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README.md
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README.md
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# PyLCG
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> Linear Congruential Generator for IP Sharding
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PyLCG is a Python implementation of a memory-efficient IP address sharding system using Linear Congruential Generators *(LCG)* for deterministic random number generation. This tool enables distributed scanning and network reconnaissance by efficiently dividing IP ranges across multiple machines.
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___
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## Table of Contents
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- [Project Origins & Purpose](#project-origins-and-purpose)
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- [Overview](#overview)
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- [How It Works](#how-it-works)
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- [Understanding IP Addresses](#understanding-ip-addresses)
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- [The Magic of Linear Congruential Generators](#the-magic-of-linear-congruential-generators)
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- [Sharding: Dividing the Work](#sharding-dividing-the-work)
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- [Memory-Efficient Processing](#memory-efficient-processing)
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- [Real-World Applications](#real-world-applications)
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- [Network Security Testing](#network-security-testing)
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- [Cloud-Based Scanning](#cloud-based-scanning)
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___
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## Project Origins & Purpose
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PyLCG was inspired by the elegant IP distribution system used in [masscan](https://github.com/robertdavidgraham/masscan), the popular mass IP port scanner. While masscan implements this logic as part of its larger codebase, I wanted to isolate and implement this specific component as a standalone Python library that developers can easily integrate into their own projects.
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The goal was to create a clean, well-documented implementation that:
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- Can be used as a drop-in solution for any project needing IP distribution capabilities
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- Provides the same reliable mathematical foundation as masscan's approach
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- Is easy to understand and modify for specific needs
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- Works well with modern Python async patterns
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By extracting this functionality into its own library, developers can add sophisticated IP distribution capabilities to their network tools without having to reinvent the wheel or extract code from larger projects.
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___
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## Overview
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When performing network reconnaissance or scanning large IP ranges, it's often necessary to split the work across multiple machines. However, this presents several challenges:
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1. You want to ensure each machine works on a different part of the network *(no overlap)*
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2. You want to avoid scanning IPs in sequence *(which can trigger security alerts)*
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3. You need a way to resume scans if a machine fails
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4. You can't load millions of IPs into memory at once
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PyLCG solves these challenges through clever mathematics and efficient algorithms.
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___
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## How It Works
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### Understanding IP Addresses
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First, let's understand how IP addresses work in our system:
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- An IP address like `192.168.1.1` is really just a 32-bit number
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- A CIDR range like `192.168.0.0/16` represents a continuous range of these numbers
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- For example, `192.168.0.0/16` includes all IPs from `192.168.0.0` to `192.168.255.255` *(65,536 addresses)*
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### The Magic of Linear Congruential Generators
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At the heart of PyLCG is something called a Linear Congruential Generator *(LCG)*. Think of it as a mathematical recipe that generates a sequence of numbers that appear random but are actually predictable if you know the starting point *(seed)*.
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Here's how it works:
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1. Start with a number *(called the seed)*
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2. Multiply it by a carefully chosen constant *(1597 in our case)*
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3. Add another carefully chosen constant *(51749)*
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4. Take the remainder when divided by 2^32
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5. That's your next number! Repeat the process to get more numbers
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In mathematical notation:
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```
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Next_Number = (1597 * Current_Number + 51749) mod 2^32
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```
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Why these specific numbers?
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- `1597` and `51749` were chosen because they create a sequence that:
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- Visits every possible number before repeating *(maximum period)*
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- Spreads numbers evenly across the range
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- Can be calculated quickly on computers
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- `2^32` *(4,294,967,296)* is used because it:
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- Matches the size of a 32-bit integer
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- Is large enough to handle any IP range
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- Makes calculations efficient on modern CPUs
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### Sharding: Dividing the Work
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Let's say you want to scan a /16 network *(65,536 IPs)* using 4 machines. Here's how PyLCG handles it:
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1. **Division**: First, it divides the total IPs evenly:
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- 65,536 ÷ 4 = 16,384 IPs per shard
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- Machine 1: IPs 0-16,383
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- Machine 2: IPs 16,384-32,767
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- Machine 3: IPs 32,768-49,151
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- Machine 4: IPs 49,152-65,535
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2. **Randomization**: Within each shard, IPs are randomized using the LCG:
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- Each IP index *(0 to 65,535)* is fed through the LCG
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- The resulting numbers determine the scan order
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- Because we use the same seed, this order is consistent across runs
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Example of how IPs might be ordered in Shard 1:
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```
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Original order: 0, 1, 2, 3, 4, 5...
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LCG values: 51749, 134238, 297019, 12983...
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Final order: 3, 5, 1, 4, 2, 0... (sorted by LCG values)
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```
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### Memory-Efficient Processing
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To handle large IP ranges without consuming too much memory, PyLCG uses several techniques:
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1. **Chunked Processing**
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Instead of loading all IPs at once, it processes them in chunks:
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```python
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# Example with chunk_size = 1000
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Chunk 1: Process IPs 0-999
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Chunk 2: Process IPs 1000-1999
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...and so on
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```
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2. **Lazy Generation**
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- IPs are generated only when needed using Python's async generators
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- The system yields one IP at a time rather than creating huge lists
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- This keeps memory usage constant regardless of IP range size
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3. **Direct Calculation**
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- The LCG can jump directly to any position in its sequence
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- No need to generate all previous numbers
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- Enables efficient random access to any part of the sequence
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___
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## Real-World Applications
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### Network Security Testing
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Imagine you're testing the security of a large corporate network:
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- You have 5 scanning machines
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- You need to scan 1 million IPs
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- You want to avoid triggering IDS/IPS systems
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PyLCG helps by:
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1. Dividing the IPs evenly across your 5 machines
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2. Randomizing the scan order to avoid detection
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3. Allowing you to pause/resume scans from any point
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4. Using minimal memory on each machine
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### Cloud-Based Scanning
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In cloud environments, PyLCG is particularly useful:
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- Easily scale up/down the number of scanning instances
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- Each instance knows exactly which IPs to scan
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- Consistent results across multiple runs
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- Efficient resource usage keeps costs down
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___
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###### Mirrors for this repository: [acid.vegas](https://git.acid.vegas/pylcg) • [SuperNETs](https://git.supernets.org/acidvegas/pylcg) • [GitHub](https://github.com/acidvegas/pylcg) • [GitLab](https://gitlab.com/acidvegas/pylcg) • [Codeberg](https://codeberg.org/acidvegas/pylcg)
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pylcg.py
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pylcg.py
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#!/usr/bin/env python3
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# Python implementation of a Linear Congruential Generator for IP Sharding - Developed by acidvegas in Python (https://git.acid.vegas/pylcg)
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# pylcg.py
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import argparse
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import asyncio
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import ipaddress
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from math import ceil
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class LCG:
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'''Linear Congruential Generator for deterministic random number generation'''
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def __init__(self, seed: int, m: int = 2**32):
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self.m = m
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self.a = 1597
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self.c = 51749
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self.seed = seed
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self.current = seed
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def get_nth(self, n: int) -> int:
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'''
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Get the nth number in the sequence without generating previous numbers.
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:param n: The index of the number to get
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'''
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# For large n, use the standard next() method to avoid modular arithmetic issues
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if n > 1000:
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self.current = self.seed
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for _ in range(n):
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self.next()
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return self.current
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# For smaller n, use direct calculation
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result = self.seed
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for _ in range(n):
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result = (self.a * result + self.c) % self.m
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return result
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def next(self) -> int:
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'''Generate next random number'''
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self.current = (self.a * self.current + self.c) % self.m
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return self.current
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class IPRange:
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'''Memory-efficient IP range iterator'''
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def __init__(self, cidr: str):
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network = ipaddress.ip_network(cidr)
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self.start = int(network.network_address)
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self.end = int(network.broadcast_address)
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self.total = self.end - self.start + 1
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def get_ip_at_index(self, index: int) -> str:
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'''
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Get IP at specific index without generating previous IPs
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:param index: The index of the IP to get
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'''
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if not 0 <= index < self.total:
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raise IndexError('IP index out of range')
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return str(ipaddress.ip_address(self.start + index))
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async def get_shard_ips(cidr: str, shard_num: int, total_shards: int, seed: int, chunk_size: int = 1000):
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'''
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Asynchronously generate IPs for the specified shard.
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:param cidr: The CIDR range to shard
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:param shard_num: The number of the shard to generate
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:param total_shards: The total number of shards
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:param seed: The seed for the random number generator
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:param chunk_size: The size of the chunks to process
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'''
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# Initialize the IP range and LCG
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ip_range = IPRange(cidr)
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lcg = LCG(seed)
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total_ips = ip_range.total
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# Calculate which indices belong to this shard
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shard_size = ceil(total_ips / total_shards)
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start_idx = shard_num * shard_size
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end_idx = min(start_idx + shard_size, total_ips)
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# Process in chunks to maintain memory efficiency
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for chunk_start in range(start_idx, end_idx, chunk_size):
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chunk_end = min(chunk_start + chunk_size, end_idx)
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chunk_indices = list(range(chunk_start, chunk_end))
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# Generate random values for this chunk
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chunk_random_values = [(i, lcg.get_nth(i)) for i in chunk_indices]
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chunk_random_values.sort(key=lambda x: x[1])
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# Yield IPs in randomized order
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for idx, _ in chunk_random_values:
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yield ip_range.get_ip_at_index(idx)
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# Allow other tasks to run (do we need this?)
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await asyncio.sleep(0)
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async def main():
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parser = argparse.ArgumentParser(description='Async IP address sharding tool')
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parser.add_argument('cidr', help='Target IP range in CIDR format')
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parser.add_argument('shard_num', type=int, help='Shard number (0-based)')
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parser.add_argument('total_shards', type=int, help='Total number of shards')
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parser.add_argument('--seed', type=int, default=12345, help='Random seed for LCG')
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parser.add_argument('--chunk-size', type=int, default=1000, help='Processing chunk size')
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args = parser.parse_args()
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if args.shard_num >= args.total_shards:
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raise ValueError('Shard number must be less than total shards')
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if args.shard_num < 0 or args.total_shards < 1:
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raise ValueError('Invalid shard configuration')
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async for ip in get_shard_ips(args.cidr, args.shard_num, args.total_shards, args.seed, args.chunk_size):
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print(ip)
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if __name__ == '__main__':
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asyncio.run(main())
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unit_test.py
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unit_test.py
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#!/usr/bin/env python3
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# Python implementation of a Linear Congruential Generator for IP Sharding - Developed by acidvegas in Python (https://git.acid.vegas/pylcg)
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# pylcg.py
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import unittest
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import asyncio
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import ipaddress
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import sys
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import time
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from pylcg import IPRange, get_shard_ips, LCG
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# ANSI color codes
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class Colors:
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BLUE = '\033[94m'
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GREEN = '\033[92m'
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YELLOW = '\033[93m'
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CYAN = '\033[96m'
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RED = '\033[91m'
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ENDC = '\033[0m'
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def progress_bar(iteration: int, total: int, prefix: str = '', length: int = 50) -> None:
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'''Simple progress bar using standard Python'''
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percent = f"{100 * (iteration / float(total)):.1f}"
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filled_length = int(length * iteration // total)
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bar = '█' * filled_length + '-' * (length - filled_length)
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sys.stdout.write(f'\r{Colors.CYAN}{prefix} |{bar}| {percent}%{Colors.ENDC} ')
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if iteration == total:
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sys.stdout.write('\n')
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sys.stdout.flush()
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def print_header(message: str) -> None:
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'''Print formatted header'''
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print(f'\n{Colors.BLUE}{"="*80}')
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print(f'TEST: {message}')
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print(f'{"="*80}{Colors.ENDC}\n')
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def print_success(message: str) -> None:
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'''Print success message'''
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print(f'{Colors.GREEN}✓ {message}{Colors.ENDC}')
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def print_progress(message: str) -> None:
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'''Print progress message'''
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print(f"{Colors.YELLOW}⟳ {message}{Colors.ENDC}")
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class TestIPSharder(unittest.TestCase):
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@classmethod
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def setUpClass(cls):
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'''Set up test parameters'''
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print_header('Setting up test environment')
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cls.test_cidr = '192.0.0.0/16' # 65,536 IPs
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cls.test_seed = 12345
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cls.total_shards = 4
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cls.chunk_size = 1000
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# Calculate expected IPs
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network = ipaddress.ip_network(cls.test_cidr)
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cls.all_ips = {str(ip) for ip in network}
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print_success(f"Initialized test environment with {len(cls.all_ips):,} IPs")
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||||||
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def setUp(self):
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'''Create event loop for each test'''
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self.loop = asyncio.new_event_loop()
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asyncio.set_event_loop(self.loop)
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||||||
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||||||
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||||||
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def tearDown(self):
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||||||
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'''Clean up event loop'''
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self.loop.close()
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||||||
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||||||
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async def collect_shard_ips(self, shard_num: int):
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'''Helper to collect IPs from a shard'''
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||||||
|
return {ip async for ip in get_shard_ips(self.test_cidr, shard_num, self.total_shards, self.test_seed, self.chunk_size)}
|
||||||
|
|
||||||
|
|
||||||
|
def test_ip_range_initialization(self):
|
||||||
|
'''Test IPRange class initialization and calculations'''
|
||||||
|
print_header('Testing IPRange initialization')
|
||||||
|
ip_range = IPRange(self.test_cidr)
|
||||||
|
|
||||||
|
self.assertEqual(ip_range.total, 65536)
|
||||||
|
print_success('IP range size correctly calculated')
|
||||||
|
|
||||||
|
first_ip = ip_range.get_ip_at_index(0)
|
||||||
|
last_ip = ip_range.get_ip_at_index(ip_range.total - 1)
|
||||||
|
print_success(f'IP range spans from {first_ip} to {last_ip}')
|
||||||
|
|
||||||
|
|
||||||
|
def test_shard_completeness(self):
|
||||||
|
'''Test that all IPs are covered exactly once across all shards'''
|
||||||
|
print_header('Testing shard completeness')
|
||||||
|
|
||||||
|
async def check_completeness():
|
||||||
|
seen_ips = set()
|
||||||
|
shard_sizes = []
|
||||||
|
|
||||||
|
for shard_num in range(self.total_shards):
|
||||||
|
progress_bar(shard_num, self.total_shards-1, prefix='Processing shards')
|
||||||
|
shard_ips = await self.collect_shard_ips(shard_num)
|
||||||
|
shard_sizes.append(len(shard_ips))
|
||||||
|
|
||||||
|
# Check for duplicates and overlap
|
||||||
|
self.assertEqual(len(shard_ips), len(set(shard_ips)),
|
||||||
|
f'Duplicates found in shard {shard_num}')
|
||||||
|
overlap = seen_ips & shard_ips
|
||||||
|
self.assertEqual(len(overlap), 0,
|
||||||
|
f'Overlap found with previous shards: {overlap}')
|
||||||
|
|
||||||
|
seen_ips.update(shard_ips)
|
||||||
|
|
||||||
|
# Verify all IPs are covered
|
||||||
|
self.assertEqual(seen_ips, self.all_ips,
|
||||||
|
'Not all IPs were covered by the shards')
|
||||||
|
print_success(f'All {len(self.all_ips):,} IPs were distributed across shards')
|
||||||
|
|
||||||
|
# Print distribution information
|
||||||
|
for i, size in enumerate(shard_sizes):
|
||||||
|
print(f"{Colors.CYAN}Shard {i}: {size:,} IPs{Colors.ENDC}")
|
||||||
|
|
||||||
|
self.loop.run_until_complete(check_completeness())
|
||||||
|
|
||||||
|
|
||||||
|
def test_lcg_sequence(self):
|
||||||
|
'''Test LCG sequence generation and performance'''
|
||||||
|
|
||||||
|
print_header('Testing LCG sequence generation')
|
||||||
|
|
||||||
|
lcg = LCG(seed=self.test_seed)
|
||||||
|
|
||||||
|
# Test small sequence
|
||||||
|
small_n = 100
|
||||||
|
start_time = time.perf_counter()
|
||||||
|
small_result = lcg.get_nth(small_n)
|
||||||
|
small_time = time.perf_counter() - start_time
|
||||||
|
print_success(f'Small sequence (n={small_n:,}) generated in {small_time:.6f}s')
|
||||||
|
|
||||||
|
# Test large sequence
|
||||||
|
large_n = 1_000_000
|
||||||
|
start_time = time.perf_counter()
|
||||||
|
large_result = lcg.get_nth(large_n)
|
||||||
|
large_time = time.perf_counter() - start_time
|
||||||
|
print_success(f'Large sequence (n={large_n:,}) generated in {large_time:.6f}s')
|
||||||
|
|
||||||
|
# Verify deterministic behavior
|
||||||
|
lcg2 = LCG(seed=self.test_seed)
|
||||||
|
print_progress('Verifying sequence determinism...')
|
||||||
|
for i in range(large_n):
|
||||||
|
if i % (large_n // 100) == 0: # Update progress every 1%
|
||||||
|
progress_bar(i, large_n, prefix='Verifying sequence')
|
||||||
|
lcg2.next()
|
||||||
|
progress_bar(large_n, large_n, prefix='Verifying sequence')
|
||||||
|
|
||||||
|
self.assertEqual(large_result, lcg2.current, 'LCG sequence is not deterministic')
|
||||||
|
print_success('LCG produces consistent results')
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
if __name__ == '__main__':
|
||||||
|
print(f"\n{Colors.CYAN}{'='*80}")
|
||||||
|
print(f"Starting IP Sharder Tests - Testing with {65536:,} IPs (/16 network)")
|
||||||
|
print(f"{'='*80}{Colors.ENDC}\n")
|
||||||
|
unittest.main(verbosity=2)
|
Loading…
Reference in New Issue
Block a user