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Simple Log Service:Ship logs to a SIEM system over HTTPS

Last Updated:Aug 04, 2023

This topic describes how to ship logs in Alibaba Cloud to a security information and event management (SIEM) system by using Splunk HTTP Event Collector (HEC).

If a SIEM system, such as Splunk, is deployed in an on-premises environment, no ports are open for security purposes. This prevents access to the SIEM system from an external environment.


Code examples in this topic are used for reference only. For more information about the latest code examples, visit GitHub or GitHub (applicable to the Logstore that has multiple data sources).

Shipping process

We recommend that you write a program based on the consumer groups in Simple Log Service. This way, you can call API operations provided by Splunk HEC to ship logs to Splunk.

Write a main program

The following code shows the control logic of a main program:

def main():
    option, settings = get_monitor_option()"*** start to consume data...")
    worker = ConsumerWorker(SyncData, option, args=(settings,) )

if __name__ == '__main__':

Configure the program

  • Specify the following information:

    • Log file of the program: is used for subsequent testing and diagnosis of potential issues.

    • Basic options: include consumer group settings and connection settings of Simple Log Service.

    • Advanced options for consumer groups: are used for performance tuning. We recommend that you do not change the settings of these options.

    • Parameters and options for the SIEM system. In this topic, Splunk is used as an example.

  • Code example

    Read the code comments in the following example and modify the parameters based on your business requirements.

    #encoding: utf8
    import os
    import logging
    from logging.handlers import RotatingFileHandler
    user = logging.getLogger()
    handler = RotatingFileHandler("{0}_{1}.log".format(os.path.basename(__file__), current_process().pid), maxBytes=100*1024*1024, backupCount=5)
    handler.setFormatter(logging.Formatter(fmt='[%(asctime)s] - [%(threadName)s] - {%(module)s:%(funcName)s:%(lineno)d} %(levelname)s - %(message)s', datefmt='%Y-%m-%d %H:%M:%S'))
    logger = logging.getLogger(__name__)
    def get_option():
        # Basic options
        # Obtain parameters and options for Log Service from environment variables. 
        endpoint = os.environ.get('SLS_ENDPOINT', '')
        accessKeyId = os.environ.get('SLS_AK_ID', '')
        accessKey = os.environ.get('SLS_AK_KEY', '')
        project = os.environ.get('SLS_PROJECT', '')
        logstore = os.environ.get('SLS_LOGSTORE', '')
        consumer_group = os.environ.get('SLS_CG', '')
        # The starting point of data consumption. The first time that you run the program, the starting point is specified by this parameter. The next time you run the program, the consumption starts from the last consumption checkpoint. 
        # You can set the parameter to begin, end, or a time in the ISO 8601 standard. 
        cursor_start_time = "2018-12-26 0:0:0"
        # Advanced options
        # We recommend that you do not modify the consumer name, especially when concurrent consumption is required. 
        consumer_name = "{0}-{1}".format(consumer_group, current_process().pid)
        # The heartbeat interval. If the server does not receive a heartbeat for a specific shard for two consecutive intervals, the consumer is considered disconnected. In this case, the server allocates the task to another consumer. 
        # If the network performance is poor, we recommend that you specify a larger interval. 
        heartbeat_interval = 20
        # The maximum interval between two data consumption processes. If data is generated at a fast speed, you do not need to adjust the parameter. 
        data_fetch_interval = 1
        # Create a consumer group that contains the consumer.
        option = LogHubConfig(endpoint, accessKeyId, accessKey, project, logstore, consumer_group, consumer_name,
        # Splunk options
        settings = {
                    "host": "",
                    "port": 80,
                    "token": "a023nsdu123123123",
                    'https': False,             # Optional. A Boolean variable.
                    'timeout': 120,             # Optional. An integer.
                    'ssl_verify': True,         # Optional. A Boolean variable.
                    "sourcetype": "",           # Optional. The type of the source.
                    "index": "",                # Optional. The index.
                    "source": "",               # Optional. The source.
        return option, settings

Consume and ship data

The following example shows how to collect data from Simple Log Service and ship the data to Splunk. Read the code comments in the following example and modify the parameters based on your business requirements.

from aliyun.log.consumer import *
from aliyun.log.pulllog_response import PullLogResponse
from multiprocessing import current_process
import time
import json
import socket
import requests

class SyncData(ConsumerProcessorBase):
    The consumer consumes data from Log Service and ships the data to Splunk. 
    def __init__(self, splunk_setting):
      """Initiate Splunk and test network connectivity."""
        super(SyncData, self).__init__()

        assert splunk_setting, ValueError("You need to configure settings of remote target")
        assert isinstance(splunk_setting, dict), ValueError("The settings should be dict to include necessary address and confidentials.")

        self.option = splunk_setting
        self.timeout = self.option.get("timeout", 120)

        # Test the network connectivity to Splunk.
        s = socket.socket()
        s.connect((self.option["host"], self.option['port']))

        self.r = requests.session()
        self.r.max_redirects = 1
        self.r.verify = self.option.get("ssl_verify", True)
        self.r.headers['Authorization'] = "Splunk {}".format(self.option['token'])
        self.url = "{0}://{1}:{2}/services/collector/event".format("http" if not self.option.get('https') else "https", self.option['host'], self.option['port'])

        self.default_fields = {}
        if self.option.get("sourcetype"):
            self.default_fields['sourcetype'] = self.option.get("sourcetype")
        if self.option.get("source"):
            self.default_fields['source'] = self.option.get("source")
        if self.option.get("index"):
            self.default_fields['index'] = self.option.get("index")

    def process(self, log_groups, check_point_tracker):
        logs = PullLogResponse.loggroups_to_flattern_list(log_groups, time_as_str=True, decode_bytes=True)"Get data from shard {0}, log count: {1}".format(self.shard_id, len(logs)))
        for log in logs:
            # Ship data to Splunk.
            event = {}
            event['time'] = log[u'__time__']
            del log['__time__']

            json_topic = {"actiontrail_audit_event": ["event"] }
            topic = log.get("__topic__", "")
            if topic in json_topic:
                    for field in json_topic[topic]:
                        log[field] = json.loads(log[field])
                except Exception as ex:
            event['event'] = json.dumps(log)

            data = json.dumps(event, sort_keys=True)

                    req =, data=data, timeout=self.timeout)
                except Exception as err:
                    logger.debug("Failed to connect to remote Splunk server ({0}). Exception: {1}", self.url, err)

                    # Add code to handle errors. For example, you can add the code to retry requests or report errors. "Complete send data to remote")


Start the program

The following code shows how to start a program named

export SLS_ENDPOINT=<Endpoint of your region>
export SLS_PROJECT=<SLS Project Name>
export SLS_LOGSTORE=<SLS Logstore Name>
export SLS_CG=<Consumer group name, such as syc_data>


Ship data from a Logstore that has multiple sources

If a Logstore has multiple data sources, you must configure a public executor. This prevents a large number of processes from running. For more information, see Ship logs from a Logstore that has multiple sources to Splunk. The following code provides an example on how to configure an executor.

exeuctor, options, settings = get_option()"*** start to consume data...")
    workers = []

    for option in options:
        worker = ConsumerWorker(SyncData, option, args=(settings,) )

        for i, worker in enumerate(workers):
            while worker.is_alive():
  "worker project: {0} logstore: {1} exit unexpected, try to shutdown it".format(
                options[i].project, options[i].logstore))
    except KeyboardInterrupt:"*** try to exit **** ")
        for worker in workers:

        # wait for all workers to shutdown before shutting down executor
        for worker in workers:
            while worker.is_alive():



You can configure up to 30 consumer groups for each Logstore in Log Service. If the system displays the ConsumerGroupQuotaExceed error message, we recommend that you log on to the Log Service console and delete consumer groups that you no longer need.

View and monitor data consumption

You can log on to the Log Service console to view the data consumption status of a consumer group. For more information, see Step 2: View the status of a consumer group.

Concurrent consumption

To consume data concurrently, you can start multiple consumer group-based programs for multiple consumers.
nohup python3 &
nohup python3 &
nohup python3 &
Note The name of each consumer is unique within a consumer group. The names of the consumers are suffixed with process IDs. The data of one shard can be consumed by only one consumer. If a Logstore contains 10 shards and each consumer group contains only one consumer, a maximum of 10 consumer groups can consume the data of all shards at the same time.


Throughput is tested in the following scenario: Python 3 is used to run the program in the preceding example, the bandwidth and receiving speed, such as the receiving speed on Splunk, are not limited, and a single consumer consumes about 20% of the single-core CPU resources. The test results indicate that the consumption speed of raw logs can reach 10 MB/s. Therefore, if 10 consumers consume data at the same time, the consumption speed of raw logs can reach 100 MB/s per CPU core. Each CPU core can consume up to 0.9 TB of raw logs per day.

High availability

A consumer group stores checkpoints on the server. When the data consumption process of one consumer stops, another consumer automatically takes over the data consumption process and continues the process from the checkpoint of the last consumption. You can start consumers on different machines. If a machine stops or is damaged, a consumer on another machine can take over the data consumption process and continue the process from the checkpoint of the last consumption. To have a sufficient number of consumers, you can start more consumers than shards on different machines.


To allow HTTPS-based communication between your program and Simple Log Service, you must configure the prefix of the endpoint to https://. Example:

The certificate for the domain name * is issued by GlobalSign. By default, most Linux and Windows servers are preconfigured to trust this certificate. If a server does not trust this certificate, download and install the certificate. For more information, see Certificate installation.