Observability platforms face a fundamental tension: you need to query massive volumes of data, but users expect instant results. Scanning terabytes of raw logs or metrics for every query doesn't scale. Pre-aggregating everything wastes storage and limits flexibility. Traditional approaches force you to choose between query speed and data completeness.

We've watched customers struggle with this trade-off. Long-running queries timing out during incidents. Dashboards taking minutes to load. Engineers waiting for results instead of investigating problems. The observability platform becomes a bottleneck when it should be accelerating troubleshooting.

Specialized log analytics platforms like Sumo Logic and Splunk solved this years ago with scheduled views: pre-computed aggregations that update automatically. We brought this capability to Kloudfuse 3.5, but unified across all telemetry types, not just logs.

The Query Performance Problem

When you query observability data, you're typically asking the same questions repeatedly. "What's the error rate for checkout-service over the last 24 hours?" "Which pods are consuming the most memory?" "What are the top log patterns in production?"

These queries scan massive datasets. Millions of metric data points. Billions of log events. Hundreds of thousands of traces. Even with efficient columnar storage and indexing, full scans take time. During incidents when every second matters, waiting 30 seconds for a dashboard to load is unacceptable.

The traditional solution is pre-aggregation. Calculate common aggregations once and store the results. When users query, return the pre-computed values instead of scanning raw data. This works, but it's inflexible. You need to predict which aggregations users want. If they ask a slightly different question, you're back to full scans.

We needed something that combined the performance of pre-aggregation with the flexibility of ad-hoc queries.

Designing Scheduled Views

Scheduled views are pre-computed datasets that update at regular intervals. Define a FuseQL query once. Configure an update schedule. Kloudfuse executes the query automatically, stores the results, and makes them available for instant querying.

The key insight: many observability queries follow patterns. You're looking at the same services, the same time windows, the same aggregations. The specific values change, but the structure stays consistent. Scheduled views capture these patterns.

For example, create a scheduled view that aggregates error rates by service every minute. The view contains pre-computed error rates for all services. When you query for current error rates, Kloudfuse returns results from the scheduled view instantly. No full scan. No aggregation computation. Just a simple lookup.

Update intervals range from one minute to hours or days, depending on your needs. Real-time operational dashboards might update every minute. Capacity planning reports might update daily. You balance freshness against computation cost.

Implementation Across All Telemetry Types

Scheduled views in Kloudfuse 3.5 work across all telemetry types, not just logs. Create scheduled views for metric aggregations, log pattern analysis, trace performance statistics, event correlations, and RUM performance metrics.

This unified approach matters because observability questions often span multiple telemetry types. A scheduled view might correlate service error rates (from traces) with infrastructure CPU usage (from metrics) and error log patterns (from logs). Query the scheduled view and get correlated insights instantly, without scanning three separate data streams.

The underlying architecture leverages Kloudfuse's unified data lake. All telemetry flows through the same OLAP engine. Scheduled views execute FuseQL queries against this unified store and materialize results back into queryable tables. This creates a feedback loop: raw data feeds scheduled views, which feed dashboards and alerts.

Query Performance Impact

The performance difference is significant. Customers running global platforms query service performance metrics across hundreds of microservices every minute. Without scheduled views, these queries scanned millions of data points taking 15-20 seconds. With scheduled views updating every minute, the same queries return results in under one second.

Log pattern analysis queries that previously scanned billions of events and frequently timed out during peak traffic now complete in under two seconds using scheduled views that pre-aggregate patterns by severity and service.

Cost analysis queries tracking ingestion volumes by team and service for chargeback models load instantly instead of requiring expensive full-table scans, even as data volumes scale.

Balancing Freshness and Performance

Scheduled views introduce a trade-off: query performance versus data freshness. Results reflect the last update, not real-time data. For some use cases, this is acceptable. For others, it's not.

We designed scheduled views to make this trade-off explicit. Configure update intervals based on your requirements. Critical operational dashboards might accept one-minute staleness for 10x faster queries. Weekly capacity reports can tolerate hour-old data.

You can also query raw data alongside scheduled views. Use scheduled views for high-level dashboards that load quickly. Drill down into raw data when you need real-time precision. The scheduled view gets you 90% of the way there instantly. Raw data queries provide the final 10% when needed.

Complementing Scheduled Search

Kloudfuse 3.5 also introduces scheduled search: cron-based queries that execute automatically and store results. While similar to scheduled views, scheduled search serves different use cases.

Scheduled views optimize query performance for repeated access patterns. You query the view many times after each update. Scheduled search automates routine investigations that run once per interval. Daily security audits. Weekly capacity reports. Hourly compliance checks.

Together, these capabilities transform observability workflows. Scheduled views make dashboards and alerts fast. Scheduled search automates recurring analysis. Both reduce cognitive load and manual repetition.

What We Learned

Building scheduled views taught us something about observability platforms. Speed matters. Not just for user experience, but for operational effectiveness.

During incidents, every second counts. Engineers need immediate answers to guide their investigation. Waiting for queries creates cognitive breaks that slow troubleshooting. Fast queries maintain flow state and accelerate resolution.

Outside incidents, query performance affects platform adoption. Slow dashboards discourage usage. Engineers create workarounds instead of using observability tools. Fast, responsive interfaces encourage teams to engage with their data proactively.

Scheduled views deliver the performance that makes observability platforms useful rather than frustrating. Combined with Kloudfuse's unified data lake, they enable query patterns that weren't practical before: correlating multiple telemetry types, analyzing long time windows, tracking high-cardinality dimensions.

Observability platforms should accelerate understanding, not create bottlenecks. Scheduled views in Kloudfuse 3.5 ensure that performance never stands between engineers and insights.

Learn more about advanced query capabilities in Kloudfuse 3.5 in our launch announcement.