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MongoDB vs PostgreSQL: What to consider when choosing a database

Sharding implies that the data is stored across multiple computers while partitioning groups this data within a single database instance. BSON skips the keys that aren’t useful for the query, thus making it faster to retrieve data. A user could further define the document’s structure and undertake some development by introducing new fields, reworking data, or developing it whenever they see fit. MongoDB has a document model, making collaboration and development easier and faster to implement. Relational databases often store information about tables, databases, columns, etc. in system catalogs. These “data dictionaries” appear to the user as tables, but they do have information stored internally by the database system.

difference between postgresql and mongodb

PostgreSQL complies with a wealth of security standards and includes various features for backup, reliability, and disaster recovery (typically via third-party tooling). PostgreSQL employs an engineering-centric approach to almost everything. The company has stated that it works to conform with the latest SQL standard when that doesn’t contradict conventional features or may contribute to ill-founded architectural choices.

PostgreSQL use cases

Document databases can do JOINs, but they are done differently from multi-page SQL statements that are sometimes required and often automatically generated by BI tools. That said, MongoDB does have a SQL connector that allows SQL access, mostly from BI tools. The plumbing that makes MongoDB scalable is based on the idea of intelligently partitioning (sharding) data across instances in the cluster. MongoDB does not break documents apart; documents are independent units which makes it easier to distribute them across multiple servers while preserving data locality.

In addition to a mature query planner and optimizer, PostgreSQL offers performance optimizations including parallelization of read queries, table partitioning, and just-in-time (JIT) compilation of expressions. The object part of PostgreSQL relates to the many extensions that enable it to include other data types such as JSON data objects, key/value stores, and XML. It also offers Atlas Search powered by Lucene, and with features that support data lakes built on cloud object storage. MongoDB has seen massive adoption and is the most popular modern database, and based on a Stackoverflow developer survey, the database developers most want to use. Thanks to the efforts of MongoDB engineering and the community, we have built out a complete platform to serve the needs of developers.

Online Transaction Processing (OLTP) Compared: PostgreSQL 11.1 and MongoDB 4.0

Having a database to collect customer information, such as likes, dislikes, order history, or articles read, allows a business or organization to target their consumers more readily. This will lead to higher sales, more traffic, and better targeted ads. There are several different flavors of normalization, but the high level explanation is that it reduces redundancy difference between postgresql and mongodb and anomalies in your data. The retail store example from above could have certainly used a computerized database to increase productivity and reduce the amount of manual tabulating. One of the things that we may struggle with as developers when working on a green field project is our stack. Choosing the right tech to solve a problem can be a harrowing experience.

difference between postgresql and mongodb

The manual method is effective, but it requires a lot of time and resources. Migrating data from MongoDB to PostgreSQL is a tedious and time-consuming process, but data integration tools like RestApp make this process easy and time-saving. MongoDB is a popular NoSQL database that many companies have adopted due to its flexibility and scalability. When connecting the two, PostgreSQL will act as a central point for data management and extraction.

Why Fortune 50 Banks are Leaving Oracle for EDB Postgres

MongoDB shines as a consistency and partition tolerant document store while PostgreSQL focuses on consistency and availability. MongoDB has a single master in a replica set that can accept reads and writes, and the secondaries can be configured for reading. PostgreSQL has a similar setup with a single master, and passive nodes can be configured for reading. Scaling is inherently built into MongoDB, but with PostgreSQL an extension is required to add that capability. There are numerous extensions to choose from to achieve scalability with PostgreSQL. You can have as many nodes as needed in a sharded cluster with MongoDB, and PostgreSQL has no limit on database size.

  • There are several different flavors of normalization, but the high level explanation is that it reduces redundancy and anomalies in your data.
  • MongoDB is a non-relational or NoSQL database with a flexible data model.
  • As a result, a transaction would be necessary to update every record at once.
  • You can now model and sync your data with a Drag-and-Drop Editor (NoSQL, SQL, and Python built-in functions).
  • Aditi Prakash is an experienced B2B SaaS writer who has specialized in data engineering, data integration, ELT and ETL best practices for industry-leading companies since 2021.
  • These systems can deal with challenges related with the distribution of streams among nodes via thread keys and can handle differences in event and processing time.

Like MySQL and other open source relational databases, PostgreSQL has been proven in the cauldron of demanding use cases across many industries. If data aligns with objects in application code, then it can be easily represented by documents. MongoDB is a good fit during development and in production, especially if you have to scale. MongoDB is adept at handling data structures generated by modern applications and APIs and is ideally positioned to support the agile, rapidly changing development cycle of today’s development practices. PostgreSQL has a full range of security features including many types of encryption.

Online Analytical Processing (OLAP) Compared: PostgreSQL 11.1 and MongoDB 4.0

The two systems store data differently and the concept of “index” is different too. As an example the size of the index on attribute “ship_id” in MongoDB is about 6 GB while in PostgreSQL the size is 3,1 GB. As mentioned above, the data are stored in MongoDB as GeoJSON and the $geometry field contains the coordinates values, latitude and longitude. Because these data are geographical, we create a 2dsphere index type which supports geospatial queries. Respectively, in PostgreSQL we created an index of type GiST on field the_geom which contains the POINT geometry created from latitude and longitude of each record.

On the other hand, PostgreSQL’s vertical scaling is more limited by the resources available on a single instance. In this stage, several queries for index-loading and other memory-related attributes are performed, as they can affect the overall performance of the DBMSs systems. This phase is essential, as the db systems try to avoid disk requests by storing the index references in RAM. In this perspective it makes sense to focus on different subsets of a Mediterranean dataset rather than examining a very sparse dataset, e.g. in the Pacific or Atlantic ocean. Regardless of the database you choose, partnering with a third party for support and guidance is a must.

MongoDB vs. PostgreSQL: Core Differences

Normalization is the process of structuring a relational database to reduce data redundancy, minimize anomalies in data modification, and improve data integrity. On the other hand, MongoDB allows you to store data in any structure that can be quickly accessed by indexing, no matter how deeply nested in arrays or subdocuments. In the next section, we’ll elucidate the differences between MongoDB and PostgreSQL to help you make that decision easily.

Data can be stored using specific data types, and developers can define and categorize data. For an e-commerce store, product descriptions will always be a string of characters, and the price of a product is always a decimal number. The data is predictable and structured in a way that a PostgreSQL database can manage.

Which Database Should You Choose: MongoDB or PostgreSQL?

Since data is stored in structured table designs, you link data across tables using primary and foreign keys. You can link dozens of tables using primary and foreign keys, but every record is stored in a structured way with rules that define columns. Because data is structured, your SQL statement will have expected results based on the type of data in each table. Postgres employs SQL ultimately under the hood, a structured query language, to define, to access and to manipulate the database.

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