DataBuffSabertooth APM

English

简体中文

English

简体中文

Parameter Configuration ​

How to change the login account after install, and how to tune Ingest pipeline task settings. For directories and lifecycle, see Docker Operations and Kubernetes Operations. For capacity planning and additional knobs, see Performance Tuning.

1. Change login username and password ​

The open-source Web uses a built-in seed account loaded at startup (in memory). There is no UI to change credentials. Override via environment variables and restart ai-apm-web.

YAML keyEnvironment variableDefault
apm.security.seed-usernameAPM_SECURITY_SEED_USERNAMEadmin
apm.security.seed-passwordAPM_SECURITY_SEED_PASSWORDDatabuff@123

Sign in again with the new account afterward; old sessions/tokens will not work. The password is stored in env vars—protect host access accordingly. After start.sh / install completes, the summary prints the effective credentials from the running Web container (not a hard-coded default).

Docker ​

Preferred: docker-compose.override.yml (merged automatically; upgrades do not overwrite it):

bash
cd /opt/databuff-ai-apm   # or: echo $APM_INSTALL_DIR

cat > docker-compose.override.yml <<'EOF'
services:
  ai-apm-web:
    environment:
      APM_SECURITY_SEED_USERNAME: "youruser"
      APM_SECURITY_SEED_PASSWORD: "your-strong-password"
EOF

docker compose up -d ai-apm-web
docker exec ai-apm-web printenv | grep '^APM_SECURITY_SEED_'

You can edit docker-compose.yml under ai-apm-web.environment instead, but upgrades replace docker-compose.yml—re-apply changes or use override.

Kubernetes ​

Add keys to the ConfigMap and restart Web:

bash
kubectl -n databuff edit configmap ai-apm-config

Under data::

yaml
  APM_SECURITY_SEED_USERNAME: "youruser"
  APM_SECURITY_SEED_PASSWORD: "your-strong-password"

Then:

bash
kubectl -n databuff rollout restart deploy/ai-apm-web
kubectl -n databuff rollout status deploy/ai-apm-web
kubectl -n databuff exec deploy/ai-apm-web -- printenv | grep '^APM_SECURITY_SEED_'

Editing the Deployment env (kubectl -n databuff edit deploy/ai-apm-web) works the same way.

Upgrade note: Re-running install.sh / start.sh applies the stock configmap.yaml and may wipe hand-added keys. Re-apply them after upgrade, or keep a local patch checklist.

2. Adjust Ingest task parameters ​

Ingest uses worker pools and ring buffers for Trace / Metric / aggregation. Defaults live in ai-apm-ingest application.yml and can be overridden by env vars.

Common task and buffer settings ​

YAML keyEnvironment variableDefaultMeaning
ingest.pipeline.trace-tasksINGEST_TRACE_TASKS8Trace parse/assemble parallelism
ingest.pipeline.metric-tasksINGEST_METRIC_TASKS4Metric routing parallelism
ingest.pipeline.aggregate-tasksINGEST_AGGREGATE_TASKS4Minute aggregation workers
ingest.pipeline.trace-buffer-sizeINGEST_TRACE_BUFFER_SIZE8192Ring slots per trace worker (≥16)
ingest.pipeline.metric-buffer-sizeINGEST_METRIC_BUFFER_SIZE1024Ring slots per metric worker
ingest.pipeline.aggregate-buffer-sizeINGEST_AGGREGATE_BUFFER_SIZE1024Ring slots per aggregate worker

When buffers fill, events are dropped (overflow). If the UI misses data but ingest logs look healthy, raise *_BUFFER_SIZE or *_TASKS first. For Doris flush and related knobs, see Performance Tuning — Ingest pipeline.

Docker ​

Prefer docker-compose.override.yml:

bash
cd /opt/databuff-ai-apm

cat > docker-compose.override.yml <<'EOF'
services:
  ai-apm-ingest:
    environment:
      INGEST_TRACE_TASKS: "16"
      INGEST_TRACE_BUFFER_SIZE: "16384"
      INGEST_METRIC_TASKS: "8"
EOF

docker compose up -d ai-apm-ingest
docker exec ai-apm-ingest printenv | grep '^INGEST_'

If the same override already sets Web credentials, append under the matching services entries instead of replacing the whole file.

Kubernetes ​

bash
kubectl -n databuff edit configmap ai-apm-config

Under data::

yaml
  INGEST_TRACE_TASKS: "16"
  INGEST_TRACE_BUFFER_SIZE: "16384"
  INGEST_METRIC_TASKS: "8"

Restart ingest:

bash
kubectl -n databuff rollout restart deploy/ai-apm-ingest
kubectl -n databuff rollout status deploy/ai-apm-ingest
kubectl -n databuff exec deploy/ai-apm-ingest -- printenv | grep '^INGEST_'

Same upgrade caveat as login credentials: re-applying the stock ConfigMap drops custom keys.

Tuning tips ​

  • Change one or two knobs at a time so you can roll back easily.
  • Fix drops first (larger buffers / tasks), then tune against CPU and Doris Stream Load pressure.
  • Higher *_TASKS uses more CPU and memory—see the baselines in Performance Tuning.