Benchmarking ClickHouse vs Snowflake for Quantum Data: Throughput, Cost, and Query Patterns

Context: Why 2025–2026 matters for analytical databases and quantum data

Database vendors accelerated feature rollouts in late 2025 and early 2026. ClickHouse's ecosystem expansion (including a $400M funding round in 2025 that highlighted growing adoption) pushed aggressive optimizations for analytical workloads. Snowflake continued investing in Snowpark, VARIANT handling, and multi-cloud orchestration — improving developer ergonomics for complex data types including arrays and nested pulse traces. For reproducible publishing and long-form repo releases see advice on modular publishing workflows.

Benchmark design — reproducible, shareable, and realistic

We designed the benchmark notebook with three principles: reproducible (single repo and seed scripts), public-data friendly (use public quantum archives where possible), and workload-realistic (shots-heavy and pulse-level traces). The notebook (example repo referenced at the end) contains everything you need — from synthetic generators to exact DDL and measurement scripts.

Workloads

1. Shots aggregation: Many quantum experiments log one row per shot. Typical queries: counts, bitstring frequency, conditioning on calibration metadata.
2. Pulse trace retrieval: Retrieve a full analog waveform (time-series) for a shot or set of shots. These are larger blobs per row and exercise storage + IO paths.
3. Join-heavy analysis: Join shot table with calibration metadata, device telemetry, and experiment annotations to compute error rates and correlations.

Datasets

Use public sources whenever possible to make results auditable: IBM Qiskit export traces, Rigetti/Forest experiment dumps, and community archives on Zenodo or Kaggle. When public sets are too small, the notebook includes a generator that expands datasets deterministically (seeded RNG) so results are reproducible across runs. For tips on archiving and DOIs, see the guide on publishing reproducible datasets.

Schema recommendations (ClickHouse vs Snowflake)

Quantum data mixes high-cardinality shot metadata with large pulse arrays. Schema choices dominate performance.

ClickHouse DDL (recommended)

CREATE TABLE shots (
  experiment_id String,
  circuit_id String,
  shot UInt32,
  bitstring FixedString(64),
  meas_time DateTime64(6),
  cal_tag String,
  pulse_id String,
  pulse_trace Array(Float32)
) ENGINE = MergeTree()
PARTITION BY toYYYYMM(meas_time)
ORDER BY (experiment_id, circuit_id, shot);

Notes: use Array(Float32) for pulse traces; partition by time and order by experiment/circuit/shot for efficient range scans. ClickHouse supports compression codecs (LZ4, ZSTD) and encodings such as LowCardinality for repeatable tags.

Snowflake DDL (recommended)

CREATE TABLE shots (
  experiment_id STRING,
  circuit_id STRING,
  shot NUMBER,
  bitstring STRING,
  meas_time TIMESTAMP_NTZ,
  cal_tag STRING,
  pulse_id STRING,
  pulse_trace VARIANT  -- store as JSON array or use external staged Parquet
);
-- Use clustering on (experiment_id, circuit_id, meas_time)
ALTER TABLE shots CLUSTER BY (experiment_id, circuit_id, meas_time);

Notes: Snowflake handles nested VARIANTs well; you can store pulse traces as a JSON array or use external staging with Parquet to reduce storage cost and speed loading. Clustering improves selective scans but requires maintenance when data distribution changes.

Ingestion patterns and tips

• Batch versus streaming: For shots-heavy bulk loads, prefer batch load (Parquet/ORC) — both engines ingest columnar files fast. For live experiment telemetry, ClickHouse's native HTTP or Kafka integration often yields lower-latency ingestion than Snowflake streaming APIs.
• Compression: Compress traces aggressively. Float32 arrays compress well with ZSTD and delta encoding; ClickHouse's column codecs and Snowflake's internal micro-partition compression both help, but the raw on-wire size matters for egress costs.
• Shredding pulse traces: For queries that read only parts of a trace (e.g., amplitude peaks), consider precomputing features (max, RMS, timestamp of peak) and storing both the raw trace and features to speed common patterns.

Measurement harness — how the notebook times queries

The notebook uses a minimal, vendor-agnostic harness so results are comparable:

import time
import pandas as pd

def time_query(execute_fn, warmups=2, repeats=5):
    # execute_fn runs the query and returns rowcount
    for _ in range(warmups):
        execute_fn()
    times = []
    for _ in range(repeats):
        t0 = time.perf_counter()
        rc = execute_fn()
        t1 = time.perf_counter()
        times.append(t1 - t0)
    return {
        'p50': np.percentile(times,50),
        'p95': np.percentile(times,95),
        'mean': np.mean(times),
        'rows': rc
    }

Use provider-specific connectors (clickhouse-driver for ClickHouse, snowflake-connector-python for Snowflake) inside execute_fn so network/client overheads are similar between runs. Instrumentation and observability are critical to make sure your timing harness isolates DB runtime from client-side delays.

Query patterns we benchmarked

1. Large scan + group by: SELECT circuit_id, count(*) FROM shots WHERE meas_time BETWEEN ... GROUP BY circuit_id
2. Selective read: fetch pulse_trace for a single shot or 1000 shots — tests latency and retrieval throughput
3. Join + aggregation: Join shots with device_calibration ON (experiment_id, meas_time range) and compute aggregated error rates

Representative results (patterns, not a one-size-fits-all number)

Benchmarks vary by cluster size, data layout, and compression. These distilled patterns reflect many runs across realistic dataset sizes:

• Scan throughput: ClickHouse delivered higher sustained rows/sec for full-table scans when the cluster resources were dedicated. Snowflake matched or exceeded ClickHouse on single-shot small-scan latency when using automatically scaled warehouses for bursty, small queries.
• Pulse trace retrieval: For retrieving large arrays per row, ClickHouse's direct columnar read had lower end-to-end latency if traces were stored inline. Snowflake performed well when traces were stored externally in compressed Parquet and staged, but egress and staging latency added overhead.
• Concurrency: Snowflake's serverless model makes concurrency simple — many analysts may run ad-hoc queries without impacting others. ClickHouse can handle concurrency with replicas but requires more ops tuning for resource isolation.

Cost modeling — build a parametric model for your lab

Absolute cost numbers change, but the structure of cost does not. Use a parametric model so you can plug in vendor quotes for 2026. See more on modern cloud cost optimization to align model assumptions with current vendor pricing trends.

Cost model formula

# high-level model
Total_Cost = Storage_Monthly + Compute_Cost + Egress_Cost + Ops_Cost

# Storage_Monthly = storage_size_TB * storage_price_per_TB_month
# Compute_Cost = sum_over_queries( compute_price_per_hour * runtime_hours )
# Egress_Cost = bytes_out * egress_price_per_GB
# Ops_Cost = team_time * hourly_rate  (only for self-hosting)

Important levers: re-run factor (how many times you re-run experiments), fraction of queries that are full scans vs selective, and team ops overhead for self-hosting ClickHouse.

Example scenario (parametric example)

Assume a dataset: 10 TB raw (after compression stored as 2 TB columnar), 10 active analysts, 500 full-scan queries/month each running 10 minutes, and 2,000 selective pulse fetches/month.

# plug-in numbers (example; replace with vendor quotes)
storage_price_per_TB_month = $20   # vendor-specific
compute_price_per_hour = $5         # averaged per cluster/hour
egress_price_per_GB = $0.09

# compute cost: 500*10min = 500*0.1667 = 83.35 hours
Compute_Cost = 83.35 * $5 = $416.75
Storage = 2 TB * $20 = $40/month
Egress = (assume 1 TB of exports) * 1024 GB * $0.09 = ~$92.2
Total ≈ $549/month + Ops

Interpretation: For this workload, compute dominates monthly costs for repeated full scans. ClickHouse self-hosted would trade off lower per-query cost for higher Ops_Cost; ClickHouse Cloud often sits between self-hosted and Snowflake in pricing.

Practical recommendations by use-case

For heavy, repeatable analytics (large scans, nightly reprocessing)

• Favor ClickHouse with tuned MergeTree partitions and compression.
• Precompute features and materialized views for common aggregates to reduce repeated full scans.
• Use scheduled compaction and TTL to age out raw traces if you keep features.

For multi-team ad-hoc exploration and sharing

• Snowflake is attractive for low-ops and predictable elasticity; invest in clustering and materialized views to control compute cost.
• Combine Snowflake with external staged Parquet for bulk archival of pulse traces to control storage costs.

For reproducible publications and cross-institution benchmarks

• Publish a notebook that contains: seed generator, exact DDL, ingest scripts, and measurement harness. Put it on GitHub and pin a DOI with Zenodo best practices.
• Use deterministic seeds for synthetic growth and version your datasets in an archive so others can reproduce the experiment — this reduces ambiguity in performance comparisons.

Example notebook workflow (what to include in your repo)

1. README with costs assumptions and provider pricing links (update yearly).
2. data_generator.py — deterministic generator for shots & pulse arrays (seeded RNG).
3. ddl_clickhouse.sql and ddl_snowflake.sql — exact statements used in tests.
4. ingest scripts — upload to cloud stage or clickhouse-client bulk ingest commands.
5. benchmark_harness.py — timing harness and query definitions.
6. results.ipynb — graphs, statistical summaries, reproducible outputs, and instructions to re-run.

Common pitfalls and how to avoid them

• Comparing apples to oranges: Make sure both systems receive the exact same files and data layout. Use checksums and row counts to validate ingests.
• Warm-up effects: Cache, micro-partitions, and pipelined plans can bias first-run timings. Warm up each query pattern before measuring.
• Network variance: Run benchmarks from a colocated environment or within the same cloud region to avoid egress and cross-region latency noise. See notes on cloud cost optimization for region-aware pricing impacts.

For research reproducibility, the benchmark is only useful if it’s executable end-to-end by an independent team — that means scripts, precise DDL, and dataset checksums.

Advanced strategies for 2026 and beyond

As quantum experiments generate ever-larger pulse datasets, a few advanced strategies matter:

• Hybrid storage: Keep raw traces in cold storage (S3/GB-scale object stores) and load selected partitions into ClickHouse/Snowflake for hot analysis. Use a manifest table to locate raw blobs and reduce duplicated storage.
• Feature stores: Extract and centrally store time-series features (peaks, integrals) for ML pipelines. This reduces database load for downstream analytics.
• Containerized test beds: Run ClickHouse and Snowflake-compatible tests in CI using Docker/Kubernetes (ClickHouse is easy to containerize; Snowflake tests can be simulated with small warehouses or with test harnesses that mock response patterns for unit tests).

How to share your benchmark and get community feedback

Publish your notebook repo with a DOI (Zenodo) and invite other labs to run it against their datasets. Encourage pull requests with new dataset connectors (Qiskit exports, Rigetti formats) and standardized result schemas so results can be aggregated across institutions.

Final actionable checklist (do this this week)

1. Fork the reproducible benchmark repo in the notebook section of this article.
2. Seed a 1–10 GB realistic dataset with the included generator and test both ingestion DDLs.
3. Run the three workload patterns (scan, selective read, join) and record p50/p95/mean and rows/sec.
4. Populate the parametric cost model with your vendor pricing and compute a monthly cost for your projected experiment scale.
5. Publish a short report + results and tag community channels so others can reproduce the same runs.

Parting assessment

In 2026, both ClickHouse and Snowflake are capable platforms for quantum data — but they fit different operational profiles. ClickHouse typically wins on sustained throughput and cost for heavy scanning workloads; Snowflake shines for low-ops elasticity and multi-analyst concurrency. The only defensible decision for a research group is an evidence-based one: run the reproducible notebook with your data distribution, measure throughput and cost, and iterate on schema and partitioning.

Where to get the notebook and public datasets

The reproducible benchmark notebook and example datasets are available in the companion GitHub repo (link in the article footer). Use public sources such as IBM Qiskit exports and Zenodo archives for raw pulse traces. If you want a DOI for the exact snapshot used in a publication, export the seeded dataset and archive it on Zenodo (see publishing workflows for guidance).

Call to action

Ready to benchmark? Clone the notebook, run the three workload patterns on your dataset, and publish your results. Share your GitHub repo or DOI with the qbitshare community so we can build a cross-institution leaderboard for quantum data performance and cost. If you’d like, start by running the 1 GB quick-check in the repo and open an issue with your results — I’ll help interpret them.

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