Backend Architecture on K-Life Hack | Seoul Gastronomy & Travel Guide

Improving Cache Throughput and Eliminating Latency Spikes by Migrating to Valkey 8.0

Sat, 23 May 2026 09:55:55 +0900

Redis Limitations and Latency Occurrences Due to AI Agent Burst Traffic

As of May 2026, concurrent requests from Claude Code and Cursor are surging in the AI agent infrastructure, leading to confirmed performance degradation in the Redis 7.2 cluster operated as the backend cache layer. Specifically, in vector search metadata caching and session management, P99 latency frequently spiked from a normal 2ms to over 150ms.

Analysis via monitoring tools such as Prometheus and Grafana revealed CPU saturation caused by the single-threaded model of Redis. While I/O thread separation is available in Redis 7.x, it reached throughput limits for the advanced parallel processing requirements of 2026 workloads. Consequently, the decision was made to migrate to Valkey 8.0, developed under the Linux Foundation.

Technical Details of the Occurring Failures

The following log is an excerpt from the slow query log on a Redis 7.2 node. Complex pipeline requests generated by AI agents occupied the main thread for extended periods. This delay caused cascading timeouts in upstream gRPC services, dropping overall system availability to 98.2%.

# Redis Slow Log Excerpt
1) (integer) 1024
2) (integer) 1717143615 # 2026-05-31 14:20:15
3) (integer) 45000 # Execution time: 45ms
4) 1) "MGET"
 2) "session:ai_agent:user_992834..."
 3) "metadata:vector:index_442..."

Valkey 8.0 Migration Procedures and Multi-thread Optimization Settings

For the migration, Valkey-specific multi-threading extensions were enabled while maintaining full protocol compatibility with Redis. In Valkey 8.0, parallelization of command execution has been enhanced, with significant performance improvements expected in large-scale MGET and SCAN operations.

Installation and Build Process

Dependencies were organized via uv, the standard package manager for the 2026 environment, and build/deployment was executed using the following steps.

# Valkey 8.0.1 source acquisition and build
git clone --branch 8.0.1 https://github.com/valkey-io/valkey.git
cd valkey
make -j$(nproc)
sudo make install

# Migration and optimization from existing Redis configuration
cp /etc/redis/redis.conf /etc/valkey/valkey.conf
sed -i 's/redis/valkey/g' /etc/valkey/valkey.conf

Configuration Changes for Throughput Improvement

To maximize Valkey performance, the following parameters were adjusted in valkey.conf. Optimization of io-threads and server-threads is key to handling the 2026 infrastructure load.

# valkey.conf optimization for 2026 infrastructure
maxmemory 32gb
maxmemory-policy allkeys-lru
io-threads 8
io-threads-do-reads yes
# Valkey 8.0 specific: Enhanced multi-threading for command execution
server-threads 4
cluster-enabled yes

Post-Migration Performance Verification and Throughput Measurement

After completing the migration, comparative verification with the legacy Redis environment was conducted using valkey-benchmark. The verification environment utilized AWS r7g.2xlarge instances (Graviton 4).

Executing Benchmark Commands

# Load test execution for Valkey 8.0
valkey-benchmark -h 10.0.4.12 -p 6379 -c 200 -n 2000000 -t set,get,mget -P 16 --threads 8

Comparison Data of Verification Results

Metric	Redis 7.2 (Legacy)	Valkey 8.0 (New)	Improvement Rate
GET Throughput (RPS)	420,000	1,350,000	+221%
MGET (10 keys) RPS	85,000	290,000	+241%
P99 Latency (ms)	12.4ms	1.8ms	-85%
CPU Usage (Peak)	98% (1 core)	45% (Distributed)	Load balancing successful

Metric Changes and Log Evidence in Operational Monitoring

After introducing Valkey, checking the node operation status confirmed that contention between threads was minimized. Below is the statistical information output from the valkey-cli info command.

# Valkey Stats Excerpt
valkey_version:8.0.1
multiplexing_api:epoll
io_threads_active:1
server_threads_active:4
instantaneous_ops_per_sec:1284902
total_net_input_bytes:15829304822
total_net_output_bytes:89230492833
rejected_connections:0

Notably, rejected_connections remains at 0. In the legacy environment, an average of 150 connection rejections per hour occurred due to TCP backlog overflow.

Issues Encountered and Troubleshooting

In the early stages of migration, an issue occurred where some client libraries (legacy redis-py 4.x series) failed to recognize nodes in Valkey’s cluster bus communication.

Root Cause

The metadata format included in the Valkey 8.0 CLUSTER NODES response conflicted with some old regex-based parsers.

Solution

Resolved by updating client-side libraries to the 2026 standard valkey-py or the latest redis-py 5.5.0 or higher. Additionally, project-wide dependencies were forcibly synchronized using uv.

# Dependency update
uv add valkey&gt;=8.0.0
uv lock

Final Confirmation and System Impact Assessment

Through this migration, the cache layer now provides stable responses without becoming a bottleneck, even against bursty requests from AI agents. As of May 31, 2026, the error rate in the production environment is suppressed to less than 0.01%.

Throughput: Secured approximately 3x the previous processing capacity.
Latency: Spikes eliminated, P99 stable at 2ms or less.
Resource Efficiency: Multi-threading allows for efficient utilization of multi-core CPU computing resources.

Moving forward, the plan is to verify native support for vector indices, a new feature of Valkey 8.0, to contribute to faster inference for AI agents.

Implementing Snort IPS Inline Mode and PCRE Rules for SQL Injection Prevention

Thu, 21 May 2026 01:32:15 +0900

Transitioning from Passive IDS to Active IPS Inline Mode

Modern network security architectures require a transition from passive monitoring to active mitigation to prevent malicious traffic from saturating backend connection pools. While an Intrusion Detection System provides visibility by monitoring traffic via TAP or SPAN ports, it lacks the capability to terminate malicious sessions in real-time. Consequently, an Intrusion Prevention System must be deployed in an inline configuration, where every packet passes through the inspection engine before reaching its destination. This architectural shift allows the system to execute a drop action instead of a mere alert, effectively neutralizing threats at the perimeter. Furthermore, the Snort engine must be invoked with specific flags to enable the Data Acquisition inline module, as changing an action to drop in a standard Host-based IDS environment results in no operational change.

Implementing ICMP Drop Rules and Validating Inline Blocking

By modifying the local rules configuration file, administrators can replace legacy alert rules with drop directives to secure the 10.10.11.10 internal node. In addition, the execution of the Snort binary requires the -Q parameter to facilitate inline packet processing. When a client attempts to reach the target via ICMP, the inline IPS intercepts the request and returns a destination port unreachable message. Consequently, this mechanism ensures that unauthorized reconnaissance traffic never reaches the backend infrastructure, which is verified by the Snort console logging the drop events with high precision.

# Configuration in /etc/snort/rules/local.rules
# Deactivating the passive alert rule
# alert icmp any any -&gt; 10.10.11.10 any (msg: "ICMP ping Request Inline mode"; sid: 1000001;)

# Activating the active drop rule for IPS mode
drop icmp any any -&gt; 10.10.11.10 any (msg: "ICMP ping Request Inline mode"; sid: 1000001;)

# Starting Snort in Inline Mode with DAQ
snort -A console -q -u snort -g snort -c /etc/snort/snort.conf -Q

Analyzing NAT Packet Transformations in Multi-Tiered Architectures

In complex backend environments, Network Address Translation introduces layers of complexity to packet inspection. When a client at 192.168.100.1 accesses a web server, the packet undergoes Destination Network Address Translation to map the public-facing IP to the internal 10.10.11.10 address. Consequently, understanding the L2, L3, and L4 headers at each stage is vital for writing accurate Snort rules. Furthermore, the IPS must be aware of these transformations to correctly apply filters to the post-NAT traffic, ensuring that security policies are enforced on the actual internal endpoints rather than the gateway aliases.

Engineering Robust Snort Rules for UNION-Based SQL Injection

Protecting web applications from SQL injection requires deep packet inspection beyond simple string matching. The implementation of sid:1000002 demonstrates the use of Perl Compatible Regular Expressions to identify complex attack patterns like UNION SELECT. By leveraging the http_uri modifier and established flow state tracking, the engine reduces false positives by only inspecting traffic that has completed the TCP three-way handshake. In addition, the use of ungreedy matching in regex patterns optimizes the inspection latency, preventing the security layer from becoming a bottleneck during high-traffic periods.

# Advanced SQL Injection Detection Rule
alert tcp any any -&gt; $HOME_NET 80 (
msg: "&gt;&gt;&gt; WEB-Attack SQL injection attempt using UNION SELECT &lt;&lt;&lt;";
flow:to_server,established;
content:"UNION"; nocase; http_uri;
content:"SELECT"; nocase; http_uri;
pcre:"/UNION.+SELECT/Ui";
sid:1000002;
rev:1;
)

The integration of these rules into the production pipeline provides a robust defense-in-depth strategy. By combining inline blocking for protocol-level attacks with regular expression-based inspection for application-layer threats, engineers can ensure the integrity of the backend ecosystem against evolving cyber threats. Furthermore, this proactive security posture mitigates the risk of resource exhaustion within backend connection pools. Consequently, maintaining optimized rule definitions allows the system to sustain high throughput while actively neutralizing malicious payloads at the perimeter.