Userid | first_name | last_name | logins | upgraded | city | state Let's look at an example query to see how this works.Ĭonsider the PostgreSQL created table “users.” postgres=# select * from users However, you could not store multiple address entities. You could create an address field and create nested state and city fields as in the example below. You can still map values in each row to fields deeper in your documents you just can’t embed documents. Each row in the TSV will become a document. However, it only works if the original PostgreSQL schema is relatively simple and you don't need to embed documents in other documents with a one-to-many relationship.įor example, if you need to create a collection of documents that represent customers and plan on embedding each customer's order in these documents, TSV won’t work because it doesn’t support hierarchical data structures. The TSV format is the quick and easy option. Let’s look at the two different ways to extract the data: returning queries as tab-separated values (TSV) or as JSON. To migrate data, you’ll extract it from PostgreSQL and then import it to MongoDB using the mongoimport tool. 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 process for transferring data from PostgreSQL to MongoDB is clear-cut. How to move data from PostgreSQL to MongoDB To keep our following example simple, we'll only consider the migration of data for a one-to-one mapping. In that case, you should become familiar with best practices for MongoDB schema design, including anti-patterns. However, you might want to restructure your data to fit better within a MongoDB schema design. The migration for some of your tables might be simple. Once you’ve considered any changes needed to your application, the next step is to migrate the data. If you're just diving into document databases for the first time, MongoDB provides excellent documentation on SQL to MongoDB Mappings. For example, if you're using an ORM that does not support both relational and document databases, you’ll need to find a new library that can connect to MongoDB. You'll also need to examine the details of the applications that access your database. While switching from PostgreSQL to MongoDB is not difficult, the process often involves more than just extracting and migrating data. Preparing to switch from PostgreSQL to MongoDB Scaling can even be reconfigured online, without downtime, thanks to online resharding. With MongoDB, horizontal scaling is easier as collections are self-contained and not coupled relationally. However, horizontal scaling is very difficult with relational databases, due to the difficulty in spreading out related data across nodes. Horizontal scaling-adding new nodes to a database cluster to share the workload-is often a better choice. However, vertical scaling has physical limitations and can quickly become expensive. Improving performance by increasing the power (CPU, memory, etc) of a database server-called vertical scaling-is possible on both MongoDB and PostgreSQL. MongoDB scales both vertically and horizontally with relative ease. You'll find that MongoDB is the best choice for fast querying and seamless scaling. While data models and schema flexibility are a consideration, scalability is also important. Your application might already be tightly coupled to SQL-based clients and frameworks, such that it might make sense to hold off on a migration, and consider redesigning the application as a long-term solution. On the other hand, if you have a stable, relational schema that does not change over time, an RDMBS like PostgreSQL might be sufficient for your application needs. MongoDB Atlas offers a free tier, which is a great way to experiment and learn. In addition, MongoDB Atlas (the cloud offering of MongoDB as a DBaaS) lets you quickly start and scale your MongoDB clusters. With MongoDB, you can change the structure of documents on the fly without searching through application code to update queries and table references. If your data model and schema evolve regularly-such as in an agile environment- MongoDB, with its flexible schema, is an excellent choice.
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