Making ClickHouse Production-Ready: How Docker Hardened Images Solve Security Blocks

Imagine you're about to deploy a critical analytics database—ClickHouse—into production on Kubernetes. Everything looks good until the security scanner flags three critical vulnerabilities. But these aren't in ClickHouse itself; they're in the base image's packages your application never uses. Your security team blocks the deployment, and you're stuck investigating false positives and writing risk exceptions. This common scenario frustrates DevOps teams everywhere. The solution lies in using Docker Hardened Images (DHI) that strip away unnecessary components, reducing the attack surface and eliminating irrelevant CVEs. Below, we dive into the specifics of this problem, how DHI resolves it, and what makes ClickHouse tick under the hood.

1. Why was the ClickHouse Docker deployment blocked by security?

In November 2025, a team self-hosting Langfuse uploaded their ClickHouse image to AWS ECR for production. The pipeline scanner returned three critical vulnerabilities—not in ClickHouse but in the underlying base image. The security team, adhering to strict policies, blocked the deployment because those CVEs were formally present, even though they were irrelevant to ClickHouse's actual workload. This is a common enterprise hurdle: scanners detect vulnerabilities in packages like system libraries that the application never touches, yet the default response is to halt until exceptions are approved. The team spent a day investigating, wrote risk exceptions, and still faced rejection because the vulnerabilities were technically real. This situation underscores a fundamental mismatch between developer-optimized base images (which include many packages for ease of use) and the hardened requirements of production environments.

2. What exactly are Docker Hardened Images (DHI) and how do they fix this?

Docker Hardened Images are pre-built, minimal base images that remove unnecessary components, like unused libraries, tools, and packages. They undergo rigorous scanning to ensure a low CVE count. When you use a DHI as the foundation for your ClickHouse container, you eliminate the bulk of false-positive vulnerabilities because those packages simply aren't present. In the blocked deployment scenario, switching to a ClickHouse image built on a hardened base (e.g., a distroless or slim variant) would have prevented the security scanner from flagging those three critical CVEs. The hardened image still contains everything ClickHouse needs to run—such as glibc and essential runtime libraries—but excludes irrelevant utilities like curl, bash, or Python that often carry CVEs. This satisfies security teams while maintaining full functionality. DHI solutions are often maintained by third-party vendors or can be custom-built by trimming a standard image to only what the application requires.

3. What is ClickHouse and why is it so widely used?

ClickHouse is an open-source, column-oriented database management system designed for real-time analytical queries on large datasets. Unlike traditional row-based databases, it stores data column-by-column, enabling extremely fast compression and scanning of specific columns. Major companies like Cloudflare, Uber, and Spotify rely on ClickHouse for high-throughput analytics. With over 100 million pulls from Docker Hub, it's become the default choice for teams needing sub-second query performance on billions of rows. Its SQL interface and support for standard protocols (HTTP/TCP) make it accessible, while its distributed capabilities allow horizontal scaling. However, the default official Docker images prioritize developer convenience—bundling extra tools for debugging and configuration—rather than the hardened posture required for enterprise production. This convenience gap is precisely why deployments can encounter security blocks.

4. How is ClickHouse structured architecturally?

ClickHouse follows a layered architecture optimized for analytical speed. When an SQL query arrives over HTTP (port 8123) or TCP (port 9000), it passes through an optimizer that parses it into an abstract syntax tree (AST), prunes partitions, and rewrites the query for efficiency. A pipeline executor then distributes the work across parallel threads, each reading data from the storage layer. The heart is the MergeTree storage engine, which stores data in columnar .bin files. A sparse primary index allows skipping irrelevant granules without scanning entire columns. Background merge processes compact parts and maintain performance over time. Storage is pluggable: local disk, S3, or HDFS. This architecture enables high-speed aggregation and filtering, but it also means that the database relies on system libraries for threading, I/O, and memory management—none of which require the extra packages that trigger false positive CVEs.

5. Why does the base image matter for security vulnerabilities?

The base image provides the operating system layer for your Docker container. Official images often use full distributions like Ubuntu or Debian, which include hundreds of packages—shells, utilities, libraries, and tools—that are rarely used by the application but are present and need to be patched. Security scanners (e.g., Trivy, Snyk) check every installed package against CVE databases. Even if a vulnerability exists in a package like apt or curl that ClickHouse never invokes, it gets flagged as a critical issue. In production-hardened environments, security teams often enforce zero-tolerance policies for critical or high CVEs, leading to deployment blocks. Using a minimal base image that contains only the necessary runtime dependencies—like alpine or a distroless image—dramatically reduces the attack surface and CVE count. For ClickHouse, a hardened base eliminates the three CVEs that stopped the deployment described earlier.

6. How can teams make their ClickHouse containers production-ready while keeping security teams happy?

To avoid the security block scenario, teams should adopt a multi-pronged approach. First, choose a ClickHouse image built on a hardened base—either the official slim variant or a custom image from a vendor like Docker Hardened Images. Second, perform a pre-deployment vulnerability scan using tools like Trivy or Snyk, and whitelist only CVEs that are not reachable (though this can be cumbersome). Third, implement a security exception process that clearly documents why each flagged CVE is not exploitable in your specific context, but this is often denied as seen in the case. The most reliable solution is to use a minimal base image from the start. Many teams also adopt automated CI/CD pipelines that rebuild the ClickHouse image from scratch using a scratch or distroless base, adding only the ClickHouse binary and its required system libraries (e.g., glibc, openssl). This guarantees a minimal footprint. Finally, engage security early in the development cycle so they can validate the image's CVE posture before the final deployment block.