The New Architecture for Real-Time JOINs: Beyond Kafka and Flink

Introduction

ClickHouse has become a leading choice for OLAP (Online Analytical Processing) workloads thanks to its lightning-fast query speeds and scalability. However, its architecture, optimized for columnar storage and append-only workloads, introduces significant challenges when it comes to performing JOIN operations on large datasets.

In this article, we’ll explore:

• Why JOINs are problematic in ClickHouse.
• Why Kafka alone can’t solve this.
• Why solutions like Flink can work but introduce major complexity.
• The benefits of simpler alternatives like Glasflow

What Is Apache Kafka?

Apache Kafka is an open-source platform designed for real-time data streaming. It allows developers to publish, subscribe to, and process streams of records in a high-throughput, low-latency environment.

Key Features:

• Distributed and scalable.
• Handles millions of messages per second.
• Widely used for building real-time data pipelines.

“Decisions are where data meets judgment.” — Cassie Kozyrkov

What Is Apache Flink?

Apache Flink is an open-source stream processing framework that can handle stateful computations over unbounded and bounded data streams. It’s known for its ability to perform complex transformations and aggregations in real-time.

Key Features:

• Distributed processing engine.
• Supports event-time and processing-time semantics.
• Allows for efficient JOIN operations across streams.

“Data is a precious thing and will last longer than the systems themselves.” — Tim Berners-Lee

Why JOINs Are Challenging in ClickHouse

ClickHouse’s strengths come from design decisions that make it ideal for analytical queries:

• Columnar Storage
• Append-mostly architecture
• Batch-oriented writes

But these same strengths limit its ability to handle complex JOINs efficiently:

• JOINs across large datasets create performance bottlenecks.
• Using ReplacingMergeTree and FINAL can help—but these increase query latency.

ClickHouse was designed for fast, scalable analytics, not relational complexity.

Why Kafka Isn’t the Solution

Many teams use Apache Kafka to manage high-volume data streams and integrate them into ClickHouse. Kafka is great at:

• High-speed data transport
• Efficient data ingestion into ClickHouse

However:

• Kafka does not support complex data operations like JOINs.
• It’s built to move data, not to transform or relate it.

“Torture the data, and it will confess to anything.” — Ronald Coase

Why Flink Can Help — But Comes With a Price

One common workaround is to move JOIN operations out of ClickHouse entirely, using a powerful stream processing engine like Apache Flink.

Typical ETL Flow with Flink

1. Ingest Data: Flink consumes data streams from Kafka.
2. Transform & JOIN: Flink applies transformations and performs JOINs between multiple streams using keys and time-based windows.
3. Write to ClickHouse: Pre-joined and optimized data is ingested into ClickHouse.

Benefits

• JOINs happen before ClickHouse, reducing query-time complexity.
• Real-time processing becomes possible at scale.

Challenges

But this approach introduces significant operational complexity:

• Additional Infrastructure: Flink requires significant compute and memory resources.
• Increased Complexity: Developing and maintaining Flink pipelines demands specialized expertise.
• Connector Management: Reliable connectors between Kafka, Flink, and ClickHouse add further complexity.
• State Management: Flink’s stateful operations require careful configuration and maintenance.
• Dependency Management: Keeping ecosystem components compatible is challenging.
• Higher Costs: More infrastructure and specialized talent mean higher costs.

“The goal is to turn data into information, and information into insight.” — Carly Fiorina

A Newer, Simpler Alternative: Managed Solutions

For teams seeking the benefits of pre-joined, real-time data without the overhead of building Flink pipelines, newer managed solutions have emerged.

GlassFlow is one such tool:

• Ingests data directly from Kafka.
• Performs JOINs and deduplication within a managed environment.
• Writes optimized, clean data into ClickHouse.

Benefits of Using GlassFlow

• 40% faster query performance thanks to pre-processed data.
• Minimal configuration required, saving engineering time.
• Built-in deduplication to avoid data inconsistencies.
• Auto-scaling pipelines that handle changing workloads.
• No need for complex infrastructure like Flink clusters.

“Real-time data is only useful if it’s actionable.” — Jay Kreps

If you’re ready to simplify your real-time data pipelines, reduce query latency by up to 40%, and eliminate the maintenance burden of complex JOIN operations:

👉 Try GlassFlow now 👈

Take control of your data pipeline’s performance today!

Conclusion

ClickHouse’s architecture makes complex JOINs inherently difficult.

• Kafka helps with ingestion, but not JOINs.
• Flink enables JOINs — but adds massive complexity and cost.
• Managed solutions like GlassFlow offer a middle ground, simplifying data pipelines without sacrificing power.

As real-time data needs grow, choosing the right strategy to manage JOINs can mean the difference between operational excellence and unmanageable technical debt.

👉 Download and try GlassFlow here.