-7%
HEAVY DUTY GENERATOR
HEAVY DUTY GENERATOR - Image 2
HEAVY DUTY GENERATOR - Image 3
HEAVY DUTY GENERATOR - Image 4

HEAVY DUTY GENERATOR

Share With Friends

AVAILABLE IN STOCK

TSh14,000,000.0 TSh15,000,000.0

1. Handling Large Data Streams

Generators excel at memory efficiency. Use them to process large files, databases, or infinite sequences without loading everything into memory.

Example: Large File Processing

python

Copy

Download

def read_large_file(file_path, chunk_size=1024):
    with open(file_path, 'r') as file:
        while True:
            chunk = file.read(chunk_size)
            if not chunk:
                break
            yield chunk

# Process a 10GB file in 1KB chunks
for chunk in read_large_file('huge_data.log'):
    process(chunk)  # Custom processing function

2. Parallel Processing with Generators

Combine generators with concurrent.futures or asyncio for parallel execution (e.g., processing multiple data streams).

Example: Async Generator (Python 3.6+)

python

Copy

Download

import asyncio

async def async_data_fetcher(urls):
    for url in urls:
        data = await fetch(url)  # Assume `fetch` is an async function
        yield data

async def main():
    urls = ['url1', 'url2', 'url3']
    async for data in async_data_fetcher(urls):
        print(data)

asyncio.run(main())

3. Generator Pipelines

Chain generators to create data processing pipelines (e.g., filtering → transforming → aggregating).

Example: Data Pipeline

python

Copy

Download

def filter_positive(numbers):
    for n in numbers:
        if n > 0:
            yield n

def square(numbers):
    for n in numbers:
        yield n ** 2

def pipeline(data):
    filtered = filter_positive(data)
    squared = square(filtered)
    yield from squared

# Execute the pipeline
data = [-2, -1, 0, 1, 2]
result = pipeline(data)
print(list(result))  # Output: [1, 4]

4. Stateful Generators

Retain complex state between iterations (e.g., tracking progress, managing connections).

Example: Batch Database Queries

python

Copy

Download

def batch_query(query, batch_size=1000):
    offset = 0
    while True:
        results = execute_query(f"{query} LIMIT {batch_size} OFFSET {offset}")
        if not results:
            break
        yield results
        offset += batch_size

# Process batches of 1000 records
for batch in batch_query("SELECT * FROM huge_table"):
    process_batch(batch)

5. Infinite Generators

Generate sequences indefinitely (e.g., sensor data, real-time streams).

Example: Real-Time Sensor Data

python

Copy

Download

import random
import time

def sensor_data_stream():
    while True:
        yield {
            'timestamp': time.time(),
            'value': random.uniform(0, 100)
        }
        time.sleep(1)  # Simulate delay

# Monitor indefinitely
for data in sensor_data_stream():
    print(f"Time: {data['timestamp']}, Value: {data['value']}")

6. Advanced Generator Control

Use .send(), .throw(), and .close() for bidirectional communication and error handling.

Example: Generator with .send()

python

Copy

Download

def interactive_generator():
    total = 0
    while True:
        value = yield total  # Pause and wait for input via `.send()`
        if value is None:
            break
        total += value

gen = interactive_generator()
next(gen)  # Prime the generator
print(gen.send(10))  # Output: 10
print(gen.send(20))  # Output: 30
gen.close()  # Terminate the generator

7. Memory-Optimized Generators

Avoid materializing large datasets with yield from and itertools.

Example: Combining Generators with itertools

python

Copy

Download

import itertools

def generate_combinations(elements):
    for r in range(1, len(elements)+1):
        yield from itertools.combinations(elements, r)

# Generate combinations on-the-fly
for combo in generate_combinations(['A', 'B', 'C']):
    print(combo)

8. Error Handling in Heavy-Duty Generators

Gracefully handle exceptions during iteration.

Example: Fault-Tolerant Generator

python

Copy

Download

def fault_tolerant_generator(data):
    for item in data:
        try:
            result = process_risky_item(item)  # May raise exceptions
            yield result
        except Exception as e:
            log_error(e)
            continue

Performance Tips

  • Lazy Evaluation: Delay computation until needed.

  • Avoid Materialization: Use yield instead of building lists.

  • Optimize Chunk Sizes: For I/O-bound tasks (e.g., file reading), tune chunk_size to balance memory and speed.

  • Use C Extensions: For CPU-heavy tasks, pair generators with libraries like numpy or Cython.

When to Use Heavy-Duty Generators

  • Big Data: Process terabytes of data without OOM errors.

  • Real-Time Streams: Handle live data feeds (e.g., logs, sensors).

  • Resource-Constrained Environments: Minimize memory footprint.

Category:

Related products

SHOPPING CART

close