Revisiting Covid-19 contact tracing with Neo4j 4.3's relationship indexes

Last week was a great week at the "office". One that I don't think I will easily forget. Not only did we host our Nodes 2021 conference, but we also launched our new website, published a MASSIVE trillion-relationship graph, and announced a crazy $325M series F investment round that will fuel our growth in years to come. 

In all that good news, the new release of Neo4j 4.3 kinda disappeared into the background - which is why I thought it would be fun to write a short blogpost about one of the key features that are part of this new release: relationship property indexes.

This is a really interesting feature for a number of different reasons. But let's draw your attention to two main points of attention:

1. Relationship indexes will lead to performance improvements: all of a sudden the Neo4j Cypher query planner is going to be able to use a lot more information, provided by these relationship indexes. The planner is becoming smarter - and therefore queries will become faster. We will explore this below.
2. Relationship indexes will actually have interesting modelling implications: the introduction of these indexes could have far-reaching implications with regards to how we model certain things. Here's what we mean with that

You can see that both alternative models could have good use, but that the second model is simpler and potentially more elegant. It will depend on the use case to decide between the two - but in the past we would most often use the first model for performance reasons - and we will see below that that will no longer be a main reason with the addition of relationship indexes. Let's investigate.

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Creating a Contact Tracing Testbed with Neo4j and Faker

Over the past few weeks and months, I have been living through the Covid-19 pandemic like many others. It's not been easy - but at the same time I feel very fortunate to have been able to stay healthy, active, working, and connected. There's a lot of people out there that are a lot less fortunate, and my heart goes out to them.

On this blog, I have been writing about using graphs for Contact Tracing quite a bit. See 

• the original blogpost series over here,
• the demo movies that I made
• the Neo4j Browser guide that you can use

Fortunately, these articles were very well received by the community - we have had a ton of discussions with a variety of different individuals, companies and governments about how to use this technology to prevent that the next lockdown would again require immobilising so many healthy people. If the pandemic's second wave hits, we all want people at risk / sick people to be separated from the healthy population, and manage the spread of the disease in this way. But all of that requires contact tracing to be effective and operational - which is not a trivial thing to do.

This is why I have been looking at creating a very easy to use testbed for Contact Tracing in Neo4j. I wanted to make it super easy for people to create synthetic contact tracing datasets, and then work with them to gain experience - valuable experience for the "real deal" when we have to manage that. That's what this post is about.

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