![]() ![]() Then you can use database routers to tell Django which database to go to, transparently for your application.ĭisclaimer: this is how I think it should work, I have never used MongoDB in Django, nor have I tested that my code actually works. When you can't, use Redis.Since Django 1.2, you can define multiple datbase connections in your settings.py. The moral of the story is: avoid large offset and counting over many records. I have an use case of transferring the data from single MongoDB collection to multiple tables in PostgreSQL. In the question What are the best databases to use for. And, if you have ties, you might end up skipping results. When comparing MongoDB vs PostgreSQL, the Slant community recommends PostgreSQL for most people. If you do know it, it requires that you persist it and pass it around. However, it requires that you know the last page's last score (which we don't in the case of the jump to my page feature). To migrate data, you’ll extract it from PostgreSQL and then import it to MongoDB using the mongoimport. Ultimately, the ease of your task depends on the complexity of the PostgreSQL database and the structure of document collections needed in the new MongoDB database. The BSON format used by MongoDB is limited to a maximum of 64 bits for representing an integer or floating point number, whereas the JSONB format used by Postgres does not have this limit. This will perform very well (Redis will still be faster). The process for transferring data from PostgreSQL to MongoDB is clear-cut. Assume we have some dummy data that looks like: db.scores.find() Testing the impact of large offsets is really simple. One of the best document databases is MongoDB. If we have 1 million scores, the average scenario will result in an offset of 500 000. In mongodb vs PostgreSQL let us first understand both databases one by one. However, we provide a leaderboard view which jumps straight to the page containing the user. Figure 1 shows that MongoDBs mean response time was lower than PostgreSQLs. According to MongoDB, for write-intensive applications. ![]() MongoDB stores objects exactly as they were designed. Mongo stores its data in a binary format called BSONb which is (roughly) just a binary representation of a superset of JSON. Its database can be organized into groups of comparable data using an automatic load. ![]() Storing JSON is what Mongo is optimized to do. It ensures high performance as it is a schema-less database written in C++. Most users only care about the first or second pages of a leaderboard, meaning the offset is small. MongoDB may beat Postgres in dimension of horizontal scale. ![]() In mogade we provide two features that are tied to paging/offset performance: leaderboard pages and ranks. Both the MongoDB and PostgreSQL documentation are clear that developers should avoid large offsets. PostgreSQL on the other hand uses an RDBMS structure and SQL to store and access data respectively. MongoDB, being a NoSQL database, leverages documents to store data, allowing users to access it using MQL. You can have pictures and videos collections separately if you want and wire them with your page collection using some foreign key like pageid. The syntax supported by both databases is quite different from each other. Hopefully you already know that paging with large offsets tends to be inefficient with most systems. Anyways, by using MongoDB, you can have different records (called documents in MongoDB) with different columns based on attributes of your page in a single Collection (Tables in SQL). ![]()
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |