Slow Consumer

AI / RAG Application

A Retrieval-Augmented Generation (RAG) architecture that combines vector similarity search for semantic document retrieval with relational metadata filtering, using PostgreSQL with pgvector as the unified store for both embeddings and structured data. Redis provides a semantic cache to avoid redundant embedding model inference and reduce vector index query load for repeated or similar queries. Kafka manages the asynchronous embedding generation pipeline that keeps the vector index current as source documents are added or updated.

Analytics Pipeline

An OLAP-oriented analytics architecture that ingests operational changes from PostgreSQL via WAL-based CDC into Kafka, then routes them to a columnar analytics store (ClickHouse or Snowflake) for product analytics, business intelligence, and operational reporting. The CQRS separation ensures analytical queries never degrade transactional write performance, and materialized views provide pre-aggregated query acceleration for the most expensive analytical patterns.

Multi-Tenant SaaS

A multi-tenant developer tooling platform providing CI/CD pipeline execution, log aggregation, code analysis, and dependency scanning across isolated tenant organizations. Tenant isolation is the primary correctness constraint: a security boundary violation between tenants is a critical incident, not a performance event. PostgreSQL row-level security enforces data isolation; Redis manages job queues and distributed locks; Elasticsearch indexes pipeline log output for search; Kafka delivers webhook events to tenant-registered endpoints; MinIO stores pipeline artifacts. Resource quota enforcement prevents any single tenant's burst from affecting others.

Write-Heavy Application

A durable background job execution platform where jobs are enqueued via API and executed by competing consumer worker pools. PostgreSQL is the durable job store, job definitions, retry state, scheduling metadata, and dead-letter records persist in PostgreSQL with ACID guarantees, surviving any worker or queue infrastructure failure. Redis tracks in-flight job state (which worker claimed which job, visibility timeout lease expiry) to enable fast lease checks without PostgreSQL queries on the hot path. Temporal provides workflow orchestration for multi-step jobs that require coordination across multiple execution stages, with built-in state machine semantics and durable activity execution. Kafka carries job completion events to downstream consumers (analytics, billing triggers, notification fan-out). Backpressure between the job enqueue rate and worker execution rate prevents runaway job accumulation when workers are degraded.

Realtime Collaboration

A real-time location tracking platform for moving entities: vehicles, delivery couriers, field workers, and assets: receiving position updates at 1–30 second intervals from thousands of simultaneously tracked objects. Redis geospatial indexes (GEOADD / GEOSEARCH) serve sub-10ms proximity queries against the live position surface; PostgreSQL stores durable entity metadata and historical location records; TimescaleDB stores high-frequency time-series location data with automatic hypertable chunking and retention policies; Kafka streams location events to downstream consumers (dispatch systems, customer tracking apps, analytics pipelines). Geofence evaluation runs at ingestion time: each incoming position update is tested against active geofences for the entity's region, and geofence entry/exit events are emitted as Kafka messages to downstream consumers.

Write-Heavy Application

A high-rate device telemetry ingestion architecture designed for millions of devices emitting metrics at 1–60 second intervals. Kafka absorbs device writes as an ingestion buffer, decoupling device-facing ingest endpoints from the storage write path so that downstream storage pressure never propagates back to devices. TimescaleDB provides time-series storage with automatic chunk partitioning by time range, native compression, and continuous aggregate views for rollup queries. ClickHouse serves as the OLAP layer for device fleet analytics queries. Redis caches last-known device state (current readings per device) for real-time alerting queries that must not scan historical storage. Backpressure on the Kafka consumer side prevents storage write throughput from being overwhelmed by burst ingestion events from device reconnect storms.

AI / RAG Application

A low-latency feature serving platform for ML model inference, providing both batch (offline) and real-time (online) feature access with strict training-serving consistency. Redis serves the hot feature cache with p99 latency targets below 5ms for online inference requests; PostgreSQL provides point-in-time feature lookups for offline training jobs with temporal consistency guarantees; Cassandra stores high-cardinality feature entities at write scale beyond PostgreSQL's single-primary ceiling; Kafka streams feature computation events from online feature pipelines to update the cache; Qdrant stores vector features for embedding-based model inputs and similarity lookups; ClickHouse serves feature analytics and drift monitoring across training dataset populations. Feature versioning is first-class: every feature value is tagged with a pipeline_version and computed_at timestamp to support model reproducibility and training-serving skew diagnosis.

Event-Driven System

A multi-channel notification delivery architecture that accepts upstream business events (order placed, payment received, comment posted, threshold alert triggered) and routes them to per-channel delivery workers (push via FCM/APNs, email via SendGrid, SMS via Twilio, in-app via WebSocket). Kafka carries raw business events from upstream producers. RabbitMQ handles per-channel fan-out with separate exchanges and queues per delivery channel, isolating email queue backlog from push notification delivery. PostgreSQL provides durable notification state tracking (sent, failed, bounced, suppressed). Redis enforces per-user rate limiting (notification frequency caps to prevent fatigue) and stores deduplication tokens to prevent duplicate sends across retry attempts. The inbox pattern on the consumer side ensures idempotent delivery even when Kafka produces duplicate events.

Analytics Pipeline

A metrics, logs, and traces ingestion and query platform built to absorb the telemetry output of a production system fleet: including the telemetry volume spikes that accompany the incidents the platform is meant to detect. ClickHouse stores metrics data with automatic time-based rollup via continuous materialized views; TimescaleDB provides complementary time-series storage for high-cardinality alert evaluation; Kafka buffers the ingestion stream against downstream write pressure, decoupling ingest acceptance rate from storage write throughput; Elasticsearch serves log full-text search and structured field filtering; Redis caches dashboard query results and active alert state for sub-100ms alert evaluation latency. The alert engine evaluates threshold and anomaly rules against pre-computed materialized views, not raw data, to bound alert evaluation cost independent of ingestion volume.

Event-Driven System

A video and audio streaming architecture where content ingestion triggers an async multi-variant transcoding pipeline, CDN delivery handles 95%+ of playback traffic, and Cassandra absorbs the write volume of per-user viewing history. Kafka decouples upload events from transcoding workers; MinIO stores raw and encoded assets; Redis maintains playback session state and view counters. The architecture must handle upload spikes without blocking delivery, and cache cold starts without cascading database load.