Dragonfly ConsultingShared-Nothing Threads · Dash Table (-30 to 50% RAM) · Zero-Fork io_uring

Dragonfly Consulting & Architecture Review

JusDB provides Dragonfly consulting, architecture reviews, and multi-terabyte Redis Cluster consolidation for engineering teams adopting Dragonfly's shared-nothing, thread-per-core in-memory engine. Our database reliability engineers calibrate proactor threads, audit Dash-table memory efficiency (delivering 30-50% lower memory overhead than fork-based engines), tune zero-fork io_uring snapshots and multi-stream replication, and execute live REPLICAOF cutovers.

Book a 30-min scoping call Request a Dragonfly review

The first 30-minute call covers your databases, current ownership, and required coverage. No production access is needed. Or request a free audit of one database instance.

NDA before access
Read-only by default
Written scope before work starts

Dragonfly engine & consolidation architecture

Select a Dragonfly subsystem

Shared-Nothing Proactor Fiber Architecture

4M+ QPS per vertical node

Dragonfly partitions its keyspace across N pinned CPU threads (--proactor_threads) using cooperative user-space fibers and Very Lightweight Locking (VLL) instead of a single global event loop.

Allows a single 32-vCPU or 64-vCPU instance to replace a 20-to-40-node horizontally sharded Redis Cluster while preserving multi-key commands without {hashtag} slot rules.

Inspect Dragonfly flag & consolidation specimen

Verified against Dragonfly shared-nothing Dash-table specifications and in-memory engine benchmarks.

Dragonfly subsystems covered: Shared-nothing --proactor_threads, Dash hash tables, --cache_mode=true LFRU, zero-fork io_uring .dfs snapshots, and VLL
Initial-response targets: 15 minutes for a critical (P1) incident under a 24x7 support agreement; 2 to 4 hours in business hours. See details
Coverage hours: Business hours (03:30–12:30 UTC) by default; 24x7 on-call available under a 24x7 support agreement

Production failure modes

Common Dragonfly architecture and tuning pitfalls we resolve

Dragonfly's shared-nothing C++ proactor engine eliminates Redis's single-thread and fork() bottlenecks, but undeclared Lua keys, disabled cache_mode, or missing io_uring kernel privileges can still stall production.

Dragonfly production symptoms, root causes, and first consulting actions
Dragonfly / OS signal	What is usually happening	Where we start
VLL Cross-Thread LockMulti-core Dragonfly throughput plateaus under heavy Lua or MGET load	Lua scripts that generate key names dynamically instead of declaring them in the KEYS[] array force Dragonfly's VLL coordinator to lock all proactor threads globally.	Audit EVAL/EVALSHA scripts, declare all accessed keys in KEYS[], avoid --default_lua_flags=allow-undeclared-keys, or co-locate related keys with hash tags.
io_uring EPERM / FallbackSnapshot saves stall proactor threads and spike p99 latency	Running Dragonfly on an older Linux kernel (< 5.10) or inside a container with a default Docker/containerd seccomp profile blocks io_uring syscalls, falling back to slower epoll/pthread I/O.	Upgrade host kernels to 5.15+ or 6.1+, configure RuntimeDefault/unconfined io_uring seccomp rules, and provision dedicated NVMe or high-MiB/s EBS gp3 storage for --dir.
Hot-Key Fiber StarvationOne CPU core pins at 99% while 31 sibling proactor threads sit at 20%	Because each key hashes deterministically to a single proactor thread, a single celebrity key receiving 300k RPS saturates its assigned core's fiber queue.	Inspect Prometheus dragonfly_commands_total per thread, enable RESP3 client-side caching (CLIENT TRACKING), or replicate hot read keys across read replicas.
cache_mode Eviction MissDragonfly returns OOM command not allowed when used memory > maxmemory	Unlike Redis maxmemory-policy, Dragonfly defaults to durable store mode (no eviction) unless explicitly started with --cache_mode=true to enable Dash 2-bit LFRU eviction.	Pass --cache_mode=true and --maxmemory=80% of physical RAM for cache workloads, and monitor dragonfly_evicted_keys_total and hit ratios.
DFS vs RDB Restore MismatchBackup restore loads only 1/Nth of the dataset or fails tool parsing	By default, Dragonfly writes N parallel shard files (dump-0000.dfs .. dump-0031.dfs) plus a summary file; automation expecting a single legacy dump.rdb archives only one file.	Update S3/GCS backup scripts to archive the complete *.dfs + summary.dfs set atomically, or configure --df_snapshot_format=false only when exporting to Redis/Valkey.
Multi-Socket NUMA PenaltyScaling from 32 to 64+ vCPUs increases p99 latency on dual-socket hosts	Cross-socket QPI/UPI memory bus traffic between proactor threads during multi-key pipelines degrades cache-line locality on dual-NUMA bare-metal or metal cloud instances.	Bind Dragonfly instances to single-NUMA-node instance families (such as AWS r7g.8xlarge / r7i.8xlarge) or deploy one Dragonfly process per NUMA socket.

Consulting scope

What does our Dragonfly consulting service include?

We review your keyspace, size vertical compute and Dash memory budgets, harden io_uring snapshots and multi-stream replication, and lead Redis-to-Dragonfly consolidation projects.

Multi-TB Redis Cluster to Dragonfly consolidation
Replace sprawling 15-to-50-node Redis Clusters with vertically scaled shared-nothing Dragonfly HA pairs, cutting cloud VM and memory footprints by 40-60%.
- Model shard-to-vertical-core consolidation math across AWS r7g, GCP c3/n2d, and Azure Ebdsv5
- Eliminate CROSSSLOT cluster errors while preserving multi-million QPS throughput
Vertical consolidation · --proactor_threads · VLL transactions · TCO model
Dragonfly migration services
Dash-table memory & cache_mode review
We audit your key-value size distribution, TTL patterns, and eviction requirements to maximize Dragonfly's Dash-table memory density.
- Benchmark Dash table vs jemalloc overhead across strings, hashes, sets, and ZSETs
- Calibrate --cache_mode=true (2-bit LFRU eviction) and --maxmemory up to 80-85% of RAM
Dash hash table · --cache_mode=true · --maxmemory · INFO memory
Dragonfly performance tuning
Zero-fork io_uring snapshot consulting
Dragonfly snapshots never call OS fork(), eliminating copy-on-write OOM kills - provided your Linux kernel and storage subsystem are tuned for parallel .dfs writes.
- Configure --dir, --dbfilename, --snapshot_cron, and parallel .dfs S3/GCS archival
- Tune Linux kernel 5.15+/6.x io_uring queues and NVMe / EBS gp3 throughput ceilings
io_uring · *.dfs parallel snapshots · --snapshot_cron · S3 archival
Multi-stream replication & HA architecture
Unlike single-socket PSYNC2, Dragonfly replicates each proactor thread shard over parallel TCP streams. We architect resilient multi-AZ primary-replica failover.
- Design Multi-AZ Dragonfly primary + replica topologies with Sentinel or Operator failover
- Validate full-sync and stable-state parallel stream replication under peak write load
Parallel DFLY replication · ROLE · REPLICAOF · Sentinel / Operator HA
Lua script, VLL & pipeline latency review
We inspect your application's multi-key transactions, Lua scripts, and connection pools so every proactor thread runs lock-free.
- Refactor undeclared Lua keys that trigger global VLL barrier locks across threads
- Tune Lettuce, ioredis, go-redis, and redis-py connection counts to match proactor threads
VLL lock profiler · EVALSHA audit · --conn_io_threads · RESP3 pipelining
Emulated Cluster Mode & Kubernetes strategy
Migrating an application that already uses a Redis Cluster client? We configure Dragonfly's emulated 16,384-slot Cluster API or Kubernetes Operator CRDs.
- Configure --cluster_mode=emulated so existing Cluster SDKs connect to a single vertical node
- Architect Dragonfly Operator deployments on EKS, GKE, and AKS with static CPU pinning
--cluster_mode=emulated ·CLUSTER SLOTS · Dragonfly Operator · K8s QoS
Dragonfly on Kubernetes

Shared responsibility & response targets

Where legacy Redis assumptions fail on Dragonfly, and what JusDB runs

Treating Dragonfly like a drop-in single-threaded Redis binary leaves half your RAM unused and risks global VLL Lua locks; we calibrate Dragonfly for its shared-nothing architecture.

Comparison between naive Dragonfly setups using legacy Redis habits and JusDB Dragonfly engineering
Dragonfly subsystem	Where default / Redis habits stop	What JusDB engineers deliver
Memory reservation & snapshot overhead	Applies legacy Redis rules (reserving 40-50% idle RAM for BGSAVE copy-on-write forks) and wastes cloud memory.	Leverages Dragonfly's zero-fork io_uring snapshotting and Dash-table density to safely utilize 80-85% of host RAM.
Multi-TB cluster topology design	horizontally shards workloads across dozens of small VMs, multiplying cross-AZ network transfer and CROSSSLOT errors.	Consolidates 15-40 Redis shards into vertically scaled 16-to-64 vCPU Dragonfly nodes with native multi-key VLL support.
Lua script & VLL concurrency	Copies legacy Redis Lua scripts with undeclared dynamic keys, inadvertently serializing all Dragonfly proactor threads.	Audits all EVAL/EVALSHA scripts, enforces strict KEYS[] declarations, and eliminates cross-shard VLL lock contention.
Linux kernel & io_uring configuration	Deploys Dragonfly on older kernels or restricted container runtimes where io_uring silently falls back to slow synchronous I/O.	Verifies kernel 5.15+/6.x io_uring rings, tunes memlock ulimits, and aligns NVMe/EBS throughput with parallel .dfs saves.
Eviction policy & cache_mode tuning	Expects Redis maxmemory-policy flags to work identically, resulting in unexpected OOM write rejections at maxmemory.	Configures --cache_mode=true to activate Dash 2-bit LFRU eviction and tunes memory budgets for predictable hit rates.
Vendor-neutral engine selection	Pitches one engine for every workload regardless of ecosystem fit.	Benchmarks Dragonfly alongside BSD-3 Valkey 8.0 and Redis so you adopt Dragonfly where vertical multi-core scale shines.

Published initial-response targets by severity

Standard business hours: 03:30–12:30 UTC

Initial-response targets are 2 to 4 hours during covered business hours, Mon–Fri 09:00–18:00 IST (UTC+5:30). Where your service agreement includes 24x7 on-call, the initial-response target for P1 incidents is 15 minutes. Targets are not resolution guarantees.

Severity	Initial response	Definition	Channel & coverage
P1 · Critical	15 minutes	Production database down, primary unreachable, data corruption risk, or active failover event.	24x7 on-call (under your service agreement) · Direct PagerDuty, Opsgenie, or phone page plus incident bridge
P2 · High	30 minutes	Severe latency spike, replication lag climbing rapidly, connection exhaustion, or degraded HA node.	24x7 on-call (under your service agreement) · On-call alert plus shared Slack or Teams channel
P3 · Medium	60 minutes	Non-critical slow query regression, staging issue, backup warning, or replication check alert.	24x7 on-call (under your service agreement) · Shared Slack or Teams channel or support ticket
P4 · Low	90 minutes	Configuration question, index advisory, planned schema review, or general operational request.	24x7 on-call (under your service agreement) · Shared Slack or Teams channel or support ticket

Our engineering team is in India; business-hours coverage is Mon–Fri 09:00–18:00 IST (UTC+5:30), and 24x7 on-call is available under your service agreement.

Initial-response targets, not resolution guarantees. The target for your engagement is set in your service agreement.

Interactive consolidation simulator

How Dragonfly consolidates multi-shard Redis Clusters

Compare a 24-node horizontally sharded Redis Cluster against an uncalibrated Dragonfly drop-in and a properly tuned 2-node vertical Dragonfly HA pair.

Multi-TB Redis Cluster to Dragonfly consolidation simulator

Select a state to inspect memory overhead, thread utilization, and snapshot mechanics.

Client SDKs, Proactor Threads & Snapshot I/O

12 primaries + 12 replicas required just to parallelize single-threaded Redis loops and reserve 45% RAM for BGSAVE copy-on-write forks.

384 GiB provisioned for 180 GiB data · CoW fork waste

12x Redis Primaries (r6i.xlarge)
16,384 hash slots · Single main thread per VM
FRAGMENTED · 32% memory overhead + CoW reserve
12x Redis Replicas (Multi-AZ)
Single-stream PSYNC2 · High cross-AZ network bill
EXPENSIVE · 24 total instances to patch & monitor
Application SDKs
Forced to use {hashtag} workarounds for MGET/Lua
COMPLEX · Frequent CROSSSLOT exceptions

Because traditional Redis is single-threaded and relies on OS fork() for snapshots, scaling past 50-100 GiB forces teams into complex 20+ node clusters with 40-50% wasted RAM.

Representative consulting deliverable

How a Dragonfly memory and consolidation audit is delivered

Every engagement includes the Dash-table memory benchmark, the production Dragonfly startup flag configuration, and kernel io_uring validation.

Sample finding · 18-Node Redis Cluster consolidated to 2-Node Dragonfly HA pairDragonfly v1.24 · AWS r7g.8xlarge (256 GiB)

Consolidation outcome

210 GiB Redis dataset compressed into 128 GiB Dash table with zero BGSAVE fork spikes

An 18-node Redis Cluster (9x r6i.2xlarge primaries + 9 replicas = 1,152 GiB provisioned RAM) was consolidated onto 2 r7g.8xlarge Dragonfly nodes (512 GiB total RAM) using --cluster_mode=emulated and --cache_mode=true.

Dataset RAM footprint210 GiB to 128 GiB

Provisioned EC2 nodes18 VMs to 2 VMs

1. Production Dragonfly flag configuration (32-core vertical cache & parallel `.dfs` snapshots)

dragonfly.flagfile · shared-nothing production profile

--proactor_threads=32
--conn_io_threads=8
--maxmemory=205G
--cache_mode=true
--cluster_mode=emulated
--lock_on_hashtags=true

# Zero-fork parallel .dfs snapshots via Linux io_uring (writes 32 shard files)
--dir=/var/lib/dragonfly/snapshots
--dbfilename=prod-dump
--snapshot_cron="0 */4 * * *"

# Multi-stream replication & client buffer guardrails
--Replica_partial_sync=true
--pipeline_squash=10

2. Linux kernel `io_uring` ulimit & Dash memory verification

bash · kernel check & redis-cli telemetry

# Ensure Linux kernel >= 5.15 and unlimited memlock for io_uring rings
uname -r && ulimit -l unlimited

# Inspect Dash table memory efficiency, object overhead, and parallel stream replication
redis-cli -p 6379 INFO memory | grep -E "used_memory_human|used_memory_rss_human|object_used_memory"
redis-cli -p 6379 INFO replication

Ways to work together

How to engage JusDB for Dragonfly consulting

Start with a free read-only memory and consolidation audit, scope a focused Dragonfly architecture or migration project, or put your Dragonfly estate under ongoing SRE support.

Scoped consulting projectThis page

Consolidate Redis or harden Dragonfly in production.

A fixed-scope engagement covering staging benchmarks, flag and kernel calibration, and live REPLICAOF consolidation cutover.

Common Dragonfly scopes agreed in writing first

Multi-TB Redis consolidation
Dash memory & cache_mode tuning
Zero-fork io_uring .dfs snapshots
VLL & Lua concurrency refactoring
Multi-stream HA replication

Dragonfly migration services Dragonfly performance tuning

Free Dragonfly audit

One Dragonfly node or Redis Cluster, audited read-only.

We inspect your keyspace memory distribution, proactor thread balance, Lua scripts, and snapshot configuration, then deliver a written report within one week.

What we check during the audit

Dash-table RAM savings vs your current Redis Cluster
VLL Lua lock contention & hot-key fiber skew
io_uring kernel readiness & .dfs snapshot throughput

Request a free Dragonfly audit

Ongoing Dragonfly SRE & support

Named engineers covering your Dragonfly estate.

Proactive Prometheus monitoring, .dfs backup restore drills, version upgrades, and incident response under a 24x7 support agreement.

Page on-callavailable under a 24x7 support agreement
15 minP1 initial-response target
Written RCAafter every P1

Dragonfly support Dragonfly on Kubernetes

Dragonfly deployment targets and source platforms we support

Dragonfly Server (Vertical HA)Shared-NothingDash Table

Dragonfly on KubernetesDragonfly OperatorCRD

AWS EC2 Graviton3/4 (r7g/r8g)Single-NUMAEBS gp3/NVMe

Google Cloud Compute / GKEC3 / N2DLocal SSD

Azure VMs & AKS (Ebdsv5)Premium SSD v2Azure

Redis Cluster Consolidation--cluster_mode=emulated

ElastiCache to DragonflyTCO ReductionREPLICAOF

S3 Parallel .dfs SnapshotsZero-Forkio_uring

For procurement and security teams

Mutual NDA first, read-only diagnostics, and written scope

We work inside your cloud VPC or Kubernetes security perimeter under mutual NDA. Your team approves all production flag and topology changes and keeps every benchmark and runbook.Raguraman, Engineering Manager · Allen D, Lead Database SRE

Mutual NDA
Written statement of work
Vendor-neutral sizing
Least-privilege ACLs

NDA before access
Signed before any diagnostic review. The 30-minute scoping call requires no credentials.
Written statement of work
Target instances, consolidation milestones, cutover windows, and fees agreed in writing first.
Read-only by default
Initial reviews require only read-only INFO, Prometheus metrics, and flag inspection.
Runbooks you keep
Every dragonfly.flagfile, snapshot archival script, and failover playbook stays with your team. Meet the JusDB team.

JusDB's security practices are aligned with ISO 27001 and SOC 2 Type II, and JusDB is pursuing both certifications (see Terms, section 12). Read the stated status.

Technical FAQ

Dragonfly consulting common questions

Answers on Dash-table 30-50% memory savings, --cache_mode LFRU eviction, zero-fork io_uring .dfs snapshots, VLL Lua transactions, and Redis Cluster consolidation.

Still have a question?

Ask it on a scoping call. No production access needed.

Book a 30-min scoping call

What does JusDB's Dragonfly consulting service cover?

Our Dragonfly consulting practice covers architecture reviews, multi-terabyte Redis Cluster consolidation modeling, Dash-table memory and --cache_mode (2-bit LFRU) tuning, zero-fork io_uring parallel snapshot (.dfs) configuration, multi-stream replication and failover design, VLL Lua script concurrency audits, Dragonfly Operator deployments on Kubernetes, and live REPLICAOF migrations.

How does Dragonfly achieve 30-50% lower memory usage than Redis?

Dragonfly saves memory in two ways: (1) Its Dash hash table (based on the Dash extendible hashing paper) packs keys into 64-slot segments with roughly 2 bytes of metadata overhead per key compared to 24-32 bytes per dictEntry in Redis, plus inline small-string and integer compression; and (2) Dragonfly performs background snapshots using asynchronous shared-nothing fiber iteration and Linux io_uring without calling OS fork(), eliminating the 30-40% copy-on-write RAM buffer required by Redis BGSAVE and BGREWRITEAOF.

When should we consolidate a Redis Cluster into Dragonfly versus migrating to Valkey 8.0?

If you run small-to-medium instances (< 32-64 GiB) or require permissive BSD-3 open-source licensing and AWS ElastiCache managed service compatibility, Linux Foundation Valkey 8.0 is the ideal choice. If you operate large, multi-hundred-gigabyte or multi-terabyte self-managed or Kubernetes caches currently split across 15 to 50+ Redis Cluster shards, consolidating into vertically scaled 32-to-64 vCPU Dragonfly nodes dramatically simplifies operations and cuts memory and VM spend.

What is Dragonfly --cache_mode=true and how does its LFRU eviction work?

By default, Dragonfly runs as a durable in-memory store and rejects writes with OOM errors once --maxmemory is reached. Passing --cache_mode=true enables Dragonfly's novel 2-bit LFRU (Least Frequently Recently Used) eviction algorithm built directly into the Dash table segments. Each segment tracks probationary and protected slots with zero extra pointer overhead, delivering higher cache hit ratios than random-sampled LRU without main-thread eviction stalls.

How do multi-key operations and Lua scripts work across Dragonfly's shared-nothing threads?

Dragonfly partitions keys across N proactor threads (1 per vCPU). When an application executes MGET, MSET, SUNION, or a Lua script touching keys on different threads, Dragonfly coordinates atomicity using Very Lightweight Locking (VLL) - an optimistic, deterministic lock-ordering protocol over user-space fibers. However, if a Lua script generates key names dynamically without declaring them in KEYS[], Dragonfly must lock all threads globally; we audit and refactor Lua scripts to keep execution lock-free.

Why does Dragonfly recommend .dfs snapshots over single-file .rdb snapshots?

In a 32-core Dragonfly instance, funneling 200 GiB of memory from 32 independent threads into a single serialized dump.rdb file creates a massive I/O bottleneck. When --dbfilename is configured without a .rdb extension, Dragonfly writes 32 parallel shard files (for example dump-0000.dfs through dump-0031.dfs plus dump-summary.dfs) concurrently via io_uring, completing backups and cold restores up to 10x faster.

Can our existing Redis Cluster clients connect to a single vertical Dragonfly node?

Yes. By starting Dragonfly with --cluster_mode=emulated, Dragonfly responds to CLUSTER SLOTS and CLUSTER NODES commands by announcing that the single vertical instance owns all 16,384 hash slots (0-16383). Existing Lettuce, Jedis, ioredis, and go-redis Cluster clients connect without changing application code.

What are your response targets for Dragonfly consulting and incident support?

Initial-response targets are 2 to 4 hours during covered business hours, Mon–Fri 09:00–18:00 IST (UTC+5:30). Where your service agreement includes 24x7 on-call, the initial-response target for P1 incidents is 15 minutes. Targets are not resolution guarantees.

Related Dragonfly services & comparisons

Explore sibling Dragonfly and in-memory services

Pair Dragonfly architectural consulting with Kubernetes Operator automation, live Redis Cluster consolidation, or 24x7 incident coverage under a 24x7 support agreement.

Cloud Native

In-memory architecture comparisons and operations guides

Architecture Comparison
Redis vs Valkey vs Dragonfly: choosing the right in-memory engine
When to use BSD-3 Valkey 8.0 io-threads and when to consolidate multi-TB clusters onto Dragonfly.
Sibling Practice
Redis Consulting & Cluster Architecture
Running hybrid Redis and Dragonfly estates? Explore our vendor-neutral Redis consulting practice.
Managed Operations
Dragonfly Support & Remote DBA
Ongoing Prometheus monitoring, .dfs snapshot restore drills, and version upgrades.

Dragonfly Consulting & Architecture Review

Common Dragonfly architecture and tuning pitfalls we resolve

What does our Dragonfly consulting service include?

Multi-TB Redis Cluster to Dragonfly consolidation

Dash-table memory & cache_mode review

Zero-fork io_uring snapshot consulting

Multi-stream replication & HA architecture

Lua script, VLL & pipeline latency review

Emulated Cluster Mode & Kubernetes strategy

Where legacy Redis assumptions fail on Dragonfly, and what JusDB runs

Published initial-response targets by severity

How Dragonfly consolidates multi-shard Redis Clusters

How a Dragonfly memory and consolidation audit is delivered

210 GiB Redis dataset compressed into 128 GiB Dash table with zero BGSAVE fork spikes

1. Production Dragonfly flag configuration (32-core vertical cache & parallel `.dfs` snapshots)

2. Linux kernel `io_uring` ulimit & Dash memory verification

How to engage JusDB for Dragonfly consulting

Consolidate Redis or harden Dragonfly in production.

One Dragonfly node or Redis Cluster, audited read-only.

Named engineers covering your Dragonfly estate.

Dragonfly deployment targets and source platforms we support

Mutual NDA first, read-only diagnostics, and written scope

NDA before access

Written statement of work

Read-only by default

Runbooks you keep

Dragonfly consulting common questions

What does JusDB's Dragonfly consulting service cover?

How does Dragonfly achieve 30-50% lower memory usage than Redis?

When should we consolidate a Redis Cluster into Dragonfly versus migrating to Valkey 8.0?

What is Dragonfly --cache_mode=true and how does its LFRU eviction work?

How do multi-key operations and Lua scripts work across Dragonfly's shared-nothing threads?

Why does Dragonfly recommend .dfs snapshots over single-file .rdb snapshots?

Can our existing Redis Cluster clients connect to a single vertical Dragonfly node?

What are your response targets for Dragonfly consulting and incident support?

Redis vs Valkey vs Dragonfly: choosing the right in-memory engine

Redis Consulting & Cluster Architecture

Dragonfly Support & Remote DBA

Talk to an engineer about your Dragonfly architecture or consolidation

Related Dragonfly Services

Common Dragonfly architecture and tuning pitfalls we resolve

What does our Dragonfly consulting service include?

Multi-TB Redis Cluster to Dragonfly consolidation

Dash-table memory & cache_mode review

Zero-fork io_uring snapshot consulting

Multi-stream replication & HA architecture

Lua script, VLL & pipeline latency review

Emulated Cluster Mode & Kubernetes strategy

Where legacy Redis assumptions fail on Dragonfly, and what JusDB runs

Published initial-response targets by severity

How Dragonfly consolidates multi-shard Redis Clusters

How a Dragonfly memory and consolidation audit is delivered

210 GiB Redis dataset compressed into 128 GiB Dash table with zero BGSAVE fork spikes

1. Production Dragonfly flag configuration (32-core vertical cache & parallel .dfs snapshots)

2. Linux kernel io_uring ulimit & Dash memory verification

How to engage JusDB for Dragonfly consulting

Consolidate Redis or harden Dragonfly in production.

One Dragonfly node or Redis Cluster, audited read-only.

Named engineers covering your Dragonfly estate.

Dragonfly deployment targets and source platforms we support

Mutual NDA first, read-only diagnostics, and written scope

NDA before access

Written statement of work

Read-only by default

Runbooks you keep

Dragonfly consulting common questions

What does JusDB's Dragonfly consulting service cover?

How does Dragonfly achieve 30-50% lower memory usage than Redis?

When should we consolidate a Redis Cluster into Dragonfly versus migrating to Valkey 8.0?

What is Dragonfly --cache_mode=true and how does its LFRU eviction work?

How do multi-key operations and Lua scripts work across Dragonfly's shared-nothing threads?

Why does Dragonfly recommend .dfs snapshots over single-file .rdb snapshots?

Can our existing Redis Cluster clients connect to a single vertical Dragonfly node?

What are your response targets for Dragonfly consulting and incident support?

Explore sibling Dragonfly and in-memory services

Dragonfly on Kubernetes

Dragonfly Performance Tuning

Redis to Dragonfly Migration

Dragonfly 24x7 Support (where contracted)

Valkey Consulting

All Dragonfly Database Services

In-memory architecture comparisons and operations guides

Redis vs Valkey vs Dragonfly: choosing the right in-memory engine

Redis Consulting & Cluster Architecture

Dragonfly Support & Remote DBA

Talk to an engineer about your Dragonfly architecture or consolidation

Related Dragonfly Services

1. Production Dragonfly flag configuration (32-core vertical cache & parallel `.dfs` snapshots)

2. Linux kernel `io_uring` ulimit & Dash memory verification