Description

We're comparing data models as we continue our deep dive into Designing Data-Intensive Applications as Coach Joe is ready to teach some basketball, Michael can't pronounce 6NF, and Allen measured some geodesic distances just this morning.

For those reading these show notes via a podcast player, this episode's full show notes can be found at https://www.codingblocks.net/episode123 where you can also join in on the conversation.

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Data Models

• Data models are one of the most important pieces of developing software.
  • It dictates how the software is written.
  • And it dictates how we think about the problems we're solving.
• Software is typically written by stacking layers of modeling on top of each other.
  • We write objects and data structures to reflect the real world.
  • These then get translated into some format that will be persisted in JSON, XML, relational tables, graph db's, etc.
    • The people that built the storage engine had to determine how to model the data on disk and in memory to support things like search, fast access, etc.
      • Even further down, those bits have to be converted to electrical current, pulses of light, magnetic fields and so on.
• Complex applications commonly have many layers: APIs built on top of APIs.
  • What's the purpose of these layers? To hide the complexity of the layer below it.
    • The abstractions allow different groups of people (potentially with completely different skillsets) to work together.
• There are MANY types of data models, all with different usages and needs in mind.
  • It can take a LOT of time and effort to master just a single model.
  • Data models have a HUGE impact on how you write your applications, so its important to choose one that makes sense for what you're trying to accomplish.

Relational Model vs Document Model

• Best-known model today is probably the ones based on SQL.
• The relational model was proposed by Edgar Codd back in 1970.
• The relational model organizes data into relations (i.e. tables in SQL) where each relation contains an unordered collection of tuples (i.e. rows in SQL).
  • People originally doubted it would work but it's dominance has lasted since the mid-80's, which the author points out is basically an eternity in software.
• Origins were based in business data processing, particularly transaction processing.
• There have been a number of competing data storage and querying approaches over the years.
  • Network and Hierarchical models in 70's and 80's,
  • Object databases were competitors in the late 80's and early 90's,
  • XML databases,
  • Basically a number a competitors over the years but nobody has dethroned the relational database.
• Almost everything you see and use today has some sort of relational database working behind it.

NoSQL

• NoSQL is the latest competitor to Relational Databases.
  • It was originally intended as a catchy Twitter hashtag for a meetup about open source, distributed, non-relational databases.
  • It has since been re-termed to "Not only SQL".
• What needs does NoSQL aim to address?
  • The need for greater scalability than traditional RDBMS's can typically achieve, including very large datasets and fast writes.
  • The desire for FOSS (free and open source software), as opposed to very expensive, commercial RDBMS's.
  • Specialized query operations that are not supported well in the relational model.
  • Shortcomings of relational models – need for more dynamic and/or expressive data models.
• Different applications (or even different pieces of the same application) have different needs and may require different data models. For that reason, it's very likely that NoSQL won't replace SQL, but rather it'll augment it.
  • This is referred to as polyglot persistence.

Object-Relational Mismatch

• Most applications today are written in an object oriented programming language.
• There's typically a translation layer required to map the relational data models to an object model.
  • The disconnect between models can be referred to as impedance mismatch.
• Frameworks like ActiveRecord, Hibernate, Entity Framework, etc., can reduce the boilerplate code needed for the translation but typically don't fully hide the impedance mismatch issues.

Resources We Like

• Designing Data-Intensive Applications: The Big Ideas Behind Reliable, Scalable, and Maintainable Systems by Martin Kleppmann (Amazon)
• Grokking the System Design Interview (Educative.io)
• Monitor Azure DevOps workflows and pipelines with Datadog (Datadog)
• Monitor Amazon EKS on AWS Fargate with Datadog (Datadog)
• Best practices for tagging your infrastructure and applications (Datadog)
• Introducing: Educative Subscriptions (Educative.io)
• Santosh Hari – Not all data is created equal: NoSQL (YouTube)
• TIOBE Index (tiobe.com)
• Database Schema for Multiple Types of Products
•  

Tip of the Week

• Got data? Use DataGrip. One tool for many databases. (JetBrains)
• KafkaHQ – A Kafka GUI for topics, data, consumer groups, schema registry and more. (GitHub)
• Grafka – A GraphQL interface for Apache Kafka (GitHub)
• Use Google Maps to measure geodesic distances (citylab.com)
• How to undo (almost) anything with Git (GitHub)
• Will Save the Galaxy for Food by Yahtzee Croshaw (Amazon)