NoSQL Databases

Jalaz Kumar · January 10, 2021

Database

NoSQL databases are non tabular, and store data differently than relational tables.

  • Provides flexible schemas.
  • Scale easily with large amounts of data and high user loads.

Features:

  • Non relational
  • Schema-less
  • Distributed
  • Simple API

Need for NoSQL

  • Relationship data
    • NoSQL DBs store relationship data differently than relational databases do.
    • Modeling relationship data is easier than in SQL databases.
  • Storage Cost
    • Drastic drop in the storage cost after 90s, developer cost became major one. For easing developer’s effort NoSQL DBs were introduced & optimised.
    • Software engineers used to normalize their databases in order to reduce data duplication during earlier days.
  • Randomness & Quanity of data
    • Data started coming in varied shapes & sizes.
    • Amount of data generated & fed also increased manifold.
    • NoSQL DBs allow developers to store huge amounts of unstructured data, giving them a lot of flexibility.
  • Introduction of cloud-computing
    • Enterprises desired their applications & data hosted on cloud. This data was required to be distributed on multiple servers & regions for application resilience.
    • Also helped in better geo-placing their data.
    • NoSQL DBs like Mongo provided these capabilities

no-sql

Types of NoSQL Databases

Document Databases:

  • Stores data in documents similar to JSON objects.
  • Each document contains pairs of fields and values.
  • Due to presence of variety of field types & powerful query languages, used as general purpose databases.
  • Can easily scale-out for storing humongous amount of data.
  • Example: MongoDB, MariaDB, Apache Solr

Key-value Databases:

  • Store data in the form of keys and values.
  • Simpler type of database
  • Great when large amounts of data is stored but no need of performing complex queries to retrieve that data.
  • Great for caching purposes or storing user preferences.
  • Example: Redis, DyanoDB

Wide-column Stores:

  • Store data in tables, rows, and dynamic columns.
  • Provides lot of flexibility over relational databases as each row is not required to have the same columns.
  • Great when large amounts of data is stored and the future query patterns can be preicted.
  • Commonly used for storing IoT data and user profile data. (Clickstream data)
  • Example: Cassandra, HBase

Graph Databases:

  • Store data in nodes and edges
  • Nodes store information about entities(users, company, products) while edges store information about the relationships between these nodes.
  • Great for usecases where we need to traverse relationships to look for patterns such as social networks, fraud detection, and recommendation engines.
  • Example: Neo4j, JanusGraph

nosql-db

When should NoSQL be used over SQL

  • Development Pace
    • Is higher for NoSQL as compared to SQL
    • Due to sprints & frequent code changes, NoSQL supports quick changes as opposed to SQL, where schema needs to be updated by DB Admin, Data unload & Data load is required for each change.
  • Mutli-form Data
    • NoSQL DBs can better handle & are evolved for varied forms of data.
    • NoSQL DBs are better suited for storing & modeling structured, semi-structured & unstructured data together.
    • NoSQL DBs store data in a form that is similar to the objects used in application thus, reducing the efforts & cost in translation.
  • Supports large amount of data
    • NoSQL DBs use scale-out mechanism which is much better than the scale-up method of SQL DBs.
    • Scale-out architecture is one of the most affordable ways to handle large volumes of traffic
    • Scale-out architectures also provide benefits such as being able to upgrade a database or change its structure with zero downtime.
  • Multi Application paradigm support
    • NoSQL can serve both transactional and analytical workloads from the same database. In SQL DBs, separate data warehouse is required to support analytics.
    • NoSQL have superior integration capabilities with real-time streaming technologies.

Advantages

  • High Availability
    • Auto-replication feature of NoSQL DBs supports this.
    • In case of failure, data automatically replicates to a previous consistent state making the system available most of the time.
  • High Scalability
    • NoSQL DBs use sharding for horizontal scaling.
      • Sharding: Partitioning of data and placing it on multiple machines in such a way that the order of the data is preserved.
    • NoSQL can handle such huge amount of data because of this scalability, as the data grows NoSQL scale itself to handle that data in efficient manner.

scale-up-scale-out

Drawbacks

  • Narrow focus
    • Mainly designed for storage & lacks transactional management best features.
  • Open-source
    • Due to this, no reliable software standards.
  • No GUI
    • GUI mode tools for db access are not available.
  • Large document size
    • Data stored is of immense size & stored in formats like JSON making these packets quite costly in networking.
  • Management challenge
    • Data management in NoSQL is more challenging than SQL DBs.
    • Setting up of NoSQL is also cumbersome.

SQL vs NoSQL Databases

CAP theorem states that it’s impossible for a distributed data store to offer more than two out of three:

  • Consistency:
    • Data should remain consistent even after the execution of an operation.
    • Once data is written, any future read request should contain that data.
  • Availability:
    • DB should always be available and responsive.
    • Should not have any downtime.
  • Partition Tolerance:
    • System should continue to function even if the communication among the servers is not stable.

sql-nosql

Just like SQL DBs follow ACID rules, NoSQL DBs follow BASE rules.

  • Basically available means DB is available all the time as per 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

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