statsd/statsd
Statsd
StatsD is a metrics aggregator and UDP collection server that collects system counters and timers. It functions as a time-series data forwarder, receiving high-frequency metric updates via a lightweight line protocol and summarizing them before flushing the data to a backend.
The project features a pluggable metrics backend framework, allowing aggregated statistics to be routed to various third-party monitoring services or time-series databases such as Graphite. It supports horizontal scaling and high availability through a proxy ring distribution system that forwards incoming packets across a cluster of instances.
The system provides capabilities for real-time metric aggregation, including event rate tracking, state gauge monitoring, and unique event counting. It performs statistical analysis on timing data using histogram-based sampling to calculate medians and percentiles for latency analysis. Metrics are organized using a hierarchical namespace structure to allow for logical grouping and filtering.
The daemon can be deployed via a container image and is managed through a remote administration interface over TCP.
Features
- UDP Metric Ingestion - Receives lightweight line-protocol metric updates over connectionless UDP sockets to minimize client overhead.
- Metric Aggregators - Collects counters, timers, and gauges from multiple sources and summarizes them over fixed intervals.
- Batch Aggregators - Summarizes incoming counters and timers in memory over fixed time windows before flushing to backends.
- Metrics Storage Backends - Implements a pluggable framework to export aggregated statistics to various third-party monitoring services.
- Timing - Calculates percentiles and distributions for timing data by binning samples into frequency distributions.
- Latency - Computes medians and top percentiles from timing metrics to analyze system response latency.
- Time-Series Data Forwarding - Routes aggregated real-time metrics to external storage systems like Graphite for long-term visualization.
- Time-Series Data Forwarding - Routes aggregated system statistics to time-series databases like Graphite for long-term storage.
- Statistical Metric Calculators - Calculates percentiles and standard deviation for duration samples using histogram binning.
- Latency Histograms - Uses histogram-based aggregation to calculate medians and percentiles for analyzing system response times.
- Distributed Monitoring Tools - Gathers performance data across multiple servers and languages using lightweight network protocols.
- Socket-Based Ingestion - Receives system metrics over network ports using both connectionless UDP packets and stream-based TCP connections.
- Metric Namespace Organization - Organizes metrics into a hierarchical tree structure using dot-separated identifiers for logical grouping.
- Gauge Observers - Tracks persistent gauge values that maintain state across flush intervals until explicitly updated.
- System Monitoring - Tracks application performance and system health by aggregating custom metrics sent to a central daemon.
- Cardinality Tracking - Tracks the number of unique events occurring between flush intervals using a set-based approach.
- Metric Flushing - Exports aggregated telemetry snapshots to upstream storage backends after a set flush interval.
- Metric Rate Tracking - Increments counters for specific buckets and resets totals at each flush interval to report rates.
- Proxy Ring Distribution - Forwards incoming packets across a cluster of instances to enable horizontal scaling and high availability.
- Database Exporters - Sends aggregated statistics to time-series databases or cloud monitoring services for long-term storage.
- Custom Metrics Routing - Allows routing metrics to proprietary or third-party services by integrating custom modules that implement a standard interface.
- Consistent Hashing Distribution - Distributes incoming metrics across a cluster using a proxy ring to ensure scalability and high availability.
- Metric Ingestion Protocols - Provides a standard interface for integrating custom protocols to receive external metric data.
- Polyglot Client Support - Provides client libraries across multiple programming languages to send counters and timers to the aggregator.
- Metric Push Clients - Pushes precalculated system statistics to remote monitoring services at defined flush intervals.
- Pluggable Backends - Implements a modular architecture for routing aggregated statistics to various external monitoring backends.
- Random Sampling - Reduces the volume of timing data by randomly sampling a subset of events to minimize overhead.
- Batch Metric Ingestion - Receives multiple metric updates in a single network packet to reduce transmission overhead.
- Multi-Transport Ingestion - Receives metrics through various transport layers including UDP, TCP, and Unix sockets.
- Graphite Metric Ingestors - Provides a dedicated integration for exporting aggregated metrics using the Graphite plaintext protocol.
- Server Metrics - Retrieves real-time dumps of counters, gauges, and timers along with server uptime and processing statistics.
- Performance Monitoring Tools - Tracks event counts and timing percentiles across distributed systems to analyze latency and throughput.
- Metrics Collection - Daemon for aggregating statistics like counters and timers.
- Observability and Monitoring - Daemon for aggregating application statistics.
