Scaling VictoriaMetrics Stream Aggregation with a Distributed Gateway
This article explores the challenges of scaling VictoriaMetrics' native stream aggregation feature for real-time metric cardinality reduction in a distributed environment. It highlights issues like collection gaps, inconsistent routing, and data loss, proposing a custom-built distributed gateway, `vm-receive-route`, to address these problems and ensure accurate, scalable metric processing.
Read original on Dev.to #architectureVictoriaMetrics provides stream aggregation to reduce metric cardinality. While effective for single instances, scaling this feature to millions of time series in a distributed system introduces significant architectural challenges. The article dives into the limitations of the native implementation and presents a solution built to overcome them.
Challenges with Native Stream Aggregation at Scale
The native stream aggregation in `vmagent` processes metrics by accumulating deltas. This approach assumes timely and ordered data arrival, which is often not the case in large-scale distributed systems. Key problems identified include:
- Collection Gap Problem: Network issues or service restarts cause gaps in metric collection. When data resumes, a large delta can inflate aggregation results within a window, or corrupt calculations across windows if the gap crosses boundaries.
- Inconsistent Routing: Without a dedicated routing strategy, metrics for the same dimension can be processed by different nodes, leading to partial and incorrect aggregations that cannot be reliably combined.
- Data Loss from Out-of-Order Handling: VictoriaMetrics discards later-arriving values for the same dimension set within a time window, leading to silent data loss when multiple nodes independently compute results.
- Resource Imbalance: Uneven distribution of metric streams results in some aggregation nodes being overloaded while others are underutilized.
- Dimension Explosion: Adding internal task IDs for routing can inadvertently introduce new dimensions, negating the cardinality reduction goal of stream aggregation.
Architecting a Distributed Stream Aggregation Gateway
To address the limitations, a distributed frontend module, `vm-receive-route`, was developed. This gateway acts as an entry point to intelligently manage and route incoming metric samples before they reach the aggregation engines. The design acknowledges that the native aggregation has a generous 50% grace period for late data within its time window, which can exacerbate issues with stale data influence. The gateway's primary role is to ensure consistent routing and proper handling of distributed data streams.
System Design Takeaway
When building distributed aggregation or analytical systems, consider how late or out-of-order data impacts windowing logic. A robust solution often involves a dedicated ingestion layer (like this gateway) that handles data consistency, routing, and potential de-duplication before aggregation occurs, rather than relying solely on the downstream aggregation engine's basic handling.