Trench takes a practical approach to the challenge of real-time event tracking and analytics by bundling the core components you need into a single production-ready Docker image. Instead of managing separate Kafka brokers, ClickHouse instances, and an API layer on different hosts or containers, Trench packages them together with sensible defaults and a compatible Segment API interface. This makes it easier to deploy and operate a high-throughput event pipeline without the usual overhead of stitching multiple distributed systems together.
What trench does and how it is built
At its core, Trench is an open-source event tracking system designed for high-volume analytics. It supports the Segment API methods Track, Group, and Identify, allowing it to integrate easily with existing event producers that already use Segment-compatible tracking. Under the hood, it uses Apache Kafka for event streaming and ClickHouse as its OLAP (online analytical processing) database to enable fast, real-time queries over large event datasets.
The project is implemented in TypeScript and ships as a single Docker image that bundles Kafka, ClickHouse, and a Node.js API server. This server exposes the tracking API endpoints and also offers features like webhook destinations and the ability to run raw SQL queries against the stored event data. The architecture targets throughput of thousands of events per second on a single node, with recommended minimum specs of 4GB RAM and 4 CPU cores for production.
The system is designed with privacy compliance in mind, supporting GDPR and PECR regulations by avoiding cookie-based tracking. It offers both a self-hosted deployment option and a managed cloud service with 99.99% SLAs for users who want zero operational overhead.
Technical strengths, tradeoffs, and code quality
The main strength of Trench lies in its pragmatic architecture that combines two powerful open-source tools: Kafka and ClickHouse. Kafka acts as a durable, scalable event bus that can ingest large volumes of events with low latency. ClickHouse, a columnar OLAP database, excels at running complex aggregate queries quickly on large datasets. By packaging these with a Node.js API and providing a Segment-compatible interface, Trench offers a production-ready solution that many teams would otherwise have to build from scratch.
This integration is opinionated: bundling Kafka and ClickHouse into one Docker Compose environment simplifies deployment but also means you trade off flexibility in scaling individual components independently. For example, if your event volume grows beyond a single node’s capacity, you’ll need to move beyond the default setup and manage Kafka and ClickHouse clusters separately. The minimum hardware recommendations reflect this tradeoff.
The codebase is TypeScript-based, which provides a good developer experience with type safety and modern JavaScript features. The API layer is straightforward, focusing on compatibility and ease of use rather than elaborate customization. The inclusion of raw SQL querying capabilities against ClickHouse is useful for advanced analytics workloads beyond the typical event tracking use case.
Overall, the code is surprisingly clean and well-structured given the complexity of the underlying systems it packages. The repository also includes tooling for local development, testing, and configuration management via environment files.
Quick start with trench self-hosted
Trench provides two deployment options: a self-hosted version and a fully-managed cloud service. The self-hosted version is designed to be easy to get running locally or in production with Docker and Docker Compose. Here are the exact steps from the project’s quickstart guide:
# Clone the repo and navigate to the trench app directory
git clone https://github.com/frigadehq/trench.git
cd trench/apps/trench
cp .env.example .env
# Start the local development server with Kafka and ClickHouse included
docker-compose -f docker-compose.yml -f docker-compose.dev.yml up --build --force-recreate --renew-anon-volumes
This command boots a development server on http://localhost:4000 that includes Kafka and ClickHouse instances. You should see a confirmation message in your browser once the server is running.
To send a sample event, update the .env file with your API keys (public and private), then use a curl command like this:
curl -i -X POST \
-H "Authorization:Bearer public-d613be4e-di03-4b02-9058-70aa4j04ff28" \
-H "Content-Type:application/json" \
-d '{
"events": [
{
"userId": "550e8400-e29b-41d4-a716-446655440000",
"type": "track",
"event": "ConnectedAccount",
"properties": {
"totalAccounts": 4
}
}
]
}' \
http://localhost:4000/api/track
This demonstrates how to interact with the API using Segment-compatible event tracking calls.
who trench is for and final thoughts
Trench is a solid choice for teams or developers looking to run their own high-throughput event tracking and analytics infrastructure without the hassle of managing separate Kafka and ClickHouse clusters. Its Docker Compose packaging lowers the barrier for operational complexity, making it accessible for medium-scale workloads that fit on a single node.
The tradeoff is that for very large-scale or highly available deployments, you’ll need to extend or separate the components yourself. The bundled approach prioritizes ease of deployment and developer experience over fine-grained scaling control.
It’s also worth noting that Trench’s focus on Segment API compatibility means it fits nicely into existing analytics stacks that use Segment or want a drop-in replacement.
If you want to experiment with real-time event tracking, test how Kafka and ClickHouse can work together, or need a starting point for building your own analytics backend, Trench is worth a look. Just be mindful of its hardware requirements and scaling limits when moving beyond development or small production workloads.
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