Unless you've maximally-provisioned your ELK systems, search performance can be slow when searching across lots of data, especially if your Grok filters are not working perfectly. In ELK, if your rows get wider, it gets slower to read back the full row, and you'll have to update more indexes at write time. ELK relies on storing whole rows of data plus additional indexes for speed, while Honeycomb's underlying architecture means that our column store doesn't get slower the more data you store per-row. You'll have to have defined the fields and indexes you want ahead of time, and you can't really discover what's in your data easily. When it comes to observability with Elastic stack, you'll be using Kibana to search your data, and that means learning a new query language and syntax. Observability disadvantages with Elastic Stack Honeycomb has tracing support built right in, and with our Beelines, you get automatic and immediate instrumentation that includes traces. This is sub-optimal when time-to-understanding (and ultimately, resolution) is critical. You can ask your team to use a second tool to access them, but in doing so they have to re-orient their investigation and drill into a UI with a different approach and mindset-which can derail their investigation. Switching interfaces for tracing can mean losing speed and context during an investigationĮlastic stack doesn't include traces or the ability to view them side-by-side with events from your systems.
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