Honeycomb vs SignalFx: 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, SignalFx is detailed as "Monitoring and Operational Intelligence for the Cloud". We provide operational intelligence for today’s elastic architectures through monitoring specifically designed for microservices and containers with: -powerful and proactive alerting -metrics aggregation -visualization into time series data.
Honeycomb and SignalFx belong to "Performance Monitoring" category of the tech stack.
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, SignalFx provides the following key features:
- Beautiful streaming visualizations
- Meaningful, fast alerting using SignalFlow analytics
- High resolution metrics up to 1 sec
What is Honeycomb?
What is SignalFx?
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Why do developers choose Honeycomb?
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What are the cons of using Honeycomb?
What are the cons of using SignalFx?
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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.