BlockSci¶
The Bitcoin blockchain — currently 170 GB and growing — contains a massive amount of data that can give us insights into the Bitcoin ecosystem, including how users, businesses, and miners operate. BlockSci enables fast and expressive analysis of Bitcoin’s and many other blockchains. The accompanying working paper explains its design and applications: https://arxiv.org/pdf/1709.02489.pdf
Current tools for blockchain analysis depend on general-purpose databases that provide “ACID” guarantees. But that’s unnecessary for blockchain analysis where the data structures are append-only. We take advantage of this observation in the design of our custom in-memory blockchain database as well as an analysis library. BlockSci’s core infrastructure is written in C++ and optimized for speed. (For example, traversing every transaction input and output on the Bitcoin blockchain takes only 1 second on our r4.2xlarge EC2 machine.) To make analysis more convenient, we provide Python bindings and a Jupyter notebook interface.
Documentation¶
Documentation is available for the Python interface.
Additionally, a demonstration Notebook is available in the Notebooks folder.
For installation instructions, see below. More detailed documentation is coming soon. Meanwhile, feel free to contact us at blocksci@lists.cs.princeton.edu.
Latest release (BlockSci v0.4.5)¶
Version 0.4.5 introduces full bech32 address support, adds segwit size support, fixes a bug which had been preventing use of continuous incremental blockchain updates, and introduces the concept of Equivalent addresses. The “Local setup” section contains best practices for setting up automatic blockchain updates. You can read more details about the release in the release notes. We are releasing an new AMI running version 0.4.5 (explained under “Quick setup” below).
Quick setup using Amazon EC2¶
AMI Last updated on March 20, 2018.
If you want to start using BlockSci immediately, we have made available an EC2 image: ami-0650807b. We recommend using instance with 60 GB of memory or more for optimal performance (r4.2xlarge). On boot, a Jupyter Notebook running BlockSci will launch immediately. To access the notebook, you must set up port forwarding to your computer. Inserting the name of your private key file and the domain of your ec2 instance into the following command will make the Notebook available on your machine.
ssh -i .ssh/your_private_key.pem -N -L 8888:localhost:8888 ubuntu@your_url.amazonaws.com
This sets up an SSH tunnel between port 8888 on your remote EC2 instance and port 8888 on your localhost. You can use whichever port you like on your local machine. Next, you can navigate to http://localhost:8888/ in your browser and log in with the password ‘blocksci’. A demo notebook will be available for you to run and you can begin exploring the blockchain. Don’t forget to shut down the EC2 instance when you are finished since EC2 charges hourly.
AWS instances suffer from a known performance issue when starting up from an existing AMI. When the machine starts up it doesn’t actually load all of the data on the disk so that startup can be instant. Instead it only loads the data when it is accessed for the first time. Thus BlockSci will temporarily operate slowly when the image has first been launched. Within about 20 minutes after launch, the most crucial data files will be loaded to disk from the network, and most queries should run at full speed, including all examples in the demo Notebook. After about 3.5 hours, all data will be loaded to disk and all queries will reach full speed.
There is no need for user intervention to resolve this issue since the machine will do so automatically on launch.
The AMI contains a fully updated version of the Bitcoin blockchain as of the creation date of the AMI (March 8, 2017). Additionally it will automatically start a Bitcoin full node and update the blockchain once every hour to the latest version of the chain.
Local setup¶
To run BlockSci locally, you must be running a full node (such as bitcoind or an altcoin node) since BlockSci requires the full serialized blockchain data structure which full nodes produce.
Parser¶
The next step is parsing the blockchain downloaded by the full node. The blockchain_parser binary which is built from this repo performs the transformation of this blockchain data into our customized BlockSci database format. It has two modes.
Disk mode is optimized for parsing Bitcoin’s data files. It reads blockchain data directly from disk in a rapid manner. However this means that it does not work on many other blockchains which have different serialization formats than Bitcoin.
blocksci_parser --output-directory bitcoin-data update disk --coin-directory .bitcoin
RPC mode uses the RPC interface of a cryptocurrency to extract data regarding the blockchain. It works with a variety of cryptocurrencies which have the same general model as Bitcoin, but with minor changes to the serialization format which break the parser in disk mode. Examples of this are Zcash and Namecoin. To use the parser in RPC mode, you’re full node must be running with txindex enabled.
blocksci_parser --output-directory bitcoin-data update rpc --username [user] --password [pass] --address [ip] --port [port]
BlockSci can be kept up to date with the blockchain by setting up a cronjob to periodically run the parser command. Updates to the parser should not noticeably impact usage of the analysis library. It is recommended that the Blockchain be kept approximately 6 blocks back from the head of the chain in order to avoid imperfect reorg handling in BlockSci.
For example you can set BlockSci to update hourly and stay 6 blocks behind the head of the chain via adding
@hourly /usr/local/bin/blocksci_parser --output-directory /home/ubuntu/bitcoin-data update --max-block -6 disk --coin-directory /home/ubuntu/.bitcoin
to your system crontab.
Using the analysis library¶
After the parser has been run, the analysis library is ready for use. This can again be used through two different interfaces
C++¶
In order to use the C++ library, you must compile your code against the BlockSci dynamic library and add its headers to your include path. The Blockchain can then be constructed given the path to the output of the parser.
#include <blocksci/blocksci.hpp>
int main(int argc, const char * argv[]) {
blocksci::Blockchain chain{"file_path_to_output-directory"};
}
Python¶
To use the BlockSci in python, you only need to import the BlockSci library. By default the library is installed into BlockSci/Notebooks. To use the library first open the Python interpreter in that folder:
cd BlockSci/Notebooks
python3
With the python interpreter open, the following code will load a Blockchain object created from the data output by the parser:
import blocksci
chain = blocksci.Blockchain("file_path_to_parser_output-directory")
If you would like to use BlockSci through a web interface, we recommend the use of Jupyter Notebook. Once Jupyter is installed, simply navigate into BlockSci/Notebooks and run:
jupyter notebook
which will open a window in your browser to the Jupyter server.
Supported Compilers¶
BlockSci require GCC 6.3 or above or Clang 5 or above.
BlockSci compilation instructions¶
Here are the steps for compiling BlockSci on Ubuntu 16.04.
Note that BlockSci only actively supports python 3.
sudo add-apt-repository ppa:ubuntu-toolchain-r/test -y
sudo apt-get update
sudo apt install build-essential cmake libssl-dev libboost-all-dev libsqlite3-dev autogen \
autoconf libcurl4-openssl-dev libjsoncpp-dev libjsonrpccpp-dev libjsonrpccpp-tools \
python3-dev python3-pip liblmdb-dev libsparsehash-dev libargtable2-dev libmicrohttpd-dev \
libhiredis-dev catch gcc-7 g++-7 libgflags-dev libsnappy-dev zlib1g-dev libbz2-dev \
liblz4-dev libzstd-dev
sudo update-alternatives --install /usr/bin/gcc gcc /usr/bin/gcc-7 60 --slave /usr/bin/g++ g++ /usr/bin/g++-7
git clone https://github.com/bitcoin-core/secp256k1
cd secp256k1
./autogen.sh
./configure --enable-module-recovery
make
sudo make install
cd ~
wget https://cmake.org/files/v3.10/cmake-3.10.0.tar.gz
tar xzf cmake-3.10.0.tar.gz
cd cmake-3.10.0/
cmake .
make
sudo make install
exec bash
cd ~
git clone https://github.com/facebook/rocksdb --branch v5.10.4
cd rocksdb
make static_lib
make shared_lib
sudo make install
cd ~
git clone https://github.com/citp/BlockSci.git
cd BlockSci
git submodule init
git submodule update --recursive
sudo cp -r libs/range-v3/include/meta /usr/local/include
sudo cp -r libs/range-v3/include/range /usr/local/include
cd libs/bitcoin-api-cpp
mkdir release
cd release
cmake -DCMAKE_BUILD_TYPE=Release ..
make
sudo make install
cd ../../..
mkdir release
cd release
cmake -DCMAKE_BUILD_TYPE=Release ..
make
sudo make install
sudo -H pip3 install --upgrade pip
sudo -H pip3 install --upgrade multiprocess psutil jupyter pycrypto matplotlib pandas dateparser
Team & contact info¶
BlockSci was created by Harry Kalodner, Steven Goldfeder, Alishah Chator, Malte Möser, and Arvind Narayanan at Princeton University. It is supported by NSF grants CNS-1421689 and CNS-1651938 and an NSF Graduate Research Fellowship under grant number DGE-1148900. We’ve released a paper describing BlockSci’s design and a few applications that illustrate its capabilities. You can contact the team at blocksci@lists.cs.princeton.edu.