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News & Announcements Observability
NEW HONEYCOMB FEATURES RAISE THE BAR FOR WHAT OBSERVABILITY SHOULD DO FOR YOU
By Christine Yen | Last modified on October 26, 2022
As long as humans have written software, we’ve needed to understand why our
expectations (the logic we thought we wrote) don’t match reality (the logic
being executed). To that end, we developed techniques to help measure
reality—logging text strings, or capturing aggregated metrics—and persevered,
seeking out newer and fancier logging or monitoring solutions over the
intervening decades.
But by anchoring ourselves in those original techniques of “logs or metrics,”
we’ve accepted a fatal flaw: a world with disconnected, siloed data stores that
many struggle to piece together into something meaningful (no wonder some folks
call them “pillars”).
Honeycomb entered the scene with a fundamentally different way of viewing
software telemetry: what is a metric, if not logs that have been aggregated
along a single dimension? What is a trace, if not structured logs tied together
into a useful hierarchy? These different types of data are, ultimately, just
different visualizations of the same reality in your system.
We built Honeycomb in a fundamentally different way, to reflect the experience
we wanted to have in understanding our production systems. That meant extracting
whatever visualization our hearts desired, from a single source of truth, with
no “dead ends” in a given exploration. Filtering, grouping, and aggregation all
became levers to pull in order to tune the visualization and answer questions,
instead of investigation-limiting characteristics of the traditional data
collection or storage itself.
Looking for additional context beyond this image? Dive into the technical
details in this blog post.
Honeycomb has always seen the world a little differently from legacy logging,
monitoring, and APM vendors—and based on the mainstream embrace of
“observability” and recognition from analysts, the industry is hungry for this
fresh perspective.
With the release of our latest innovations—a new Service Map and improvements to
make BubbleUp more useful and accessible than ever—we’re setting the new
standard for what observability tools should be able to do for you. Let’s see
what’s in the box.
A SERVICE MAP YOU CAN DO SOMETHING WITH
I’ll be blunt: service maps in observability or monitoring tools are nothing
new. Of course it’s valuable to visually represent the various services in your
system. When we set out to design our take on a service map, we wanted to build
beyond a table-stakes implementation and imbue it with the same qualities that
differentiate the core Honeycomb experience: a truly interactive, debuggable,
useful view into your system.
Honeycomb’s Service Map isn’t a piece of art for your walls (or your
execs’)—it’s meant to be touched, filtered, zoomed in and out, and used as a
jumping-off point into other visualizations of your system like traces or
aggregate queries. We built on top of the full power of Honeycomb’s data model
and query engine—so while the usual signals like upstream/downstream
relationships, relative throughput, and latency cues are right there where you’d
expect, you can now filter services or highlight trace paths that match any
criteria in your data.
Want to see which services are touched for users with empty shopping carts?
Easy. What about highlighting the path traveled for requests with a given
feature flag enabled? Done. Or understanding which services are involved in any
traces that were triggered by user 4289cb26? Simple.
Use our Service Map to highlight relevant trace paths, filter displayed
services, zoom into an example trace, then zoom out to an analytical view of
checkout-service spans.
To learn more, see our Service Map launch blog post.
WHAT TO DO WHEN A GRAPH LOOKS OFF: BUBBLE UP ON THOSE OUTLIERS
One of the consistent themes we hear about is the institutional knowledge and
intuition gap between new and seasoned engineers on a given team. Transitioning
from “something looks off” to “I know what it might be” to “let’s check if it’s
happening everywhere, or only under certain conditions” can leave an engineer
less familiar with a system struggling to catch up.
Those seasoned engineers on a team can often simply sense that, say, increased
latency in Service X is due to an increase in requests that fit Criteria Y. They
can confirm it with data, then move forward into figuring out why Criteria Y is
suddenly overrepresented. But what about the rest of us?
We introduced BubbleUp in 2019 to try and short-circuit the path to intuition
and help answer the question, “these data points are interesting, but why?
What’s different about them?” In its earliest incarnation, users would literally
draw a box around “odd-looking” artifacts on a heatmap visualization. BubbleUp
would then take each and every field in your data and compare the distribution
across those fields between the selected points and the rest of the dataset.
Use BubbleUp to indicate a weird (or interesting) area on a heatmap, and quickly
jump to dimensions that are disproportionately represented in the “weird”
section.
But we couldn’t stop there! Once we realized the value of being able to compare
a subset of interesting data points against a baseline, we wove it in as the
core mechanic behind our best-in-class support for Service Level Objectives
(SLOs), immediately highlighting “what’s different” about requests failing an
SLO’s associated Service Level Indicator (SLI).
After all, when checking an SLO, what subset of requests are more “interesting”
than the ones actively eating into your error budget?
Using BubbleUp’s mechanic to make SLOs actionable—understand what makes events
that fail your SLI different from the ones that pass the SLI, and continue your
investigation directly.
We’ve taken BubbleUp one step further: you can now bubble up on anything, not
just numeric values in a heatmap. Any group—any set of outliers that you can
describe as a subset, like “requests to /checkout with fewer than five items in
the shopping cart,” or “invocations of the email service with foo@example.com as
the recipient”—can be compared against the rest of your traffic, to help
identify what’s unique about your chosen subset.
BubbleUp was built to offer a path forward when you simply don’t know which
fields are important, or where to look next. We’re delighted to have made it
easier and more accessible than ever to utilize BubbleUp. If more users can
figure out what question to answer next, that means a faster path to
understanding why and how reality has deviated from our expectations—and how to
fix it.
You can learn more about BubbleUp, and the piles of additional usability
improvements we’ve made to the experience in its standalone blog post.
OBSERVABILITY ISN’T A TOOL OR CORPUS OF DATA; IT’S ALL ABOUT WHAT PRACTITIONERS
CAN DO
I’m done debating definitions of observability. I’d much rather talk about what
all the functionality we’ve built into Honeycomb lets you do (“observability”
has “ability” in the word, after all):
1. Eliminate dead ends. Every view in the Honeycomb product is the entry point
into another; every visualization can connect to another; every graph can be
zoomed into or refined. It’s all drawing from the same source of truth;
after all, the tool you’re using shouldn’t get in the way when following a
given clue.
You can start with a graph, look under the hood (flip to raw data), zoom
into an exemplar (view a representative trace), and zoom back out to find
other patterns (query for similar traces)—all without worrying about whether
tags (or other metadata) were correctly configured to allow that movement.
This experience is core to Honeycomb and fundamental to the unique
experience we deliver.
2. Orient yourself with an overview of your services, then actually use it to
answer questions. Honeycomb’s Service Map is meant to be played around with,
filtered, and used to track any subset of traffic (as defined by any
dimensions), down to—you guessed it—a single trace, and back out.
3. Introduce the full potential of observability to everyone on your team.
Honeycomb has always excelled at enabling power users to answer arbitrary
questions about system telemetry; now we’re taking the same care to make it
easy for all users to figure out which questions to ask. Our latest BubbleUp
enhancements and Service Map provide friendlier entry points into a given
investigation.
Concepts like Service Maps, Distributed Tracing, Anomaly Detection, and SLOs
have been around for some time. But the way all of these have historically been
implemented reflects the siloed approach of treating logs, metrics, and traces
as fundamentally separate sources of truth. This leaves you on your own to
connect the dots, often guessing at patterns because one visualization is
disconnected from another.
Honeycomb challenges this traditional approach—we’ve taken a wrecking ball to
the three pillars and laid our own foundation: a data store and query engine
that are refreshingly silo-free. We offer the ability to look at your system at
any level of granularity, zooming in and out without losing context—because at
the end of the day, engineers answering questions need the big picture, the
details, and several steps in between.
In the spirit of answering questions, I’ll end with one for you: can your
“observability” tool do this?
Start from your Service Map, highlight traces that match a failing SLI, run a
query to analyze those traces, run BubbleUp on a particular subset of trace
spans, zoom in to an example trace, and view the raw metadata on that span—all
without losing your context or running into a dead end.
If you’re curious to see what you can do with Honeycomb, we invite you to get
started today—for free. Our free tier covers up to 20 million events per month,
and all users get access to our Service Map until the end of 2022. Additionally,
we’re hosting a live discussion on November 9th where we’ll apply our new
releases to real-world concepts and best practices. Register here.
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