Vmagent OTLP Delta Temporality: Reduce Ingestion Volume

In the ever-evolving landscape of observability, efficiently managing the sheer volume of metrics generated by modern applications is a paramount concern. When working with OpenTelemetry (OTLP), a powerful standard for instrumenting applications, one crucial aspect that significantly impacts ingestion volume, network traffic, and storage footprint is temporality. Specifically, the ability of tools like vmagent to understand and process Delta temporality offers a compelling solution to reduce overhead. Currently, vmagent’s OTLP receiver primarily expects Cumulative temporality, which, for high-frequency counter metrics, leads to unnecessary data transmission and storage. This article delves into why Delta temporality is so important and advocates for its inclusion in vmagent to unlock substantial efficiency gains.

The Challenge of Cumulative Temporality for High-Frequency Metrics

Many applications, especially those operating in real-time workflows, naturally generate metrics that represent incremental changes. Think of counters tracking the number of requests processed, errors encountered, or messages sent – these values typically increase over time. The natural behavior for such high-frequency counters is to report delta increments, meaning you only care about the change in the value since the last observation. However, the current OTLP receiver in vmagent operates under the assumption of Cumulative temporality. This means that instead of just sending the difference, the exporter is forced to send the full metric value at every collection interval, regardless of whether the value has actually changed since the last report. This creates a cascade of inefficiencies. Imagine a counter that increments by 1 every second. If your collection interval is 15 seconds, under Cumulative temporality, you’d be sending the same value (incremented by 15) repeatedly for 14 of those intervals, only for the value to change on the 15th. This results in transmitting metrics even when the increment is zero, generating a significantly larger number of packets than necessary. Consequently, this leads to an inflated ingestion volume and means that your storage system, like vmstorage, ends up storing far more samples than are truly informative. In scenarios with millions of such high-frequency counters, this translates into a staggering amount of unnecessary data being processed and stored daily, impacting performance and increasing operational costs.

Why Delta Temporality is a Superior Approach

The core advantage of Delta temporality lies in its elegant simplicity and efficiency. Instead of transmitting the absolute value of a counter at each interval, Delta temporality focuses on reporting only the change or the increment that has occurred since the last data point was collected. This fundamental shift in how data is transmitted offers several critical benefits. Firstly, it allows applications to suppress metric export entirely if the value hasn’t changed. If a counter hasn’t seen any activity, no data is sent for it. This drastically reduces the amount of data that needs to be transmitted over the network, leading to a significant reduction in network I/O. Furthermore, by only sending the actual increments, the number of samples stored in systems like vmstorage can be reduced by an order of magnitude. This not only saves storage space but also speeds up query performance and reduces the overall cost of maintaining your observability infrastructure. Crucially, Delta temporality does all of this while still preserving the accuracy of your metrics. You still get a complete picture of how your counters are behaving; you're just receiving that information in a much more efficient package. This is the natural and most effective way for high-frequency applications to generate and report statistics, aligning perfectly with the design principles of modern distributed systems and observability standards.

The Crucial Request: Adding OTLP Delta Aggregation Temporality Support in vmagent

To fully leverage the benefits of efficient metric reporting, the critical missing piece is explicit support for OpenTelemetry Delta Aggregation Temporality within vmagent. The documentation does mention that vmagent can accept OTLP metrics, but the current implementation’s reliance on Cumulative temporality creates a bottleneck for many use cases. The explicit request is to enhance vmagent to accept and correctly interpret metrics using AggregationTemporality::kDelta. This enhancement would empower applications that naturally emit delta-incrementing counters to send their data in the most efficient format. Instead of being forced to translate their delta metrics into a cumulative format – a process that often involves complex logic, potential for errors, and the aforementioned inefficiencies – applications could directly send OTLP metrics with Delta temporality. This would streamline the entire data pipeline, from application instrumentation to ingestion and storage. It ensures that the OpenTelemetry exporter in the application can operate as designed, sending only the changes. This not only simplifies the application's telemetry configuration but also ensures that vmagent can ingest this data optimally, maximizing the benefits of reduced network traffic and storage. Ultimately, adding this support would allow vmagent to fully align with the OpenTelemetry specification and provide a much more performant and cost-effective solution for users dealing with high-volume, high-frequency metric data. This feature is not just a nice-to-have; it’s essential for truly optimizing the ingestion of real-time telemetry data.

Workarounds and the Path Forward

Given the current limitation, users facing high-frequency counter metrics often explore workarounds to mitigate the inefficiencies caused by vmagent’s lack of Delta temporality support for OTLP. One common approach involves modifying the OpenTelemetry exporter within the application itself. Instead of relying on the exporter to automatically handle temporality, developers might implement custom logic to calculate and send delta values manually. This could involve storing the previous metric value and computing the difference before exporting. However, this approach adds complexity to the application code, increases the risk of introducing bugs, and essentially duplicates functionality that should ideally be handled by the collector or agent. Another workaround might involve pre-processing the OTLP data before it reaches vmagent, perhaps using another OpenTelemetry Collector instance configured to handle delta temporality and then forward the data in a different format. While this can work, it introduces an extra hop in the data pipeline, increasing latency and operational overhead. The ideal solution, however, remains the direct implementation of OTLP Delta Aggregation Temporality support within vmagent itself. This would eliminate the need for complex workarounds, reduce the burden on application developers, and ensure the most efficient path for metric data. By implementing this feature, vmagent would become a more robust and versatile tool for modern observability stacks, capable of handling a wider range of metric reporting strategies with maximum efficiency. The community eagerly awaits this enhancement to unlock the full potential of OTLP ingestion.

For further insights into VictoriaMetrics troubleshooting and best practices, you can explore the official documentation:

• General VictoriaMetrics Troubleshooting: https://docs.victoriametrics.com/troubleshooting/
• vmagent Specific Troubleshooting: https://docs.victoriametrics.com/vmagent/troubleshooting/