Failure Modes and Edge Cases

Source Availability Cascade: The most common failure mode is source slowness or unavailability cascading through your system. If your virtual query touches 5 systems and one is down 1% of the time, your aggregate availability drops to approximately 95% assuming independence. In practice it is worse because failures often correlate: network issues, data center problems, or peak load events affect multiple systems simultaneously. When one source has a p99 latency of 2 seconds while others respond in 300ms, your composite p99 becomes dominated by the slowest link. Platforms like Denodo mitigate this with timeouts, retries, and partial result strategies. But then you face a difficult choice: return incomplete data with a warning, fail the entire query, or substitute cached stale data. Each option has consequences for correctness and user experience. Consistency Anomalies: Virtual queries often span multiple transactional systems that do not share a transaction boundary. A Customer 360 read might see a new order in the transactional database but a stale customer address still cached in the CRM system. This creates anomalies where related data appears inconsistent. If you need strict read after write correctness across systems, you face a hard choice. You can accept stale reads with eventual consistency, adding seconds to minutes of lag. You can fall back to a consolidated store with defined snapshot semantics, losing the freshness benefit. Or you can implement distributed transaction protocols, adding significant complexity and latency. Most production systems choose eventual consistency and design UIs to handle inconsistencies gracefully. Cache Stampede: Caching reduces source load and improves latency, but creates edge cases. If cache Time To Live (TTL) is too long, consumers see outdated data. If TTL is too short or invalidation too aggressive, cache hit rates drop and source load spikes. At 10x scale, an unexpectedly popular dashboard can generate thousands of near identical virtual queries, stampeding a backend that was never sized for that Queries Per Second (QPS). A production incident example: a new executive dashboard goes viral internally, generating 500 requests per minute. With a 60 second cache TTL, the first query of each minute hits all 5 backend systems. Four systems handle the load, but the CRM API rate limits at 10 requests per second. Queries start timing out, triggering retries, which amplify the problem. The solution involved longer TTLs for this specific query pattern and pre warming caches before dashboard launches. High Cardinality Join Explosion: Joining high cardinality datasets from slow systems over the network can explode memory or hit timeouts. Imagine cross joining a CRM table with 10 million customers and an orders table with 100 million rows over a high latency API. Even with filters, if selectivity estimates are wrong, the optimizer may attempt to pull gigabytes across the network and perform the join in memory, exhausting resources or timing out after 30 seconds. Production systems address this with join hints, cardinality caps, and monitoring for expensive query patterns. Some platforms automatically detect problematic joins and suggest materialization or reject the query with guidance to refactor it.