Performance

Performance Optimization¶

This guide covers performance optimization techniques for LDA projects, especially when dealing with large datasets, many files, or complex tracking requirements.

File Tracking Performance¶

Hash Calculation Optimization¶

Choose the right hash algorithm for your needs:

# lda_config.yaml
tracking:
  hash_algorithm: "xxhash"  # Fastest
  # hash_algorithm: "md5"    # Fast, less secure
  # hash_algorithm: "sha256" # Default, balanced
  # hash_algorithm: "sha512" # Most secure, slower

Performance comparison (1GB file): - xxhash: ~0.3s - md5: ~2.1s - sha256: ~5.8s (default) - sha512: ~4.2s

Parallel Processing¶

Enable parallel file processing:

performance:
  parallel_tracking: true
  max_workers: 8  # CPU cores
  chunk_size: 100  # Files per batch

Selective Tracking¶

Track only what you need:

tracking:
  patterns:
    include:
      - "*.csv"
      - "*.parquet"
    exclude:
      - "*.tmp"
      - "*.log"
      - "__pycache__/"

  # Skip large files
  size_limit: "1GB"

  # Track by modification time
  track_if_modified: true

Incremental Tracking¶

Use incremental tracking for large projects:

# Python API
from lda.core.tracking import IncrementalTracker

tracker = IncrementalTracker(manifest)
changes = tracker.scan_changes()
tracker.update_changed_only(changes)

Memory Management¶

Large File Handling¶

Process large files in chunks:

# lda_config.yaml
performance:
  large_file_threshold: "100MB"
  chunk_size: "10MB"

  # Memory mapping for huge files
  use_mmap: true
  mmap_threshold: "1GB"

Manifest Optimization¶

For projects with many files:

performance:
  manifest:
    format: "binary"  # vs "json"
    compression: "gzip"
    index_type: "btree"  # Fast lookups
    cache_size: "100MB"

Memory Profiling¶

Profile memory usage:

# Profile memory usage
lda track --profile-memory

# Generate memory report
lda debug memory-report

# Python API
from lda.profiling import memory_profile

@memory_profile
def process_large_dataset():
    # Your code here
    pass

Database Performance¶

PostgreSQL Optimization¶

-- Optimized schema
CREATE TABLE lda.file_tracking (
    id BIGSERIAL PRIMARY KEY,
    project_id INTEGER NOT NULL,
    file_path VARCHAR(500) NOT NULL,
    hash VARCHAR(64) NOT NULL,
    size BIGINT,
    modified_at TIMESTAMP,
    analyst VARCHAR(100),
    -- Indexes for common queries
    CONSTRAINT uk_project_file UNIQUE (project_id, file_path)
);

CREATE INDEX idx_modified ON lda.file_tracking(modified_at);
CREATE INDEX idx_hash ON lda.file_tracking(hash);
CREATE INDEX idx_analyst ON lda.file_tracking(analyst);

-- Partitioning for large projects
CREATE TABLE lda.file_tracking_2024 PARTITION OF lda.file_tracking
FOR VALUES FROM ('2024-01-01') TO ('2025-01-01');

Connection Pooling¶

database:
  postgres:
    pool_size: 20
    max_overflow: 10
    pool_timeout: 30
    pool_recycle: 3600

Query Optimization¶

# Batch operations
from lda.db import batch_insert

# Instead of individual inserts
for file in files:
    db.insert(file)  # Slow

# Use batch insert
batch_insert(files, batch_size=1000)  # Fast

Caching Strategies¶

File System Cache¶

performance:
  cache:
    enabled: true
    type: "filesystem"
    path: ".lda_cache"
    size_limit: "1GB"
    ttl: 3600  # seconds

Redis Cache¶

performance:
  cache:
    type: "redis"
    host: "localhost"
    port: 6379
    db: 0
    ttl: 86400

Memory Cache¶

from lda.cache import MemoryCache

cache = MemoryCache(max_size="500MB")

@cache.memoize
def expensive_operation(file_path):
    # Cached computation
    return process_file(file_path)

Network Performance¶

S3 Upload Optimization¶

integrations:
  s3:
    multipart_threshold: "100MB"
    multipart_chunksize: "10MB"
    max_concurrency: 10
    use_threads: true

    # Transfer acceleration
    use_accelerate_endpoint: true

Compression¶

performance:
  compression:
    enabled: true
    algorithm: "zstd"  # Best ratio/speed
    level: 3  # 1-9
    threshold: "1MB"

Monitoring and Profiling¶

Performance Monitoring¶

monitoring:
  enabled: true
  metrics:
    - operation_duration
    - memory_usage
    - disk_io
    - network_bandwidth

  export:
    prometheus:
      port: 9090
    grafana:
      dashboard: "lda-performance"

Built-in Profiler¶

# Profile specific operation
lda track --profile

# Generate performance report
lda debug performance-report --output perf.html

# Continuous monitoring
lda monitor --interval 60

Custom Metrics¶

from lda.metrics import Timer, gauge

# Time operations
with Timer("data_processing"):
    process_large_dataset()

# Track metrics
gauge("memory_usage", get_memory_usage())
gauge("active_files", len(tracked_files))

Best Practices¶

1. Optimize Configuration¶

# Optimized production config
performance:
  parallel_tracking: true
  max_workers: 16
  chunk_size: 1000

  cache:
    enabled: true
    type: "redis"

  compression:
    enabled: true
    algorithm: "zstd"

  database:
    pool_size: 50
    batch_size: 1000

2. Lazy Loading¶

from lda.core import LazyManifest

# Load manifest on demand
manifest = LazyManifest("manifest.json")

# Only loads required sections
section_files = manifest.get_section("sec01")

3. Async Operations¶

import asyncio
from lda.async import AsyncTracker

async def track_files_async():
    tracker = AsyncTracker()

    tasks = []
    for file in files:
        task = tracker.track_file_async(file)
        tasks.append(task)

    results = await asyncio.gather(*tasks)
    return results

4. Resource Limits¶

performance:
  limits:
    max_memory: "4GB"
    max_cpu_percent: 80
    max_disk_io: "100MB/s"

  throttling:
    enabled: true
    rate_limit: 1000  # ops/sec

Benchmarking¶

Built-in Benchmarks¶

# Run performance benchmarks
lda benchmark --all

# Specific benchmarks
lda benchmark --hash-algorithms
lda benchmark --file-operations
lda benchmark --database

Custom Benchmarks¶

from lda.benchmark import Benchmark

bench = Benchmark("custom_operation")

# Warm up
bench.warmup(iterations=10)

# Run benchmark
results = bench.run(
    function=my_operation,
    iterations=1000,
    parallel=True
)

print(results.summary())

Troubleshooting Performance¶

Slow File Tracking¶

Check hash algorithm
Enable parallel processing
Exclude unnecessary files
Use incremental tracking

# Diagnose slow tracking
lda debug trace-tracking

High Memory Usage¶

Enable chunked processing
Reduce cache size
Use memory mapping
Implement pagination

# Memory analysis
lda debug memory-analysis

Database Bottlenecks¶

Add appropriate indexes
Enable connection pooling
Use batch operations
Consider partitioning

# Database performance
lda debug db-performance