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Identity Graph and Identity Resolution in SQL

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In the age of data science, the need for sophisticated identity resolution strategies has never been more critical. By harnessing big data from different sources, both offline and online, and utilizing advanced matching algorithms, organizations can craft single views of their customers, leading to enhanced customer experiences and streamlined data management. Through these approaches, businesses can reconcile multiple IDs associated with the same entity, in processes often referred to as deterministic or probabilistic matching.

In this article, we detail how you can achieve identity resolution and create an identity graph with SQL. We will cover the technical challenges behind solving this important step towards creating a customer 360, and we’ll propose a new algorithm to solve those challenges. But first, let’s get started with the basics.

What is identity resolution?

Identity resolution is a data management technique used to connect multiple identities and attributes related to the same individual or entity across different data sources or touchpoints.

What is an identity graph?

An Identity Graph is a comprehensive data structure that links an individual's identifiers (such as email addresses, device IDs, and social handles) across multiple platforms and channels. It uses identity resolution to create a unified, single view of a customer, enhancing personalized marketing strategies, improving customer experiences, and aiding in data privacy and security.

The real-world impact of ID mapping

In real-world applications, the successful implementation of ID mapping — managing and reconciling various customer identity datasets — has profound effects. It enables businesses to understand their customers more deeply, optimize marketing efforts, and deliver a more personalized and seamless customer experience.

The ability to reconcile multiple identifiers into a single identity, while respecting privacy regulations like GDPR, can significantly enhance analytics capabilities and enable more personalized customer experiences.

ID mapping and customer data platforms (CDPs)

Customer Data Platforms (CDPs) often use ID mapping to integrate and organize customer data from multiple sources. By resolving entity identities accurately, CDPs can provide a single source of truth for all customer data, leading to more insightful customer profiles and enabling better personalization strategies.

The problem of identity resolution

In our previous blog, The Tale of Identity Graph and Identity Resolution, we described the problem of identity resolution. We used a concrete example of a user visiting an eCommerce site from multiple websites. Specifically, we showed how the app events can be associated with multiple identities and how these identities can be tied together using the identify() call. We captured the association using the following identity graph:

identity graph capturing associations

The identity graph is stored in a SQL database in the

identify

table as below:

Anonymous IDUser IDMapped ID
A-WebU-PhoneVirtual-ID-1
A-WebU-EmailVirtual-ID-1
A-MobU-PhoneVirtual-ID-1
A-Web2U-EmailVirtual-ID-1

These identifiers can be any data point such as a phone number or a These identifiers can be any data point such as a phone number or a virtual id, collected from different sources.

Here, we’ll show how we can associate a virtual ID with all these IDs. This association between the anonymous or user ID and the virtual ID will let us tie all the events as originating from one end user. The system tags each of these events with individual IDs.

The connected components algorithm

It’s easy to see that this task is the same as identifying the connected components in the identity graph. Furthermore, this task includes associating a unique ID with each connected component. We generally implement the connected components algorithm using depth-first-search. However, the goal here is to implement this algorithm in SQL.

This article by David R. Maclver shows one way to implement the connected graph components algorithm. The idea is to keep a set of connected components, initially initialized to individual nodes as connected components. After this, you need to recursively merge the components if they have a link between them.

The algorithm requires a JOIN between the table of nodes with itself (self-join) and the table of edges (cartesian product). Unfortunately, self-joins and cartesian products can be prohibitively costly to implement in distributed data warehouses. For example, BigQuery considers self-joins anti-pattern and strongly discourages it.

Identity graphs and ID resolution: the proposed algorithm

To address this and optimize the process, we propose an identity resolution algorithm to find the connected component by propagating unique identifiers across the edges. In the world of customer data, this implies assigning each node, or customer record, the ID that is the minimum of its own ID and its neighbors. If you repeat this step enough times, all the nodes in a connected component will get a minimum node ID in the connected component. You can see that you need to repeat the step as many times as the diameter of the largest connected component.

The easiest way to do this is to keep two tables for edges and nodes. At each stage, we can join these two tables and update the node IDs. However, as explained earlier, these self-joins and cartesian products can be very costly.

However, in the case of a bi-partite graph, we can simplify this quite a bit. Nodes on the right side can only update the nodes on the left side and vice versa. The following diagram shows the successive steps of this algorithm for the identity graph above:

The successive steps to implement the connected components algorithm for our identity graph

How it works

Each step assigns an ID to each node, which is the minimum of its ID and its neighbors. Assuming the ordering

A1<A2<A3<U1<U2

, after step 1:

  • The node

    A1

    gets

    A1

    (minimum of it’s ID

    A1

    and neighbors

    U1, U2

    )
  • Node

    A2

    gets

    A2

    (minimum of it’s ID

    A2

    and neighbors

    U1

    )
  • Node

    U1

    gets

    A1

    (minimum of ID’s ID and neighbors

    A1, A2

    )
  • and so on

You can see the node IDs after the subsequent steps in the above figure. The ID in bold is the latest ID associated with the node. As shown in the previous figure, above After-Step-2, the IDs have stabilized with each node getting assigned the same ID

A1

.

Code Implementation in SQL

The following SQL code implements this algorithm. We create successive graphs

ID_GRAPH_0, ID_GRAPH_1

, and so on. Each table has four columns:

  • orig_anon_id

    : Anonymous ID in the initial graph
  • orig_user_id

    : User ID in the initial graph
  • curr_anon_id

    : Anonymous ID at Nth iteration of the update
  • curr_user_id

    : User ID at the Nth iteration of the update

You can create

ID_GRAPH_N+1 from ID_GRAPH_N

by taking the min as described using this deterministic identity resolution algorithm that works effectively across different sources of data:

SQL
CREATE TABLE ID_GRAPH_0 (
orig_anon_id varchar(32),
orig_user_id varchar(32),
curr_anon_id varchar(32),
curr_user_id varchar(32),
version_anon_id int,
version_user_id int,
);
CREATE TABLE ID_GRAPH_1 AS
(
SELECT
orig_anon_id,
orig_user_id,
CASE
WHEN curr_anon_id is NULL THEN NULL
WHEN tmp_anon_id < curr_anon_id THEN tmp_anon_id
ELSE curr_anon_id
END AS curr_anon_id,
CASE
WHEN curr_user_id is NULL THEN NULL
WHEN tmp_user_id < curr_user_id THEN tmp_user_id
ELSE curr_user_id
END as curr_user_id,
CASE
WHEN curr_anon_id is NULL THEN version_anon_id
WHEN tmp_anon_id < curr_anon_id THEN curr_version
ELSE version_anon_id
END AS version_anon_id,
CASE
WHEN curr_user_id is NULL THEN version_usrer_id
WHEN tmp_user_id < curr_user_id THEN curr_version
ELSE version_user_id
END as version_user_id
FROM
(
SELECT
orig_anon_id,
orig_user_id,
curr_anon_id,
curr_user_id,
version_anon_id,
version_user_id,
MIN(curr_user_id)
OVER(PARTITION BY orig_anon_id)
as tmp_anon_id,
MIN(curr_anon_id)
OVER(PARTITION BY orig_user_id)
as tmp_user_id
FROM
ID_GRAPH_0
WHERE orig_anon_id IN (
SELECT orig_anon_id from ID_GRAPH_0 WHERE version_anon_id == curr_version
) OR
orig_user_id IN (
SELECT orig_user_id from ID_GRAPH_0 WHERE version_user_id == curr_version
)
) AS TMP_GRAPH_0
)

As noted above, you should run this as many times as the largest diameter of the connected component. Users rarely have more than four devices and identities (emails, phones). So, it would help if you ran this seven to eight times before the IDs stabilize. Moreover, you would have to run this continuously as new nodes and edges are added to the identity graph. As a result, large connected components that haven’t had IDs spread throughout will eventually do, as we discuss in the next section.

Incremental updates

To create a real-time customer 360, the system will update the identity graph continuously as new users sign up, or as users form new associations. One data management strategy to keep the identity map up-to-date is to run the proposed identity resolution algorithm at regular intervals. Something like once every few hours should suffice.

Unfortunately, that is not the most efficient way as the above code processes the entire graph in every run. Ideally, we should be able to process the new nodes and edges that the user introduces.

Also, we should process the nodes/edges/connected components touched by those new edges. The latter is important because a new edge can connect two independently connected components, as shown in the following figure. In this case, the two connected components need to be merged and must be assigned the same ID as shown:

Identity graph: connected components merged

Using version numbers to improve efficiency

We address this by assigning a version number with all the edges starting with one. Since RudderStack loads the data in batches, a monotonically increasing version number is associated with each load. You can also use the following algorithm to use timestamps with minor tweaks.

The Nth run of the ID mapping algorithm only touches nodes with edges with version N (or higher). You need to apply this process transitively. If the algorithm touches a node in the version N run, you need to process all the edges (even the older version ones) and nodes connected to those edges in the next run. This is required to handle the case described in the identity graph above. It is not enough to only process New Edge. You need to update all the nodes connected to New Edge. Furthermore, you need to update all the other edges from those nodes and their transitive closure.

SQL
CREATE TABLE ID_GRAPH_0 (
orig_anon_id varchar(32),
orig_user_id varchar(32),
curr_anon_id varchar(32),
curr_user_id varchar(32),
version_anon_id int,
version_user_id int,
);
CREATE TABLE ID_GRAPH_1 AS
(
SELECT
orig_anon_id,
orig_user_id,
CASE
WHEN curr_anon_id is NULL THEN NULL
WHEN tmp_anon_id < curr_anon_id THEN tmp_anon_id
ELSE curr_anon_id
END AS curr_anon_id,
CASE
WHEN curr_user_id is NULL THEN NULL
WHEN tmp_user_id < curr_user_id THEN tmp_user_id
ELSE curr_user_id
END as curr_user_id,
CASE
WHEN curr_anon_id is NULL THEN version_anon_id
WHEN tmp_anon_id < curr_anon_id THEN curr_version
ELSE version_anon_id
END AS version_anon_id,
CASE
WHEN curr_user_id is NULL THEN version_usrer_id
WHEN tmp_user_id < curr_user_id THEN curr_version
ELSE version_user_id
END as version_user_id
FROM
(
SELECT
orig_anon_id,
orig_user_id,
curr_anon_id,
curr_user_id,
version_anon_id,
version_user_id,
MIN(curr_user_id)
OVER(PARTITION BY orig_anon_id)
as tmp_anon_id,
MIN(curr_anon_id)
OVER(PARTITION BY orig_user_id)
as tmp_user_id
FROM
ID_GRAPH_0
WHERE orig_anon_id IN (
SELECT orig_anon_id from ID_GRAPH_0 WHERE version_anon_id == curr_version
) OR
orig_user_id IN (
SELECT orig_user_id from ID_GRAPH_0 WHERE version_user_id == curr_version
)
) AS TMP_GRAPH_0
)

Profiling

We did quick profiling of the base version with the incremental version on an identity graph with 4 million nodes with 1 percent new random edges introduced between every run. The base version requires ~60secs, while the incremental version requires ~10 secs. This means 5-6 times speed-up over the base version.

Conclusion

In this blog, we demonstrated how to achieve ID mapping in a big data environment efficiently using SQL, thereby optimizing the data management process. While the focus was on the use of SQL, it's worth noting that there are APIs available from various providers that also aid in identity resolution. Since originally publishing this post, we’ve also released RudderStack Profiles, a customer 360 product. Profiles includes a refined identity resolution algorithm that takes care of the SQL gruntwork of identity resolution for you and automatically builds a configurable identity graph in your warehouse. Read about how we optimized our identity resolution algorithm for performance, and check out RudderStack profiles today.

Build a customer 360 in your warehouse

Check out RudderStack Profiles to learn how you can quickly build a customer 360 in your warehouse without complex SQL.

May 13, 2020
Soumyadeb Mitra

Soumyadeb Mitra

Founder and CEO of RudderStack