Alternatives to Wavefront logo

Alternatives to Wavefront

Datadog, Prometheus, SignalFx, New Relic, and ruxit are the most popular alternatives and competitors to Wavefront.
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What is Wavefront and what are its top alternatives?

Enterprise-grade cloud monitoring and analytics at over 1 million data points per second. Reduce downtime. Boost performance. Be at the Wavefront.
Wavefront is a tool in the Performance Monitoring category of a tech stack.

Wavefront alternatives & related posts

related Datadog posts

Robert Zuber
Robert Zuber
CTO at CircleCI · | 8 upvotes · 386.6K views
atCircleCICircleCI
Datadog
Datadog
PagerDuty
PagerDuty
Honeycomb
Honeycomb
Rollbar
Rollbar
Segment
Segment
Amplitude
Amplitude
PostgreSQL
PostgreSQL
Looker
Looker

Our primary source of monitoring and alerting is Datadog. We’ve got prebuilt dashboards for every scenario and integration with PagerDuty to manage routing any alerts. We’ve definitely scaled past the point where managing dashboards is easy, but we haven’t had time to invest in using features like Anomaly Detection. We’ve started using Honeycomb for some targeted debugging of complex production issues and we are liking what we’ve seen. We capture any unhandled exceptions with Rollbar and, if we realize one will keep happening, we quickly convert the metrics to point back to Datadog, to keep Rollbar as clean as possible.

We use Segment to consolidate all of our trackers, the most important of which goes to Amplitude to analyze user patterns. However, if we need a more consolidated view, we push all of our data to our own data warehouse running PostgreSQL; this is available for analytics and dashboard creation through Looker.

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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.

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related Prometheus posts

Conor Myhrvold
Conor Myhrvold
Tech Brand Mgr, Office of CTO at Uber · | 12 upvotes · 1.5M views
atUber TechnologiesUber Technologies
Prometheus
Prometheus
Graphite
Graphite
Grafana
Grafana
Nagios
Nagios

Why we spent several years building an open source, large-scale metrics alerting system, M3, built for Prometheus:

By late 2014, all services, infrastructure, and servers at Uber emitted metrics to a Graphite stack that stored them using the Whisper file format in a sharded Carbon cluster. We used Grafana for dashboarding and Nagios for alerting, issuing Graphite threshold checks via source-controlled scripts. While this worked for a while, expanding the Carbon cluster required a manual resharding process and, due to lack of replication, any single node’s disk failure caused permanent loss of its associated metrics. In short, this solution was not able to meet our needs as the company continued to grow.

To ensure the scalability of Uber’s metrics backend, we decided to build out a system that provided fault tolerant metrics ingestion, storage, and querying as a managed platform...

https://eng.uber.com/m3/

(GitHub : https://github.com/m3db/m3)

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Raja Subramaniam Mahali
Raja Subramaniam Mahali
Prometheus
Prometheus
Kubernetes
Kubernetes
Sysdig
Sysdig

We have Prometheus as a monitoring engine as a part of our stack which contains Kubernetes cluster, container images and other open source tools. Also, I am aware that Sysdig can be integrated with Prometheus but I really wanted to know whether Sysdig or sysdig+prometheus will make better monitoring solution.

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SignalFx logo

SignalFx

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Monitoring and Operational Intelligence for the Cloud
SignalFx logo
SignalFx
VS
Wavefront logo
Wavefront
New Relic logo

New Relic

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SaaS Application Performance Management for Ruby, PHP, .Net, Java, Python, and Node.js Apps.
New Relic logo
New Relic
VS
Wavefront logo
Wavefront

related New Relic posts

Sebastian Gębski
Sebastian Gębski
CTO at Shedul/Fresha · | 4 upvotes · 440.7K views
atFresha EngineeringFresha Engineering
CircleCI
CircleCI
Jenkins
Jenkins
Git
Git
GitHub
GitHub
New Relic
New Relic
AppSignal
AppSignal
Sentry
Sentry
Logentries
Logentries

Regarding Continuous Integration - we've started with something very easy to set up - CircleCI , but with time we're adding more & more complex pipelines - we use Jenkins to configure & run those. It's much more effort, but at some point we had to pay for the flexibility we expected. Our source code version control is Git (which probably doesn't require a rationale these days) and we keep repos in GitHub - since the very beginning & we never considered moving out. Our primary monitoring these days is in New Relic (Ruby & SPA apps) and AppSignal (Elixir apps) - we're considering unifying it in New Relic , but this will require some improvements in Elixir app observability. For error reporting we use Sentry (a very popular choice in this class) & we collect our distributed logs using Logentries (to avoid semi-manual handling here).

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Jerome Dalbert
Jerome Dalbert
Senior Backend Engineer at StackShare · | 4 upvotes · 169.9K views
atStackShareStackShare
Heroku
Heroku
New Relic
New Relic
Skylight
Skylight
Rails
Rails
Pingdom
Pingdom
Slack
Slack

We currently monitor performance with the following tools:

  1. Heroku Metrics: our main app is Hosted on Heroku, so it is the best place to get quick server metrics like memory usage, load averages, or response times.
  2. Good old New Relic for detailed general metrics, including transaction times.
  3. Skylight for more specific Rails Controller#action transaction times. Navigating those timings is much better than with New Relic, as you get a clear full breakdown of everything that happens for a given request.

Skylight offers better Rails performance insights, so why use New Relic? Because it does frontend monitoring, while Skylight doesn't. Now that we have a separate frontend app though, our frontend engineers are looking into more specialized frontend monitoring solutions.

Finally, if one of our apps go down, Pingdom alerts us on Slack and texts some of us.

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ruxit logo

ruxit

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Full stack availability and performance monitoring powered by artificial intelligence
ruxit logo
ruxit
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Wavefront logo
Wavefront
AppDynamics logo

AppDynamics

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Application management for the cloud generation
AppDynamics logo
AppDynamics
VS
Wavefront logo
Wavefront
Azure Application Insights logo

Azure Application Insights

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It is an extensible Application Performance Management (APM) service for web developers
Azure Application Insights logo
Azure Application Insights
VS
Wavefront logo
Wavefront
Dynatrace logo

Dynatrace

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Monitor, optimize, and scale every app, in any cloud
Dynatrace logo
Dynatrace
VS
Wavefront logo
Wavefront