A database is a collection of information that is organized so that it can be easily accessed, managed and updated. Before starting with the most popular NoSQL databases. You must have to know about NoSQL databases. Most of the programmer doesn’t know what it stands for. It’s Not Only SQL. After getting knowledge of the NoSQL database, we will jump into Best NoSQL databases for the 2021 year and we also see Cassandra vs MongoDB vs HBase. Don’t forget to learn the CAP theorem before choosing any particular NoSQL database.
We have published lots of tutorials related to Databases but its really difficult to get everything in one place. So, we are going to share out 11 years of experience of databases in this ultimate guide for No SQL databases. Using this guide, you will able to know what actually No SQL is, which DB to choose, how many types of NoSQL databases are available, how to scale, comparison between databases and which popular companies are using each database in 2020. We will also share online courses, books to read and get started with NoSQL databases.
What is NoSQL Database?
NoSQL databases (additionally called Not Only SQL Databases) are non-relational database systems used for storing and retrieving data. In today’s world, we should not store all the data in table format only which has not predefined fixed schemas( fix no of columns). Like User-generated data, GEO location data, IoT generated data, social graphs are examples of real-world data which has been increasing exponentially. These huge amounts of data required lots of processing also. Here, the NoSQL database comes into the picture. Using NoSQL database we can store and retire document, key-value, graph-based data easily & faster. We can easily avoid complex SQL joins operations. Easy to scale horizontally for real-world problems(web and enterprise business applications) using NoSQL DBs. Carlo Strozzi came with NoSQL term in the 1998 year. The motivation of using NoSQL – the simplicity of design, horizontal scaling to clusters of machines which is difficult to achieve in RDMS databases.
NoSQL Database Types
- Document Databases – These Db usually pair each key with a complex data structure which is called a document. Documents can contain key-array pairs or key-value pairs or even nested documents. Examples of document NoSQL: MongoDB, Apache CouchDB, Raven DB, ArangoDB, Couchbase, Cosmos DB, IBM Domino, MarkLogic, OrientDB.
- Key-value stores – Every single item is stored as a Key-value pair. Key-value stores are the most simple database among all NoSQL Databases. Examples of Key-value NoSQL – Redis, Memcached, Apache Ignite, Riak.
- Wide-column stores – These types of Databases are optimized for queries over large datasets, and instead of rows, they store columns of data together. Examples of Wide column NoSQL – Cassandra, Hbase, Scylla.
- Graph stores – These store information about graphs, networks, such as social connections, road maps, transport links. Examples of Graph NoSQL – Neo4j, AllegroGraph.
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Best NoSQL Databases 2021
- It is an open-source NoSQL database that is document-oriented. MongoDB uses JSON like documents to store any data. It is written in C++.
- It was developed at Facebook for an inbox search. Cassandra is a distributed data storage system for handling very large amounts of structured data.
- Redis is the most famous key-value store. Redis is composed in C language. It is authorized under BSD.
- It is a distributed and non-relational database that is designed for the BigTable database by Google.
- Neo4j is referred to as a native graph database because it effectively implements the property graph model down to the storage level.
- RavenDB is the original NoSQL Document Database to offer fully transactional (ACID) data integrity across multiple documents of your database and throughout your entire database cluster.
- Oracle NoSQL
- Oracle NoSQL Database implements a map from user-defined keys to opaque data items.
- Amazon DynamoDB
- DynamoDB uses a NoSQL database model, which is nonrelational, allowing documents, graphs and columnar among its data models.
- Couchbase Server is a NoSQL document database for interactive web applications. It has a flexible data model, is easily scalable, provides consistently high performance.
- It is an open-source, high-performance, distributed memory caching system intended to speed up dynamic web applications by reducing the database load.
- Provides high performance
- Run over multiple servers
- Supports Master-Slave replication
- Data is stored in the form of JSON style documents
- index any field in a document
- It has an automatic load balancing configuration because of data placed in shards
- Supports regular expression searches
- Easy to administer in the case of failures
Pros of MongoDB
- Easy to setup MongoDB
- MongoDB Inc. provides professional support to its clients
- Support ad-hoc query
- High-Speed Database
- Schema-less database
- Horizontally scalable database
- Performance is very high
Cons of MongoDB
- Doesn’t support joins
- Data Size is High
- Nesting of documents is limited
- Increase unnecessary usage of memory
Cassandra was developed at Facebook for inbox search. Cassandra is a distributed data storage system for handling very large amounts of structured data. Generally, these data are spread out across many commodity servers. You can also add storage capacity of your data keeping your service online and you can do this task easily. As all the nodes in a cluster are same, there is no complex configuration to deal with. Cassandra is written in Java. Cassandra Query Language (CQL) is a SQL-like language for querying Cassandra Database. As a result, Cassandra stands 2nd in best open source databases. Cassandra is being used by some of the biggest companies such as Facebook, Twitter, Cisco, Rackspace, eBay, Twitter, Netflix, and more.
- Linearly scalable
- Maintains a quick response time
- Supports properties like Atomicity, Consistency, Isolation, and Durability (ACID)
- Supports MapReduce with Apache Hadoop
- Maximal flexibility to distribute the data
- Highly scalable
- Peer-to-peer architecture
Pros of Cassandra
- Highly scalable
- No single point of failure
- Multi-DC Replication
- Integrate tightly with other JVM based applications
- More suitable for multiple data-center deployments, redundancy, failover and disaster recovery
Cons of Cassandra
- Limited support for aggregations
- Unpredictable Performance
- Doesn’t Support ad-hoc query
Don’t forget to see free data-structures books for programmers ( this month offer only)
Redis(Remote Dictionary Server) is a key-value store. Furthermore, it is the most famous key-value store. Redis has support for some C++, PHP, Ruby, Python, Perl, Scala and so forth. Redis is composed in C language. Furthermore, it is authorized under BSD. Some fun facts about Redis NoSQL Database – It can handle up to 2 ³² keys and was tested in practice to handle at least 250 million keys per instance. It is an in-memory but persistent on-disk database. It means it will store all data in RAM only for backup only use disk(HDD or SSD).
- Automatic failover
- Holds its database entirely in the memory
- Lua scripting
- Replicate data to any number of slaves
- Keys with a limited time-to-live
- LRU eviction of keys
- Supports Publish/Subscribe
Pros of Redis
- Supports a huge variety of data types
- Easy to install
- Very fast(perform about 110000 SETs per second, about 81000 GETs per second)
- Operations are atomic
- Multi-utility tool(used in a number of use cases)
- Redis Sentinel is featured provided by Redis to create replication into a distributed system.
Cons of Redis
- Doesn’t support joins
- Knowledge required of Lua for stored procedures
- the dataset has to fit comfortably in memory
HBase is a distributed and non-relational database which is designed for the BigTable database by Google. One of the main goals of HBase is to host Billions of rows X millions of columns. You can add servers anytime to increase capacity. And multiple master nodes will ensure high availability of your data. HBase is composed in Java 8. It’s authorized under Apache. Hbase accompanies simple to utilize Java API for customer access also.
- Support automatic failure
- Linearly scalable
- Provides data replication
- Integrates with Hadoop, both as a source and a destination
Pros of HBase
- Provides fast lookups for larger tables.
- Provides low latency access to single rows from billions of records
- Easy Java API for client
- Handle large datasets on top of HDFS file storage
- Flexible on schema design
Cons of HBase
- Doesn’t support transaction
- No permissions or built-in authentication
- Indexed and sorted only on key.
- Single point of failure (when only one HMaster is used)
- Doesn’t support for SQL structure
- Memory issues on the cluster
Neo4j is referred to as a native graph database because it effectively implements the property graph model down to the storage level. This means that the data is stored exactly as you whiteboard it, and the database uses pointers to navigate and traverse the graph. Neo4j has both a Community Edition and Enterprise Edition of the database. The Enterprise Edition includes all that Community Edition has to offer, plus extra enterprise requirements such as backups, clustering, and failover abilities.
- It supports UNIQUE constraints
- Neo4j supports full ACID(Atomicity, Consistency, Isolation, and Durability) rules
- Java API: Cypher API and Native Java API
- Indexes by using Apache Lucence
- Easy query language Neo4j CQL
- Contains a UI to execute CQL Commands: Neo4j Data Browser
Pros of Neo4j
- Easy to retrieve its adjacent node or relationship details without Joins or Indexes
- Easy to learn Neo4j CQL query language commands
- Not require complex Joins to retrieve data
- Represents semi-structured data very easily
- High availability for large enterprise real-time applications
- Simplified tuning
Cons of Neo4j
- Doesn’t support Sharding
RavenDB is the original NoSQL Document Database to offer fully transactional (ACID) data integrity across multiple documents of your database and throughout your entire database cluster. An open source distributed database, RavenDB offers high availability and lightning performance. It is simple to use with lots of native tools to eliminate the need for addons, externals, or unnecessary support to boost developer productivity.
A secure multi-node database cluster can be set up on-premise or in the cloud in a matter of minutes. RavenDB offers a Database as a Service solution on all major cloud platforms, allowing you to pass on your database operations to us so you can focus exclusively on your application. RavenDB has it’s own storage engine, Voron, that reaches speeds up to 1.5 million reads per second and 150,000 writes per second on a single node using simple commodity hardware.
DBaaS Cloud Instance: RavenDB Cloud
- RQL, the native query language, uses 80% of SQL syntax, making it easy to read for 99% of developers.
- Storage engine, full-text search engine, MapReduce engine, GUI Studio, Documents Compression are all part of your database.
- Multi-Model. Document, Key-Value, Counter, Graph, Time Series models all included.
- RavenDB can go virtually unattended. It is ideal for embedded solutions.
- RavenDB is easy to setupand secure. You can do it in a matter of minutes.
- Easy to use and fast to production. A large enterprise was up and running in 90 days.
- ETL Replication and Reverse ETL Pull Replication. Migrations to relational databases makes working with standard databases easy. Migration tools available for CosmosDB and MongoDB along with relational databases. Pull replication is ideal for cloud / on-prem hybrid architectures.
- RavenDB’s Management Studio GUI lets you monitor operations and performance of your database.Perform functions like adding new nodes with just a click.
- Master-Master replication keeps your database running even if nodes are temporarily offline.
- Fully Transactional (ACID) Database over multiple documents and throughout your database cluster.
- RavenDB Cloud is a DBaaS Managed Cloud Hosting available on all major cloud platforms.
- Automatic indexing boosts performance with each data query as RavenDB learns from your data to continuously improve your indexes.
PROS OF RavenDB
- RavenDB features are built to minimize developer headache and overhead
- Memory usage remains steady over new versions. RavenDB’s optimal use of memory makes it a good fit on Raspberri Pi and ARM servers
- Everything you need is in the box, reducing third party integrations and minimizing complexity
- Schemaless database
- Dev Support from friendly developers who built the database
- Supports multiple server operating systems: Linux, macOS, Raspberry Pi, Windows
CONS OF Ravendb
- Doesn’t support data sharding
- Doesn’t support JOINs
Oracle just started NoSQL database with Oracle NoSQL. It’s becoming popular in the year 2018. It less popular compare to MongoDB and Casandra databases. Oracle NoSQL Database implements a map from user-defined keys to opaque data items. Although it records internal version numbers for key/value pairs, it only maintains the single latest version in the store. The version of Oracle, 12c, is designed for the cloud and can be hosted on a single server or multiple servers, and it enables the management of databases holding billions of records. Some of the features of the latest version of Oracle include a grid framework and the use of both physical and logical structures. Oracle Database 18c now provides customers with a high-performance, reliable and secure platform to easily and cost-effectively modernize their transactional and analytical workloads either in the Cloud, or on-premises or in a Hybrid Cloud configuration.
- Oracle NoSQL Database handle big data
- Supports SQL, and it can be accessed from Oracle relational databases
- Oracle NoSQL Database using Java/C API to read and write data
- Distributed database
- Provides access to the data through the node for the requested key.
Pros of Oracle NoSQL
- Based on PL/SQL Programming construct
- Peer to peer communities help to solve all problems
- Oracle database is secure and ensures that user data is not tampered with through prompt updates.
Cons of Oracle NoSQL
- High cost for small organizations
- Require significant resources for installation
- Hardware upgrades may be required to even implement Oracle
- takes up a lot of space
DynamoDB uses a NoSQL database model, which is nonrelational, allowing documents, graphs and columnar among its data models. Each DynamoDB query is executed by a primary key identified by the user, which uniquely identifies each item. It also relieves the customers from the burden of operating and scaling a distributed database. Hence, hardware provisioning, setup, configuration, replication, software patching, cluster scaling, etc. is managed by Amazon.
- High Scalable
- Hash-Range for indexing a range of values
- Stores data in partitions
- Utilizes JSON as a transport protocol, not as a storage format
Pros of DynamoDB
- Easy to set up
- Provide a low-level AWS DynamoDB API
- Reduces the complexity of managing the high availability and scaling for peak usage times.
- Encryption at rest
- Security for DynamoDB is governed by AWS Identity
Cons of DynamoDB
- Doesn’t back up your tables for free
- Size limit
The focus is on the ease of use, embracing the web. It is a NoSQL document store database. Couchbase Server is a NoSQL document database for interactive web applications. It has a flexible data model, is easily scalable, provides consistently high performance. Couchbase Server, JSON documents are used to represent application objects and the relationships between objects.
- Auto-FailoverDeploying and Managing Couchbase at Scale With Kubernetes
- Index partitioning
- Support JSON data natively via N1QL queries
- Data Compression
- Couchbase Eventing Service
Pros of Couchbase
- Aggregate optimization
- reduces the cost of network, memory, and storage
- great admin panel that provides tons of insights into how your cluster is performing
Cons of Couchbase
- Couchbase is not open source
Memcached is an open source, high-performance, distributed memory caching system intended to speed up dynamic web applications by reducing the database load. It is a key-value dictionary of strings, objects, etc., stored in the memory, resulting from database calls, API calls, or page rendering. It is now being used by Netlog, Facebook, Flickr, Wikipedia, Twitter, and YouTube among others.
- Client-server application over TCP or UDP
- Reduces the database load
- Memcached server is a big hash table
- Efficient for websites with high database load
- Distributed under Berkeley Software Distribution license
- Combine memory caches into a logical pool
Pros of Memcached
- Installation is fast
- Widely documented with a huge community
Cons of Memcached
- Only supported on Linux operating systems and systems that are similar to BSD
- Doesn’t support data redundancy
- Doesn’t support for locks, read-through, CAS
- Map/Reduce List and Show
- Provide database-level security
- Authentication opens via a session cookie like a web application
- JSONP for Free
- Follow document storage
- Support ACID Properties
- Provide the simplest form of replication
- Browser-based GUI to handle your data, permission, and configuration
Pros of CouchDB
- Map/Reduce, querying data is somewhat separated from the data itself
- Store any JSON data
Cons of CouchDB
- Arbitrary queries are expensive
- A bit of extra space overhead with CouchDB
- Doesn’t support XML
Before we summarize the article we compare the three databases:
|Description||High scalability, strong security by lowering overall cost of ownership||Traded in JSON format, Schema-less database||Key-value stores which run on top of HDFS|
|Data Model||Key Spaces||Flexible Schema||Column-Oriented DB|
|Performance||More Durable and Slight better among the three of them||Less durable compared to Cassandra||Less durable compared to Cassandra|
|Thrift Server role
|Replication Methods||Selective Replication Factor||Master Salve Replication||Selective Replication Factor|
|Competitive Advantage||No chance of Failure and it ensures 100% availability, High-Scalability||Best of Traditional Database, Giant-Ideas||Store Large dataset on top of HDFS, Aggregate and analyze billions of rows in HBase table for online analytics|
|Application Areas||Used in fraud detection applications. Twitter and Netflix used Cassandra.||Used in a mobile single view, real-time analytics||Used in medical to store the genome sequence, sports, storing match histories for better analytics, Web use Hbase for better customer targetting|
|Market Metrics||40% of the Fortune Hundred Companies||40 million downloads||7% of the companies in the world|
Before ending the list for NoSQL database. I must recommend preparing for Database interview -> Most popular quires for databases. We have recently been taking a Best NoSQL DB survey from different programmers who are available on social media for all SQL and NoSQL databases. Here we have seen the most popular database with its features, pros, and cons. Now you can decide which one is best for your project. Bonus point – Learn the CAP theorem before choosing any particular database. We need to know how to scale our database also as data increases in applications. Each Database administrator should learn the first CAP theorem and should learn each DB where it’s fitting based on application needs and then should choose it.
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