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Dragonfly on Kubernetes

Dragonfly onKubernetes

In short: Running Dragonfly on Kubernetes means using the Dragonfly Operator to provision Dragonfly custom resources as StatefulSets, Services, and PersistentVolumeClaims for snapshot/AOF persistence. The operator manages primary-replica replication with automatic failover for HA, plus rolling zero-downtime updates — exposing a Redis- and Memcached-compatible API for drop-in migration.

Deploy Dragonfly on Kubernetes for a modern, multi-threaded in-memory datastore with Redis-compatible API, 25x throughput over Redis, and lower memory usage — fully managed with the Dragonfly Operator.

25x
Redis Throughput
Redis-Compatible
Drop-in API
Multi-Threaded
All CPU Cores

Comprehensive Dragonfly on Kubernetes Services

From initial deployment to production optimization, we provide end-to-end Dragonfly on Kubernetes solutions for high-performance in-memory workloads.

Dragonfly Operator Deployment
Install and configure the Dragonfly Operator for automated lifecycle management on Kubernetes
  • Dragonfly Operator CRD installation
  • Custom resource configuration
  • RBAC and service account setup
  • Namespace isolation and multi-tenancy
Helm Chart Management
Production-grade Helm chart deployment with customized values for your environment
  • Helm chart customization and tuning
  • Values file management per environment
  • Chart versioning and rollback strategy
  • GitOps integration (ArgoCD / Flux)
HA & Replication on K8s
High availability with primary-replica replication, automatic failover, and pod anti-affinity
  • Primary-replica replication setup
  • Automatic failover orchestration
  • Pod anti-affinity and topology spread
  • Multi-AZ deployment strategies
Storage & Persistence
Configure snapshot and AOF persistence backed by Kubernetes PersistentVolumes for durability
  • RDB-compatible snapshot configuration
  • Append-only file (AOF) persistence
  • PVC storage class optimization
  • Fork-free snapshot (no memory spike)
Monitoring on K8s
Comprehensive observability with Prometheus metrics, Grafana dashboards, and alerting
  • Prometheus metrics exporter setup
  • Grafana dashboard templates
  • Alertmanager rules for key metrics
  • Resource usage and latency tracking
Migration from Redis to Dragonfly
Zero-downtime migration from Redis to Dragonfly with data replication and traffic cutover
  • Redis-to-Dragonfly replication setup
  • Application endpoint cutover planning
  • Data validation and consistency checks
  • Rollback strategy and testing

Why Choose Dragonfly on Kubernetes?

Dragonfly delivers a modern, multi-threaded alternative to Redis with dramatically higher throughput, lower memory usage, and full Redis API compatibility — all on Kubernetes.

Multi-Threaded Architecture

Unlike single-threaded Redis, Dragonfly utilizes all available CPU cores with a shared-nothing, per-thread memory design. Each thread manages its own keyspace partition, eliminating lock contention and delivering linear scaling with core count.

Redis & Memcached Compatible

Dragonfly supports both Redis (RESP) and Memcached protocols. Existing applications, client libraries, and tooling work without modification. Switch from Redis or Memcached with zero code changes.

25x Throughput Over Redis

Benchmarks show Dragonfly achieving up to 25x the throughput of Redis on equivalent hardware. This is achieved through multi-threading, io_uring for async I/O, and a cache-friendly dashtable hash structure.

Lower Memory Usage

Dragonfly uses more efficient memory encoding and eliminates the fork-based snapshot overhead that causes Redis memory to spike during BGSAVE. The same dataset typically uses significantly less RAM in Dragonfly.

Dragonfly Key Metrics

Throughput vs Redis(On same hardware)
25x
API Compatibility(RESP protocol)
Redis + Memcached
Threading Model(All CPU cores)
Multi-threaded
Memory Efficiency(vs Redis)
Up to 40% less
Snapshot Overhead(No memory spike)
Zero fork
K8s Integration(Cloud native)
Operator + Helm

Dragonfly vs Redis on Kubernetes

Understanding the architectural differences that make Dragonfly a compelling alternative to Redis for high-throughput Kubernetes workloads.

Modern Alternative
Dragonfly
Multi-threaded, shared-nothing, Redis-compatible
Multi-threaded — uses all CPU cores
Up to 25x throughput vs Redis
Redis + Memcached API compatible
Fork-free snapshots (no memory spike)
Up to 40% lower memory usage
Dashtable hash structure (cache-friendly)
io_uring for efficient async I/O
Dragonfly Operator for Kubernetes
Best for: High-throughput caching, session stores, real-time apps
Established Standard
Redis
Single-threaded, battle-tested, extensive ecosystem
Single-threaded event loop
Baseline throughput (1x)
Redis API only
Fork-based BGSAVE (2x memory spike)
Baseline memory usage
dict hash table implementation
epoll/kqueue for I/O
Redis Operator / Helm charts
Best for: General caching, pub/sub, mature ecosystem needs

Our Dragonfly on Kubernetes Implementation Process

A proven methodology for deploying production-ready Dragonfly on Kubernetes with comprehensive testing and validation.

1

Assessment & Planning

Analyze your current Redis/Memcached workload, throughput requirements, and data model. Design the Dragonfly topology, resource requests, and storage strategy for Kubernetes.

2

Operator & Deployment

Install the Dragonfly Operator, deploy Dragonfly instances via Helm or CRDs, configure persistence, resource limits, and network policies on your Kubernetes cluster.

3

Migration & Testing

Replicate data from Redis to Dragonfly, validate application compatibility, run load tests to confirm throughput gains, and test failover scenarios.

4

Production & Monitoring

Cut over production traffic to Dragonfly, enable Prometheus monitoring and Grafana dashboards, configure alerting, and provide ongoing operational support.

Dragonfly on Kubernetes — Frequently Asked Questions

Common questions about Dragonfly deployment, migration from Redis, and Kubernetes integration.

Ready to Deploy Dragonfly on Kubernetes?

Let our experts deploy Dragonfly on your Kubernetes cluster for 25x Redis throughput, lower memory usage, and full Redis API compatibility with zero code changes.