Lynn Root - Tracing, Fast and Slow: Digging into and improving your web service’s performance

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Lynn Root | SRE | @roguelynn Tracing: Fast & Slow Digging into and improving your web service’s performance

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$ whoami

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—

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agenda —

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agenda • Overview and problem space —

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agenda • Overview and problem space • Approaches to tracing —

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agenda • Overview and problem space • Approaches to tracing • Tracing at scale —

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agenda • Overview and problem space • Approaches to tracing • Tracing at scale • Diagnosing performance issues —

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agenda • Overview and problem space • Approaches to tracing • Tracing at scale • Diagnosing performance issues • Tracing services & systems —

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Tracing Overview —

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machine-centric • Focus on a single machine —

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machine-centric • Focus on a single machine • No view into a service’s dependencies —

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workflow-centric • Understand causal relationships —

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workflow-centric • Understand causal relationships • End-to-end tracing —

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No content

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why trace? —

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why trace? • Performance analysis —

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why trace? • Performance analysis • Anomaly detection —

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why trace? • Performance analysis • Anomaly detection • Profiling —

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why trace? • Performance analysis • Anomaly detection • Profiling • Resource attribution —

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why trace? • Performance analysis • Anomaly detection • Profiling • Resource attribution • Workload modeling —

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Tracing Approaches —

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manual

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def request_id(f): @wraps(f) def decorated(*args, **kwargs): req_id = request.headers.get( "X-Request-Id", uuid.uuid4()) return f(req_id, *args, **kwargs) return decorated @app.route("/") @request_id def list_services(req_id): # log w/ ID for wherever you want to trace # app logic

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upstream appserver { 10.0.0.0:80; } server { listen 80; # Return to client add_header X-Request-ID $request_id; location / { proxy_pass http://appserver; # Pass to app server proxy_set_header X-Request-ID $request_id; } }

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log_format trace '$remote_addr … $request_id'; server { listen 80; add_header X-Request-ID $request_id; location / { proxy_pass http://app_server; proxy_set_header X-Request-ID $request_id; # Log $request_id access_log /var/log/nginx/access_trace.log trace; } }

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blackbox

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metadata propagation

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Tracing at Scale —

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four things to think about —

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four things to think about • What relationships to track —

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four things to think about • What relationships to track • How to track them —

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four things to think about • What relationships to track • How to track them • Which sampling approach to take —

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four things to think about • What relationships to track • How to track them • Which sampling approach to take • How to visualize to employ —

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what to track

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Request One Request Two Submitter Flow PoV

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Request One Request Two Trigger Flow PoV

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how to track

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request ID

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request ID + logical clock

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request ID + logical clock + previous trace points

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tradeoffs —

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tradeoffs • Payload size —

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tradeoffs • Payload size • Explicit relationships —

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tradeoffs • Payload size • Explicit relationships • Collate despite lost data —

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tradeoffs • Payload size • Explicit relationships • Collate despite lost data • Immediate availability —

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how to sample

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sampling approaches • Head-based —

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sampling approaches • Head-based • Tail-based —

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sampling approaches • Head-based • Tail-based • Unitary —

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what to visualize

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gantt chart — GET /home GET /feed GET /profile GET /messages GET /friends Trace ID: de4db33f

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— request flow graph A call B call C call C call D call E call E reply D reply B reply C reply C reply A reply 2200µs 1500µs 500µs 300µs 400µs 600µs 800µs 500µs 500µs 700µs 500µs 400µs 600µs 100µs

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— context calling tree A B C C D E

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keep in mind • What do I want to know? —

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keep in mind • What do I want to know? • How much can I instrument? —

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keep in mind • What do I want to know? • How much can I instrument? • How much do I want to know? —

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suggested for performance —

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suggested for performance — • Trigger PoV

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suggested for performance — • Trigger PoV • Head-based sampling

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suggested for performance — • Trigger PoV • Head-based sampling • Flow graphs

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Diagnosing —

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questions to ask — • Batch requests?

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questions to ask • Batch requests? • Any parallelization opportunities? —

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questions to ask • Batch requests? • Any parallelization opportunities? • Useful to add/fix caching? —

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questions to ask • Batch requests? • Any parallelization opportunities? • Useful to add/fix caching? • Frontend resource loading? —

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questions to ask • Batch requests? • Any parallelization opportunities? • Useful to add/fix caching? • Frontend resource loading? • Chunked or JIT responses? —

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Systems & Services —

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OpenTracing

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self-hosted

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Zipkin (Twitter) —

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Zipkin (Twitter) • Out-of-band reporting to remote collector —

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Zipkin (Twitter) • Out-of-band reporting to remote collector • Report via HTTP, Kafka, and Scribe —

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Zipkin (Twitter) • Out-of-band reporting to remote collector • Report via HTTP, Kafka, and Scribe • Python libs only support propagation via HTTP —

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Zipkin (Twitter) • Out-of-band reporting to remote collector • Report via HTTP, Kafka, and Scribe • Python libs only support propagation via HTTP • Limited web UI —

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def http_transport(span_data): requests.post( "http://zipkinserver:9411/api/v1/spans", data=span_data, headers={"Content-type": "application/x-thrift"}) @app.route("/") def index(): with zipkin_span(service_name="myawesomeapp", span_name="index", # need to write own transport func transport_handler=http_transport, port=app_port, # 0-100 percent sample_rate=100): # do something

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Jaeger (Uber) —

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Jaeger (Uber) • Local daemon to collect & report —

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Jaeger (Uber) • Local daemon to collect & report • Storage support for only Cassandra —

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Jaeger (Uber) • Local daemon to collect & report • Storage support for only Cassandra • Lacking in documentation —

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Jaeger (Uber) • Local daemon to collect & report • Storage support for only Cassandra • Lacking in documentation • Cringe-worthy client library —

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import opentracing as ot config = Config(…) tracer = config.initialize_tracer() @app.route("/") def index(): with ot.tracer.start_span("ASpan") as span: span.log_event("test message", payload={"life": 42}) with ot.tracer.start_span("AChildSpan", child_of=span) as cspan: span.log_event("another test message") # wat time.sleep(2) # yield to IOLoop to flush the spans tracer.close() # flush any buffered spans

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honorable mentions • AppDash • LightStep (private beta) —

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services

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Stackdriver Trace (Google) —

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Stackdriver Trace (Google) • No Python client libraries; no gRPC client support —

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Stackdriver Trace (Google) • No Python client libraries; no gRPC client support • Forward traces from Zipkin —

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Stackdriver Trace (Google) • No Python client libraries; no gRPC client support • Forward traces from Zipkin • Storage limitation of 30 days —

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X-Ray (AWS) —

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X-Ray (AWS) • No first class Python support; Boto available —

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X-Ray (AWS) • No first class Python support; Boto available • Configurable sampling, but not for Boto —

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X-Ray (AWS) • No first class Python support; Boto available • Configurable sampling, but not for Boto • Flow graphs with latency, response %, sample % —

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honorable mentions • Datadog • New Relic —

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TL;DR —

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tl;dr — • You need this

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tl;dr — • You need this • Docs are lacking

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tl;dr — • You need this • Docs are lacking • Language support lacking

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tl;dr — • You need this • Docs are lacking • Language support lacking • One size fits all approaches

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tl;dr — • You need this • Docs are lacking • Language support lacking • One size fits all approaches • But there’s an open spec!

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Thanks! — Sources & links: rogue.ly/tracing Lynn Root | SRE | @roguelynn