- #Mongodb performance vs postgres software#
- #Mongodb performance vs postgres code#
- #Mongodb performance vs postgres free#
GraphQL is another example of a technology which while helpful can drive developers into domain binding. A better way would be to convert the GraphQL into a single aggregation which can be run and returned.
However, to be blunt the implementation of the GraphQL library I used forced me into doing hundreds of queries to fulfill requests. Dynamic modification of a schema would be better than rebuilding.Īlso built a bunch of functions to do various queries which are not really part of GraphQL syntax, but can be supported. Only if you are able to add or modify schema at runtime what do you do? It was a little fiddly, in that I had to have a trigger mechanism when the data design changes in order to programatically rebuild the GraphQL schema. If it was an orthodox app this would be quite easy, you just write the classes required to get data from the model. One of the requirements I was given was to introduce GraphQL endpoints to our system. The Java GraphQL implementation assumes a fixed schema burnt into the binary same orthodox philosophy.It took me a while to be able to generate the schema dynamically in order to expose GraphQL endpoints that would dynamically change as users modified the data structures. watch?v=uwZj4XF6zic Like comment: Like comment: 45 likes I made a video about all of this in 2017. It isn't the only option by any means, but it is a pretty decent start. If you accept this philosophy you will find MongoDB is a useful tool to help you on the path.
#Mongodb performance vs postgres free#
By smashing that dependency and embracing an approach which is more universal and flexible you are free to write applications which are more general purpose. I would rather say that you should challenge the orthodoxy of tight coupling between schema and binary. There is no point using it as a drop in replacement to SQL, because you can't beat SQL for being an SQL database.īut this is all backwards. If you are going to embrace MongoDB you should also be embracing its strengths. That kind of thing is just a non issue with MongoDB. Imagine trying to change a schema on the fly while there are users online. Dynamic changes to SQL can be time consuming and potentially dangerous. I've seen a similar application as the one I'm working on and the pipeline of SQL schema scripts and messing about is scary.
It delivers a kind of flexibility and adaptability that allows us to do things which are essentially impossible to do with SQL databases. You can store arbitrary data easily and allow users to define what data they want to store at run time rather than design time. The door MongoDB opens is the ability to write applications that are not tightly coupled to the binary. If you are simply going to build applications like you did with SQL and expect it to be feature for feature identical you are missing the benefits. The who point of MongoDB is the flexibility. If you simply treat MongoDB as any other relational database and build applications tightly coupled with a schema it is like complaining that your trail bike doesn't go as fast as your Ferrari or carry as much as your pickup truck. However it became clear to me that using MongoDB directly would give me all the same features with far better performance. Later this technology adopted MongoDB under the covers.
#Mongodb performance vs postgres code#
It actually used PostgereSQL under the covers, but it allowed huge flexibility that decoupled the code from schema. In 2013 I began working on a Automation Engine which used a data storage mechanism which was flexible. Where tight coupling between code and schema is acceptable. There are workloads where the data structures are mature and unchanging, where flexibility and adaptability isn't critically important. I was a dBase programmer back in the early ninties.
I spent decades dealing with this kind of problem from even before SQL. Changing the data structure becomes almost impossible without management of both binary deployment and schema changes. You will build business rules into the domain objects. when you bind the data structure to the binary artifacts you cripple flexibility. The model is usually established by the SQL and object model, with a mapping using Hibernate or whatever other object relational mapper.
#Mongodb performance vs postgres software#
In this statement there is the core weakness of orthodox software development, mainly that the foundation stone of our software is the model. The orthodox thinking is on clear display when Noah says "one of the most important parts of designing a system is fleshing out your data model".
And to be frank some of the comments below indicate that using non SQL data storage is still considered heterodox. There was a time when the idea of using anything but a SQL database was heretical. Long time user of SQL of various varieties and PostgreSQL specifically.
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