Solving ClickHouse’s JOIN Limitations
In the last blog, we saw what ClickHouse was and the primary limitations it has when it comes to advanced transformations like JOIN operations. Basically, the design that allows ClickHouse to be so fast with analytical queries creates a huge bottleneck when it comes to expensive operations like Joins, specially on bigger datasets.
Here is an overview of the limitations that ClickHouse has when it comes to JOIN operations.
Figure 1. ClickHouse and it’s limitations with JOIN: An Overview
Now, the main question is: how to overcome these drawbacks? That is exactly what we will be covering in this blog: ways to overcome the JOIN problem that ClickHouse suffers with. So, let’s start by considering what alternatives do we have.
Let’s consider a scenario where you are dealing with a lot of streaming data from an mobile application (imagine that every interaction with the app is recorded). What if you have to join this table with a table that contains the information of all the users of that platform. This is something that ClickHouse would struggle with because of the huge data size and the complexity of the use case.
To solve this problem, ClickHouse does provide various integrations and once of them is a very popular solution for handling streaming data: Apache Kafka. But, the big question is: will it solve our JOIN problem? Let’s figure it out.
Alternative 1- Apache Kafka: A well integrated solution for ClickHouse?
In case your application has to deal with streaming data, the volume and velocity of data will be huge. If you need to perform JOIN operations on that data, it would be a huge challenge. However, what if we use Kafka as a connector between the two?
Figure 2. Kafka and it’s integration with ClickHouse (source)
Simply speaking, Apache Kafka is a tool that helps move large amounts of data quickly between different systems, acting as a messenger between them. It’s great for handling continuous streams of data, making sure everything flows smoothly in real time, which is very necessary in operations requiring realtime data. Because of this, it might seem like a possible way to work around ClickHouse’s JOIN limitations—but unfortunately, it’s not. Why? Let me explain.
The problem is that Kafka is designed for sending and receiving data, not for complex data relationships like JOINs. Even if you use Kafka to connect your application with ClickHouse, it won’t change the fact that ClickHouse struggles with JOIN operations. While Kafka can help get data into ClickHouse efficiently, it doesn’t improve ClickHouse’s ability to combine data across multiple tables. So, in the end, it’s not a solution for this issue and we still have a missing piece in our puzzle that would help us solve the JOIN problem in ClickHouse.
Figure 3. The missing piece is the solution to our JOIN problem!
It’s not all bad news tho! We now have an important tool in our data pipeline that can help us handle streaming data very effectively irrespective of its size and complexity. We just have to focus on the JOIN problem now, which was our primary goal from the start.
Here is another idea; What if we use another framework or application that can perform transformations on the data that Kafka handles and then transfer it into the corresponding ClickHouse table. That way, we won’t have to perform the expensive and challenging JOIN operations in ClickHouse. The big question now is, what could be this framework/application?
Good news is that the open source software community is full of innovative solutions so we already have a feasible solution for this issue: Apache Flink. Since Kafka alone cannot solve the JOIN problem, Flink allows us to process and transform data before it reaches ClickHouse, ensuring that JOINs happen efficiently outside ClickHouse’s constraints. But could Flink be the missing piece? What additional infrastructure is required to make it work seamlessly? And does this solution introduce its own set of trade-offs? Let’s see.
Alternative 2- Apache Flink: A Solution to ClickHouse’s JOIN Problem?
Figure 4. Is Flink a feasible solution for JOIN operations in ClickHouse?
Now that we know that Kafka alone cannot help with this, let’s explore Apache Flink as a potential solution. Flink is a powerful stream processing framework designed to handle real-time transformations efficiently. It enables us to preprocess data, perform complex operations like JOINs, and then store the final enriched data in ClickHouse for fast querying. But how does this work in practice, and what challenges come with it? Let’s break it down.
Introduction to Apache Flink
Apache Flink is an open-source framework for distributed stream processing. Unlike Kafka, which primarily acts as a data transport layer, Flink allows real-time computation on incoming data, making it well-suited for operations like aggregations, filtering, and—most importantly—JOINs. By leveraging Flink, we can shift the heavy lifting of JOIN operations away from ClickHouse, ensuring that only preprocessed and optimized data lands in our database.
Figure 5. How can Flink help with JOIN Operations?
Flink operates on streams, meaning it continuously processes data as it arrives, rather than waiting for batch loads (which is what a typical batch processing pipeline does). This makes it ideal for scenarios where real-time JOINs are necessary. However, setting up a Flink pipeline to perform JOINs and integrating it with ClickHouse requires careful design. Let’s go through the typical implementation process.
How to Setup a Flink Pipeline for Performing JOINs : A Starter Guide
Figure 6. How to use Apache Flink as an intermediary to solve the JOIN problem
As with any data pipeline, this also follows the traditional ETL (Extract, Transform and Load) flow. Let’s break the process into parts for easier understanding:
1. Data Ingestion
- Flink consumes data from sources like Kafka, databases, or event streams.
- The incoming data is processed as a continuous stream or as micro-batches, depending on the use case.
2. Data Stream Transformations
- Flink applies filtering, enrichment, and other transformations.
- A JOIN operation is performed by matching records from multiple streams using keys (e.g., user IDs, timestamps).
- Windowing techniques are often used for time-based joins to handle late-arriving data.
3. Writing to ClickHouse
- Once the JOINs and transformations are complete, the processed data is written to ClickHouse.
- This results in pre-joined, optimized tables, eliminating the need for ClickHouse to perform costly JOIN operations at query time.
For more details on the technical implementation of this approach, refer to the following article (link to Armend’s Article).
While this approach offloads JOIN complexity from ClickHouse, it introduces its has it’s own set of challenges. The primary one is that it requires a lot of additional setup and expertise as well. Let’s elaborate on this.
Problems with this Approach
There are several problems with the current approach of using Apache Flink to address the JOIN challenge:
- Additional Infrastructure – Running Apache Flink requires significant additional resources, including compute power and memory, leading to increased operational overhead and cost.
- Increased Complexity – Building and maintaining data pipelines for Apache Flink requires specialized expertise, additional development time, and continuous monitoring for smooth operation.
- Connector Management – Managing a reliable connector between Kafka and Flink, along with configuring batch sizes, retry logic, and ingestion mechanisms, adds complexity to the overall data pipeline.
- State Management – Ensuring proper state management within Flink is critical, requiring careful setup and maintenance to handle large-scale joins effectively.
- Dependency Management – Handling dependencies and ensuring compatibility between different components within the Flink ecosystem can be challenging as the pipeline becomes more and more complex.
- Storage and Integration Challenges – Setting up custom connectors to external storage systems like ClickHouse and managing ingestion introduces further operational difficulties.
Apache Flink offers a powerful solution for processing streaming data and performing complex transformations. However, the effort required to set up, manage, and fine-tune Flink—especially for handling JOIN operations—makes it a resource-intensive and costly choice. The missing pieces, such as external/custom connectors and integration challenges, further increase the complexity, making Flink a less ideal solution for this specific use case.
Conclusion
Based on our above discussion, this is what we need: “A tool that requires minimal setup and can ingest streaming data from sources like Kafka, perform JOIN operations and then load the resulting data into ClickHouse”.
I have the perfect solution that has been specifically designed to solve this problem: Glassflow! It is a fully managed stream processing solution that eliminates the friction of heavy infrastructure (Flink!), gets you up and running in minutes, and is managed.
It’s so easy to setup a JOIN operation pipeline for ClickHouse in Glassflow:
It’s as easy as dragging a component on the screen, selecting the join keys and connecting the result into a ClickHouse connector! Everything else is manage for you by Glassflow: State management, ClickHouse Write operation, etc.
Did we make you curious? Would you like to know more about Glassflow and try it?
