Alternatives to Stackdriver logo

Alternatives to Stackdriver

Datadog, New Relic, Splunk, Prometheus, and ELK are the most popular alternatives and competitors to Stackdriver.
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What is Stackdriver and what are its top alternatives?

Google Stackdriver provides powerful monitoring, logging, and diagnostics. It equips you with insight into the health, performance, and availability of cloud-powered applications, enabling you to find and fix issues faster.
Stackdriver is a tool in the Cloud Monitoring category of a tech stack.

Stackdriver alternatives & related posts

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Robert Zuber
Robert Zuber
CTO at CircleCI · | 8 upvotes · 113.9K views
atCircleCICircleCI
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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
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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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New Relic logo

New Relic

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SaaS Application Performance Management for Ruby, PHP, .Net, Java, Python, and Node.js Apps.
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Sebastian Gębski
Sebastian Gębski
CTO at Shedul/Fresha · | 4 upvotes · 245.4K views
atFresha EngineeringFresha Engineering
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GitHub
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Git
Git
Jenkins
Jenkins
CircleCI
CircleCI

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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Julien DeFrance
Julien DeFrance
Principal Software Engineer at Tophatter · | 3 upvotes · 62.5K views
atStessaStessa
Datadog
Datadog
New Relic
New Relic
#APM

Which #APM / #Infrastructure #Monitoring solution to use?

The 2 major players in that space are New Relic and Datadog Both are very comparable in terms of pricing, capabilities (Datadog recently introduced APM as well).

In our use case, keeping the number of tools minimal was a major selection criteria.

As we were already using #NewRelic, my recommendation was to move to the pro tier so we would benefit from advanced APM features, synthetics, mobile & infrastructure monitoring. And gain 360 degree view of our infrastructure.

Few things I liked about New Relic: - Mobile App and push notificatin - Ease of setting up new alerts - Being notified via email and push notifications without requiring another alerting 3rd party solution

I've certainly seen use cases where NewRelic can also be used as an input data source for Datadog. Therefore depending on your use case, it might also be worth evaluating a joint usage of both solutions.

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Splunk logo

Splunk

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Search, monitor, analyze and visualize machine data
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    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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    Prometheus logo

    Prometheus

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    An open-source service monitoring system and time series database, developed by SoundCloud
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    Conor Myhrvold
    Conor Myhrvold
    Tech Brand Mgr, Office of CTO at Uber · | 10 upvotes · 568.8K views
    atUber TechnologiesUber Technologies
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    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
    Sysdig
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    Kubernetes
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    Prometheus
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    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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    ELK logo

    ELK

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    The acronym for three open source projects: Elasticsearch, Logstash, and Kibana
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      Wallace Alves
      Wallace Alves
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      Docker Docker Compose Portainer ELK Elasticsearch Kibana Logstash nginx

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      Johnny Bell
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      For my portfolio websites and my personal OpenSource projects I had started exclusively using React and JavaScript so I needed a way to track any errors that we're happening for my users that I didn't uncover during my personal UAT.

      I had narrowed it down to two tools LogRocket and Sentry (I also tried Bugsnag but it did not make the final two). Before I get into this I want to say that both of these tools are amazing and whichever you choose will suit your needs well.

      I firstly decided to go with LogRocket the fact that they had a recorded screen capture of what the user was doing when the bug happened was amazing... I could go back and rewatch what the user did to replicate that error, this was fantastic. It was also very easy to setup and get going. They had options for React and Redux.js so you can track all your Redux.js actions. I had a fairly large Redux.js store, this was ended up being a issue, it killed the processing power on my machine, Chrome ended up using 2-4gb of ram, so I quickly disabled the Redux.js option.

      After using LogRocket for a month or so I decided to switch to Sentry. I noticed that Sentry was openSorce and everyone was talking about Sentry so I thought I may as well give it a test drive. Setting it up was so easy, I had everything up and running within seconds. It also gives you the option to wrap an errorBoundry in React so get more specific errors. The simplicity of Sentry was a breath of fresh air, it allowed me find the bug that was shown to the user and fix that very simply. The UI for Sentry is beautiful and just really clean to look at, and their emails are also just perfect.

      I have decided to stick with Sentry for the long run, I tested pretty much all the JS error loggers and I find Sentry the best.

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      Conor Myhrvold
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      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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      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)
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      Open source macOS app for monitoring the status of cloud services (AWS, Heroku, Twilio, etc)
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                    Monitor your infrastructure and applications, running on-premise or in the cloud, all within a single product
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