Four Major Types of NoSQL Databases
- Key-value store
- Document store
- Column-oriented database
- Graph database
Each type solves problems that relational databases can't address.
Key-Value Pair Based
Data is stored in key/value pairs, designed to handle large amounts of data and heavy load.
Key-value pair storage databases use a hash table where each key is unique, and the value can be a JSON, BLOB (Binary Large Object), string, etc.
Key-value stores store everything as a key and a value.
The value in a key-value store can be anything: a string, a number, or an entirely new set of key-value pairs within an object.
Examples of key-value stores include Redis, Voldemort, Riak, and Amazon's DynamoDB.
Key-value nested structure.
Document-Oriented:
Document stores are one step up in complexity from key-value stores: a document store does assume a certain document structure that can be specified with a schema. Document stores appear the most natural among the NoSQL database types because they’re designed to store everyday documents as is, and they allow for complex querying and calculations on this often already aggregated form of data. The way things are stored in a relational database makes sense from a normalization point of view: everything should be stored only once and connected via foreign keys. Document stores care little about normalization as long as the data is in a structure that makes sense. A relational data model doesn’t always fit well with certain business cases.
Newspapers or magazines, for example, contain articles. To store these in a relational database, you need to chop them up first: the article text goes in one table, the author and all the information about the author in another, and comments on the article when published on a website go in yet another. As shown below, a newspaper article can also be stored as a single entity; this lowers the cognitive burden of working with the data for those used to seeing articles all the time.
Amazon SimpleDB, CouchDB, MongoDB, Riak, Lotus Notes are popular Document originated DBMS systems.
Document stores save documents as a whole, whereas RDMS cuts up the article and saves it in several tables.
Column-based
Column-oriented databases work on columns and are based on BigTable paper by Google. Every column is treated separately. The values of single column databases are stored contiguously.
They deliver high performance on aggregation queries like SUM, COUNT, AVG, MIN, etc. as the data is readily available in a column.
Column-based NoSQL databases are widely used to manage data warehouses, business intelligence, CRM, Library card catalogs,
HBase, Cassandra, HBase, Hypertable are examples of a column-based database.
Graph-Based
A graph type database stores entities as well the relations amongst those entities. The entity is stored as a node with the relationship as edges. An edge gives a relationship between nodes. Every node and edge has a unique identifier.
Compared to a relational database where tables are loosely connected, a Graph database is a multi-relational in nature. Traversing relationships as fast as they are already captured into the DB, and there is no need to calculate them.
Graph base databases mostly used for social networks, logistics, spatial data.
Neo4J, Infinite Graph, OrientDB, FlockDB are some popular graph-based databases.
Query Mechanism tools for NoSQL
The most common data retrieval mechanism is the REST-based retrieval of a value based on its key/ID with GET resource
Document store Database offers more difficult queries as they understand the value in a key-value pair. For example, CouchDB allows defining views with MapReduce.
Advantages of NoSQL
- Can be used as Primary or Analytic Data Source
- Big Data Capability
- No Single Point of Failure
- Easy Replication
- No Need for Separate Caching Layer
- It provides fast performance and horizontal scalability.
- Can handle structured, semi-structured, and unstructured data with equal effect
- Object-oriented programming which is easy to use and flexible
- NoSQL databases don’t need a dedicated high-performance server
- Support Key Developer Languages and Platforms
- Simple to implement than using RDBMS
- It can serve as the primary data source for online applications.
- Handles big data which manages data velocity, variety, volume, and complexity
- Excels at distributed database and multi-data center operations
- Eliminates the need for a specific caching layer to store data
- Offers a flexible schema design which can easily be altered without downtime or service disruption
Disadvantages of NoSQL
- No standardization rules
- Limited query capabilities
- RDBMS databases and tools are comparatively mature
- It does not offer any traditional database capabilities, like consistency when multiple transactions are performed simultaneously.
- When the volume of data increases it is difficult to maintain unique values as keys become difficult
- Doesn’t work as well with relational data
- The learning curve is stiff for new developers
- Open-source options so not so popular for enterprises.
NoSql Technical Comparison Report.