Ever experienced that frustrating moment where your application works perfectly but would randomly fail after running for a period of time? To help you troubleshoot such elusive issues, I’ve compiled a list of top reasons why you application is failing intermittently and how to handle them.

The Perils of Date and Time

A surprising number of bugs stem from improper handling of date and time operations. If your application deal with workflows, scheduling and reporting, you might want to keep an eye out for this.

Symptoms:

  • Incorrect timezone conversions
  • Bugs caused by serialization/deserialization (i.e. formatting and parsing of date/time)
  • Unexpected result from Date arithmetic
  • Bugs caused by daylight saving time transitions

How to avoid:

  • Use date-time libraries
  • Implement rigorous date-time validation
  • Standardize timezones to UTC and only convert to local timezone for display purposes
  • Always use 4-digits year format to avoid ambiguity in 2-digit representations

Race Conditions

In complex applications, multiple threads/processes might attempt to access and modify the same resource simultaneously. If not managed carefully, this can lead to unpredictable behaviour and intermittent issues which are difficult to reproduce.

Symptoms:

  • Corrupted state data occurring sporadically
  • Unexpected errors occurring especially under load

How to avoid:

  • Use thread-safe programming constructs such as atomic operations, synchronized data structures, locks, etc.
  • Leverage on concurrency libraries to manage threads and synchronization
  • Limit the scope of critical sections

Resource Constraints

Your application might run well during normal conditions but falter under heavy load.

Symptoms:

  • Uptick in response times and timeouts
  • High CPU/memory usage
  • Disk is almost full

How to avoid:

  • Monitor your CPU/memory/disk usage
  • Scaling your application to handle the increased load
  • Profile your application to identify and resolve memory leaks
  • Conduct performance and load testing to validate that your system is able to support the required capacity

Depleted Entropy

Applications relying heavily on cryptographic secure randomness may occasionally face issues if the entropy pool is exhausted.

Symptoms:

  • Slow or unresponsive cryptographic operations
  • Your system does not have a good source of entropy (e.g. Virtual Machines, embedded systems)

How to avoid:

  • For Linux-based systems, monitor /dev/random accordingly
  • Use dedicated hardware (HSMs) for cryptographic operations
  • Consider using non-blocking APIs that generates pseudo randomness which does not deplete entropy

Depleted File Descriptors

Applications that handle a large number of files or network connections might run into problems when file handles are depleted.

Symptoms:

  • Unable to establish network connections
  • Unable to open files for reading/writing

How to avoid:

  • Configure ulimits to increase the maximum number of allowed file handles
  • Implement proper resource management to close network connections or file handles when they are no longer needed
  • For systems dealing with large number of concurrent connections, consider scaling with a load balancer to distribute the load across multiple instances

Understanding these potential pitfalls are crucial for developers to build a stable and reliable system. Combined with robust testing and proactive monitoring, we can minimize intermittent failures leading to a smoother user experience and lesser headaches for the technical teams.