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Evaluation of Databases to Find the best from SQL vs. NoSQL vs. NewSQL

SQL vs. NoSQL vs. NewSQL

With SQL getting invaded by the NoSQL wave and then it is getting outrun by the NewSQL camp, the users are confused as to how to choose between these for their purpose. When it comes to selecting enterprise application databases, the expert opinion is always ‘there is no one size fits all.’ The ideal now is that any single database product may not be able to satisfy the use cases in full.

The volume and diversity of data are also exploding lately. With the invention of AI and big data analytics, etc., it becomes more and more critical for organizations to keep data in an easily deliverable and interpretable manner to make quick data-centered decisions. In this article, we are exploring the evolution of databases from time to time with a baseline comparison of the new eras in database management of SQL, NoSQL, and the latest NewSQL.

However, if you are content with the performance, availability, and scalability aspects of your legacy SQL database management systems, i.e., MySQL, Oracle, SQL Server, etc., then there is no reason for you to read through this further. However, if you face challenges in these areas and want to find out more operable solutions about these, then the offerings from NoSQL or NewSQL may be apt for you to go further.   

Choosing the most appropriate database tool for the job in hand could be 75% of the solution for your issues, and the rest of the 25% comes from understanding the problem you are trying to address. In this article, we are attempting a quick rundown of the benefits and disadvantages of the conventional SQL databases (no OldSQL) versus the NoSQL and NewSQL, which you help you to focus better on your future datastore selections.

SQL / OldSQL

The traditional relational SQL databases are there for quite long now and act as the foundation of many legacy applications across the globe. If you already have a deployed SQL application that is performing acceptably, it is apt to leave it that way, and there is no need to think of an upgrade now. One should realize that porting your database or resizing it will take a lot of work initially along with some risks.

Database replacement rarely acts as a feature-for-feature candidate by fixing bug-for-bug with the database getting replaced. Also, one should beware of the vendors who suggest the options to you. It is essential to choose the right provider if you are looking for a third-party database administration consulting. For expert help in choosing database and remote administration support, feel free to chat with RemoteDBA.com experts.

Advantages of SQL

1. It is a well-proven database technology with reliable support, existing for decades now.

2. SQL is compatible with the ORM layers like ActiveRecord or Hibernate.

3. It enables better client-side transactions.

4. Querying and reporting can be done ad hoc.

5. Being existing for long, SQL has a well-established market and has a robust ecosystem with many standard tools available.

SQL disadvantages

1. It is not a scale-out solution. The capacity of a single machine often guides the transactional throughput of SQL.

2. Scaling up or down requires managed sharding or data partitioning etc.

3. The old-school SQL systems are built for one size fits all applications.  So, there are only suitable for applications which are for general purpose with moderate requirements only.

NoSQL

When dealing with petabytes of daily growing data, it needs to be regularly handled by the database, which is not possible with a relatively static DB. On the other hand, you may want to store it in the cloud, and it also may want to handle the programming challenges too without tampering the consistency. The need may be to ensure decentralized access, multiple datacenter replications, and the highest availability 24×7. This is where NoSQL comes into place.

The most popular NoSQL DBMS are CouchDB, MariaDB, and MongoDB, which follows a document model that expands by replacing the conventional key-value store modes with JSON-structured value documents. Each of these further have sub-values, sub-keys, value arrays, or hierarchies of all the above. These are also described to be schema-less and do not enforce any premeditated schema across the stored data. This further has a different approach to managing schemas, which is less rigid, but a bit messier if not appropriately handled.

The other existing systems tend to expand on the key-value stores with the given organizational features. Among these, Redis is much acceptable in terms of creating multiple sorted data lists, which makes creating leaderboards and ranking easier. Adding more complex functions and statistics can focus better on the functionality key to specific use cases.

The best examples for this approach are the systems that are optimized for graphical processing like Neo4j. The array databases are another example among which SciDB uses R and Python to access the MPP array data for big-scale scientific researches. Accumulo is another variation of the wide-column-store model, which is popularized by the NoSQL DBs like BigTable and Cassandra.

NewSQL

As of now, term NewSQL is not as broad as what we know about NoSQL. All existing NewSQL systems have started with the concept of relational data and SQL query language, and now all these try to additionally address the scalability and inflexibility challenges existing in its predecessors. So, the approach of NewSQL is to combine the advantages of both SQL and NoSQL in one hand by addressing the challenges of those on the other.

Considering the available options in NewSQL, NuoDB offers a cluster-first SQL DB model that focuses on cloud-based operations by running on many nodes and better managing data consistency and locality. However, this comes with a toll on the flexibility and performance of the database. Some other NewSQL systems, like MemSQL, focus on the concept of clustered analytics. With distributed compatibility of MySQL, the NewSQL version of MemSQL offers a quicker OLAP analytics than any standard SQL systems with a higher level of concurrency and capability to update the data as it is analyzed. 

So, lately, the discussions in the data management space are substantial, and DBAs tend to talk more about the categories as SQL, NoSQL, and NewSQL vs. the ability of each to handle the challenges. This is the right approach for any enterprise database user to identify the best solution for the challenges they face.

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