shargri-la / shargri-la

Shargri-La is a transaction-level sharding simulator for protocol testing against users' behavior on a sharded blockchain. The goal of Shargri-La is to help researchers to design or refine sharding protocols.

Shargri-La performs a discrete-event simulation, which proceeds with slots. At each slot, Shargri-La simulates transaction creation by users, block proposals by validators, and state transitions.

Scope of Shargri-La

Shargri-La is a transaction-level simulator, meaning that it focuses on users’ behavior on a sharded blockchain. Therefore, Shargri-La covers the following things:

• Transaction creation by users
• Application-level state transitions
• Cross-shard transactions
• Transaction selection by block proposers
• Transaction fee mechanism and gas auction

On the other hand, Shargri-La does not simulate the following things:

• P2P network (gossip protocols, peer discovery, etc.)
• Consensus algorithm
• Data availability mechanism
• Cryptographic processing (signatures, hashes, etc.)

Version 0.1.0: EIP-1559 and ETH Transfers

For simplicity, we assume that all the on-chain activities are only the transfers of ETH.

We partially adopt Eth1x64 Variant 1 "Apostille," i.e., each shard contains the Eth1 state transition rule and supports receipt-based cross-shard communication. Also, we adopt EIP-1559 as the transaction pricing mechanism.

Usage

We verified to work them with Rust 1.45.0.

cargo run --release

Options

FLAGS:
        --popular_user_exists         Whether or not there ia a popular user
        --popular_user_is_switcher    Whether or not the popular user is a switcher
    -h, --help                        Prints help information
    -V, --version                     Prints version information

OPTIONS:
        --csv <BIG_QUERY_CSV>
            A BigQuery Ethereum transactions csv file

        --end_slot <END_SLOT>                                                    The number of slots
        --output_dir_path <OUTPUT_DIR_PATH>                                      The path of the output directory
        --percentage_of_decreasing_minimum <PERCENTAGE_OF_DECREASING_MINIMUM>    
        --percentage_of_minimum <PERCENTAGE_OF_MINIMUM>                          
        --percentage_of_weighted_random <PERCENTAGE_OF_WEIGHTED_RANDOM>          
        --user_num <USER_NUM>                                                    The maximum number of users

(WIP) Using BigQuery

Use transactions table of the crypto_ethereum datasets.

cargo run --release -- --csv BIG_QUERY_ETHEREUM_TRANSACTION_CSV

Visualizer

Simulation results can be visualized by the sample programs using Matplotlib. We verified to work them with Python 3.8.5 (venv).

Install:

pip install -r requirements.txt

Visualize:

make visualize

Result images are saved in the data directory.