Part of the database involved representing artifacts found at a site using a specific type hierarchy defined by the state government. Representing that hierarchy within Mongo was difficult, and querying it even more so. It was hard to find how to query a recursive structure using the MongoDB query language. Recently I worked on a web app designed for tracking, editing, and reporting on archaeological survey data. When I joined the project, it used React on the front-end, Node.js on the back-end, GraphQL as the API interface, and MongoDB as the database.

You can see a part of the checks that were performed on the table holding the vouchers. This is the most interesting piece, so the rest of this query is cut here. The same kind of query was performed on the campaign’s data right after that. Whenever some interesting findings came up we fixed them in Mongo, so that the effect of this action was preserved if the migration had to be aborted for some reason. The first thing we wanted to find was if one of the old migrations of voucher and campaign types is fully done. We decided to have these types with a NON NULL constraint in the final SQL form, so all entries must have been equipped with some value before the migration.

Set up a PostgreSQL database and migrate a small subset of the data. Use FME to convert data from PostgreSQL databases into JSON for loading into MongoDB NoSQL databases without writing any code. Transformers Gallery right-arrow Browse tools for transforming data.

Mongodb Vs Postgresql

PostgreSQL doesn’t like those, so it will reject the SSL connection when it figures that out. When you add the Heroku PostgreSQL add-on to your site, Heroku automatically creates an environment variable for you called DATABASE_URL with the connection info. We asked for a table named user, but Sequelize decided we needed a table named Users instead. That’s not going to work very well for our existing code. The first step is to install PostgreSQL locally, and that’s pretty simple. Since the beginning, this project has evolved quite a bit, and it’s clear that it will continue to change going forward.

The Connection Pool is necessary to determine the appropriate database column types. Also, we need to specify the statement type and Table name to insert data into that table, as shown in the above image. Then after it creates the DBcontroller service, we need to configure select the created controller service in the value in the client service. PostgreSQL also has the capability of altering tables in various ways withoutrequiring to lock it for every operation. For example, adding a column that does not have a default value and can be set to NULL can be done quickly without locking the entire table. What started out as a single product developed by a Ruby development agency grew into a set of different products and many different applications as the years passed.

But I have to point out that, in fairness, 700MB is a really small database for 34,000 users. But the biggest question in my head is whether it’s good or bad that we can go full circle like that? So the projects were pretty agile and have grown with newly discovered requirements. On the other hand, migrating the data mentioned in steps 1 and 5 only took about 45 minutes. Migrate production data to the new database and deploy the new platform.

The home of the most advanced Open Source database server on the worlds largest and most active Front … Press J to jump to the feed. Here we need to select the JDBC connection pool as we created the connection in the above step. We need to specify the JDBC Connection Pool to convert the JSON message to a SQL statement.

For many collections, it may be easy to simply export it as CSV and use Postgres’s COPY command to import it into the appropriate table. However, many things won’t be so simple and might require writing your own script that reads the exported CSV or JSON and writes it to the database, altering data as needed. This might be necessary for example to change MongoDB’s object IDs to match a datatype in PostgreSQL, such as https://globalcloudteam.com/ an identity column , serial, or UUID. While switching from PostgreSQL to MongoDB is not difficult, the process often involves more than just extracting and migrating data. You’ll also need to examine the details of the applications that access your database. 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.

Chapter 3 Migrating From Internal Satellite Databases To External Databases

The second thing is that for the same short time window we will have a duplicated entry while fetching from the parent table. It would produce an error in our code and would probably be seen by the customer. This scenario is about a very tiny time window, but the second case is more interesting. DMS is a great tool which can be used to migrate data between different types of databases – including Mongo to Postgres transfer.

• While we do have one service still using MongoDB we intend to also migrate this to PostgreSQL in the future.
• If you want to use MongoDB as an external database, the following information can help you decide if this option is right for your Satellite configuration.
• Ensure your database are up to date with log-based incremental replication.
• It allowed us to join two tables together in a hierarchical order to obtain a parent table having multiple independent child tables.

This was a good opportunity to experiment with something we’d not used before to see if it would be useful for us. Although Ammonite allowed us to use a familiar mongodb to postgresql language there were downsides. Whilst Intellij now supports Ammonite, at the time it did not, which meant we lost autocomplete and automatic imports.

Should I Replace Postgresql With Mongodb?

Since we have a legacy MongoDB database, we will want to match the collection names to the new table names in PostgreSQL. There are several existing tools that migrate data from MongoDB to PostgreSQL. I chose to roll my own to have ultimate control over what was migrating. Another motivating factor was to learn how to use Sequelize migrations on a going forward rather than a one-time migration. You can install only the same version of PostgreSQL that is installed with the satellite-installer tool during an internal database installation. You can install PostgreSQL using Red Hat Enterprise Linux Server 7 repositories or from an external source, as long as the version is supported.

Next, to make the migration as safe as possible we decided to transfer the data in two steps. First, each Mongodb collection would be copied to a corresponding temporary table, inheriting from the appropriate parent table. Next, we would move the data to parent tables and switch boolean flags in a vaguely known order. Finally, we managed to connect DMS with both source and target databases, having the MongoDB source running in a document mode. The main problem that we had to solve next, was about writing a mapper that links fields from source json objects to the appropriate SQL columns.

Export Mongodb To Sql In 5 Steps

Most of these steps were described in detail already, let’s quickly talk about the leftovers. The third point is about creating the same indexes for the child tables as indexes that the parent tables have. We had to do that even though we moved majority of the project’s data to final tables before making the switch. The reason behind this are database queries from other projects.

We went for the option that seemed the safest, if for instance we make a mistake while setting up the DMS task. This was unlikely to happen, but we wanted to use the safest path and see if we can stand the burden that will be put on us. Before we were sure that DMS was an acceptable tool for this job, we still had to overcome many technical obstacles. Initially we planned to do two rounds of the migration, one for campaigns and second for vouchers. We imagined that each round would have only two steps for each Project – run DMS with ongoing replication and switch a boolean flag in the project’s config.

Modeling And Migrating Data

The APIs have a flag in configuration for this; the value was true for the Mongo backed API and false for the Postgres backed one. Simply changing the proxy to talk to Postgres first wasn’t enough as the message wouldn’t get sent on the Kinesis stream until the request reached Mongo too. The proxy had been built with the idea that it would only exist temporarily, so it perhaps hadn’t been as carefully designed as other apps. Also, it was built using Akka Http, which none of the team members had used before. We decided to start a big refactoring job to improve readability that included using for comprehensions instead of the growing nested logic we had before, and adding even more logging markers.

It provides a web-based User Interface to create, monitor, and control data flows. Gathering data using rest API calls is widely used to collect real-time streaming data in Big data environments to capture, process, and analyze the data. Here in this scenario, we will fetch data from the MongoDB table, migrate it to the Postgres database table, and store it.

There are two modes – graphical and json – where the first does not support all the features of the json mode. So going through the documentation is necessary to understand all of the possible filters and transformations. However, after we managed to set up the migration process using the json mode, it became way better to use DMS that way.

Step 4: Set The Database Connection Details¶

With some combinations of SQL clients and databases, results might not show up immediately. Click on Add export units.Each export unit corresponds to one single export of a source collection. You can then simply choose the saved connection for future migrations. Now we need to connect to our MongoDB server that holds the collections we wish to migrate.

There are two bad scenarios that could happen in theory. Imagine that we moved all data from the child table, made a switch, and suddenly one last change was transferred by the DMS that occurred in the source database. We can see that there is one more element to be moved to the parent table at that moment, so we fire the query moving the data once again counting on its part updating an existing entry. The first bad thing is that it already takes some additional seconds, which could in theory enable for voucher code overuses because counting redemptions would be inaccurate for a while.

So, if we get results, we map those down to just the data to make it look more like the data we used to get from MongoDB. The following code will find all of the rows using the like operator. If we’ve indexed the itemName column, this will be a fast operation. Using Sequelize with PostgreSQL in Node.JS/Express is pretty straightforward. Inserts, updates, and deletes are very well covered elsewhere, including in the Sequelize documentation. The one thing that puzzled me a little bit was the like operator, so I’ll cover that here.

Updating Applications

Just authenticate your MongoDB account and destination, and your new MongoDB data integration will adapt to schema / API changes. You can opt for getting the raw data, or to explode all nested API objects in separate tables. Stitch integrates with leading databases and SaaS products. No API maintenance, ever, while you maintain full control over replication behavior.