HeliosDB Performance Tuning Guide v7.0

Version: 7.0 (Phase 3B) Date: December 9, 2025 Audience: Database Administrators, DevOps Engineers, Performance Engineers

Introduction
Performance Baseline
System Configuration
Storage Optimization
Cache Tuning
Query Optimization
Multi-Tenancy Performance
Network Optimization
Monitoring & Profiling
Troubleshooting Common Issues
Production Best Practices

Introduction

This guide provides comprehensive performance tuning recommendations for HeliosDB v7.0 (Phase 3B). Following these guidelines can improve:

Query latency by 40-60%
Throughput by 50-100%
Resource utilization by 30-50%
System stability and predictability

Quick Wins Checklist

Before deep tuning, apply these quick optimizations:

Enable compression (zstd level 3)
Configure appropriate cache sizes (4-16 GB)
Set write buffer size (256-512 MB)
Enable batching (10ms window)
Create proper indexes
Configure connection pooling
Enable query result caching
Set appropriate resource quotas

Performance Baseline

Benchmark Your System

1. Single-Node Performance

# Run comprehensive benchmark
heliosdb benchmark --workload mixed --duration 300s

# Expected results (reference hardware):
# - Read latency (p50): 2-5ms
# - Read latency (p99): 10-25ms
# - Write latency (p50): 3-8ms
# - Write latency (p99): 15-35ms
# - Throughput: 10,000-50,000 QPS
# - Cache hit rate: 85-95%

2. Multi-Node Performance

# Test cluster scalability
heliosdb benchmark --workload distributed --nodes 4 --duration 300s

# Expected linear scalability:
# - 2 nodes: 95-98% of theoretical (2x)
# - 4 nodes: 93-97% of theoretical (4x)
# - 8 nodes: 90-95% of theoretical (8x)

Reference Hardware Specifications

Development/Testing:

CPU: 4 cores @ 2.5 GHz
RAM: 16 GB
Storage: 500 GB SSD (NVMe)
Network: 1 Gbps
Expected QPS: 5,000-10,000

Production (Small):

CPU: 8 cores @ 3.0 GHz
RAM: 32 GB
Storage: 1 TB NVMe SSD
Network: 10 Gbps
Expected QPS: 20,000-40,000

Production (Medium):

CPU: 16 cores @ 3.5 GHz
RAM: 64 GB
Storage: 2 TB NVMe SSD
Network: 10 Gbps
Expected QPS: 50,000-100,000

Production (Large):

CPU: 32 cores @ 3.5 GHz
RAM: 128 GB
Storage: 4 TB NVMe SSD
Network: 25 Gbps
Expected QPS: 100,000-200,000

System Configuration

Operating System Tuning

1. Linux Kernel Parameters

# Increase max open files
fs.file-max = 1000000

# TCP tuning for high throughput
net.core.rmem_max = 16777216
net.core.wmem_max = 16777216
net.ipv4.tcp_rmem = 4096 87380 16777216
net.ipv4.tcp_wmem = 4096 65536 16777216
net.ipv4.tcp_max_syn_backlog = 8192
net.core.netdev_max_backlog = 5000

# Virtual memory tuning
vm.swappiness = 10
vm.dirty_ratio = 15
vm.dirty_background_ratio = 5
vm.dirty_writeback_centisecs = 100

# Hugepages (optional, for large deployments)
vm.nr_hugepages = 1024

# Apply changes
sudo sysctl -p

2. Filesystem Tuning

# Use XFS or ext4 with optimal mount options
# XFS (recommended)
/dev/sdb1  /data  xfs  noatime,nodiratime,nobarrier  0 0

# ext4 (alternative)
/dev/sdb1  /data  ext4  noatime,nodiratime,data=writeback  0 0

# Remount
sudo mount -o remount /data

3. I/O Scheduler

# Use deadline or noop for SSDs
echo noop > /sys/block/sdb/queue/scheduler

# Verify
cat /sys/block/sdb/queue/scheduler
# Output: noop [deadline] cfq

# Make persistent
# /etc/udev/rules.d/60-scheduler.rules
ACTION=="add|change", KERNEL=="sd[a-z]", ATTR{queue/scheduler}="noop"

4. CPU Governor

# Use performance governor for predictable latency
sudo cpupower frequency-set -g performance

# Verify
cpupower frequency-info

HeliosDB Configuration

config.toml

[server]
# Bind address
listen_addr = "0.0.0.0:8080"

# Number of worker threads (2x CPU cores recommended)
worker_threads = 16

# Connection limits
max_connections = 1000
connection_timeout_ms = 30000

[storage]
# Data directory (use fast SSD/NVMe)
data_dir = "/data/heliosdb"

# Write-ahead log (WAL) settings
wal_sync_mode = "normal"  # Options: none, normal, full
wal_buffer_size_mb = 64

# Write buffer (MemTable) size
write_buffer_size_mb = 256

# Number of write buffers
max_write_buffer_number = 4

# Background compaction threads
max_background_compactions = 4
max_background_flushes = 2

# SSTable block size
block_size_kb = 16

# Bloom filter bits per key
bloom_filter_bits_per_key = 10

[compression]
# Enable compression
enabled = true

# Algorithm: zstd, lz4, snappy, none
algorithm = "zstd"

# Compression level (1-22 for zstd, higher = better compression, slower)
level = 3

# Minimum size to compress (bytes)
min_size_bytes = 1024

[cache]
# Unified cache size (20-40% of RAM recommended)
size_mb = 16384  # 16 GB

# Block cache size
block_cache_mb = 12288  # 12 GB

# Row cache size
row_cache_mb = 4096  # 4 GB

# Cache policy: lru, lfu, arc
policy = "lru"

# Cache TTL (seconds, 0 = no TTL)
ttl_seconds = 3600

[performance]
# Enable batching
enable_batching = true

# Batch window (milliseconds)
batch_window_ms = 10

# Max batch size (operations)
max_batch_size = 1000

# Enable query result caching
enable_query_cache = true

# Query cache size (MB)
query_cache_mb = 2048

# Query cache TTL (seconds)
query_cache_ttl = 600

# Parallel query execution
max_parallel_queries = 8

# SIMD acceleration
enable_simd = true

[multitenancy]
# Enable multi-tenancy
enabled = true

# Default isolation mode
default_isolation_mode = "logical"

# Tenant metadata cache size
tenant_cache_mb = 256

# RLS policy cache size
rls_cache_mb = 128

[monitoring]
# Enable metrics
metrics_enabled = true

# Metrics endpoint
metrics_addr = "0.0.0.0:9090"

# Enable detailed statistics
enable_statistics = true

# Statistics collection interval (seconds)
stats_interval_seconds = 60

Storage Optimization

LSM-Tree Tuning

1. Write Amplification Reduction

[storage]
# Reduce write amplification
max_write_buffer_number = 6  # More buffers = less frequent flushes
write_buffer_size_mb = 512   # Larger buffers = fewer SSTables

# Compaction strategy
compaction_style = "leveled"  # Options: leveled, universal, fifo

# Level 0 file limits
level0_file_num_compaction_trigger = 4
level0_slowdown_writes_trigger = 20
level0_stop_writes_trigger = 36

# Target file size
target_file_size_base_mb = 64
target_file_size_multiplier = 2

2. Read Amplification Reduction

[storage]
# Bloom filters reduce read amplification
bloom_filter_bits_per_key = 10  # 1% false positive rate

# Block cache for frequently accessed data
block_cache_mb = 16384

# Enable index and filter caching
cache_index_and_filter_blocks = true
pin_l0_filter_and_index_blocks_in_cache = true

3. Space Amplification Reduction

[compression]
# Aggressive compression
enabled = true
algorithm = "zstd"
level = 5  # Higher level for better compression

[storage]
# More aggressive compaction
max_bytes_for_level_base_mb = 512
max_bytes_for_level_multiplier = 8

HCC (Hierarchical Columnar Compression)

Configuration:

[hcc]
# Enable HCC
enabled = true

# Compression ratio (4-15x typical)
target_ratio = 8.0

# Chunk size (KB)
chunk_size_kb = 64

# Enable adaptive compression
adaptive = true

# Cold data threshold (days)
cold_data_threshold_days = 30

Benefits:

Storage reduction: 60-80%
Cost savings: 60-80% for cloud storage
Performance impact: <5% for reads, <10% for writes

Cloud Tiering

Configuration:

[cloud_tiering]
# Enable tiering
enabled = true

# Cloud provider: s3, gcs, azure
provider = "s3"

# S3 configuration
[cloud_tiering.s3]
bucket = "heliosdb-cold-storage"
region = "us-west-2"
endpoint = "https://s3.us-west-2.amazonaws.com"

# Tiering policies
[cloud_tiering.policy]
# Move to cloud after N days
cold_data_threshold_days = 90

# Access frequency threshold
access_frequency_threshold = 0.01  # <1% access rate

# Storage class: standard, glacier, deep_archive
storage_class = "glacier"

Performance Impact:

Hot data (local SSD): 2-5ms latency
Warm data (local with prefetch): 5-15ms latency
Cold data (S3 standard): 50-200ms latency
Cold data (Glacier): Minutes to hours (restore first)

Cache Tuning

Multi-Level Cache Architecture

Level 1: In-Memory Metadata Cache

[cache.l1]
# Tenant metadata
tenant_metadata_mb = 256

# RLS policy cache
rls_policy_mb = 128

# Configuration cache
config_mb = 64

# TTL (seconds, 0 = infinite)
ttl_seconds = 0  # Metadata rarely changes

Level 2: Query Result Cache (Redis)

[cache.l2]
# Enable distributed caching
enabled = true

# Redis configuration
redis_url = "redis://localhost:6379"

# Cache size
size_mb = 4096

# TTL (seconds)
ttl_seconds = 600  # 10 minutes

# Eviction policy: lru, lfu, random
eviction_policy = "lru"

Level 3: Block Cache

[cache.l3]
# Block cache for storage
size_mb = 16384  # 20-40% of RAM

# Index and filter block caching
cache_index_and_filter_blocks = true

# Pin frequently accessed blocks
pin_l0_filter_and_index_blocks_in_cache = true

Cache Sizing Guidelines

Total RAM: 32 GB

OS and System:        4 GB  (12%)
Application:          4 GB  (12%)
Block Cache:         12 GB  (38%)
Row Cache:            4 GB  (12%)
Write Buffers:        2 GB  (6%)
Query Cache:          2 GB  (6%)
Tenant Metadata:    512 MB  (2%)
Remaining:            4 GB  (12%)

Total RAM: 64 GB

OS and System:        8 GB  (12%)
Application:          8 GB  (12%)
Block Cache:         24 GB  (38%)
Row Cache:            8 GB  (12%)
Write Buffers:        4 GB  (6%)
Query Cache:          4 GB  (6%)
Tenant Metadata:      1 GB  (2%)
Remaining:            7 GB  (11%)

Cache Monitoring

# Get cache statistics
curl http://localhost:9090/metrics | grep cache

# Key metrics:
# - heliosdb_cache_hit_rate (target: >90%)
# - heliosdb_cache_evictions_total (monitor for sizing)
# - heliosdb_cache_memory_usage_bytes (utilization)

Optimization Guidelines:

Hit rate < 80%: Increase cache size
Hit rate > 95%: Cache may be oversized
High evictions: Increase cache or reduce TTL
Low utilization: Reduce cache size

Query Optimization

Index Strategy

1. B-Tree Indexes (Range Queries)

-- Primary key index (automatic)
CREATE TABLE orders (
    order_id BIGINT PRIMARY KEY,
    ...
);

-- Secondary indexes for common filters
CREATE INDEX idx_user_id ON orders(user_id);
CREATE INDEX idx_created_at ON orders(created_at);
CREATE INDEX idx_status ON orders(status);

-- Composite indexes for multi-column filters
CREATE INDEX idx_user_status ON orders(user_id, status);
CREATE INDEX idx_user_created ON orders(user_id, created_at DESC);

2. Vector Indexes (Similarity Search)

-- HNSW index for high-recall searches
CREATE VECTOR INDEX idx_product_embedding ON products
USING hnsw (embedding)
WITH (
    dimension = 768,
    metric = 'cosine',
    m = 16,              -- Connectivity (higher = better recall, slower build)
    ef_construction = 200, -- Build-time parameter
    ef_search = 100       -- Query-time parameter
);

-- IVF index for large-scale searches
CREATE VECTOR INDEX idx_large_embedding ON large_table
USING ivf (embedding)
WITH (
    dimension = 768,
    metric = 'l2',
    nlist = 1024,        -- Number of clusters
    nprobe = 32          -- Clusters to search
);

3. Full-Text Indexes

CREATE FULLTEXT INDEX idx_content ON documents(content)
WITH (
    language = 'english',
    stemming = true,
    min_word_length = 3,
    stop_words = true
);

Query Patterns

1. Use EXPLAIN ANALYZE

EXPLAIN ANALYZE
SELECT *
FROM orders
WHERE user_id = 12345
  AND created_at >= '2025-01-01'
  AND status = 'completed';

-- Output shows:
-- - Execution plan
-- - Index usage
-- - Row counts
-- - Actual timing

2. Optimize WHERE Clauses

-- Bad: Function on indexed column
SELECT * FROM orders
WHERE DATE(created_at) = '2025-12-09';

-- Good: Range on indexed column
SELECT * FROM orders
WHERE created_at >= '2025-12-09 00:00:00'
  AND created_at < '2025-12-10 00:00:00';

**3. Avoid SELECT ***

-- Bad: Retrieves unnecessary data
SELECT * FROM orders WHERE user_id = 12345;

-- Good: Select only needed columns
SELECT order_id, amount, status, created_at
FROM orders
WHERE user_id = 12345;

4. Use LIMIT for Large Results

-- Always use LIMIT for pagination
SELECT order_id, amount, created_at
FROM orders
WHERE user_id = 12345
ORDER BY created_at DESC
LIMIT 100 OFFSET 0;

5. Batch Inserts

-- Bad: Individual inserts
INSERT INTO orders (order_id, user_id, amount) VALUES (1, 100, 99.99);
INSERT INTO orders (order_id, user_id, amount) VALUES (2, 100, 149.99);

-- Good: Batch insert
INSERT INTO orders (order_id, user_id, amount) VALUES
    (1, 100, 99.99),
    (2, 100, 149.99),
    (3, 101, 199.99);
    -- Up to 1000 rows per batch

Query Result Caching

[performance]
enable_query_cache = true
query_cache_mb = 2048
query_cache_ttl = 600  # 10 minutes

# Cache key includes:
# - Query SQL (normalized)
# - Query parameters
# - Tenant context
# - User context (for RLS)

Benefits:

Identical query latency: <1ms (cache hit)
Reduces database load: 40-60%
Especially effective for dashboards and analytics

Multi-Tenancy Performance

Tenant Isolation Modes

1. Logical Isolation (Cost-Effective)

[multitenancy]
default_isolation_mode = "logical"

# Shared resources with quotas
[multitenancy.quotas]
default_storage_quota_gb = 100
default_qps_limit = 1000
default_max_connections = 50

Performance Characteristics:

Latency overhead: <5%
Throughput impact: <10%
Resource efficiency: 95%+
Tenant density: 1,000+ per node

2. Physical Isolation (High-Performance)

[multitenancy]
default_isolation_mode = "physical"

# Dedicated resources per tenant
[multitenancy.physical]
dedicated_cpu_cores = 4
dedicated_ram_gb = 16
dedicated_storage_gb = 500

Performance Characteristics:

Latency overhead: <1%
Throughput impact: <2%
Resource efficiency: 60-70%
Tenant density: 10-50 per node

3. Hybrid Isolation (Balanced)

[multitenancy]
default_isolation_mode = "hybrid"

# Shared storage, dedicated compute
[multitenancy.hybrid]
dedicated_cpu_cores = 2
shared_storage = true
dedicated_ram_gb = 8

Row-Level Security (RLS) Performance

Optimization:

-- Create indexes for RLS predicates
CREATE INDEX idx_tenant_user ON orders(tenant_id, user_id);

-- RLS policy automatically uses index
CREATE POLICY user_own_orders ON orders
FOR SELECT
USING (tenant_id = current_tenant_id() AND user_id = current_user_id());

Performance Impact:

Policy lookup: <0.5ms (cached)
Query rewrite: <1ms
Execution overhead: <5% (with proper indexes)

Quota Enforcement Performance

[multitenancy.quota_enforcement]
# Check frequency
check_interval_ms = 100

# Soft limit warnings
soft_limit_threshold = 0.9  # 90% of quota

# Hard limit enforcement
hard_limit_action = "throttle"  # Options: throttle, reject

# Quota cache TTL
quota_cache_ttl_ms = 1000

Performance:

Quota check latency: <0.1ms (cached)
Storage quota overhead: <1%
QPS quota overhead: <2%

Network Optimization

Connection Pooling

Client-Side Configuration:

use heliosdb_client::{ConnectionPool, PoolConfig};

let pool = ConnectionPool::new(PoolConfig {
    // Pool size (2-4x application threads)
    max_size: 20,
    min_idle: 5,

    // Timeouts
    connection_timeout_ms: 5000,
    idle_timeout_ms: 300000,  // 5 minutes
    max_lifetime_ms: 1800000,  // 30 minutes

    // Validation
    test_on_checkout: false,  // Disable for performance
    test_on_return: false,
}).await?;

Server-Side Configuration:

[server]
max_connections = 1000
connection_timeout_ms = 30000
keepalive_timeout_ms = 60000

HTTP/2 and Compression

[server.http]
# Enable HTTP/2
enable_http2 = true

# Response compression
enable_compression = true
compression_algorithm = "br"  # Brotli (better than gzip)
compression_level = 4
min_compression_size_bytes = 1024

# Request decompression
enable_decompression = true

Performance:

HTTP/2 multiplexing: 40% fewer connections
Brotli compression: 20-30% smaller responses
Network bandwidth reduction: 60-70%

TCP Tuning

# Server-side TCP tuning
net.ipv4.tcp_fin_timeout = 30
net.ipv4.tcp_keepalive_time = 600
net.ipv4.tcp_keepalive_intvl = 60
net.ipv4.tcp_keepalive_probes = 3

# Enable TCP Fast Open
net.ipv4.tcp_fastopen = 3

# BBR congestion control (Linux 4.9+)
net.core.default_qdisc = fq
net.ipv4.tcp_congestion_control = bbr

Monitoring & Profiling

Key Performance Metrics

1. Latency Metrics

# Query latency percentiles
heliosdb_query_latency_seconds{quantile="0.5"}   # p50
heliosdb_query_latency_seconds{quantile="0.95"}  # p95
heliosdb_query_latency_seconds{quantile="0.99"}  # p99
heliosdb_query_latency_seconds{quantile="0.999"} # p999

# Target thresholds:
# p50 < 10ms
# p95 < 50ms
# p99 < 100ms
# p999 < 500ms

2. Throughput Metrics

# Queries per second
rate(heliosdb_query_total[1m])

# Throughput by operation
rate(heliosdb_query_total{operation="read"}[1m])
rate(heliosdb_query_total{operation="write"}[1m])

# Target: >10,000 QPS per node

3. Resource Metrics

# CPU utilization (target: 60-80%)
heliosdb_cpu_usage_percent

# Memory utilization (target: 70-85%)
heliosdb_memory_usage_bytes / heliosdb_memory_total_bytes

# Disk I/O (IOPS)
rate(heliosdb_disk_reads_total[1m])
rate(heliosdb_disk_writes_total[1m])

# Network I/O (bytes/sec)
rate(heliosdb_network_received_bytes[1m])
rate(heliosdb_network_sent_bytes[1m])

4. Cache Metrics

# Cache hit rate (target: >90%)
rate(heliosdb_cache_hits_total[1m]) /
  (rate(heliosdb_cache_hits_total[1m]) + rate(heliosdb_cache_misses_total[1m]))

# Cache evictions (monitor for capacity issues)
rate(heliosdb_cache_evictions_total[1m])

Profiling Tools

1. CPU Profiling

# Enable profiling endpoint
curl http://localhost:9090/debug/pprof/profile?seconds=30 > cpu.prof

# Analyze with pprof
go tool pprof cpu.prof

2. Memory Profiling

# Heap profile
curl http://localhost:9090/debug/pprof/heap > heap.prof

# Analyze
go tool pprof heap.prof

3. Query Profiling

-- Enable query logging
SET log_queries = true;
SET log_min_duration_ms = 100;  -- Log queries >100ms

-- View slow queries
SELECT * FROM heliosdb_slow_queries
ORDER BY duration_ms DESC
LIMIT 10;

Troubleshooting Common Issues

High Latency

Symptoms:

p99 latency > 200ms
Slow dashboard loads
Timeout errors

Diagnosis:

# Check cache hit rate
curl http://localhost:9090/metrics | grep cache_hit_rate
# If <80%, increase cache size

# Check compaction
curl http://localhost:9090/metrics | grep compaction
# If high, adjust compaction threads

# Check disk I/O wait
iostat -x 1
# If >10%, use faster storage

# Check slow queries
SELECT * FROM heliosdb_slow_queries WHERE duration_ms > 100;

Solutions:

Increase cache size
Add missing indexes
Optimize slow queries
Upgrade to faster storage (NVMe)
Enable query result caching

Low Throughput

Symptoms:

QPS < 5,000 per node
CPU utilization < 40%
High request queuing

Diagnosis:

# Check connection pool
curl http://localhost:9090/metrics | grep connections
# If maxed out, increase pool size

# Check batch settings
# If disabled, enable batching

# Check parallelism
curl http://localhost:9090/metrics | grep parallel_queries

Solutions:

Increase worker threads
Enable batching
Increase connection pool
Enable parallel query execution
Scale horizontally (add nodes)

High Memory Usage

Symptoms:

Memory usage > 90%
OOM errors
Frequent cache evictions

Diagnosis:

# Check memory breakdown
curl http://localhost:9090/metrics | grep memory

# Check cache sizes
# block_cache + row_cache + write_buffers + query_cache

# Check for leaks
pmap -x $(pidof heliosdb) | tail -20

Solutions:

Reduce cache sizes
Reduce write buffer size
Reduce max_write_buffer_number
Enable compression
Add more RAM or scale horizontally

Production Best Practices

1. Configuration Template

config.prod.toml (64 GB RAM, 16 CPU node)

[server]
listen_addr = "0.0.0.0:8080"
worker_threads = 32  # 2x CPUs
max_connections = 2000

[storage]
data_dir = "/data/heliosdb"
write_buffer_size_mb = 512
max_write_buffer_number = 6
max_background_compactions = 8
max_background_flushes = 4

[compression]
enabled = true
algorithm = "zstd"
level = 3

[cache]
size_mb = 32768  # 50% of RAM
block_cache_mb = 24576
row_cache_mb = 8192
policy = "lru"

[performance]
enable_batching = true
batch_window_ms = 10
max_batch_size = 1000
enable_query_cache = true
query_cache_mb = 4096
max_parallel_queries = 16
enable_simd = true

[multitenancy]
enabled = true
default_isolation_mode = "logical"

[monitoring]
metrics_enabled = true
metrics_addr = "0.0.0.0:9090"
enable_statistics = true
stats_interval_seconds = 60

2. Deployment Checklist

3. Monitoring Alerts

Critical Alerts:

p99 latency > 500ms (5 minutes)
Error rate > 1% (5 minutes)
CPU usage > 90% (10 minutes)
Memory usage > 95% (5 minutes)
Disk usage > 90% (immediate)
Replication lag > 10s (5 minutes)

Warning Alerts:

p99 latency > 200ms (10 minutes)
Cache hit rate < 80% (15 minutes)
Compaction lag > 100 files (15 minutes)
Connection pool exhausted (5 minutes)
Disk I/O wait > 20% (10 minutes)

Conclusion

Performance tuning is an iterative process. Start with the quick wins, establish baselines, monitor metrics, and continuously optimize based on workload patterns.

Key Takeaways:

Cache is critical: 90%+ hit rate is the goal
Indexes matter: Create indexes for all query patterns
Batch operations: 10x throughput improvement
Monitor everything: You can’t optimize what you don’t measure
Test under load: Production workloads differ from synthetic benchmarks

Related Documentation:

Document Information:

Version: 7.0
Last Updated: December 9, 2025
Lines: 1,000+
Status: Production Ready

HeliosDB Performance Tuning Guide v7.0

HeliosDB Performance Tuning Guide v7.0

Table of Contents

Introduction

Quick Wins Checklist

Performance Baseline

Benchmark Your System

Reference Hardware Specifications

System Configuration

Operating System Tuning

HeliosDB Configuration

Storage Optimization

LSM-Tree Tuning

HCC (Hierarchical Columnar Compression)

Cloud Tiering

Cache Tuning

Multi-Level Cache Architecture

Cache Sizing Guidelines

Cache Monitoring

Query Optimization

Index Strategy

Query Patterns

Query Result Caching

Multi-Tenancy Performance

Tenant Isolation Modes

Row-Level Security (RLS) Performance

Quota Enforcement Performance

Network Optimization

Connection Pooling

HTTP/2 and Compression

TCP Tuning

Monitoring & Profiling

Key Performance Metrics

Profiling Tools

Troubleshooting Common Issues

High Latency

Low Throughput

High Memory Usage

Production Best Practices

1. Configuration Template

2. Deployment Checklist

3. Monitoring Alerts

Conclusion