Shulou(Shulou.com)06/02 Report--

In this issue, the editor will bring you about how to achieve a simulation block chain in Python. The article is rich in content and analyzes and describes for you from a professional point of view. I hope you can get something after reading this article.

1. Overview of the development package

The main features of the simwiz.py blockchain simulation package are as follows:

Based on the discrete event simulation mechanism, the block chain network with a large number of nodes can be simulated on a single machine.

Built-in simulation network and block chain data structure, which can quickly carry out simulation experiments of consensus algorithm and block chain network.

Simple API interface, very easy to use, convenient for secondary development

Simwiz.py runs in Python 3.x environment, and the current version 1.0.0. The main classes and relationships are shown in the following figure:

For the main code files of simwiz.py, please see the official website description: http://sc.hubwiz.com/codebag/simwiz-python/.

2. Use the demo program

At the terminal, enter the simwiz.py package directory and execute the following command to run the built-in demo program:

~ $cd simwiz.py~/simwiz.py$ python3 demo.py

In the demo program, the block chain network composed of four nodes will periodically output its internal block chain composition, and the results are as follows:

It can be seen that each node (0 ~ 3) maintains the consistency of the block chain data through the consensus algorithm in each block cycle (1 ~ 7).

3. Start the simulation block chain

Use the Simulation class to create a simulation instance and call its run () method to start the simulation. For example, the following code creates a simulation object for a blockchain network with four default nodes and performs a simulation of 1000 milliseconds:

From simwiz import * # introduces simwiz package minters = 4nodes = [# create 4-node BlockchainNode (interval=100) Minters=minters) for i in range (minters)] net = Network (nodes) # 4 nodes networking sim = Simulation (net) # creating simulation object sim.run (1000) # Simulation 1 second 4, implementation of custom consensus algorithm

The consensus algorithm used by the BlockchainNode class built into the simwiz.py package is similar to DPoS, which determines the current out-of-block node based on the time slot. Custom consensus algorithms can be implemented by inheriting BlockchainNode and overriding its on_timer and on_message methods. For example, let each node come out of the block at a preset probability:

From simwiz import * import randomclass PoxNode (BlockchainNode): def _ _ init__ (self,prob,interval,minters): super.__init__ (interval,minters) self.prob = prob # def on_timer (self) of the current node Sim): if random.random () < self.prob: block = self.blockchain.create_next_block () # chunk msg = pojo ({# create chunk message 'type':' block', 'payload': block}) sim.broadcast (self Msg) # broadcast block message def on_message (self,sim,message): if message.type = = 'block': self.blockchain.commit_block (message.payload) # submit received block if message.type = =' transaction': self.blockchain.cache_tx (message.payload) # temporarily store received transactions

The block chain consensus algorithm based on probability can be simulated by this PoxNode model, such as PoW (workload proof) consensus algorithm, where the block probability of each node corresponds to the computing power of the node, that is, the probability of blocking of each node is equal to the computing power of the node / the computing power of the whole network.

The following code uses PoxNode to simulate a PoW network with 100 nodes:

Miners = 100,100 mining nodes prob = 0.011sampled according to uniform distribution, the block probability of each node is interval = 100# the block period is set to 100ms nodes = [ # create a node object PoxNode (prob) Interval,miners) for i in range (100)] sim = Simulation (Network (nodes)) # create a simulation object sim.run (10000) # execute the simulation for 10 seconds this is how to implement a simulation block chain in the Python shared by the editor. If you happen to have similar doubts, you might as well refer to the above analysis to understand. If you want to know more about it, you are welcome to follow the industry information channel.

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