Honeycomb vs SeaLion: What are the differences?
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; SeaLion: Quickly diagnose problems with your Linux servers. SeaLion is a cloud based system monitoring tool for Linux servers. Getting started is as easy as executing a command. It installs an agent at /usr/local/sealion-agent and runs as an unprivileged user (sealion). This agent will collect data at regular intervals across servers and this data will be available on your workspace. The latest version is shipped with 5 default services namely Apache, NGINX, MongoDB, MySQL, Redis.
Honeycomb and SeaLion 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, SeaLion provides the following key features:
- Raw output. No learning curve
- Quickly identify critical issues
- Historical analysis
What is Honeycomb?
What is SeaLion?
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Why do developers choose Honeycomb?
Why do developers choose SeaLion?
What are the cons of using Honeycomb?
What are the cons of using SeaLion?
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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.