ClickHouse Unveils CoalescingMergeTree: Revolutionizing Sparse Data Handling for IoT and Beyond

Handling sparse, high-velocity data updates—like telemetry from electric vehicles or user profile enrichments—has long challenged engineers. Traditional approaches either drown databases in redundant rows or force inefficient row-by-row updates. Enter CoalescingMergeTree, ClickHouse's newest table engine, which elegantly solves this by physically consolidating sparse records during background operations while preserving data fidelity.

The Sparse Data Dilemma

Consider modern IoT ecosystems: A Tesla might emit fragmented updates—battery levels from the power system, GPS coordinates from navigation, and firmware versions from the software module—each updating independently. Storing every sparse update as separate rows in a standard MergeTree table bloats storage and slows queries, forcing complex argMax() aggregations to reconstruct the latest state:

-- Traditional argMax() for latest state (inefficient at scale)
SELECT 
  vin,
  argMax(battery_level, last_update) AS batt,
  argMax(lat, last_update) AS lat
FROM electric_vehicle_state
GROUP BY vin;

This works but becomes prohibitively expensive at trillion-row scales, requiring full scans to resolve the latest non-null values per device.

How CoalescingMergeTree Works

CoalescingMergeTree automates consolidation. When background merges occur, it collapses rows with the same sorting key (e.g., a vehicle’s VIN), retaining only the latest non-null value per column based on insertion order:

CREATE TABLE electric_vehicle_state
(
    vin String,
    battery_level Nullable(UInt8),
    lat Nullable(Float64),
    ...
)
ENGINE = CoalescingMergeTree
ORDER BY vin;  -- Sorting key for consolidation

After inserting sparse updates (e.g., battery level alone, then GPS coordinates), merges produce a single row per vehicle with all fields populated.

*Image: Sparse inserts (battery, GPS, sensors) merge into consolidated rows*

Key Advantages

Radical Storage Reduction: Fewer rows = smaller parts = faster scans.
Efficient Queries: Use FINAL for on-demand consolidation or rely on pre-merged data:
```
SELECT * FROM electric_vehicle_state FINAL; -- In-memory merge
```
Append-Only Ingestion: Avoid update contention—just insert new values. ClickHouse’s lock-free writes handle billions of events/sec.
Eventual Consistency: Trade immediate durability for ingest speed; merges catch up asynchronously.

Production Pattern: Raw + Coalesced Tables

For resilience, pair CoalescingMergeTree with a raw MergeTree table:
- Raw table: Stores every immutable event for auditing/reprocessing.
- Coalescing table: Materialized view for real-time state queries.

Beyond IoT: Universal Applications

User Profiles: Gradually enriched attributes (email, location).
Security Logs: Slowly evolving audit trails with late-arriving context.
Healthcare: Patient records updated sporadically with lab results or notes.

CoalescingMergeTree isn’t just another engine—it rethinks state management for the age of sparse, high-cardinality data. By shifting consolidation to the background, ClickHouse lets engineers focus on insights, not storage gymnastics.

Source: ClickHouse Blog

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