Leveraging ClickHouse for High-Throughput Quantum Experiment Telemetry

Hook: When every shot matters — telemetry that keeps up with quantum experiments

ClickHouse labs run at a pace classical observability stacks weren’t designed for. You’re juggling millisecond-granularity experiment logging, bursts of tens or hundreds of thousands of events per second, and the need to run ad-hoc analytical queries to debug a failed calibration or a drifting qubit. The result: blind spots, slow queries, and frustrated teams. This guide shows how to architect a ClickHouse-backed telemetry pipeline that meets the throughput, latency, and analytical requirements of modern quantum experiments in 2026.

Why ClickHouse for quantum telemetry in 2026?

ClickHouse is now a mainstream choice for high-throughput, time-series and observability workloads — adoption has accelerated since major funding and product investments in 2024–2025. For quantum labs, ClickHouse brings three practical advantages:

• High ingest throughput: columnar storage and vectorized execution handle millions of rows per second per cluster when architected correctly.
• Low-latency analytical queries: sub-second aggregation and filter queries across large windows make interactive debugging feasible.
• Flexible integrations: native Kafka engine, S3/MinIO object store support, and cloud-managed ClickHouse offerings simplify deployment and tiering.

Core design principles for a quantum telemetry pipeline

Design decisions should align with the unique telemetry characteristics of quantum experiments:

• Events are small, high-rate, and timestamp-critical: store per-shot metadata and events with micro- or millisecond timestamps.
• Large binary artifacts belong off-cluster: IQ traces and waveform dumps can be stored in object storage (S3/MinIO) and referenced from ClickHouse.
• Narrow writes, wide reads: optimized write path for high ingest and pre-aggregated materialized views for interactive queries.
• Idempotency and burst buffering: use Kafka, Buffer or intermediate services to smooth bursts and enable retries without duplication.

Reference architecture — components and flow

The example architecture below balances throughput, durability, and analytics speed.

1. Quantum control system / RTOS (QCoDeS, Qiskit Pulse-based controllers) emits telemetry packets.
2. A lightweight collector (Go/Rust) batches and compresses records, pushes to Apache Kafka (or Pulsar).
3. ClickHouse Kafka engine consumes topics and writes to an internal staging MergeTree via a MATERIALIZED VIEW.
4. Large binary payloads (IQ waveforms, oscilloscope dumps) are stored on S3; ClickHouse stores object pointers.
5. Materialized views produce real-time rollups: per-experiment throughput, latencies, error rates, and per-qubit metrics.
6. Visualization: Grafana (ClickHouse plugin) and ad-hoc SQL for debugging. Alerts via Prometheus metrics exported from ClickHouse or the collector.

Why Kafka (or a streaming layer)?

Kafka acts as an elastic buffer: it decouples bursty experiment logging from ClickHouse ingestion, provides durable retention for reprocessing, and supports multiple consumers (analytics, archivers). In practice, the ClickHouse Kafka engine + materialized view pattern gives near-real-time ingestion with backpressuring handled by Kafka.

Schema design patterns for telemetry

Two complementary tables typically suffice: a high-throughput events table and a set of aggregates for interactive diagnostics.

Events table (raw, high-throughput)

Store one row per event or shot. Keep it narrow and typed strictly for performance.

CREATE TABLE telemetry.events
  (
    experiment_id String,
    shot_id UInt64,
    node_id String,
    channel_id String,
    event_time DateTime64(6), -- microsecond precision
    ingest_time DateTime64(6) DEFAULT now64(6),
    status UInt8, -- 0 success, 1 warning, 2 error
    readout_value Float32,
    tags Array(String),
    payload_s3_path String
  )
  ENGINE = ReplicatedMergeTree('/clickhouse/tables/{shard}/telemetry/events', '{replica}')
  PARTITION BY toYYYYMMDD(event_time)
  ORDER BY (experiment_id, node_id, event_time)
  SETTINGS index_granularity = 8192;
  

Key notes:

• Use DateTime64(6) or (9) depending on timestamp resolution requirement.
• Partition by day for predictable partition sizes; tune if you have very large or very small clusters.
• ORDER BY fields affect data locality and query speed. Put experiment_id and node_id first if most queries filter on them.
• Keep binary blobs out of the table — use payload_s3_path pointers.
• Use ReplicatedMergeTree for production clusters for durability.

Aggregates and rollups

Create materialized views that compute rollups at the tempo you need for debugging — per-second, per-minute, per-experiment summaries. These serve interactive dashboards and greatly reduce latency for common queries.

CREATE MATERIALIZED VIEW telemetry.mv_per_sec
  TO telemetry.events_1s
  AS
  SELECT
    experiment_id,
    node_id,
    toStartOfSecond(event_time) AS ts,
    count() AS shots,
    avg(readout_value) AS avg_readout,
    max(readout_value) AS max_readout,
    sum(status != 0) AS errors
  FROM telemetry.events
  GROUP BY experiment_id, node_id, ts;
  

Combine these rollups with retention rules to keep hot indexes small and fast.

Ingest strategies and operational tuning

ClickHouse ripples in performance based on insert patterns. Follow these practical rules:

• Batch inserts: Aim for 1k–50k rows per batch, 1–4 MB per insert is a useful rule of thumb. Too-small inserts increase CPU overhead.
• Use the Kafka engine + MATERIALIZED VIEW: This lets Kafka handle peak bursts and consumer lag. The view can transform/parsing JSON/Protobuf into typed columns.
• Buffer engine: If you need a simple buffer inside ClickHouse to absorb spikes, the Buffer engine can help, but Kafka is preferable for durability.
• Native client vs HTTP: Native TCP protocol is fastest for bulk inserts; the HTTP interface is simpler but can be slower. Many labs use gRPC services to batch and forward events.
• Avoid frequent ALTERs: Schema changes are expensive at scale; plan columns carefully and use JSON columns for ad-hoc fields sparingly.

Idempotency and deduplication

Retries are inevitable. Options:

• Producer-side deduplication using unique shot_id and at-least-once semantics.
• ReplacingMergeTree with version column when you must accept de-duplicates and latest-wins semantics.
• Use Kafka with exactly-once producers to reduce duplicates.

Storage tiering and retention — manage cost without losing fidelity

Quantum experiments generate high-volume telemetry. Use a tiered retention plan:

• Hot tier (ClickHouse MergeTree): keep high-resolution per-shot rows for the last 7–30 days depending on capacity.
• Warm tier (compressed/longer-term ClickHouse partitions or cheaper nodes): keep hourly/minute rollups for 90–180 days.
• Cold tier (S3/object storage): move raw payloads and older partitions to S3 using ClickHouse's object storage integrations and explicit partition MOVE/ATTACH, or use backups/archival jobs.

Implement TTL policies to automate ageing:

ALTER TABLE telemetry.events
  MODIFY TTL event_time + toIntervalDay(30) TO VOLUME 'cold_storage';
  

Note: the exact syntax and capabilities for automatic tier movement depend on your ClickHouse version and deployment (self-hosted vs ClickHouse Cloud). As of 2026, cloud providers and ClickHouse releases have matured tiering capabilities, making S3-based colder tiers easier to operate.

Query patterns and performance tips for debugging

Debug workflows often need joins across experiment metadata, calibration records, and events. Keep these patterns fast:

• Pre-join or denormalize: include common metadata (e.g., qubit_id, pulse_config_id) in the events table to avoid expensive joins at query time.
• Use low_cardinality(String) for high-cardinality strings: it reduces in-memory footprint for joins and group-bys.
• Avoid large cross joins: push filters early and use EXISTS/ANY semantics or 'JOIN USING' with pre-aggregated small lookup tables.
• Approximate functions for high-cardinality metrics: uniqCombined/uniqExact tradeoffs; for telemetry dashboards, approximate counts are usually acceptable and faster.

Example diagnostic queries

-- 1) Per-instrument shot throughput and error rate in the last minute
  SELECT node_id, count() AS shots, sum(status != 0) AS errors
  FROM telemetry.events
  WHERE event_time >= now64(6) - INTERVAL 1 MINUTE
  GROUP BY node_id
  ORDER BY shots DESC;

  -- 2) Distribution of readout_value for a qubit over the last hour
  SELECT quantiles(0.5,0.9,0.99)(readout_value) AS q
  FROM telemetry.events
  WHERE experiment_id = 'exp-20260115' AND channel_id = 'q1' AND event_time >= now64(6) - INTERVAL 1 HOUR;
  

Handling large waveform payloads

IQ traces and raw waveforms can be huge and expensive to store in ClickHouse. Recommended approach:

1. Persist binary payloads to object store (S3/MinIO) with a consistent naming scheme and compressed format (ZSTD).
2. Store metadata (S3 path, length, checksum, sample_rate) in ClickHouse so you can query and retrieve the object only when needed.
3. Optionally create a lightweight index table that maps shot_id <-> payload path for quick retrieval during debugging.

Observability, monitoring and alerting

Monitor both the telemetry pipeline and the ClickHouse cluster:

• Export ClickHouse metrics (query latency,_insert rates, merge queue size) to Prometheus and visualize in Grafana.
• Instrument collector and Kafka with application metrics (lag, producer errors, pacing) and alerts for consumer lag > threshold.
• Track business/experiment-level SLOs: e.g., 99th percentile time to ingest & availability of last N minutes of data.

Security, governance and compliance

Secure telemetry data:

• Use TLS for all client-to-ClickHouse and inter-node communication.
• Implement RBAC and restrict write privileges to collectors only.
• Encrypt sensitive payloads at rest on object storage and control key access.
• Audit logs for experiment access — ClickHouse audit plugins and cloud provider logs can be integrated into your SIEM.

2026 trends and future-proofing your design

As of 2026, several trends matter for lab architects:

• ClickHouse Cloud maturation: managed ClickHouse services offer built-in S3 tiering, autoscaling ingest and simplified multi-region replication — useful for distributed labs.
• Streaming-first observability: tighter coupling between Kafka/streaming layers and OLAP engines reduces ingestion latency — plan for streaming-first topologies.
• Edge collectors in Rust/Go: lower-latency, low-overhead collectors that batch into Kafka are increasingly standard in lab automation stacks.
• Hybrid analytics: expect more integrations between vectorized query engines and ML libraries for anomaly detection on telemetry streams (i.e., model scoring in SQL or UDFs). See work on cloud-native orchestration and model pipelines.

"Build for bursts, query for agility." — practical mantra for quantum telemetry architectures in 2026.

Operational checklist — quick wins you can implement this week

1. Switch per-shot timestamps to DateTime64(6) if you still use DateTime or second resolution.
2. Batch writes in your collector: target 1–4 MB per insert, 1k–50k rows per batch.
3. Push large binary payloads to S3 and store pointers in ClickHouse.
4. Deploy a Kafka topic per experiment or per instrument family to minimize consumer contention.
5. Create materialized views for 1s/1m rollups used by dashboards to keep interactive queries fast.
6. Enable Prometheus metrics and alert on ClickHouse merge queue length and Kafka consumer lag.

Example end-to-end snippet

Minimal flow to get from experiment to ClickHouse using Kafka:

# 1) Collector batches JSON and posts to Kafka topic: telemetry.events
# 2) ClickHouse table that consumes Kafka
CREATE TABLE kafka.telemetry_raw (
  key String,
  value String
) ENGINE = Kafka SETTINGS kafka_broker_list = 'kafka:9092', kafka_topic_list = 'telemetry.events', kafka_group_name = 'ch-consumer', kafka_format = 'JSONEachRow';

CREATE TABLE telemetry.events (
  experiment_id String,
  shot_id UInt64,
  node_id String,
  event_time DateTime64(6),
  readout_value Float32
) ENGINE = MergeTree() PARTITION BY toYYYYMMDD(event_time) ORDER BY (experiment_id, event_time);

CREATE MATERIALIZED VIEW kafka_to_events TO telemetry.events AS
SELECT
  JSONExtractString(value, 'experiment_id') AS experiment_id,
  JSONExtractUInt(value, 'shot_id') AS shot_id,
  JSONExtractString(value, 'node_id') AS node_id,
  parseDateTime64BestEffort(JSONExtractString(value, 'event_time')) AS event_time,
  JSONExtractFloat(value, 'readout_value') AS readout_value
FROM kafka.telemetry_raw;
  

This is intentionally minimal — production pipelines add validation, enrichment, and deduplication steps.

Final notes and pitfalls to avoid

Common mistakes that cause pain:

• Storing raw binary waveforms in ClickHouse — quickly consumes disk and slows merges.
• Using second-resolution timestamps — loses fidelity and complicates debugging.
• Unbounded partitions or tiny partitions — leads to many small files and degraded merges.
• Unmonitored Kafka lag — leads to silent data loss from late consumers or backlogs.

Actionable takeaways

• Adopt DateTime64 for timestamps to preserve millisecond/microsecond fidelity.
• Use Kafka + ClickHouse Kafka engine to absorb bursts and enable reprocessing.
• Keep ClickHouse tables narrow — store large payloads in S3 and reference them.
• Create materialized views for the dashboard rollups your team queries frequently.
• Plan tiered retention and monitor merge/ingest metrics to keep queries fast and costs predictable.

Call to action

Ready to build a production-ready ClickHouse telemetry pipeline for your quantum lab? Start with the operational checklist above. If you want hands-on help, clone our reference repo (example collectors, Kafka configs, ClickHouse DDLs and Grafana dashboards), or reach out to the askqbit team for an architecture review tailored to your throughput and retention targets.

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