We have a classified website. I read few things but still not got a solid reason to choose between these.
My requirement is to just create user, listing and manage these. These are paid listings so monetary transactions would be there.
As per my research (https://www.mongodb.com/compare/mongodb-mysql) I found that mongo db has edge over other options except for complex transactions. What are complex transaction in this context? Any specific examples?
What factors should I consider before choosing one. I have experience with My SQL and Entity framework so its more appealing to me now but are these really bad for scaling when compared with mongo?
Feel free to suggest any other good performance option apart from these choices.
There is a vast difference between mongoDB and MySQL or SQL Server.
MongoDB is a NoSQL database meaning it is schemaless and doesnot follow CODD Rules of RDBMS whereas MySQL/SQL Server is relational Database.
Each are meant for different use cases. As per mongoDB doc
Why use MongoDB instead of MySQL?
Organizations of all sizes are adopting MongoDB because it enables them to build applications faster, handle highly diverse data types, and manage applications more efficiently at scale.
Development is simplified as MongoDB documents map naturally to modern, object-oriented programming languages. Using MongoDB removes the complex object-relational mapping (ORM) layer that translates objects in code to relational tables.
MongoDB’s flexible data model also means that your database schema can evolve with business requirements. For example, the ALTER TABLE command required to add a single, new field to Craiglist’s MySQL database would take months to execute. The Craigslist team migrated to MongoDB because it can accommodate changes to the data model without such costly schema migrations.
MongoDB can also be scaled within and across multiple distributed data centers, providing new levels of availability and scalability previously unachievable with relational databases like MySQL. As your deployments grow in terms of data volume and throughput, MongoDB scales easily with no downtime, and without changing your application. In contrast, to achieve scale with MySQL often requires significant, custom engineering work.
What are common use cases for MongoDB?
MongoDB is a general purpose database that is used for a variety of use cases. The most common use cases for MongoDB include Single View, Internet of Things, Mobile, Real-Time Analytics, Personalization, Catalog, and Content Management.
When would MySQL be a better fit?
While most modern applications require a flexible, scalable system like MongoDB, there are use cases for which a relational database like MySQL would be better suited. Applications that require complex, multi-row transactions (e.g., a double-entry bookkeeping system) would be good examples. MongoDB is not a drop-in replacement for legacy applications built around the relational data model and SQL.
A concrete example would be the booking engine behind a travel reservation system, which also typically involves complex transactions. While the core booking engine might run on MySQL, those parts of the app that engage with users – serving up content, integrating with social networks, managing sessions – would be better placed in MongoDB
As there is no concept of join in MongoDB so if you want to store data, either you have to create a hack to store data in two table(although it is expensive cal to join in MongoDB) or you have to store data as a single document,, making your document too complex.In these cases MySQL has upperhand.
Related
I want to use aws for hosting a django application and use aws rds for database purpose. The application is kind of blog like system.
I am not able to decide which RDS I should choose over MySql or Postgres? Both price wise and performance wise according to aws pricing policy.
This can be very broad and may be opinionated , I would try to keep it short as i read it somewhere:
MySQL would be very good for any CMS Site as it works very well with it and MyISAM tables are quite nice here.
From What I read where PostgreSQL does better than MySQL:
Multi-application databases
Advanced data modelling
What Advance Data Modelling means is that PostgreSQL is far more mature at doing complex data modelling than MySQL is. It has a very mature extensible type system, a wide range of procedural languages, and a great deal of flexibility in how these languages can be plugged into existing queries.
If that wasn't enough, the fact is that you can essentially build your data model in PostgreSQL based not only on what information you are storing but what information is commonly derived from what you are storing. This makes things like not-first-normal-form actually sane to use where they are needed. Add collections and multiple inheritance in table structure and you have a very sophisticated data modelling platform, this blog would help you understand it better.
Besides the content management system market, MySQL's other major market is in applications where data is not expected to be exposed to more than one writing application at a time. This leads to a significant difference in handling data validation, etc.
In PostgreSQL validation is always equally strict. If the app expects special error treatment it had better call functions or casts to handle this explicitly.
MySQL however places the application in charge of defining the data validation rules.So while PostgreSQL allows the relational and object-relational interface to serve as a public API, it is essentially intended largely to be a private API for applications in MySQL. This is a huge difference and not readily understood by many people trying to make the choice. This leads to major differences in application design.
MySQL is a data storage and reporting solution for your application.
PostgreSQL is a data centralization, modelling, and reporting solution
for your organization. The two are remarkably different.
Now coming to Second Question based on pricing as you can see from MySQL Pricing Page and PostgreSQL Pricing Page MySQL is bit cheaper than PostgrSQL , reading on the answer you can make informed decision what would be best for you.
Hope this Helps!
I'm gonna offer you a 3rd option: Aurora - try it. It's cheaper than those 2 and is MySQL compatible.
This article may be of help to you when deciding.
For simple blog-like thingie I'd go with MySQL (or Aurora MySQL compatible version)
For data-critical and highly relational solutions I might also consider Postgres (Aurora)
Are there any benefits to using MongoDB for a Node.js application rather than a traditional SQL database such as MySQL, if I'm not planning to have large (>1000 item) collections and am already comfortable with SQL?
MongoDB is schema-less document based database. This means you can insert a JSON object with other nested objects. This can make development easier, especially for prototyping.
For a small project, why not? For a larger project you should do more research. Large or small, doesn't hurt to do the research anyway. You want to consider how your application uses the database (reads vs writes) and how MongoDB scales horizontally, and how it handles failures.
There's a thing called the CAP theorem that defines NoSQL databases. MongoDB is CP. This visual guide shows the relationships between different databases. What is most important to you and your application?
Something else to consider is that most NoSQL databases are not ACID compliant. If you're using MySQL with InnoDB, that can be something significant to give up, depending on your application. For example, transactions might be something you might not want to give up.
Lots of pros and cons. Best thing to ask yourself is: What am I gaining? What am I giving up? There are many things, and it really depends on your use-case.
There are lots of reasons to stick with a simple dbms for a small-scale application. One of them is the widespread availability of cheap hosting services providing MySQL. Another is ease of deployment and maintenance.
Of course, if you're trying to learn to use MongoDB, go for it!
I am building a data ware house that is the range of 15+ TBs. While storage is cheap, but due to limited budget we have to squeeze as much data as possible in to that space while maintaining performance and flexibility since the data format changes quiet frequently.
I tried Infobright(community edition) as a SQL solution and it works wonderful in term of storage and performance, but the limitation on data/table alteration is making it almost a no go. and infobright's pricing on enterprise version is quiet steep.
After checking out MongoDB, it seems promising except one thing. I was in a chat with a 10gen guy, and he stated that they don't really give much of a thought in term of storage space since they flatten out the data to achieve the performance and flexibility, and in their opinion storage is too cheap nowadays to be bother with.
So any experienced mongo user out there can comment on its storage space vs mysql (as it is the standard for what we comparing against to right now). if it's larger or smaller, can you give rough ratio? I know it's very situation dependent on what sort of data you put in SQL and how you define the fields, indexing and such... but I am just trying to get a general idea.
Thanks for the help in advance!
MongoDB is not optimized for small disk space - as you've said, "disk is cheap".
From what I've seen and read, it's pretty difficult to estimate the required disk space due to:
Padding of documents to allow in-place updates
Attribute names are stored in each collection, so you might save quite a bit by using abbreviations
No built in compression (at the moment)
...
IMHO the general approach is to build a prototype, insert data and see how much disk space your specific use case requires. The more realistic you can model your queries (inserts and updates) the better your result will be.
For more details see http://www.mongodb.org/display/DOCS/Excessive+Disk+Space as well.
Pros and Cons of MongoDB
For the most part, users seem to like MongoDB. Reviews on TrustRadius give the document-oriented database 8.3 out of 10 stars.
Some of the things that authenticated MongoDB users say they like about the database include its:
Scalability.
Readable queries.
NoSQL.
Change streams and graph queries.
A flexible schema for altering data elements.
Quick query times.
Schema-less data models.
Easy installation.
Users also have negative things to say about MongoDB. Some cons reported by authenticated users include:
User interface, which has a fairly steep learning curve.
Lack of joins, which can make some data retrieval projects difficult.
Occasional slowness in the cloud environment.
High memory consumption
Poorly structured documentation.
Lack of built-in analytics.
Pros and Cons of MySQL
MySQL gets a slightly higher rating (8.6 out of 10 stars) on TrustRadius than MongoDB. Despite the higher rating, authenticated users still mention plenty of pros and cons of choosing MySQL.
Some of the positive features that users mention frequently include MySQL’s:
Portability that lets it connect to secondary databases easily.
Ability to store relational data.
Fast speed.
Excellent reliability.
Exceptional data security standards.
User-friendly interface that helps beginners complete projects.
Easy configuration and management.
Quick processing.
Of course, even people who enjoy using MySQL find features that they don’t like. Some of their complaints include:
Reliance on SQL, which creates a steeper learning curve for users who
do not know the language.
Lack of support for full-text searches in InnoDB tables.
Occasional stability issues.
Dependence on add-on features.
Limitations on fine-tuning and common table expressions.
Difficulties with some complex data types.
MongoDB vs MySQL Performance
When comparing the performance of MongoDB and MySQL, you must consider how each database will affect your projects on a case-by-case basis. While some performance features may appear to be objectively promising, your team members may never use the features that drew you to a database in the first place.
MongoDB Performance
Many people claim that MongoDB outperforms MySQL because it allows them to create queries in multiple ways. To put it another way, MongoDB can be used without knowing SQL. While the flexibility improves MongoDB's performance for some organizations, SQL queries will suffice for others.
MongoDB is also praised for its ability to handle large amounts of unstructured data. Depending on the types of data you collect, this feature could be extremely useful.
MongoDB does not bind you to a single vendor, giving you the freedom to improve its performance. If a vendor fails to provide you with excellent customer service, look for another vendor.
MySQL Performance
MySQL performs extremely well for teams that want an open-source relational database that can store information in multiple tables. The performance that you get, however, depends on how well you configure the MySQL database. Configurations should differ depending on the intended use. An e-commerce site, for example, might need a different MySQL configuration than a team of research scientists.
No matter how you plan to use MySQL, the database’s performance gets a boost from full-text indexes, a high-speed transactional system, and memory caches that prevent you from losing crucial information or work.
If you don’t get the performance that you expect from MySQL data warehouses and databases, you can improve performance by integrating them with an excellent ETL tool that makes data storage and manipulation easier than ever.
MySQL vs MongoDB Speed
In most speed comparisons between MySQL and MongoDB, MongoDB is the clear winner. MongoDB is much faster than MySQL at accepting large amounts of unstructured data. When dealing with large projects, it's difficult to say how much faster MongoDB is than MySQL. The speed you get depends on a number of factors, including the bandwidth of your internet connection, the distance between your location and the database server, and how well you organise your data.
If all else is equal, MongoDB should be able to handle large data projects much faster than MySQL.
Choosing Between MySQL and MongoDB
Whether you choose MySQL or MongoDB probably depends on how you plan to use your database.
Choosing MySQL
For projects that require a strong relational database management system, such as storing data in a table format, MySQL is likely to be the better choice. MySQL is also a great choice for cases requiring data security and fault tolerance. MySQL is a good choice if you have high-quality data that you've been collecting for a long time.
Keep in mind that to use MySQL, your team members will need to know SQL. You'll need to provide training to get them up to speed if they don't already know the language.
Choosing MongoDB
When you want to use data clusters and search languages other than SQL, MongoDB may be a better option. Anyone who knows how to code in a modern language will be able to get started with MongoDB. MongoDB is also good at scaling quickly, allowing multiple teams to collaborate, and storing data in a variety of formats.
Because MongoDB does not use data tables to make browsing easy, some people may struggle to understand the information stored there. Users can grow accustomed to MongoDB's document-oriented storage system over time.
We are building a large scale e-comm web site to service over 100,000 users, but we expect the number of users to grow rapidly over the first year. In general, the site functions very much like ebay where users can create, update, and remove listings. User can also search listings and purchase an item of interest. Basically, the system has transactional and non-transactional requirements:
**Transactional**
Create a listing (multi-record update)
Remove a listing
Update a listing
Purchase a listing (multi-record update)
**Non-Transactional**
Search listings
View a listing
We want to leverage the power of scalable, document-based NoSQL data stores such as Couch or MongoDB, but at the same time we need a relational store to support our ACID transactional requirements. So we have come up with a hybrid solution which uses both technologies.
Since the site is "read mostly", and, to meet the scalablity needs, we set up a MongoDB data store. For the transactional needs, we set up a MySQL Cluster. As the middleware component we use JBoss App server cluster.
When a "search" request comes in, JBoss directs the request to Mongo to handle the search which should produce very quick results while not burdening MySQL. When a listing is created, updated, removed, or purchased, JBoss services the transactions against MySQL. To keep MongoDB and MySQL synchronized, all transactional requests handled by JBoss against MySQL would include a final step in the business logic that updates the corresponding document in MongoDB via the listing id; we plan to use the MongoDB Java API to facilitate this integration of updating the document.
So, in essence, since the site is read mostly, the architecture allows us to scale out MongoDB horizontally to accommodate more users. Using MySQL allows us to leverage the ACID properties of relational databases while keeping our MongoDB store updated through the JBoss middleware.
Is there anything wrong with this architecture? No platform can offer consistency, availability, and partition-tolerance at the same time -- NoSQL systems usually give up consistency -- but at least with this hybrid approach we can realize all three at the cost of additional complexity in the system, and we are ok with that since all of our requirements are being met.
There is nothing wrong with this approach.
Infact Currently am also working on the application (E-Commerce) which leverages both SQL & NonSQL. Ours is a rails application and 90% of the data is stored in mongo and only transactional & inventory items stored in mysql. All the transactions are handled in Mysql, and everything else goes to mongo.
If you have already built it, there isn't too much wrong with the architecture aside from being a little too enterprisey. Starting from scratch on a system like this though, I'd probably leave out SQL and the middleware.
The loss of consistency in NoSQL data stores isn't as complete as you suggest. Aside from the fact that many of them do support transactions and can be set up for immediate consistency on particular queries, I suspect some of your requirements are simply an artefact of designing things relationally. Your concern seems to be around operations that require updates to multiple records - Is a listing really multiple records, or just set up that way because SQL records have to have a flat structure?
Also, if search and view are handled outside of MySQL, you have effectively set up an eventual consistency system anyway.
I have been working to learn Mongodb effectively for one week in order to use for my project. In my project, I will store a huge geolocation data and I think Mongodb is the most appropriate to store this information. In addition, speed very important for me and Mongodb responds faster than Mysql.
However, I will use some joins for some parts of the project, and I'm not sure whether I store user's information in Mongodb or not. I heard some issues can occur in mongodb during writing process. should I use only mongodb with collections (instead of join) or both of them?
In most situations I would recommend choosing one db for a project, if the project is not huge. On really big projects (or enterprises in general), I think long term organizations will use a combination of
RDBMS for highly transactional OLTP
NoSQL
a datawarehousing/BI project
But for things of more reasonable scope, just pick the one that does the core of the use case, and use it for everything.
IMO storing user data in mongodb is fine -- you can do atomic operations on single BSON documents so operations like "allocate me this username atomically" are doable. With redo logs (--journal) (v1.8+), replication, slavedelayed replication, it is possible to have a pretty high degree of data safety -- as high as other db products on paper. The main argument against safety would be the product is new and old software is always safer.
If you need to do very complex ACID transactions -- such as accounting -- use an RDBMS.
Also if you need to do a lot of reporting, mysql may be better at the moment, especially if the data set fits on one server. The SQL GROUP BY statement is quite powerful.
You won't be JOINing between MongoDB and MySQL.
I'm not sure I agree with all of your statements. Relative speed is something that's best benchmarked with your use case.
What you really need to understand is what the relative strengths and weaknesses of the two databases are:
MySQL supports the relational model, sets, and ACID; MongoDB does not.
MongoDB is better suited for document-based problems that can afford to forego ACID and transactions.
Those should be the basis for your choice.
MongoDB has some nice features in to support geo-location work. It is not however necessarily faster out of the box than MySQL. There have been numerous benchmarks run that indicate that MySQL in many instances outperforms MongoDB (e.g. http://mysqlha.blogspot.com/2010/09/mysql-versus-mongodb-yet-another-silly.html).
Having said that, I've yet to have a problem with MongoDB losing information during writing. I would suggest that if you want to use MongoDB, you use if for the users as well, which will avoid having to do cross database 'associations', and then only migrate the users to MySQL away if it becomes necessary.