Alternatives to New Relic

We just launched the Segment Config API (try it out for yourself here) — a set of public REST APIs that enable you to manage your Segment configuration. Behind the scenes the Config API is built with Go , GRPC and Envoy.

At Segment, we build new services in Go by default. The language is simple so new team members quickly ramp up on a codebase. The tool chain is fast so developers get immediate feedback when they break code, tests or integrations with other systems. The runtime is fast so it performs great at scale.

For the newest round of APIs we adopted the GRPC service #framework.

The Protocol Buffer service definition language makes it easy to design type-safe and consistent APIs, thanks to ecosystem tools like the Google API Design Guide for API standards, uber/prototool for formatting and linting .protos and lyft/protoc-gen-validate for defining field validations, and grpc-gateway for defining REST mapping.

With a well designed .proto, its easy to generate a Go server interface and a TypeScript client, providing type-safe RPC between languages.

For the API gateway and RPC we adopted the Envoy service proxy.

The internet-facing segmentapis.com endpoint is an Envoy front proxy that rate-limits and authenticates every request. It then transcodes a #REST / #JSON request to an upstream GRPC request. The upstream GRPC servers are running an Envoy sidecar configured for Datadog stats.

The result is API #security , #reliability and consistent #observability through Envoy configuration, not code.

We experimented with Swagger service definitions, but the spec is sprawling and the generated clients and server stubs leave a lot to be desired. GRPC and .proto and the Go implementation feels better designed and implemented. Thanks to the GRPC tooling and ecosystem you can generate Swagger from .protos, but it’s effectively impossible to go the other way.

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.

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!

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 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.

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.

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.

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)

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.

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.

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)

I am new to DevOps and looking for training in DevOps. Some institutes are offering Nagios while some Prometheus in their syllabus. Please suggest which one is being used in the industry and which one should I learn.