Object Store from Start to Finish
In the vast landscape of data management, Object Store emerged as a revolutionary force. Designed to handle large amounts of unstructured data, it offered a unique approach: storing data as discrete objects rather than in rigid tables or file hierarchies. This flexibility, combined with its ability to scale effortlessly, made Object Store an appealing choice for many early adopters, particularly those embarking on projects with uncertain future requirements.
Our example project, initiated over 25 years ago, was one such adopter. At its core, Object Store provided a simple yet effective way to store and retrieve objects, making it an ideal solution for the project’s nascent stage. It aligned perfectly with the project’s initial needs, supporting its growth from a small-scale initiative into a robust, evolving system. For years, it served as a dependable backbone, accommodating increasing amounts of data without faltering, and adapting through various stages of development, ownership changes, and even returns to previous hands—a testament to the effectiveness of big data development services in driving scalable and resilient solutions.
As we look to the future, the project’s continued success may well depend on a shift away from its foundational technology. This journey, moving beyond the confines of Object Store, begins with a careful consideration of alternatives, and MongoDB stands as a compelling candidate to lead the way forward.
Object Store provided a simple way to store and retrieve objects and supported transactions, but it did not ensure cascade deletion or strict referential integrity between tables. This meant that many functions typically handled by SQL databases had to be implemented manually in the project code. Ensuring referential integrity and enforcing uniqueness constraints and entity relationships fell entirely on the developers. This approach complicated the code and made it more prone to errors, especially when dealing with large volumes of data and complex interrelationships.
Object Store, being a third-party product with its own rules, conditions, and pricing policy, became the source of recent disagreements between the project owners and its suppliers. These disagreements forced a search for an alternative data storage solution. Given the project’s scale and its current and future needs, a decision was made to transition to a different data storage system. The new choice must not only meet all project requirements but also provide flexibility and stability for future development. Avoiding costs associated with licensing and support would be an additional benefit.
What to Choose: SQL or NoSQL Databases?
At this point, the question arose: should we choose a traditional relational database (SQL) or try something new, such as a NoSQL database? This decision required a thoughtful approach, considering various factors, such as the development team’s experience and the ability to quickly implement new solutions. It was important to consider that existing customers and users of the system did not have significant experience with SQL queries or scripting for data operations. This factor was crucial in choosing between SQL and NoSQL, as the system needed to remain accessible and easy to use for all users.
Additionally, considerations regarding data schema management played a significant role. In relational databases, the schema is strictly defined, which is an advantage for projects with a stable and predictable data structure. However, our project, which was closely tied to data analytics services, was dynamic and constantly evolving, often requiring the addition of new data types or changes to the structure of existing data. This approach does not always fit well into a relational model with rigid structural requirements. In this context, NoSQL offered greater flexibility, allowing the data structure to be changed almost “on the fly” without the need for complex migration operations. This capability became another argument in favor of moving away from relational databases.
Another important factor in choosing a database was the realization that most NoSQL databases do not require the complex organization of script delivery and execution control. This significantly reduces the costs of system maintenance and minimizes the risks associated with potential errors in working with scripts. These advantages made NoSQL a more attractive option for our project, as it eliminated the need to organize a complex process for administering and controlling SQL script execution.
All these considerations led to a deeper analysis of different types of databases to determine which approach would be most suitable for the current and future needs of the project. It was important to choose a system that would not only provide high performance and reliability but also be easily scalable and flexible in the face of changing requirements.
SQL databases have many advantages, such as support for complex transactions, strict data structure, cascading updates, and record deletions. These features make them ideal for applications with a well-defined structure and strict data integrity requirements. However, for this project, it was decided to avoid SQL. The main reason was the desire to avoid additional complexities associated with organizing the delivery and control mechanisms for executing scripts. Switching to an SQL database would have required significant efforts to ensure this mechanism, leading to additional development and maintenance costs. Moreover, the clients and users of the system previously had no experience working with complex SQL scripts, which could have created significant challenges in deploying and maintaining the system, increasing the likelihood of errors and reducing overall performance.
Which NoSQL database to choose?
Thus, the decision was made to opt for a NoSQL database. NoSQL databases offer greater flexibility in working with data and do not require strict adherence to a data schema. This approach was very appealing given the dynamic and ever-changing requirements of the system. NoSQL databases allow for much faster changes to data structures without requiring complex migration procedures or application modifications. All these factors played a decisive role in choosing NoSQL as the most suitable solution for the future development of the project.
The next step was to choose among specific NoSQL solutions. Among the many available options, databases such as Cassandra, Redis, CouchDB, and MongoDB were considered. Each of these has its strengths and weaknesses, but ultimately, MongoDB was selected.
MongoDB was chosen for several key reasons:
Schema Flexibility: MongoDB does not require a rigid schema for data, making it an ideal choice for projects where the data structure may evolve as the system develops. This flexibility allows for the easy addition of new fields or modification of existing ones without breaking existing functionality.
Support for Complex Queries and Indexing: Although MongoDB is a NoSQL database, it supports powerful query and indexing capabilities, allowing for easy and efficient execution of complex data operations. This was critical for the project, where data needed to be retrieved and analyzed based on various criteria.
Broad Community and Support: MongoDB has a large and active user community, as well as extensive documentation and support from the developers. This facilitates the process of integrating the database into the project and allows for quicker resolution of emerging questions and issues.
Scalability: MongoDB easily scales both vertically and horizontally, making it an excellent choice for applications with growing data volumes and high query loads. This was an important advantage in a situation where an increase in data volume and the number of system users was anticipated.
Wide Support in the Developer Ecosystem: Support at the level of development tools and many programming languages also played a significant role. MongoDB has client libraries for most popular programming languages, making its integration into the existing codebase relatively seamless.
After a thorough analysis of all the pros and cons of different databases, MongoDB was chosen. This choice provided the flexibility and power in data handling that was needed for the current and future development of the system.
Transitioning from the Object Store database to MongoDB allowed us to avoid many limitations associated with the previous architecture. The new data storage solution provided the flexibility required for a dynamically evolving project and simplified the process of data management while retaining powerful querying and analytics capabilities. As a result, it was possible not only to improve the system’s performance and reliability but also to prepare it for future challenges and requirements that might arise.
