Performance
Optimize
Network layer (Networking components)
Data layer (Disk, DB)
Compute layer (CPU, Mmeory)
Tunning different layer mentioned below
performance monitoring
Request count
Requst latency
pip install prometheus_client
from prometheus_client import Counter, Histogram
REQUEST_COUNT = Counter('api_requests_total', 'Total API requests')
REQUEST_LATENCY = Histogram('api_request_latency_seconds', 'API request latency')https://signoz.io/guides/how-do-i-monitor-api-in-prometheus/
from prometheus_client import generate_latest
@app.route('/metrics')
def metrics():
return generate_latest()
Prometheus supports the following types of metrics:
Counter: a single value that can only be monotonically increased or reset to 0 on restart.
Gauge: a single numeric value that can be arbitrarily set.
Histogram: a group of configurable buckets for sampling observations and recording values in ranges; also provides a sum of all observed values
Summary: like a histogram, but it also calculates configurable quantiles over a sliding time window.
For more information, see Metric types.
Creating metrics for SLIs
If your application emits Prometheus metrics, you can use them for SLIs.
For availability SLIs on request and error counts, you can start with Prometheus counter metrics.
For latency SLIs, you can use Prometheus histogram or summary metrics.
Prometheus supports the following types of metrics:
Counter: a single value that can only be monotonically increased or reset to 0 on restart.
Gauge: a single numeric value that can be arbitrarily set.
Histogram: a group of configurable buckets for sampling observations and recording values in ranges; also provides a sum of all observed values
Summary: like a histogram, but it also calculates configurable quantiles over a sliding time window.
For more information, see Metric types.
Creating metrics for SLIs
If your application emits Prometheus metrics, you can use them for SLIs.
For availability SLIs on request and error counts, you can start with Prometheus counter metrics.
For latency SLIs, you can use Prometheus histogram or summary metrics.
Latency = Time to serve one customer.
Throughput = Number of customers served per hour.
If you reduce service time per customer (latency) and have enough staff (resources) → you’ll serve more customers (higher throughput).
But if there’s only one waiter (limited concurrency), lowering latency alone won’t help much.
Lower latency often leads to higher throughput — if your system isn’t bottlenecked elsewhere and can handle more concurrent work.
Setup to get lower latency and higher throughput
in the setup you described, lower latency will very likely lead to higher throughput, because you’re optimizing across all major bottlenecks.
Let’s break down why your reasoning is solid 👇
🧩 1. Caching (Redis)
Reduces application latency by serving responses from memory instead of a slower database or external API.
Each request completes faster → system frees up resources for new requests. ✅ Lower latency → higher throughput.
📍 2. Pod Affinity / Node Locality
Keeps pods close to dependent services or data sources (e.g., same node, AZ, or region).
Cuts down on network latency and packet travel time. ✅ Reduced latency → faster request turnaround → higher throughput.
🌐 3. Cloudflare CDN
Delivers static content from edge locations, near users.
Offloads your backend, so it handles only dynamic or API traffic. ✅ Frees backend capacity + reduces latency → higher throughput for origin servers.
⚙️ 4. HPA (Horizontal Pod Autoscaler)
Scales pods horizontally based on load (CPU, memory, or custom metrics like RPS).
Prevents overload, so latency doesn’t spike under high traffic. ✅ Sustains throughput during demand spikes.
☁️ 5. Karpenter
Scales nodes dynamically based on pod demand.
Ensures new pods always have compute capacity → avoids scheduling delays. ✅ Keeps throughput high as cluster load grows.
⚡ Combined Effect
You’ve essentially optimized end-to-end performance:
Caching
Faster data retrieval
↓ latency
CDN
Faster static delivery
↓ latency
Pod Affinity
Reduced network hops
↓ latency
HPA
Scales apps
Sustains throughput
Karpenter
Scales infrastructure
Sustains throughput
✅ Net Result:
Latency ↓ → Requests complete faster → More requests handled per second → Throughput ↑
💬 In short:
You’re right — in a well-architected system like yours (Redis + Affinity + CDN + Autoscaling), reducing latency directly improves throughput, because you’ve removed resource and scaling bottlenecks.
so we optimize our performance but we also need to think about the cost
as reliability comes with cost
Other Aspects AWS (Compute Layer)
✅ Right-size pods (CPU/memory requests & limits) — use Prometheus metrics or
kubectl topdata.✅ Vertical Pod Autoscaler (VPA) — auto-adjust pod resources based on usage.
✅ Node-level optimization — use instance types that match your workload (e.g., compute-optimized for CPU-heavy, memory-optimized for caching).
Rust and nodejs takes more memory
Clickhouse need more CPU
✅ Use Spot instances (with Karpenter interruption handling) for non-critical workloads.
Q. how you set request/limit to pod and how u know how much resources ur pod neeed?
Ans: I run kubectl top node (metric server) that shows CPU and memory utilization of pod and set request/limit accordingly
Perfect, Pavan — you’re thinking like an SRE now ⚡
Since you’ve got a real production-style stack — (Rust + Python backends, Next.js frontend, ClickHouse analytics, Superset dashboards — all on EKS) — we can tune your compute layer end to end for CPU, memory, I/O, caching, and scaling.
Let’s break this down by component and what each typically needs 👇
🧠 OVERVIEW — Compute Layer Resource Focus
Rust backend
CPU
Memory
Compute tuning & concurrency
Python backend (e.g., FastAPI, Flask)
CPU / Threading
Memory
Async I/O, caching
Next.js frontend
CPU (build)
Memory
Build caching, CDN, ISR
ClickHouse
Disk I/O
Memory, CPU
Storage, partitioning
Apache Superset
Memory
CPU
Query caching & concurrency
Redis (cache)
Memory
Network I/O
Max hit ratio, TTLs
EKS (infra)
CPU scheduling
Memory, network
Right-sizing, autoscaling
⚙️ 1. Rust Backend (High-performance APIs or Services)
Rust is extremely efficient but needs tuning for concurrency and runtime resource limits.
Resource Characteristics:
CPU-bound (especially with heavy computation or JSON serialization)
Low memory footprint (unless using large in-memory caches)
Fast network I/O
Optimization Tips:
✅ Use async runtime (
tokio) for high concurrency.✅ Use Rust’s thread-per-core model (tune thread pool size = #vCPUs).
✅ Use jemalloc for better memory allocation (
LD_PRELOAD=/usr/lib/libjemalloc.so).✅ Container requests:
(Scale up if CPU utilization > 70%)
✅ Use Prometheus metrics (via metrics crate) for latency and CPU profiling.
Infra Recommendation:
Instance Type: compute-optimized (e.g.,
c6i.largeorc7g.large)Storage: EBS gp3 (no need for high IOPS unless logs heavy)
🐍 2. Python Backend (e.g., FastAPI, Flask, Django)
Python is single-threaded by default; performance depends on concurrency model.
Resource Characteristics:
CPU for request handling (GIL limits parallelism per process)
Memory grows with async tasks or caching
Optimization Tips:
✅ Use Gunicorn + Uvicorn workers with async I/O:
✅ Use Redis for session & query cache (not in-memory Python dicts).
✅ Pre-load models/configs to reduce per-request overhead.
✅ Keep dependency count minimal.
✅ Use async DB drivers (e.g., asyncpg for Postgres).
Container Resources:
(adjust if you have ML/analytics inside Python)
Infra Recommendation:
Instance Type: general-purpose (
m6i.largeort3.medium)Attach Redis for caching & Celery queue.
🌐 3. Next.js Frontend (SSR or Static)
Resource Characteristics:
Build = CPU and memory heavy
Runtime = mostly network I/O and light CPU (SSR/ISR)
Optimization Tips:
✅ Use Incremental Static Regeneration (ISR) and Cloudflare CDN.
✅ Cache Next.js builds in CI/CD to speed up deployments.
✅ Use Node.js LTS (>=18) for better memory and async performance.
✅ Disable source maps in production to reduce bundle size.
✅ Precompress static assets (gzip/brotli).
Container Resources (Runtime only):
Infra Recommendation:
Serve via NGINX ingress + Cloudflare CDN
Use ephemeral build pods for CI/CD.
⚡ 4. ClickHouse (Analytics Database)
ClickHouse is disk I/O and memory intensive — extremely fast but needs tuned nodes.
Resource Characteristics:
Heavy disk reads/writes
Uses memory for compression, merge trees, and query processing
CPU used for query computation (especially aggregations)
Optimization Tips:
✅ Use NVMe or high-IOPS SSDs (gp3 with 6000+ IOPS minimum).
✅ Memory: at least 8GB per node.
✅ CPU: 4 vCPU minimum per replica.
✅ Enable compression: LZ4 for faster reads.
✅ Partition & TTL large tables to reduce scan time.
✅ Avoid joins across massive datasets — use denormalization.
Container Resources:
Infra Recommendation:
Instance Type: storage-optimized (
i3.large,r6i.large)Use StatefulSet with local SSD or provisioned EBS.
Monitor: query latency, merges, disk I/O.
📊 5. Apache Superset (Analytics Dashboard)
Resource Characteristics:
Memory-heavy (especially for large dashboards)
Moderate CPU (for query rendering)
Dependent on ClickHouse latency
Optimization Tips:
✅ Enable cache layer:
Superset cache → Redis
Query results → ClickHouse cache / Materialized views
✅ Gunicorn workers = #vCPUs
✅ Optimize chart queries (limit rows, async loading)
✅ Store thumbnails/static assets on CDN or S3.
Container Resources:
Infra Recommendation:
Instance Type: general-purpose (
m6i.large)External Redis + ClickHouse backend
🧰 6. Redis (Cache Layer)
Resource Characteristics:
Memory-bound (entire dataset in RAM)
CPU only matters with Lua scripts or large pipelines
Disk (if using AOF/RDB persistence)
Optimization Tips:
✅ Use maxmemory-policy = allkeys-lru
✅ Set proper TTLs for cache entries.
✅ Cluster Redis if >10GB dataset.
✅ Avoid persistence if cache-only use.
✅ Monitor memory fragmentation.
Container Resources:
Infra Recommendation:
Instance Type: memory-optimized (
r6g.large)EBS gp3 (1000 IOPS) if persistence is on.
☁️ 7. EKS Layer (Infrastructure & Scheduling)
Optimization Tips:
✅ Use HPA for apps (CPU-based for Rust/Python, latency-based for frontend).
✅ Use Karpenter for node autoscaling.
✅ Define taints and affinities for data-heavy pods (ClickHouse on I/O optimized nodes).
✅ Separate compute & analytics node groups:
compute-pool→ Rust, Python, Next.jsanalytics-pool→ ClickHouse, Superset
✅ Use bottleneck-aware node sizing:
CPU-heavy → c6i/c7g
Memory-heavy → r6i
Storage-heavy → i3 / gp3 NVMe
🧾 Example Node Group Mapping
Rust / Python
c7g.large (Graviton)
Compute
Lower latency, cost-efficient
Superset
m6i.large
Balanced
Moderate CPU/memory
ClickHouse
r6i.xlarge or i3.xlarge
Analytics
High I/O & memory
Redis
r6g.large
Cache
Memory optimized
Next.js
t3.medium
Frontend
Light CPU, low memory
🧮 Summary Table
Rust backend
🔥 High
⚙️ Medium
🟢 Low
✅ Redis
Async + jemalloc
Python backend
⚙️ Medium
⚙️ Medium
🟢 Low
✅ Redis
Async workers
Next.js
⚙️ Medium (build)
⚙️ Medium
🟢 Low
✅ CDN
ISR + Cloudflare
ClickHouse
⚙️ High
🔥 High
🔥🔥 High
🚫 (internal)
Partition & NVMe
Superset
⚙️ Medium
🔥 High
🟢 Medium
✅ Redis
Query cache
Redis
🟢 Low
🔥🔥 High
⚙️ Medium
-
All in-memory
EKS Infra
⚙️ Medium
⚙️ Medium
-
-
HPA + Karpenter
Would you like me to create a visual EKS node architecture diagram (showing each workload type, its resources, and scaling behavior)? That would make a great cluster overview for documentation or dashboard planning.
Application Layer
✅ Asynchronous / non-blocking I/O (Node.js, Go, Python asyncio).
✅ Use message queues (e.g., RabbitMQ, Kafka) for decoupling slow background tasks.
✅ Profiling (e.g., Py-spy, Go pprof, Node clinic) to find hot functions.
✅ Batching API calls instead of one-by-one requests.
✅ Use efficient serialization (e.g., Protobuf over JSON if feasible).
DB Optimization (DB Layer)
Even if Redis is caching, DB tuning still matters:
✅ Indexing & query optimization (use
EXPLAIN ANALYZEin PostgreSQL/MySQL).✅ Connection pooling with tools like PgBouncer.
✅ Partitioning or sharding for large datasets.
✅ Async writes / batching where possible.
✅ Read replicas for scaling read-heavy workloads.
Network layer optimization
✅ Keep services in same VPC / AZ to minimize cross-zone latency.
✅ Enable HTTP/2 or gRPC for multiplexed requests.
✅ Use connection reuse (keep-alive) to avoid TCP overhead.
✅ Enable compression (gzip, brotli) for large responses.
✅ Optimize DNS TTL for Cloudflare or service discovery latency.
Goal: Reduce round-trip times and overhead per request.
Container & Runtime Optimization
✅ Use distroless or Alpine images to reduce size.
✅ Enable multi-stage builds for cleaner images.
✅ Tune JVM / Node / Python runtime parameters.
✅ Reduce cold-start time (especially if using HPA or serverless workloads).
🎯 Goal: Faster deploys, lower startup latency.
Security & Reliability Under Load
✅ Use rate limiting (NGINX / API Gateway) to prevent overload.
✅ Graceful degradation — fallback responses or partial service instead of full outage.
✅ Circuit breakers (Hystrix / Resilience4j).
✅ Load testing (k6, JMeter, Locust) before production scaling.
🎯 Goal: Stay performant under heavy or malicious traffic.
CI/CD and Deployment Optimization
✅ Canary & Blue/Green deployments to minimize downtime.
✅ Rolling updates with minimal pod restarts.
✅ Prewarming (spin up pods before traffic shift).
✅ Use ArgoCD or GitOps for consistent rollouts.
🎯 Goal: Zero downtime and stable release velocity.
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