When you invoke a function in asynchronous mode, requests are persisted to the internal queue of Function Compute before it is processed in a reliable manner. If you want to track and save states of asynchronous invocations in each phase to obtain better task control and observability capabilities, you can enable the task mode to process asynchronous requests. This topic describes the background information, limits, and common features of asynchronous tasks.
Background
After you enable the asynchronous task feature, you can obtain the following capabilities:
State transition information, such as invocation inputs, execution results, and error information, for each function invocation is recorded.
You can manage each invocation. For example, you can terminate an invocation.
Latency is increased for function invocations and executions if the asynchronous task feature is enabled because status information needs to be saved. The increased latency does not generate additional fees. For more information about the billing of Function Compute, see Billing overview.
Limits
Scenario limits
Although the asynchronous task feature provides more capabilities, more system overheads are incurred. We recommend that you disable the asynchronous task feature in the following scenarios:
Your business applications are sensitive to latencies in request processing links and require the average latency to be less than 100 milliseconds.
You need to initiate at least thousands of asynchronous invocations per second.
Regions
Asynchronous tasks are supported in the following regions: China (Hangzhou), China (Shanghai), China (Qingdao), China (Beijing), China (Zhangjiakou), China (Shenzhen), China (Chengdu), China (Hong Kong), Singapore, UK (London), US (Silicon Valley), US (Virginia), Germany (Frankfurt), Australia (Sydney), Malaysia (Kuala Lumpur), Indonesia (Jakarta), Thailand (Bangkok), India (Mumbai) Closing Down, Japan (Tokyo), and South Korea (Seoul).
Validity period
You can query the task status information only within the last seven days.
Feature comparison
If you want to build an asynchronous task processing platform or run simple scheduled tasks, you can also use Kubernetes jobs. The following table compares asynchronous tasks of Function Compute and Kubernetes jobs.
Item | Asynchronous tasks of Function Compute | Kubernetes jobs |
Scenarios | Real-time tasks that last tens of milliseconds and offline tasks that last dozens of hours. | Offline tasks with fixed loads and low requirements on task submission speed and timeliness. |
Observability | Supported. Logs, metrics such the number of queued tasks, and observability capabilities such as querying of task link durations and task status, are provided. | You must integrate open source software to implement observability capabilities. |
Automatic scaling of task instances | Supported. Automatic scaling based on the number of queued tasks and instance resource usage can be performed. | You must use task queues to manually implement scaling and load balancing of instances, which is more complicated. |
Scaling speed of task instances | In milliseconds. | In minutes. |
Resource utilization of task instances | You need to only select the instance type. The instances are automatically scaled. You are charged based on the actual task-processing durations. The resource utilization is high. | You must determine the specifications and number of instances when jobs are submitted. It is difficult to implement auto scaling and load balancing of instances. The resource utilization is low. |
Task submission speed | A single user can submit tens of thousands of tasks per second. | Up to hundreds of jobs can be started in a cluster per second. |
Scheduled or deferred task submission | Supported. | Scheduled task submission is supported. Deferred task submission is not supported. |
Task deduplication | Supported. | Not supported. |
Task termination | Supported. | Supported under specific conditions. You can terminate a task by stopping a task instance. |
Task throttling | Supported. Throttling can be performed at different granularities, such as at the user level or at the task-processing function level. | Not supported. |
Automatic result callback for a task | Supported. | Not supported. |
Development and O&M costs | You need to only implement the task processing logic. | You must maintain a Kubernetes cluster. |
Common features
See the following topics to learn about common features of asynchronous tasks: