example, the overhead of messaging and synchronization makes this a lot slower than its Python counterpart: sum(i + i for i in xrange(100)) The synchronization step is costly, so you should avoid using chords as much as possible. Still, the chord is a powerful primitive to have in your toolbox as synchronization is a required step for many parallel algorithms. Let’s break the chord expression down: >>> task: @app.task(ignore_result=False) def another_task(project): do_something() By default the synchronization step is implemented by having a recurring task poll the completion of the group every second

example, the overhead of messaging and synchronization makes this a lot slower than its Python counterpart: sum(i + i for i in xrange(100)) The synchronization step is costly, so you should avoid using chords as much as possible. Still, the chord is a powerful primitive to have in your toolbox as synchronization is a required step for many parallel algorithms. Let’s break the chord expression down: >>> @app.task(ignore_result=False) def another_task(project): do_something() By default the synchronization step is implemented by having a recurring task poll the completion of the group every second

example, the overhead of messaging and synchronization makes this a lot slower than its Python counterpart: sum(i + i for i in xrange(100)) The synchronization step is costly, so you should avoid using chords as much as possible. Still, the chord is a powerful primitive to have in your toolbox as synchronization is a required step for many parallel algorithms. Let’s break the chord expression down: >>> value (but remember to never have a task wait for other tasks) Important Notes By default the synchronization step is implemented by having a recurring task poll the completion of the taskset every second

example, the overhead of messaging and synchronization makes this a lot slower than its Python counterpart: >>> sum(i + i for i in xrange(100)) The synchronization step is costly, so you should avoid using chords as much as possible. Still, the chord is a powerful primitive to have in your toolbox as synchronization is a required step for many parallel algorithms. Let’s break the chord expression down: >>> task: @app.task(ignore_result=False) def another_task(project): do_something() By default the synchronization step is implemented by having a recurring task poll the completion of the group every second

example, the overhead of messaging and synchronization makes this a lot slower than its Python counterpart: >>> sum(i + i for i in xrange(100)) The synchronization step is costly, so you should avoid using chords as much as possible. Still, the chord is a powerful primitive to have in your toolbox as synchronization is a required step for many parallel algorithms. Let’s break the chord expression down: >>> @app.task(ignore_result=False) def another_task(project): do_something() By default the synchronization step is implemented by having a recurring task poll the completion of the group every second

example, the overhead of messaging and synchronization makes this a lot slower than its Python counterpart: >>> sum(i + i for i in xrange(100)) The synchronization step is costly, so you should avoid using chords as much as possible. Still, the chord is a powerful primitive to have in your toolbox as synchronization is a required step for many parallel algorithms. Let’s break the chord expression down: >>> @app.task(ignore_result=False) def another_task(project): do_something() By default the synchronization step is implemented by having a recurring task poll the completion of the group every second

example, the overhead of messaging and synchronization makes this a lot slower than its Python counterpart: >>> sum(i + i for i in xrange(100)) The synchronization step is costly, so you should avoid using chords as much as possible. Still, the chord is a powerful primitive to have in your toolbox as synchronization is a required step for many parallel algorithms. Let’s break the chord expression down: >>> task: @app.task(ignore_result=False) def another_task(project): do_something() By default the synchronization step is implemented by having a recurring task poll the completion of the group every second

example, the overhead of messaging and synchronization makes this a lot slower than its Python counterpart: >>> sum(i + i for i in xrange(100)) The synchronization step is costly, so you should avoid using chords as much as possible. Still, the chord is a powerful primitive to have in your toolbox as synchronization is a required step for many parallel algorithms. Let’s break the chord expression down: >>> task: @app.task(ignore_result=False) def another_task(project): do_something() By default the synchronization step is implemented by having a recurring task poll the completion of the group every second

example, the overhead of messaging and synchronization makes this a lot slower than its Python counterpart: >>> sum(i + i for i in xrange(100)) The synchronization step is costly, so you should avoid using chords as much as possible. Still, the chord is a powerful primitive to have in your toolbox as synchronization is a required step for many parallel algorithms. Let’s break the chord expression down: >>> @app.task(ignore_result=False) def another_task(project): do_something() By default the synchronization step is implemented by having a recurring task poll the completion of the group every second

example, the overhead of messaging and synchronization makes this a lot slower than its Python counterpart: >>> sum(i + i for i in xrange(100)) The synchronization step is costly, so you should avoid using chords as much as possible. Still, the chord is a powerful primitive to have in your toolbox as synchronization is a required step for many parallel algorithms. Let’s break the chord expression down: >>> task: @app.task(ignore_result=False) def another_task(project): do_something() By default the synchronization step is implemented by having a recurring task poll the completion of the group every second