Alternatives to Datadog logo

Alternatives to Datadog

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

Datadog is a popular monitoring and analytics platform that offers features such as real-time data visualization, alerting, log management, and APM. However, Datadog can be expensive for small to medium-sized businesses and lacks some advanced customization options.

  1. New Relic: New Relic is a comprehensive observability platform that provides monitoring solutions for applications, infrastructure, and logs. It offers real-time insights, advanced visualizations, and customizable alerting. Pros include a user-friendly interface and detailed performance metrics, but cons include high pricing for larger organizations.
  2. Splunk: Splunk is a data analytics platform that offers monitoring, investigation, and visualization of machine data in real-time. It provides powerful search capabilities, machine learning features, and customizable dashboards. Pros include scalability and flexibility, while cons include a steep learning curve and high costs.
  3. Dynatrace: Dynatrace is an application performance management platform that uses AI and automation to provide insights and analytics on application performance. It offers end-to-end monitoring, root cause analysis, and automatic problem detection. Pros include AI-driven insights and automatic instrumentation, but cons include resource-intensive deployment.
  4. Prometheus: Prometheus is an open-source monitoring and alerting toolkit designed for reliability and scalability. It gathers metrics from targets like containers and databases and stores them for visualization and alerting. Pros include a flexible query language and integrations with various tools, while cons include a steeper learning curve compared to Datadog.
  5. Grafana: Grafana is an open-source analytics and monitoring platform that allows users to query, visualize, and alert on data. It supports various data sources, including Datadog, Prometheus, and InfluxDB. Pros include a wide range of integrations and a large community, but cons include a lack of built-in data collection agents.
  6. Zabbix: Zabbix is an open-source monitoring software that offers monitoring of servers, networks, and applications. It provides real-time monitoring, alerting, and reporting capabilities. Pros include a low cost of ownership and extensive monitoring capabilities, but cons include a steep learning curve and limited support for cloud environments.
  7. Sysdig: Sysdig is a container security and monitoring platform that provides visibility into containers, Kubernetes, and cloud services. It offers deep container inspection, real-time alerts, and troubleshooting tools. Pros include strong container-focused capabilities and cloud-native monitoring, but cons include a complex setup process.
  8. AppDynamics: AppDynamics is an application performance monitoring solution that provides real-time insights into the performance of business-critical applications. It offers end-user monitoring, database visibility, and business transaction tracking. Pros include deep application insights and business impact analysis, while cons include high costs for full-feature access.
  9. Icinga: Icinga is an open-source monitoring tool that checks the availability of network resources, notifies users of outages, and generates performance data. It offers customizable alerts, reporting, and REST APIs for integration. Pros include extensibility through plugins and a vibrant community, but cons include a potentially steep learning curve for beginners.
  10. Observium: Observium is a network monitoring platform that collects and displays data from routers, switches, servers, and other network devices. It offers autodiscovery, alerting, and historical data storage. Pros include ease of setup and customization, but cons include limited support for custom devices and less flexibility compared to Datadog.

Top Alternatives to Datadog

  • New Relic
    New Relic

    The world’s best software and DevOps teams rely on New Relic to move faster, make better decisions and create best-in-class digital experiences. If you run software, you need to run New Relic. More than 50% of the Fortune 100 do too. ...

  • Splunk
    Splunk

    It provides the leading platform for Operational Intelligence. Customers use it to search, monitor, analyze and visualize machine data. ...

  • Prometheus
    Prometheus

    Prometheus is a systems and service monitoring system. It collects metrics from configured targets at given intervals, evaluates rule expressions, displays the results, and can trigger alerts if some condition is observed to be true. ...

  • Grafana
    Grafana

    Grafana is a general purpose dashboard and graph composer. It's focused on providing rich ways to visualize time series metrics, mainly though graphs but supports other ways to visualize data through a pluggable panel architecture. It currently has rich support for for Graphite, InfluxDB and OpenTSDB. But supports other data sources via plugins. ...

  • AppDynamics
    AppDynamics

    AppDynamics develops application performance management (APM) solutions that deliver problem resolution for highly distributed applications through transaction flow monitoring and deep diagnostics. ...

  • Sentry
    Sentry

    Sentry’s Application Monitoring platform helps developers see performance issues, fix errors faster, and optimize their code health. ...

  • Elasticsearch
    Elasticsearch

    Elasticsearch is a distributed, RESTful search and analytics engine capable of storing data and searching it in near real time. Elasticsearch, Kibana, Beats and Logstash are the Elastic Stack (sometimes called the ELK Stack). ...

  • LogicMonitor
    LogicMonitor

    LogicMonitor provides the end-to-end visibility needed to maintain the performance and availability of business applications. It leverages automation and built-in intelligence to monitor today's complex and distributed infrastructures. ...

Datadog alternatives & related posts

New Relic logo

New Relic

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New Relic is the industry’s largest and most comprehensive cloud-based observability platform.
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PROS OF NEW RELIC
  • 415
    Easy setup
  • 344
    Really powerful
  • 245
    Awesome visualization
  • 194
    Ease of use
  • 151
    Great ui
  • 107
    Free tier
  • 80
    Great tool for insights
  • 66
    Heroku Integration
  • 55
    Market leader
  • 49
    Peace of mind
  • 21
    Push notifications
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    Email notifications
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    Heroku Add-on
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    Error Detection and Alerting
  • 13
    Multiple language support
  • 11
    Server Resources Monitoring
  • 11
    SQL Analysis
  • 9
    Transaction Tracing
  • 8
    Azure Add-on
  • 8
    Apdex Scores
  • 7
    Analysis of CPU, Disk, Memory, and Network
  • 7
    Detailed reports
  • 6
    Application Response Times
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    Error Analysis
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    Performance of External Services
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    Application Availability Monitoring and Alerting
  • 5
    JVM Performance Analyzer (Java)
  • 5
    Most Time Consuming Transactions
  • 4
    Top Database Operations
  • 4
    Browser Transaction Tracing
  • 4
    Easy to use
  • 3
    Application Map
  • 3
    Weekly Performance Email
  • 3
    Pagoda Box integration
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    Custom Dashboards
  • 2
    Easy to setup
  • 2
    App Speed Index
  • 2
    Background Jobs Transaction Analysis
  • 1
    Real User Monitoring Analysis and Breakdown
  • 1
    Incident Detection and Alerting
  • 1
    Team Collaboration Tools
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    Metric Data Retention
  • 1
    Metric Data Resolution
  • 1
    Worst Transactions by User Dissatisfaction
  • 1
    Real User Monitoring Overview
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    Free
  • 1
    Time Comparisons
  • 1
    Access to Performance Data API
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    Best of the best, what more can you ask for
  • 1
    Best monitoring on the market
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    Rails integration
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    Super Expensive
  • 0
    Proce
  • 0
    Exceptions
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    Price
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    Cost
CONS OF NEW RELIC
  • 20
    Pricing model doesn't suit microservices
  • 10
    UI isn't great
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    Expensive
  • 7
    Visualizations aren't very helpful
  • 5
    Hard to understand why things in your app are breaking

related New Relic posts

Cooper Marcus
Director of Ecosystem at Kong Inc. · | 17 upvotes · 111.2K views
Shared insights
on
New RelicNew RelicGitHubGitHubZapierZapier
at

I've used more and more of New Relic Insights here in my work at Kong. New Relic Insights is a "time series event database as a service" with a super-easy API for inserting custom events, and a flexible query language for building visualization widgets and dashboards.

I'm a big fan of New Relic Insights when I have data I know I need to analyze, but perhaps I'm not exactly sure how I want to analyze it in the future. For example, at Kong we recently wanted to get some understanding of our open source community's activity on our GitHub repos. I was able to quickly configure GitHub to send webhooks to Zapier , which in turn posted the JSON to New Relic Insights.

Insights is schema-less and configuration-less - just start posting JSON key value pairs, then start querying your data.

Within minutes, data was flowing from GitHub to Insights, and I was building widgets on my Insights dashboard to help my colleagues visualize the activity of our open source community.

#GitHubAnalytics #OpenSourceCommunityAnalytics #CommunityAnalytics #RepoAnalytics

See more
Julien DeFrance
Principal Software Engineer at Tophatter · | 16 upvotes · 3.2M views

Back in 2014, I was given an opportunity to re-architect SmartZip Analytics platform, and flagship product: SmartTargeting. This is a SaaS software helping real estate professionals keeping up with their prospects and leads in a given neighborhood/territory, finding out (thanks to predictive analytics) who's the most likely to list/sell their home, and running cross-channel marketing automation against them: direct mail, online ads, email... The company also does provide Data APIs to Enterprise customers.

I had inherited years and years of technical debt and I knew things had to change radically. The first enabler to this was to make use of the cloud and go with AWS, so we would stop re-inventing the wheel, and build around managed/scalable services.

For the SaaS product, we kept on working with Rails as this was what my team had the most knowledge in. We've however broken up the monolith and decoupled the front-end application from the backend thanks to the use of Rails API so we'd get independently scalable micro-services from now on.

Our various applications could now be deployed using AWS Elastic Beanstalk so we wouldn't waste any more efforts writing time-consuming Capistrano deployment scripts for instance. Combined with Docker so our application would run within its own container, independently from the underlying host configuration.

Storage-wise, we went with Amazon S3 and ditched any pre-existing local or network storage people used to deal with in our legacy systems. On the database side: Amazon RDS / MySQL initially. Ultimately migrated to Amazon RDS for Aurora / MySQL when it got released. Once again, here you need a managed service your cloud provider handles for you.

Future improvements / technology decisions included:

Caching: Amazon ElastiCache / Memcached CDN: Amazon CloudFront Systems Integration: Segment / Zapier Data-warehousing: Amazon Redshift BI: Amazon Quicksight / Superset Search: Elasticsearch / Amazon Elasticsearch Service / Algolia Monitoring: New Relic

As our usage grows, patterns changed, and/or our business needs evolved, my role as Engineering Manager then Director of Engineering was also to ensure my team kept on learning and innovating, while delivering on business value.

One of these innovations was to get ourselves into Serverless : Adopting AWS Lambda was a big step forward. At the time, only available for Node.js (Not Ruby ) but a great way to handle cost efficiency, unpredictable traffic, sudden bursts of traffic... Ultimately you want the whole chain of services involved in a call to be serverless, and that's when we've started leveraging Amazon DynamoDB on these projects so they'd be fully scalable.

See more
Splunk logo

Splunk

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Search, monitor, analyze and visualize machine data
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PROS OF SPLUNK
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    API for searching logs, running reports
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    Alert system based on custom query results
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    Dashboarding on any log contents
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    Custom log parsing as well as automatic parsing
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    Ability to style search results into reports
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    Query engine supports joining, aggregation, stats, etc
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    Splunk language supports string, date manip, math, etc
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    Rich GUI for searching live logs
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    Query any log as key-value pairs
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    Granular scheduling and time window support
CONS OF SPLUNK
  • 1
    Splunk query language rich so lots to learn

related Splunk posts

Shared insights
on
SplunkSplunkDjangoDjango

I am designing a Django application for my organization which will be used as an internal tool. The infra team said that I will not be having SSH access to the production server and I will have to log all my backend application messages to Splunk. I have no knowledge of Splunk so the following are the approaches I am considering: Approach 1: Create an hourly cron job that uploads the server log file to some Splunk storage for later analysis. - Is this possible? Approach 2: Is it possible just to stream the logs to some splunk endpoint? (If yes, I feel network usage and communication overhead will be a pain-point for my application)

Is there any better or standard approach? Thanks in advance.

See more
Shared insights
on
KibanaKibanaSplunkSplunkGrafanaGrafana

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.

See more
Prometheus logo

Prometheus

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An open-source service monitoring system and time series database, developed by SoundCloud
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PROS OF PROMETHEUS
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    Powerful easy to use monitoring
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    Flexible query language
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    Dimensional data model
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    Alerts
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    Active and responsive community
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    Extensive integrations
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    Easy to setup
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    Beautiful Model and Query language
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    Easy to extend
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    Nice
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    Written in Go
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    Good for experimentation
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    Easy for monitoring
CONS OF PROMETHEUS
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    Just for metrics
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    Bad UI
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    Needs monitoring to access metrics endpoints
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    Not easy to configure and use
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    Supports only active agents
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    Written in Go
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    TLS is quite difficult to understand
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    Requires multiple applications and tools
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    Single point of failure

related Prometheus posts

Matt Menzenski
Senior Software Engineering Manager at PayIt · | 16 upvotes · 999.2K views

Grafana and Prometheus together, running on Kubernetes , is a powerful combination. These tools are cloud-native and offer a large community and easy integrations. At PayIt we're using exporting Java application metrics using a Dropwizard metrics exporter, and our Node.js services now use the prom-client npm library to serve metrics.

See more
Conor Myhrvold
Tech Brand Mgr, Office of CTO at Uber · | 15 upvotes · 4.5M views

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 logo

Grafana

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Open source Graphite & InfluxDB Dashboard and Graph Editor
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PROS OF GRAFANA
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    Beautiful
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    Graphs are interactive
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    Free
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    Easy
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    Nicer than the Graphite web interface
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    Many integrations
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    Can build dashboards
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    Easy to specify time window
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    Can collaborate on dashboards
  • 9
    Dashboards contain number tiles
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    Open Source
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    Integration with InfluxDB
  • 5
    Click and drag to zoom in
  • 4
    Authentification and users management
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    Threshold limits in graphs
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    Alerts
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    It is open to cloud watch and many database
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    Simple and native support to Prometheus
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    Great community support
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    You can use this for development to check memcache
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    You can visualize real time data to put alerts
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    Grapsh as code
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    Plugin visualizationa
CONS OF GRAFANA
  • 1
    No interactive query builder

related Grafana posts

Matt Menzenski
Senior Software Engineering Manager at PayIt · | 16 upvotes · 999.2K views

Grafana and Prometheus together, running on Kubernetes , is a powerful combination. These tools are cloud-native and offer a large community and easy integrations. At PayIt we're using exporting Java application metrics using a Dropwizard metrics exporter, and our Node.js services now use the prom-client npm library to serve metrics.

See more
Conor Myhrvold
Tech Brand Mgr, Office of CTO at Uber · | 15 upvotes · 4.5M views

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
AppDynamics logo

AppDynamics

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Application management for the cloud generation
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PROS OF APPDYNAMICS
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    Deep code visibility
  • 13
    Powerful
  • 8
    Real-Time Visibility
  • 7
    Great visualization
  • 6
    Easy Setup
  • 6
    Comprehensive Coverage of Programming Languages
  • 4
    Deep DB Troubleshooting
  • 3
    Excellent Customer Support
CONS OF APPDYNAMICS
  • 5
    Expensive
  • 2
    Poor to non-existent integration with aws services

related AppDynamics posts

Farzeem Diamond Jiwani
Software Engineer at IVP · | 8 upvotes · 1.4M views

Hey there! We are looking at Datadog, Dynatrace, AppDynamics, and New Relic as options for our web application monitoring.

Current Environment: .NET Core Web app hosted on Microsoft IIS

Future Environment: Web app will be hosted on Microsoft Azure

Tech Stacks: IIS, RabbitMQ, Redis, Microsoft SQL Server

Requirement: Infra Monitoring, APM, Real - User Monitoring (User activity monitoring i.e., time spent on a page, most active page, etc.), Service Tracing, Root Cause Analysis, and Centralized Log Management.

Please advise on the above. Thanks!

See more

We are evaluating an APM tool and would like to select between AppDynamics or Datadog. Our applications are largely hosted on Microsoft Azure but we would keep the option to move to AWS or Google Cloud Platform in the future.

In addition to core Azure services, we will be hosting other components - including MongoDB, Keycloak, PagerDuty, etc. Our applications are largely C# and React-based using frontend for Backend patterns and Azure API gateway. In addition, there are close to 50+ external services integrated using both REST and SOAP.

See more
Sentry logo

Sentry

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See performance issues, fix errors faster, and optimize code health.
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PROS OF SENTRY
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    Consolidates similar errors and makes resolution easy
  • 121
    Email Notifications
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    Open source
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    Slack integration
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    Github integration
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    Easy
  • 44
    User-friendly interface
  • 28
    The most important tool we use in production
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    Hipchat integration
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    Heroku Integration
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    Good documentation
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    Free tier
  • 11
    Self-hosted
  • 9
    Easy setup
  • 7
    Realiable
  • 6
    Provides context, and great stack trace
  • 4
    Feedback form on error pages
  • 4
    Love it baby
  • 3
    Gitlab integration
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    Filter by custom tags
  • 3
    Super user friendly
  • 3
    Captures local variables at each frame in backtraces
  • 3
    Easy Integration
  • 1
    Performance measurements
CONS OF SENTRY
  • 12
    Confusing UI
  • 4
    Bundle size

related Sentry posts

Lucas Litton
Founder & CEO at Macombey · | 24 upvotes · 277.8K views

Sentry has been essential to our development approach. Nobody likes errors or apps that crash. We use Sentry heavily during Node.js and React development. Our developers are able to see error reports, crashes, user's browsers, and more, all in one place. Sentry also seamlessly integrates with Asana, Slack, and GitHub.

See more
Johnny Bell

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.

See more
Elasticsearch logo

Elasticsearch

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Open Source, Distributed, RESTful Search Engine
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PROS OF ELASTICSEARCH
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    Powerful api
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    Great search engine
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    Open source
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    Restful
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    Near real-time search
  • 98
    Free
  • 85
    Search everything
  • 54
    Easy to get started
  • 45
    Analytics
  • 26
    Distributed
  • 6
    Fast search
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    More than a search engine
  • 4
    Great docs
  • 4
    Awesome, great tool
  • 3
    Highly Available
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    Easy to scale
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    Potato
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    Document Store
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    Great customer support
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    Intuitive API
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    Nosql DB
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    Great piece of software
  • 2
    Reliable
  • 2
    Fast
  • 2
    Easy setup
  • 1
    Open
  • 1
    Easy to get hot data
  • 1
    Github
  • 1
    Elaticsearch
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    Actively developing
  • 1
    Responsive maintainers on GitHub
  • 1
    Ecosystem
  • 1
    Not stable
  • 1
    Scalability
  • 0
    Community
CONS OF ELASTICSEARCH
  • 7
    Resource hungry
  • 6
    Diffecult to get started
  • 5
    Expensive
  • 4
    Hard to keep stable at large scale

related Elasticsearch posts

Tim Abbott

We've been using PostgreSQL since the very early days of Zulip, but we actually didn't use it from the beginning. Zulip started out as a MySQL project back in 2012, because we'd heard it was a good choice for a startup with a wide community. However, we found that even though we were using the Django ORM for most of our database access, we spent a lot of time fighting with MySQL. Issues ranged from bad collation defaults, to bad query plans which required a lot of manual query tweaks.

We ended up getting so frustrated that we tried out PostgresQL, and the results were fantastic. We didn't have to do any real customization (just some tuning settings for how big a server we had), and all of our most important queries were faster out of the box. As a result, we were able to delete a bunch of custom queries escaping the ORM that we'd written to make the MySQL query planner happy (because postgres just did the right thing automatically).

And then after that, we've just gotten a ton of value out of postgres. We use its excellent built-in full-text search, which has helped us avoid needing to bring in a tool like Elasticsearch, and we've really enjoyed features like its partial indexes, which saved us a lot of work adding unnecessary extra tables to get good performance for things like our "unread messages" and "starred messages" indexes.

I can't recommend it highly enough.

See more
Tymoteusz Paul
Devops guy at X20X Development LTD · | 23 upvotes · 8.7M views

Often enough I have to explain my way of going about setting up a CI/CD pipeline with multiple deployment platforms. Since I am a bit tired of yapping the same every single time, I've decided to write it up and share with the world this way, and send people to read it instead ;). I will explain it on "live-example" of how the Rome got built, basing that current methodology exists only of readme.md and wishes of good luck (as it usually is ;)).

It always starts with an app, whatever it may be and reading the readmes available while Vagrant and VirtualBox is installing and updating. Following that is the first hurdle to go over - convert all the instruction/scripts into Ansible playbook(s), and only stopping when doing a clear vagrant up or vagrant reload we will have a fully working environment. As our Vagrant environment is now functional, it's time to break it! This is the moment to look for how things can be done better (too rigid/too lose versioning? Sloppy environment setup?) and replace them with the right way to do stuff, one that won't bite us in the backside. This is the point, and the best opportunity, to upcycle the existing way of doing dev environment to produce a proper, production-grade product.

I should probably digress here for a moment and explain why. I firmly believe that the way you deploy production is the same way you should deploy develop, shy of few debugging-friendly setting. This way you avoid the discrepancy between how production work vs how development works, which almost always causes major pains in the back of the neck, and with use of proper tools should mean no more work for the developers. That's why we start with Vagrant as developer boxes should be as easy as vagrant up, but the meat of our product lies in Ansible which will do meat of the work and can be applied to almost anything: AWS, bare metal, docker, LXC, in open net, behind vpn - you name it.

We must also give proper consideration to monitoring and logging hoovering at this point. My generic answer here is to grab Elasticsearch, Kibana, and Logstash. While for different use cases there may be better solutions, this one is well battle-tested, performs reasonably and is very easy to scale both vertically (within some limits) and horizontally. Logstash rules are easy to write and are well supported in maintenance through Ansible, which as I've mentioned earlier, are at the very core of things, and creating triggers/reports and alerts based on Elastic and Kibana is generally a breeze, including some quite complex aggregations.

If we are happy with the state of the Ansible it's time to move on and put all those roles and playbooks to work. Namely, we need something to manage our CI/CD pipelines. For me, the choice is obvious: TeamCity. It's modern, robust and unlike most of the light-weight alternatives, it's transparent. What I mean by that is that it doesn't tell you how to do things, doesn't limit your ways to deploy, or test, or package for that matter. Instead, it provides a developer-friendly and rich playground for your pipelines. You can do most the same with Jenkins, but it has a quite dated look and feel to it, while also missing some key functionality that must be brought in via plugins (like quality REST API which comes built-in with TeamCity). It also comes with all the common-handy plugins like Slack or Apache Maven integration.

The exact flow between CI and CD varies too greatly from one application to another to describe, so I will outline a few rules that guide me in it: 1. Make build steps as small as possible. This way when something breaks, we know exactly where, without needing to dig and root around. 2. All security credentials besides development environment must be sources from individual Vault instances. Keys to those containers should exist only on the CI/CD box and accessible by a few people (the less the better). This is pretty self-explanatory, as anything besides dev may contain sensitive data and, at times, be public-facing. Because of that appropriate security must be present. TeamCity shines in this department with excellent secrets-management. 3. Every part of the build chain shall consume and produce artifacts. If it creates nothing, it likely shouldn't be its own build. This way if any issue shows up with any environment or version, all developer has to do it is grab appropriate artifacts to reproduce the issue locally. 4. Deployment builds should be directly tied to specific Git branches/tags. This enables much easier tracking of what caused an issue, including automated identifying and tagging the author (nothing like automated regression testing!).

Speaking of deployments, I generally try to keep it simple but also with a close eye on the wallet. Because of that, I am more than happy with AWS or another cloud provider, but also constantly peeking at the loads and do we get the value of what we are paying for. Often enough the pattern of use is not constantly erratic, but rather has a firm baseline which could be migrated away from the cloud and into bare metal boxes. That is another part where this approach strongly triumphs over the common Docker and CircleCI setup, where you are very much tied in to use cloud providers and getting out is expensive. Here to embrace bare-metal hosting all you need is a help of some container-based self-hosting software, my personal preference is with Proxmox and LXC. Following that all you must write are ansible scripts to manage hardware of Proxmox, similar way as you do for Amazon EC2 (ansible supports both greatly) and you are good to go. One does not exclude another, quite the opposite, as they can live in great synergy and cut your costs dramatically (the heavier your base load, the bigger the savings) while providing production-grade resiliency.

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