Python script for transferring data between three drives in two separate stages

Overview

Waterlock

Waterlock is a Python script meant for incrementally transferring data between three folder locations in two separate stages. It performs hash verification and persistently tracks data transfer progress using SQLite.

I am not responsible for any lost data. This was an evening coding project. Use at your own discretion.

Use Case & Features

The use-case Waterlock was designed for is moving files from one computer (i.e. your home server) to a intermediary drive (i.e. a portable hard drive), and then from the hard drive to another computer (i.e. an offsite backup server).

  • It will fill the intermediary drive with as many files as it can, aside from a user-configurable amount of reserve-space.
  • It performs blake2 checksums with every file copy, comparing it to the initial hash value stored in the SQLite database to ensure that data is not corrupted.
  • It uses a SQLite database to track what data has been moved. As a result, you can incrementally move data from one location to another with minimal user input.
  • Every time Waterlock is run on the source location, it will check for any files that have been recently modified (based on timestamp, not hash). Any modified files will have their hash & modification timestamps updated in the database, in addition to being marked as unmoved such that they are transferred again and updated. Note that Waterlock does not version files. Nevertheless, silently corrupted files should theoretically not be transferred over unless their modification timestamp has been adjusted.
  • Every time Waterlock is run on the source location, it will check for any files that were previously moved to the intermediary drive but did not reach the destination. If these files are no longer on the intermediary drive due to accidental deletion for instance, Waterlock will move those files to the intermediary drive again.

Example Use Case: I use Waterlock to transfer large files that are too large to transfer over the network to an offsite backup location at a relatives house. Each time I visit I run the script on my home server to load the external drive, then run it again on the offsite-backup server.

Usage

Change the settings at the top of the script, using absolute file paths. While relative paths may work, they are more error prone due to string formatting issues. Store the script on the intermediary drive itself and run it from there. It will automatically create waterlock.db and a cargo folder where the data will be stored. Note that after the final transfer to the destination, Waterlock will not delete data on the intermediary drive.

python waterlock.py

If you are familiar with Python, you can also fully verify all the files on the middle or destination drives to ensure that the hashes match what is stored in the database. This is done using two additional class functions called verify_middle() and verify_destination(). The code to verify files on the destination would be as follows:

if __name__ == "__main__":
    wl = Waterlock( source_directory=source_directory, 
                    end_directory=end_direcotry, 
                    reserved_space=reserved_space
                    )
    wl.start()
    wl.verify_destination()

Why 'Waterlock'?

It is named Waterlock after marine locks used to move ships through waterways of different water levels in multiple stages.

You might also like...
Catalogue data - A Python Scripts to prepare catalogue data

catalogue_data Scripts to prepare catalogue data. Setup Clone this repo. Install

This is a python script to navigate and extract the FSD50K dataset

FSD50K navigator This is a script I use to navigate the sound dataset from FSK50K.

Python script to automate the plotting and analysis of percentage depth dose and dose profile simulations in TOPAS.

topas-create-graphs A script to automatically plot the results of a topas simulation Works for percentage depth dose (pdd) and dose profiles (dp). Dep

fds is a tool for Data Scientists made by DAGsHub to version control data and code at once.
fds is a tool for Data Scientists made by DAGsHub to version control data and code at once.

Fast Data Science, AKA fds, is a CLI for Data Scientists to version control data and code at once, by conveniently wrapping git and dvc

A data parser for the internal syncing data format used by Fog of World.
A data parser for the internal syncing data format used by Fog of World.

A data parser for the internal syncing data format used by Fog of World. The parser is not designed to be a well-coded library with good performance, it is more like a demo for showing the data structure.

Functional Data Analysis, or FDA, is the field of Statistics that analyses data that depend on a continuous parameter. Fancy data functions that will make your life as a data scientist easier.
Fancy data functions that will make your life as a data scientist easier.

WhiteBox Utilities Toolkit: Tools to make your life easier Fancy data functions that will make your life as a data scientist easier. Installing To ins

A Big Data ETL project in PySpark on the historical NYC Taxi Rides data

Processing NYC Taxi Data using PySpark ETL pipeline Description This is an project to extract, transform, and load large amount of data from NYC Taxi

Created covid data pipeline using PySpark and MySQL that collected data stream from API and do some processing and store it into MYSQL database.
Created covid data pipeline using PySpark and MySQL that collected data stream from API and do some processing and store it into MYSQL database.

Created covid data pipeline using PySpark and MySQL that collected data stream from API and do some processing and store it into MYSQL database.

Releases(latest)
Owner
David Swanlund
PhD student at SFU studying spatial privacy and spatial data anonymization
David Swanlund
NumPy aware dynamic Python compiler using LLVM

Numba A Just-In-Time Compiler for Numerical Functions in Python Numba is an open source, NumPy-aware optimizing compiler for Python sponsored by Anaco

Numba 8.2k Jan 07, 2023
Anomaly Detection with R

AnomalyDetection R package AnomalyDetection is an open-source R package to detect anomalies which is robust, from a statistical standpoint, in the pre

Twitter 3.5k Dec 27, 2022
HyperSpy is an open source Python library for the interactive analysis of multidimensional datasets

HyperSpy is an open source Python library for the interactive analysis of multidimensional datasets that can be described as multidimensional arrays o

HyperSpy 411 Dec 27, 2022
PyEmits, a python package for easy manipulation in time-series data.

PyEmits, a python package for easy manipulation in time-series data. Time-series data is very common in real life. Engineering FSI industry (Financial

Thompson 5 Sep 23, 2022
Maximum Covariance Analysis in Python

xMCA | Maximum Covariance Analysis in Python The aim of this package is to provide a flexible tool for the climate science community to perform Maximu

Niclas Rieger 39 Jan 03, 2023
Statsmodels: statistical modeling and econometrics in Python

About statsmodels statsmodels is a Python package that provides a complement to scipy for statistical computations including descriptive statistics an

statsmodels 8k Dec 29, 2022
Hydrogen (or other pure gas phase species) depressurization calculations

HydDown Hydrogen (or other pure gas phase species) depressurization calculations This code is published under an MIT license. Install as simple as: pi

Anders Andreasen 13 Nov 26, 2022
A Python and R autograding solution

Otter-Grader Otter Grader is a light-weight, modular open-source autograder developed by the Data Science Education Program at UC Berkeley. It is desi

Infrastructure Team 93 Jan 03, 2023
Business Intelligence (BI) in Python, OLAP

Open Mining Business Intelligence (BI) Application Server written in Python Requirements Python 2.7 (Backend) Lua 5.2 or LuaJIT 5.1 (OML backend) Mong

Open Mining 1.2k Dec 27, 2022
Statistical package in Python based on Pandas

Pingouin is an open-source statistical package written in Python 3 and based mostly on Pandas and NumPy. Some of its main features are listed below. F

Raphael Vallat 1.2k Dec 31, 2022
WaveFake: A Data Set to Facilitate Audio DeepFake Detection

WaveFake: A Data Set to Facilitate Audio DeepFake Detection This is the code repository for our NeurIPS 2021 (Track on Datasets and Benchmarks) paper

Chair for Sys­tems Se­cu­ri­ty 27 Dec 22, 2022
A program that uses an API and a AI model to get info of sotcks

Stock-Market-AI-Analysis I dont mind anyone using this code but please give me credit A program that uses an API and a AI model to get info of stocks

1 Dec 17, 2021
PyChemia, Python Framework for Materials Discovery and Design

PyChemia, Python Framework for Materials Discovery and Design PyChemia is an open-source Python Library for materials structural search. The purpose o

Materials Discovery Group 61 Oct 02, 2022
NFCDS Workshop Beginners Guide Bioinformatics Data Analysis

Genomics Workshop FIXME: overview of workshop Code of Conduct All participants s

Elizabeth Brooks 2 Jun 13, 2022
A collection of robust and fast processing tools for parsing and analyzing web archive data.

ChatNoir Resiliparse A collection of robust and fast processing tools for parsing and analyzing web archive data. Resiliparse is part of the ChatNoir

ChatNoir 24 Nov 29, 2022
Statistical & Probabilistic Analysis of Store Sales, University Survey, & Manufacturing data

Statistical_Modelling Statistical & Probabilistic Analysis of Store Sales, University Survey, & Manufacturing data Statistical Methods for Decision Ma

Avnika Mehta 1 Jan 27, 2022
Datashader is a data rasterization pipeline for automating the process of creating meaningful representations of large amounts of data.

Datashader is a data rasterization pipeline for automating the process of creating meaningful representations of large amounts of data.

HoloViz 2.9k Jan 06, 2023
Generate lookml for views from dbt models

dbt2looker Use dbt2looker to generate Looker view files automatically from dbt models. Features Column descriptions synced to looker Dimension for eac

lightdash 126 Dec 28, 2022
Detecting Underwater Objects (DUO)

Underwater object detection for robot picking has attracted a lot of interest. However, it is still an unsolved problem due to several challenges. We take steps towards making it more realistic by ad

27 Dec 12, 2022
Using Python to scrape some basic player information from www.premierleague.com and then use Pandas to analyse said data.

PremiershipPlayerAnalysis Using Python to scrape some basic player information from www.premierleague.com and then use Pandas to analyse said data. No

5 Sep 06, 2021