Enforced Invariant Confluence
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benchmark Updated plots with same colors. May 1, 2018
doc Updated style file and year. Sep 23, 2018
examples Added TPC 02 check. Apr 30, 2018
iconfluence
lucy Updated plot for multi plot. Apr 25, 2018
scripts Updated arbitrary start checkers. Mar 23, 2018
.gitignore Some progress on a Python I-confluence checker. Feb 6, 2018
README.md Updated README.md Apr 24, 2018
requirements.txt Added colored logging. Mar 18, 2018

README.md

Invariant Confluence

Strong consistency allows programmers to ignore many of the complexities of a distributed system, treating it as if it were running on a single machine. However, enforcing strong consistency requires coordination, and coordination leads to unavailability (at worst) or increased latency (at best). However, coordination cannot always be avoided. Certain application invariants require coordination in order to be globally enforced. Bailis et al. developed the notion of invariant-confluence as a necessary and sufficient condition for coordination freedom. Intuitively, a replicated object (with some start state and set of transactions) is invariant-confluent with respect to some invariant if the replicated object never takes on a value that violates the invariant. Bailis characterized many common invariants (e.g. uniqueness constraints, foreign key constraints), showing which could be maintained without coordination. However, this characterization required hand-written proofs. This research project expands on Bailis' research in two ways. First, we design a decision procedure to algorithmically determine when an object is invariant-confluent. Second, we develop segmented invariant-confluence: a generalization of invariant-confluence. This repository contains some documentation describing the research and accompanying implementations of the stuff described in the documentation.

Decision Procedure

Our decision procedures require python version >= 3.6. We recommend creating a conda environment to work in:

$ conda create --name iconfluence python=3.6
$ source activate iconfluence

Then, pip install the dependencies

$ pip install -r requirements.txt

Then, install z3 and z3py. Unfortunately, z3py is not pippable, so you'll have to install it by hand.

$ cd $HOME
$ git clone https://github.com/Z3Prover/z3
$ cd z3
$ python scripts/mk_make.py --python
$ cd build
$ make
$ sudo make install
$ echo 'export PYTHONPATH="$PYTHONPATH:$HOME/z3/build/python"'

Then, play with the examples in examples/.

$ PYTHONPATH+=:. python -i -m examples.all_datatypes
$ PYTHONPATH+=:. python -i -m examples.auction
$ PYTHONPATH+=:. python -i -m examples.bank_deposit_and_withdraw
$ PYTHONPATH+=:. python -i -m examples.bank_deposit_only
$ PYTHONPATH+=:. python -i -m examples.foreign_key
$ PYTHONPATH+=:. python -i -m examples.subsets
$ PYTHONPATH+=:. python -i -m examples.two_ints

Runtime

Our runtime requires C++14 or newer. First, install glog, protobuf, the protobuf compiler. On Ubuntu 14.04, this can be done as follows:

$ # Use a newer version of protobuf than is otherwise provided.
$ sudo add-apt-repository ppa:maarten-fonville/protobuf
$ sudo apt-get update
$ sudo apt-get install -y libgoogle-glog-dev libprotobuf-dev protobuf-compiler

Then, install libuv:

$ cd $HOME
$ git clone https://github.com/libuv/libuv
$ cd libuv
$ sh autogen.sh
$ ./configure
$ make
$ make check
$ sudo make install

and make sure that /usr/local/lib is on your LD_LIBRARY_PATH by adding something like export LD_LIBRARY_PATH="$LD_LIBRARY_PATH:/usr/local/lib" to your ~/.bashrc.

Then, cd into the lucy directory and run make (or make DEBUG=0 or make VERBOSE=1). Note that the code refers to segmented invariant-confluence as stratified invariant-confluence. The name's are different, but they are 100% the same thing.