MongoDB is an open source document-oriented database system. It is part of the NoSQL family of database systems. Instead of storing data in tables as is done in a "classical" relational database, MongoDB stores structured data as JSON-like documents with dynamic schemas (MongoDB calls the format BSON), making the integration of data in certain types of applications easier and faster.
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PostgreSQL
Score 8.6 out of 10
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PostgreSQL (alternately Postgres) is a free and open source object-relational database system boasting over 30 years of active development, reliability, feature robustness, and performance. It supports SQL and is designed to support various workloads flexibly.
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PostgreSQL
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MongoDB
PostgreSQL
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Fully managed, global cloud database on AWS, Azure, and GCP
Looking into PostgreSQL happened post move to Mongo. Had we considered both options at the time we likely would have went with PostgreSQL. We may migrate at some point in the future but currently it doesn't make sense.
We have [measured] the speed in reading/write operations in high load and finally select the winner = MongoDBWe have [not] too much data but in case there will be 10 [times] more we need Cassandra. Cassandra's storage engine provides constant-time writes no matter how big your …
Both Couchbase and MongoDB are document-oriented NoSQL databases, so they have very similar features. While they do have some fundamental differences in terms of how they scale, shard, etc. the one key reason why we went with MongoDB is its availability and support from the …
The flexible structure underlying MongoDB's construction is not found in other competitors; the ability to easily change the structure without affecting other stored documents. It is very ideal for projects that you cannot predict that the structure will change this way. Of …
MongoDB is our go-to database solution for any project, and the more we work with it the more we love it. Some say that NoSQL is pointless... Our developers wholeheartedly disagree, because they love working with it. Though both NoSQL and SQL have their purposes, in most …
Your default choice should not be MongoDB in my opinion. Most user-facing systems are relational by nature so a well known and reliable SQL database would be easier to maintain and simpler to develop long term. If you highly value speed of development go with Firebase. If you …
We tend to choose MongoDB when we're faced with a particular situation: we know that we need a NoSQL database in general, and want an open-source implementation that allows us to prevent against platform lock-in. Amazon's new DocumentDB product even allows us to choose to use …
MongoDB is the best NoSQL database out there. There are others, but Mongo has the largest community, is very easy to set up, and is extremely performant. Compared to a relational DB (like MySQL or Postgres) is like comparing apples and oranges. One isn't better or worse than …
I recently tried out Firestore from the Google firebase family of development products. While it allows structuring of data similar to MongoDB, it handles things a little differently. MongoDB documents are incredibly flexible and can be structured really any way you can …
MongoDB is my only NoSQL database that I have used. I have used SQL databases and don't find them as enjoyable. I code in full stack JavaScript and it blends perfectly with this. I know that there are competitors in this space, and I need to take time to try them all out. I …
I selected MongoDB because it works for well with web interfaces. All of the RDBMS alternatives would have required a lot more time writing schemas and working around retrieving data and mapping it. That could have been somewhat mitigated with Entity Framework, but that again …
The features between these database are quite comparable - except for possibly MongoDB. MongoDB being a different type of database and geared towards big data - I don't compare it to PostgreSQL. The other two I have used and would say PostgreSQL does fairly well when compared …
Despite being all open source options, what ended up making us choose PostgreSQL was the robustness of its core, which allows the great workflow that can support timely and efficient response to the demand and demand for resources. In the case of MongoDB, it is a non-relational …
MySQL is a popular open-source alternative to PostgreSQL, but in my experience it lacks the robustness, durability, and flexibility of PostgreSQL. It has also changed hands frequently, so support isn't the greatest. MongoDB and other NoSQL databases are helpful in certain …
First It's open source and it's cost-effective compared to other databases.PostgreSQL can be easily integrated with numerous platforms. It is well known and appreciated so relying on it as our system database can be easily accepted by our customers. And if your developing a …
PostgrPostgreSQL as a transaction db engine against oracle and sql server works well. TPM wise compared to MySQL and MariaDB, on an evan scale. SQL function supports, far outweighs compared to MySQL and MariaDB. PG Extensions allow for flexibiltity and scalability. Allows …
When we were originally evaluating Redshift we ran into some issue with dates. Either way, Postgres is a better choice than Redshift because it avoids vendor lockin. We ended up choosing Postgres over MySQL because it was easier at the time to get a hosted Postgres cluster up …
Much more mature and stable when compared to MySQL with features such as MVCC, complex subquery plans, ORDBMS, and NoSQL support. With Oracle retaining rights to MySQL its future as an open database is less secure and is no longer in the hands of the community. PostgreSQL also …
We selected PostgreSQL due to the number of employees who have used it in the past. The data consistency guarantees. The multiple transaction isolation levels support.
PostgreSQL outperforms every other option. It is faster, more flexible, more reliable, easier to maintain, and more consistent in behaviour than any of the other offerings.
It's a viable alternative, with a rich feature set and a reliable system. PostgreSQL is one of the best RDBMS's currently on the market in 2020, it serves just as well as a starter, PoC DB for any software idea as a final, highly valuable database solution for big systems.
PostgreSQL is the proper tool when data consistency matters and other BASE or document-based databases are simply improper. I think PostgreSQL has a fantastic system of slony replication, triggers, and other data maintenance functionality that other databases generally don't …
Compared to MySQL, it works well if you need to extend to your use case Compared to Spark, it works better w.r.t development time in a central database setting Like Redis, it cannot be used for caching and quick access of non-structured data
As I said, Postgres and MySQL are open source which is important for small start ups. Oracle is EXPENSIVE :) Postgres is faster than MySQL (Big factor) MySQL supports replication which makes it more scalable.
I am currently using MySQL and it is difficult to notice much of a difference at all. For free relational databases, there hasn't been enough for me to choose a clear winner. If you're already using a free solution, there would be no reason to change. In terms of comparing to a …
If asked by a colleague I would highly recommend MongoDB. MongoDB provides incredible flexibility and is quick and easy to set up. It also provides extensive documentation which is very useful for someone new to the tool. Though I've used it for years and still referenced the docs often. From my experience and the use cases I've worked on, I'd suggest using it anywhere that needs a fast, efficient storage space for non-relational data. If a relational database is needed then another tool would be more apt.
PostgreSQL, unlike other databases, is user-friendly and uses an open-source database. Ideal for relational databases, they can be accessed when speed and efficiency are required. It enables high-availability and disaster recovery replication from instance to instance. PostgreSQL can store data in a JSON format, including hashes, keys, and values. Multi-platform compatibility is also a big selling point. We could, however, use all the DBMS’s cores. While it works well in fast environments, it can be problematic in slower ones or cause multiple master replication.
Being a JSON language optimizes the response time of a query, you can directly build a query logic from the same service
You can install a local, database-based environment rather than the non-relational real-time bases such a firebase does not allow, the local environment is paramount since you can work without relying on the internet.
Forming collections in Mango is relatively simple, you do not need to know of query to work with it, since it has a simple graphic environment that allows you to manage databases for those who are not experts in console management.
The stability it offers, its speed of response and its resource management is excellent even in complex database environments and with low-resource machines.
The large amount of resources it has in addition to the many own and third-party tools that are compatible that make productivity greatly increase.
The adaptability in various environments, whether distributed or not, [is a] complete set of configuration options which allows to greatly customize the work configuration according to the needs that are required.
The excellent handling of referential and transactional integrity, its internal security scheme, the ease with which we can create backups are some of the strengths that can be mentioned.
An aggregate pipeline can be a bit overwhelming as a newcomer.
There's still no real concept of joins with references/foreign keys, although the aggregate framework has a feature that is close.
Database management/dev ops can still be time-consuming if rolling your own deployments. (Thankfully there are plenty of providers like Compose or even MongoDB's own Atlas that helps take care of the nitty-gritty.
The query syntax for JSON fields is unwieldy when you start getting into complex queries with many joins.
I wish there was a distinction (a flag) you could set for automated scripts vs working in the psql CLI, which would provide an 'Are you sure you want to do X?' type prompt if your query is likely to affect more than a certain number of rows. Especially on updates/deletes. Setting the flag in the headless(scripted) flow would disable the prompt.
Better documentation around JSON and Array aggregation, with more examples of how the data is transformed.
I am looking forward to increasing our SaaS subscriptions such that I get to experience global replica sets, working in reads from secondaries, and what not. Can't wait to be able to exploit some of the power that the "Big Boys" use MongoDB for.
NoSQL database systems such as MongoDB lack graphical interfaces by default and therefore to improve usability it is necessary to install third-party applications to see more visually the schemas and stored documents. In addition, these tools also allow us to visualize the commands to be executed for each operation.
Postgresql is the best tool out there for relational data so I have to give it a high rating when it comes to analytics, data availability and consistency, so on and so forth. SQL is also a relatively consistent language so when it comes to building new tables and loading data in from the OLTP database, there are enough tools where we can perform ETL on a scalable basis.
The data queries are relatively quick for a small to medium sized table. With complex joins, and a wide and deep table however, the performance of the query has room for improvement.
Finding support from local companies can be difficult. There were times when the local company could not find a solution and we reached a solution by getting support globally. If a good local company is found, it will overcome all your problems with its global support.
There are several companies that you can contract for technical support, like EnterpriseDB or Percona, both first level in expertise and commitment to the software.
But we do not have contracts with them, we have done all the way from googling to forums, and never have a problem that we cannot resolve or pass around. And for dozens of projects and more than 15 years now.
The online training is request based. Had there been recorded videos available online for potential users to benefit from, I could have rated it higher. The online documentation however is very helpful. The online documentation PDF is downloadable and allows users to pace their own learning. With examples and code snippets, the documentation is great starting point.
While the setup and configuration of MongoDB is pretty straight forward, having a vendor that performs automatic backups and scales the cluster automatically is very convenient. If you do not have a system administrator or DBA familiar with MongoDB on hand, it's a very good idea to use a 3rd party vendor that specializes in MongoDB hosting. The value is very well worth it over hosting it yourself since the cost is often reasonable among providers.
We have [measured] the speed in reading/write operations in high load and finally select the winner = MongoDBWe have [not] too much data but in case there will be 10 [times] more we need Cassandra. Cassandra's storage engine provides constant-time writes no matter how big your data set grows. For analytics, MongoDB provides a custom map/reduce implementation; Cassandra provides native Hadoop support.
Postgres stacks up just [fine] along the other big players in the RDBMS world. It's very popular for a reason. It's very close to MySQL in terms of cost and features - I'd pick either solution and be just as happy. Compared to Oracle it is a MUCH cheaper solution that is just as usable.
Open Source w/ reasonable support costs have a direct, positive impact on the ROI (we moved away from large, monolithic, locked in licensing models)
You do have to balance the necessary level of HA & DR with the number of servers required to scale up and scale out. Servers cost money - so DR & HR doesn't come for free (even though it's built into the architecture of MongoDB
The user-role system has saved us tons of time and thus money. As I mentioned in the "Use Case" section, Postgres is not only used by engineering but also finance to measure how much to charge customers and customer support to debug customer issues. Sure, it's not easy for non-technical employees to psql in and view raw tables, but it has saved engineering hundreds of man-hours that would have had to be spent on building equivalent tools to serve finance or customer support.
It provides incredibly trustworthy storage for wherever customer data dumped in. In our 6 years of Postgres existence, we have not lost a byte of customer data due to Postgres messing up a transaction or during the multiple times the hard-drives failed (thanks to ACID compliance!).
This is less significant, but Postgres is also quite easy to manage (unless you are going above and beyond to squeeze out every last bit of performance). There's not much to configure, and the out of the box settings are quite sane. That has saved us engineers lots of time that would have gone into Postgres administration.