Mastering Entity Resolution in PostgreSQL: A Guide to Incremental Record Linkage

Описание: Discover effective strategies for implementing `incremental entity resolution` in PostgreSQL while scaling to handle billions of events.
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This video is based on the question https://stackoverflow.com/q/62691185/ asked by the user 'Vojtěch Kurka' ( https://stackoverflow.com/u/5336351/ ) and on the answer https://stackoverflow.com/a/62693560/ provided by the user 'Mike Organek' ( https://stackoverflow.com/u/13808319/ ) at 'Stack Overflow' website. Thanks to these great users and Stackexchange community for their contributions.

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Mastering Entity Resolution in PostgreSQL: A Guide to Incremental Record Linkage

Entity resolution, also known as record linkage, is a crucial task in data management that identifies and merges records representing the same real-world entity across different datasets. As data continues to grow exponentially, performing this task efficiently and incrementally becomes challenging, particularly with large event streams. This guide delves into exploring the optimal data model and processing techniques to achieve deterministic entity resolution using PostgreSQL while keeping scalability and performance in mind.

The Problem Statement

The problem arises when you have a continuous stream of events, each carrying a unique identifier along with various attributes (like cookies, emails, and phone numbers). For example, consider the following sequence of events coming from web tracking:

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From these events, the desired output should reveal entities connected through shared identifiers, ultimately forming a cohesive representation. The initial goal is to maintain these relationships and update them as new events arrive.

As we introduce more complexity (like emails and phone numbers), the problem compounds, and it becomes essential to find a solution that can handle a scale of 1 billion events efficiently.

The Proposed Solution

1. Data Modeling

To effectively organize and manipulate the incoming data, we recommend structuring it in a PostgreSQL table that includes the following columns:

identifier: The unique identifier for the event (e.g., EID|1, Email|a@ example.com).

grouping_id: An integer to group identifiers that belong to the same entity.

event_id_orig: The original event ID.

Here is a simplified version of the table structure:

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2. Incremental Processing of Events

When a new event is processed, follow these steps:

Create a set of identifiers for the incoming event, including the event ID.

Before inserting the new identifiers, merge existing records to avoid duplication.

You can achieve this using a PostgreSQL query structure, as shown below:

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3. Reviewing Results

Once you've repeated this for all events, you can easily query the table to retrieve the identifiers and their associated event IDs:

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This will provide a clear, cohesive view of how entities are connected based on shared identifiers.

Conclusion

Implementing an efficient entity resolution solution requires a strong understanding of data modeling, processing logic, and the ability to handle scalability in your database. With PostgreSQL, you can achieve a structured approach to managing and updating linked entities incrementally. As your data grows, ensure that performance benchmarks are maintained and consider further optimization techniques based on your specific workload.

If you're facing challenges with this method or have questions, do not hesitate to reach out! Happy querying!