ClickHouse vs. Traditional Time‑Series Databases for IoT: When to Choose an OLAP Engine

Cut costs and complexity while keeping high‑fidelity IoT analytics: which engine actually wins?

Every engineering team building IoT pipelines in 2026 faces the same pressure: ingest more telemetry at higher cardinality, run complex analytics in near‑real time, and keep cloud bills and operational toil under control. The fundamental decision — use an OLAP engine like ClickHouse or a purpose‑built time‑series DB (TSDB) — shapes architecture, cost, and developer velocity. This guide gives a pragmatic decision framework, concrete examples, and operational patterns so you can choose the right tool for your IoT workload.

Executive summary — the one‑minute recommendation

Pick ClickHouse (OLAP) when: you need high‑cardinality analytics across millions of devices, complex ad‑hoc JOINs, vectorized analytics, or sub‑second analytical queries across large historical windows.

Pick a dedicated TSDB (TimescaleDB, InfluxDB, QuestDB) when: your workload is primarily metric/series monitoring, you require fast single‑point reads and built‑in retention/continuous aggregate features with minimal operational tuning.

Consider a hybrid approach when: you need operational monitoring and alerting (TSDB) plus exploratory analytics and ML (ClickHouse). In practice, many teams in 2026 are combining both to balance cost, latency, and functionality.

The 2026 landscape: why this comparison matters now

Through late 2025 and into 2026, OLAP engines like ClickHouse have accelerated adoption in analytics and real‑world telemetry. ClickHouse’s large funding round in late 2025 underscored investor confidence in columnar OLAP for high‑throughput analytics; enterprises are increasingly using it as a primary store for telemetry and observability. At the same time, hardware and storage economics shifted — rising SSD costs (a trend highlighted by semiconductor industry developments in 2025) mean storage efficiency and compression matter more than ever.

That combination — OLAP performance and tighter storage economics — explains why engineering teams reconsider the default “use a TSDB for time‑series” assumption. But that doesn’t mean OLAP always wins. The right answer depends on cardinality, query patterns, retention, and operational constraints.

When to choose ClickHouse (OLAP): practical scenarios

ClickHouse is a high‑performance columnar OLAP database optimized for analytical queries on large datasets. Choose it when your IoT workload exhibits one or more of these characteristics:

• Very high cardinality: millions of device_ids, firmware versions, nested tags, or combinatorial dimensions that make index cardinality explode.
• Analytical and ad‑hoc queries: cross‑device joins, cohort analysis, predictive feature engineering, or SQL analytics that span long retention windows.
• Batch and streaming hybrid workloads: ingesting high‑throughput streams (hundreds of thousands to millions of rows/sec) and running complex aggregations over that stream.
• Cost‑sensitive long‑term retention: requires storing months or years of raw telemetry for ML training or regulatory compliance — columnar compression and efficient storage help.

Concrete ClickHouse example: high‑cardinality telematics

Fleet telematics often has tens of millions of unique vehicle_id + sensor_type combinations. A sample table and query:

CREATE TABLE telemetry (
  timestamp DateTime64(3),
  vehicle_id UInt64,
  sensor_type String,
  value Float64,
  tags Nested(key String, value String)
) ENGINE = MergeTree()
PARTITION BY toYYYYMM(timestamp)
ORDER BY (vehicle_id, timestamp)
TTL timestamp + INTERVAL 365 DAY
SETTINGS index_granularity = 8192;

INSERT INTO telemetry (timestamp, vehicle_id, sensor_type, value) VALUES
('2026-01-18 10:00:00', 123456, 'engine_temp', 88.3);

-- Analytical query across millions of vehicles
SELECT
  vehicle_id,
  avgIf(value, sensor_type = 'engine_temp') AS avg_temp,
  maxIf(value, sensor_type = 'speed') AS max_speed
FROM telemetry
WHERE timestamp >= now() - INTERVAL 30 DAY
GROUP BY vehicle_id
ORDER BY avg_temp DESC
LIMIT 100;

ClickHouse excels here because of its columnar layout, vectorized execution, and compression for repeated string/tag values.

When to keep a dedicated TSDB

TSDBs remain the better choice when your telemetry patterns are simpler and operational concerns dominate:

• Monitoring and alerting: Prometheus/Thanos are designed for high cardinality? — actually, they’re optimized for metric series with relatively low cardinality and fast point reads for alerting.
• High write throughput with small read patterns: time‑based reads (last X minutes) or rollups for dashboards and alerts.
• Built‑in time‑series features: continuous aggregates, retention policies, downsampling, and easy integrations with metrics exporters and visualization tools.
• Developer and Ops familiarity: if your SREs already run Prometheus + Thanos or TimescaleDB for metrics, the operational cost of switching may outweigh benefits.

Timescale example — efficient continuous aggregates

TimescaleDB adds time‑series primitives to PostgreSQL, making it a pragmatic choice when you need SQL + time‑aware functions without managing a separate OLAP engine.

-- Create hypertable
CREATE TABLE sensor_metrics (
  time TIMESTAMPTZ NOT NULL,
  device_id BIGINT NOT NULL,
  metric_name TEXT NOT NULL,
  value DOUBLE PRECISION
);
SELECT create_hypertable('sensor_metrics', 'time');

-- Continuous aggregate for 1‑minute rollups
CREATE MATERIALIZED VIEW sensor_1m WITH (timescaledb.continuous) AS
SELECT time_bucket('1 minute', time) AS bucket,
       device_id,
       metric_name,
       avg(value) AS avg_val
FROM sensor_metrics
GROUP BY bucket, device_id, metric_name;

For telemetry used primarily for dashboards and alerts, continuous aggregates plus native PostgreSQL tooling are compelling.

Technical tradeoffs: cardinality, query patterns, and cost

Below are the most important technical considerations to weigh with concrete guidance.

Cardinality

Why it matters: cardinality drives index size, memory pressure, and query complexity. High cardinality across dimensions (device_id × sensor_type × firmware × region) is where OLAP shines because columnar storage compresses repeated values and avoids maintaining per‑series state.

Practical rule: if you expect millions of distinct series identifiers and need analytics across them, favor ClickHouse. If your cardinality is in the tens to low hundreds of thousands and usage is alert/read heavy, a TSDB may be simpler.

Query patterns

Ask: are most queries simple range scans and last‑value reads (TSDB) or ad‑hoc aggregations, JOINs, and multi‑dimensional analytics (OLAP)? ClickHouse supports complex SQL operations and large aggregations efficiently. TSDBs shine for predictable, time‑windowed queries.

Storage and compression

Columnar engines compress repeated values (tags, device types) very well. With increasing SSD costs, compression reduces direct storage spend and I/O. ClickHouse’s codecs and data skipping indexes often beat TSDBs on per‑TB cost when storing raw telemetry long‑term.

Note: In late 2025 the semiconductor market signaled higher SSD pricing pressure, making storage efficiency a more significant factor in architecture decisions.

Real‑time latency and ingestion

For sub‑second operational metrics and alerting, TSDBs tuned for time‑series ingestion (like InfluxDB or Prometheus push or remote_write patterns) are often easier to operate. ClickHouse can handle high ingest rates but requires batching, configuration (buffer table, Kafka/streaming pipelines), and careful schema design to get low‑latency writes.

Scalability and operations

ClickHouse can scale horizontally (sharding and replication) and is battle‑tested for petabyte datasets. But operational complexity (cluster management, replica synchronization, merges) is higher than running a single TimescaleDB instance with managed storage. Evaluate team skills and SRE capacity before committing.

Cost modeling

Build a cost model using three axes: storage, compute, and operational effort. Example considerations:

• Storage: price/TB × required retention × compression factor (ClickHouse may be 2–5× more efficient for telemetry).
• Compute: CPU for queries — OLAP queries can be CPU‑heavy if interactive analytics are common.
• Ops: time to manage, patch, upgrade, and tune. Managed TSDB services reduce ops burden.

Hybrid patterns: best of both worlds

Most real production stacks in 2026 use hybrids. Here are validated patterns:

• Hot TSDB, Cold ClickHouse: keep recent minutes/hours in a TSDB for alerts, stream to ClickHouse for analytics and long retention.
• Dual writes (not recommended without idempotency): write to both systems with deduplication or idempotency guarantees.
• Stream ETL pattern: ingest via Kafka/KSQ, write raw events to ClickHouse and emit aggregated metrics to a TSDB.
• Materialized rollup exports: compute continuous aggregates in ClickHouse and export to Prometheus/TSDB for monitoring.

Sample streaming pipeline

Architecture outline:

1. Edge devices → MQTT/HTTP → Ingestion gateway (edge filtering/transform)
2. Gateway → Kafka (topic per device class)
3. Stream processors (Flink/ksqlDB) → write raw events to ClickHouse, write aggregated metrics to a TSDB
4. Analytics + BI → ClickHouse; Dashboards + Alerts → TSDB

Decision checklist — run this before choosing

1. Estimate cardinality: expected distinct device_id × tag combinations per retention window.
2. List query types: ad‑hoc joins, cohort queries, last‑value reads, rolling aggregates, ML feature extraction.
3. Retention requirements: how long do you need raw data vs aggregated rollups?
4. SLAs: alert latency, query latency, ingestion durability.
5. Team skills: SQL/OLAP familiarity, operational bandwidth, preference for managed services.
6. Cost constraints: target monthly spend for storage and compute; include growth estimation.

If you answer “high” to cardinality, ad‑hoc analytics, and long retention — lean ClickHouse. If you answer “low” to cardinality and “high” to alerting and minimal ops — lean TSDB.

Operational recommendations and patterns (actionable)

Practical steps and configurations you can apply today.

ClickHouse tuning tips

• Use appropriate engine: MergeTree variants (Replacing/Collapsing/Replicated) depending on dedup and replication needs.
• Partitioning: partition by month or day depending on your query window and TTLs to speed deletes/TTL merges.
• ORDER BY and primary key: choose columns that optimize your most common GROUP BY and WHERE filters (device_id, timestamp or sensor_type).
• TTL and compression: set TTL for raw data and compress older partitions; choose compression codecs for numeric vs string columns.
• Use materialized views: pre‑aggregate heavy queries into summarized tables for interactive dashboards.
• Batch ingestion: insert in micro‑batches or via Kafka ingestion to reduce merge churn and latency.

TSDB best practices

• Continuous aggregates and retention: use continuous aggregates (Timescale) or downsampling (InfluxDB) to cap storage.
• Cardinality caps: enforce tag normalization and cardinality caps at the ingestion boundary to prevent blowups.
• Sharding and HA: use Thanos/Prometheus federation for large clusters and long retention.

Mini case studies — patterns I’ve seen work

1) Connected fleet operator (millions of vehicles)

Problem: need per‑vehicle analytics, firmware regression analysis, and anomaly detection across 3 years of raw telemetry.

Solution: raw telemetry → ClickHouse for storage and analytics; Prometheus/Thanos for operational metrics. Result: faster ML feature engineering, 3× storage reduction vs naive TSDB approach, and sub‑second analytics for rollup dashboards.

2) Industrial sensors for factory floors

Problem: real‑time control loops require low latency plus long retention for traceability.

Solution: Edge gateway does control decisioning, pushes metrics to InfluxDB for alerts, replicates raw traces to ClickHouse for forensic analytics. Result: reliable control loops and fast forensic queries.

3) SaaS IoT platform with mixed customers

Problem: multi‑tenant ingestion with variable cardinality and unpredictable query patterns.

Solution: Multi‑tenant ClickHouse with tenant_id partitioning, per‑customer retention policies and separate TSDB for SaaS metrics. Result: predictable cost per tenant and flexible analytics SLAs.

Future predictions — what to expect in 2026 and beyond

Expect more convergence: OLAP engines will continue adding time‑series specific features (compression codecs optimized for time data, native TTLs and downsampling), and TSDBs will improve SQL capabilities. The big trend is practical hybridization — orchestration layers and managed services will make it easier to route data between systems automatically based on access patterns.

Also expect storage economics to remain important. With SSD pricing under pressure in late 2025 and into 2026, compression and filterable storage will be decisive for architectures that keep long windows of raw telemetry.

Key takeaways

• Match tool to workload: high‑cardinality, ad‑hoc analytics → ClickHouse; monitoring and low‑latency single‑series reads → TSDB.
• Hybrid is pragmatic: many teams run both — TSDB for hot alerting, ClickHouse for analytics and long retention.
• Plan for growth: model cardinality, retention, and query patterns before committing.
• Optimize for cost: factor compression and storage trends into your TCO — 2026 hardware trends favor solutions that minimize raw storage.
• Operational readiness matters: ClickHouse offers scale and performance but requires operational expertise; managed services can reduce that gap.

Next steps — a pragmatic implementation checklist

1. Run a 30‑day pilot: pick representative telemetry, sample expected cardinality, and run both ClickHouse and a TSDB to measure ingestion, query latency, and storage.
2. Implement retention policies and downsampling in both systems and measure cost delta.
3. Automate the routing: use Kafka + stream processors to write to both places or to switch hot/cold paths dynamically.
4. Establish SRE playbooks: TTL enforcement, compaction/merge tuning, backup and restore drills.

Call to action

If you’re evaluating ClickHouse vs TSDB for an upcoming IoT project, run the pilot checklist above and capture cardinality and query shape metrics. Need help designing the pilot, modeling costs, or implementing the hybrid pipeline? Contact our engineering team at realworld.cloud for a 2‑week assessment and an architecture blueprint tailored to your telemetry profile.

Related Reading

• Hiring Data Engineers in a ClickHouse World: Interview Kits and Skill Tests
• Preparing for Hardware Price Shocks: What SK Hynix’s Innovations Mean for Remote Monitoring Storage Costs
• Hybrid Studio Ops 2026: Low‑Latency Capture, Edge Encoding, and Streamer‑Grade Monitoring
• Composable UX Pipelines for Edge‑Ready Microapps: Advanced Strategies and Predictions for 2026
• Universes Beyond: How Crossovers Like Fallout and TMNT Are Shaping MTG Collections
• Curated Keepsakes for Tech Lovers: From Smart Lamps to Custom Watch Bands
• SRE Playbook: Instrumenting Sites for Campaign-Driven Traffic and Cost Efficiency
• Integrating WCET and Timing Analysis into CI/CD for Embedded Software
• How to Curate a Limited-Run 'Bridge at Dusk' Home Ambience Box