What is the difference between hdfs mongodb

hdfs The difference between mongodb is: 1. MongoDB is suitable for various data without strict transaction requirements, while HDFS has a relatively large storage overhead for a large number of small files and is suitable for large file processing; 2. MongoDB is suitable for caching. It is more suitable for write once and read many application scenarios.

The difference between hdfs mongodb is:

MongoDB: log collection and storage, small files Distributed storage, similar to the data storage of Internet Weibo applications

1) Suitable for various data without strict transaction requirements, such as object data, JSON format data

2) Due to Very high performance, very suitable for real-time insertion, update and search, and highly scalable

3) Suitable for caching

HDFS: suitable for large file storage, can be appended, but Unchangeable. Suitable for Hadoop offline data analysis and Apache Spark data storage.

1) HDFS has a relatively large storage overhead for a large number of small files, so it is suitable for large file processing. If there are multiple small files, they can be merged into large files for processing

2) HDFS Suitable for high throughput, but not suitable for low-latency access

3) HDFS is suitable for streaming reading, and is not suitable for multiple users to write to a file, random writing, and file overwriting operations

4) HDFS is more suitable for write once and read multiple application scenarios

mongodb is suitable for the following scenarios:

a. Website data: Mongo is very suitable for real-time insertion, update and query, and has the replication and high scalability required for real-time data storage of the website.

b. Caching: Due to its high performance, mongo is also suitable as a caching layer for information infrastructure. After the system is restarted, the persistent cache built by mongo can prevent the underlying data source from being overloaded.

c. Large size, low value data: It may be more expensive to store some data using traditional relational databases. Before this, many programmers often chose traditional files for storage.

d. High scalability scenarios: mongo is very suitable for databases composed of dozens or hundreds of servers.

e. Used for storage of objects and JSON data: mongo’s BSON data format is very suitable for document formatted storage and query.

Unsuitable scenarios:

a. Highly transactional systems: such as banking or accounting systems. Traditional relational databases are currently more suitable for applications that require a large number of atomic and complex transactions.

b. Traditional business intelligence applications: BI databases targeting specific problems will produce highly optimized query methods. For such applications, a data warehouse may be a more suitable choice.

c. Questions that require SQL.

HDFS applicable scenarios

GB, TB, or even PB level data

Number of files exceeding one million

10K nodes scale

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