When I started working on Hyperfeed in 2016, it was not distributed; its components all ran on a single machine (this is not strictly true as it also used a Postgres database running on a separate server). It also has the built-in graphing and alerting capabilities we were looking for.However, we quickly realized that Zabbix was not the tool for the job. Being aware of both of these types of changes is critical to having confidence in your predictions.This time last year, the Predict crew was relatively new at FlightAware and its production services were small and simple. For any given airport, we train two models, one for EON and one for EIN, which make predictions for flights inbound to that airport. Refer to Add a data source for instructions on how to add a data source to Grafana. However, we quickly started running into limitations; the first, and ultimately deciding factor, was that Prometheus is a pure time-series database. Select "Prometheus" as the type.
Exporters act as translators, observing an application directly with queries and probes, converting the output of those observations into a format that Prometheus understands, and exposing the resultant metrics over HTTP in response to scrapes from a Prometheus server. But as the demands placed on Hyperfeed grew, so did its operational complexity.Eventually, a single-machine architecture was not enough to handle the increased volume of Hyperfeed’s input, which was precipitated by FlightAware acquiring Given their origin, our custom tools were not designed with a distributed setting in mind. In 2016, Prometheus was the second project accepted into the Cloud Native Computing Foundation after Kubernetes, and also to the second to graduate in 2018. Moreover, the aforementioned custom tools fed their data into Zabbix so that they could be used for alerting an engineer if something went haywire. While this tool had a lot to commend it—tracking the count of events over time is a seemingly simple but immensely powerful technique—it also suffered from a host of problems that led to its retirement: it ended up negatively impacting performance, its data model made aggregation and rate-calculation difficult, and we lacked suitable visualization for exploring its data. While it has some auto-discovery features, we found them difficult to use, especially in our case of needing >1200 error metrics.
A service that hosts Grafana, Loki, and Prometheus at scale. It wasn’t always like this, though.
Grafana Labs uses cookies for the normal operation of this website. This topic explains options, variables, querying, and other options specific to the Prometheus data source.
High availability was a critical component of the service, and to aid in this we chose to run models redundantly. It vastly increased the number of critical models to keep an eye on. In the case of Prometheus it comes straight from the code itself. Right off the bat we were impressed by its visualization and alerting capabilities.
Click on "Data Sources". On top of the aforesaid monitoring upgrades, Prometheus and Grafana also provide a number of additional pluses. To this end, Prometheus provides time-series metrics, which track aggregations of events in a system over some period of time, where an event refers to anything that might occur and can be quantified or measured. Not only do you need to manage typical software conditions, like whether the system is running, hung, or stopped, you also need to track the accuracy of the machine learning models themselves. Both Prometheus and Grafana are built around time-series data – with Prometheus primarily on the gathering side and Grafana on the reporting side. To militate against any doubts of this type, we will now look at two specific cases where Prometheus supersedes our current praxis and contributes to our general excitement and enthusiasm about adopting it for monitoring one of the core pieces of FlightAware’s infrastructure.The first case involves an in-house library, currently out of commission, but used for several years in Hyperfeed for whitebox monitoring. Each streamer typically supports up to 20 airports at a time, so to gain coverage we run multiple copies of the streamer in parallel.Because of the heavy use of machine learning in this software, monitoring became even more critical than normal. Models can have short bursts of inaccuracy, e.g.
If an exporter does not exist for a particular service, it is fairly easy to write a one and Prometheus’ official documentation At this point in the discussion, although the power of Prometheus has been outlined—it is conceptually straightforward but powerful, boasts strong visualization support via Grafana, was built for dynamic, distributed systems, generalizes to other wings at FlightAware, has equally strong support for whitebox and blackbox monitoring and excels at systems-level observations and analysis—it still might not be clear what benefits Prometheus provides over the current Hyperfeed monitoring system. We make predictions for around 75,000 flights per day; if we only stored two error values per flight (much fewer that we wanted), it would require making 100 inserts per minute.These issues with Zabbix led us to look into Grafana as an alternative monitoring solution.
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