How can you optimize web application background jobs?

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Selecting right queue systems has multiple influencers considering the current digital transformations organisations are going through. Look at your enterprise and how the data is flowing between internal and external systems. With the streaming solutions in place like Kafka, Solace or messaging systems in Hyperscalers, consider non-functional requirements such as Data redundancy, data integrity along with security, reliability. On the other hand, considering API Driven architectures and Event Driven Architecture which plays a major role in Digital Transformations and pick the right scalable Messaging platforms. Also work on cost benefit analysis, like Kafka Vs Confluence for example.

Selecting the optimal queue system is like choosing the right kitchen equipment - assess the types of meals (workloads) needed now and in the future, how many meals (scale) per day, available counter space (infrastructure), capacity of cooks (resources), and budget; upgrading later may be difficult so choose wisely from the start.

Planning before coding is the best way to do so. Identify what you need and let your code be to the point. use the best practices like comments and clean codes.

Being in the middleware and integration technology landscape for more than 23 years when I look back the number of synchronous Vs asynchronous data syncs it is very clear that 60-65% of the data syncs can be scheduled, 10-15% data syncs can be event driven and 20-25% are of real time data syncs. Considering these metrics based on my experience I recommend to schedule the jobs in different intervals, prioritise the jobs like inventory and pricing updates over the products availability on a e-commerce platform, then look at the data dependencies to ensure data quality. Chunk the data to ensure the hardware performance is optimised while running the jobs so that cost can be minisised as most of these systems are running in Cloud now a days.

Monitor your jobs with real time notifications for any data loss, publish as much information as possible to the monitoring solutions, add servers and runtime monitoring solutions, bring the logs into log management and alerting solutions like Splunk or ELK that helps in identifying the issues very quickly.

Best practices are very much dependent on the type of background job nature. One of the best practice is to publish the data into messaging solutions like Kafka to ensure the data reliability. Do not sync the complete data all times, try to get the delta data based on the updated timestamps to reduce the overall job runtime. Try to insert data only once to ensure the data integrity. Have enough logging and sync the logs to log management solutions for better monitoring.

In my experience having a health probe to continuously check if background job is running as configured is very important, else system will always be in risk.