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Simple task scheduler deployed in a cluster
posted by Basit Anwer

Working in a startup poses a lot of challenges and the biggest one is time to market. This drives the development team to build things fast but of quality.

One such task that you come across is having a simple task scheduler.

Uses of a task scheduler could be many. You might want to

  1. Send regular emails at set intervals
  2. Perform heavy computations based on a set schedule
  3. One off tasks such as deleting customer data at off-peak times.

There could be many more reasons you need a task scheduler for and these needs grow over time. Ideally you should probably go for RabbitMQ or Kafka (depending on the need) for message queuing but if you’re in for a quick solution you either opt in for a cron job. Problems with cron jobs or schedulers within an application lifetime is when you distribute your application across a cluster, you don’t want to send the same email twice or the number of times your application instances are running in a cluster.

Here I am presenting a simple solution or hack which works for a startup and is cluster freindly. Your tasks will also run only once and uses just a database which probably every application has.

So lets get down to business.

You’ll need a database table with the following columns

Id Column Type
1 scheduled_time timestamp
3 start_time timestamp
4 version int
5 meta_data jsonb (or string)

Now your pseudo code boils down to these simple steps

jobs = getJobs(status = `WAITING` && scheduled_time <
for job in jobs:
	success = UPDATE status = 'IN_PROGRESS',
    				 version = job.version + 1
		    		   WHERE id =
        		         AND status = 'WAITING'
                         AND version = job.version
    	job_type = job.getTypeFromMetaData()
    UPDATE status = 'DONE' WHERE id =

Just to explain the important bits of the pseudo code,

  • The version is there to ensure only one update call succeeds if multiple of those are made.
  • Updating start_time marks when the job was started
  • The status fields marks the item.

I like to keep the status as DONE instead of deleting the rows right away, it works as a way of logs as how did the jobs go. You could perform a bulk delete when your start_time is past 1 month or whatever that suits you.

We need just another set of code that checks the failed jobs if the application was killed in the process.

// Reset stale jobs
jobs = getJobs(status = `IN_PROGRESS`
				&& start_time - > stale_time )
for job in jobs:
	UPDATE status = `WAITING`,
    	   version=job.version + 1
           WHERE version = job.version

// Clean old jobs
DELETE jobs WHERE scheduled_time - > `3 months`

Here we are putting those jobs back in WAITING where the start_time was not updated to DONE for a time we expect it to have it completed. Consider it a TTL. We could also add a heartbeat column if we are expecting to have some task running for a long time.

Run thes pieces of code at a set interval of either a minute, 5 mintues or 10 minutes or whatever you think is best.

This solution is language agnostic hence no mention of implementation code.