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SQL vs. NoSQL

Common types of NoSQL

Key-value stores

• Array of key-value pairs. The “key” is an attribute name.
• Redis, Vodemort, Dynamo.

Document databases

• Data is stored in documents.
• Documents are grouped in collections.
• Each document can have an entirely different structure.
• CouchDB, MongoDB.

Wide-column / columnar databases

• Column families - containers for rows.
• No need to know all the columns up front.
• Each row can have different number of columns.
• Cassandra, HBase.

Graph database

• Data is stored in graph structures
  • Nodes: entities
  • Properties: information about the entities
  • Lines: connections between the entities
• Neo4J, InfiniteGraph

Differences between SQL and NoSQL

Storage

• SQL: store data in tables.
• NoSQL: have different data storage models.

Schema

• SQL
  • Each record conforms to a fixed schema.
  • Schema can be altered, but it requires modifying the whole database.
• NoSQL:
  • Schemas are dynamic.

Querying

• SQL
  • Use SQL (structured query language) for defining and manipulating the data.
• NoSQL
  • Queries are focused on a collection of documents.
  • UnQL (unstructured query language).
  • Different databases have different syntax.

Scalability

• SQL
  • Vertically scalable (by increasing the horsepower: memory, CPU, etc) and expensive.
  • Horizontally scalable (across multiple servers); but it can be challenging and time-consuming.
• NoSQL
  • Horizontablly scalable (by adding more servers) and cheap.

ACID

• Atomicity, consistency, isolation, durability
• SQL
  • ACID compliant
  • Data reliability
  • Gurantee of transactions
• NoSQL
  • Most sacrifice ACID compliance for performance and scalability.

Which one to use?

SQL

• Ensure ACID compliance.
  • Reduce anomalies.
  • Protect database integrity.
• Data is structured and unchanging.

NoSQL

• Data has little or no structure.
• Make the most of cloud computing and storage.
  • Cloud-based storage requires data to be easily spread across multiple servers to scale up.
• Rapid development.
  • Frequent updates to the data structure.