RRDtool vs StatsD: What are the differences?
Developers describe RRDtool as "High performance data logging and graphing system for time series data". RRDtool lets you log and analyze the data you gather from all kinds of data-sources (DS). The data analysis part of RRDtool is based on the ability to quickly generate graphical representations of the data values collected over a definable time period. On the other hand, StatsD is detailed as "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).
RRDtool and StatsD can be primarily classified as "Monitoring" tools.
"Do one thing and do it well" is the top reason why over 2 developers like RRDtool, while over 6 developers mention "Single responsibility" as the leading cause for choosing StatsD.
RRDtool and StatsD are both open source tools. StatsD with 14.2K GitHub stars and 1.83K forks on GitHub appears to be more popular than RRDtool with 571 GitHub stars and 199 GitHub forks.
What is RRDtool?
What is StatsD?
Need advice about which tool to choose?Ask the StackShare community!
Sign up to add, upvote and see more prosMake informed product decisions
What are the cons of using RRDtool?
What companies use RRDtool?
Sign up to get full access to all the companiesMake informed product decisions
Sign up to get full access to all the tool integrationsMake informed product decisions
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.
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
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).
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.