Month: November 2024

Recap 

In the first post of this series, I walked through the basic ideas of what constitutes a graph, and in the second post I discussed a particular kind of graph called a directed acyclical graph (DAG), providing a handful of examples from the world of data engineering along with an attempt to disambiguate  –  and expand  – the meaning of what a DAG is. 

To briefly recap the important points discussed in this series so far: 

• A graph consists of one or more nodes. 

• Nodes are related to other nodes via one or more edges. 

• Nodes that that can reach themselves again (in a single direction) indicate the graph has a cycle. 

• A given edge can be directed or undirected. 

• Nodes and edges can have attributes, including numerical attributes.

• Graphs without cycles, with only directed eges, are called directed acyclical graphs (DAGs). 

• Hierarchies are DAGs in which each node only has a single predecessor node.

In the following post, we’re going to discuss hierarchies in detail. We’ll look at a few examples, discuss additional attributes that emerge from the definition of a hierarchy, and we’ll introduce an initial data model that can accommodate many different types of hierarchies. 

Hierarchies 

As indicated in the last post and summarized above, a hierarchy is essentially a DAG in which any given node has a single predecessor node (except for the root node, which has no predecessor). Given this constraint, predecessor nodes are most commonly referred to as “parent” nodes (i.e. with the analogy of a parent having one or more children). Successor nodes are thus also usually referred to as “child” nodes. We’ll stick with this convention for the rest of the series, given its wider adoption within BI vernacular.

(Note: despite the ideal case a hierarchy only ever consisting of child nodes with a single parent node, there are real-world edge cases where a given node can potentially have more than one parent. Such cases are the exceptions, and they need to handled on a case-by-case basis. For our purposes, to manage scope of this overall series of posts, and in light of the expected cardinality of dimensional modeling (i.e. many-to-one from fact table to dimension tables), we’re going to move forward with the expectation that any/all hierarchies should always respect this relationship, i.e. a child node only ever having a single parent node.) 

It’s also worth noting that hierarchies can be considered an instance of a “tree” data structure (a directed rooted tree, more specifically), which is a formally defined data structure from graph theory. While graph theory is rarely invoked in discussions of business intelligence, it is important to note that the tree analogy does indeed carry over, with terms like “root nodes”, “branches” and “leaf nodes” frequently used, which we’ll discuss later. (Don’t you love mixing metaphors? Family metaphors. Tree metaphors. Family trees anyone?)

So, here’s a basic visualization of a hierarchy: 

As you can see, it’s still very much a graph (and indeed a DAG) with: 

• seven nodes 

• six edges (directed) 

• no cycles 

• each node only has a single parent 

So far so good. 

I mentioned earlier that even though hierarchies have more constraints, i.e. they’re more limited in what they can represent compared to generic graphs, they nonetheless yield additional attributes. What do I mean by that? 

Consider the following two points. 

Firstly, because I have: 

• a single starting point (i.e. the very first parent node), 

• only directed edges, 

• no cycles, 

a hierarchy therefore allows me to measure the “distance” of any node in the hierarchy by how many edges away from the starting node (or “root node” as we’ll later call it) it is. This measurement is typically referred to as which level a particular node is on (analogous to measuring how high up in a building you are by what level you’re on. And much like the British/American confusion around which floor constitutes the “first” floor, you’ll also have to decide whether you index your root node at level 0 or 1, although 0 is more common). 

Note that level is a value that can be derived from the data itself, and thus is slightly redundant as its own column – but such derivation introduces unnecessary complexity and potential performance headaches, and thus benefits from being persisted as part of transformation logic in a data pipeline.

Also note that this level attribute can be considered a node’s “natural” level, but that business requirements may dictate treating a node as though it has a different level. A more detailed discussion on such cases can be found in the 5th post of this series, where unbalanced and ragged hierarchies are addressed.

It’s also worth noting that you can, of course, measure the number of edges between any two nodes in a generic graph, but unlike with hierarchies, there’s not a single value for level per node but rather N-1 such values, i.e. since you can measure the number of edges between a given node and all other nodes in the same graph. Storing such information for all nodes introduce exponential storage requirements, i.e. O(n²), and offers very little technical or business value. Hence level is a fairly meaningless attribute in the context of generic graphs or DAGs.) 

Secondly, because you can now refer to multiple “sibling” nodes under a single parent, you can describe the sort order of each sibling node. In other words, you define a particular sort order for all the related sibling nodes under a particular parent node. (This is another example of an attribute that is meaningful for hierarchies, but really doesn’t make much sense in the context of a generic graph or DAG.) 

A classic example of this are the various lines in a financial statement such as a profit-and-loss statement: 

Financial reports are a very common use case in the world of BI, and it’s clearly extremely important to ensure that your reports display the various lines of your report in the correct order, defined by the sort order of each node in your financial statement hierarchy.

It’s worth noting that this sort order attribute doesn’t have a canonical name in the world of BI as such. But for a variety of reasons, my preference is “sequence number” (SEQNR as its column name), so we’ll stick with that for now. 

(Also, for folks new to hierarchies, you might find it a worthwhile exercise to analyse the financial statement structure above as a hierarchy: How many nodes does it have? How many edges? How many levels? How many child nodes per parent? How might you actually model this data in a relational structure? We’ll come back to this in a bit.)

Two more points I’d like to make, as we work towards a generic/reusable data model for hierarchies: 

• This should be obvious, but there are many different types of hierarchies, such as (in an enterprise context): profit centre hierarchies, cost centre hierarchies, WBS structure hierarchies, cost element hierarchies, financial statement hierarchies, and the list goes on. 

• A particular hierarchy type might have multiple hierarchy instances. For example, a time dimension is actually a hierarchy, and almost all large enterprises care about tracking metrics along both the calendar year as well as the fiscal year, each of which is its own hierarchy.

And lastly, let me introduce a bit more vernacular from the tree analogy introduced earlier: 

• As mentioned above, a given hierarchy has one root node

• Any nodes that have parent and child nodes are called intermediate or inner nodes. 

• Any nodes that have parent but no child nodes themselves are called leaf nodes. 

Ok, let’s now look at a basic, generic and reusable data model for storing hierarchies. This is the beginning of the discussion, and not the end, but it gives a strong foundation for how to accommodate many different types and instances of hierarchies.

Rather than write the SQL, I’m just going to provide a poor man’s data dictionary, especially given that your naming conventions (and probably also data types) may well differ. (For a detailed review of SQL that may help you ingest and model hierarchies, see the 6th post in this series.)

Here’s what the example hierarchy above would look like in this model (primary key fields highlighted):

It’s worth taking a moment to talk through the model and the data a bit. 

• This model denormalizes three levels of data: hierarchy types, hierarchy instances, and hierarchy nodes, and it also embeds a text description of hierarchy types (instead of a typical key/text pair). This is based on a pragmatic model that has been used successfully at multiple very large enterprises, but it’s worth considering whether your use case warrants a more normalized model. And in either case, this model excludes separate language-dependent text tables that would typically join on HIERARCHY_ID and CHILD_NODE_ID columns. Keep that in mind if you’re implementing a hierarchy data model for a multinational organization.

• I’d suggest playing around a bit with some sample data, and sorting by the different columns, to familiarise yourself even more with this data model. For example, you could sort by NODE_TYPE to quickly find all the INNER nodes and LEAF nodes. You could sort by LEVEL and then SEQNR to “read” the hierarchy as you would with its visualised equivalent, i.e. top-to-bottom, left-to-right. 

• Note that root nodes are defined as those records for which PARENT_NODE_ID is NULL. This is a commonly accepted convention, especially for query languages that can traverse parent-child hierarchies natively (such as MDX, which we’ll come back to), so I’d highly suggest adopting it (especially if you’re modeling in a strictly OLAP context), but there’s clearly no “formal” reason, i.e. from relational theory or anything else, that requires this. 

• If this were a data model for a generic graph or even a DAG, we would have to include PARENT_NODE_ID as part of the primary key, since a node could have more than one parent. However, since hierarchy nodes only have a single parent node, we don’t need to do this, and in fact, we don’t want to do this (as, from a data quality perspective, it would let us insert records with multiple parent records, which breaks the definition set forth previously for hierarchies, and could lead to incorrect results in our BI queries by inflating metrics when joining hierarchies to fact tables due to M:N join cardinality instead of M:1). 

• SEQNR might be a bit confusing at first glance, because again, it only defines the sort order for a given node compared to its immediate siblings (again, consider the financial statement hierarchy structure that we glanced at earlier. “Revenue” related figures always come before “Expense” figures, which is the sort order at level 1. Sort order is also enforced amongst each set of siblings nodes at each level.) The typical convention for populating SEQNR is to restart its numerical sequence for each set of siblings nodes (although it’s technically possible to implement a contiguous sequence that spans all records).

• It’s also worth noting that in some cases, the only sort order that really matters is the inherent alphanumeric sort order (or the node IDs or their associated text descriptions), such as with the time dimension (so long as any alphanumeric representation is padded with zeroes so that the string “11” doesn’t appear before “2”, for example. For more on this, see the ISO 8601 standard.) 

Before I forget to mention, you might hear this parent-child structure referred to as an “adjacency list”, which is another technical term from graph theory. Technically, an adjacency list maps a single node to all of its adjacent nodes within a single record (which could be accomplished by packing all adjacent nodes in an ARRAY or JSON data type). In practice though, folks often refer to the parent/child data model above as an adjacency list, even though each child node gets its own row.

Extending the Model

The basic hierarchy data model we’ve reviewed above can clearly accommodate multiple hierarchies of multiple different types, and after a bit of reflection it becomes obvious that each hierarchy type likely corresponds with its own dedicated master data and/or dimension (depending on how we want to refer to our database entities, and also depending on which layer of our pipeline architecture we’re referring to – but for simplicity I’ll just refer to these as dimensions) with its own attributes. Also given the fact that hierarchies can certainly be ingested from different source systems, we’ll very likely want to ensure we’re generating our own surrogate keys so that we’re not mixing system-dependent primary key semantics.

So, we’ll extend our model with one additional generic table as a kind of placeholder for any/all dimensions that correspond with each hierarchy type that we’re persisting (keeping in mind that we may need to further extend the model to accommodate additional semantics around multilingual texts, currencies, units of measure, etc.).

In Summary 

We’ve now gone through graphs, DAGs, and hierarchies, and we’ve walked through a basic, scalable data model for parent-child hierarchies and discussed some of the nuances of this model. 

In the next post, we’ll review an alternative way to model hierarchies: the level hierarchy. We’ll discuss tradeoffs of the parent-child and level hierarchy, indicating when the level hierarchy might be more appropriate, and review a particularly helpful SQL query for “flattening” a parent-child hierarchy into a level hierarchy.

Recap

In the first post of this series, I walked through the basic ideas of what constitutes a graph (from a somewhat formal computer science perspective) with a simplified example from supply chain logistics.

To briefly recap:

• A graph consists of one or more nodes, which are connect to one another via one or more edges.
• A graph has a cycle if a node can reach itself by traversing the graph in a single direction.
• A given edge can be directed or undirected.
• Nodes and edges often represent real-world objects with various associated attributes.
• Graphs, nodes and edges can be modeled in relational databases.

This next post will further flesh out the technical context for hierarchies by exploring a subset of graphs called directed acyclical graphs (DAGs) and disambiguate the use of this term and related concepts.

Directed Acyclical Graphs (DAGs)

This post is not about ETL tools as such, but I would say most modern day Data Engineers are at least loosely familiar with the concept of DAGs, as it is a term that has experienced quite a bit of adoption in data pipeline orchestration tools like Apache Airflow and Dagster.

The definition of a DAG is pretty straightforward now that we’ve fleshed out the concept of a graph:

• A DAG is a graph where each and every edge is directed, and
• that has no cycles, i.e. is acyclical

Perhaps this is obvious, but the concept of DAGs originated as an abstract concept from graph theory with wide applicability across a large number of domains (and even within data engineering extends well beyond the typical use of the term to categorize what otherwise might be called data orchestration graphs). So, if when/as you leave the bubble of data engineering, it’s helpful to recognize that DAGs are an altogether much broader concept than that typically used within data engineering.

In fact, the intention of this post is largely to illustrate many of the different instances of DAGs within the field of data engineering, and to then disambiguate DAGs from hierarchies and indicate how hierarchies are technically a subset of DAGs.

So, let’s look at a number of concepts from data engineering that can be understood as DAGs.

Queries

Queries are DAGs. Look at the EXPLAIN PLAN of even the most basic query, and you’ll see that the visualised execution plan is a DAG, where:

• Each node is a particular operation (such as join, filter, aggregate).
• Each edge visualises how the output of a given operation serve as an input to the next operation.

Data Lineage

Data lineage, which represents the flow of data across a data pipeline, is also a DAG. (What I find interesting to note is that in the case of data lineage, each edge represents a particular operation, and each node represents data. This is the opposite of a query plan, where each edge represents data, and each node represents an operation).

Data Pipelines

Data pipelines are, rather obviously, DAGs.

Gantt Charts

Gantt charts? Aren’t Gantt charts something from project management? Yes, but they’re also used to visualise query plans by visualising the time each operation takes within a query execution plan, by expanding the length of the node to represent the amount of time it takes.

(Random tangent: I’ve always wanted a dynamic Gantt chart visualisation for query plans, where instead of being limited just to time (represented by bar width), I’d have the choice to select from things like memory consumption, CPU time, any implicit parallelization factors, disk I/O for root nodes, etc… any product managers out there want to take up the mantle?)

Relational Data Models

Okay, so, cycles can be found in some real-world relational data models, but even so, all relational data models are directed, and many are acyclical, so I decided to include them here.

• Entities are nodes.
• Primary/foreign key relationships are the edges.
• The direction of the directed edges are from primary keys to foreign keys.
• Clearly large enterprise data models with multiple subject areas can consist of multiple sub-graphs.

And if you’re in the mood for a bit of nuance, it’s absolutely fair to refer to the data model itself (i.e. the metadata) as a DAG, and separately calling out the data itself also as a DAG (i.e. the records themselves and their primary/foreign key relationships).

Hierarchies

Hierarchies are DAGs? Yes, hierarchies are DAGs (and DAGs are graphs – but not all graphs are DAGs – and not all DAGs are hierarchies. In fact, most arent.) Hierarchies will be introduced and discussed in much more detail in the next post, but to give a technical description – a hierarchy can be though of as a DAG where any particular node only has a single predecessor node (often called a “parent” node).

The reality that you should be aware of is that the use of the term “hierarchy”, like most terms in any human language, is often used with a certain amount of ambiguity, which can be frustrating in a technical context when precision is important.

One common example (of many) that illustrates this ambiguity is usage of the term “hierarchy” when describing how database roles in role-based access control (RBAC) can be setup. Consider this language from Snowflake’s documentation around their RBAC model (which is comparable to language from most database vendors with an RBAC model): “Note that roles can also be assigned to other roles, creating a role hierarchy.”

Consider the following diagram from Snowflake’s documentation that, at first glance, does look like a hierarchy:

If you look closely, however, you’ll see that Role 3 inherits two different privileges, which means that this structure, technically speaking is not strictly a hierarchy, but rather is a DAG (as the node we’re calling “Role 3” has more than one predecessor node.)

Now, it’s not really reasonable to say that this categorization is “wrong” because that’s not how language works. The term “hierarchy” means different things in different contexts. However, given that Snowflake is often a component in a larger BI landscape, it is important to disambiguate different senses or meanings of terms like “hierarchy”. In the case of RBAC, use of the term “hierarchy” is short-hand for “a model that supports inheritance” and has little bearing on the use of the term “hierarchy” when it comes to dimensional data warehousing hierarchy models.

(A more detailed disambiguation is provided in the next post in this series)

Summary

This is a fairly quick post to summarise:

• DAGs are directed, acyclical graphs
• The common use of the term DAG by Data Engineers is usually limited to the code artifacts of many modern data pipeline/orchestration tools, although it’s clearly a much broader concept.
• Even within the field of Data Engineering, DAGs can be found everywhere, as they describe the structure of things like: query execution plans, data lineage visualisations, data pipelines, and entity-relationship models.
• The terms DAG and hierarchy are often, and confusingly, used interchangeably, despite not representing exactly the same thing. Being aware of this ambiguity can help improve documentation and communication in the context of larger data engineering and BI efforts.

In the next post, we’ll further flesh out the concept of a hierarchy (particularly in the context of BI) including a few examples, a discussion of components of a hierarchy, an introduction to a data model for hierarchies, and a handful of additional considerations.