Project Name
How Ksolves Cut Redis Write Time by 89% for a Global Satellite Technology Provider
A global satellite technology company’s mission-critical connectivity infrastructure was under threat. Their 3-node Redis cluster, the backbone of operational data delivery for satellite services worldwide, was crashing under load. Master node failures triggered slow and unreliable failovers. Out-of-memory errors were occurring during benchmarking. Write latency was running 50% above optimized baselines.
The client is a B2B satellite technology company delivering mission-critical connectivity services to enterprise and government clients across multiple countries. Their Redis cluster handled real-time operational data for satellite services where any latency spike or crash has direct consequences for service-level agreements and customer commitments. Stability was not a preference. It was a contractual requirement.
Ksolves, an AI-first company, was brought in to stabilize, tune, and stress-test the entire Redis environment through a structured remote engagement. Using AI-assisted log analysis and configuration review, the team identified root causes quickly and delivered production-ready results without requiring any on-site access.
The client's Redis environment had seven specific problems, all identified during the initial assessment:
- Master Node Crashes Under Load: The Redis master node was failing during high-traffic periods, causing the cluster to enter failover. The failover process was slow and unreliable, leaving services without a functioning master for longer than acceptable.
- Out-of-Memory Errors in Benchmarking: Redis was running into OOM (Out of Memory) errors during benchmark testing because no memory limit had been configured. Without a maxmemory setting, Redis consumed all available RAM until the system ran out.
- Write Latency 50% Above Baseline: Write operations were taking significantly longer than they should. The cluster was processing writes at 250 seconds under load, far above the optimized target.
- 519 Page Faults Per 60 Seconds: High page fault rates indicated that Redis was accessing memory that was not physically loaded, causing disk reads and adding significant latency to every affected operation.
- Transparent Huge Pages Causing Latency Spikes: Linux's Transparent Huge Pages (THP) feature was enabled on the host, which Redis explicitly warns against. THP causes unpredictable latency spikes and memory fragmentation in Redis environments.
- Non-Functional Init Scripts: The Redis and Redis Sentinel startup scripts were not working correctly, meaning the services would not reliably restart after a system reboot or crash, creating a hidden availability risk.
- No Validated Performance Baseline: The client had no confirmed benchmark figures for their environment. Without a tested baseline, there was no way to measure whether the cluster was performing correctly or to detect degradation over time.
Ksolves Redis consulting team applied an AI-first delivery approach to this engagement, using AI-assisted log analysis and configuration diagnostics to identify root causes across all seven issues before any changes were made. This compressed the assessment phase significantly and allowed the team to move directly into targeted fixes with high confidence. The work was structured across three phases:
The work was structured across three phases:
-
Phase 1: Environment Assessment
A full health check was run across the Redis cluster, Redis Sentinel configuration, and underlying Linux OS. redis-benchmark and Linux perf tools were used to establish a performance baseline and identify exactly where latency and memory issues were originating. The page fault rate of 519 per 60 seconds and the 250-second write time were documented as the primary remediation targets. -
Phase 2: Configuration Optimization
Targeted changes were made to both Redis configuration and Linux kernel parameters:
- maxmemory set to 8GB with an appropriate eviction policy. This gave Redis a defined memory boundary and eliminated OOM errors entirely.
- hash-max-ziplist-entries set to 512. This reduced memory overhead for hash data structures without affecting functionality.
- hz set to 50. This raised the frequency of Redis background tasks, making the cluster more responsive under load.
- Transparent Huge Pages disabled at the OS level. This removed the primary source of unpredictable latency spikes in the Redis environment.
- net.core.somaxconn set to 65535. This raised the connection backlog limit and prevented connection failures under peak concurrency.
- tcp_fin_timeout set to 10 seconds. This reduced the time spent on closed TCP connections and freed up network resources faster.
- vm.overcommit_memory set to 1. This eliminated background save failures caused by Linux memory allocation restrictions.
- Redis Sentinel configuration corrected. Failover detection and master promotion settings were fixed. The cluster can now switch to a healthy master quickly when a failure occurs.
- Init scripts fixed for both Redis and Redis Sentinel. Both services now restart reliably after a crash or reboot.
-
Phase 3: Performance Validation
Stress tests were run at both 30-minute and 2-hour durations to validate the configuration changes under sustained load. Results were compared against the pre-optimization baseline to confirm the improvements were real, stable, and production-ready.
Technology Stack
| Component | Details |
|---|---|
| Database | Redis, 3-node master-replica cluster |
| High Availability | Redis Sentinel |
| OS Layer | Linux (kernel para no meter tuning) |
| Benchmarking | redis-benchmark |
| Profiling | Linux perf |
| Delivery Model | Remote advisory, zero on-site access required |
| AI Tooling | AI-assisted log analysis, configuration diagnostics |
The three-phase optimization delivered measurable improvements across write performance, memory stability, high availability, and read throughput:
- 89% Faster Write Performance: Master write time dropped from 250 seconds to 27.43 seconds under load. The write bottleneck that was causing service degradation during peak traffic was eliminated. The cluster can now sustain production traffic spikes without write queue buildup.
- OOM Errors Eliminated: Configuring maxmemory at 8GB gave Redis a defined memory boundary. Out-of-memory errors dropped to zero, and the cluster no longer risks crashing because of unconstrained memory consumption.
- Page Faults Reduced from 519 to 300 Per 60 Seconds: Disabling Transparent Huge Pages and tuning memory parameters brought page faults down significantly. It reduced the disk reads that were adding latency to every affected operation.
- Read Throughput at 608 Requests Per Second: Post-optimization benchmark testing confirmed a read throughput of 608 req/s, establishing a validated performance baseline the client can use to monitor for future degradation.
- Zero Failover Events During 30-Minute and 2-Hour Stress Tests: The Redis cluster held through both sustained stress test windows without a single master node failure, validating that the configuration changes resolved the root cause of the crashes.
- Latency Spikes Eliminated: Disabling Transparent Huge Pages removed the primary source of unpredictable latency spikes, resulting in consistent, predictable response times across the cluster.
- Init Scripts Fixed and Validated: Redis and Redis Sentinel now restart reliably after crashes or reboots, closing the hidden availability risk that existed before the engagement.
- Production-Ready Baseline Established: The client now has a documented, tested performance baseline for their Redis environment. This makes future performance monitoring and capacity planning possible for the first time.
“We knew our Redis setup had problems, but we did not know how deep they went until Ksolves ran the full assessment. The three-phase approach gave us confidence in every configuration change before it went near production. The performance results speak for themselves, but what we valued most was the structured remote delivery model. They did not need to be on-site to deliver production-ready outcomes.”
– Head of Infrastructure, Global Satellite Technology Company
Before this engagement, the client’s Redis cluster was crashing under load, running out of memory during benchmarks, and processing writes at 250 seconds with no validated baseline to measure against. Today, write time is down to 27.43 seconds, OOM errors are gone, and the cluster has held through two hours of sustained stress testing without a single failover event.
Ksolves, with its AI-first delivery approach, delivered all of this through a structured remote engagement with no on-site access required. The satellite technology company now has a stable, tuned Redis environment and a documented performance baseline they can rely on as their infrastructure grows.
For enterprises and technology providers running Redis in production, find out how a structured optimization engagement can stabilize and improve your environment with Ksolves Redis Consulting and Support Services.
Is Your Redis Cluster Stable Under Peak Load? We Can Help!
