LogStratav1.0.0
Guide

Getting Started

Learn how to deploy LogStrata's controller daemon and fluentd collection agents in minutes.


System Prerequisites

Before installing LogStrata, verify that your target environment contains the following tools:

  • Kubernetes cluster version v1.24+
  • Helm package manager installed locally
  • Aggregated Elasticsearch database context or open port access
  • Write access to patch Deployment scale limits

1. Install via Helm

Add the official Helm repository context and pull down the LogStrata platform package:

# Register registry
$ helm repo add logstrata https://helm.logstrata.io
$ helm repo update

# Install charts into custom namespace
$ helm install logstrata logstrata/logstrata \
  --namespace logstrata-system \
  --create-namespace

2. Verify Ingestion

Ensure that the collection agent DaemonSet and Controller components are running correctly:

$ kubectl get pods -n logstrata-system

Expected output:

NAME                                   READY   STATUS    RESTARTS   AGE
logstrata-controller-5fc6d5b78-xyz12   1/1     Running   0          2m
fluentd-logging-agent-j4k2s            1/1     Running   0          2m
fluentd-logging-agent-l8f9d            1/1     Running   0          2m

3. Apply Your First Scaling Policy

Create a simple policy configuration to automatically scale your application on high request latency. Save the file as slsa-policy.yaml:

apiVersion: core.logstrata.io/v1alpha1
kind: LogAutoscalerPolicy
metadata:
  name: frontend-latency-scaler
spec:
  scaleTargetRef:
    apiVersion: apps/v1
    kind: Deployment
    name: main-frontend
  metricSources:
    - type: ElasticSearchQuery
      elasticsearch:
        query: "status:200 AND path:/home"
        timeWindow: "30s"
        trigger:
          metricName: throughput_rps
          threshold: 300.0
          scaleFactor: 1.5
  cooldownPeriod: "45s"
  minReplicas: 3
  maxReplicas: 15

Apply it directly to your cluster context:

$ kubectl apply -f slsa-policy.yaml

Next Steps

Read about how the internal architecture manages metric querying, or explore the configuration variables.