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Logging

  • Implement Python logging in UDFs and application endpoints
  • Track execution time, errors, API call latency
  • Use structured logging (JSON) for log aggregation
import logging
import time
import pixeltable as pxt

logger = logging.getLogger(__name__)

@pxt.udf
def process_video(video: pxt.Video) -> pxt.Json:
    start = time.time()
    try:
        # Your processing logic here
        result = {'processed': True}
        logger.info(f"Processed in {time.time() - start:.2f}s")
        return result
    except Exception as e:
        logger.error(f"Processing failed: {e}")
        raise

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Resource Monitoring

  • Monitor CPU, RAM, Disk I/O, Network on Pixeltable host
  • Track UDF execution time and model inference latency
  • Alert on resource exhaustion
Key Metrics to Track:
MetricWhat to Watch
CPUSustained high usage during inference
MemoryGrowth over time (potential leaks)
Disk I/OBottlenecks during media processing
NetworkAPI call latency to external services

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Optimization

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Batch Operations

Use batch processing for better throughput: 
# Batch UDF execution for GPU models
@pxt.udf(batch_size=32)
def embed_batch(texts: pxt.Batch[str]) -> pxt.Batch[list[float]]:
    # Process multiple items at once
    return model.encode(texts)

# Batch inserts (more efficient than individual inserts)
table.insert([row1, row2, row3, ...])

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Performance Tips

  • Batch Operations: Use @pxt.udf(batch_size=32) for GPU model inference
  • Batch Inserts: Insert multiple rows at once: table.insert([row1, row2, ...])
  • Profile UDFs: Add execution time logging to identify bottlenecks
  • Embedding Indexes: Use pgvector for efficient similarity search

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Rate Limiting

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Built-In Provider Limits

Automatic rate limiting for OpenAI, Anthropic, Gemini, etc. is configured per-model in config.toml: 
# ~/.pixeltable/config.toml
[openai]
requests_per_minute = 500
tokens_per_minute = 90000

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Custom API Rate Limiting

Use resource_pool to throttle calls to self-hosted models or custom endpoints: 
# Default: 600 requests per minute
@pxt.udf(resource_pool='request-rate:my_service')
async def call_custom_api(prompt: str) -> dict:
    # Your logic to call custom endpoint
    return await custom_api_call(prompt)

# Example: Custom rate-limited UDF for self-hosted model
@pxt.udf(resource_pool='request-rate:my_ray_cluster')
async def call_ray_model(prompt: str, model: str) -> dict:
    # Your logic to call FastAPI + Ray cluster
    return await custom_api_call(prompt, model)

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Advanced Features

LLM & Agent Workflows

Build complex agent workflows as computed columns with tool calling, MCP integration, and persistent state.

Data Sharing

Publish and replicate tables across Pixeltable instances for team collaboration.

Snapshots

Create immutable point-in-time copies for reproducible ML experiments.

Label Studio Integration

Sync tables with annotation projects for human-in-the-loop workflows.
Last modified on January 29, 2026