NoSQL Tutorial: Types of NoSQL Databases & Example

¿Qué es NoSQL?

Base de datos NoSQL is a non-relational data management system that does not require a fixed schema. It avoids joins, and is easy to scale. The major purpose of using a NoSQL database is for distributed data stores with humongous data storage needs. NoSQL is used for big data and real-time web apps. For example, companies like Twitter, Facebook, and Google Recopilamos terabytes de datos de usuarios cada día.

Base de datos NoSQL stands for “Not Only SQL” or “Not SQL”. Though a better term would be “NoREL”, NoSQL caught on. Carl Strozzi introduced the NoSQL concept in 1998.

Traditional RDBMS uses SQL syntax to store and retrieve data for further insights. Instead, a NoSQL database system encompasses a wide range of database technologies that can store structured, semi-structured, unstructured, and polymorphic data. Let us understand about NoSQL with a diagram in this NoSQL database tutorial:

¿Por qué NoSQL?

El concepto de bases de datos NoSQL se popularizó entre gigantes de Internet como Google, Facebook, Amazon, etc. que manejan grandes volúmenes de datos. El tiempo de respuesta del sistema se vuelve lento cuando utiliza RDBMS para volúmenes masivos de datos.

Para resolver este problema, podríamos "ampliar" nuestros sistemas actualizando nuestro hardware existente. Este proceso es costoso.

The alternative for this issue is to distribute the database load on multiple hosts whenever the load increases. This method is known as “scaling out”.

NoSQL database is non-relational, so it scales out better than relational databases, as they are designed with web applications in mind.

Breve historia de las bases de datos NoSQL

• 1998 – Carlo Strozzi uses the term NoSQL for his lightweight, open-source relational database.
• 2000 – Graph database Neo4j is launched.
• 2004 - Google BigTable is launched.
• 2005 - CouchDB se pone en marcha.
• 2007 – The research paper on Amazon Dynamo is released.
• 2008 – Facebook open sources the Cassandra proyecto.
• 2009 – The term NoSQL was reintroduced.

Características de NoSQL

No relacional

• Las bases de datos NoSQL nunca siguen el modelo relacional.
• Never provide tables with flat fixed-column records.
• Work with self-contained aggregates or BLOBs.
• Do not require object-relational mapping and data normalization.
• No complex features like query languages, query planners, referential integrity joins, or ACID.

Sin esquemas

• NoSQL databases are either schema-free or have relaxed schemas.
• Do not require any sort of definition of the schema of the data.
• Offer heterogeneous structures of data in the same domain.

API simple

• Offers easy-to-use interfaces for storage and querying data.
• APIs allow low-level data manipulation and selection methods.
• Text-based protocols mostly used with HTTP REST with JSON.
• Mostly used no standard-based NoSQL query language.
• Web-enabled databases running as internet-facing services.

Distribuido

• Multiple NoSQL databases can be executed in a distributed fashion.
• Offers auto-scaling and fail-over capabilities.
• Often the ACID concept can be sacrificed for scalability and throughput.
• Mostly no synchronous replication between distributed nodes; asynchronous multi-master replication, peer-to-peer, HDFS replication.
• Only providing eventual consistency.
• Shared-nothing architecture. This enables less coordination and higher distribution.

Tipos de bases de datos NoSQL

Bases de datos NoSQL are mainly categorized into four types: Key-value pair, Column-oriented, Graph-based, and Document-oriented. Every category has its unique attributes and limitations. None of the above-specified databases is better at solving all the problems. Users should select the database based on their product needs.

Tipos de bases de datos NoSQL:

• Basado en pares clave-valor
• Gráfico orientado a columnas
• Basado en gráficos
• Orientado a documentos

Basado en pares clave-valor

Data is stored in key/value pairs. It is designed in such a way to handle lots of data and heavy load. Key-value pair storage databases store data as a hash table where each key is unique, and the value can be a JSON, BLOB (Binary Large Objects), string, etc.

Por ejemplo, un par clave-valor puede contener una clave como “Sitio web” asociada con un valor como “Guru99 ".

It is one of the most basic NoSQL database examples. This kind of NoSQL database is used as a collection, dictionaries, associative arrays, etc. Key-value stores help the developer to store schema-less data. They work best for shopping Contenido del carrito.

Redis, Dynamo, and Riak are some NoSQL examples of key-value store databases. They are all based on AmazonEl papel Dynamo.

Basado en columnas

Column-oriented databases work on columns and are based on the BigTable paper by Google. Every column is treated separately. 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, inteligencia empresarial , CRM, and library card catalogs.

HBase, Cassandra, and Hypertable are NoSQL query examples of column-based databases.

Orientado a documentos

Document-Oriented NoSQL DB stores and retrieves data as a key-value pair, but the value part is stored as a document. The document is stored in JSON or XML formats. The value is understood by the DB and can be queried.

In this diagram on your left, you can see we have rows and columns, and on the right, we have a document database which has a similar structure to JSON. Now for the relational database, you have to know what columns you have, and so on. However, for a document database, you have a data store like a JSON object. You do not need to define it, which makes it flexible.

The document type is mostly used for CMS systems, blogging platforms, real-time analytics, and e-commerce applications. It should not be used for complex transactions which require multiple operations or queries against varying aggregate structures.

Amazon base de datos simple, CouchDB, MongoDB, Riak, and Lotus Notes are popular document-oriented sistemas DBMS.

Basado en gráficos

A graph type database stores entities as well as 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 multi-relational in nature. Traversing relationships is fast, as they are already captured in the DB, and there is no need to calculate them. Graph base databases are mostly used for social networks, logistics, and spatial data.

Neo4J, gráfico infinito, OrientDB, and FlockDB are some popular graph-based databases.

Herramientas del mecanismo de consulta para NoSQL

The most common data retrieval mechanism is the REST-based retrieval of a value based on its key/ID with a GET resource.

Document store databases offer more difficult queries, as they understand the value in a key-value pair. For example, CouchDB allows defining views with MapReduce.

¿Qué es el teorema CAP?

CAP theorem is also called Brewer’s theorem. It states that it is impossible for a distributed data store to offer more than two out of three guarantees:

1. Consistencia
2. Disponibilidad
3. Tolerancia de partición

Consistencia: Los datos deben permanecer consistentes incluso después de la ejecución de una operación. Esto significa que una vez que se escriben los datos, cualquier solicitud de lectura futura debe contener esos datos. Por ejemplo, después de actualizar el estado del pedido, todos los clientes deben poder ver los mismos datos.

Disponibilidad: La base de datos siempre debe estar disponible y responsiva. No debería tener ningún tiempo de inactividad.

Tolerancia de partición: Tolerancia de partición significa que el sistema debe continuar funcionando incluso si la comunicación entre los servidores no es estable. Por ejemplo, los servidores se pueden dividir en varios grupos que pueden no comunicarse entre sí. Aquí, si parte de la base de datos no está disponible, otras partes nunca se ven afectadas.

Coherencia eventual

The term “eventual consistency” means to have copies of data on multiple machines to get high availability and scalability. Thus, changes made to any data item on one machine have to be propagated to other replicas.

Data replication may not be instantaneous, as some copies will be updated immediately while others in due course of time. These copies may be mutually inconsistent, but in due course of time, they become consistent. Hence, the name eventual consistency.

BASE: Basicamente Adisponible Sestado a menudo, Econsistencia ventual

• Basically available means the DB is available all the time as per the CAP theorem.
• Soft state means even without an input, the system state may change.
• Eventual consistency means that the system will become consistent over time.

Ventajas de NoSQL

• Can be used as a primary or analytic data source.
• Big data capability.
• No single point of failure.
• Easy replication.
• No need for a separate caching layer.
• Proporciona un rendimiento rápido y escalabilidad horizontal.
• Can handle structured, semi-structured, and unstructured data with equal effect.
• Object-oriented programming which is easy to use and flexible.
• NoSQL databases do not need a dedicated high-performance server.
• Support key developer languages and platforms.
• Simpler to implement than using RDBMS.
• Puede servir como fuente de datos principal para aplicaciones en línea.
• 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.

Desventajas de NoSQL

• No standardization rules.
• Limited query capabilities.
• RDBMS databases and tools are comparatively mature.
• No ofrece ninguna capacidad de base de datos tradicional, como consistencia cuando se realizan múltiples transacciones simultáneamente.
• When the volume of data increases, it is difficult to maintain unique values as keys become difficult.
• Does not work as well with relational data.
• The learning curve is stiff for new developers.
• Open source options are not so popular for enterprises.

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