Honeycomb vs Inspeqtor: 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 Inspeqtor? Easy application infrastructure monitoring. Inspeqtor monitors your application infrastructure. It gathers and verifies key metrics from all the moving parts in your application and alerts you when something looks wrong. It understands the application deployment workflow so it won't bother you during a deploy.
Honeycomb and Inspeqtor 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, Inspeqtor provides the following key features:
- Monitor init.d-, systemd-, upstart-, runit- or launchd-managed services
- Monitor process memory and CPU usage
- Monitor daemon-specific metrics (e.g. redis, memcached, mysql, nginx...)
Inspeqtor is an open source tool with 1.63K GitHub stars and 73 GitHub forks. Here's a link to Inspeqtor's open source repository on GitHub.
What is Honeycomb?
What is Inspeqtor?
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Why do developers choose Honeycomb?
What are the cons of using Honeycomb?
What are the cons of using Inspeqtor?
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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.