Obfuscating one script 1.1.4. Obfuscating one package 1.1.5. Expiring obfuscated scripts 1.1.6. Binding obfuscated scripts to device 1.1.7. Packaging obfuscated scripts 1.1.8. Something need to know 1 script 1.3.3. More options to protect package 1.3.4. Copying package data files 1.3.5. Checking runtime key periodically 1.3.6. Binding to many machines 1.3.7. Using outer file to store runtime key 1.3.8 package 1.1.4.1. Distributing the obfuscated package 1.1.5. Expiring obfuscated scripts 1.1.6. Binding obfuscated scripts to device 1.1.7. Packaging obfuscated scripts 1.1.8. Something need to know 1

Obfuscating one script 1.1.4. Obfuscating one package 1.1.5. Expiring obfuscated scripts 1.1.6. Binding obfuscated scripts to device 1.1.7. Packaging obfuscated scripts 1.1.8. Something need to know 1 script 1.3.3. More options to protect package 1.3.4. Copying package data files 1.3.5. Checking runtime key periodically 1.3.6. Binding to many machines 1.3.7. Using outer file to store runtime key 1.3.8 different applications 2.1.4. Reforming scripts to improve security 2.2. Protecting Runtime Memory Data 2.3. Packing with outer key 2.4. Building obfuscated wheel 2.5. Protecting system packages 2.6. Fix

AMQPs flexible routing model. Tasks can be routed to specific servers, or a cluster of servers by binding workers to different queues. A single worker node can be bound to one or more queues. Multiple messaging they’re not used – Avoid launching synchronous subtasks • Performance and Strategies – Granularity – Data locality – State – Database transactions • Example – blog/models.py – blog/views.py – blog/tasks processing other waiting tasks. However, executing a task does have overhead. A message needs to be sent, data may not be local, etc. So if the tasks are too fine-grained the additional overhead may not be worth

AMQPs flexible routing model. Tasks can be routed to specific servers, or a cluster of servers by binding workers to different queues. A single worker node can be bound to one or more queues. Multiple messaging they’re not used – Avoid launching synchronous subtasks • Performance and Strategies – Granularity – Data locality – State – Database transactions • Example – blog/models.py – blog/views.py – blog/tasks processing other waiting tasks. However, executing a task does have overhead. A message needs to be sent, data may not be local, etc. So if the tasks are too fine-grained the additional overhead may not be worth

AMQPs flexible routing model. Tasks can be routed to specific servers, or a cluster of servers by binding workers to different queues. A single worker node can be bound to one or more queues. Multiple messaging if they’re not used Avoid launching synchronous subtasks Performance and Strategies Granularity Data locality State Database transactions Example blog/models.py blog/views.py blog/tasks.py This guide processing other waiting tasks. However, executing a task does have overhead. A message needs to be sent, data may not be local, etc. So if the tasks are too fine-grained the additional overhead may not be worth

they’re not used – Avoid launching synchronous subtasks • Performance and Strategies – Granularity – Data locality – State – Database transactions 2.1.1 Basics A task is a class that encapsulates a function reconsider your strategy. There is no universal answer here. Data locality The worker processing the task should be as close to the data as possible. The best would be to have a copy in memory, the worst the data is far away, you could try to run another worker at location, or if that’s not possible, cache often used data, or preload data you know is going to be used. The easiest way to share data between

AMQPs flexible routing model. Tasks can be routed to specific servers, or a cluster of servers by binding workers to different queues. A single worker node can be bound to one or more queues. Multiple messaging if they’re not used Avoid launching synchronous subtasks Performance and Strategies Granularity Data locality State Database transactions Example blog/models.py blog/views.py blog/tasks.py This guide processing other waiting tasks. However, executing a task does have overhead. A message needs to be sent, data may not be local, etc. So if the tasks are too fine-grained the additional overhead may not be worth

Using Redis Redis is also feature-complete, but power failures or abrupt termination may result in data loss. • Using SQLAlchemy • Using the Django Database Using a database as a message queue is not AMQPs flexible routing model. Tasks can be routed to specific servers, or a cluster of servers by binding workers to different queues. A single worker node can be bound to one or more queues. Multiple messaging they’re not used – Avoid launching synchronous subtasks • Performance and Strategies – Granularity – Data locality – State – Database transactions • Example – blog/models.py – blog/views.py – blog/tasks

Documentation, Release 1.0.6 (stable) is no universal answer here. Data locality The worker processing the task should be as close to the data as possible. The best would be to have a copy in memory, the worst the data is far away, you could try to run another worker at location, or if that’s not possible, cache often used data, or preload data you know is going to be used. The easiest way to share data between between workers is to use a distributed caching system, like memcached. For more information about data-locality, please read http://research.microsoft.com/pubs/70001/tr-2003-24.pdf State Since celery

reconsider your strategy. There is no universal answer here. Data locality The worker processing the task should be as close to the data as possible. The best would be to have a copy in memory, the worst the data is far away, you could try to run another worker at location, or if that’s not possible, cache often used data, or preload data you know is going to be used. The easiest way to share data between a distributed caching system, like memcached [http://memcached.org/]. For more information about data-locality, please read http://research.microsoft.com/pubs/70001/tr-2003-24.pdf State Since celery