Infrastructure Scaling, Capacity Planning, and High Availability Questions

Plan Summary
I will validate scaling and latency of a Redis cluster via controlled load tests that vary read/write ratios, data sizes, concurrency, and hit/miss ratios while collecting latency percentiles, throughput, and resource metrics. Tests include warm-up, steady-state, and scale-up phases.

Workload profiles

• Read-heavy: 95% GET / 5% SET (cache-serving scenario)
• Read-mostly: 80% GET / 20% SET
• Balanced: 50/50 (session store or counters)
• Write-heavy: 20% GET / 80% SET (ingest use)
• Pub/Sub or Lua-heavy test where applicable

Data size / key

• Small: 100 B — typical cache object
• Medium: 1 KB — JSON payload
• Large: 10 KB — larger session or blob
  Use realistic key distribution: Zipfian with working set sizes: 1%, 10%, 100% of aggregate memory.

Warm-up strategy

• 10–20 minutes prefill phase to load working set
• 5–10 minutes ramp-up applying target qps to stabilize JIT/OS caches
• Verify hit ratio and memory occupancy before measuring

Concurrency & hit/miss

• Concurrency: ramp concurrent clients: 50, 200, 1000, 5000 (depends on client machine)
• Hit/miss ratios: 99/1, 95/5, 80/20 to simulate cache warm/cold

Metrics to collect

• Latency: p50, p95, p99, max per op type (GET/SET/LUA)
• Throughput: ops/sec per node and aggregate
• Resource: CPU, memory, net I/O, context switches, thread pools
• Redis internals: evictions, keyspace hits/misses, expirations, blocked clients, replication lag
• OS: disk, NIC stats, TCP retransmits

Pass criteria

• p95 < target_latency (e.g., 2 ms) and p99 < upper bound (e.g., 10 ms)
• No node CPU > 85% sustained; memory < 90% max; replication lag < 200 ms
• Throughput scales linearly (or acceptable diminishing returns) with nodes
• Error rate < 0.1% and acceptable eviction rate per SLA

Scaling during tests

• Start single primary-replica set; run baseline
• Add shards/primaries (cluster slots) incrementally: 2, 4, 8 primaries
• At each scale point: rebalance data, allow rehashing to complete, re-warm if needed, then run full benchmark profile
• For vertical scaling: increase instance CPU/memory sizes and repeat hot paths
• Observe bottlenecks: if CPU-bound, scale vertically or add primaries; if network-bound, increase NIC or distribute clients; if memory-bound, add nodes or reduce working set.

Tools & validation

• Use memtier_benchmark or redis-benchmark for load; collect metrics via Prometheus + Grafana, node-exporter, and Redis INFO
• Repeat each scenario 3 times, report median and variance, and produce heatmaps of p95/p99 vs concurrency and nodes.

Situation & goal
I’d treat sustained CPU saturation as a controlled investigation: determine whether pods are under-provisioned, bursty, or node capacity-limited, then choose short- and medium-term remediation.

Step-by-step investigative plan

1. Pod-level checks

• Inspect requests/limits: kubectl get pods -o json / describe — look for missing/low cpu requests.
• QoS class: check Guaranteed/Burstable/BestEffort; BestEffort more likely evicted.
• Pod CPU usage: kubectl top pod and metrics-server/Prometheus queries (rate of cpu usage per pod).
• Eviction/oom events: kubectl describe pod and kubectl get events -n <ns>.

2. Node-level checks

• Node CPU allocatable vs capacity: kubectl describe node and kubectl top node.
• System vs kube-reserved vs eviction thresholds (kubelet config).
• Check kernel, load, throttling (cAdvisor/Prometheus node-exporter metrics: cpu_throttling, load1).

3. Autoscaling & controllers

• HPA: kubectl get hpa -o yaml and check current/target metrics, min/max replicas, metricServer/Prometheus adapter metrics.
• VPA: check recommendation mode (Auto/Recommend/Off) and recommendations for cpu.
• Cluster-Autoscaler: inspect logs on CA pod or cloud provider autoscaler events for scale-up failures or unrecognized taints/insufficient resources.

Remediation options

• Short: increase pod CPU requests (match 95th percentile), adjust HPA target to CPU or custom metric, cordon low-perf nodes.
• Medium: resize node-pool (larger instance types or more nodes), add mixed node-pool with higher CPU density, tune kubelet reserved/eviction settings.
• Long: add taints/tolerations and nodeSelectors to move heavy workloads to dedicated pools; use VPA in Recommend mode for safe tuning; implement resource quotas and alerting for sustained CPU saturation.

Validation

• After changes, monitor pod CPU usage, pod restarts/evictions, HPA behavior, and autoscaler events for 30–60 minutes; iterate.

Definition — High Availability (HA)
High availability means systems stay operational with minimal downtime through redundancy, load balancing, and fast failover. Goal: continuous service.

Definition — Disaster Recovery (DR)
Disaster recovery is the plan and processes to restore systems after catastrophic failure (region outage, data corruption). Focus: restore data and service to an acceptable point and time.

Key differences

• HA prevents/mitigates short interruptions; DR handles large-scale restorations.
• HA emphasizes immediate failover; DR emphasizes recovery procedures, backups, and possibly manual steps.

Concrete examples (HTTP service + DB)

• HTTP service: HA via multiple app instances across AZs, fronted by a load balancer and health checks; auto-scaling + blue/green deploys. Failover is automatic within seconds to minutes. Typical targets: RTO = 0–5 minutes, RPO = 0 (stateless or session replication).
• Database: HA via synchronous replication (primary + synchronous replica) for no data loss; async cross-region replica for DR. For DB in same region: RTO = 1–15 minutes, RPO = 0 for sync, minutes for async. For cross-region DR: RTO = 30–120 minutes, RPO = minutes–hours depending on replication.

Strategies to meet targets

• HA: health checks, automated failover, DNS TTL tuning, multi-AZ deployment, connection draining.
• DR: regular backups, point-in-time recovery, offsite/georedundant replicas, runbooks, periodic DR drills, RTO/RPO validation.

Trade-offs
Synchronous replication increases latency and cost but yields lower RPO; async reduces latency and cost but increases potential data loss. Choose per business impact.

Clarify requirements

• RPO/RTO targets, retention windows, legal hold duration, regions, throughput for restores, allowable costs, and audit evidence retention.

High-level architecture

• Primary object store (e.g., S3 standard or equivalent) + immutable cold tier (WORM via Object Lock/GOVERNANCE/COMPLIANCE) + cross-region replicated archive (CRR -> Glacier Deep Archive or equivalent).
• Orchestrator service (Lambda/Functions + Step Functions) to manage backups, manifests, and recovery workflows.
• Metadata/Index DB (ElastiCache/RDS/Cosmos) storing file manifests, chunks, checksums, locations, and retention/hold flags.

Immutable backups & replication

• Enforce WORM using object-lock at ingestion with retention/hold metadata; block delete/overwrite via bucket policies and SCPs.
• CRR asynchronously copies locked objects to secondary region; lifecycle rules transition to archive tier.

Restore indexing & speed

• Maintain searchable index of manifests and object chunk locations with materialized views for fast lookup.
• Pre-warm critical datasets by keeping recent monthly snapshots in infrequent-access tier; use parallel multi-part ranged GETs and transfer acceleration or signed URLs to maximize throughput and meet RTO.

Cost optimization / tiering

• Hot: S3 Standard for current working sets.
• Warm: S3 IA or Nearline for monthly snapshots.
• Cold: Archive Deep tiers for long-term retention.
• Lifecycle policies automate transitions; intelligent-tiering for unpredictable access.

Access controls & encryption

• Enforce least privilege via IAM roles, ABAC tags, and segregation of duties.
• Server-side encryption with KMS CMKs; use HSM-backed keys for regulated data.
• Audit-only service accounts for restore operations; require multi-party approval (approval workflow, MFA + digital signatures) for high-risk restores.

Proving compliance / auditability

• Immutable logs: enable object access logs, CloudTrail, and write-once audit logs stored in separate immutable repository.
• Evidence package per retention period: manifests, checksums, KMS key access logs, Object Lock retention proofs, replication receipts, and signed approval records.
• Continuous attestation: periodic integrity scans (checksums), automated reports, and SIEM dashboards; exportable compliance reports with signed hashes and timestamps.

Trade-offs

• Faster restores cost more (keep warm copies); stricter immutability increases storage and operational complexity.
• Use automated policy-driven controls to balance cost vs RTO/RPO.

I would implement this as repeatable IaC modules, automated tests for retention/immutability, and run tabletop drills to validate RTO/RPO and audit evidence generation.

Approach summary
Design a unified observability pipeline that ingests cloud-native metrics from AWS/GCP/Azure, normalizes labels and cardinality, stores time-series efficiently, exposes aggregated headroom metrics, and powers Grafana dashboards + alerts with cost controls.

Ingestion

• Pull from cloud metrics APIs (CloudWatch, Stackdriver/Cloud Monitoring, Azure Monitor) via collectors (Prometheus + exporters or vendor SDKs).
• Use Prometheus-compatible scraping where possible (kube-state-metrics, node-exporter). Centralize ingestion through a federated Prometheus or remote-write gateway.

Normalization & cardinality

• Apply relabeling and mapping rules in the gateway: normalize provider-specific labels (region/zone → region, instance_type → instance_family).
• Enforce label whitelist and aggregate high-cardinality labels (instance_id → instance_role or hashed bucket) to limit series explosion.
• Convert provider units/timestamps and synthesize common metrics (e.g., cpu_utilization_pct).

Transformation & aggregation

• Use a stream processor (e.g., Prometheus remote-write to Cortex/Thanos + a processing layer like Telegraf/Vector or Kafka + Flink) to compute per-region aggregates: sum(capacity), sum(used), headroom = capacity - used, headroom_pct.
• Precompute rollups at minute/5m/1h resolutions for dashboard efficiency.

Storage

• Long-term TSDB: Thanos or Cortex/Mimir with object-store backend (S3/GCS/Azure Blob). Keep high-resolution recent data (14d), downsampled older data (90–365d) to control cost.
• Use metric retention tiers and compaction to reduce storage costs.

Cost considerations

• Reduce cardinality, sample high-frequency metrics, downsample old data.
• Apply ingestion filters to ignore low-value metrics.
• Rightsize retention and use object storage lifecycle policies.
• Use cost-aware alerting to avoid noisy alerts that trigger expensive investigation.

Presentation: per-region headroom dashboards & alerts

• Grafana dashboard with panels: capacity vs used, headroom (absolute + %), projected depletion (linear regression), top consumers by service.
• Drilldowns: per-zone, per-instance-family.
• Alerts:
  • Static thresholds (headroom_pct < 15% for 15m).
  • Trend alerts (projected depletion < 72h).
  • Anomaly detection for sudden drops using rolling z-score.
• Alert routing by region and runbook link + automated remediation playbooks (scale-out, quota requests).

Why this works

• Normalization abstracts provider differences, cardinality controls keep ingestion and storage affordable, precomputed aggregates make dashboards responsive, and multi-tier retention balances fidelity vs cost.