Interpret container stdout in real-time. Patch HPA bounds and block malicious ingress instantly, seconds before CPU capacity limits are breached.
Traditional CPU/memory-based autoscaling reacts too late. LogStrata tails access log event patterns directly from containerd sockets, triggering pod scale-up and IP rate-limiting policy adjustments within milliseconds of load shifts.
Kubernetes Horizontal Pod Autoscalers rely on metrics-server CPU/memory polling. During sudden load events, your containers will crash-loop before the system registers resource spikes.
Metrics server averages CPU usage over 60-second windows. During flash-sale traffic spikes, request queues fill up instantly. Latency rises exponentially, causing database socket depletion and gateway timeouts before pods scale.
LogStrata parses individual HTTP access logs on the fly. By monitoring the exact throughput rate of checkout endpoints, the scaling trigger is fired immediately—allocating replicas before CPU metrics even begin to move.
A zero-overhead daemonset listens directly on the container socket, transforming raw stdout streams into instant scaling actions.
DaemonSet tails container stdout files directly from /var/log/pods, completely bypassing the user network path.
Extracts latency fields, response status, endpoints, and incoming client subnet IPs at sub-millisecond speeds.
Matches extracted values against policy criteria (RPS ceilings, error percentages, rate limits, cooldown bounds).
Triggers mutations on Kubernetes API servers to scale pods or update ingress rate-limiting rules.
Observe and autoscale within a single control loop. Skip third-party metric collectors entirely.
Parse container stdout socket streams directly from containerd. Our high-performance extraction loop decodes JSON lines in micro-seconds, logging throughput metrics, request paths, response duration and query parameters.
Configure autoscaling parameters through YAML Custom Resource Definitions (CRDs). Set custom metrics thresholds, scale boundaries, cooldown periods, and threat locks.
apiVersion: core.logstrata.io/v1alpha1 kind: LogAutoscalerPolicy metadata: name: checkout-rules spec: scaleTargetRef: name: commerce-frontend
Protect workloads from malicious requests by blocking IPs at the ingress layer. LogStrata monitors fail counts and pattern repetitions, dynamically updating Nginx Ingress ConfigMaps to drop connections.
LogStrata analyzes in-memory logs asynchronously to identify traffic anomalies. Rather than waiting for metrics-server or Prometheus queries, the controller makes immediate decisions and propagates them to the Kubernetes endpoint, protecting downstream resources from queue overload.
Monitor ingress rates, autoscale transitions, and client blocklists inside a single unified dashboard interface.
Evaluator matched 1 active scaling target.
42 rogue IPs blocked in the last hour.
stabilized from 450ms peak load.
A decoupled log controller analyzes multi-dimensional container stdout log patterns dynamically, applying feedback to the Kubernetes HPA scaling loop without adding request latency.
Deploy the LogStrata controller using Helm, apply configurations, and start monitoring logs.
Review standard Custom Resource Definitions (CRDs) and metric parameter schemas supported by the evaluator core.
| CRD Group / Resource | Description | Primary Config Parameters |
|---|---|---|
| LogAutoscalerPolicy | Defines real-time pod replicas boundaries and triggering log criteria. | minReplicas, maxReplicas, trigger.threshold |
| LogThreatPolicy | Specifies rate limiting and host blocking rules based on log failure loops. | threatMetrics.pattern, action.duration |
| IngressRateLimiter | Configures ConfigMap routing rules to enforce firewall blocks on client IPs. | blocklistConfigMap, duration |
Local testing is always free. Scale up to staging and multi-cluster production environments dynamically.
Ideal for local evaluation and minikube environments
For small-to-medium clusters in staging and production
For mission-critical global scale multi-cluster operations
Everything you need to know about log-driven autoscaling, security loops, and container telemetry.
Switch scaling decisions from reactive resource consumption limits to predictive log-level insights. Deploy to minikube or cloud clusters in seconds.