Publishing Reproducible OLAP Workflows: A Guide to Archiving ClickHouse-Backed Analyses

Stop sending static exports and hope for the best: make OLAP analyses on ClickHouse truly reproducible

Teams building analytics on ClickHouse face a recurring set of problems: fragmented artifacts (schema DDL scattered in dashboards, ad-hoc data exports, notebooks with hidden state), brittle migrations that behave differently across clusters, and no automated way to verify that a published analysis still runs as advertised. In 2026, with ClickHouse adoption accelerating after the company's major 2025 funding round and growing enterprise usage, these risks cost time and research credibility. This guide gives a practical, code-first playbook to package datasets, schema migrations, queries, and notebooks so other teams can reproduce OLAP analyses on ClickHouse—using packaging, checksums, and CI verification.

Why reproducible OLAP matters now (2026 context)

ClickHouse has moved from an experimental fast-column store to a mainstream OLAP option for analytics teams and data platforms. Recent capital and product momentum has broadened deployments—from single-node analytics to multi-cluster, cloud-hosted ClickHouse Cloud setups—making reproducibility both more valuable and harder. When you publish an analysis in 2026, readers expect to run it end-to-end: restore the dataset, apply the exact schema and migrations, run the same queries, and get the same results (or a well-documented delta). That expectation is the baseline for collaboration, auditing, and multi-institution research.

Overview: The reproducible archive model

Build a single, versioned archive that contains everything needed to re-run an analysis. At minimum, an archive should include:

• Schema & migrations — deterministic DDL and migration scripts
• Datasets — raw or preprocessed files (Parquet/CSV/Arrow) plus provenance
• Queries & expected outputs — saved SQL with golden result hashes or sample outputs
• Notebooks — runnable notebooks with clear environment metadata
• Manifest & checksums — machine-readable manifest listing every file and its checksum
• Environment — Docker images / compose files or instructions to reproduce the runtime

Design principles

• Immutability and provenance: Every archive is content-addressed; files are checksummed and signed.
• Deterministic migrations: Migrations are idempotent and repeatable; schema drift is explicit.
• Minimal runtime assumptions: Use containerized ClickHouse and explicit client versions.
• Automated verification: CI runs migrations, loads data, runs queries, and compares results.

Step 1 — Package your schema and migrations

Store schema as plain SQL files and expose a deterministic migration strategy. A good layout in your repository looks like:

migrations/
  0001_create_raw_tables.sql
  0002_transform_event_schema.sql
  0003_add_materialized_view.sql
  schema.current.sql  # optional snapshot of fully applied schema
  

Best practices:

• Idempotence: Use CREATE TABLE IF NOT EXISTS or wrapped checks in migration scripts so applying a migration twice does not break the state.
• Reproducible DDL: Avoid server-side non-determinism (server-generated defaults, random UUIDs without seeds). If defaults are needed, make them explicit in test fixtures.
• Schema snapshot: Commit a complete schema snapshot (schema.current.sql) to enable quick bootstrapping and to serve as authoritative documentation.
• Migration runner: Use or implement a small runner that applies migrations in order, records applied migrations in a schema_migrations table, and supports a --dry-run mode.

Example: idempotent migration header

-- 0001_create_raw_tables.sql
  CREATE TABLE IF NOT EXISTS events_raw (
    event_time DateTime,
    user_id UInt64,
    payload String
  ) ENGINE = MergeTree()
  ORDER BY (event_time);
  

Step 2 — Package datasets with provenance and efficient formats

Large datasets are the main friction point. The goal is efficient, verifiable transfer and a known-loading path into ClickHouse.

• Preferred formats: Parquet or Arrow for columnar efficiency; compressed CSV for small datasets and human readability.
• Chunking: Split large datasets into logical shards (e.g., by day or partition) so CI can process subsets quickly.
• Provenance metadata: Provide a data provenance file (data_provenance.json) with source, extraction query, row counts, and timestamps.
• Storage and distribution: Host on S3-compatible object storage or an institutional data repository. For public experiments, use a DOI-like persistent identifier.

Manifest example (manifest.json)

{
    "version": "1.0",
    "files": [
      { "path": "data/events_2025-12-01.parquet", "sha256": "...", "rows": 423452 },
      { "path": "migrations/0001_create_raw_tables.sql", "sha256": "..." }
    ]
  }
  

Step 3 — Use checksums and digital signatures

Checksums are the minimum guarantee. In 2026, teams should use SHA-256 everywhere and optionally sign manifests with GPG for non-repudiation.

• File checksums: Compute SHA-256 for every file and store in manifest.json. Example: sha256sum data/events_2025-12-01.parquet >> checksums.sha256
• Manifest signing: Sign the manifest file (manifest.json) with a repository or project GPG key. Consumers verify the manifest signature before using the archive.
• Content-addressed storage: If you publish artifacts to S3 or IPFS, the object hash should match the checksum in the manifest.

Step 4 — Publish notebooks as runnable artifacts

Notebooks are essential for explainability but often rely on hidden variables. Publish them with explicit environment metadata and small, reproducible data slices.

• Environment files: Include a requirements.txt, environment.yml, or a pipfile plus a Dockerfile for the notebook environment. Pin library versions (pandas, clickhouse-connect, polars, etc.).
• Data access: Avoid notebooks that call internal endpoints; bundle a small sample dataset or provide instructions to download shards.
• Outputs as artifacts: Save notebook outputs (HTML or executed notebook) and include expected result hashes so CI can detect drift.

Notebook header example

---
  title: "Event aggregation example"
  environment:
    python: "3.11"
    requirements: "requirements.txt"
  data: "data/events_2025-12-01.parquet"
  expected_results_sha256: "..."
  ---
  

Step 5 — Create a reproducible runtime: Docker + ClickHouse

To remove cluster variability, provide a containerized runtime. Include a Docker Compose that launches a ClickHouse server pre-configured for tests plus a runner container that applies migrations and loads data.

version: '3.8'
  services:
    clickhouse-server:
      image: clickhouse/clickhouse-server:23.8
      ports:
        - "9000:9000"
        - "8123:8123"
      volumes:
        - ./clickhouse_config:/etc/clickhouse-server
    test-runner:
      build: .
      depends_on:
        - clickhouse-server
  

Note: Pin the ClickHouse image version to avoid surprises. In 2026, ClickHouse releases are frequent; a reproducible archive must lock the server version.

Step 6 — CI verification: run it automatically

CI is where reproducibility becomes enforceable. A complete CI job will:

1. Fetch the archive and verify manifest signature and checksums.
2. Spin up the containerized ClickHouse instance (or connect to a sandbox cluster).
3. Apply migrations using your runner.
4. Load datasets (full or sample chunks), respecting order and batching.
5. Execute queries or notebooks and capture outputs.
6. Compare results to golden outputs or hashes, allowing configurable tolerances for floating point results.

Example GitHub Actions job (abbreviated)

name: verify-reproducible-archive
  on: [push, workflow_dispatch]
  jobs:
    test-archive:
      runs-on: ubuntu-latest
      steps:
        - uses: actions/checkout@v4
        - name: Verify manifest
          run: ./scripts/verify-manifest.sh manifest.json
        - name: Start ClickHouse
          run: docker-compose up -d clickhouse-server
        - name: Apply migrations
          run: ./scripts/run-migrations.sh --host localhost --port 9000
        - name: Load sample data
          run: ./scripts/load-data.sh data/events_2025-12-01.parquet
        - name: Run verification queries
          run: ./scripts/verify-queries.sh
  

Key details:

• Fast feedback: CI should run against a small subset of data for pull requests and a full run on tags or release events.
• Tolerance handling: For statistical queries, compare result hashes against a tolerance using a checksum of normalized output (e.g., sorted CSV with fixed precision).
• Cache artifacts: Cache downloaded datasets (or rely on S3) so CI is fast while still being deterministic.

Step 7 — Publish and document access patterns

Publishing is not just pushing a ZIP. Provide a README that documents:

• How to verify the manifest and signature.
• How to boot the environment (local Docker vs cloud ClickHouse instance).
• How to run full verification locally and in CI.
• Known differences between local and cloud deployments (e.g., filesystem, cluster settings).

Advanced strategies and patterns

1. Content-addressed archival and dataset deduplication

Store artifacts in an object store with keys based on SHA-256; archives refer to content-addressed identifiers. This eliminates accidental mutation and reduces storage costs for repeated datasets.

2. Golden result hashes vs. sample validators

For heavy workloads, compute golden hashes on canonical small inputs and use statistical validators for full runs. Include unit-test style queries that validate key invariants (row counts, distribution quantiles).

3. Managing schema evolution

When schema changes are expected, publish a compatibility matrix that shows which migration versions are compatible with which dataset snapshots. Tag archives with migration version ranges and provide upgrade/downgrade scripts that can be executed safely.

4. Security and access control

When datasets are sensitive, provide a public synthetic sample for reproducing query logic and a private production dataset accessible via signed URLs or short-lived credentials. Always require manifest signature verification locally even for private artifacts.

Common pitfalls and how to avoid them

• Hidden state in notebooks: Clear outputs and provide executed notebooks plus a script that recreates outputs from scratch.
• Environment drift: Pin dependencies and ClickHouse server images. Use Docker to isolate versions.
• Non-deterministic queries: Avoid ORDER BY without deterministic keys when generating golden outputs; sort before hashing.
• Large dataset CI costs: Use sample shards for PR checks, full verification on release tags, and spot-checks on a schedule for long-term maintenance.

Case study: Reproducible session for a multi-team analytics project (example)

In late 2025 a cross-institution project published a ClickHouse-backed OLAP analysis with the following approach:

1. All raw extracts published as Parquet shards on S3 with SHA-256 checksums in manifest.json.
2. Migrations stored in sequential SQL files; a small migration runner recorded applied migration IDs to a schema_migrations table.
3. Jupyter notebooks executed in a pinned Python 3.11 Docker image with clickhouse-connect v0.6.2 and pandas 2.1. Notebooks included a bootstrap script that loaded a 1-day sample for fast iteration.
4. CI pipeline verified manifest signatures, spun up a ClickHouse container, applied migrations, loaded sample data, ran queries, and compared sorted CSV outputs to golden checksums.

Result: external collaborators could reproduce figures and tables in under 30 minutes on commodity hardware, and auditors could validate the pipeline without access to private production data.

Future-proofing for 2026 and beyond

As ClickHouse ecosystems evolve—managed services, multi-region clusters, and new SQL extensions—expect tighter integrations with artifact registries and dataset catalogs. Build archives with versioned metadata and machine-readable manifests so they can be indexed by catalog tools and validated automatically. Consider adopting or contributing to community standards for OLAP experiment archives; a lightweight spec (manifest + checksums + runtime descriptor) will likely emerge as best practice across organizations in 2026.

Actionable checklist — publish a reproducible ClickHouse archive

1. Collect: Save schema SQL, ordered migrations, datasets (Parquet preferred), notebooks, and scripts into a repository.
2. Checksum: Compute SHA-256 for every file and assemble manifest.json.
3. Sign: GPG-sign manifest.json and publish the public key.
4. Containerize: Provide a Docker Compose + pinned ClickHouse image and notebook runtime.
5. CI: Add a verification workflow that validates checksums, applies migrations, loads data, and runs queries.
6. Document: Add a README with step-by-step reproduction instructions and expected run times.

Quick reference: tools and idioms

• Formats: Parquet, Arrow, CSV (compressed)
• Checksums: SHA-256 for files and manifests
• Packaging: tar.gz with manifest.json or direct S3 object layout
• Runtime: clickhouse-server Docker image, clickhouse-client or clickhouse-connect for Python
• CI: GitHub Actions / GitLab CI with Docker Compose and artifact caching

Closing: reproducibility is a product feature

Reproducible OLAP workflows are not an academic nicety—they reduce time-to-collaboration, simplify audits, and make your analytics a reliable product for other teams. In 2026, with ClickHouse increasingly used for enterprise analytics, investing an hour to package an archive correctly yields months of saved debugging, fewer support tickets, and clearer knowledge transfer.

Ready to make your ClickHouse analyses reproducible? Start with the checklist above: package your next analysis as an archive, add checksums and a simple CI verification, and publish the manifest. If you want a template repository that implements these practices (Docker Compose, migration runner, manifest verification, and a CI pipeline), download our starter kit and adapt it for your projects.

Call to action

Download the free reproducibility starter kit, or join our community call to walk through converting a real ClickHouse analysis into a verified archive. Share your archive links and results—let's build standards that make OLAP research and engineering reproducible across teams.

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