Frequently Asked Questions (FAQ)¶

General Questions¶

What is SAGE Kernel?¶

SAGE Kernel is a high-performance streaming framework designed for real-time data processing. It provides APIs for creating data streams, processing pipelines, and managing execution environments.

How does SAGE Kernel differ from other streaming frameworks?¶

Lightweight: Minimal overhead with efficient memory management
Flexible: Supports both local and remote execution environments
Extensible: Plugin architecture for custom functionality
Developer-friendly: Simple API with comprehensive documentation

What are the system requirements?¶

Python 3.8 or higher
4GB RAM minimum (8GB recommended for production)
Linux, macOS, or Windows
Optional: Redis for distributed operations

Installation and Setup¶

How do I install SAGE Kernel?¶

pip install intsage-kernel

For development:

pip install intsage-kernel[dev]

Why am I getting import errors?¶

Common causes:

Wrong package name: Use from sage.core.api import LocalEnvironment
Missing dependencies: Install with pip install intsage-kernel[all]
Virtual environment: Ensure you're in the correct environment

How do I configure the environment?¶

Create a configuration file or use environment variables:

from sage.core.api import LocalEnvironment

env = LocalEnvironment(
    config={"log_level": "INFO", "buffer_size": 1000, "max_workers": 4}
)

API Usage¶

How do I create a basic stream?¶

from sage.core.api import LocalEnvironment

env = LocalEnvironment()
stream = env.create_stream("my_data")

# Add data
stream.write({"key": "value"})

# Process data
result = stream.map(lambda x: x["key"]).collect()

What's the difference between LocalEnvironment and RemoteEnvironment?¶

LocalEnvironment: Runs on your local machine, good for development and small-scale processing
RemoteEnvironment: Connects to remote SAGE clusters, used for production and distributed processing

How do I handle errors in streams?¶

def safe_processor(data):
    try:
        return process_data(data)
    except Exception as e:
        logger.error(f"Processing error: {e}")
        return None


# Filter out None results
result = stream.map(safe_processor).filter(lambda x: x is not None)

Can I process multiple streams together?¶

Yes, use ConnectedStreams:

from sage.core.api.connected_streams import ConnectedStreams

stream1 = env.create_stream("source1")
stream2 = env.create_stream("source2")

connected = ConnectedStreams([stream1, stream2])
result = connected.process(your_processor_function)

Performance¶

My streams are running slowly. How can I optimize?¶

Increase buffer size:

stream = env.create_stream("data", config={"buffer_size": 10000})

Use batch processing:

stream.batch(100).map(batch_processor)

Enable parallel processing:

stream.map_parallel(processor_func, max_workers=8)

Optimize your processing functions:
Avoid expensive operations in tight loops
Use generators for large datasets
Cache expensive computations

How much memory does SAGE Kernel use?¶

Memory usage depends on:

Buffer sizes (configurable)
Number of active streams
Size of processed data
Processing complexity

Monitor with:

import psutil

memory_usage = psutil.Process().memory_info().rss / 1024 / 1024  # MB

Can I control the number of worker threads?¶

Yes, configure when creating the environment:

env = LocalEnvironment(config={"max_workers": 8})

Or per operation:

stream.map_parallel(func, max_workers=4)

Data Handling¶

What data types can I process?¶

SAGE Kernel can process any Python object:

Dictionaries (most common)
Lists and tuples
Custom classes
Pandas DataFrames
NumPy arrays
Binary data

How do I handle large datasets?¶

Use streaming: Process data incrementally rather than loading everything into memory
Implement windowing: Process data in time or count-based windows
Use lazy evaluation: Only compute what you need

# Good: Streaming approach
large_stream = env.create_stream("large_data")
result = large_stream.filter(filter_func).take(1000)

# Avoid: Loading everything
all_data = large_stream.collect()  # May run out of memory

Can I process real-time data?¶

Yes, SAGE Kernel is designed for real-time processing:

# Real-time data ingestion
def ingest_realtime_data():
    while True:
        data = get_next_data_point()
        stream.write(data)
        time.sleep(0.1)


# Background ingestion
threading.Thread(target=ingest_realtime_data, daemon=True).start()

# Real-time processing
processed = stream.map(process_realtime).sink(output_handler)

Integration¶

How do I integrate with databases?¶

import sqlite3


def save_to_db(data):
    conn = sqlite3.connect("data.db")
    cursor = conn.cursor()
    cursor.execute("INSERT INTO table (data) VALUES (?)", (str(data),))
    conn.commit()
    conn.close()


stream.sink(save_to_db)

Can I use SAGE Kernel with Pandas?¶

Yes, SAGE Kernel works well with Pandas:

import pandas as pd


def process_dataframe(df_dict):
    df = pd.DataFrame(df_dict)
    # Process with pandas
    result = df.groupby("category").sum()
    return result.to_dict()


stream.map(process_dataframe)

How do I integrate with Apache Kafka?¶

from kafka import KafkaProducer, KafkaConsumer

# Kafka consumer to SAGE stream
consumer = KafkaConsumer("input_topic")
for message in consumer:
    stream.write(json.loads(message.value))

# SAGE stream to Kafka producer
producer = KafkaProducer()
stream.sink(lambda data: producer.send("output_topic", json.dumps(data)))

Can I use custom functions?¶

Yes, register custom functions:

from sage.core.api.functions import register_function


@register_function
def my_custom_function(data):
    return process_data(data)


# Use in streams
stream.map(my_custom_function)

Debugging¶

How do I debug stream processing?¶

Enable debug logging:

import logging

logging.basicConfig(level=logging.DEBUG)

Add debug prints:

def debug_processor(data):
    print(f"Processing: {data}")
    result = process_data(data)
    print(f"Result: {result}")
    return result


stream.map(debug_processor)

Use peek() for inspection:

stream.peek(lambda x: print(f"Data: {x}")).map(processor)

My stream seems to hang. What should I check?¶

Buffer full: Check if buffers are full and increase size
Blocking operations: Ensure processing functions don't block
Deadlocks: Check for circular dependencies between streams
Resource limits: Monitor CPU and memory usage

How do I handle connection failures in RemoteEnvironment?¶

from sage.core.api import RemoteEnvironment

env = RemoteEnvironment(
    endpoint="https://api.sage-cluster.com",
    config={"timeout": 30, "retry_attempts": 3, "retry_delay": 5},
)

# Test connection
if not env.test_connection():
    print("Failed to connect to remote environment")

Production Deployment¶

How do I deploy SAGE Kernel in production?¶

Use containers:

FROM python:3.11-slim
RUN pip install intsage-kernel
CMD ["python", "my_sage_app.py"]

Configure for production:

env = LocalEnvironment(
    config={"log_level": "WARNING", "buffer_size": 10000, "max_workers": 16}
)

Monitor performance: Use metrics and logging

How do I scale SAGE Kernel applications?¶

Horizontal scaling: Deploy multiple instances
Vertical scaling: Increase resources per instance
Use RemoteEnvironment: Distribute across cluster
Optimize processing: Batch operations, use efficient algorithms

What about security considerations?¶

Input validation: Always validate incoming data
Authentication: Use tokens for RemoteEnvironment
Network security: Use HTTPS for remote connections
Resource limits: Set appropriate memory and CPU limits

def validate_input(data):
    if not isinstance(data, dict):
        raise ValueError("Data must be dictionary")
    if "id" not in data:
        raise ValueError("Missing required field: id")
    return data


stream.map(validate_input)

Troubleshooting¶

Common Error Messages¶

"Stream buffer is full"¶

Cause: Data being written faster than processed
Solution: Increase buffer size or optimize processing

"Connection timeout"¶

Cause: Network issues with RemoteEnvironment
Solution: Check network connectivity, increase timeout

"Memory allocation failed"¶

Cause: Insufficient memory
Solution: Reduce buffer sizes, optimize data structures

"Function not found"¶

Cause: Custom function not registered
Solution: Use @register_function decorator

Performance Issues¶

Slow processing¶

Profile your processing functions
Use batch processing for small operations
Enable parallel processing
Optimize data structures

High memory usage¶

Reduce buffer sizes
Process data in smaller chunks
Use generators instead of lists
Clean up resources properly

CPU usage high¶

Reduce number of worker threads
Optimize processing algorithms
Use more efficient data structures
Consider caching expensive operations

Getting Help¶

Where can I find more documentation?¶

API Reference
Architecture Guide
Best Practices
Examples - 查看导航栏中的教程章节

How do I report bugs?¶

Check existing issues on GitHub
Create a minimal reproduction case
Include version information and logs
Submit issue with clear description

How do I contribute?¶

Fork the repository
Create a feature branch
Add tests for new functionality
Submit a pull request

Community Support¶

GitHub Issues: Bug reports and feature requests
Discussions: General questions and community help
Documentation: Comprehensive guides and examples

Still have questions? Check our GitHub repository or create an issue for support.