Reasons to migrate from document database to relational database

7 min readDec 15, 2023

Let’s talk with examples so it’s easier to relate to. MongoDB and PostgreSQL are two popular database systems that have different strengths and weaknesses. MongoDB is a document-oriented database that follows the NoSQL paradigm, while PostgreSQL is an object-relational database that is SQL compliant. Depending on your application’s requirements, you may find that one of them suits your needs better than the other.

In this blog post, we will explore some of the reasons why you might want to migrate from document database to relational database if you faced similar problems with document database in your application.

Why We Shouldn’t Fit Data with Well-Defined Schema into Document Database

Document databases, also known as NoSQL databases, are popular choices for storing and querying unstructured or semi-structured data. They offer flexibility, scalability, and performance advantages over traditional relational databases. However, they are not suitable for every use case, especially when the data has a well-defined schema that is unlikely to change.

In this blog post, we will explore some of the reasons why we shouldn’t fit data with well-defined schema into document database, and what are the alternatives.

Data Consistency and Integrity

One of the main drawbacks of document databases is that they do not enforce data consistency and integrity at the database level. This means that different documents in the same collection can have different fields, types, and values, and there is no guarantee that the data is valid or complete. This can lead to problems such as:

Data duplication: Since there is no schema, the same data can be stored in multiple documents, wasting storage space and increasing the risk of inconsistency.
Data corruption: Since there is no schema, the data can be easily modified or deleted by mistake, causing data loss or errors.
Data quality: Since there is no schema, the data can be of poor quality, such as missing, incomplete, or malformed, affecting the accuracy and reliability of the data.

To avoid these problems, the application layer has to implement the logic and rules to ensure data consistency and integrity, which can be complex and error-prone. Moreover, the application layer has to be updated whenever the data schema changes, which can be costly and time-consuming.

Data Modeling and Querying

Another drawback of document databases is that they do not support complex data modeling and querying. This means that the data has to be denormalized and embedded into documents, and the queries have to be written in a specific syntax that is dependent on the database vendor. This can lead to problems such as:

Data complexity: Since the data has to be denormalized, the documents can become large and nested, making them hard to read and maintain.
Data performance: Since the data has to be embedded, the queries have to scan the entire documents, which can be slow and inefficient, especially for large datasets or complex queries.
Data flexibility: Since the queries have to be written in a specific syntax, the queries have to be rewritten or translated whenever the database vendor changes, which can be tedious and risky.

To avoid these problems, the data model and the queries have to be carefully designed and optimized, which can be challenging and time-consuming. Moreover, the data model and the queries have to be revised whenever the data schema changes, which can be disruptive and costly.

Data Migration and Integration

A final drawback of document databases is that they do not support easy data migration and integration. This means that the data has to be converted and transferred between different formats and systems, and the data has to be synchronized and reconciled across different sources. This can lead to problems such as:

Data compatibility: Since the data has to be converted, the data can lose some of its original properties, such as precision, order, or relationships, affecting the fidelity and usability of the data.
Data availability: Since the data has to be transferred, the data can encounter delays, errors, or failures, affecting the accessibility and reliability of the data.
Data security: Since the data has to be synchronized, the data can be exposed to unauthorized or malicious access, affecting the confidentiality and integrity of the data.

To avoid these problems, the data migration and integration have to be carefully planned and executed, which can be complicated and risky. Moreover, the data migration and integration have to be repeated whenever the data schema changes, which can be frequent and expensive.

Alternatives to Document Database

Given the drawbacks of document databases, what are the alternatives for storing and querying data with well-defined schema? Here are some of the options:

Relational database: A relational database is a type of database that stores data in tables, where each table has a fixed set of columns and each row has a unique identifier. A relational database enforces data consistency and integrity at the database level, using constraints, triggers, and transactions. A relational database supports complex data modeling and querying, using a standard language called SQL. A relational database supports easy data migration and integration, using tools such as ETL, APIs, and connectors. A relational database is suitable for data that is structured, stable, and transactional, such as financial, operational, or inventory data.
Graph database: A graph database is a type of database that stores data as nodes and edges, where each node represents an entity and each edge represents a relationship. A graph database enforces data consistency and integrity at the database level, using schemas, indexes, and validations. A graph database supports complex data modeling and querying, using a language called Cypher. A graph database supports easy data migration and integration, using tools such as importers, exporters, and drivers. A graph database is suitable for data that is highly connected, dynamic, and analytical, such as social, network, or recommendation data.
Columnar database: A columnar database is a type of database that stores data in columns, where each column contains values of a single attribute and each row contains values of multiple attributes. A columnar database enforces data consistency and integrity at the database level, using schemas, compression, and encoding. A columnar database supports complex data modeling and querying, using a language called SQL or a variant. A columnar database supports easy data migration and integration, using tools such as loaders, unloaders, and connectors. A columnar database is suitable for data that is sparse, variable, and analytical, such as event, log, or sensor data.

Benefits of having a well-defined schema compared to schemaless data

This is only relevant if your data has a well defined schema.

A well-defined schema ensures data integrity, as it enforces data validation and integrity constraints, such as primary keys, foreign keys, unique constraints, check constraints, and triggers.
A well-defined schema enhances data retrieval speed, as it allows for efficient indexing and querying of data, especially for complex queries that span multiple tables or statements.
A well-defined schema simplifies complex queries, as it provides a clear structure and type for data, and supports ACID transactions that guarantee data consistency and isolation.
A well-defined schema helps maintain data quality, by taking advantage of database constraints such as consistent formatting and enforcing referential integrity during lookups. It also helps minimize data duplication.
A well-defined schema supports data analytics, as it enables data modeling and design of data structures that suit the analytical requirements, such as star schema or snowflake schema.

Benefits for developers by having a well defined schema

Disadvantages of using document database for data with well defined schema and relationships

Here are some possible disadvantages of using MongoDB for storing data with well-defined schema and relationships:

MongoDB does not support ACID transactions, which means that it cannot guarantee data consistency and integrity across multiple operations or documents. This can lead to data corruption, duplication, or loss, especially when dealing with complex or nested data structures.
MongoDB does not support joins, which means that it cannot perform queries that involve multiple collections or documents. This can limit the query capabilities and performance of MongoDB, as well as increase the complexity and redundancy of the data model.
MongoDB relies heavily on RAM for data storage and retrieval, which means that it can be expensive and inefficient for large or complex data sets. MongoDB also does not support indexing on nested data structures, which can affect the query speed and performance.
MongoDB has a flexible schema, which means that it does not enforce any data validation or integrity constraints, such as primary keys, foreign keys, unique constraints, check constraints, and triggers. This can result in data inconsistency, duplication, or loss, as well as increased complexity and maintenance of the application logic.

Conclusion

Document databases are great for storing and querying unstructured or semi-structured data, but they are not ideal for data with well-defined schema that is unlikely to change. For such data, we should consider other types of databases, such as relational, graph, or columnar, that offer better data consistency, integrity, modeling, querying, migration, and integration. By choosing the right database for the right data, we can ensure the quality, performance, and value of our data.