Honeycomb vs Kadira: What are the differences?
What is Honeycomb? 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.
What is Kadira? Performance Monitoring for Meteor. See what’s going on with your app with different performance metrics and traces. Kadira tracks all your client and server errors automatically. You can profile your app in production or locally with Kadira and analyze it using an easy-to-use CPU analyzer.
Honeycomb and Kadira 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, Kadira provides the following key features:
- Performance Metrics and Traces
- Error Tracking
- CPU Profiling
Kadira is an open source tool with 214 GitHub stars and 88 GitHub forks. Here's a link to Kadira's open source repository on GitHub.
What is Honeycomb?
What is Kadira?
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Why do developers choose Honeycomb?
What are the cons of using Honeycomb?
What are the cons of using Kadira?
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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.