splink implements Fellegi-Sunter's canonical model of record linkage in Apache Spark, including the EM algorithm to estimate parameters of the model.
It:
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Works at much greater scale than current open source implementations (100 million records+).
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Runs quickly - with runtimes of less than an hour.
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Has a highly transparent methodology; match scores can be easily explained both graphically and in words
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Is highly accurate
It is assumed that users of Splink are familiar with the probabilistic record linkage theory, and the Fellegi-Sunter model in particular. A series of interactive articles explores the theory behind Splink.
The statistical model behind splink is the same as that used in the R fastLink package. Accompanying the fastLink package is an academic paper that describes this model. This is the best place to start for users wanting to understand the theory about how splink works.
Data Matching, a book by Peter Christen, is another excellent resource.
splink is a Python package. It uses the Spark Python API to execute data linking jobs in a Spark cluster. It has been tested in Apache Spark 2.3, 2.4 and 3.1.
Install splink using:
pip install splink
Note that Splink requires pyspark and a working Spark installation. These are not specified as explicit dependencies becuase it is assumed users have an existing pyspark setup they wish to use.
You can run demos of splink in an interactive Jupyter notebook by clicking the button below:
The best documentation is currently a series of demonstrations notebooks in the splink_demos repo.
splink_graph is a graph utility library for use in Apache Spark. It computes graph metrics on the outputs of data linking. The repo is here
- Quality assurance of linkage results and identifying false positive links
- Computing quality metrics associated with groups (clusters) of linked records
- Automatically identifying possible false positive links in clusters
splink_comparison_viewer produces interactive dashboards that help you rapidly understand and quality assure the outputs of record linkage. A tutorial video is available here.
splink_cluster_studio creates an interactive html dashboard from Splink output that allows you to visualise and analyse a sample of clusters from your record linkage. The repo is here.
This code is able to generate realistic test datasets for linkage using the WikiData Query Service.
It has been used to performance test the accuracy of various Splink models.
We also provide an interactive splink settings editor and example settings here.
A tool to generate custom m and u probabilities can be found here.
You can read a short blog post about splink here.
You can find a short video introducing splink and running though an introductory demo here.
A 'best practices and performance tuning' tutorial can be found here.
We are very grateful to ADR UK (Administrative Data Research UK) for providing funding for this work as part of the Data First project.
We are also very grateful to colleagues at the UK's Office for National Statistics for their expert advice and peer review of this work.