# Agent 'out of the box' idle experiment. Represents an agent install with the
# default configuration and no active workload.
# Further documentation:
# https://datadoghq.atlassian.net/wiki/spaces/agent/pages/4294836779/Performance+Quality+Gates#idle

optimization_goal: memory
erratic: false

target:
  name: datadog-agent
  cpu_allotment: 4
  # Set to 30% higher than the memory_usage check value
  memory_allotment: 233 MiB

  environment:
    DD_API_KEY: a0000001
    DD_HOSTNAME: smp-regression

  profiling_environment:
    DD_INTERNAL_PROFILING_BLOCK_PROFILE_RATE: 10000
    DD_INTERNAL_PROFILING_CPU_DURATION: 1m
    DD_INTERNAL_PROFILING_DELTA_PROFILES: true
    DD_INTERNAL_PROFILING_ENABLED: true
    DD_INTERNAL_PROFILING_ENABLE_GOROUTINE_STACKTRACES: true
    DD_INTERNAL_PROFILING_MUTEX_PROFILE_FRACTION: 10
    DD_INTERNAL_PROFILING_PERIOD: 1m
    DD_INTERNAL_PROFILING_UNIX_SOCKET: /smp-host/apm.socket
    DD_PROFILING_EXECUTION_TRACE_ENABLED: true
    DD_PROFILING_EXECUTION_TRACE_PERIOD: 1m
    DD_PROFILING_WAIT_PROFILE: true
    DD_APM_INTERNAL_PROFILING_ENABLED: true

    DD_INTERNAL_PROFILING_EXTRA_TAGS: experiment:quality_gate_idle

checks:
  - name: memory_usage
    description: "Memory usage quality gate. This puts a bound on the total agent memory usage."
    bounds:
      series: total_pss_bytes
      # When updating this, update the memory_allotment in the target section to 30% higher
      upper_bound: "179 MiB"

  - name: intake_connections
    description: "Connections established to intake APIs. This puts a bound on total connections per Agent instance."
    bounds:
      series: "connection.current"
      upper_bound: 5

  - name: total_bytes_received
    description: "Bytes received quality gate. This bounds total network egress."
    bounds:
      series: total_bytes_received
      upper_bound: "0.8 MiB"

report_links:
  - text: "bounds checks dashboard"
    link: "https://app.datadoghq.com/dashboard/vz3-jd5-bdi?fromUser=true&refresh_mode=paused&tpl_var_experiment%5B0%5D={{ experiment }}&tpl_var_job_id%5B0%5D={{ job_id }}&view=spans&from_ts={{ start_time_ms }}&to_ts={{ end_time_ms }}&live=false"