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CTO Craft Con Keynote: Observability is due for a version change: are you ready for it?

CTO Craft Con Keynote: Observability is due for a version change: are you ready for it?

The time has come: the DevOps revolution is winding down, and we’re entering the post-DevOps era. We’re at the precipice of a massive generational shift in how we build and understand our software, and CTOs need to prepare.

In the past, we were only interested in basic metrics on how we operated our software: reliability, uptime, MTTR, MTTD. Observability 1.0. Companies that settle for these basic data points will not survive in this new era.

As engineering best practices around separating deploys from releases, testing in production, and observability-driven development have gone mainstream, the metrics-driven approach to telemetry has stalled, and it’s time for a new version: Observability 2.0. Learn what this new version means for your engineers, and how to embrace this breaking change to:

* Save them from drowning in symptom-based alerting
* Help fewer people work together to build better software
* Create fast feedback loops throughout the entire organization through highly granular visibility into all their systems

Charity Majors

May 27, 2024

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  1. It really pays to be on a high-performing team. High-performing

    teams get to spend 
 most of their time working on interesting, 
 novel problems that move the business materially forward. The team is the smallest viable unit of software ownership. Individuals don’t own software.
  2. How do we build 
 high-performing teams? “By hiring all

    the smartest people and greatest engineers and ex-Googlers we can get our hands on” NO.
  3. and more to do with the sociotechnical system 

    participate in. Your ability to ship code swiftly + safely has less to do with your knowledge of algorithms & data structures, “How well does your team perform?” != “How good are you at engineering?”
  4. If technical leaders 
 have ✨one job✨ it is this:

    Constructing + tightening the 
 feedback loops at the heart of their system
  5. Engineers own their code in production Practice observability-driven development Test

    in production Separate deploys from releases using feature flags Continuous deployment (or at least delivery) Modern software development practices
  6. Get your code into production 
 as fast as possible

    after writing it. FAST FEEDBACK LOOPS Modern software development best practices are ✨ALL✨ about: speed is safety. When it comes to software,
  7. The cost of fi nding and fi xing bugs goes

 exponentially from the moment you write them.
  8. Your ability to move swiftly, with confidence, is grounded in

    the quality of your observability. But what does that even mean, these days?? (Great question! 🙋)
  9. 2016 2017 2018 2019 2020 2021 2022 2023 2024 A

    chronological history of observability in software “In control theory, observability is a measure of how well internal states of a system can be inferred from knowledge of its external outputs. The observability and controllability of a system are mathematical duals." — wikipedia Technical Definition for Observability 1. High-cardinality 2. High-dimensionality 3. Arbitrarily-wide structured log events 4. Preserve access to raw events 5. Read-time aggregation & querying 6. Persist context through execution 7. No pre-defined indexes or schemas 8. Tracing is a visualization over time 9. Client-side dynamic sampling 10. Exploratory, ad hoc interface 11. Service Level Objectives 12. Real time, interactive speeds Observability has “three pillars”: metrics, logs and traces — Peter Bourgon Gartner adds APM/Observability Magic Quadrant, adopting our technical definition for observability Observability comes for LLMs and the front end
  10. Observability is a ✨property✨ of complex systems. But … how

    then to account for the enormous step function in value, usability, cost model, etc between generations of tooling?
  11. Observability 1.0 Metrics, logs and traces, captured separately Many. APM,

    RUM, logging, tracing, metrics, analytics… Static dashboards Debug based on intuition, scar tissue from past outages, and guesswork Page on symptoms appearing in metrics Pay to store your data many times Data: Source of truth: Interface: Debugging: . Alerts: Cost:
  12. Observability 2.0 Wide, rich structured logs (aka events or spans),

    with high cardinality and high dimensionality One Exploratory, interactive; no dead ends Follow the trail of breadcrumbs. It’s in the data. Page on customer pain via SLOs Pay to store your data once Data: . Source of truth: Interface: Debugging: Alerts: Cost:
  13. You have observability only if you have… 1.High-cardinality 2.High-dimensionality 3.Arbitrarily-wide

    structured log events 4.Preserve access to raw events 5.Read-time aggregation & querying 6.Persist context through execution 7.No pre-defined indexes or schemas 8.Tracing is a visualization over time 9.Client-side dynamic sampling 10. Exploratory, ad hoc interface 11. Service Level Objectives 12. Real time, interactive speeds If you have three pillars, and many tools: Observability 1.0 If you have a single source of truth: Observability 2.0
  14. OBSERVABILITY 1.0 OBSERVABILITY 2.0 How the data gets stored •

    Metrics • Logs • Traces • APM • RUM • … • Tracing is an entirely different tool • Siloed tools, with no connective tissue or only a few, predefined connective bits • Arbitrarily-wide structured data blobs • Single source of truth • Tracing is just visualizing over time • It’s just data. Treat your data like data. • Write-time aggregation • Read-time aggregation; raw events
  15. There are only three types of data: 1. The metric

    2. Unstructured logs (strings) 3. Structured logs RUM tools are built on top of metrics to understand browser user sessions. APM tools are built on top of metrics to understand application performance.
  16. Tiny, fast, and cheap Each metric is a single number,

    with some tags appended Stored in TSDBs NO context. NO high cardinality. NO data structures. NO ability to correlate or dig deeper Only basic static dashboards. EXTREMELY limited. Metrics are the right tool for summarizing vast quantities of data, and aggregating it in a way so it can cheaply age out and lose fidelity over time. They are not equipped to help you introspect and understand your software.
  17. Unstructured Logs To understand our systems, we turn to logs.

    Even unstructured logs are more powerful than metrics, because they preserve SOME context and connective dimensions. However, you have to know what you’re looking for in order to find it. And the only thing you can do is string search, which is slloooooowwwww.
  18. OBSERVABILITY 1.0 OBSERVABILITY 2.0 Who uses it, and how? •

    About MTTR, MTTD, and reliability • Usually a checklist item before shipping code to production — “how will we monitor this?” • An “ops concern” • No support for structured data • Underpins the entire software development lifecycle. • Part of the development process • High cardinality • High dimensionality • Static dashboards • Exploratory, open-ended interface
  19. Observability 1.0 is about how you ✨operate✨ software Observability 2.0

    is about how you ✨develop✨ software It is what underpins the entire software development lifecycle, allowing you to hook up tight feedback loops and move swiftly, with confidence. It is traditionally focused more on bugs, errors, MTTR, MTTD, reliability, monitoring, and performance.
  20. OBSERVABILITY 1.0 OBSERVABILITY 2.0 How you interact with production •

    You deploy your code and wait to get paged. 🤞 • Your job is done when you commit your code and tests pass • You practice Observability-Driven Development • Your job isn’t done until you’ve verified it works in production. • These worlds are porous and overlapping • You are in constant conversation with your code. 💜 • Your world is broken up into two very different universes, Dev & Prod
  21. OBSERVABILITY 1.0 OBSERVABILITY 2.0 How you debug • You flip

    from dashboard to dashboard, pattern-matching with your eyeballs • You lean heavily on intuition, past experience, and a rich mental model of the system • The best debuggers are always the engineers who have been there the longest and seen the most. • You form a hypothesis, ask a question, consider the results, and ask another based on the answer. • You don’t have to guess. You follow the trail of breadcrumbs to the answers, every time. • Analysis-first • The best debuggers are the people who are the most curious. • Search-first
  22. OBSERVABILITY 1.0 OBSERVABILITY 2.0 The cost model • You pay

    to store your data again and again and again and again, multiplied by the number of tools • Cost goes up (at best) linearly, driven by the number of custom metrics you define • Keeping costs under control requires ongoing investment from engineering • You pay to store your data ✨once✨ • You can store infinite “custom metrics”, appended to your events • Powerful, surgical options for controlling costs via head-based or tail-based dynamic sampling • Cost for individual metrics can spike massively and unpredictably
  23. Why does observability 1.0 cost so much? Because you have

    to pay for so many different tools / pillars, your costs rise at a multiplier of your traffic (5x? 7x?) Because so many of those tools are built on metrics https://www.honeycomb.io/blog/cost-crisis-observability-tooling Because of the high overhead of ongoing engineering labor to manage costs and billing data Because of the dark matter of lost engineering cycles.
  24. Envelope math: cost of a custom metric 5 hosts, 4

    endpoints, 2 status codes as a Count metric: 40 custom metrics request.Latency 1000 hosts, 5 methods, 20 handlers, 63 status codes as a Count metric: 6.3M custom metrics 1000 hosts, 5 methods, 20 handlers, 63 status codes as a histogram using defaults (max, median, avg, 95pct, count): 31.5M custom metrics 1000 hosts, 5 methods, 20 handlers, 63 status codes as a histogram using defaults (max, median, avg, 95pct, count) plus distribution (99pct, 99.5pct, 99.9pct, 99.99pct: 63M custom metrics A DataDog acct comes with 100-200 free custom metrics, and costs 10 cents for every 100 over. 63M custom metrics costs you $63,000/month for request.Latency
  25. OBSERVABILITY 1.0 OBSERVABILITY 2.0 The cost model • Ballooning costs

    are baked in to the 1.0 model. ☹ • As your bill goes up, the value you get out of your tools actually goes down. • Your costs go up as your traffic goes up and as you add more spans for finer-grained inspection • As your bill goes up, the value you get out of your tools goes up too. • Metrics and unstructured logs both suffer from opaque, bursty billing and degrade in punishing ways • Costs effectively nothing to widen structured data & add more context
  26. With observability 1.0 tools, as costs go up, the value

    you get out goes down. Ballooning costs are baked into observability 1.0. ☹ Observability 2.0 isn’t “cheap”, but its costs are predictable and aligned with engineering value.
  27. Observability 2.0 is faster, cheaper, and simpler to use. The

    way you are doing it NOW is the hard way.
  28. We have learned to be insanely clever when it comes

    to wringing every last bit of utility out of metrics and unstructured logs. What if it was all just … data. What if we didn’t have to work that hard?
  29. Metrics are a bridge to our past. Structured logs are

    the bridge to the future. Metrics aren’t completely useless; they still have their place! (In infrastructure 😛.) ❤
  30. Envelope math: cost of a structured log request.Latency With structured

    logs, you should be able to capture each of these dimensions endpoint method status_code handler hostname and just slice and dice, break down and group by any combination of dimensions. app_id user_id shopping_cart_id build_id … etc AND SO MUCH MORE …
  31. You build better systems by building software this way. You

    become a better engineer by building software this way.
  32. What you can do ✨NOW✨ to start moving towards observability

    2.0: 1. Instrument your code using the principles of canonical logs. It is difficult to overstate the value of doing this. Make them wide. 2. Add trace IDs and span IDs, so you can trace your code using the same events instead of having to hop between tools 3. Feed your data into a columnar store, to move away from predefined schemas or indexes 4. Use a storage engine that supports high cardinality 5. Adopt tools with explorable interfaces, or at least dynamic dashboards.
  33. We used to be able to reason about 

    architecture. Not anymore. Now we have to instrument for observability, or we are screwed. 2003 2023 2013 What got us here won’t get us there.
  34. Here’s the dirty little secret: It can’t be done. The

    next generation of systems won’t be built and run by burned-out, exhausted people, or command-and-control teams just following orders. Our systems have become too complicated…too hard. The shit that can be done on autopilot will be automated out of existence.
  35. Writing code is not the hard part. It never has

    been. The hard part of software is understanding it, maintaining it, extending it, scaling it, operating it, migrating it, refactoring it, crafting the right level of abstractions, instrumenting it, reasoning about it.
  36. Those who try will lose. We can no longer hold

    a model of these systems in our heads and reason about them, or intuit the solution. Our systems are emergent and unpredictable. Runbooks and canned playbooks won’t work; it takes your full creative self.
  37. Observability 2.0 advances the craft of software engineering. We are

    trying to make it faster and safer to bring change to the world. We are trying to make this a humane profession.
  38. The biggest obstacle between us and a better world, is

    when we don’t believe one is actually possible. Demand more from your tools. Demand more from your vendors. Everyone writes code. Everyone owns their code in production, And everybody deserves the tools to do it efficiently and well.