Big Data has been playing a role of big game changer for the majority of the industries over the past couple of years. The main goal of Big Data applications is to help businesses make business decisions by analysing massive volumes of information. Let us go through the capability of laravel framework, to handle big data with MongoDB,CouchDB NoSQL and MySQL. Laravel is the MVC architecture framework of PHP,
A document database. It stores data in rich structures like maps of maps of lists, which contains integers and floating point data. Used for scaling (horizontal). Also, used when you’ve a simpler data model which requires less joins and need a relatively ease of development. MongoDB supports easy sharding, much easier than SQL.
MySQL and MongoDB are just two rather specific types of databases. Generally you first pick the storage method you desire. The storage approaches divide themselves into two groups: relational and NoSQL. Along with the NoSQL storage methods can be divided in about four classes (there’s a good deal of division methods): column shop, key-value shop, chart store, file store. MongoDB is a document store database program
Comparing MongoDB vs MySQL performance is difficult, since both data management systems are extremely useful and the core differences underlie their basic operations and initial approach.
MySQL is best used for:
- Data structure fits for tables and rows
- Strong dependence on multi-row transactions
- Frequent updates and modifications of large volume of records
- Relatively small datasets
MongoDB is best used for:
- High write loads
- Unstable schema
- Your DB is set to grow big
- Data is location based
- HA (high availability) in unstable environment is required
- No database administrators (DBAs)
When should you use MongoDB?
- If your data seems complex to model in a relational database system, or if you find yourself de-normalizing your database schema or coding around performance issues you should consider using MongoDB.
- If you find yourself trying to store serialized arrays or JSON objects, that’s a good sign that you are better off MongoDB.
- If you can’t pre-define your schema or you want to store records in the same collection that have different fields, that’s another good reason.
MySQL and MongoDB are both tremendously useful, and there are much more important differences in their basic operations than simply performance.
Let us say you own a college, and there’s courses which have pupils, and students attend classes, and classes have educators, who teach pupils, give out grades, etc. Each noun in that sentence can be made to a desk, using fixed, intrinsic connections with the other tables. A pupil never teaches courses, a teacher never receives scores, etc. You could still earn a relational database which permits students to teach courses, using institution tables, however if this keeps happening you will most likely need a different sort of database altogether.
NoSQL databases in general are for if the data you are saving doesn’t possess (that many) fixed and intrinsic inner connections. Say, you want to store your error logs. A log has a code, a message, a level, and so on. But those are attributes of your log, not independent entities with many-to-many relations with other things.
Now, among the other shop methods I said is key-value shop. That means getting rid of all connections between information. You have got only one row using a key and your worth (s) (using JSON you can store as many values in a single area as you want). Document store can be called a variant of this, except it accommodates more complicated values more easily.
The answer depends on how much you want your app to scale. I would say that up to 500 transactions per second, it doesn’t matter. You can use whatever is easier for you to develop and maintain. If you want to go above 500 user transactions per second then you need a nosql database. Of course you can also vertically scale a SQL database and throw bigger hardware at it, but that’s what people used to do back in the day with big expensive Oracle instances. You can also shard a SQL database, but that will complicate your architecture a lot and honestly it’s not worth it in most cases.
Have a look at other NoSQL solutions namely Cassandra and Couchbase, Mongo might be popular, but there’s more to be considered.
Building Big Data applications with Laravel & MongoDB
Laravel is the most popular PHP framework till today. It’s directory structure is good, well organised, defined and straight-forward. Working with the default project structure provided by Laravel is absolutely fine when we are working in a medium or large sized project
Today, all the leading organisations are engaging the Big Data to develop a robust pack of innovative business applications.
Advantages of Using Laravel with MongoDB
- Client-side delivery along with the Geospatial Indexing makes MongoDB a better choice
- However, Laravel doesn’t support MongoDB directly yet the Jenssegers API allows you connect to Laravel’s Eloquent.
- When these both are used in a combination, you can get the best outcome in the form of real-time analytical applications involving many large chunks batches of information and long-running ETL.
- The laravel is best known for the web app development and MongoDB is already proven to be the best at handling the unstructured Big Data. And, both in the combination results in rendering the best-performing web and mobile applications dealing with Big Data.
Our team members are expert at handling Big Data utilizing MongoDB and Laravel in the best way possible.
We have developed several brilliant Custom Applications, Web Applications, CRM (Customer Relationship Management), API Portals and so on that are successfully running and have live up to all the standards and challenges of our clients.
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- Laravel website Development
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