ledger design. Every movement of money is recorded as two rows: a debit and a credit.

We utilized PostgreSQL's NUMERIC type for arbitrary precision arithmetic, eliminating the risk of floating-point errors.

-- The Ledger Table: Immutable log of all movements
CREATE TABLE ledger_entries (
    id BIGSERIAL PRIMARY KEY,
    transaction_id UUID NOT NULL,
    account_id UUID NOT NULL,
    entry_type VARCHAR(10) CHECK (entry_type IN ('DEBIT', 'CREDIT')),
    amount NUMERIC(20, 4) NOT NULL CHECK (amount > 0),
    currency CHAR(3) NOT NULL,
    created_at TIMESTAMPTZ DEFAULT NOW(),
    metadata JSONB -- Retaining some flexibility for contextual data
);

-- Indexing for rapid balance calculation
CREATE INDEX idx_ledger_account ON ledger_entries(account_id, currency);
CREATE INDEX idx_ledger_tx ON ledger_entries(transaction_id);

Implementing Atomic Transfers

The application layer (Go/Rust services) was refactored to rely on database-level constraints rather than application logic checks. Here is how a transfer is handled inside a SERIALIZABLE transaction block to prevent write skew and ensure funds exist.

BEGIN;

-- 1. Lock the sender's balance (prevent double spend)
-- We sum the ledger rather than storing a mutable 'balance' column to ensure auditability.
WITH balance AS (
    SELECT 
        SUM(CASE WHEN entry_type = 'CREDIT' THEN amount ELSE -amount END) as current_balance
    FROM ledger_entries
    WHERE account_id = 'uuid-alice' AND currency = 'USD'
)
SELECT 1 FROM balance WHERE current_balance >= 100.00;

-- If above returns row, proceed. Else ROLLBACK.

-- 2. Insert Debit for Sender
INSERT INTO ledger_entries (transaction_id, account_id, entry_type, amount, currency, metadata)
VALUES ('uuid-tx-123', 'uuid-alice', 'DEBIT', 100.00, 'USD', '{"note": "payment"}');

-- 3. Insert Credit for Receiver
INSERT INTO ledger_entries (transaction_id, account_id, entry_type, amount, currency, metadata)
VALUES ('uuid-tx-123', 'uuid-bob', 'CREDIT', 100.00, 'USD', '{"note": "payment"}');

COMMIT;

The Migration Strategy: Change Data Capture (CDC)

We could not afford downtime. We used a "strangler fig" pattern with CDC.

  1. Dual Writes: The application wrote to Mongo (primary) and queued messages for Postgres.
  2. Backfill: We used a custom Go worker to read historical Mongo documents, normalize them into ledger rows, and insert them into Postgres.
  3. Verification: A reconciliation script ran continuously, comparing Mongo $_id sums against Postgres SUM(amount).
  4. Cutover: Once parity was reached (0.000% deviation), we flipped the read/write flags in the gateway services.

Architecture & Performance Benefits

Post-migration, the system metrics improved drastically, validating the architectural shift.

1. Referential Integrity

By enforcing Foreign Keys between ledger_entries and accounts, we eliminated "orphaned transactions." In the NoSQL implementation, if a user was deleted but their transaction history wasn't cleaned up perfectly, it broke reconciliation. Postgres prevents this at the engine level.

2. Analytical Throughput

End-of-day reconciliation reports, which previously took 45 minutes to run via MongoDB Aggregation Pipelines, now run in under 3 seconds using SQL window functions and materialized views.

-- Example of the simplified reporting capability
CREATE MATERIALIZED VIEW daily_balances AS
SELECT 
    account_id, 
    currency,
    DATE_TRUNC('day', created_at) as ledger_date,
    SUM(CASE WHEN entry_type = 'CREDIT' THEN amount ELSE -amount END) as balance
FROM ledger_entries
GROUP BY 1, 2, 3;

3. Storage Efficiency

Despite the row explosion (two rows per transaction), the storage footprint decreased by roughly 30%. Storing repetitive field names in BSON documents is costly. PostgreSQL's heap storage and compression on TOASTed JSONB columns proved far more efficient for this specific data shape.

How CodingClave Can Help

Migrating a live financial engine from a NoSQL store to a relational architecture is not a task for the faint of heart. It is one of the most high-risk operations an engineering team can undertake. A failed migration results in data corruption, lost funds, and catastrophic regulatory liability.

While the code snippets above outline the destination, they do not cover the treacherous journey of getting there without downtime.

CodingClave specializes in high-stakes architecture modernization.

We don't just write code; we engineer stability. If your internal team is struggling with database scaling bottlenecks, consistency issues, or technical debt in your transaction layer, do not attempt a "big bang" rewrite alone.

Partner with us to secure your infrastructure.

Book a Technical Audit with CodingClave — Let’s build a roadmap to migrate your legacy systems to a scalable, audit-proof architecture.