Update README.md

README.md

@@ -10,7 +10,7 @@ This is useful when you'd want:
   * [Running multiple schedulers for high availability isn't safe](https://groups.google.com/forum/#!topic/airbnb_airflow/-1wKa3OcwME) so it isn't the way to go in the first place. [Someone in the internet tried to implement a wrapper](https://stackoverflow.com/a/39595535) to implement leader election on top of the scheduler so that only one scheduler executes the tasks at a time. It is possbile but can't we just utilize a kind of cluster manager here? This is where Kubernetes comes into play.
 * Easy parallelism of task executions
   * The common way to scale out workers in Airflow is to utilize Celery. However, managing a H/A backend database and Celery workers just for parallelising task executions sounds like a hassle. This is where Kubernetes comes into play, again. If you already had a K8S cluster, just let K8S manage them for you.
-  * If you have ever considered to avoid Celery for task parallelism, yes, K8S can still help you for a while. Just keep using `LocalExecutor` instead of `CeleryExecutor` and delegate actual tasks to Kubernetes by calling e.g. `kubectl run --restart=Never ...` from your tasks. It will work until the concurrent `kubectl run` executions(up to the concurrency implied by scheduler's `max_threads` and LocalExecutor's `parallelism`) consumes all the resources a single airflow-scheduler pod provides, which will be after the pretty long time.
+  * If you have ever considered to avoid Celery for task parallelism, yes, K8S can still help you for a while. Just keep using `LocalExecutor` instead of `CeleryExecutor` and delegate actual tasks to Kubernetes by calling e.g. `kubectl run --restart=Never ...` from your tasks. It will work until the concurrent `kubectl run` executions(up to the concurrency implied by scheduler's `max_threads` and LocalExecutor's `parallelism`. See [this SO question](https://stackoverflow.com/questions/38200666/airflow-parallelism) for gotchas) consumes all the resources a single airflow-scheduler pod provides, which will be after the pretty long time.
 
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