Designing Data-Intensive Applications – Data Models: Query Languages

We dive into declarative vs imperative query languages as we continue to dive into Designing Data-Intensive Applications while Allen is gallivanting around London, Michael had a bullish opinion, and Joe might not know about The Witcher.

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Query Languages

Declarative vs Imperative

• The relational model introduced a declarative query language: SQL.
• Prior models used imperative code.
• An imperative language performs certain operations in a certain order, i.e. do this, then do that.
• With a declarative query language, you specify the pattern of data you want, the conditions that must be met, any sorting, grouping, etc.
  • Note that you don't specify how to retrieve the data. That is left to the optimizer to figure out.
• Declarative languages are attractive because they are shorter and easier to work with.
  • Consider UI frameworks where you declaratively describe the UI without needing to write code that actually draws a button of a specific size in a specific place with a specific label, etc.
• Additionally, declarative languages hide the implementation details.
  • This means it's easier to continue using the code as the underlying engine is updated, be it a database, UI framework, etc.
  • This also means that the declarative code can take advantage of performance enhancements with little to no change (often) to the declarative code.
• Because declarative languages only specify the result, instead of how to get the result, they are often more likely to be able to take advantage of parallel execution.
  • Conversely, because imperative code needs to happen in a specific order, it's more difficult to parallelize.

MapReduce

• Made popular by Google, MapReduce is a programming model meant for processing large amounts of data in bulk in a horizontally distributed fashion.
• Some NoSQL databases, such as MongoDB and CouchDB, support MapReduce in a limited form as a way to perform read-only queries across many documents.
• MapReduce isn't a declarative query language but it's also not completely an imperative query API either.
  • This is because to use it, you're implementing the Template Pattern (episode 16).
• With MapReduce, you implement two methods: map() and reduce().
• The map() and reduce() functions are pure functions.
  • They can only use the data passed into them, they can't perform additional queries, and they must not have side effects.
  • Pure functions are a concept used in functional programming.
• From a usability perspective though, it does require writing two functions that are somewhat tied to each other, which may be more effort than just writing a single SQL query.
  • Plus a purely declarative SQL query is better able to take advantage of the optimizer.
    • For this reason, MongoDB added a declarative query language called the aggregation pipeline to wrap the MapReduce functionality.
      • It's expessiveness is similar to a subset of SQL but in a JSON syntax.

Graph-Like Data Models

• Relationships, particularly many-to-many, are an important feature for distinguishing between when to use which data model.
• As relationships get even more complicated, graph models start to feel more natural.
• Where as document databases have documents, and relational databases have tables, rows, and columns, graph databases have:
  • Vertices: Nodes in the graph
  • Edges: Define the relationships between nodes, and can contain data about those relationships.
• Examples of graph-like data:
  • Social graphs: Vertices are the entities (people, media, articles), and edges are the relationships (friends with, likes, etc.)
  • Web graph: Vertices are the pages, and edges are the links.
  • Maps: Addresses are the vertices, and roads, rails, sidewalks are the edges.
• There are some things that are trivial to express in a graph query that are really hard any other way.
  • For example, fetch the top 10 people that are friends with my friends, but not friends with me, and liked pages that I like sorted by the count of our common interests.
• These queries work just like graph algorithms, you define how the graph is traversed.
• Graph databases tend to be highly flexible since you can keep adding new vertices and nodes without changing any other relationships.
• This makes graphs great for evolvability.

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)
• Design Patterns Part 2 – Oh behave! (episode 16)
• Developer Survey Results 2019 (Stack Overflow)
• Graph Algorithms (episode 85)
• CloudSQL with Amy Krishnamohan (gcppodcast.com)

Tip of the Week

• Is there an equivalent of tail -f on Windows? (Stack Overflow)
• Recursively find files whose content matches a regex pattern and display the first 10 lines for context:

Get-ChildItem .\*.txt -Recurse | Select-String -Pattern 'MyPattern' -context 10,0

• Use the Microsoft Application Inspector to identify and surface well-known features and other interesting characteristics of a component's source code to determine what it is and/or what it does. (GitHub)
• Automatically silence those pesky, or worse: embarrassing, notifications while screensharing on your Mac. (Muzzle)