Observability 3 ways at SCaLE 17x

Observability 3 ways at SCaLE 17x

This was an updated variant of my https://speakerdeck.com/adriancole/observability-3-ways-logging-metrics-and-tracing deck given at the Southern California Linux Expo

https://www.socallinuxexpo.org/scale/17x/presentations/observability-3-ways-logging-metrics-and-distributed-tracing

What's different are a couple more advice slides, the last a challenge to positioning logging, tracing and metrics as solutions. I also tried to emphasize that "3 ways" is not the same as "the only 3 ways"

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Adrian Cole

March 09, 2019
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  1. Observability 3 ways Logging, Metrics and Tracing @adrianfcole works at

    Pivotal works on Zipkin let’s talk about observability.. how we understand production.. through three common tools
  2. Unifying theory • Logging - recording events • Metrics -

    data combined from measuring events • Tracing - recording events with causal ordering Everything is based on events credit: coda hale note: metrics take in events and emit events! ex a reading of requests per second is itself an event data combined from measuring events put more specifically: metrics are “statistical aggregates of properties of events which generate periodic events recording the instantaneous values of those aggregates"
  3. Tracing Request scoped Logging Events Metrics Aggregatable* credit: peter bourgon

    Focal areas often confused because they have things in common, like a timeline. start with logging: crappy error happened tracing: impact of that error metrics: how many errors of this type are happening in the system logs: discrete events: debug, error, audit, request details crappy error happened; tracing can tell you the impact of that error. for example did it cause a caller to fail or did it delay it? tracing: request-scope causal info: latency, queries, IDs metrics: gauge counter histogram; success failure or customer how many errors of this type are happening in this cluster? not all metrics are meaningfully aggregatable, ex percentiles or averages https://peter.bourgon.org/blog/2017/02/21/metrics-tracing-and-logging.html
  4. Let’s use latency to compare a few tools • Log

    - event (response time) • Metric - value (response time) • Trace - tree (response time) event value and tree are outputs of each corresponding system
  5. Logs show response time [20/Apr/2017:11:07:07 +0000] "GET / HTTP/1.1" 200

    7918 "" "Mozilla/5.0 (X11; U; Linux i686; en-US; rv: 1.8.1.11) Gecko/20061201 Firefox/2.0.0.11 (Ubuntu- feisty)" **0/95491** Look! this request took 95 milliseconds! often a field or other to derive duration from logs. note there’s some complexity in this format, and often latency is timestamp math between events.
  6. Metrics show response time Is 95 milliseconds slow? How fast

    were most requests at 11:07? 95ms wouldn't have set off an alert if we are monitoring max. response time can also be complicated as it not a normal distribution (long tail) and can be multimodal (multiple simultaneous peak latencies)
  7. Alert on max, performance tune to high percentiles. @jon_k_schneider I

    can't control the processes which cause max (GC, VM pauses etc which happens less than 1%), but I can control what might happen at 99% and can use that statistic for performance analysis.
  8. What caused the request to take 95 milliseconds? åȇȇȇȇȇȇȇȇȇȇȇȇ95491 microseconds───────────────────────────å

    │䡢䡢䡢䡢䡢䡢䡢䡢䡢䡢䡢䡢䡢䡢䡢䡢䡢䡢䡢䡢䡢䡢䡢䡢䡢䡢䡢䡢䡢䡢䡢䡢䡢䡢䡢䡢䡢䡢䡢䡢䡢䡢䡢䡢䡢䡢䡢䡢䡢䡢䡢䡢䡢䡢䡢䡢│ │䡢䡢䡢䡢䡢䡢䡢䡢䡢│ åȇȇȇȇȇȇȇȇȇȇȇȇ 55231 microseconds───────────å │䡢䡢䡢䡢䡢䡢䡢䡢䡢䡢䡢䡢䡢䡢䡢䡢䡢䡢䡢䡢䡢䡢䡢䡢䡢䡢䡢䡢䡢䡢䡢䡢䡢䡢䡢䡢䡢䡢䡢│ │䡢䡢䡢䡢䡢䡢䡢䡢䡢䡢䡢䡢䡢䡢䡢䡢䡢䡢䡢䡢䡢䡢䡢䡢䡢䡢䡢│ │䡢䡢䡢䡢䡢䡢䡢䡢䡢䡢䡢䡢│ Traces show response time an error delayed the request, which would have otherwise been performant.
  9. First thoughts… • Log - easy to “grep”, manually read

    • Metric - can identify trends • Trace - identify cause across services
  10. How do you write timing code? • Log - time

    and write formatted or structured logs • Metric - time and store the number • Trace - start, propagate and finish a “span” Jargon alert! span == one operation in the call graph
  11. Logging response time Find the thing you want and time

    it, format the result into a log statement. long tookMs = TimeUnit.NANOSECONDS.toMillis(System.nanoTime() - startNs); logger.log("<-- " + response.code() + ' ' + response.message() + ' ' + response.request().url() + " (" + tookMs + "ms" + (!logHeaders ? ", " + bodySize + " body" : "") + ')'); often global reference to a logger, as events share no state between them Code is simplified and uncompilable variant of okhttp request logger
  12. Metric’ing response time Initialize something to record duration and add

    to it def apply(request: Req, service: Service[Req, Rep]): Future[Rep] = { val sample = Timer.start(Clock.SYSTEM) service(request).respond { response => sample.stop( Metrics.timer(“request.latency”, “code”, response.status()) ) } } often endpoint-scoped reference to a stat, api often limited to the value type Code is simplified and uncompilable variant of finagle tracing using micrometer
  13. Tracing response time Create and manage a span. Pass it

    on via headers Span span = handler.handleReceive(extractor, httpRequest); try { chain.doFilter(httpRequest, httpResponse); } finally { servlet.handleAsync(handler, httpRequest, httpResponse, span); } managing spans ensures parent/child links are maintained. this allows the system to collate spans into a trace automatically. Code is simplified and uncompilable variant of servlet tracing
  14. Impact of timing code • Log - ubiquitous apis, but

    requires coordination • Metric - easy, but least context • Trace - hardest, as identifiers must be passed within and between services logging and metrics require less state, so are easier to program
  15. Should you write timing code? • Frameworks usually have metrics

    built-in • Many frameworks have tracing built-in • Lots of edge cases in this sort of code! edge cases like clock skew, sparse traces, overhead management
  16. How to not see tracing code? • Buddy - another

    process intercepts yours • Agent - code patches code • Framework - code intercepts or configures code
  17. Buddy tracing web service processing Use a service mesh to

    trace around your services propagate context headers Image is from linkerd blog https://blog.buoyant.io/2017/03/14/a-service-mesh-for-kubernetes-part-vii-distributed-tracing-made-easy/ without propagation of context, stitching traces together is heuristic, if possible. propagation supports other things like retry budgets or routing overrides
  18. Agent tracing We have ways of making code traced.. if

    ("spark/webserver/JettyHandler".equals(className)) { ClassPool cp = new ClassPool(); cp.appendClassPath(new LoaderClassPath(loader)); CtClass ct = cp.makeClass(new ByteArrayInputStream(classfileBuffer)); CtMethod ctMethod = ct.getDeclaredMethod("doHandle"); ctMethod.insertBefore("{ $4.setHeader(\”TraceId\", MagicTraceId.get()); }"); return ct.toBytecode(); } Code is from https://github.com/bsideup/javaagent-boilerplate Most APMs use agents. Allows tracing of libraries that never planned to be traced, and more advanced propagation as system or otherwise difficult classes can be modified.
  19. Framework Tracing Framework code configures libraries @Configuration @AutoConfigureAfter(TraceAutoConfiguration.class) @ConditionalOnClass(HystrixCommand.class) @ConditionalOnBean(Tracer.class)

    public class SleuthHystrixAutoConfiguration { @Bean SleuthHystrixConcurrencyStrategy sleuthHystrixConcurrencyStrategy( Tracer tracer, TraceKeys traceKeys) { return new SleuthHystrixConcurrencyStrategy(tracer, traceKeys); } } Code is from https://github.com/spring-cloud/spring-cloud-sleuth Users trust frameworks to setup tracing on their behalf. Usually there are ways to customize data. Frameworks are limited to version compatibility and hooks that are available.
  20. How is timing data shipped? • Log - pull raw

    events into a parsing pipeline • Metric - report duration buckets near-real time • Trace - report spans near-real time logs usually imply disk writes and/or store forward pipelines which can be available minutes later. There are different architectures behind services, so these are just tendencies. Ex all three are sometimes placed over the same pipeline. Also, sometimes there’s buffering in log pipelines for recent data,.
  21. Parsing latency from events Identify the pattern and parse into

    indexable fields input { file { path => "/var/log/http.log" } } filter { grok { match => { "message" => "%{IP:client} %{WORD:method} % {URIPATHPARAM:request} %{NUMBER:bytes} %{NUMBER:duration}" } } } an example of reading a file and parsing duration from it. also includes other fields so you can rollup by ip config is from elasticsearch grok plugin docs
  22. Bucketing duration define boundaries up front… boundaries[0] = 1; //

    0 to < 1ms boundaries[1] = 1000; // 1ms to < 1s boundaries[2] = 50000; // 1s to < 50s add values by incrementing count in a bucket for (int i = 0; i < boundaries.length; i++) { if (duration < boundaries[i]) { bucket[i]++; return; } } bucket[boundaries.length]++; // overflow! Code is simplified from google-instrumentation, which is an opinionated way not shared by others. Some prefer recording into an ordinary histogram, later using that to make a cumulative one. static bucketing isn’t always used, HDRHistogram is dynamic for example.
  23. Shipping spans Spans represent operations and are structured !""""""""""""""""""""""""""""""""# $

    ""% structure and report span &""""▶ $ $ ${ $ $ "traceId": "aa", $ $ "id": "6b", $ $ "name": "get", $ $ "timestamp": 1483945573944000,$ $ "duration": 95491, $ $ "annotations": [ $ $--snip-- $ '""""""""""""""""""""""""""""""""( This structure can change, but instrumentation code can live forever! json is in zipkin format
  24. How timing data grows • Log - grows with traffic

    and verbosity • Metric - fixed wrt traffic • Trace - grows with traffic ex trace data doesn't necessarily grow based on garbage collection or audit events
  25. Means to reduce volume • Log - don’t log irrelevant

    data, filtering • Metric - read-your-writes, coarser grain • Trace - sampling, but needs to be consistent Each have different retention, too! logging and tracing generally increase volume with traffic log systems can lose 10% and there’s often no way to say which 10% will be dropped you typically can’t get per-request details from a metric people can trust tracing too much and not expect data to ever be absent sampling is also possible in metrics, but it can interfere with aggregations. accidental sampling a lot of folks ship an unholy amount of data at statsd over UDP w/o recording packet loss - coda
  26. Stitching “all” 3 together Trace ID Pod Service Name Metrics

    Logging Tracing with all three you can identify knock-on effects of things like pauses or deployment events
  27. Correlating Metrics and Tracing Data https://medium.com/observability/want-to-debug- latency-7aa48ecbe8f7

  28. Leverage strengths while understanding weaknesses • Log - monoliths, black

    boxes, exceptional cases • Metric - identify patterns and/or alert • Trace - distributed services “why is this slow” all tools are sometimes needed. sometimes you have software you can’t affect “black boxes” scraping logs can help.
  29. Logging, metrics & distributed tracing  — These are problems, not solutions!

    @autoletics Instrumenting simulated episodes derived from real systems is not new, but rarely discussed. We need to consider all options even if some feel easier to reach https://medium.com/@autoletics/logging-metrics-distributed-tracing-these-are-problems-not-solutions-d528c09a4dbe https://medium.com/@autoletics/data-in-motion-reconstructing-the-past-4859749983bf other simulation focused attempts https://github.com/adrianco/spigo canopy decoupled recording from model creation https://blog.acolyer.org/2017/11/22/canopy-an-end-to-end-performance-tracing-and-analysis-system/
  30. Was this helpful? If so, thank folks who helped with

    this! @adrianfcole @munroenic @basvanbeek @bogdandrutu @jeanneretph If not, blame me, @peterbourgon @felix_b @abhik5ingh https://peter.bourgon.org/blog/ @coda @jon_k_schneider @autoletics Peter’s blog led to this talk. read it! https://peter.bourgon.org/blog/2017/02/21/metrics-tracing-and-logging.html The others offered time reviewing, sometimes multiple passes. Each of which improved this content.