Bridge

Lead Architect · 2026 · 1 Week · 2 people · 3 min read

A high-performance 'Data-to-API' layer that leverages DuckDB and Apache Arrow to provide sub-10ms analytical queries directly from Parquet and S3, bypassing traditional warehouse latency.

Overview

Developed Bridge, a modern API template designed to eliminate the friction between raw data files and downstream applications. By moving the query engine into the application process, Bridge creates a 'Serverless Analytics' environment that is faster and more cost-effective than SQL warehouses like Snowflake or BigQuery.

Problem

Traditional data architectures suffer from high latency and significant serialization overhead. Querying cloud warehouses for real-time apps is expensive, and converting SQL rows to JSON for API delivery is CPU-intensive. There was a clear need for a solution that queries static files directly without managing a full database instance.

Constraints

  • Must achieve sub-10ms response times for million-row datasets
  • Requires zero-copy data transfer to minimize memory overhead
  • Must support secure, sanitized raw SQL execution for analytical flexibility
  • Needs to handle auto-discovery of schemas from S3/R2 storage

Approach

Utilized FastAPI for the web layer and integrated DuckDB as an in-process OLAP engine. I implemented a 'Zero-Copy' pipeline using Apache Arrow to stream data from the engine to the client. To ensure security, I built a custom regex-based 'Whole-Word' sanitization layer to permit complex queries while blocking destructive commands.

Key Decisions

In-Process DuckDB Engine

Reasoning:

By using an in-process database, we eliminate the network hop between the API and the data store. DuckDB's vectorized execution allows us to process analytical workloads with the speed of a dedicated warehouse but the footprint of a library.

Alternatives considered:
  • External Postgres Instance (High latency and maintenance overhead)
  • Pandas-based Querying (Slower execution and higher memory usage)

Apache Arrow IPC Streaming

Reasoning:

Traditional JSON serialization is a bottleneck. By using the Arrow IPC format, we can stream data chunks directly to the client. This 'zero-copy' approach means the memory format used by the database is the same as the transmission format, drastically reducing CPU cycles.

Alternatives considered:
  • Standard JSON (High serialization/deserialization cost)
  • CSV Streaming (Loss of type safety and slow parsing)

Tech Stack

  • FastAPI
  • DuckDB
  • Apache Arrow
  • Python
  • S3 / R2
  • Docker

Result & Impact

  • < 10ms on 1M+ rows
    Query Latency
  • 90% vs. Cloud Warehouses
    Cost Reduction
  • Shared memory via zero-copy
    Memory Efficiency

Bridge successfully proved that high-performance analytics do not require high-cost infrastructure. It enables 'Serverless Analytics' where data is treated as a first-class citizen, allowing developers to deploy production-ready API layers on standard cloud instances with minimal configuration.

Learnings

  • Vectorized execution engines like DuckDB change the math on where data processing should live.
  • The bottleneck in modern APIs is often serialization (JSON), not the logic itself; Arrow is the antidote.
  • Regex-based SQL sanitization requires careful 'Whole-Word' detection to support complex table naming conventions.

Additional Context

The most critical part of the project was the Zero-Copy Architecture. Traditional APIs fetch data as SQL rows, convert them to Python dictionaries, and then serialize them into JSON strings. Bridge bypasses this entire cycle. By using the Apache Arrow memory format, the data remains in a columnar structure from the moment it is read from a Parquet file until it reaches the client’s memory buffer.

[Image of hydrogen fuel cell] (Note: Replace with analytical architecture diagram)

The implementation of the Smart Querying Layer was the core technical hurdle. We needed to allow data scientists to run complex SQL (including joins and window functions) without the risk of DROP TABLE or DELETE commands. We moved from simple string matching to a robust regex-based detection system that ensures keywords are only flagged when they function as commands, not as part of a column or table name.

Furthermore, leveraging FastAPI’s Lifespan events allowed us to manage DuckDB connection pools and S3 filesystem mounts efficiently. This ensures that the first request to a cold-started Lambda or container doesn’t suffer from ‘initialization lag,’ as the data environment is pre-warmed during the application startup phase.

All projects