04 A map of NoSQL technologies

1. Key-Value Store:

   A key that refers to a payload (actual content / data). E.g. MemcacheDB, Azure Table Storage, Redis

   Key-value databases work by storing dictionaries or hash tables, which are a collection of key-value pairs in which a key serves as a unique identifier to retrieve an associated value. Values can be anything from simple objects, like integers or strings, to more complex objects, like JSON structures. A key-value database treats any data held within it as an opaque blob; it’s up to the application to understand how it’s structured.

   Key-value databases are often described as highly performant, efficient, and scalable. Com- mon use cases for key-value databases are caching, message queuing, and session management.

   Column Store

   Columnar databases are database systems that store data in columns. The goal of a columnar database is to efficiently write and read data to and from hard disk storage in order to speed up the time it takes to return a query.

   This design allows queries to only read the columns they need, rather than having to read every row in a table and discard unneeded data after it’s been stored in memory. Varius storage optimizations are possible since the data stored in this way can be highly compresses.

   In terms of performance, there’s a breakeven point in which relational databases are convenient vs columnar databases since traditional queries always read all the rows. If columnar databases are required to read too much data, after a certain point, they are then outperformed by relational databases (whose read performance is constant).

   Columnar databases have become widely used for data analytics since the columnar data model lends itself well to fast query processing. They’re also seen as advantageous in cases where an application needs to frequently perform aggregate functions. More in general, columnar databases excel at:

   • Queries that involve only a few columns
   • ColumnAggregation Store:queries Columnagainst vast amount of data is saved together, as opposed to row data. E.g. Hadoop, Cassandra, Hypertable
   • Column-wise compression

   Document / XML / Object Store

   Document-oriented databases are NoSQL databases that store data in the form of documents. Document stores are a type of key-value store: each document has a unique identifier and the document itself serves as the value.

   The main difference between these two models is that, in a key-value database, the data is treated as opaque and the database doesn’t know or care about the data held within it; it’s up to the application to understand what data is stored. In a document store, however, each docu- ment contains some kind of metadata that provides a degree of structure to the data which can be queried.

   Document stores are considered highly scalable, with sharding being a common horizontal scaling strategy. They are also excellent for keeping large amounts of unrelated, complex information that varies in structure.

   Key features document stores:

   • Flexible schema: Keystructure (andof possiblyindividual otherdocuments indexes)does pointnot athave ato serializedbe object.consistent; E.g.easier MongoDB,to CouchDB,integrate RavenDBnew information
   • Better read performance: information is contained in a single location (a document), no relations needed to access nested data

   Graph Store:

   Nodes are stored independently, and the relationship between nodes (edges) are stored with data. E.g. Neo4j

   Graph databases can be thought of as a subcategory of the document store model, in that they store data in documents and don’t insist that data adhere to a predefined schema. The difference though is that graph databases add an extra layer to the document model by highlighting the relationships between individual documents.

   These databases are commonly used in cases where it’s crucial to be able to gain insights from the relationships between data points as in a social network.

   The main advantages of graph databases are on:

   • Performance: in contrast to relational databases, where join-intensive query performance deteriorates as the dataset gets bigger, with a graph database performance tends to remain relatively constant, even as the dataset grows.

Flexibility: As developers and architects, we want to connect data as the domain dictates, thereby allowing structure and schema to emerge in tandem with our growing understanding of the problem space, rather than being imposed upfront.

Agility: schema-free nature of graph data mode