README.md

This is an attempt at reproducing behavior in Grafana Tempo where traces are delayed for hours after being submitted.

The files included here are:

• docker-compose.yaml: Runs a tempo, grafana, and jaeger instance to send data to
• .vscode/launch.json: VS Code launch file with configurations for running the parser script to send data to Tempo and to Jaeger
• telemetry-tools-env-vars/telemetry-env-vars: This is where traceparent gets set. We compute it ourselves instead of randomly generating it, so that we can reproduce it in different places rather than carry it around.
• bump_time.py: Used to update timestamps in all_jobs.json so that data is new enough for Tempo to consider it
• all_jobs.json: Job timing information obtained from the GitHub Actions API. The input to the parser.

To run this example:

• Start services with docker compose

docker compose up

• Install prereqs:

python -m venv create tempo_repro
. tempo_repro/bin/activate
pip install -r requirements.txt

• Use launch.json to run either the tempo or the jaeger configuration

OR

• Run the script manually, setting a few env vars:

Tempo is on port 4318 in docker-compose.yaml

OTEL_EXPORTER_OTLP_ENDPOINT="http://localhost:4318" OTEL_EXPORTER_OTLP_PROTOCOL="http/protobuf" OTEL_TRACES_EXPORTER="otlp_proto_http" python send_trace.py

Jaeger is on port 4320 in docker-compose.yaml

OTEL_EXPORTER_OTLP_ENDPOINT="http://localhost:4320" OTEL_EXPORTER_OTLP_PROTOCOL="http/protobuf" OTEL_TRACES_EXPORTER="otlp_proto_http" python send_trace.py

all_jobs.json

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{"name": "Post Telemetry setup", "status": "completed", "conclusion": "success", "number": 4, "started_at": "2024-12-13T13:24:59Z", "completed_at": "2024-12-13T13:24:59Z"}, {"name": "Complete job", "status": "completed", "conclusion": "success", "number": 5, "started_at": "2024-12-13T13:24:59Z", "completed_at": "2024-12-13T13:25:00Z"}], "check_run_url": "https://api.github.com/repos/rapidsai/rmm/check-runs/34296465890", "labels": ["ubuntu-latest"], "runner_id": 1456, "runner_name": "GitHub Actions 495", "runner_group_id": 2, "runner_group_name": "GitHub Actions"}, {"id": 34296470808, "run_id": 12289568170, "workflow_name": "pr", "head_branch": "pull-request/1750", "run_url": "https://api.github.com/repos/rapidsai/rmm/actions/runs/12289568170", "run_attempt": 2, "node_id": "CR_kwDOCZBTeM8AAAAH_DqdGA", "head_sha": "8c92a920d184eb8ebcc35b554755184042df3401", "url": "https://api.github.com/repos/rapidsai/rmm/actions/jobs/34296470808", "html_url": 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"2024-12-13T13:41:49Z"}, {"name": "Generate test report", "status": "completed", "conclusion": "success", "number": 10, "started_at": "2024-12-13T13:41:50Z", "completed_at": "2024-12-13T13:41:50Z"}, {"name": "Upload additional artifacts", "status": "completed", "conclusion": "success", "number": 11, "started_at": "2024-12-13T13:41:51Z", "completed_at": "2024-12-13T13:41:51Z"}, {"name": "Post checkout code repo", "status": "completed", "conclusion": "success", "number": 19, "started_at": "2024-12-13T13:41:51Z", "completed_at": "2024-12-13T13:41:51Z"}, {"name": "Post Run aws-actions/configure-aws-credentials@v4", "status": "completed", "conclusion": "success", "number": 20, "started_at": "2024-12-13T13:41:51Z", "completed_at": "2024-12-13T13:41:51Z"}, {"name": "Post Telemetry setup", "status": "completed", "conclusion": "success", "number": 21, "started_at": "2024-12-13T13:41:51Z", "completed_at": "2024-12-13T13:41:51Z"}, {"name": "Stop containers", "status": "completed", "conclusion": 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bump_time.py

import json
import datetime

with open('all_jobs.json') as f:
    jobs = json.load(f)

parse_time = lambda x: int(datetime.datetime.strptime(x, "%Y-%m-%dT%H:%M:%SZ").timestamp() * 1e9)

start_time = parse_time(jobs[0]['created_at'])
needed_time = parse_time(jobs[-3]['completed_at']) - parse_time(jobs[0]['created_at'])
new_start_time = datetime.datetime.utcnow() - datetime.timedelta(minutes=60)

for idx, job in enumerate(jobs):
    if job['created_at']:
        job['created_at'] = (new_start_time + datetime.timedelta(seconds=(parse_time(job['created_at']) - start_time)/1e9)).strftime("%Y-%m-%dT%H:%M:%SZ")
    if job['started_at']:
        job['started_at'] = (new_start_time + datetime.timedelta(seconds=(parse_time(job['started_at']) - start_time)/1e9)).strftime("%Y-%m-%dT%H:%M:%SZ")
    if job['completed_at']:
        job['completed_at'] = (new_start_time + datetime.timedelta(seconds=(parse_time(job['completed_at']) - start_time)/1e9)).strftime("%Y-%m-%dT%H:%M:%SZ")
    steps = []
    for step in job['steps']:
        if step['started_at']:
            step['started_at'] = (new_start_time + datetime.timedelta(seconds=(parse_time(step['started_at']) - start_time)/1e9)).strftime("%Y-%m-%dT%H:%M:%SZ")
        if  step['completed_at']:
            step['completed_at'] = (new_start_time + datetime.timedelta(seconds=(parse_time(step['completed_at']) - start_time)/1e9)).strftime("%Y-%m-%dT%H:%M:%SZ")
        steps.append(step)
    job['steps'] = steps

    jobs[idx] = job


with open("all_jobs.json", "w") as f:
    json.dump(jobs, f)

datasources.yaml

apiVersion: 1

datasources:
- name: Tempo
  type: tempo
  access: proxy
  orgId: 1
  url: http://tempo:3200
  basicAuth: false
  isDefault: true
  version: 1
  editable: false
  apiVersion: 1
  uid: tempo
  jsonData:
    httpMethod: GET

docker-compose.yaml

x-default-logging: &logging
  driver: "json-file"
  options:
    max-size: "5m"
    max-file: "2"
    tag: "{{.Name}}"

networks:
  default:
    name: opentelemetry-demo
    driver: bridge

services:
  # Tempo runs as user 10001, and docker compose creates the volume as root.
  # As such, we need to chown the volume in order for Tempo to start correctly.
  init:
    image: &tempoImage grafana/tempo:latest
    user: root
    entrypoint:
      - "chown"
      - "10001:10001"
      - "/var/tempo"
    volumes:
      - ./tempo-data:/var/tempo

  tempo:
    image: *tempoImage
    container_name: tempo
    command: ["-config.file=/etc/tempo.yaml"]
    environment:
      {}
      # - GODEBUG=http2debug=2
      # - GRPC_GO_LOG_VERBOSITY_LEVEL=99
      # - GRPC_GO_LOG_SEVERITY_LEVEL=info
    volumes:
      - ./tempo.yaml:/etc/tempo.yaml
      - ./tempo-data:/var/tempo
    ports:
      - "3200:3200" # tempo
      - "4318:4318" # OTLP HTTP
    depends_on:
      - init
      # - fluentbit
    logging: *logging

  prometheus:
    image: prom/prometheus:latest
    container_name: prometheus
    command:
      - --config.file=/etc/prometheus.yaml
      - --web.enable-remote-write-receiver
      - --enable-feature=exemplar-storage
      - --enable-feature=native-histograms
      - --enable-feature=otlp-write-receiver
    volumes:
      - ./prometheus.yaml:/etc/prometheus.yaml
    logging: *logging
    ports:
      - "9090:9090"

  grafana:
    image: grafana/grafana:11.2.0
    container_name: grafana
    volumes:
      - datasources.yaml:/etc/grafana/provisioning/datasources/datasources.yaml
      - grafana.ini:/etc/grafana/grafana.ini
      - ./openssl:/certs
    environment:
      - GF_AUTH_ANONYMOUS_ENABLED=true
      - GF_AUTH_ANONYMOUS_ORG_ROLE=Admin
      - GF_AUTH_DISABLE_LOGIN_FORM=true
      - GF_LOG_LEVEL=debug
      # - GF_FEATURE_TOGGLES_ENABLE=traceqlEditor traceQLStreaming metricsSummary featureToggleAdminPage
      - GF_FEATURE_TOGGLES_ENABLE=traceqlEditor metricsSummary featureToggleAdminPage
    ports:
      - "3000:3000"

  jaeger:
    image: jaegertracing/all-in-one:latest
    container_name: jaeger
    restart: always
    ports:
      - "16686:16686"
      - "4320:4318"
      - "14268"
      - "14250"

grafana.ini

[server]
root_url = http://localhost:3000/grafana
serve_from_sub_path = true

launch.json

{
    "version": "0.2.0",
    "configurations": [
        {
            "name": "send-jaeger",
            "type": "debugpy",
            "request": "launch",
            "program": "send_trace.py",
            "console": "integratedTerminal",
            "cwd": "${workspaceFolder}",
            "justMyCode": false,
            "env": {
                "OTEL_EXPORTER_OTLP_ENDPOINT": "http://localhost:4320",
                "OTEL_EXPORTER_OTLP_PROTOCOL": "http/protobuf",
                "OTEL_TRACES_EXPORTER": "otlp_proto_http"
            }
        },
        {
            "name": "send-tempo",
            "type": "debugpy",
            "request": "launch",
            "program": "send_trace.py",
            "console": "integratedTerminal",
            "cwd": "${workspaceFolder}",
            "justMyCode": false,
            "env": {
                "OTEL_EXPORTER_OTLP_ENDPOINT": "http://localhost:4318",
                "OTEL_EXPORTER_OTLP_PROTOCOL": "http/protobuf",
                "OTEL_TRACES_EXPORTER": "otlp_proto_http"
            }
        }
    ]
}

requirements.txt

opentelemetry-api==1.29.*
opentelemetry-sdk==1.29.*
opentelemetry-exporter-otlp-proto-http==1.29.*

send_trace.py

from __future__ import annotations
from datetime import datetime, timezone
import hashlib
import json
import logging
import os
from pathlib import Path
from typing import Optional, Dict

from opentelemetry import trace
from opentelemetry.context import attach, detach
from opentelemetry.propagate import extract
from opentelemetry.sdk.trace import TracerProvider
from opentelemetry.sdk.resources import Resource
from opentelemetry.sdk.trace.export import BatchSpanProcessor
from opentelemetry.trace.status import StatusCode
from opentelemetry.sdk.trace.id_generator import IdGenerator

match os.getenv("OTEL_EXPORTER_OTLP_PROTOCOL"):
    case "http/protobuf":
        from opentelemetry.exporter.otlp.proto.http.trace_exporter import OTLPSpanExporter
    case "grpc":
        from opentelemetry.exporter.otlp.proto.grpc.trace_exporter import OTLPSpanExporter
    case _:
        from opentelemetry.sdk.trace.export import ConsoleSpanExporter as OTLPSpanExporter


import logging
logging.basicConfig(level=logging.WARNING)

SpanProcessor = BatchSpanProcessor


def parse_attribute_file(filename: str) -> Dict[str, str]:
    attributes = {}
    with open(filename, "r") as attribute_file:
        for line in attribute_file.readlines():
            key, value = line.strip().split('=', 1)
            attributes[key] = value
    return attributes


def date_str_to_epoch(date_str: str, value_if_not_set: Optional[int] = 0) -> int:
    if date_str:
        # replace bit is to attach the UTC timezone to our datetime object, so
        # that it doesn't "help" us by adjusting our string value, which is
        # already in UTC
        timestamp_ns = int(datetime.strptime(date_str, "%Y-%m-%dT%H:%M:%SZ").replace(tzinfo=timezone.utc).timestamp() * 1e9)
    else:
        timestamp_ns = value_if_not_set or 0
    return timestamp_ns


def map_conclusion_to_status_code(conclusion: str) -> StatusCode:
    if conclusion == "success":
        return StatusCode.OK
    elif conclusion == "failure":
        return StatusCode.ERROR
    else:
        return StatusCode.UNSET

def load_env_vars():
    env_vars = {}
    with open('telemetry-env-vars') as f:
        for line in f.readlines():
            k, v = line.split("=", 1)
            env_vars[k] = v.strip().strip('"')
    return env_vars

class LoadTraceParentGenerator(IdGenerator):
    def __init__(self, traceparent) -> None:
        # purpose of this is to keep the trace ID constant if the same data is sent different times,
        # which mainly happens during testing. Having the trace ID be something that we control
        # will also be useful for tying together logs and metrics with our traces.
        ctx = extract(
            carrier={'traceparent': traceparent},
        )
        self.context = list(ctx.values())[0].get_span_context()


    def generate_span_id(self) -> int:
        """Get a new span ID.

        Returns:
            A 64-bit int for use as a span ID
        """
        return self.context.span_id

    def generate_trace_id(self) -> int:
        """Get a new trace ID.

        Implementations should at least make the 64 least significant bits
        uniformly random. Samplers like the `TraceIdRatioBased` sampler rely on
        this randomness to make sampling decisions.

        See `the specification on TraceIdRatioBased <https://github.com/open-telemetry/opentelemetry-specification/blob/main/specification/trace/sdk.md#traceidratiobased>`_.

        Returns:
            A 128-bit int for use as a trace ID
        """
        return self.context.trace_id

class RapidsSpanIdGenerator(IdGenerator):
    def __init__(self, trace_id, job_name) -> None:
        self.trace_id = trace_id
        self.job_name = job_name
        self.step_name = None

    def update_step_name(self, step_name):
        self.step_name = step_name

    def generate_span_id(self) -> int:
        """Get a new span ID.

        Returns:
            A 64-bit int for use as a span ID
        """
        span_id = hashlib.sha256()
        span_id.update(str(self.trace_id).encode())
        span_id.update(bytes(self.job_name.encode()))
        if self.step_name:
            span_id.update(bytes(self.step_name.encode()))
        return int(span_id.hexdigest()[:16], 16)

    def generate_trace_id(self) -> int:
        """Get a new trace ID.

        Implementations should at least make the 64 least significant bits
        uniformly random. Samplers like the `TraceIdRatioBased` sampler rely on
        this randomness to make sampling decisions.

        See `the specification on TraceIdRatioBased <https://github.com/open-telemetry/opentelemetry-specification/blob/main/specification/trace/sdk.md#traceidratiobased>`_.

        Returns:
            A 128-bit int for use as a trace ID
        """
        return self.trace_id


class GithubActionsParserGenerator(IdGenerator):
    def __init__(self, traceparent) -> None:
        # purpose of this is to keep the trace ID constant if the same data is sent different times,
        # which mainly happens during testing. Having the trace ID be something that we control
        # will also be useful for tying together logs and metrics with our traces.
        ctx = extract(
            carrier={'traceparent': traceparent},
        )
        self.context = list(ctx.values())[0].get_span_context()

    def update_span_job_name(self, new_name):
        self.job_name = new_name

    def update_span_step_name(self, new_name):
        self.step_name = new_name


    def generate_span_id(self) -> int:
        """Get a new span ID.

        Returns:
            A 64-bit int for use as a span ID
        """
        return self.context.span_id

    def generate_trace_id(self) -> int:
        """Get a new trace ID.

        Implementations should at least make the 64 least significant bits
        uniformly random. Samplers like the `TraceIdRatioBased` sampler rely on
        this randomness to make sampling decisions.

        See `the specification on TraceIdRatioBased <https://github.com/open-telemetry/opentelemetry-specification/blob/main/specification/trace/sdk.md#traceidratiobased>`_.

        Returns:
            A 128-bit int for use as a trace ID
        """
        return self.context.trace_id


def main(args):
    with open("all_jobs.json") as f:
        jobs = json.loads(f.read())

    env_vars = load_env_vars()

    first_timestamp = date_str_to_epoch(jobs[0]["created_at"])
    # track the latest timestamp observed and use it for any unavailable times.
    last_timestamp = date_str_to_epoch(jobs[0]["completed_at"])

    attribute_files = list(Path.cwd().glob(f"telemetry-tools-attrs-*/*"))
    if attribute_files:
        attribute_file = attribute_files[0]
        attributes = parse_attribute_file(attribute_file.as_posix())
    else:
        attributes = {}
    global_attrs = {}
    for k, v in attributes.items():
        if k.startswith('git.'):
            global_attrs[k] = v

    global_attrs['service.name'] = env_vars['OTEL_SERVICE_NAME']

    provider = TracerProvider(resource=Resource(global_attrs), id_generator=LoadTraceParentGenerator(env_vars["TRACEPARENT"]))
    provider.add_span_processor(span_processor=SpanProcessor(OTLPSpanExporter()))
    tracer = trace.get_tracer("GitHub Actions parser", "0.0.1", tracer_provider=provider)

    with tracer.start_as_current_span("workflow root", start_time=first_timestamp, end_on_exit=False) as root_span:
        for job in jobs:
            job_name = job["name"]
            job_id = job["id"]
            logging.info(f"Processing job '{job_name}'")
            job_create = date_str_to_epoch(job["created_at"], first_timestamp)
            job_start = date_str_to_epoch(job["started_at"], first_timestamp)
            # this may get later in time as we progress through the steps. It is
            # common to have the job completion time be earlier than the end of
            # the final cleanup steps
            job_last_timestamp = date_str_to_epoch(job["completed_at"], job_start)

            if job_start == 0:
                logging.info(f"Job is empty (no start time) - bypassing")
                continue

            attribute_file = Path.cwd() / f"telemetry-tools-attrs-{job_id}/attrs-{job_id}"
            attributes = {}
            if attribute_file.exists():
                logging.debug(f"Found attribute file for job '{job_id}'")
                attributes = parse_attribute_file(attribute_file.as_posix())
            else:
                logging.debug(f"No attribute metadata found for job '{job_id}'")

            attributes["service.name"] = job_name

            job_id_generator = RapidsSpanIdGenerator(trace_id=root_span.get_span_context().trace_id, job_name=job_name)

            job_provider = TracerProvider(resource=Resource(attributes=attributes), id_generator=job_id_generator)
            job_provider.add_span_processor(span_processor=SpanProcessor(OTLPSpanExporter()))
            job_tracer = trace.get_tracer("GitHub Actions parser", "0.0.1", tracer_provider=job_provider)

            with job_tracer.start_as_current_span(job['name'], start_time=job_create, end_on_exit=False) as job_span:
                job_span.set_status(map_conclusion_to_status_code(job["conclusion"]))

                job_id_generator.update_step_name('start delay time')
                with job_tracer.start_as_current_span(
                        name="start delay time",
                        start_time=job_create,
                        end_on_exit=False,
                        ) as delay_span:
                    delay_span.end(job_start)

                for step in job["steps"]:
                    start = date_str_to_epoch(step["started_at"], job_last_timestamp)
                    end = date_str_to_epoch(step["completed_at"], start)
                    job_id_generator.update_step_name(step['name'])

                    if (end - start) / 1e9 > 1:
                        logging.info(f"processing step: '{step['name']}'")
                        with job_tracer.start_as_current_span(
                                name=step['name'],
                                start_time=start,
                                end_on_exit=False,
                            ) as step_span:
                            step_span.set_status(map_conclusion_to_status_code(step["conclusion"]))
                            step_span.end(end)
                            # print("  start:", datetime.fromtimestamp(start/1e9, timezone.utc).strftime("%Y-%m-%d %H:%M:%SZ"))
                            # print("  end:", datetime.fromtimestamp(end/1e9, timezone.utc).strftime("%Y-%m-%d %H:%M:%SZ"))

                        job_last_timestamp = max(end, job_last_timestamp)

                job_end = max(date_str_to_epoch(job["completed_at"], job_last_timestamp), job_last_timestamp)
                last_timestamp = max(job_end, last_timestamp)
                job_span.end(job_end)
        root_span.end(last_timestamp)


if __name__ == "__main__":
    import sys

    main(sys.argv[1:])

telemetry-env-vars

OTEL_RESOURCE_ATTRIBUTES="git.repository=rapidsai/rmm,git.ref=refs/heads/pull-request/1750,git.sha=8c92a920d184eb8ebcc35b554755184042df3401,git.job_url=https://github.com/rapidsai/rmm/actions/runs/12289568170"
OTEL_SERVICE_NAME="pr-rmm"
TRACEPARENT="00-111111dda0211f70d114f7ab88b7eabc-1c4d7db9450b0000-01"

tempo.yaml

stream_over_http_enabled: true
server:
  http_listen_port: 3200
  log_level: info


query_frontend:
  search:
    duration_slo: 5s
    throughput_bytes_slo: 1.073741824e+09
  trace_by_id:
    duration_slo: 5s

distributor:
  receivers:                           # this configuration will listen on all ports and protocols that tempo is capable of.
    otlp:
      protocols:
        http:
  log_received_spans:
    enabled: true
    include_all_attributes: true
    filter_by_status_error: true
  log_discarded_spans:
    enabled: true
    include_all_attributes: true
    filter_by_status_error: true

ingester:
  max_block_duration: 1m               # cut the headblock when this much time passes. this is being set for demo purposes and should probably be left alone normally
  trace_idle_period: 5s

querier:
  frontend_worker:
    frontend_address: tempo:9095

compactor:
  compaction:
    block_retention: 720h                # overall Tempo trace retention. set for demo purposes

metrics_generator:
  metrics_ingestion_time_range_slack: 3h
  registry:
    external_labels:
      source: tempo
      cluster: docker-compose
  storage:
    path: /var/tempo/generator/wal
    remote_write:
      - url: http://prometheus:9090/api/v1/write
        send_exemplars: true
  traces_storage:
    path: /var/tempo/generator/traces
  processor:
    local_blocks:
      filter_server_spans: false
      flush_to_storage: true
    span_metrics:
      intrinsic_dimensions:
        span_kind: false

storage:
  trace:
    backend: local                     # backend configuration to use
    wal:
      path: /var/tempo/wal             # where to store the wal locally
    local:
      path: /var/tempo/blocks

overrides:
  defaults:
    metrics_generator:
      processors: [service-graphs, span-metrics, local-blocks] # enables metrics generator
      generate_native_histograms: both