Navigating the Transition: From StatsD to Prometheus in a Month
October 31, 2024, 6:47 am
Location: United States, New York
Employees: 501-1000
Founded date: 2014
Total raised: $804M
In the fast-paced world of technology, change is the only constant. For Mixpanel, a company that thrives on data, the transition from StatsD to Prometheus was not just a shift; it was a leap into a new era of metrics collection. This migration, completed in just one month, was both a challenge and an opportunity. It reshaped our approach to infrastructure metrics and taught us valuable lessons along the way.
The journey began with a realization. StatsD, while effective in its time, was no longer sufficient for our evolving needs. Originally developed in 2011, StatsD served us well during our early days. It was simple, efficient, and fit our infrastructure like a glove. But as we scaled, the limitations of StatsD became apparent. Our services, now running on Kubernetes, generated thousands of metrics per second. The stakes were high: we needed a solution that could handle this influx without losing data.
Enter Prometheus. Born from the need for a more robust metrics system, Prometheus offered a decentralized model that promised scalability and reliability. Its architecture allowed for better performance, eliminating the risk of lost metrics—a critical factor for our engineering team. The decision to migrate was not taken lightly, but the benefits were clear.
The migration process was divided into three main challenges: collecting metrics, transforming their format, and rewriting queries. Each step required careful planning and execution.
**Collecting Metrics**
The first hurdle was ensuring a seamless transition in metrics collection. We needed a solution that allowed us to send metrics to both StatsD and Prometheus simultaneously. This dual approach would ensure no data was lost during the migration. The goal was clear: no code rewrites for our services and no performance degradation.
We explored various tools, ultimately settling on a custom solution. While the statsd_exporter provided a good starting point, it didn’t meet all our needs. Our services aggregated metrics in a way that required a more integrated approach. By embedding the statsd_exporter directly into our internal SDK, we could process metrics in memory, allowing for a smoother transition to Prometheus without the need for a sidecar.
This decision paid off. It allowed our teams to gradually adopt the official Prometheus Go SDK at their own pace, ensuring a more manageable migration process.
**Transforming Metrics**
Next came the challenge of transforming our metrics. The difference in naming conventions and tagging between StatsD and Prometheus was significant. StatsD often encoded dimensions in metric names, while Prometheus encouraged the use of labels. This posed a risk: queries could break if we didn’t adapt our metrics correctly.
We tackled this by replacing our RPC metrics interceptors with Prometheus’s open-source alternatives. This single change had a massive impact, simplifying our metrics and making them more compatible with Prometheus’s querying capabilities.
However, not all metrics could be easily transformed. For those, we implemented a configuration interface in the statsd_exporter to define metric mappings on the fly. This allowed us to adapt our metrics without requiring extensive code rewrites, minimizing disruption for our engineers.
**Rewriting Queries**
The final challenge was the most daunting: rewriting our dashboards and alert queries in PromQL. With approximately 300 dashboards and 600 alerts to convert, the task seemed monumental. Initially, we explored automated tools for this process, but the complexity of the task quickly became apparent.
Instead, we focused on making the manual process as painless as possible. We set aside dedicated hours for engineers to seek help with PromQL, ensuring they had the support they needed. Documentation became our ally, guiding engineers through the intricacies of the new system.
In total, our team of 20 engineers managed to rewrite around 4,000 queries in just over a month. The sense of accomplishment was palpable. We had not only migrated our metrics but had also strengthened our infrastructure in the process.
**Final Thoughts**
The transition from StatsD to Prometheus was more than a technical upgrade; it was a testament to our team’s resilience and adaptability. We emerged from the process with a more robust metrics system, ready to meet the demands of our growing infrastructure.
As we look to the future, we are excited about the possibilities that Prometheus offers. The open standards of PromQL and Grafana provide us with flexibility and freedom, allowing us to explore new platforms without being tied to a single vendor.
In a world where data is king, our ability to adapt and evolve is crucial. The lessons learned during this migration will guide us as we continue to innovate and improve our services. The journey may have been challenging, but the rewards are already evident. With Prometheus, we are not just keeping pace; we are setting the standard for metrics collection in our industry.
The journey began with a realization. StatsD, while effective in its time, was no longer sufficient for our evolving needs. Originally developed in 2011, StatsD served us well during our early days. It was simple, efficient, and fit our infrastructure like a glove. But as we scaled, the limitations of StatsD became apparent. Our services, now running on Kubernetes, generated thousands of metrics per second. The stakes were high: we needed a solution that could handle this influx without losing data.
Enter Prometheus. Born from the need for a more robust metrics system, Prometheus offered a decentralized model that promised scalability and reliability. Its architecture allowed for better performance, eliminating the risk of lost metrics—a critical factor for our engineering team. The decision to migrate was not taken lightly, but the benefits were clear.
The migration process was divided into three main challenges: collecting metrics, transforming their format, and rewriting queries. Each step required careful planning and execution.
**Collecting Metrics**
The first hurdle was ensuring a seamless transition in metrics collection. We needed a solution that allowed us to send metrics to both StatsD and Prometheus simultaneously. This dual approach would ensure no data was lost during the migration. The goal was clear: no code rewrites for our services and no performance degradation.
We explored various tools, ultimately settling on a custom solution. While the statsd_exporter provided a good starting point, it didn’t meet all our needs. Our services aggregated metrics in a way that required a more integrated approach. By embedding the statsd_exporter directly into our internal SDK, we could process metrics in memory, allowing for a smoother transition to Prometheus without the need for a sidecar.
This decision paid off. It allowed our teams to gradually adopt the official Prometheus Go SDK at their own pace, ensuring a more manageable migration process.
**Transforming Metrics**
Next came the challenge of transforming our metrics. The difference in naming conventions and tagging between StatsD and Prometheus was significant. StatsD often encoded dimensions in metric names, while Prometheus encouraged the use of labels. This posed a risk: queries could break if we didn’t adapt our metrics correctly.
We tackled this by replacing our RPC metrics interceptors with Prometheus’s open-source alternatives. This single change had a massive impact, simplifying our metrics and making them more compatible with Prometheus’s querying capabilities.
However, not all metrics could be easily transformed. For those, we implemented a configuration interface in the statsd_exporter to define metric mappings on the fly. This allowed us to adapt our metrics without requiring extensive code rewrites, minimizing disruption for our engineers.
**Rewriting Queries**
The final challenge was the most daunting: rewriting our dashboards and alert queries in PromQL. With approximately 300 dashboards and 600 alerts to convert, the task seemed monumental. Initially, we explored automated tools for this process, but the complexity of the task quickly became apparent.
Instead, we focused on making the manual process as painless as possible. We set aside dedicated hours for engineers to seek help with PromQL, ensuring they had the support they needed. Documentation became our ally, guiding engineers through the intricacies of the new system.
In total, our team of 20 engineers managed to rewrite around 4,000 queries in just over a month. The sense of accomplishment was palpable. We had not only migrated our metrics but had also strengthened our infrastructure in the process.
**Final Thoughts**
The transition from StatsD to Prometheus was more than a technical upgrade; it was a testament to our team’s resilience and adaptability. We emerged from the process with a more robust metrics system, ready to meet the demands of our growing infrastructure.
As we look to the future, we are excited about the possibilities that Prometheus offers. The open standards of PromQL and Grafana provide us with flexibility and freedom, allowing us to explore new platforms without being tied to a single vendor.
In a world where data is king, our ability to adapt and evolve is crucial. The lessons learned during this migration will guide us as we continue to innovate and improve our services. The journey may have been challenging, but the rewards are already evident. With Prometheus, we are not just keeping pace; we are setting the standard for metrics collection in our industry.