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StatsD
StatsD

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126
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27
Statsite
Statsite

4
8
+ 1
0
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StatsD vs Statsite: What are the differences?

StatsD: Simple daemon for easy stats aggregation. StatsD is a front-end proxy for the Graphite/Carbon metrics server, originally written by Etsy's Erik Kastner. StatsD is a network daemon that runs on the Node.js platform and listens for statistics, like counters and timers, sent over UDP and sends aggregates to one or more pluggable backend services (e.g., Graphite); Statsite: C implementation of statsd. Statsite is a metrics aggregation server. Statsite is based heavily on Etsy's StatsD https://github.com/etsy/statsd, and is wire compatible.

StatsD and Statsite can be primarily classified as "Monitoring" tools.

Some of the features offered by StatsD are:

  • buckets: Each stat is in its own "bucket". They are not predefined anywhere. Buckets can be named anything that will translate to Graphite (periods make folders, etc)
  • values: Each stat will have a value. How it is interpreted depends on modifiers. In general values should be integer.
  • flush: After the flush interval timeout (defined by config.flushInterval, default 10 seconds), stats are aggregated and sent to an upstream backend service.

On the other hand, Statsite provides the following key features:

  • Multiple metric types
  • Efficient summary metrics for timer data
  • Dynamic set implementation

StatsD and Statsite are both open source tools. StatsD with 14.2K GitHub stars and 1.83K forks on GitHub appears to be more popular than Statsite with 1.73K GitHub stars and 241 GitHub forks.

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What is StatsD?

StatsD is a front-end proxy for the Graphite/Carbon metrics server, originally written by Etsy's Erik Kastner. StatsD is a network daemon that runs on the Node.js platform and listens for statistics, like counters and timers, sent over UDP and sends aggregates to one or more pluggable backend services (e.g., Graphite).

What is Statsite?

Statsite is a metrics aggregation server. Statsite is based heavily on Etsy's StatsD https://github.com/etsy/statsd, and is wire compatible.
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          What are some alternatives to StatsD and Statsite?
          collectd
          collectd gathers statistics about the system it is running on and stores this information. Those statistics can then be used to find current performance bottlenecks (i.e. performance analysis) and predict future system load (i.e. capacity planning). Or if you just want pretty graphs of your private server and are fed up with some homegrown solution you're at the right place, too.
          Prometheus
          Prometheus is a systems and service monitoring system. It collects metrics from configured targets at given intervals, evaluates rule expressions, displays the results, and can trigger alerts if some condition is observed to be true.
          InfluxDB
          InfluxDB is a scalable datastore for metrics, events, and real-time analytics. It has a built-in HTTP API so you don't have to write any server side code to get up and running. InfluxDB is designed to be scalable, simple to install and manage, and fast to get data in and out.
          Logstash
          Logstash is a tool for managing events and logs. You can use it to collect logs, parse them, and store them for later use (like, for searching). If you store them in Elasticsearch, you can view and analyze them with Kibana.
          Telegraf
          It is an agent for collecting, processing, aggregating, and writing metrics. Design goals are to have a minimal memory footprint with a plugin system so that developers in the community can easily add support for collecting metrics.
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          Decisions about StatsD and Statsite
          StackShare Editors
          StackShare Editors
          Grafana
          Grafana
          StatsD
          StatsD
          Airflow
          Airflow
          PagerDuty
          PagerDuty
          Datadog
          Datadog
          Celery
          Celery
          AWS EC2
          AWS EC2
          Flask
          Flask

          Data science and engineering teams at Lyft maintain several big data pipelines that serve as the foundation for various types of analysis throughout the business.

          Apache Airflow sits at the center of this big data infrastructure, allowing users to “programmatically author, schedule, and monitor data pipelines.” Airflow is an open source tool, and “Lyft is the very first Airflow adopter in production since the project was open sourced around three years ago.”

          There are several key components of the architecture. A web UI allows users to view the status of their queries, along with an audit trail of any modifications the query. A metadata database stores things like job status and task instance status. A multi-process scheduler handles job requests, and triggers the executor to execute those tasks.

          Airflow supports several executors, though Lyft uses CeleryExecutor to scale task execution in production. Airflow is deployed to three Amazon Auto Scaling Groups, with each associated with a celery queue.

          Audit logs supplied to the web UI are powered by the existing Airflow audit logs as well as Flask signal.

          Datadog, Statsd, Grafana, and PagerDuty are all used to monitor the Airflow system.

          See more
          Łukasz Korecki
          Łukasz Korecki
          CTO & Co-founder at EnjoyHQ · | 6 upvotes · 79.1K views
          atEnjoyHQEnjoyHQ
          collectd
          collectd
          Google Compute Engine
          Google Compute Engine
          StatsD
          StatsD
          Clojure
          Clojure
          Stackdriver
          Stackdriver

          We use collectd because of it's low footprint and great capabilities. We use it to monitor our Google Compute Engine machines. More interestingly we setup collectd as StatsD replacement - all our Clojure services push application-level metrics using our own metrics library and collectd pushes them to Stackdriver

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          Sentry
          Sentry
          StatsD
          StatsD
          Graphite
          Graphite
          Grafana
          Grafana
          PagerDuty
          PagerDuty
          Amazon CloudWatch
          Amazon CloudWatch

          A huge part of our continuous deployment practices is to have granular alerting and monitoring across the platform. To do this, we run Sentry on-premise, inside our VPCs, for our event alerting, and we run an awesome observability and monitoring system consisting of StatsD, Graphite and Grafana. We have dashboards using this system to monitor our core subsystems so that we can know the health of any given subsystem at any moment. This system ties into our PagerDuty rotation, as well as alerts from some of our Amazon CloudWatch alarms (we’re looking to migrate all of these to our internal monitoring system soon).

          See more
          Interest over time
          Reviews of StatsD and Statsite
          No reviews found
          How developers use StatsD and Statsite
          Avatar of Stream
          Stream uses StatsDStatsD

          StatsD is used to track the number of messages we're publishing and the type of realtime subscribers. So it shows the number of longpoll connections, the number of websocket connections etc. It also tracks how Redis is performing.

          Avatar of Chris Hartwig
          Chris Hartwig uses StatsDStatsD

          Business and system counters go through StatsD and are pushed to InfluxDB

          Avatar of Tongliang Liu
          Tongliang Liu uses StatsDStatsD

          Arm yourself with sensor all over your application

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          How much does Statsite cost?
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