Materialized Views in ClickHouse: Benefits, Limitations, and Alternatives

Data engineers constantly face the challenge of transforming raw data into actionable insights efficiently. In high-performance analytical systems like ClickHouse, where query speed is paramount, executing complex transformations or aggregations repeatedly on massive datasets can become a bottleneck. Precomputing results is a common strategy to address this, and Materialized Views are a feature often considered for this purpose. Within the ClickHouse ecosystem, they offer a specific approach to automating certain data transformations upon ingestion.

What Are Materialized Views in ClickHouse?

At its core, a Materialized View stores the result of a query, persisting it physically like a regular table. This pre-computation allows queries against the view to be significantly faster as the complex calculation or join has already been done.

How they differ from regular views:

• Regular View (Normal View): A stored query definition. When you query a regular view, the underlying query runs against the source tables at that moment. It's just syntactic sugar, offering no inherent performance gain beyond query simplification.
• Materialized View (in ClickHouse): Stores the result of its defining query. Querying the materialized view reads precomputed data.

Materialized Views vs. Tables in ClickHouse:

This is where ClickHouse's implementation is crucial. A ClickHouse Materialized View isn't queried directly in the traditional sense. Instead, it acts like an INSERT trigger on a source table.

1. Data is INSERTed into the Source Table.
2. The Materialized View definition (a SELECT query) runs on this incoming batch of data.
3. The result of this query is automatically INSERTed into a separate Target Table.

So, conceptually, a ClickHouse materialized view connects a source table, a transformation query, and a target table.

Why Use Materialized Views?

The primary driver for using materialized views in ClickHouse is performance and efficiency, achieved through pre-computation triggered on data ingestion.

• Performance Benefits: Querying the (often aggregated or filtered) target table is much faster than running the complex transformation on the raw source table every time.
• Resource Optimization: Reduces read amplification and computation overhead during query time, shifting some load to insertion time.
• Near Real-time Transformation: As data flows into the source table, it's automatically transformed and made available in the target table with minimal delay (insert latency).
• Simplified Query Complexity: End users or applications can query the simpler, pre-aggregated target table, hiding the complexity of the underlying transformation logic.
• Cost Efficiency: For cloud deployments where compute/scan costs matter, querying smaller, pre-aggregated target tables can be cheaper than repeatedly scanning large source tables.

How Materialized Views Work in ClickHouse

As mentioned, ClickHouse materialized views are essentially INSERT triggers.

• Implementation: When you CREATE MATERIALIZED VIEW materialized view_name TO target_table AS SELECT ... FROM source_table, ClickHouse creates a hidden trigger on source_table. Each INSERT into source_table activates this trigger, which executes the SELECT query over the newly inserted block of data and inserts the result into target_table.
• Refresh Mechanisms:
  • Automatic (on Insert): The primary mechanism. Triggered by INSERTs.
  • Manual Population (POPULATE): materialized views only process new data inserted after their creation. To process existing data in the source table, you must use the POPULATE keyword during creation (CREATE MATERIALIZED VIEW ... POPULATE AS ...) or run INSERT INTO target_table SELECT ... FROM source_table manually later. POPULATE can be resource-intensive for large source tables.
• Relationship with Source Data: The materialized view trigger only reacts to INSERT. It does not react to UPDATE or DELETE operations on the source table. Data in the target table reflects the state of the transformed source data at the moment it was inserted.

Example: Using Materialized Views in ClickHouse

Let's create a simple materialized view to aggregate event counts per minute.

1. Create Source Table:

2. Create Target Table (for materialized view results): It's best practice to use an aggregating engine if your materialized view performs aggregation.

3. Create Materialized View: This connects the source, the query, and the target.

4. Insert Data into Source Table:

5. Query the Target Table (NOT the materialized view name): Use finalizeAggregation or SimpleAggregateFunction in the target table definition for simpler querying. Here we use finalizeAggregation.

Best Practices for Materialized Views

• Design First: Plan your target schema carefully. Consider aggregation granularity and the query patterns it needs to support.
• Use Appropriate Engines: Use AggregatingMergeTree or SummingMergeTree for the target table when doing aggregations to leverage ClickHouse's optimizations.
• Monitor Target Table: Keep an eye on the size and query performance of the target table. It is a real table consuming storage and resources.
• Test Thoroughly: Ensure the materialized view logic correctly transforms the data as expected under various load conditions.
• Idempotency: If possible, design insert processes and materialized view logic so that re-inserting the same source data doesn't corrupt the target table state (often tricky, depends on target engine).
• Consider POPULATE Impact: Understand that POPULATE is a one-time, potentially heavy operation. Plan for it during maintenance or initial setup.

When Not to Use Materialized Views

Materialized views are not a silver bullet. Avoid them when:

• Source Data Requires Updates/Deletes: materialized views don't automatically reflect these changes. This is a major limitation.
• Complex Multi-Stage Transformations: materialized views execute a single SELECT. Complex logic involving multiple steps or dependencies is better handled externally.
• High Cardinality Aggregations: Grouping by columns with very high unique values can cause the target table to become enormous, negating performance benefits.
• Need for Backfills/Recomputation: Changing the materialized view logic requires dropping/recreating the materialized view and the target table, often involving a complex and slow backfill process from the original source data.
• Insert Performance is Paramount: The materialized view query runs synchronously during the INSERT process. A complex or slow materialized view query can significantly slow down data ingestion into the source table.
• Handling Streaming Data: This is a critical anti-pattern for ClickHouse materialized views. Here’s why:
  • The Streaming Challenge: Streaming data often arrives in frequent, small batches (sometimes even row-by-row if not buffered properly). ClickHouse, however, performs best when ingesting data in larger batches (thousands or tens of thousands of rows at once)
  • Synchronous Materialized View Overhead: The materialized views’ transformation query runs synchronously within the INSERT transaction for every single batch.
  • High Cost per Small Batch: When batches are small, the fixed overhead of triggering the MV, executing its query (even if simple), and writing to the target table becomes disproportionately large compared to the amount of data processed. This overhead is incurred repeatedly for each small streaming batch.
  • Drastic Throughput Reduction: The materialized view execution becomes a significant bottleneck. The time taken by the materialized view query directly adds to the latency of inserting data into the source table. This can dramatically reduce the overall ingestion throughput, preventing ClickHouse from keeping up with a high-velocity stream (e.g., reducing ingestion from 100k rows/sec to potentially 10k rows/sec or less, depending on materialized view complexity and batch size).
  • Increased Insert Latency: Individual inserts take much longer, impacting upstream systems and delaying data visibility even in the source table. Latency can jump from milliseconds to hundreds of milliseconds or even seconds per batch.
  • Resource Contention & Cost: Frequent materialized view executions consume significant CPU, memory, and I/O, especially if the materialized view query is non-trivial (e.g., uses complex functions like uniqCombined, performs joins, or aggregates over large windows). This leads to:
    • Higher Compute Costs: Needing larger or more ClickHouse nodes to handle the combined load of inserts and synchronous materialized view processing.
    • System Instability: Potential for resource starvation impacting other queries or essential background tasks like data merging in MergeTree tables.
    • Operational Cost: Requires constant monitoring of insert performance and potentially complex upstream buffering strategies to create larger batches before insertion, adding complexity to the pipeline.
  • Target Table Fragmentation: Frequent small inserts into the materialized view's target table can create many small data parts, increasing the load on background merges and potentially slowing down queries against the target table until merges consolidate the parts.

Deep Dive: Limitations of Materialized Views in ClickHouse

This is where understanding the trade-offs is critical:

• Refresh & Staleness: Only refresh on INSERT. No periodic refresh, no reaction to source updates/deletes. Data in the target table can become stale if the source changes without new inserts.
• Update/Delete Behavior: This is the most significant limitation. Changes in the source table are not propagated. Workarounds (e.g., using ReplacingMergeTree in the target table and carefully managing inserts) are complex and often brittle.
• Consistency: Eventual consistency based on inserts flowing through the source. There's no transactional guarantee between source and target across multiple inserts or failures.
• Resource Utilization: Doubles the write amplification (write to source + write to target). The materialized view query consumes CPU/memory during inserts. Target tables consume storage.
• Complex Transformations: Limited to what a single SELECT statement can achieve efficiently. Chaining materialized views is possible but adds complexity and potential failure points.
• Schema Evolution: Changing the materialized view logic or source/target table schemas is cumbersome. Typically requires:
  1. Stopping inserts (optional but safer).
  2. Dropping the materialized view .
  3. Altering source/target tables (if needed).
  4. Recreating the materialized view (potentially with POPULATE or manual backfill).
  5. Resuming inserts.

Limitations Summary Table:

Real-World Use Case: When Materialized Views Fall Short

Scenario: An IoT platform receives millions of events per minute from devices. They need a dashboard showing the count of distinct active devices sending data each second.

Attempt:

1. Source table iot_events (timestamp DateTime, device_id String, payload String).
2. Target table distinct_devices_per_second (second DateTime, distinct_devices AggregateFunction(uniqCombined, String)) using AggregatingMergeTree.
3. Materialized view CREATE MATERIALIZED VIEW devices_materialized view TO distinct_devices_per_second AS SELECT toStartOfSecond(timestamp) as second, uniqCombinedState(device_id) FROM iot_events GROUP BY second;

Problem Encountered:

• The INSERT rate into iot_events becomes severely throttled.
• CPU usage spikes during ingestion periods.
• Dashboard queries on distinct_devices_per_second are fast if the data gets there, but ingestion lag means the dashboard isn't real-time.

Root Cause Analysis:

• Calculating uniqCombinedState (even the intermediate state) is computationally more expensive than a simple countState.
• Running this aggregation synchronously for every micro-batch inserted into iot_events creates a bottleneck. The materialized view trigger processing time adds directly to the INSERT latency.
• At millions of events per minute, the overhead becomes unsustainable, limiting overall ingestion throughput.

Lessons Learned: ClickHouse materialized views are powerful for simple aggregations/filtering on append-only data, but heavy computations (like high-cardinality distinct counts) within the materialized view trigger can cripple insert performance, making them unsuitable for high-throughput, low-latency ingestion pipelines requiring complex aggregation.

Alternative Solutions

When ClickHouse materialized views aren't the right fit, especially due to update/delete requirements, complex transformation, or insert performance, you can consider using external stream processing like GlassFlow.

Using an external stream processing tool like GlassFlow alongside ClickHouse offers a significant advantage: it enables a clear separation of concerns. GlassFlow can handle the complex, stream processing logic – such as real-time transformations, data enrichment, filtering, complex event processing, or aggregations – before the data even reaches ClickHouse. This pre-processing means ClickHouse receives data that is already cleaned, structured, and potentially aggregated, allowing it to focus entirely on its core strength: incredibly fast ingestion and analytical querying (OLAP) on large datasets. This offloading reduces the processing burden on ClickHouse, potentially improving its ingestion performance and query speeds, while allowing for more sophisticated real-time data manipulation than might be practical solely within ClickHouse itself.

Conclusion

ClickHouse Materialized Views are a specialized tool, acting as INSERT triggers that populate target tables with transformed data. They excel at accelerating queries on append-only data streams requiring simple aggregations or filtering, offering performance gains by precomputing results during ingestion.

However, their synchronous nature, inability to handle updates/deletes automatically, and the complexities of schema evolution and backfills are significant limitations. Their synchronous processing, triggered on every insert batch, can become a major bottleneck, particularly for streaming data where insert performance is paramount and batches may be small. This often makes materialized views unsuitable for demanding real-time pipelines. They are not a general-purpose solution for all data transformation needs within ClickHouse.

For handling streaming data, performing complex real-time transformations, or managing updates/deletes effectively, strongly consider an external stream processing system like GlassFlow which decouple processing from processing from ClickHouse ingestion.

Did we make you curious? Would you like to know more about Glassflow and try it?

Notify me when repo is available

Resources

• ClickHouse Official Documentation: CREATE MATERIALIZED VIEW
• ClickHouse Official Documentation: AggregatingMergeTree
• ClickHouse Official Documentation: Working with Materialized Views