Database Paradigms
Relational DB
- Rigid structure enforced by table's
schema - Has support for
transactions(ACID compliant) - Difficult to scale
- Slower read operations
-
On-line Transaction Processing (OLTP)
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Implementations
- MySQL
- Postgres
- SQL Server
- RDS & Aurora (AWS)
Key-Value DB
- It's a large scale hash table
- Data stored in RAM
-
Perfect use-case for caches and counters
-
Implementations
- Redis
- Memcached
- Etcd
Document Oriented DB
- Each document is a container for key-value pairs
- No schema
-
Documents are grouped together in collections
-
Implementations
- MongoDB
- Firestore
- DynamoDB
- CouchDB
Graph DB
- Data is represented as
nodes(circles/vertexes) - Relationships are represented as
edges(arrows) -
Edges can also be weighted
-
Operations
- Link
- Traverse
-
Analyze
-
Pros
- Good for n:n relationships!
- Good for fraud detection in finance and recomendation systems
-
Good for recommendation engines
-
Implementations
- Neo4j
- DGraph
- AWS Neptune
Wide-Column DB
- Keys store multiple columns (values)
- No schema
-
Good for time-series data, historical records, high-write, low-read
-
Implementations
- Cassandra
- HBase
Search DB
- Similar to document-oriented DBs
- Search DB analyze all the text in the document and create index of the searchable terms
-
Good for type-ahead search bars
-
Implementations
- Lucene
- Solr
- ElasticSearch
- Algolia
- MeiliSearch
Multi Model DB
- Implementations
- Fauna: describe how to access the data with GraphQL