Performance Optimization

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MAIF is designed for high-performance operations. This guide covers optimization techniques for maximum throughput and efficiency.

Performance Features

MAIF provides:

• Memory-Mapped I/O: Efficient file access
• Write Buffering: Batched writes for throughput
• Compression: Reduce storage and I/O
• Batch Processing: Process data efficiently
• Streaming: Handle large files

Memory-Mapped I/O

Enable memory mapping for large files:

from maif.core import MAIFEncoder

# Enable memory mapping
encoder = MAIFEncoder(
    "large.maif",
    agent_id="performance-agent",
    enable_mmap=True
)

# Large write operations are more efficient
for i in range(10000):
    encoder.add_text_block(f"Document {i}")

encoder.finalize()

Block Storage with mmap

from maif.block_storage import BlockStorage

# Direct block access with memory mapping
storage = BlockStorage("large.maif", enable_mmap=True)

# Efficient random access
block = storage.get_block("block-id")

Write Buffering

The encoder uses buffering for better performance:

from maif.core import MAIFEncoder

# Configure buffer size
encoder = MAIFEncoder(
    "buffered.maif",
    agent_id="buffered-agent",
    buffer_size=128 * 1024  # 128KB buffer
)

# Writes are buffered automatically
for i in range(1000):
    encoder.add_text_block(f"Item {i}")

# Buffer flushed on finalize
encoder.finalize()

Compression

Enable compression to reduce file size and I/O:

from maif.core import MAIFEncoder

encoder = MAIFEncoder(
    "compressed.maif",
    agent_id="compressed-agent",
    enable_compression=True
)

# Data is automatically compressed
encoder.add_text_block("Large document content...")
encoder.finalize()

Compression Manager

from maif.compression_manager import CompressionManager

# Create compression manager
compression = CompressionManager()

# Compress data
compressed = compression.compress(large_data)

# Decompress when needed
original = compression.decompress(compressed)

Embedding Compression

from maif_api import create_maif

maif = create_maif("embedding-agent")

# Compress large embedding sets
embeddings = [[0.1, 0.2, 0.3] for _ in range(10000)]
maif.add_embeddings(embeddings, model_name="bert", compress=True)

maif.save("compressed_embeddings.maif")

Batch Processing

Process data in efficient batches:

from maif.batch_processor import BatchProcessor

# Create batch processor
processor = BatchProcessor(batch_size=100)

# Process items in batches
items = [f"document_{i}" for i in range(10000)]

for batch in processor.process(items):
    # Each batch has up to 100 items
    for item in batch:
        process(item)

Stream Batch Processing

from maif.batch_processor import StreamBatchProcessor

# For streaming data
processor = StreamBatchProcessor(
    batch_size=100,
    timeout=5.0  # Flush after 5 seconds
)

# Add items as they arrive
for item in data_stream:
    processor.add(item)
    
    if processor.is_ready():
        batch = processor.get_batch()
        process_batch(batch)

# Process remaining
final_batch = processor.flush()

Parallel Processing

from maif.batch_processor import DistributedBatchProcessor

# Multi-worker processing
processor = DistributedBatchProcessor(
    batch_size=1000,
    num_workers=4
)

# Process with parallelism
results = processor.process_parallel(items)

Streaming

For large files, use streaming operations:

from maif.streaming import MAIFStreamWriter, MAIFStreamReader

# Write incrementally
with MAIFStreamWriter("large.maif") as writer:
    for i in range(100000):
        writer.write_text_chunk(f"Document {i}")

# Read without loading entire file
with MAIFStreamReader("large.maif") as reader:
    for block in reader.read_blocks():
        process(block)

Hot Buffer

For high-throughput caching:

from maif.hot_buffer import HotBuffer

# Create hot buffer
buffer = HotBuffer(max_size=1024 * 1024)  # 1MB

# Cache frequently accessed data
buffer.put("key1", data1)
buffer.put("key2", data2)

# Fast retrieval
data = buffer.get("key1")

Rate Limiting

Control throughput in production:

from maif.rate_limiter import RateLimiter, RateLimitConfig

# Configure rate limiting
config = RateLimitConfig(
    requests_per_second=100,
    burst_size=20
)
limiter = RateLimiter(config)

# Use rate limiter
with limiter:
    perform_operation()

Cost-Based Rate Limiting

from maif.rate_limiter import CostBasedRateLimiter

# Limit by cost
limiter = CostBasedRateLimiter(max_cost_per_hour=1000)

# Operations have different costs
limiter.track_cost(operation="write", cost=1)
limiter.track_cost(operation="search", cost=5)

Metrics

Track performance metrics:

from maif.metrics_aggregator import (
    MetricsAggregator,
    initialize_metrics,
    get_metrics
)

# Initialize metrics
metrics = initialize_metrics(namespace="my-app")

# Record metrics
metrics.record("write_latency", 0.05)
metrics.record("blocks_written", 100)

# Get aggregated metrics
summary = get_metrics()
print(f"Average latency: {summary['write_latency_avg']}")

Cost Tracking

Track resource usage:

from maif.cost_tracker import (
    CostTracker,
    initialize_cost_tracking,
    with_cost_tracking
)

# Initialize tracking
tracker = initialize_cost_tracking()

# Track operation costs
@with_cost_tracking
def expensive_operation():
    # Operation code
    pass

# Get cost summary
costs = tracker.get_summary()

Performance Tips

1. Enable Memory Mapping for Large Files

encoder = MAIFEncoder("fast.maif", agent_id="fast", enable_mmap=True)

2. Use Compression

encoder = MAIFEncoder("efficient.maif", agent_id="efficient", enable_compression=True)

3. Batch Operations

# Don't write one at a time
for item in items:
    encoder.add_text_block(item)

# Better: batch writes are efficient
encoder.add_text_block(large_content)

4. Stream Large Files

# Don't load entire file
with MAIFStreamReader("huge.maif") as reader:
    for block in reader.read_blocks():
        process(block)

5. Use Parallel Processing

from maif.batch_processor import DistributedBatchProcessor

processor = DistributedBatchProcessor(num_workers=4)
results = processor.process_parallel(items)

Performance Benchmarks

Typical performance characteristics:

Operation	Throughput	Latency
Write (text)	100K blocks/sec	<1ms
Write (binary)	400+ MB/sec	<5ms
Read (sequential)	500+ MB/sec	<1ms
Search (semantic)	<50ms	-
Compression	200+ MB/sec	<10ms

Actual performance depends on hardware and configuration.

Complete Example

from maif.core import MAIFEncoder
from maif.batch_processor import BatchProcessor
from maif.metrics_aggregator import initialize_metrics
import time

# Initialize metrics
metrics = initialize_metrics()

# Create optimized encoder
encoder = MAIFEncoder(
    "optimized.maif",
    agent_id="optimized",
    enable_mmap=True,
    enable_compression=True,
    buffer_size=256 * 1024
)

# Batch processor
processor = BatchProcessor(batch_size=500)

# Generate test data
documents = [f"Document {i} with content..." for i in range(10000)]

# Process in batches
start = time.time()
for batch in processor.process(documents):
    for doc in batch:
        encoder.add_text_block(doc)

encoder.finalize()
elapsed = time.time() - start

print(f"Processed {len(documents)} documents in {elapsed:.2f}s")
print(f"Throughput: {len(documents)/elapsed:.0f} docs/sec")

Available Performance Components

Component	Module	Purpose
MAIFEncoder	maif.core	Create files (mmap, buffer options)
MAIFStreamWriter	maif.streaming	Streaming writes
MAIFStreamReader	maif.streaming	Streaming reads
BatchProcessor	maif.batch_processor	Batch processing
DistributedBatchProcessor	maif.batch_processor	Parallel processing
CompressionManager	maif.compression_manager	Data compression
HotBuffer	maif.hot_buffer	High-speed caching
RateLimiter	maif.rate_limiter	Throughput control
MetricsAggregator	maif.metrics_aggregator	Performance metrics
CostTracker	maif.cost_tracker	Resource tracking

Next Steps

• Streaming → - Real-time processing
• Architecture → - System design
• API Reference → - Complete API documentation