Naboria Ads — Technology Stack

A high-performance analytics and ad delivery pipeline built with Rust, Kafka, ClickHouse, and an ML layer powered by dbt and XGBoost.

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Event Tracking Pipeline

How page views, clicks, and ad events flow from client to ClickHouse.

Tracking & Storage Flow

Async Page tracking is fire-and-forget — the server responds immediately and processes the event in the background, keeping client latency minimal. Sync Ad tracking is synchronous — the server confirms the write before responding, ensuring impressions and clicks are reliably recorded.

Ad Delivery Flow

How ads are served from MongoDB to clients in real time.

Ad Serving — Server-Sent Events (SSE)

SSE (Server-Sent Events) A persistent one-way connection from the server to the browser. The web client connects once and receives personalised ads as they rotate every 30 seconds — no polling required. The mobile app fetches ads on launch and rotates them locally on a timer.

Analytics & ML Pipeline

How raw events become dashboards and trained models.

Analytics & ML Flow

graph LR subgraph CH["ClickHouse"] RAW["Raw Events\n(views, clicks,\nad impressions)"] end subgraph DBT["dbt Transform Layer"] STG["Staging\n(cleaned events)"] INT["Sessions\n(per-user sessions)"] MART["Feature Datasets\n(CTR, booking, user)"] end PARQUET["Exported Datasets\n(Parquet)"] subgraph ML["ML Training"] XGB1["CTR Model"] XGB2["Booking Conversion\nModel"] ARTIFACTS["Trained Model\nArtifacts"] end DAG["Pipeline Scheduler\n(Dagster)"] RDFS[("Redis\nOnline Feature Store")] SERVER["API Server\n(model loaded at startup)"] DASH["Analytics Dashboards\n+ Grafana"] RAW --> STG STG --> INT INT --> MART MART -->|export| PARQUET PARQUET --> XGB1 PARQUET --> XGB2 XGB1 --> ARTIFACTS ARTIFACTS -->|deployed to| SERVER DAG -->|scheduled refresh| RDFS MART -->|feature materialisation| RDFS RDFS -->|features at inference| SERVER RAW -->|live queries| DASH

Components

Every piece of the Naboria Ads stack and what it does.

Server

Rust / Axum

A fast, reliable backend that handles all tracking requests and serves ads to clients.

Messaging

Apache Kafka

A message queue that absorbs high volumes of events, ensuring no data is lost under load.

Analytics Storage

ClickHouse

A high-performance analytics database purpose-built for querying billions of events in real time.

Ad Storage

MongoDB

A flexible document store for ad definitions, supporting banner and video ad formats.

Personalisation

Redis

Caches per-user ad scores and click history so the SSE stream can serve the most relevant ad without blocking the request path.

Tracker

naboria-tracker.js

A lightweight JavaScript library that integrates into any web or mobile app with a single init() call.

Data Pipeline

dbt

Transforms raw events into clean, analytics-ready datasets using version-controlled SQL.

Machine Learning

XGBoost + MLflow

Predictive models for ad click-through rate and booking conversion, with full experiment tracking.

Dashboards

Analytics UI

Real-time dashboards for views, clicks, ad performance, and user activity — refreshed automatically.

Orchestration

Dagster

Schedules the feature materialisation pipeline every 10 minutes, keeping the online feature store fresh for ML inference.

Reverse Proxy

Caddy

Handles HTTPS termination with automatic Let's Encrypt certificate provisioning. Zero configuration for production TLS.

Observability

Prometheus + Grafana

Scrapes request rates, latency percentiles, and active SSE connections every 15 seconds. Pre-built dashboard included.

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