Challenges and Best Practices in Transactional Database Scaling

database scaling

Database scaling is one of the first problems enterprise application architecture face lately. The relational databases we use conventionally are not meant to scale like any distributed model as in the cloud. However, as modern-day apps are expected to be available to everyone in the world, we must rethink the traditional database designs.

Databases are primarily the storage system for different built-in software to manage and manipulate the internal data movements. A typical DBMS (database management system) controls the other programs and processes related to any application. Irrespective of the size and functionality of an application, the design and implementation of a comprehensive database is a challenging endeavor. As the data sources and volume of data are continuously increasing day by day, newer types of databases to meet specific uses should be evolved.

Relational Databases

As we can see, most of the traditional database platforms still use relational databases with SQL storage. The data storage usually occurs in memory, which can be lost if there is a power outage. The long-term data gets saved in the storage space, which is retained and adequately backed up irrespective of whether the system is on or off.

Transactional Databases

The objective of transactional databases is to record the transactions. We can consider the bank transactions or e-commerce purchases of a user. Each transaction is assigned with a unique transaction ID clubbed with the list of items related to that specific transaction. Transactional databases include various tables containing different information related to the transactions, such as the descriptions of items purchases, location, user info, salesperson’s info, and more. Most of the transactional databases are ACID compliant.

Scaling of Transactional Databases

The scalability of a database describes its ability to handle the increasing volume of data and queries coming to it. Scalable systems can accommodate the workload by evolving continuously and adjusting the resources designed to work with the growing amount of data without failing or resultant downtime.

The capacity of data servers to accommodate more data and workload is represented by both scalability and elasticity. Elasticity is primarily referring to the ease with which database adapts to the increasing volume of work by ensuring on-demand resources. As we can see, relational databases are less elastic as they function in the predefined method.

Scalability requires more availability and a database management system with the flexibility to accommodate the administrative changes or upgrades without tampering the end-user experience. Scalable systems can get automatically adjusted to the increasing workloads without tampering accessibility.

1. Identify the Actual Problem

The first thing to understand with a database scaling need in hand is where the actual bottleneck is. Identifying what causes your database to slow down or crash will act as the pointer to your solution. Most of the time, it may be stored. A poorly written SQL query may cause a cascading effect and delay in getting the needed information from the storage. Some other times, it could be the application code or stored procedure errors creating the issues. So, all the time, adding additional resources may not be a quick solution. Sometimes, it may be about merely correcting the code than other resources.

2. Increase the Memory

If you identify the bottleneck as the storage, then you may first think of adding more memory. By enhancing the information in given memory, you may not be accessing the disks as often, so speed may be improved. For example, in cases, you may find success by adding the memory and merely dedicating the server to cached database info in the memory.

3. Avoid Sharding of Database

Most of the time, database admins may try to go for an overly complicated solution to scale up relational databases horizontally using “sharding.” Simply put, sharding is the process of splitting your existing database in half. By putting the relational DB into groups, it will leave your application to deal with two databases. Further, it will increase the complexity and risk this way, which is not always recommended.

Latest Database Scaling Trends

The need for database scaling is ever-changing, and newer modes or scaling are getting introduced day by day. The latest scaling models include the memory databases and the latest NoSQL database systems like Mongo DB and Cassandra.

Databases like Cassandra can support replication across various datacenters, which will further provide lower latency to the users. MongoDB is also a highly scalable database, which is an open-source system built in C++. As a rule of thumb, NoSQL systems are widely scalable and commonly distributed geographically with more fault-tolerance. However, one major challenge with these systems may be that with increased scalability, the administration’s complexity may also scale up in some ways.

Many options are out there for those who do not want to go with the NoSQL track. For example, the VMware’ vFabricSQLFire, a solid data management system offering high memory speed, a conventional SQL interface, and horizontal scalability at its best. SQLFire will let the normal SQL queries run, but on the other hand, it can be distributed geographically as in the case of NoSQLDBs. Being in between the SQL and NoSQL, SQLFire is also considered to be as NewSQL.

We can also notice that the relational databases are quickly becoming a legacy in light of the modern-day apps which need greater scalability. Erstwhile relational DBs are not meant for the needs of today’s apps. Of course, there are ways to scale the relational databases, but most of them may not scale up or out to the magnitude which many new DB technologies can.

It is possible to migrate your data from an old RDBMS to the latest NoSQL databases, but the major challenge in this is changing your application to integrate with the NoSQL database. NoSQL databases are not relational anymore, but these are key-value stores.

These practices, which we discussed above, may help you out to keep your relational databases to be tweaked for the changing needs of your applications. However, eventually, the older applications may have to be re-architected to comply with the new technologies and meet the needs of the new-age users.

Author Bio

Kristen Smith is a web developer and experienced professional in database management and administration. She says you must deploy credible companies like to help you maintain and secure any database system with success!



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