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This article will explain in detail what are the differences between hdfs and mongodb. The editor thinks it is very practical, so I share it for you as a reference. I hope you can get something after reading this article.

The differences of hdfs mongodb are: 1. MongoDB is suitable for all kinds of data without strict transactional requirements, while HDFS has a large storage overhead for a large number of small files, which is suitable for processing large files. 2. MongoDB is suitable for caching, and is more suitable for writing once and reading multiple times.

The difference between hdfs mongodb is:

MongoDB: collection and storage of logs, distributed storage of small files, data storage similar to Internet Weibo applications

1) suitable for all kinds of data without strict transactional requirements, such as object data and JSON format data

2) because of its high performance, it is very suitable for real-time insertion, update and search, and has a high degree of scalability.

3) suitable for caching

HDFS: suitable for large file storage, can be append, but can not be modified. Suitable for Hadoop offline data analysis and Apache Spark data storage.

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

2) HDFS is suitable for high throughput, but not for access with low time latency

3) HDFS is suitable for streaming reading, but not suitable for multiple users to write a file, random write and file overwrite operation.

4) HDFS is more suitable for writing once and reading multiple times.

Mongodb applies to 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 website real-time data storage.

b. Caching: due to its high performance, mongo is also suitable as a cache layer for the information infrastructure. After the system is rebooted, the persistence cache built by mongo can avoid the overload of data sources in the lower layer.

c. Large-size, low-value data: it may be expensive to use traditional relational databases to store some data. before that, many programmers tend to choose traditional files for storage.

d. Highly scalable scenarios: mongo is ideal for databases made up of dozens or hundreds of servers.

e. For the storage of objects and JSON data: mongo's BSON data format is very suitable for document format storage and query.

Unsuitable scenarios:

a. A highly physical system, such as a banking or accounting system. At present, the traditional relational database is more suitable for applications that require a large number of atomic complex transactions.

b. Traditional business intelligence applications: BI databases for specific problems will produce highly optimized queries. For such applications, data warehouse may be a more appropriate choice.

c. A problem that requires SQL.

HDFS applicable scenario

GB, TB, and even PB-level data

Number of documents with a scale of more than one million

10K + node scale

What are the differences between hdfs and mongodb to share here, I hope that the above content can be of some help to you, can learn more knowledge. If you think the article is good, you can share it for more people to see.

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