Honeycomb vs Instrumental: What are the differences?
Developers describe Honeycomb as "Observability for a distributed world--designed for high cardinality data and collaborative problem solving 🐝💖". We built Honeycomb to answer the hard questions that come up when you're trying to operate your software–to debug microservices, serverless, distributed systems, polyglot persistence, containers, and a world of fast, parallel deploys. On the other hand, Instrumental is detailed as "Visualize any aspect of your application performance, measure production code in real-time, and share with your entire team". Instrumental is a real-time application and server monitoring platform. With over a million datapoints processed every second, we're one of the highest scale monitoring services.
Honeycomb and Instrumental can be primarily classified as "Performance Monitoring" tools.
Some of the features offered by Honeycomb are:
- High-performance querying against high-cardinality or sparse events.
- Accepts any structured JSON objects with a write key.
- Submit events via API.
On the other hand, Instrumental provides the following key features:
- Application monitoring - we accept any volume of arbitrary data, making it easy to set up graphs to measure the performance of your application in real time
- Server monitoring - Ruby apps can use our instrumental tools gem to monitor information like CPU statistics, memory usage, disk space, and all the other things you'll need to monitor your servers. Language support - Ruby is supported using a custom library, and all other languages are supported with our Statsd backend
- RESTful API - upon authentication, data is collected and returned in strict JSON format
What is Honeycomb?
What is Instrumental?
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Why do developers choose Honeycomb?
What are the cons of using Honeycomb?
What are the cons of using Instrumental?
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What tools integrate with Instrumental?
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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.