Duke is a fast and flexible deduplication (or entity resolution, or record linkage) engine written in Java on top of Lucene. The latest version is 1.2 (see ReleaseNotes).
Duke can find duplicate customer records, or other kinds of records in your database. Or you can use it to connect records in one data set with other records representing the same thing in another data set. Duke has sophisticated comparators that can handle spelling differences, numbers, geopositions, and more. Using a probabilistic model Duke can handle noisy data with good accuracy.
- High performance.
- Highly configurable.
- Support for CSV, JDBC, SPARQL, NTriples, and JSON.
- Many built-in comparators.
- Plug in your own data sources, comparators, and cleaners.
- Genetic algorithm for automatically tuning configurations.
- Command-line client for getting started.
- API for embedding into any kind of application.
- Support for batch processing and continuous processing.
- Can maintain database of links found via JNDI/JDBC.
- Can run in multiple threads.
The GettingStarted page explains how to get started and has links to further documentation. The examples of use page lists real examples of using Duke, complete with data and configurations. This presentation has more of the big picture and background.
Contributions, whether issue reports or patches, are very much welcome. Please fork the repository and make pull requests.
Supports Java 1.7 and 1.8.
If you have questions or problems, please register an issue in the
issue tracker, or post to the the mailing
list. If you don't want to
join the list you can always write to me at
larsga [a] garshol.priv.no, too.
Using Duke with Maven
Duke is hosted in Maven Central, so if you want to use Duke it's as easy as including the following in your pom file:
<dependency> <groupId>no.priv.garshol.duke</groupId> <artifactId>duke</artifactId> <version>1.2</version> </dependency>
Building the source
If you have Maven installed, this is as
easy as giving the command
mvn package in the root directory. This
will produce a
.jar file in the
target/ subdirectory of each
This blog post describes the basic approach taken to match records. It does not deal with the Lucene-based lookup, but describes an early, slow O(n^2) prototype. This early presentation describes the ideas behind the engine and the intended architecture