Project Name
How Ksolves Optimized Redis Infrastructure for a Leading Satellite Technology Provider
A global satellite technology company providing mission-critical connectivity services operated a 3-node Redis cluster in master-slave mode with a Redis Sentinel for high availability. The setup stored and retrieved sensitive operational data central to their service delivery. To ensure long-term stability and peak performance, Ksolves applied its Redis expert services to harden the cluster, optimize memory, and improve high availability. While the system performed adequately under normal conditions, high-load scenarios triggered alarming instability, master node crashes, sluggish failovers, and soaring write latency, putting service continuity and data integrity at serious risk.
- Master Node Stability Risks: The master Redis node crashed under sudden heavy workloads, causing slow failovers and risking data loss during peak traffic
- Frequent Out-of-Memory (OOM) Failures: Benchmarking consistently produced Out-Of-Memory (OOM) errors due to dangerously misconfigured memory management settings
- High Write Latency: Write operations ran with 50% higher latency compared to optimized configurations, impacting application responsiveness
- CPU Saturation Under Load: The master node experienced excessive CPU spikes under sudden load due to unoptimized thread handling and KEYS command usage
- Excessive Page Faults: System profiling revealed 519 page-faults per 60-second window, indicating suboptimal OS-level memory management
- Unreliable Service Management: Redis and Redis-Sentinel init scripts were non-functional, making automated service management and recovery unreliable
- Suboptimal OS & Kernel Configuration: Linux kernel parameters, file descriptor limits, TCP settings, Transparent Huge Pages, and memory overcommit were set at defaults unsuitable for high-throughput Redis deployments
Ksolves implemented a structured three-phase optimization strategy to stabilize, tune, and validate the client’s Redis environment. The engagement was executed remotely with continuous collaboration from the client’s technical team.
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Phase 1: Environment Assessment & Sanity Check
- Performed a comprehensive health audit of the existing Redis deployment.
- Reviewed cluster architecture and validated the 3-node master–replica topology.
- Assessed Sentinel configuration for high availability and failover readiness.
- Analyzed memory utilization policies and capacity planning.
- Evaluated replication behavior, persistence mechanisms (RDB/AOF), and failover scenarios.
- Identified configuration gaps, operational risks, and performance bottlenecks.
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Phase 2: Configuration Optimization
Redis Parameter Tuning
- Configured maxmemory to 8GB with an appropriate eviction policy to prevent out-of-memory (OOM) failures.
- Enabled compact data encoding:
- hash-max-ziplist-entries = 512
- zset-max-ziplist-entries = 512
- Stabilized replication using:
- min-replicas-to-write
- min-replicas-max-lag
- repl-diskless-sync
- Optimized client-output-buffer limits for normal and replica clients (512MB / 128MB thresholds).
- Increased hz to 50 with dynamic-hz enabled for faster internal processing cycles.
- Enabled latency monitoring with a 100ms threshold.
- Implemented a hybrid persistence strategy (RDB + AOF) for improved durability and recovery.
OS-Level Kernel Optimization
Alongside Redis tuning, our team implemented production-grade OS and networking hardening through DevOps consulting services, ensuring the infrastructure could sustain high-throughput workloads without failovers or latency spikes.- Increased file descriptor limits from 1,024 to 100,000 to support high-concurrency workloads.
- Set net.core.somaxconn=65,535 to handle larger TCP connection queues.
- Enabled vm.overcommit_memory=1 to prevent false OOM termination.
- Tuned networking:
- Reduced tcp_fin_timeout to 10 seconds
- Enabled tcp_tw_reuse for faster socket recycling.
- Set vm.swappiness = 1 to minimize swapping and keep data in RAM.
- Disabled Transparent Huge Pages (THP) to eliminate latency spikes and memory fragmentation.
Service & System Stability Fixes
- Disabled SELinux enforcement to resolve service permission conflicts.
- Corrected file ownership and directory permissions for Redis.
- Fixed Redis and Redis-Sentinel init scripts for reliable service management and restart operations.
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Phase 3: Performance Validation & Stress Testing
- Conducted benchmarking using redis-benchmark under realistic production-like conditions.
- Executed load tests with:
- 10,000 requests
- 200 concurrent clients
- 1MB payload size
- Performed both 30-minute and 2-hour continuous stress tests.
- Monitored performance metrics using Linux perf and Redis latency tools.
- Validated cluster stability, throughput consistency, and resource utilization under sustained load.
- Significant Write Performance Improvement: Master write time dropped from 250 seconds to 27.43 seconds, a 50%+ improvement in write performance
- Elimination of OOM Failures: OOM crashes were eliminated after applying Redis and OS-level memory configuration changes
- High Availability Stability Achieved: Zero failover events occurred during both the 30-minute and 2-hour sustained stress tests
- Improved Memory Efficiency: Page faults reduced from 519 to 300 per 60-second window, a 30% improvement in memory efficiency
- Restored and Enhanced Replication Throughput: A peak write throughput of 430 requests/second was achieved on Slave Node 2, up from a non-functional baseline
- Reliable Service Lifecycle Management: Redis and Redis-Sentinel services are now reliably started, stopped, and managed via init containers
- Higher Read Performance Across Nodes: Read throughput improved across all nodes, with the master reaching 608 req/s post-tuning
Through focused Redis and OS-level configuration optimization, Ksolves transformed an unstable, crash-prone Redis cluster into a robust, high-performance infrastructure. The satellite technology company can now confidently handle sustained traffic spikes without risk of master node failure, OOM crashes, or service degradation. This engagement demonstrates Ksolves’ deep expertise in Big Data infrastructure consulting, delivering measurable, production-ready results through a structured, remote advisory model.
Struggling with Redis instability or performance bottlenecks?
