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etsy avatar

etsy/statsd

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Statsd

StatsD is a network-based metrics daemon and aggregator that collects application performance data, such as counters and timers, for periodic delivery to backend services. It functions as system monitoring middleware, receiving telemetry via UDP to minimize performance overhead on monitored services.

The system acts as a distributed metrics router, employing consistent hashing to distribute data points across clusters and ensure aggregation accuracy. It includes cluster health monitoring to track node availability and automatically recalculate routing paths when services go offline.

The project covers metrics aggregation and organization through the use of hierarchical namespaces. It also provides mechanisms for external metrics export, pushing aggregated system health and performance statistics to external monitoring services at defined intervals.

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Features

  • Metric Routers - Functions as a system for routing collected telemetry across clusters to maintain aggregation accuracy.
  • Cluster Request Routing - Distributes unique metric data points to specific instances using consistent hashing to ensure aggregation accuracy.
  • Metric Flushing - Pushes aggregated metric snapshots to external storage services at a defined frequency using a scheduled timer.
  • Metric Buckets - Provides in-memory buckets for accumulating counters and timers before periodic backend delivery.
  • Metric Aggregations - Groups and summarizes individual application counters and timers into composite health indicators.
  • Background Daemons - Operates as a background process that receives application performance data over the network.
  • Consistent Hashing - Implements consistent hashing to route metric keys to specific daemon instances for consistent aggregation across clusters.
  • Distributed Monitoring Tools - Tracks health and performance across multiple server clusters using distributed data distribution.
  • UDP Metric Ingestion - Receives lightweight application metrics via non-blocking UDP datagrams to minimize overhead on monitored services.
  • Metric Aggregators - Collects counters and timers via network protocols and groups them for periodic delivery to backend services.
  • System Monitoring - Acts as a middleware layer that organizes application metrics before they reach the monitoring backend.
  • Registry Heartbeat Monitoring - Uses periodic heartbeats to track node availability and dynamically update routing paths in the cluster.
  • Cluster Health Monitoring - Tracks instance availability and automatically recalculates routing paths when services go offline.
  • Stream Routing - Routes high volumes of system metrics using namespaces to simplify consolidation in backend databases.
  • Metric Namespace Organization - Groups monitoring data under configurable prefixes to simplify how information is consolidated in the backend.
  • Metric Namespaces - Organizes incoming metrics into hierarchical groups using prefix strings to simplify backend querying.
  • Metrics Exporters - Exposes aggregated system performance data to external monitoring services at defined intervals.
  • Real-Time Monitoring Systems - Provides real-time visibility by sending aggregated statistics to external monitoring services at regular intervals.
  • Log Management - Network daemon for aggregating application statistics.
  • Log Monitoring - Network daemon for aggregating statistics and metrics.
  • Monitoring and Logging - Lightweight network daemon for aggregating and reporting application metrics.
  • Nagios Ecosystem - Daemon for aggregating and reporting application statistics.
18,046 stars·1,937 forks·JavaScript·MIT·15 views

Star history

Star history chart for etsy/statsdStar history chart for etsy/statsd

How this analysis was created: This summary and feature list are AI-generated from collected project material and can contain mistakes. Stars, license and language are imported from GitHub. Inclusion does not mean that we have tested or audited this project. Check the source documentation for any feature you depend on. Learn more on our About page.

Frequently asked questions

What does etsy/statsd do?

StatsD is a network-based metrics daemon and aggregator that collects application performance data, such as counters and timers, for periodic delivery to backend services. It functions as system monitoring middleware, receiving telemetry via UDP to minimize performance overhead on monitored services.

What are the main features of etsy/statsd?

The main features of etsy/statsd are: Metric Routers, Cluster Request Routing, Metric Flushing, Metric Buckets, Metric Aggregations, Background Daemons, Consistent Hashing, Distributed Monitoring Tools.

Which projects share features with etsy/statsd?

Projects with overlapping indexed features include: statsd/statsd — StatsD is a metrics aggregator and UDP collection server that collects system counters and timers. It functions as a… doocs/advanced-java — This project is a comprehensive Java backend engineering guide and technical reference focused on high-concurrency… tporadowski/redis — Redis is a high-performance in-memory key-value store that functions as a distributed cache, message broker, and NoSQL… apache/brpc — brpc is a high-performance C++ RPC framework and network programming library designed for building distributed… influxdata/telegraf — Telegraf is a modular, cross-platform telemetry pipeline designed to collect, process, and route metrics from diverse… victoriametrics/victoriametrics — VictoriaMetrics is a high-performance, scalable time series database and observability platform designed for long-term…

Projects sharing features with Statsd

These projects share indexed features with Statsd. Shared tags can include platform or build tooling; verify the primary use case before treating a result as a replacement.
  • statsd/statsdstatsd avatar

    statsd/statsd

    18,046View on GitHub↗

    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

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  • doocs/advanced-javadoocs avatar

    doocs/advanced-java

    78,987View on GitHub↗

    This project is a comprehensive Java backend engineering guide and technical reference focused on high-concurrency design, distributed systems, and microservices architecture. It provides detailed strategies for decomposing monolithic applications, managing service discovery, and implementing the architectural patterns required for scalable backend environments. The repository distinguishes itself through an extensive collection of big data algorithmic references and database scaling strategies. It covers memory-efficient techniques for analyzing massive datasets, such as Top-K element extrac

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    View on GitHub↗78,987
  • tporadowski/redistporadowski avatar

    tporadowski/redis

    9,987View on GitHub↗

    Redis is a high-performance in-memory key-value store that functions as a distributed cache, message broker, and NoSQL database. It provides sub-millisecond read and write access to data stored in RAM and can operate as a vector database for indexing high-dimensional embeddings. The system supports a wide range of data storage and synchronization primitives, including the management of strings, hashes, lists, sets, and JSON documents. It enables real-time data operations through atomic transactions, hybrid persistence using snapshots and append-only logs, and high-availability configurations

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  • apache/brpcapache avatar

    apache/brpc

    17,545View on GitHub↗

    brpc is a high-performance C++ RPC framework and network programming library designed for building distributed systems. It functions as a multi-protocol RPC server capable of hosting and detecting multiple communication protocols, including gRPC, Thrift, HTTP, Redis, and Memcached, on a single TCP port. The project distinguishes itself through high-throughput data transport and memory efficiency, utilizing RDMA-based transport to bypass the kernel TCP stack and zero-copy memory management to eliminate data duplication. It also implements the Raft algorithm for consensus-based state replicatio

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