SLO Support now available

Support for setting Service Level Objectives (SLOs) and getting alerts from error budgets is now available for all Enterprise users.

Learn more at our SLO Docs page or click on the golden handshake.

slo menu.png

Boards: Change the time range on all graphs

Within a board, you are now able to change the time range on all graphs at once. To reset the time range back to the original queries - simply choose "Original Board Queries" from the time picker.


Unified Storage

Now Live: Unified Storage. We’ve unified primary and secondary storage, and there’s a pretty cool story behind this update we think you’ll enjoy. Read the blog:

Click to trace from a stacked graph

Seeing something interesting in that stacked graph view of your tracing dataset? You can now click to view the trace!


Edit Derived Columns Beta Released

Many of you have requested the ability to edit your derived columns. This beta functionality is now available to everyone.

We now also show notification if your derived column is in use in a board, trigger or SLO so that you know what will be impacted if you edit or delete a derived column.

Read all about the new features here:

Python Beeline now supports asyncio

As of 2.11.0, the Honeycomb Python Beeline supports asyncio and asynchronous traces. See the docs for more info.

Honeycomb OpenTracing Proxy v2.1.0

We've published a new release of the Honeycomb OpenTracing Proxy. Version 2.1.0 includes:

  • Support for the Zipkin V2 format
  • A fix for an issue with binary annotations

Previously released version 2.0.0 includes:

  • A fix to ensure trace IDs are always positive.

BubbleUp now availble while running Usage Mode

You are now able to use BubbleUp in Usage Mode (Unweighted Mode) while exploring how your sample rates are behaving.

Understanding your historical retention :)

Now in your Dataset Settings >> Overview tab, you'll find a graph of historical retention (that is, how far back you were able to query on that dataset on any given day). Being able to visualize how big of a time window of data was available to you; which is also key for understanding the impact of sampling on a dataset.


From Bubbleup to Traces

If you're looking at a BubbleUp, and see a traceid field, you can now just leap directly from the BubbleUp directly to the Trace.