In order to start the MongoDB shell, you need to use the mongo command. The following is the syntax-
>mongo
First enter the MongoDB bin directory from the command prompt, as shown in the figure below-
This is the command to start the mongo shell as shown in the screenshot below -
This will produce the following Output-
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MongoDB has changed the way of development with its flexible documentation model and high-performance storage engine. Its advantages include: 1. Patternless design, allowing fast iteration; 2. The document model supports nesting and arrays, enhancing data structure flexibility; 3. The automatic sharding function supports horizontal expansion, suitable for large-scale data processing.

MongoDB is suitable for projects that iterate and process large-scale unstructured data quickly, while Oracle is suitable for enterprise-level applications that require high reliability and complex transaction processing. MongoDB is known for its flexible document storage and efficient read and write operations, suitable for modern web applications and big data analysis; Oracle is known for its strong data management capabilities and SQL support, and is widely used in industries such as finance and telecommunications.

MongoDB is a document-based NoSQL database that uses BSON format to store data, suitable for processing complex and unstructured data. 1) Its document model is flexible and suitable for frequently changing data structures. 2) MongoDB uses WiredTiger storage engine and query optimizer to support efficient data operations and queries. 3) Basic operations include inserting, querying, updating and deleting documents. 4) Advanced usage includes using an aggregation framework for complex data analysis. 5) Common errors include connection problems, query performance problems, and data consistency problems. 6) Performance optimization and best practices include index optimization, data modeling, sharding, caching, monitoring and tuning.

MongoDB is suitable for scenarios that require flexible data models and high scalability, while relational databases are more suitable for applications that complex queries and transaction processing. 1) MongoDB's document model adapts to the rapid iterative modern application development. 2) Relational databases support complex queries and financial systems through table structure and SQL. 3) MongoDB achieves horizontal scaling through sharding, which is suitable for large-scale data processing. 4) Relational databases rely on vertical expansion and are suitable for scenarios where queries and indexes need to be optimized.

MongoDB performs excellent in performance and scalability, suitable for high scalability and flexibility requirements; Oracle performs excellent in requiring strict transaction control and complex queries. 1.MongoDB achieves high scalability through sharding technology, suitable for large-scale data and high concurrency scenarios. 2. Oracle relies on optimizers and parallel processing to improve performance, suitable for structured data and transaction control needs.

MongoDB is suitable for handling large-scale unstructured data, and Oracle is suitable for enterprise-level applications that require transaction consistency. 1.MongoDB provides flexibility and high performance, suitable for processing user behavior data. 2. Oracle is known for its stability and powerful functions and is suitable for financial systems. 3.MongoDB uses document models, and Oracle uses relational models. 4.MongoDB is suitable for social media applications, while Oracle is suitable for enterprise-level applications.

MongoDB's scalability and performance considerations include horizontal scaling, vertical scaling, and performance optimization. 1. Horizontal expansion is achieved through sharding technology to improve system capacity. 2. Vertical expansion improves performance by increasing hardware resources. 3. Performance optimization is achieved through rational design of indexes and optimized query strategies.

MongoDB is a NoSQL database because of its flexibility and scalability are very important in modern data management. It uses document storage, is suitable for processing large-scale, variable data, and provides powerful query and indexing capabilities.


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