Self-Reflection: MongoDB Workshop
In this post, I’m going to mention what concepts and use cases I learned after attending a workshop by LinuxWorld on MongoDB Database.
MongoDB is the Document based database following the NoSQL Data model, helping in faster access of data with increased I/O performance. The I/O performance gives a better User Experience. Along with this, MongoDB’s biggest advantage is the flexibility which it provides via the schemaless model, where we can add fields on the fly according to our requirements.
- Database: Group of tables(collections) together form the Database.
- Collection: In the Document DB model, the collection is the group of records or documents.
- Document: One record in the collection which consists of fields and values meant to be stored for future reference is the document.
Concepts I learned in the Workshop:
- About File system, files, and permanent storage.
- Different types of Data Models.
- Right meanings of SQL Databases and NoSQL (Not Only SQL) Databases.
- Schema-oriented model and Schema-less oriented model.
- MongoDB’s document-based model.
- MongoDB server and client programs mongod and mongo.
- JSON format of storing data in MongoDB.
- CRUD operations on MongoDB.
- Using compass tool for WebUI for MongoDB.
- Feature of storing data in MongoDB using import tools that import data from files.
- Concepts such as Primary key, Unique key, and foreign key in the Database world.
- Indexing, which helps to give faster I/O performance. Also understood the situation where Indexing could slow down the process.
- Database cluster comprising Master and Slave nodes and distributed database system.
- Sharding of database according to criteria put on the field values.
- Replication and replica set to avoid SPOF (Single point of failure).
- IXSCAN and COLLSCAN
- The Reference Model for saving storage and tradeoff between management time and performance.
- MongoDB cluster on AWS cloud and Atlas cloud of MongoDB.
Use Cases explored
- Integration of MongoDB with Python API to use the database for storing data via the application using pymongo driver/library.
- Aggregation Pipeline framework to solve complex use cases in the database query via searching, grouping, and sorting.
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