Mastering Cross-Cluster and Federated Querying in Kusto Query Language (KQL)

Dive into the mechanics of querying across Azure Data Explorer clusters using unions and external references

In the world of big data analytics, Azure Data Explorer (ADX) stands out for its speed and scalability, powered by the Kusto Query Language (KQL). While KQL is intuitive for querying within a single database or cluster, things get more complex when you need to pull data from multiple clusters or even hybrid environments mixing cloud and on-premises sources. Cross-cluster and federated querying enable seamless data aggregation across boundaries, but they come with challenges like authentication hurdles, performance overhead, and setup intricacies. These topics are often glossed over in basic tutorials, reserved for enterprise-scale setups where data silos are the norm. In this post, we’ll dive into the mechanics of querying across Azure Data Explorer clusters using unions and external references, then explore optimizing federated joins for hybrid scenarios—think combining on-premises logs with cloud-based telemetry.

Whether you’re a data engineer dealing with distributed systems or an analyst hunting for insights across environments, understanding these advanced KQL features can unlock powerful analytics. We’ll cover syntax, best practices, and pitfalls, drawing from real-world implications.

Cross-Cluster Querying: Unions and External References

At its core, cross-cluster querying lets you reference data outside your current “database in context”—the default scope where your query runs and permissions are checked. This is crucial for scenarios where data is sharded across multiple ADX clusters, perhaps for geographic distribution or workload isolation.

Basic Syntax for Referencing Remote Data

To query a table in another database or cluster, use the cluster() and database() functions to qualify the entity name. For example:

• Within the same cluster but different database: database(”OtherDatabase”).MyTable

• In a remote cluster: cluster(”https://remotecluster.kusto.windows.net”).database(”RemoteDb”).MyTable

This qualified naming extends to functions, views, and other entities. Authentication is key here: you need at least viewer permissions on the default database and all referenced databases or clusters. Without proper access, queries will fail with permission errors, making role-based access control (RBAC) setup a prerequisite in enterprise environments.

Leveraging Unions for Multi-Cluster Aggregation

The union operator is your go-to for combining results from multiple tables across clusters. It merges rows from input tables into a single result set, handling schema differences by aligning columns where possible.

A simple example:

union
    MyLocalTable,
    database(”OtherDb”).RemoteTable,
    cluster(”https://anothercluster.kusto.windows.net”).database(”DbThere”).AnotherTable
| where Timestamp > ago(1d)
| summarize Count = count() by Category

For broader scopes, use wildcards (but not on cluster names):

union withsource=SourceTable *
| union database(”OtherDb*”).*Table
| union cluster(”https://remote.kusto.windows.net”).database(”*”).*

This is powerful for aggregating logs from regional clusters, but watch for performance: unions can process massive datasets, and default row limits (e.g., 500,000 records) apply unless you set the notruncation option. Schema caching helps efficiency but can cause issues if remote schemas change—clear the cache with .clear cache remote-schema if needed.

External references shine when incorporating non-ADX data. The externaldata operator lets you query CSV, JSON, or other files from storage like Azure Blob or ADLS directly in KQL:

externaldata (Timestamp:datetime, Message:string)
    [@”https://mystorage.blob.core.windows.net/logs/logfile.csv”]
with (format=”csv”, ignoreFirstRecord=true)
| union MyCloudTable
| where Message contains “error”

This bridges cloud data with external sources, but for true on-premises integration, you’ll often need to set up connectors or ingest data first—more on that in the hybrid section.

Cross-Cluster Joins: Merging Data Across Boundaries

Joins take cross-cluster querying further by correlating datasets, but they introduce federation complexities. A cross-cluster join merges tables from different clusters, with execution potentially “remoted” to optimize data movement.

Syntax and Strategies

The basic join syntax mirrors standard KQL, but with qualified references and an optional hint.remote for strategy:

LocalTable
| join hint.remote=right (
    cluster(”https://remotecluster.kusto.windows.net”).database(”RemoteDb”).RemoteTable
) on CommonKey

Strategies include:

• auto (default): ADX decides based on data location—often the right cluster if the left is remote.

• left, right, or local: Explicitly choose where to run the join.

For instance, if the remote table is much larger, use hint.remote=right to avoid shipping gigabytes across networks.

Authentication follows the same rules: ensure viewer access across all involved clusters. Performance hits come from data transfer and serialization—joins can be CPU-intensive, so profile with the set query_trace=true option to spot bottlenecks.

Optimizing Federated Joins in Hybrid Environments

Hybrid setups—e.g., on-premises logs (like from local servers or legacy systems) joined with cloud data in ADX—are where federated querying gets tricky. “Federated” here means querying without full data movement, often via external tables or cross-service links.

Setting Up for Hybrid Queries

For on-premises data, direct federation isn’t always native, but you can:

1. Ingest on-prem logs into ADX via tools like Azure Data Factory or Event Hubs for streaming, then query as standard tables.

2. Use external tables to reference on-prem SQL databases or files if they’re accessible via public endpoints. Create an external table in ADX:

.create external table OnPremLogs (Id: long, LogMessage: string)
kind=sql
(h@”Server=tcp:myserver.database.windows.net,1433;Database=MyDb;Authentication=Active Directory Integrated”)
with (table=”OnPremTable”)

3. Then join: CloudTable | join kind=inner OnPremLogs on Id

4. Cross-service querying with Azure Monitor/Log Analytics: If on-prem logs are routed to Log Analytics (via agents), query them from ADX:

workspace(”MyLogAnalyticsWorkspace”).Perf
| join kind=inner MyAdxTable on Computer

5. This federates cloud ADX data with on-prem telemetry collected in Log Analytics.

Performance Optimization Tips

• Filter early: Apply where clauses right after table references to reduce data scanned—e.g., filter timestamps first, then strings.

• Choose the right side: For joins, put the smaller dataset on the left; for cross-cluster, execute on the cluster with the bulk of data using hint.remote.

• Use alternatives: Swap join for in or lookup when possible—lookup is faster for small right-side datasets.

• Materialize for reuse: If subqueries are repeated, use materialize() in let statements to cache intermediates.

• Shuffle for high cardinality: Add hint.shufflekey=<key> to joins or summarizes with many unique keys to distribute processing.

• In hybrid scenarios, minimize network hops—ingest on-prem data to cloud if queries are frequent, as federation adds latency.

These tweaks can slash query times from minutes to seconds, especially in distributed setups where data volumes explode.

TLDR: Why Master This Now?

Cross-cluster and federated querying in KQL isn’t just a nice-to-have—it’s essential for modern, distributed analytics in enterprises. While authentication demands careful RBAC planning and performance requires thoughtful optimization, the payoff is unified insights without massive data migrations. Start small: test unions in your dev clusters, then scale to hybrids with external tables or Log Analytics integration.

If you’re in an enterprise setup, dive into the docs for your specific auth model (e.g., Entra ID). Experiment, monitor query metrics, and remember: the key to efficiency is reducing data movement. Got questions or war stories? Drop them in the comments—let’s demystify this further!