PostgreSQL vs MongoDB: Key Features and Benefits

MongoDB also supports database transactions across multiple documents allowing bits of related changes to be rolled back or committed as a group. Owing to its multi-document transactions capability, MongoDB is one of the few databases to coalesce the flexibility, speed, and power of the document model with the ACID guarantees of traditional databases. PostgreSQL can handle complex joins, outline relationships, and rapidly query data. As it’s structured, it can process large volumes of data and rapidly provide insight and advanced analytics. These features also allow it to integrate well into business intelligence tools and work effectively as a data warehouse. But MongoDB has succeeded, especially in the enterprise, because it opens the door to new levels of developer productivity, while static relational tables often introduce roadblocks.

Analysis: A Ransomware Attack on a PostgreSQL Database – Security Boulevard

Analysis: A Ransomware Attack on a PostgreSQL Database.

Posted: Wed, 25 Oct 2023 05:10:50 GMT [source]

MongoDB can work best when integrated into an analytics platform, as MongoDB’s speed provides dynamic performance that can help track the user’s behavior in real time. PostgreSQL is a highly stable database management system, backed by over 20 years of community development that has led to its high levels of integrity, resilience, and correctness. You can use PostgreSQL as the primary data warehouse or data source for various mobile, geospatial, analytics, and web applications.

What Is PostgreSQL?

Managed streaming data pipelines, streaming SQL transformations and turnkey connectivity to clouds, databases, and apps. In this section, you will look at how to perform data integration between MongoDB and PostgreSQL using Airbyte and Estuary. To follow along, it is assumed that you have accounts with these service providers as they all provide a cloud solution.

mongodb postgresql

In contrast, PostgreSQL is an object-relational database management system that you can use to store data as tables with rows and columns. It offers flexibility in data types, scalability, concurrency, and data integrity for structured data. PostgreSQL and MySQL are two of the most popular open-source relational databases today. While they share many things in common, the differences between them are significant and can be a source of confusion both for newcomers to database management and for experienced DBAs.

How to Secure PostgreSQL: Security Hardening Best Practices & Tips

4 in which client application always interact with the primary node and the primary node then replicates the data to the secondary ones. In case of write requests the queries are forwarder only to the primary node. SPEC, BAPco and TPC benchmarks are not suitable for large database environments and they cannot be applied for spatiotemporal data.

mongodb postgresql

These authentication mechanisms help reduce a server’s attack surface and prevent unauthorized access to data. PostgreSQL uses a vertical scaling strategy to manage vast amounts of data and increase write scalability by adding hardware resources such as disks, CPUs, and memory to existing database nodes. Comparing the performance of MongoDB and PostgreSQL is a complex task due to their distinctive approaches to data storage and retrieval. The most recent version of PostgreSQL has new features such as improved performance for queries and performance gains and space savings when B-tree index entries become duplicated.

PostgreSQL vs. MongoDB Performance

PostgreSQL, like Linux, is an example of a well-managed open source project. One of the most broadly adopted relational databases, PostgreSQL came out of the POSTGRES project at the University of California at Berkeley starting in 1986 and it has evolved with the times. NoSQL capabilities in an RDBMS database can help deal with unstructured data, for example, JSON, XML, and other TEXT data types. Replication at the table level can be achieved using external open-source tools such as Slony, Bucardo, Londiste, RubyRep, etc. PostgreSQL also supports logical replication, which performs table-level replication using WAL records and removes the complexity brought in by trigger-based replication. Initially, logical replication was supported by an extension called pglogical and has been part of the PostgreSQL core from version 10.

  • Because PostgreSQL is widely used, you can be pretty sure that most development tools and other systems have been tested with it and are compatible.
  • There are several JSON-specific operators and functions, making data searches in JSON documents very efficient.
  • You dumped a decades-tested, fully featured RDBMS for a young, beta-quality, feature-thin document store with little community support.
  • Availability ensures that even during a server outage, there’s no data downtime.
  • The rest of this article aims to provide information that helps make a safe bet.

MongoDB and PostgreSQL are different types of databases that have distinct data models. PostgreSQL can support replication but more advanced features such as automatic failover must be supported difference between postgresql and mongodb by third-party products developed independently of the database. Such an approach is more complex and can work slower and less seamlessly than MongoDB’s in-built self-healing capabilities.

MongoDB vs PostgreSQL: Relationships Among Tables

This phase is essential, as the db systems try to avoid disk requests by storing the index references in RAM. Q7i returns the haversine distances of vessels by calculating continuous distances of pairs of points and by summing these distances for every vessel passed in the query. This code is executed for a different set of ListOfTimestamps and ship_id.

mongodb postgresql

If you’d like to support me as a writer, consider signing up to become a Medium member. By clicking “Post Your Answer”, you agree to our terms of service and acknowledge that you have read and understand our privacy policy and code of conduct. Xml is the best supported of the three, with the most features, and the longest support history. Each document class gets its own table, with columns for all metadata and data. The web app we are building contains data that is clearly relational in
nature as well as data that is document-oriented. There’s a lot here and I’m not
sure it’s a good fit for the StackExchange Q&A format but I think it a) an answerable question and b) non-specific enough that it can benefit the community.

Overview of MongoDB and PostgreSQL​

PostgreSQL offers many ways to improve the efficiency of the database, but at its core it uses a scale-up strategy. The document model also has emergent properties that make development and collaboration much easier and faster. Below are a few examples of SQL statements and how they map to MongoDB. A more comprehensive list of statements can be found in the MongoDB documentation. The strength of SQL is its powerful and widely known query language, with a large ecosystem of tools. The right answer for your needs is based of course on what you are trying to do.

mongodb postgresql

So, now that we know what each database has to offer, we need to determine when to choose each depending on the data, organization, and requirements in question. The key is to identify your needs and best match the abilities and benefits with those guidelines. That being said, MongoDB has adopted more to the ACID format in 2018, though it still has yet to measure up to the rigidity of those properties. A study published in May 2020 identified a bug that affects the claim that Mongo performs ACID transactions at an acceptable level.

Security Model

Alongside the data values, each tuple also contains metadata like the primary key, which identifies each tuple within a table. As we said at the outset, the question is not “MongoDB vs. PostgreSQL? ” but “When does it make sense to use a document database vs. a relational database? ” because each database is the best version of its particular database format. 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.

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