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NoSQL Data Modeling

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Data modeling for RDBMS has been a well-defined discipline for many years. Techniques like logical to physical mapping and normalization / de-normalization have been widely practiced by professionals, including novice users. However, with the recent emergence of NoSQL databases, data modeling is facing new challenges to its relevance. Generally speaking, NoSQL practitioners focus on physical data model design rather than the traditional conceptual / logical data model process for the following reasons:

In this blog post, we explore other important mindset changes in NoSQL data modeling: development agility through flexible schemas vs. database manageability; the business and data model design process; the role of RDBMS in NoSQL data modeling; NoSQL variations that affect data modeling; and visualization approaches for NoSQL logical and physical data modeling. We end the post with a peak into the NoSQL data modeling future.

Development agility vs. database manageability

One highly touted feature in today’s NoSQL is application development agility. Part of this agility is accomplished through flexible schemas, where developers have full control over how data is stored and organized in their NoSQL databases. Developers can create or modify database objects in application code on the fly without relying on DBA execution. The result is, indeed, increased application development and deployment agility.

However, the flexible schema is not without its challenges. For example, dynamically created database objects can cause unforeseen database management issues due to the lack of DBA oversight. Furthermore, unsupervised schema changes increase DBA challenges in diagnosing associated issues. Frequently, such troubleshooting requires the DBA to review application code written in programming languages (e.g., Java) rather than in RDBMS DDL (Data Definition Language) – a skill that most DBAs do not possess.

NoSQL business and data model design process

In old-school software engineering practice, sound business and (relational) data model designs are key to successful medium- to large-scale software projects. As NoSQL developers assume business / data model design ownership, another dilemma arises: data modeling tools. For example, traditional RDBMS logical and physical data models are governed and published by dedicated professionals using commercial tools, such as PowerDesigner or ER/Studio.

Given the nascent state of NoSQL technology, there isn’t a professional-quality data modeling tool for such tasks. It is not uncommon for stakeholders to review application source code in order to uncover data model information. This is a tall order for non-technical users such as business owners or product managers. Other approaches, like sampling actual data from production databases, can be equally laborious and tedious.

It is obvious that extensive investment in automation and tooling is required. To help alleviate this challenge, we recommend that NoSQL projects use the business and data model design process shown in the following diagram (illustrated with MongoDB’s document-centric model):

design_process

Figure 1

RDBMS data modeling influence on NoSQL

Interestingly enough, old-school RDBMS data modeling techniques still play a meaningful role for those who are new to NoSQL technology. Using document-centric MongoDB as an example, the following diagram illustrates how one can map a relational data model to a comparable MongoDB document-centric data model:

mongodb_mapping

Figure 2

NoSQL data model variation

In the relational world, logical data models are reasonably portable among different RDBMS products. In a physical data model, design specifications such as storage clauses or non-standard SQL extensions might vary from vendor to vendor. Various SQL standards, such as SQL-92 and the latest SQL:2008 as defined by industry bodies like ANSI/ISO, can help application portability across different database platforms.

However, in the NoSQL world, physical data models vary dramatically among different NoSQL databases; there is no industry standard comparable to SQL-92 for RDBMS. Therefore, it helps to understand key differences in the various NoSQL database models:

The following diagram illustrates the comparison landscape based on model complexity and scalability:

nosql_comparisons

Figure 3

It is worth mentioning that for NoSQL data models, a natural evolutionary path exists from simple key-value stores to the highly complicated graph databases, as shown in the following diagram:

nosql_evolution

Figure 4

NoSQL data model visualization

For conceptual data models, diagramming techniques such as the Entity Relationship Diagram can continue to be used to model NoSQL applications. However, logical and physical NoSQL data modeling requires new thinking, due to each NoSQL product assuming a different native structure. One can intuitively use any of the following three visualization approaches, using a document-centric data model like MongoDB as an example:

Pros – It naturally conveys a complex document model through an intuitive visual representation.
Cons – Without specialized tools support, visualization results in ad-hoc drawing using non-uniform conventions or notations.

Pros – It can easily reverse engineer a hierarchical model into a visual representation from existing JSON documents stored in NoSQL databases like MongoDB.
Cons – As of this writing, JSON Designer is available only on iPhone / iPad. Furthermore, it does not include native DB objects, such as MongoDB indexes.

json_designer

Figure 5

Pros – Commercial tools support is available.
Cons – it requires tedious manual preparation and diagram arrangement to represent complex and deeply nested document structure.

power_designer

Figure 6

In a future post, we’ll cover specific data model visualization techniques for other NoSQL products such as Cassandra, which is based on the Column Family structure.

New NoSQL data modeling opportunities

Like any emerging technology, NoSQL will mature as it becomes mainstream. We envision the following new data modeling opportunities for NoSQL:

作者:eBay Tech Blog
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原文地址:NoSQL Data Modeling, 感谢原作者分享。

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