Blackfire.io vs Honeycomb: What are the differences?
What is Blackfire.io? Blackfire Profiler automatically instruments your code to gather data about consumed server resources like memory, CPU time, and I/O. Blackfire achieves a new level of accuracy by aggregating several profile samples and getting rid of non relevant data. Get even deeper and review the interactions with your entire stack (SQL database engines, cache servers, ...).
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.
Blackfire.io and Honeycomb can be categorized as "Performance Monitoring" tools.
Some of the features offered by Blackfire.io are:
- Profile a page at a mouse click thanks to the Blackfire web browser extension
- Browse through the function calls and identify performance bottlenecks thanks to the Blackfire intuitive UI
- Because Blackfire is only triggered when you profile a request, there is no overhead for other requests
On the other hand, Honeycomb provides the following key features:
- High-performance querying against high-cardinality or sparse events.
- Accepts any structured JSON objects with a write key.
- Submit events via API.
What is Blackfire.io?
What is Honeycomb?
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Why do developers choose Honeycomb?
What are the cons of using Blackfire.io?
What are the cons of using Honeycomb?
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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.