---
title: "How we built it: Real-time analytics for Stripe Billing"
description: "Stripe built a real-time streaming analytics system for Billing after customers said they needed to detect emerging behavior and adapt pricing quickly, replacing a batch pipeline whose subscription updates lagged by 24 hours on average. The architecture uses Apache Flink to maintain compressed subscription history and incrementally process analytics events, while a custom Apache Spark job generates initial Flink state from billions of historical events and supports validation and exports. A new Apache Pinot v2 query engine performs windowed aggregations and joins without offline preaggregation; most updates are processed in under one minute, nearly all reach users within 15 minutes, and production query latency is under 300 milliseconds. Metric-definition changes trigger historical recalculation while new events are buffered and later replayed, preserving a consistent Dashboard view during the transition."
---

# How we built it: Real-time analytics for Stripe Billing

[Stripe](https://yomu.fyi/company/stripe) · Reed Trevelyan · Sep 16, 2025

**Type:** Problem & solution

## Summary

Stripe built a real-time streaming analytics system for Billing after customers said they needed to detect emerging behavior and adapt pricing quickly, replacing a batch pipeline whose subscription updates lagged by 24 hours on average. The architecture uses Apache Flink to maintain compressed subscription history and incrementally process analytics events, while a custom Apache Spark job generates initial Flink state from billions of historical events and supports validation and exports. A new Apache Pinot v2 query engine performs windowed aggregations and joins without offline preaggregation; most updates are processed in under one minute, nearly all reach users within 15 minutes, and production query latency is under 300 milliseconds. Metric-definition changes trigger historical recalculation while new events are buffered and later replayed, preserving a consistent Dashboard view during the transition.

## Context

Customers needed timely, high-quality billing data to identify emerging customer behavior and adapt pricing. The previous subscription analytics system relied on batch processing, resulting in an average 24-hour lag for updates, while customers also needed flexible metric definitions and responsive Dashboard queries.

## Approach / What changed

The system transforms subscription and invoice updates into analytics events processed by Apache Flink, with a custom Apache Spark job generating initial Flink state from historical events. Apache Pinot's v2 engine handles windowed aggregations and joins at query time. Metric-definition changes use concurrent historical recalculation, temporary buffering of incoming events, state patching, and replay before the Dashboard switches to the updated data.

## Takeaways

- Apache Flink incrementally updates a compressed subscription-history state, avoiding re-analysis of every payment whenever new subscription data arrives.
- Apache Pinot v2 windowed aggregation and join support removed offline preaggregation while preserving Dashboard query latency below 300 milliseconds in production.
- During metric-definition changes, new events continue processing under the old definition while buffered events wait for historical recalculation and are then replayed over the updated state.

**Tags:** [Architecture](https://yomu.fyi/topic/architecture), [Data Pipelines](https://yomu.fyi/topic/data-pipelines), [Open Source](https://yomu.fyi/topic/open-source), [Performance](https://yomu.fyi/topic/performance), [Reliability](https://yomu.fyi/topic/reliability)

- Source: [Stripe](https://stripe.dev/blog/how-we-built-it-real-time-analytics-for-stripe-billing)
- Source URL: https://stripe.dev/blog/how-we-built-it-real-time-analytics-for-stripe-billing
- Ingested by Yomu: 2026-08-28T08:59:56.521Z

[Read original post](https://stripe.dev/blog/how-we-built-it-real-time-analytics-for-stripe-billing)