Design Patterns for Hybrid OLAP at the Edge: ClickHouse as an IoT Analytics Backend

Hook: Why your IoT analytics pipeline is breaking — and how OLAP at the edge fixes it

If your teams struggle with high-ingest bursts from millions of sensors, data silos across cloud and edge, or unpredictable query latency for operational dashboards, you're facing a common IoT reality in 2026: the bulk of value is created where devices are, not in a single cloud bucket. Traditional time-series stores or cloud-only OLAP approaches break on throughput, cost, and latency. This article presents production-hardened architectural design patterns that use ClickHouse as an OLAP engine at the edge and in hybrid deployments to solve ingestion, downsampling, and real-time query challenges for time-series workloads.

Executive summary — what you'll learn

• Why ClickHouse is a strong choice for hybrid edge analytics in 2026.
• Four architectural patterns for hybrid OLAP with edge ClickHouse nodes and a central cluster.
• Practical ingestion, downsampling, TTL, and query strategies with code snippets.
• Operational tips for replication, cost control, and handling intermittent connectivity.
• Future-proof considerations and trends trending in late 2025–early 2026.

Why ClickHouse for edge OLAP in 2026?

ClickHouse has evolved from a cloud-centric OLAP engine to the de facto high-throughput analytics choice for real-time, time-series workloads at scale. Recent funding and ecosystem expansion (notably a major raise in late 2025) accelerated contributions around cloud-native operators, improved on-device builds, and better integrations with streaming systems like Kafka, MQTT gateways, and S3-compatible object stores.

Key technical strengths that matter for edge/hybrid deployments:

• High ingest and vectorized execution — handles millions of events/sec on modest hardware.
• MergeTree family and Aggregating engines — optimized for append-only time-series with flexible aggregation strategies.
• Materialized views & TTL — implement deterministic downsampling and retention policies efficiently at ingestion time.
• Distributed/Replicated engines — support hybrid topologies with local responsiveness and central durability.
• S3/Cloud tiering — built-in or ecosystem tooling to push cold data to object storage for cost-control.

Architectural patterns — choose the right pattern for your constraints

Below are four production patterns, each suited to different constraints around latency, connectivity, and cost. All patterns assume ClickHouse is the analytics store; the differences are about placement, sync direction, and aggregation strategies.

Pattern A — Edge-first OLAP (local hot path + async cloud sync)

Best for low-latency operational control (e.g., industrial automation, building control) where decisions must be made even when connectivity is intermittent.

• Deploy a small ClickHouse instance on each site/edge cluster (VM, container, or ARM binary) for local ingest and query.
• Use the Kafka engine or local MQTT gateways ingestion gateway writing to ClickHouse via a connector (Vector/Fluent/Telegraf).
• Materialize local aggregates (minute/hour) using MATERIALIZED VIEW to support operational dashboards.
• Asynchronously replicate summarized blocks to a central ClickHouse cluster or S3 using a compact wire format (Parquet/Arrow) when connectivity allows.

Why this pattern works: Local queries are instant, ingestion scales horizontally at each edge, and only compact summaries are pushed to central storage — saving bandwidth and cloud costs.

Pattern B — Hot/Cold hybrid (local hot ClickHouse, central cold store)

Use when you need both sub-second operational queries locally and deep historical analytics centrally.

• Keep recent data (days/weeks) in local ClickHouse instances with aggressive downsampling and retention policies.
• Push raw or semi-processed blocks older than a threshold to the central cluster or S3 using ClickHouse's table engines or a scheduled backup pipeline.
• Central cluster stores full fidelity and supports cross-site joins, ML training, and long-range analytics.

Operational levers: use TTLs to automatically move or delete aged rows, and use Distributed tables to query across both local and central data from a control plane when connectivity exists.

Pattern C — Federated OLAP (query federation + central compute)

Best when central governance and heavy compute must operate on consolidated data, but edge sites must retain autonomy for basic analytics.

• Local ClickHouse nodes store recent, site-specific data.
• Central ClickHouse cluster acts as a federated query engine using remote() functions or Distributed tables to pull summarized results for cross-site queries.
• Use server-side materialized views to pre-aggregate metrics that are frequently queried centrally.

Tradeoffs: Federation reduces data movement, but complex cross-site joins may still require transferring intermediate data when queries run centrally.

Pattern D — Edge as pre-processor for central OLAP (edge transforms, central analysis)

When central analytics teams need high-fidelity data but edge compute should compress and enrich raw telemetry first.

• At the edge, run ClickHouse for enrichment (geofencing, device joins, anomaly tagging) and then stream enriched events to central Kafka topics or object store.
• Central ClickHouse consumes enriched streams for global analytics, KPI computation, and long-term storage.

Ingestion patterns — durable, high-throughput pipelines

Designing an ingestion pipeline for millions of sensors requires both throughput and durability. Below are patterns that work reliably in hybrid topologies.

Recommended ingress pipeline components

• Edge message gateway (MQTT / CoAP) -> stream buffer (Kafka, Pulsar, or local file-backed queue)
• Stream processor / sink (Vector, Fluentd, or ClickHouse native Kafka engine)
• ClickHouse table with a MergeTree engine for raw events

ClickHouse table example for raw telemetry

CREATE TABLE telemetry_raw (
    device_id String,
    ts DateTime64(3),
    metric_name String,
    value Float64,
    tags Nested(key String, value String)
  ) ENGINE = MergeTree()
  PARTITION BY toYYYYMM(ts)
  ORDER BY (device_id, ts)
  SETTINGS index_granularity = 8192;
  

Notes:

• Partition by month (or day for very high volume) to reduce compaction impact and speed deletions.
• ORDER BY device_id, ts to optimize time-windowed queries per device.

Streaming ingestion using Kafka engine (edge-local)

CREATE TABLE kafka_telemetry (
    device_id String,
    ts DateTime64(3),
    metric_name String,
    value Float64
  ) ENGINE = Kafka SETTINGS
    kafka_broker_list = 'localhost:9092',
    kafka_topic_list = 'telemetry',
    kafka_group_name = 'ch-edge-ingest',
    format = 'JSONEachRow';

  CREATE MATERIALIZED VIEW kafka_to_raw TO telemetry_raw AS
  SELECT * FROM kafka_telemetry;
  

This approach decouples producers from ClickHouse backpressure and provides durability on the edge.

Downsampling and retention — patterns you can operationalize

Downsampling is crucial to reduce storage and central bandwidth while preserving analytic fidelity. ClickHouse gives you two complementary tools: MATERIALIZED VIEW-based pre-aggregation and TTL-based data lifecycle management.

1) Near-real-time downsampling with MATERIALIZED VIEW + AggregatingMergeTree

Use AggregatingMergeTree with aggregate states to keep incremental aggregates that are cheap to merge and re-aggregate.

-- Table to store aggregate states per minute
  CREATE TABLE telemetry_minute_agg (
    device_id String,
    minute DateTime,
    value_state AggregateFunction(avgState, Float64),
    cnt_state AggregateFunction(countState, UInt64)
  ) ENGINE = AggregatingMergeTree()
  PARTITION BY toYYYYMM(minute)
  ORDER BY (device_id, minute);

  CREATE MATERIALIZED VIEW mv_telemetry_minute TO telemetry_minute_agg AS
  SELECT
    device_id,
    toStartOfMinute(ts) AS minute,
    avgState(value) AS value_state,
    countState() AS cnt_state
  FROM telemetry_raw
  GROUP BY device_id, minute;
  

To query finalized averages:

SELECT device_id, minute, avgMerge(value_state) AS avg_value, countMerge(cnt_state) AS cnt
  FROM telemetry_minute_agg
  WHERE minute >= now() - INTERVAL 1 DAY
  GROUP BY device_id, minute
  ORDER BY device_id, minute;
  

2) TTL-based compaction / migration to cold storage

Use TTL rules to automatically migrate or delete raw rows and to create downsampled copies. Example: keep raw for 7 days, keep minute aggregates for 30 days, then move to cold object storage.

CREATE TABLE telemetry_raw (
    device_id String,
    ts DateTime,
    metric_name String,
    value Float64
  ) ENGINE = MergeTree()
  PARTITION BY toYYYYMM(ts)
  ORDER BY (device_id, ts)
  TTL ts + INTERVAL 7 DAY
  TO VOLUME 'cold' -- or TO DISK 's3' with disk configuration
  SETTINGS ...;
  

Alternatively, create a TTL expression to delete raw rows after 7 days and leave aggregated tables intact. In hybrid setups, the "TO VOLUME/TO DISK" clause can be configured to push files to an object store if the ClickHouse deployment is configured with cold storage volumes.

3) Multi-resolution retention strategy

1. Raw high-frequency (1–10s) data: keep at edge for 7–14 days.
2. Minute-level aggregates: keep on central cluster for 90 days.
3. Hourly/day summary: keep for multi-year retention in cold S3 or Parquet lake.

Query strategies — speed for operational use, depth for analytics

Design queries with the right target table (raw vs aggregated) and leverage ClickHouse features to reduce latency and compute footprint.

Tip 1 — Route user-facing dashboards to aggregated tables

Dashboards should query pre-aggregated tables where possible. Use AggregatingMergeTree and materialized views for sub-second panel loads.

Tip 2 — Use Distributed tables and sharding keys for scale

Distributed tables allow queries to fan out. Choose a shard key that aligns with common query predicates (e.g., device_id or site_id) to avoid scatter-gather on large joins.

Tip 3 — Use the appropriate JOIN strategy and avoid cross-joins

For device metadata, use ClickHouse Dictionary tables when reads are frequent and updates are rare. Dictionaries are memory-resident and extremely fast for lookups.

Tip 4 — Predictable SLAs with query timeouts and resource groups

Use ClickHouse settings to limit max_memory_usage, set query_timeouts, and assign resource groups to ensure operational queries remain responsive even during heavy analytics workloads.

Edge-to-cloud synchronization patterns

Connectivity is the primary operational constraint. Build pipelines that are resilient to outages and avoid reprocessing duplication.

• At-least-once ingestion: Use Kafka or file-backed queues at the edge and idempotent ingestion at the central side (use deduplication keys, time windows, or ReplacingMergeTree with a version field).
• Batch snapshots & incremental shipping: Export Parquet/Arrow partitions from ClickHouse using clickhouse-local or clickhouse-backup and push to S3 for central re-ingestion.
• Change Data Capture: Use Materialized Views exporting to Kafka topics for central consumption.

Example: safe, resumable export from edge to central S3

-- Create a compact export
  SELECT
    device_id, ts, metric_name, value
  FROM telemetry_raw
  WHERE ts >= toDateTime('2026-01-01 00:00:00') AND ts < toDateTime('2026-01-02 00:00:00')
  INTO OUTFILE 's3://my-bucket/edge-site-01/2026-01-01.parquet'
  FORMAT Parquet;
  

Automation tip: mark exported partitions in a local metadata table to make exports idempotent and resumable.

Operational concerns — reliability, cost, and security

In production hybrid deployments, the people and processes around the system matter as much as the architecture. Key operational recommendations:

• Replication strategy: Use ReplicatedMergeTree for local redundancy within a site and asynchronous replication to central clusters for disaster recovery. ClickHouse Keeper reduces dependency on ZooKeeper in modern deployments.
• Autoscaling and resource limits: For cloud-managed central clusters, scale compute for heavy batch windows (nightly ETL). For edge nodes, set CPU/memory limits and use ops playbooks for kernel tuning (I/O scheduler, hugepages).
• Cost control: Push cold blocks to object storage and downsample aggressively. Monitor per-site ingestion and set alarms when costs or byte rates deviate from expected ranges.
• Security: Encrypt data at rest (volume/disk-level/ S3), use mutual TLS for inter-node communication, and enable RBAC and audit logging. For device identity, integrate with hardware-backed keys or device certificates.

Case study: industrial telemetry across 120 factories (composite example)

Context: A manufacturing operator needed sub-second anomaly detection locally, cross-factory ML for yield improvement, and multi-year compliance storage. They implemented a hybrid OLAP pattern combining Pattern A and B:

• Edge sites ran ClickHouse on 4 vCPU/16GB nodes ingesting 500k events/sec per site via Kafka engine.
• Local materialized views produced minute aggregates that powered local SCADA dashboards and alert rules.
• Raw data was kept for 10 days locally, then compacted and exported as Parquet to central S3 daily. Central ClickHouse re-ingested these files for cross-factory analytics and ML training.
• Cost savings: ~70% reduction in egress and cloud storage compared to cloud-first constant replication; SLA improved for local alerting from 4s to 350ms.

2026 trends and what they mean for hybrid OLAP

Late 2025 and early 2026 accelerated a few trends that directly affect hybrid ClickHouse deployments:

• Edge-native ClickHouse builds and operators: Easier deployment on ARM and Kubernetes at the edge reduces ops friction.
• S3-native tiering and object-store optimizations: Better cold storage integrations make long retention affordable.
• Streaming ecosystem maturity: More robust connectors (Vector, Flink sinks) and better schema evolution handling simplify hybrid pipelines.
• Privacy & compliance tooling: Built-in row-level TTLs and deletion semantics help meet regulatory needs for sensor data retention and erasure.

Checklist: Implementing a hybrid ClickHouse OLAP solution

1. Identify hot/nearline/cold retention windows and required SLAs for each.
2. Select an architecture pattern above that matches your latency and connectivity constraints.
3. Design ingestion: MQTT/Kafka at the edge, local buffering, idempotent keys for deduplication.
4. Implement materialized views for minute/hour aggregates and an AggregatingMergeTree strategy.
5. Configure TTLs and cold storage volumes; automate export/import jobs with checkpointing.
6. Set resource controls, query timeouts, and monitoring for cost and performance visibility.
7. Build a recovery plan and test cross-site failover and re-ingestion workflows periodically.

Actionable code recipes you can drop into playbooks

1) Materialized view with downsampling into an AggregatingMergeTree (minute-level):

CREATE MATERIALIZED VIEW IF NOT EXISTS mv_minute_agg
  TO telemetry_minute_agg AS
  SELECT
    device_id,
    toStartOfMinute(ts) AS minute,
    avgState(value) AS value_state
  FROM telemetry_raw
  GROUP BY device_id, minute;
  

2) Use ReplacingMergeTree for idempotent ingestion if device messages carry a sequence number:

CREATE TABLE telemetry_raw_dedup (
    device_id String,
    ts DateTime64(3),
    seq UInt64,
    value Float64
  ) ENGINE = ReplacingMergeTree(seq)
  PARTITION BY toYYYYMM(ts)
  ORDER BY (device_id, ts);
  

3) Export daily partitions to S3 for central ingestion (cron | orchestrator):

SELECT *
  FROM telemetry_raw
  WHERE ts >= '{start}' AND ts < '{end}'
  INTO OUTFILE 's3://company-telemetry/edge/{site}/{start}.parquet'
  FORMAT Parquet;
  

Common pitfalls and how to avoid them

• Over-retaining raw data at the edge — increases storage and ops complexity. Apply TTLs early.
• Using wrong ORDER BY — choose an order key aligned to query patterns to avoid full merges and scans.
• Underestimating network variability — prefer asynchronous export, idempotent ingest, and resumable checkpoints.
• Not setting resource limits — heavy analytical queries can starve local operational dashboards if not isolated.

Final takeaways

Hybrid OLAP with ClickHouse is a pragmatic, high-performance model for modern IoT analytics in 2026. The combination of high-throughput ingestion engines, flexible aggregation types, and mature tiering options enables architectures that are both operationally responsive and analytically powerful. Choose a pattern that balances local decision-making needs with centralized analytics, instrument everything, and treat downsampling and TTLs as first-class citizens in your data lifecycle.

"The right balance of edge processing and central analytics reduces latency and cost while enabling richer global insights."

Call to action

If you're evaluating ClickHouse for hybrid edge analytics, start with a pilot: deploy a lightweight ClickHouse node at one site, implement the Kafka ingestion + materialized view downsampling recipe above, and run a 30-day comparison of latency, cost, and operational overhead versus your current architecture. Need a reference architecture or an audit of your proposed schema and TTL rules? Contact our engineering team for a hands-on review and a tailored pilot plan.

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