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"Get or create" is a very common operation for syncing data in the database, but implementing it correctly may be trickier than you may expect. If you ever had to implement it in a real system with real-life load, you may have overlooked potential race conditions, concurrency issues and even bloat! In this article I explore ways to "get ot create" in PostgresSQL. Illustration by Abstrakt Design Ta
Concurrency is not very intuitive. You need to train your brain to consider what happens when multiple processes execute a certain code block at the same time. There are several issues I often encounter: Failing to recognize potential concurrency issues: It's not uncommon for both beginner and seasoned developers to completely miss a potential concurrency problem. When this happens, and the concur
In 2006 Microsoft conducted a customer survey to find what new features users want in new versions of Microsoft Office. To their surprise, more than 90% of what users asked for already existed, they just didn't know about it. To address the "discoverability" issue, they came up with the "Ribbon UI" that we know from Microsoft Office products today. Office is not unique in this sense. Most of us ar
Pandas is a very popular tool for data analysis. It comes built-in with many useful features, it's battle tested and widely accepted. However, pandas is not always the best tool for the job. SQL databases have been around since the 1970s. Some of the smartest people in the world worked on making it easy to slice, dice, fetch and manipulate data quickly and efficiently. SQL databases have come such
When I started my career in development, my first job was a DBA. Back then, before AWS RDS, Azure, Google Cloud and the rest of them cloud services, there were two types of DBAs: The Infrastructure DBA was in charge of setting up the database, configuring the storage and taking care of backups and replication. After setting up the database, the infrastructure DBA would pop up from time to time and
As glorified data plumbers, we are often tasked with loading data fetched from a remote source into our systems. If we are lucky, the data is serialized as JSON or YAML. When we are less fortunate, we get an Excel spreadsheet or a CSV file which is always broken in some way, can't explain it. Data from large companies or old systems is somehow always encoded in a weird way, and the Sysadmins alway
When creating a new Django Admin page a common conversation between the developer and the support personal might sound like this: Developer: Hey, I'm adding a new admin page for transactions. Can you tell me how you want to search for transactions? Support: Sure, I usually just search by the username. Developer: Cool. search_fields = ( user__username, ) Anything else? Support: I sometimes also wan
ORMs offer great utility for developers but abstracting access to the database has its costs. Developers who are willing to poke around the database and change some defaults often find that great improvements can be made. Aggregation with Filter Prior to Django 2.0 if we wanted to get something like the total number of users and the total number of active users we had to resort to conditional expr
source code The complete source code for this article can be found in this gist. Django Admin is a powerful tool for managing data in your app. However, it was not designed with summary tables and charts in mind. Luckily, the developers of Django Admin made it easy for us to customize. This is what it's going to look like at the end: Django admin dashboard Why Would I Want To Do That There are a l
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