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Function Compute (2.0):Simple Log Service triggers

Last Updated:Feb 01, 2024

You can create a Simple Log Service trigger to connect Simple Log Service to Function Compute. The Simple Log Service trigger automatically triggers a function to processes incremental logs in Logstores based on your business requirements.


  • Data cleansing and processing

    Simple Log Service allows you to quickly collect, process, query, and analyze logs.

  • Data shipping

    Simple Log Service allows you to ship data to its destination and build data pipelines between big data services in the cloud.


Functions for data processing

Function type

Function Compute trigger mechanism

An extract, transform, and load (ETL) job corresponds to a Simple Log Service trigger and is used to invoke a function. After you create an ETL job for a Logstore in Simple Log Service, a timer is started to poll data from the shards of the Logstore based on the job configurations. If data is written to the Logstore, a triple data record in the <shard_id,begin_cursor,end_cursor > format is generated as a function event. Then, the associated ETL function is invoked. Simple Log Service pushes function events to Function Compute.


If no new data is written to the Logstore and the storage system is updated, the cursor information changes. The ETL function is invoked for each shard but no data is transformed. In this case, you can use the cursor information to obtain data from the shards. If no data is obtained, the ETL function is invoked but no data is transformed. You can ignore the function invocations. For more information, see Create a custom function.

An ETL job invokes functions based on the time mechanism. For example, an ETL job triggers a function every 60 seconds and data is constantly written to Shard0 of a Logstore. In this case, the shard triggers a function invocation every 60 seconds. If data is no longer written to the shard, function invocation cannot be triggered. The inputs to execute the function are the start and end offsets of the cursor in the most recent 60 seconds. You can perform operations in the function after the cursor reads Shard0 data.


Sample scenario

You can configure a Simple Log Service trigger to periodically obtain updated data and invoke a function. Simple Log Service triggers are suitable for scenarios in which you want to incrementally consume data from a Logstore. You can invoke functions to perform custom processing tasks, such as a data cleansing task and a data processing task, and ship data to a third-party service. This example shows how to obtain and print log data.


The function that is used to process data can be a template function that is provided by Simple Log Service or a custom function.


  • Function Compute

    • A service is created. For more information, see Create a service.


      When you create a service, configure a service role to allow functions in the service to obtain the permissions of the role. If you do not configure a service role, an error is reported when you test the function code. In this example, the service role is AliyunFCDefaultRole and the AliyunLogReadOnlyAccess policy is attached to the role. For more information about service roles, see Grant Function Compute permissions to access other Alibaba Cloud services.

  • Simple Log Service

    • A Simple Log Service project and two Logstores are created. For more information, see Resource management overview.

      One Logstore is used to process logs and data sources, and the other Logstore is used to store logs that are generated by Function Compute.


      The regions where the log project and the Function Compute service reside must be the same.

Step 1: Create a Simple Log Service trigger

  1. Log on to the Function Compute console.

  2. In the left-side navigation pane, click Services & Functions.
  3. In the top navigation bar, select a region.

  4. On the Services page, find the desired service and click Functions in the Actions column.
  5. On the Functions page, click the function that you want to manage.
  6. On the function details page, click the Triggers tab, select the version or alias from the Version or Alias drop-down list, and then click Create Trigger.
  7. In the Create Trigger panel, configure the parameters and click OK. The following table describes the parameters.




    Trigger Type

    Select Log Service.

    Log Service


    Enter a trigger name.


    Version or Alias

    Default value: LATEST. If you want to create a trigger for another version or alias, select a version or alias in the upper-right corner of the function details page. For more information about versions and aliases, see Manage versions and Manage aliases.


    Log Service Project

    Select a Simple Log Service project that you created.



    Select a Logstore. The trigger that is created subscribes to the data in the Logstore at a scheduled time and sends the data to Function Compute for custom processing.


    Trigger Interval

    Enter the interval at which Simple Log Service invokes the function.

    Valid values: 3 to 600. Unit: seconds. Default value: 60.



    The maximum number of retries that is allowed for a single trigger if an error occurs when Simple Log Service triggers a function invocation.

    Valid values: 0 to 100. Default value: 3.

    • If the function is executed, the status=20 message is returned and the value of the X-Fc-Error-Type parameter in the header of the function is not UnhandledInvocationError, or HandledInvocationError. In other cases, the execution fails, and a retry is triggered.

    • If the function fails to be executed, the current request is retried until the function is successfully executed. The value of Retries determines the upper limit on retries. If the value is reached and the function still fails to be executed, the system increases the trigger interval and implements exponential backoff to retry the request.


    Trigger Log

    Select the Logstore in which you want to store the logs that are generated when Simple Log Service invokes the function.


    Invocation Parameters

    Configure a custom parameter in this editor. This parameter is passed to the function as the parameter field of the event parameter. The value of this parameter must be a string in JSON format.

    By default, this parameter is empty.


    Role Name

    Select AliyunLogETLRole.


    After you configure the preceding parameters, click OK. The first time you create a trigger of this type, click Authorize Now in the dialog box that appears.


    After the trigger is created, it is displayed on the Triggers tab. To modify or delete an existing trigger, see Manage triggers.

Step 2: Configure the input parameter of the function

  1. On the function details page, click the Code tab, click the xialatubiao icon to the right of Test Function, and select Configure Test Parameters from the drop-down list.

  2. In the Configure Test Parameters panel, click the Create New Test Event or Modify Existing Test Event tab, configure the Event Name parameter, specify the event content, and then click OK.

    In Function Compute, event is an input parameter. The following code shows the format of the event parameter:

        "parameter": {},
        "source": {
            "endpoint": "",
            "projectName": "aliyun-fc-cn-hangzhou-2238f0df-a742-524f-9f90-976ba457****",
            "logstoreName": "function-log",
            "shardId": 0,
            "beginCursor": "MTUyOTQ4MDIwOTY1NTk3ODQ2Mw==",
            "endCursor": "MTUyOTQ4MDIwOTY1NTk3ODQ2NA=="
        "jobName": "1f7043ced683de1a4e3d8d70b5a412843d81****",
        "taskId": "c2691505-38da-4d1b-998a-f1d4bb8c****",
        "cursorTime": 1529486425





    The value of Invocation Parameters.



    The log block information that you want the function to read from Simple Log Service.

    • endpoint: the endpoint of the Alibaba Cloud region in which the Simple Log Service project resides.

    • projectName: the name of the Simple Log Service project.

    • logstoreName: the name of the Logstore.

    • shardId: the ID of a specific shard in the Logstore.

    • beginCursor: the offset from which data consumption starts.

    • endCursor: the offset at which data consumption ends.

        "endpoint": "",
        "projectName": "aliyun-fc-cn-hangzhou-2238f0df-a742-524f-9f90-976ba457****",
        "logstoreName": "function-log",
        "shardId": 0,
        "beginCursor": "MTUyOTQ4MDIwOTY1NTk3ODQ2Mw==",
        "endCursor": "MTUyOTQ4MDIwOTY1NTk3ODQ2NA=="


    The name of an ETL job in Simple Log Service. Simple Log Service triggers must correspond to ETL jobs in Simple Log Service.



    For an ETL job, taskId is the identifier for a deterministic function invocation.



    The UNIX timestamp when the most recent log arrives at Simple Log Service.


Step 3: Write and test the function

After you create the Simple Log Service trigger, you can write function code and test the function code to verify whether the code is valid. The function is invoked when Simple Log Service collects incremental logs. Function Compute obtains the corresponding logs, and then displays the collected logs.

  1. On the function details page, click the Code tab, enter function code in the code editor, and then click Deploy.

    This section uses function code for Python as an example. In the sample code, the values of the access_key_id, access_key_secret, and security_token parameters are queried from context.credentials.

    The sample code is used to implement the following features:
    * Parse Simple Log Service events from the event parameter.
    * Initialize the Simple Log Service client based on the preceding information.
    * Obtain real-time log data from the source Logstore.
    This sample code is mainly doing the following things:
    * Get SLS processing related information from event
    * Initiate SLS client
    * Pull logs from source log store
    #!/usr/bin/env python
    # -*- coding: utf-8 -*-
    import logging
    import json
    import os
    from aliyun.log import LogClient
    logger = logging.getLogger()
    def handler(event, context):
        # Query the key information from context.credentials.
        # Access keys can be fetched through context.credentials
        print("The content in context entity is: \n")
        creds = context.credentials
        access_key_id = creds.access_key_id
        access_key_secret = creds.access_key_secret
        security_token = creds.security_token
        # Parse the event parameter to the OBJECT data type.
        # parse event in object
        event_obj = json.loads(event.decode())
        print("The content in event entity is: \n")
        # Query the following information from event.source: log project name, Logstore name, the endpoint to access the Simple Log Service project, start cursor, end cursor, and shard ID.
        # Get the name of log project, the name of log store, the endpoint of sls, begin cursor, end cursor and shardId from event.source
        source = event_obj['source']
        log_project = source['projectName']
        log_store = source['logstoreName']
        endpoint = source['endpoint']
        begin_cursor = source['beginCursor']
        end_cursor = source['endCursor']
        shard_id = source['shardId']
        # Initialize the Simple Log Service client.
        # Initialize client of sls
        client = LogClient(endpoint=endpoint, accessKeyId=access_key_id, accessKey=access_key_secret, securityToken=security_token)
        # Read logs that start from the start and end cursors in the source Logstore. In this example, the specified cursors include all logs of the function invocation.
        # Read data from source logstore within cursor: [begin_cursor, end_cursor) in the example, which contains all the logs trigger the invocation
        while True:
          response = client.pull_logs(project_name=log_project, logstore_name=log_store,
                                    shard_id=shard_id, cursor=begin_cursor, count=100,
                                    end_cursor=end_cursor, compress=False)
          log_group_cnt = response.get_loggroup_count()
          if log_group_cnt == 0:
"get %d log group from %s" % (log_group_cnt, log_store))

          begin_cursor = response.get_next_cursor()
        return 'success'
  2. On the Code tab, click Test Function.

    After the function is executed, you can view the result on the Code tab.