Kusto Query Language (KQL) Materialized Views for Performance Optimization

Explore how to create and query materialized views with aggregation policies to optimize performance for large datasets

Kusto Query Language (KQL) is a powerful tool for querying and analyzing large datasets in Azure Data Explorer (ADX). One of its standout features for performance optimization is materialized views, which allow pre-computed aggregations to significantly reduce query execution times, especially for high-volume logs and streaming data scenarios. However, designing and leveraging materialized views effectively—particularly with real-time update policies and optimized query patterns—remains a complex and under-discussed topic. In this blog post, we’ll explore how to create and query materialized views with aggregation policies to optimize performance for large datasets.

What Are Materialized Views in KQL?

Materialized views in KQL are pre-computed, persisted query results that store aggregated or transformed data from a source table. Unlike regular views, which are virtual and computed on-the-fly, materialized views physically store the results, making them ideal for scenarios where you need to repeatedly query aggregated data over large datasets. They are particularly useful for:

• Reducing scan times by avoiding repetitive calculations on raw data.

• Enabling real-time analytics on streaming data with minimal latency.

• Simplifying complex queries by pre-aggregating data.

Materialized views are updated automatically based on an update policy, which defines how and when the view is refreshed with new data from the source table.

Designing Update Policies for Real-Time Materialized Views

In streaming data scenarios, where data arrives continuously (e.g., IoT telemetry, application logs, or event streams), materialized views must balance performance with data freshness. The update policy determines how the view processes incoming data, and designing it correctly is critical for real-time analytics.

Key Considerations for Update Policies

1. Source Table and Ingestion:

   • Materialized views are tied to a single source table. Ensure the source table is optimized for ingestion (e.g., using partitioning or sharding for high-volume data).

   • For streaming data, use ingestion methods like Event Hubs or Kafka to ensure low-latency data arrival.

2. Update Frequency:

   • The update policy defines a time window for processing new data (e.g., every 5 minutes). Smaller windows improve data freshness but increase computational overhead.

   • Example: For a materialized view summarizing IoT sensor data, you might set a 1-minute window to ensure near-real-time insights.

3. Aggregation Scope:

   • Specify which columns to aggregate (e.g., sum, count, avg) and how to group them (e.g., by device ID or time bin).

   • Use materialize() to define the aggregation logic, ensuring it aligns with your query patterns.

4. Backfill Strategy:

   • For historical data, configure the update policy to backfill the materialized view with existing data. This is crucial when initializing a view or recovering from failures.

   • Example: Set BackfillFrom to a specific timestamp to process historical data up to the current time.

Example: Creating a Materialized View with an Update Policy

Suppose you have a source table SensorData with columns Timestamp, DeviceId, Temperature, and Humidity. You want to create a materialized view that computes the average temperature per device every 5 minutes for real-time monitoring.

.create materialized-view SensorAvgTemps on table SensorData
{
    SensorData
    | summarize AvgTemp = avg(Temperature) by DeviceId, bin(Timestamp, 5m)
}
with (
    updateInterval = 5m,
    backfillFrom = datetime(2025-09-01),
    updateMode = ‘Incremental’
)

• updateInterval: Refreshes the view every 5 minutes.

• backfillFrom: Processes historical data starting from September 1, 2025.

• updateMode: Incremental ensures only new data is processed, reducing overhead.

This materialized view pre-computes the average temperature, allowing queries to run against a smaller, aggregated dataset instead of scanning the entire SensorData table.

Best Practices for Update Policies

• Minimize Update Frequency: Balance freshness with performance. For example, a 1-minute interval may be too aggressive for low-priority analytics.

• Monitor Resource Usage: Frequent updates on large datasets can strain cluster resources. Use .show materialized-view to monitor performance and adjust as needed.

• Handle Data Gaps: Use lookback in the update policy to reprocess data if ingestion delays occur (e.g., lookback = 1h to account for late-arriving data).

Query Patterns to Leverage Materialized Views

Materialized views shine when you optimize query patterns to take advantage of their pre-aggregated data. Below are strategies to reduce scan times in high-volume log scenarios, such as analyzing application logs or telemetry data.

1. Query the Materialized View Directly

Instead of querying the raw source table, target the materialized view to avoid scanning large datasets. For example, to get the average temperature per device from the SensorAvgTemps view:

SensorAvgTemps
| where Timestamp > ago(1h)
| summarize MaxAvgTemp = max(AvgTemp) by DeviceId

This query scans the pre-aggregated view, which is significantly smaller than the raw SensorData table, reducing execution time.

2. Use Time Filters Efficiently

Materialized views are optimized for time-based queries. Always include a time filter (e.g., where Timestamp > ago(1d)) to limit the data scanned, as materialized views are often partitioned by time.

SensorAvgTemps
| where Timestamp between (datetime(2025-09-24 00:00:00) .. datetime(2025-09-24 23:59:59))
| summarize AvgTemp = avg(AvgTemp) by DeviceId

3. Combine with Joins for Complex Analysis

If you need to combine aggregated data with other tables, use the materialized view in joins to minimize the data processed. For example, joining SensorAvgTemps with a DeviceMetadata table:

SensorAvgTemps
| join kind=inner DeviceMetadata on DeviceId
| where AvgTemp > 30
| project DeviceId, DeviceName, AvgTemp

This query leverages the pre-aggregated view to reduce the join’s computational cost.

4. Avoid Over-Aggregation

While materialized views pre-aggregate data, avoid redundant aggregations in your queries. For instance, if the view already computes avg(Temperature), don’t recompute it unless necessary.

5. Monitor Query Performance

Use .show queries to analyze query execution times and verify that the materialized view is reducing scan times. If performance is suboptimal, check the view’s aggregation logic or update policy.

Challenges and Limitations

While materialized views are powerful, they come with challenges:

• Storage Overhead: Materialized views store pre-computed data, increasing storage costs. Ensure the view’s scope is focused to avoid unnecessary data retention.

• Update Latency: Even with real-time policies, there may be a slight delay (seconds to minutes) between data ingestion and view updates.

• Complexity in Maintenance: Changing the aggregation logic or update policy requires recreating the view, which can be disruptive for large datasets.

TLDR

Materialized views in KQL are a game-changer for optimizing performance in large-scale, high-volume data scenarios. By carefully designing update policies for real-time streaming data and leveraging query patterns that target pre-aggregated views, you can significantly reduce scan times and improve query performance. Start by identifying repetitive query patterns in your workload, create targeted materialized views, and fine-tune their update policies to balance freshness and resource usage. With these strategies, you’ll unlock the full potential of KQL for real-time analytics on massive datasets.

For more details on KQL materialized views, check the Azure Data Explorer documentation.