Alternatives to SignalFx logo

Alternatives to SignalFx

Datadog, Prometheus, Splunk, Zipkin, and Sumo Logic are the most popular alternatives and competitors to SignalFx.
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What is SignalFx and what are its top alternatives?

We provide operational intelligence for today’s elastic architectures through monitoring specifically designed for microservices and containers with: -powerful and proactive alerting -metrics aggregation -visualization into time series data
SignalFx is a tool in the Performance Monitoring category of a tech stack.

Top Alternatives of SignalFx

SignalFx alternatives & related posts

related Datadog posts

Robert Zuber
Robert Zuber
CTO at CircleCI · | 8 upvotes · 377.5K 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 · 1.4M 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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Splunk logo

Splunk

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Search, monitor, analyze and visualize machine data
    Be the first to leave a pro
    Splunk logo
    Splunk
    VS
    SignalFx logo
    SignalFx

    related Splunk posts

    Kibana
    Kibana
    Splunk
    Splunk
    Grafana
    Grafana

    I use Kibana because it ships with the ELK stack. I don't find it as powerful as Splunk however it is light years above grepping through log files. We previously used Grafana but found it to be annoying to maintain a separate tool outside of the ELK stack. We were able to get everything we needed from Kibana.

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    Zipkin logo

    Zipkin

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    39
    1
    37
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    A distributed tracing system
    Zipkin logo
    Zipkin
    VS
    SignalFx logo
    SignalFx
    Sumo Logic logo

    Sumo Logic

    151
    130
    19
    151
    130
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    Cloud Log Management for Application Logs and IT Log Data
    Sumo Logic logo
    Sumo Logic
    VS
    SignalFx logo
    SignalFx

    related Sumo Logic posts

    Logentries
    Logentries
    LogDNA
    LogDNA
    Timber.io
    Timber.io
    Papertrail
    Papertrail
    Sumo Logic
    Sumo Logic
    #Heroku

    Logentries, LogDNA, Timber.io, Papertrail and Sumo Logic provide free pricing plan for #Heroku application. You can add these applications as add-ons very easily.

    See more
    New Relic logo

    New Relic

    15.5K
    4.1K
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    15.5K
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    SaaS Application Performance Management for Ruby, PHP, .Net, Java, Python, and Node.js Apps.
    New Relic logo
    New Relic
    VS
    SignalFx logo
    SignalFx

    related New Relic posts

    Sebastian Gębski
    Sebastian Gębski
    CTO at Shedul/Fresha · | 4 upvotes · 436.1K 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 · 165K 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.

    See more
    Instana logo

    Instana

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    Automatic Application and Infrastructure Monitoring
    Instana logo
    Instana
    VS
    SignalFx logo
    SignalFx

    related Grafana posts

    Conor Myhrvold
    Conor Myhrvold
    Tech Brand Mgr, Office of CTO at Uber · | 12 upvotes · 1.4M 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)

    See more
    Grafana
    Grafana
    Kibana
    Kibana

    For our Predictive Analytics platform, we have used both Grafana and Kibana

    Kibana has predictions and ML algorithms support, so if you need them, you may be better off with Kibana . The multi-variate analysis features it provide are very unique (not available in Grafana).

    For everything else, definitely Grafana . Especially the number of supported data sources, and plugins clearly makes Grafana a winner (in just visualization and reporting sense). Creating your own plugin is also very easy. The top pros of Grafana (which it does better than Kibana ) are:

    • Creating and organizing visualization panels
    • Templating the panels on dashboards for repetetive tasks
    • Realtime monitoring, filtering of charts based on conditions and variables
    • Export / Import in JSON format (that allows you to version and save your dashboard as part of git)
    See more