A high-performance C implementation of the StatsD metrics aggregation server, wire-compatible with Etsy's StatsD.
Statsite is a C implementation of a StatsD-compatible metrics aggregation server that collects, aggregates, and forwards application performance data. It solves the problem of efficiently handling high volumes of metrics from distributed systems, providing summarized data to monitoring backends like Graphite or InfluxDB with minimal resource consumption.
DevOps engineers, SREs, and backend developers who need a high-performance, scalable metrics aggregation layer for monitoring distributed applications and infrastructure.
Developers choose Statsite for its exceptional performance and efficiency, leveraging C and streaming algorithms to handle millions of metrics with low memory overhead, while maintaining full compatibility with the widely adopted StatsD protocol.
C implementation of statsd
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Implemented in C with an event loop architecture, enabling efficient handling of hundreds of connections and millions of metrics per second, as noted in the README's emphasis on speed and scalability.
Uses streaming algorithms like Cormode-Muthukrishnan for approximate timer percentiles and HyperLogLog for set cardinalities, minimizing memory usage while maintaining configurable accuracy.
Supports built-in sinks for Graphite, InfluxDB, and others, plus any custom executable via stdin, allowing metrics to be forwarded to virtually any backend system.
Fully compatible with Etsy's StatsD protocol, making migration seamless for existing StatsD users while offering enhanced performance in C.
Requires building from source with autoconf, automake, and libtool, which can be error-prone and daunting for teams without prior C toolchain experience, as highlighted in the INSTALL.md dependency notes.
Timer percentiles and set cardinalities are estimates with configurable error margins (e.g., timer_eps, set_eps), not exact values, which may not suit applications demanding precise accuracy.
Acts solely as an aggregation and forwarding layer; lacks persistent storage or visualization capabilities, forcing reliance on external sinks and tools for data analysis.