Hello :) We are using Datadog on Kong to monitor the metrics and analytics.
We feel that the cost associated with Datadog is high in terms of custom metrics and indexations. So, we planned to find an alternative for Datadog and we are looking into Grafana implementation with kong.
Will the shift from Datadog to Grafana be a wise move and flawless?
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.
I'm considering moving from Flask to Quart, does anyone have some experience with this migration?
I expect possible problems with connexion which we use as OpenAPI specification.
Would be good if someone can point downsides of moving to the Quart framework so I can double-check if my plan is worth doing.
Other libs and tools used in the project: SQLAlchemy, alembic, PostgreSQL, Datadog
cons for now:
I see StatsD is commonly used in conjunction with Datadog. In fact, Datadog even has their own StatsD daemon (called DogStatsD) embedded in the DataDog agent. Can someone explain to me what it is that StatsD gives you which you don't already have with Datadog's APM and distributed tracing functionality?
The Datadog statsd agent is not really a normal statsd client: it implements a large subset of the original (etsy) features, but also some Datadog-specific features (about histograms). It is used to send metrics to Datadog APM, and its big advantage is the developer experience, which is familiar and easy to use, just like any statsd client, making it trivial to replace an existing statsd metrics client in any application with the Datadog version to publish metrics to Datadog.
Via acquisitions and internal product developments over the last year+, Splunk provides really differentiated APM and monitoring for microservices and AWS. I'd recommend giving it a peak if you haven't yet! For some validation, a recent Cloud Observability vendor report by GigaOm came out and ranked Splunk as the "top performer" in the space. Hope this helps in your search
We use a combination of Java and C# microservices on AWS. We started off with CloudWatch but found it severely lacking - even for basic logging functionality; mostly because the way it sets up log groups is not very useful for distributed applications. It gets hard to find the right logs for the right instance; the interface is rather lacking, etc.
We looked into several alternatives. Our final decision fell upon: - Datadog, bundled with every docker image during the CI/CD build process. Datadog has agents easily hook into our existing processes. No real code had to be changed other than the build script and the Dockerfile (https://docs.datadoghq.com/tracing/setup_overview/setup/java/?tab=containers). Datadog has been very good at providing insights on many different levels (performance, errors, infrastructure load) and can be set up to send automated alerts when unexpected behavior happens. - Kibana, to centralize our logging into a more easily searchable and filterable configuration.
I would also recommend considering Dynatrace. I believe it comes at a higher price, but I fondly look back to my time working with the tool in the past. Dynatrace is remarkably deep and smart; it ended up being very good at helping us find tricky issues like memory leaks, it helped us monitor performance, trace user paths throughout our apps and so much more. I understand they've evolved quite a bit since I last used them, investing heavily into AI components to improve the experience. Worth the consideration.
Coming from a Ruby background, we've been users of New Relic for quite some time. When we adopted Elixir, the New Relic integration was young and missing essential features, so we gave AppSignal a try. It worked for quite some time, we even implemented a :telemetry reporter for AppSignal . But it was difficult to correlate data in two monitoring solutions, New Relic was undergoing a UI overhaul which made it difficult to use, and AppSignal was missing the flexibility we needed. We had some fans of Datadog, so we gave it a try and it worked out perfectly. Datadog works great with Ruby , Elixir , JavaScript , and has powerful features our engineers love to use (notebooks, dashboards, very flexible alerting). Cherry on top - thanks to the Datadog Terraform provider everything is written as code, allowing us to collaborate on our Datadog setup.
We use AppOptics. I am curious what are the current leaders for APM for small companies (50 employees) that use Python, MariaDB, RabbitMQ, and Google Cloud Storage. We run both Celery and Gunicorn services. We are considering Datadog or some other deep code profiling tool that can spot I/O, DB, or other response time/request rate issues
We are running Python & Celery, our stack is based on AWS ECS. We are using NewRelic. This tool is just amazing, both for API and Offline workers. It would provide any metric I was looking for, including a profiler, SLA / SLO dashboards, infrastructure metrics. It has alerting capability that is easily integrated with Pingdom / PagerDuty / Webhooks.
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!
