The time has come when a one-second delay means lost revenue

coffeeholic 2025년 06월 11일

2874

When I was building a real-time recommendation system recently, I realized something that really stuck with me: the delay between the moment a user clicks and seeing a personalized result can be as little as a few seconds, which can completely change the user experience. With traditional batch processing, I was only able to make recommendations based on data from a day ago, but now I needed to immediately reflect user behavior in the "here and now" to be competitive.

At first, we thought, "How hard can real-time processing be?" but when we got into it, we realized it was a whole new level of complexity: data consistency, failover, backframe processing... There are so many variables that pop up that we hadn't considered in batch processing.

My biggest concern was how to reliably handle tens of thousands of events per second.

Prompt.

복사

# Real-time data processing architect

## Project Requirements

- Data volume: [expected number of events per second].

- Latency goal: [maximum acceptable latency].

- Data sources: [logs/clickstream/sensor data, etc.]

- Processing result utilization: [real-time dashboards/recommendations/alerts, etc.]

## Streaming Architecture Design

### A. Selecting a streaming platform

- Apache Kafka vs Apache Pulsar vs Amazon Kinesis comparison

- Analyze compatibility with [current infrastructure environment

- Evaluate scalability/durability/operational complexity tradeoffs

### B. Processing Engine Optimization

- Review Apache Flink vs Spark Streaming vs Kafka Streams suitability

- Windowing operations and state management strategies

- Exactly-once processing guarantee mechanisms

### C. Performance tuning strategies

- Optimize partitioning and parallelism

- Memory management and garbage collection tuning

- Backpressure and throttling control measures

### D. Ensure operational reliability

- Failover and checkpointing strategies

- Establish a monitoring and alerting system

- Stream branching design for A/B testing

Please include specific implementation examples and performance benchmarks.

After three months of building a real-time data pipeline based on this organized design, the results were truly amazing. The biggest change was the dramatic increase in business responsiveness.

For example, the moment a user searches for a specific product, that information is immediately fed into the recommendation engine, so that on the next page, we can already show them personalized products. This is done in real time, instead of a day later, which is a huge improvement in user satisfaction and conversion rates.

I also learned a lot technically, especially that it's not so much about "perfect real-time" as it is about "real-time that fits the business needs." Trying to do everything in milliseconds exponentially increases the complexity and cost of the system, when in reality, a delay of a few seconds is often imperceptible to the user.

Six months later, when we checked the reliability of the system, we were able to reliably process over 100,000 events per second while maintaining over 99.9% availability. It's also made our development team more productive, as we can see user reactions in real time, which makes A/B testing and validating new features much faster.

If you're thinking about adopting real-time data processing, don't be intimidated by the technical complexity and start by clearly defining the business value. Once you know what really needs to be real-time and what doesn't, you'll be able to create a much more efficient system!

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