Designing Data-Intensive Applications [Book Highlights]

An imperative language tells the computer to perform certain operations in a certain order.
In a declarative query language, like SQL, you just mention what conditions the data should meet and what transformations. but not how.
A declarative query language is attractive, as it hides implementation details of engine, making the database system able to introduce performance improvements without requiring any changes to queries.
Declarative languages have a better chance of getting faster in parallel execution because they specify only the pattern of the results, not the algorithm.

MapReduce is a programming model for processing large amounts of data in bulk across many machines, popularized by Google
A limited form of MapReduce is supported by some NoSQL datastores, including MongoDB and CouchDB, as a mechanism for performing read-only queries across many documents.
MapReduce is neither a declarative query language nor a fully imperative query API, but somewhere in between
It is based on the map (also known as collect) and reduce (also known as fold or inject) functions
A usability problem with MapReduce is that you have to write two carefully coordinated functions,

if many-to-many relationships are very common in your data, the connections within your data become more complex, it becomes more natural to start modeling your data as a graph.

Example of graph-structured data (boxes represent vertices, arrows represent edges).

In the property graph model, each vertex consists of:
- A unique identifier
- A set of outgoing edges
- A set of incoming edges
- A collection of properties (key-value pairs)
Each edge consists of:
- A unique identifier
- The vertex at which the edge starts (the tail vertex)
- The vertex at which the edge ends (the head vertex)
- A label to describe the kind of relationship between the two vertices
- A collection of properties (key-value pairs)
Any vertex can have an edge connecting it with any other vertex. There is no schema that restricts which kinds of things can or cannot be associated.
Given any vertex, you can efficiently find both its incoming and its outgoing edges, and thus traverse the graph
By using different labels for different kinds of relationships, you can store several different kinds of information in a single graph

Day 24/100