MongoDB vs MySQL vs. PostgreSQL Comparison

Let’s look at five of the biggest differences between MongoDB and PostgreSQL, that you should consider when deciding which database to use. Learn more about them, how they differ, and how to determine which one is right for you. The tests show that If you want a database that delivers great performance and low latency, you want Postgres. Additional testing was conducted on online transaction processing (OLTP) workloads. For the OLTP test, the industry standard sysbench benchmark was used with Postgres once again coming out on top,  performing three times faster than MongoDB on average.

Plus, MongoDB is open source, which means it’s free to use, and you can contribute to its development. So, if you’re looking for a cutting-edge database that’s easy to use and can handle any workload, MongoDB might be the perfect fit for you. For each spatial distance three experiments are executed with different amount of timestamps and waypoints of a specific vessel’s trajectory. Again the superiority of PostgreSQL is obvious as the sample grows and reduced almost at half. In case of PostgreSQL we used the fastest solution to find all vessels within some distance of a given point. The simplest way to perform this query is to use ST_DWithin with the PostGIS geography type, instead of geometry.

When to use MongoDB vs. PostgreSQL

MongoDB has implemented a modern suite of cybersecurity controls and integrations both for its on-premise and cloud versions. Users cannot do anything beyond retrieving and inserting JSON as a string. Many times it fails to find the difference between the long, integer and thus provides no support for all data types. MySQL is designed to be open-source and has a vast and active community. It is compatible with various operating systems, including Linux, OS X, Solaris, and Windows. Many prominent companies across diverse industries rely on MySQL for their data management needs.

It also creates a new revision ID for the document, which allows multiple documents with the same data to exist simultaneously. Concurrency is the ability of a database system to manage multiple transactions at the same time. Concurrency allows multiple users to access and modify data without causing inconsistency difference between postgresql and mongodb issues or conflicts. PostgreSQL uses an SQL variant, called Postgres SQL, as its query language. Although similar to SQL, it has additional features like an extensible type system, functions, and inheritance. However, PostgreSQL is still compatible with standard SQL, so you can use SQL queries as well.

Step 3: Load the Exported CSV to PostgreSQL

In specific, GeoSpark seems to be the most complete spatial analytic system because of data types and queries supported. MongoDB is a non-relational database that stores data in dynamic JSON-like documents, while PostgreSQL is an object-relational database that stores data in pre-defined tables with rows and columns. MongoDB does not require a pre-defined schema before inserting data, whereas PostgreSQL does. Because PostgreSQL is a traditional relational SQL database, it works well for basic applications where data is structured. For example, an e-commerce front end will work well with a SQL database such as PostgreSQL.

• PostgreSQL also carries no licensing cost, eliminating the risk of over-deployment.
• This code is executed for a different set of ListOfTimestamps and ship_id.
• Despite the popularity of NoSQL databases, relational databases continue to be relevant for various applications because of their robustness and strong querying abilities.
• Our goal in this article is to help to explain the personality and characteristics of each of these databases so you can better understand whether it meets your needs.
• PostgreSQL, on the other hand, supports traditional SQL transactions, which are more familiar to developers.
• If your concerns are time to market, developer productivity, supporting DevOps and agile methodologies, and building stuff that scales without operational gymnastics, MongoDB is the way to go.

Furthermore, if you’re working with a tabular data model that’s unlikely to change on a regular basis and has no need to scale-out, SQL and relational databases can be a terrific option. Thanks to ACID transactions, relational databases allow for simpler application writing. The defining and implementation of ACID transactions is highly complex, and we simply don’t have the space to detail it all here.

Send email from airflow Setup airflow email in 2023

Automatic failover and replication are also built into MongoDB where PostgreSQL requires either an extension or more configuration to support those features. Migrating to a NoSQL document database can be a challenge if you have a large data model. Take inventory of your software to check if you have business intelligence analysis and reporting tools as they may depend on a SQL database and will not be able to take advantage of a NoSQL database. MongoDB has a rich set of data types which include String, Numeric, Boolean, Min/Max keys, Arrays, Timestamps, Object, Null, Symbol, Date, Object ID, Binary, Code, and Regular Expression. In comparison, the maximum BSON document size is 16 megabytes in MongoDB, and in PostgreSQL the maximum row size is 1.6 terabytes. PostgreSQL has a numerous selection of data types which include Boolean, Character, Numeric, Temporal, UUID, Array, JSON, key-value pairs, and special types such as network address and geospatial data.

The same behaviour is observed in the other samples of timestamps for both queries. SPEC, BAPco and TPC benchmarks are not suitable for large database environments and they cannot be applied for spatiotemporal data. However only three queries from SEQUOIA 2000 and one query of PGS-DBMS include the temporal component. Furthermore, Jackpine’s micro- and macro benchmarking consist only of spatial queries. On the other hand, the 3-Dimension spatiotemporal benchmark expands the aforementioned benchmarks and includes the time component.

Comparison of MongoDB vs. PostgreSQL

The latest version of MongoDB has new features, such as support for automatic data archival, delete operations, and time series dataset distribution across shards. Launched in 2007, MongoDB now serves some of the world’s biggest companies, including EA, eBay, and Shutterfly. Common use cases for MongoDB include customer analytics, content management, business transactions, and product data. Thanks to its ability to scale, the database is also ideal for mobile solutions that need to be scaled to millions of users. MongoDB has currency control mechanisms that use document-level atomicity and optimistic locking. It assumes there are no conflicts between most concurrency write operations, which allows people to modify data at the same time without acquiring locks.

With phishing attacks, malware, and other threats on the rise, it is essential that you make the right choice to keep your data safe and process it effectively. However, it can be extremely difficult to choose from the wide variety of database solutions on the market today. MongoDB is a NoSQL database with a flexible data model, high performance, and effective horizontal scaling. While MongoDB doesn’t have the same level of community maturity, it does offer drivers for many programming languages. There is lots of community and aid to help you interact with MongoDB using one of your preferred programming languages. An index is a data structure that maps values of one or more columns to a physical location of the corresponding data on the disk.

PostgreSQL Indexes

MongoDB is an open-source software from MongoDB Inc used for non-relational database management systems. At the same time, PostgreSQL is developed and maintained by the PostgreSQL Development group, which is used for the relational database management system. Since PostgreSQL handles relational databases, it exhibits an object-oriented nature. PostgreSQL and MongoDB support all major operating systems, including Windows, Linux, and Unix. There are challenges in managing and querying the massive scale of spatial data such as the high computation complexity of spatial queries and the handling of the big data nature of them.

MongoDB benefits from a committed community of developers spanning hobbyists, massive enterprises, government agencies, and emerging startups. Not to forget the numerous systems integrators and consultants delivering an extensive range of services. As you can see from the above MongoDB vs PostgreSQL comparison, both databases have lots to recommend them.

MongoDB vs PostgreSQL: Pricing Models

Q7ii adds yet another factor, the geographical area and performs the same functionality as Q7i. Q8i returns the average speed for every vessel passed in the query whereas Q8ii takes into account the geographical area. The geographical polygons that used are uniformly selected and occupy equal size (P1ran, P2ran, P3ran). This code is executed for a different set of ListOfTimestamps andship_id. For queries Q8i and Q8ii the pseudocode is almost the same one that responds to Q7i and Q7ii and for this reason we preferred to exclude it. It is worth mentioning, that the selection of different vessels (ship_id) and time intervals follows a normal distribution, which was applied in the dataset at an early stage before the execution of queries.