If you've ever tried to push a ClickHouse container into an enterprise Kubernetes cluster, you might have run into a familiar roadblock: a security scanner flags vulnerabilities in the base image, and your deployment gets halted. This happened to a team in November 2025, when their pipeline detected three critical CVEs not in ClickHouse itself but in the underlying Linux packages. Security teams often reject such images even when the vulnerabilities are irrelevant to the workload, wasting time on risk exceptions.
This guide explains why these blocks occur and how Docker Hardened Images (DHI) can help. We'll also dive into ClickHouse's architecture, its security posture, and practical steps to get your container from blocked to production-ready. Use the links below to jump to a specific question.
- What caused the ClickHouse deployment to be blocked by security?
- Why does a security team block an image if the vulnerabilities aren't in the application itself?
- What is ClickHouse and why is it so widely adopted for analytics?
- How does ClickHouse's architecture deliver high performance?
- What are Docker Hardened Images and how do they solve these security blocks?
- How does the default ClickHouse image's security posture differ from production needs?
- What steps can teams take to avoid similar security blocking issues?
What caused the ClickHouse deployment to be blocked by security?
In November 2025, a team self-hosting Langfuse, an open-source LLM observability platform, on Kubernetes uploaded their ClickHouse image to AWS ECR as part of production preparation. The pipeline scanner returned three critical vulnerabilities. However, these were not in ClickHouse itself but in the base image's packages, like OpenSSL or glibc. The security team saw the findings and blocked the deployment from reaching production. The team then posted on GitHub Issue #286: "Our security team is not allowing us to take it to production. Please suggest alternatives." This is a common scenario: a functional container gets flagged because scanners detect CVEs that the application never even touches, leading to days of investigation and risk exceptions.
Why does a security team block an image if the vulnerabilities aren't in the application itself?
Security teams operate under strict, auditable policies. Even if a CVE is in a library that ClickHouse never loads, the scanner reports it as a real risk. Enterprise environments often forbid any container with known critical or high-severity vulnerabilities, regardless of exploitability. The reasoning is twofold: first, a vulnerability could become exploitable through future code changes, and second, compliance frameworks (like SOC 2 or PCI-DSS) require remediation of all known CVEs. So while the team might argue the CVE is irrelevant, the security team rejects the risk exception because the vulnerability is technically real. This friction forces teams to either patch the base image or use a hardened alternative that eliminates unnecessary packages.
What is ClickHouse and why is it so widely adopted for analytics?
ClickHouse is an open-source columnar database designed for analytical workloads at massive scale. It can query billions of rows and return results in milliseconds—something traditional row-oriented databases struggle with. Major companies like Cloudflare, Uber, and Spotify run it in production. With over 100 million pulls from Docker Hub, it has become the default infrastructure choice for teams needing high-performance analytics. Its columnar storage compresses data efficiently and processes only the columns requested in a query, drastically reducing I/O. Yet, its default Docker image prioritizes developer ease-of-use over the hardening that enterprise production environments demand—a gap that often triggers security blocks.
How does ClickHouse's architecture deliver high performance?
ClickHouse follows a layered architecture optimized for analytical speed. SQL queries arrive over HTTP (port 8123) or TCP (port 9000) and pass through an optimizer that parses them into an abstract syntax tree. Pruning occurs early, discarding irrelevant parts. Then a pipeline executor hands work off to parallel threads. The core storage layer is the MergeTree engine, which stores data in columnar .bin files, uses a sparse primary index to skip irrelevant granules without reading entire columns, and runs background merges to compact parts and maintain performance. Storage is pluggable—local disk, S3, HDFS—allowing flexibility. This architecture enables ClickHouse to handle tens of billions of rows per table while keeping query latency low.
What are Docker Hardened Images and how do they solve these security blocks?
Docker Hardened Images (DHI) are base images that have been stripped of unnecessary packages and components, reducing the attack surface and eliminating CVEs in unused libraries. For ClickHouse, a DHI version uses a minimal operating system (e.g., Alpine or a distroless base) and only includes the runtime dependencies that ClickHouse actually needs. When a scanner runs on such an image, the number of CVEs drops dramatically—often to zero in critical or high categories. DHI images also follow best practices like non-root users, read-only file systems, and seccomp profiles. By replacing the default ClickHouse image with a hardened build, teams can bypass security blocks without modifying the database itself, fulfilling both functional and compliance requirements.
How does the default ClickHouse image's security posture differ from production needs?
The official ClickHouse Docker image is built for developer convenience. It includes a full Linux distribution (like Ubuntu or CentOS) with many packages—networking tools, shells, compilers—that are rarely used in production but carry potential vulnerabilities. Enterprise production environments demand minimalism: only the bare essentials to run the application. Hardened images go further by removing even common utilities (e.g., curl, bash, wget) that are not required for runtime. Additionally, the default image runs as root, which is a security risk. A production-ready posture would use a non-root user, disable unnecessary capabilities, and apply regular patching to the underlying base image, all of which are standard in Docker Hardened Images.
What steps can teams take to avoid similar security blocking issues?
First, scan your ClickHouse image early in the CI pipeline—not just at deployment—to catch CVEs before they reach production. Use tools like Trivy or Snyk to identify vulnerable base packages. Second, switch to a Docker Hardened Image for ClickHouse. Many community or vendor-provided hardened images exist, or you can build your own from scratch using a minimal base like Alpine and adding only ClickHouse binaries. Third, maintain an exception process: if a CVE has no exploit path, document it with the security team and get formal acceptance. Fourth, regularly update the base image to incorporate patches. Finally, consider using a runtime security layer like AppArmor or seccomp to limit potential damage even if a vulnerability is present. Combining these steps ensures you stay both fast and compliant.