Redis Performance Optimization

Nextbrick provides redis performance optimization services for enterprise teams experiencing Redis latency, throughput, or memory pressure issues. Our structured methodology identifies and resolves root causes rather than applying surface-level workarounds.

Common Redis Performance Problems We Solve

High p99 latency spikes under load
Memory usage growing uncontrolled or hitting maxmemory
Eviction causing cache thrashing
Slow commands blocking other operations
Replication lag degrading read performance
Connection pool exhaustion under peak traffic
Key expiry causing latency bursts
Large key scans blocking the event loop

Redis Performance Optimization Methodology

Step 1: Baseline and Profiling

We establish a performance baseline using Redis INFO stats, latency monitoring, and keyspace analysis. We capture p50, p95, and p99 command latency and identify the top command patterns by frequency and execution time.

Step 2: SLOWLOG Analysis

Redis SLOWLOG captures commands exceeding the configured threshold. We analyze SLOWLOG entries to identify expensive commands, large key operations, and application-level patterns causing slowdowns. Typical findings include KEYS scans, unindexed LRANGE operations, and O(N) set operations on oversized data structures.

Step 3: Memory Analysis

We run MEMORY DOCTOR, MEMORY USAGE sampling, and DEBUG JMAP to understand memory allocation, fragmentation ratio, and per-key overhead. Common findings include poor data type selection (using strings instead of hashes for object storage), oversized values, and key proliferation from missing TTL policies.

Step 4: Eviction and Expiry Policy Review

Incorrect eviction policy selection (e.g., noeviction in a cache role, or allkeys-random instead of allkeys-lru) causes performance degradation at maxmemory. We review and tune eviction policy, maxmemory-policy, and active-expire configuration to match workload behavior.

Step 5: Pipeline and Connection Optimization

Round-trip latency compounds quickly for applications issuing many small commands. We audit application code patterns for pipelining opportunities, MULTI/EXEC batch efficiency, connection pool sizing, and Lua scripting for atomic operations that can eliminate multiple round trips.

Step 6: Infrastructure Sizing

We validate that instance size, network bandwidth, CPU allocation, and storage throughput are appropriately provisioned for the workload. Underpowered instances cause latency even when Redis configuration is correct.

Redis Performance Metrics We Optimize

Latency — p50, p95, p99 command execution times
Throughput — Operations per second under load
Hit Rate — Cache hit/miss ratio for caching workloads
Memory Efficiency — Used memory vs allocated memory, fragmentation ratio
Eviction Rate — Keys evicted per second under memory pressure
Replication Lag — Replica offset delta from primary
Blocked Clients — Clients blocked on BLPOP, BRPOP, or WAIT