How to Deal with Race Conditions – InApps is an article under the topic Devops Many of you are most interested in today !! Today, let’s learn How to Deal with Race Conditions – InApps in today’s post !

Read more about How to Deal with Race Conditions – InApps at Wikipedia

You can find content about How to Deal with Race Conditions – InApps from the Wikipedia website

“Race conditions” refers to bugs that occur due to the timing or order of execution of multiple operations. This is a fairly broad class of bugs that can present themselves in very different ways, depending on the problem space. In many cases, they can be difficult to identify and/or reproduce, even if the solution might be simple.


“Time-of-check to time-of-use” (TOCTOU) describes a type of race condition that occurs when the state of a resource changes between checking its state and using the result. TOCTOU is usually discussed in the context of filesystem operations, but variations are possible in many areas of the systems we build.

The common example of a TOCTOU race condition is checking if a file is accessible and then reading it:

If the file is deleted or otherwise modified after the initial check, at best you will end up with an unhandled exception. At worst, you could be opening the door to a security vulnerability.

Instead: Skip the access check, wrap the readFile in a try/catch and handle any errors there.


Normally we talk about “updating atomically” in the context of database systems. Consider the following contrived example:

See the problem? The user is fetched from the database, some logic is performed, then a query is executed to update the user. This is a non-atomic operation, however, and there is no guarantee that the user’s role is still set to “admin” in the database between the time of the check and the update. In this fake example, the consequence might be a security vulnerability in the application; and in a real-world scenario, it might be even more difficult to notice depending on the complexity of the system.

Instead: Craft an atomic update query that performs the update in a single statement.

Shared State

Mike Del Tito

Mike is an experienced senior developer at LogDNA, with a demonstrated history of designing, delivering and leading teams in the development of web-based software applications.

Although Node.js is single-threaded, working with shared resources and data structures asynchronously requires the same level of care as needed in multi-threaded systems.

Here’s another example:

At a quick glance, you might expect this to always output the first value of 1, but the program can print a different value every time (try it!).

Even if it’s easy to spot the issue here in an isolated example, consider a more complex concurrent program with a similar goal of doing some work only when a precondition is met. You might have multiple asynchronous workers that need to:

  • Open a write stream for a specific file for all workers to write to.
  • Create a record in a database if it doesn’t already exist.
  • Memoize the result of a very expensive operation.

In all of these scenarios, you cannot assume the state of the resource you are working with will remain the same between the check and the use. Variations of this specific “create if not exist” race condition have popped up several times recently in third-party code and even our own.

Read More:   Update The Scalability Myth

Counter Measures

Avoiding race conditions not only requires some thought about what your code is doing, but also about how other parts of the system will use your code. There are no silver bullets here, but in addition to being thoughtful about concurrent design, here are some tips:

  • Perform database updates atomically. Do not rely on previously queried information about the record you are updating to craft your update query.
  • In general, avoid sharing “global” state whenever possible, but especially with concurrency. Think about the implications of simultaneous access to data structures and how that affects the logic of the program or the correctness of the data itself.
  • If sharing state among concurrent routines is required, consider introducing a mutex (mutually exclusive) or another locking mechanism to control access to the shared resource. This comes at the expense of complexity, but is sometimes unavoidable.

Photo by cottonbro from Pexels.


Rate this post
As a Senior Tech Enthusiast, I bring a decade of experience to the realm of tech writing, blending deep industry knowledge with a passion for storytelling. With expertise in software development to emerging tech trends like AI and IoT—my articles not only inform but also inspire. My journey in tech writing has been marked by a commitment to accuracy, clarity, and engaging storytelling, making me a trusted voice in the tech community.

Let’s create the next big thing together!

Coming together is a beginning. Keeping together is progress. Working together is success.

Let’s talk

Get a custom Proposal

Please fill in your information and your need to get a suitable solution.

    You need to enter your email to download


      Success. Downloading...