Use the logstash-input-sls plug-in

Features

• Distributed collaborative consumption: Multiple servers can be configured to consume log data in a Logstore at the same time.
  Note If you want to use multiple Logstash servers to implement distributed collaborative consumption, make sure that only one pipeline with logstash-input-sls installed is deployed on each server. This is because of the limits on the logstash-input-sls plug-in. If multiple pipelines with logstash-input-sls installed are deployed on a single server, duplicated data may exist in the output.
• High performance: If you use a Java consumer group, the consumption speed of a single-core CPU can reach 20 MB/s.
• High reliability: logstash-input-sls saves the consumption progress on servers. If a server is recovered from an exception, it can continue to consume data from the last consumption checkpoint.
• Automatic load balancing: Shards are automatically allocated based on the number of consumers in a consumer group. If consumers join or leave the consumer group, logstash-input-sls automatically re-allocates the shards.

Operation

• The logstash-input-sls plug-in is installed.

  For more information, see Install and remove a plug-in.

• A Log Service project and a Logstore are created. Data is collected.

  For more information, see Quick start.

logstash-input-sls application

After the prerequisites are met, you can create a pipeline based on the instructions provided in Use configuration files to manage pipelines. When you create a pipeline, configure the pipeline parameters based on the descriptions that are provided in the table of the Parameters section. After you configure the parameters, you must save the settings and deploy the pipeline. Then, Alibaba Cloud Logstash is triggered to retrieve data from Log Service.

The following example shows how to use Alibaba Cloud Logstash to consume log data in a Logstore and transfer the data to Alibaba Cloud Elasticsearch:

input {
 logservice{
  endpoint => "your project endpoint"
  access_id => "your access id"
  access_key => "your access key"
  project => "your project name"
  logstore => "your logstore name"
  consumer_group => "consumer group name"
  consumer_name => "consumer name"
  position => "end"
  checkpoint_second => 30
  include_meta => true
  consumer_name_with_ip => true
  }
}

output {
  elasticsearch {
    hosts => ["http://es-cn-***.elasticsearch.aliyuncs.com:9200"]
    index => "<your_index>"
    user => "elastic"
    password => "changeme"
  }
}

Make the following assumptions: The Logstore has 10 shards. The data traffic of each shard is 1 MB/s. The processing capacity of each Logstash server is 3 MB/s. Five Logstash servers are allocated, and one pipeline with logstash-input-sls installed is deployed on each server. Each server uses the same settings of consumer_group and consumer_name. consumer_name_with_ip is set to true.

In this case, two shards are allocated to each server, and each shard processes data at 2 MB/s.

Parameters

The following table describes the parameters supported by logstash-input-sls.

Performance benchmark test

• Test environment
  • CPU: Intel(R) Xeon(R) Platinum 8163 processor@2.50 GHz, 4-core
  • Memory: 8 GB
  • Operating system: Linux
• Configuration of a Logstash pipeline

  input {
    logservice{
    endpoint => "cn-hangzhou-intranet.log.aliyuncs.com"
    access_id => "***"
    access_key => "***"
    project => "test-project"
    logstore => "logstore1"
    consumer_group => "consumer_group1"
    consumer_name => "consumer1"
    position => "end"
    checkpoint_second => 30
    include_meta => true
    consumer_name_with_ip => true
    }
  }
  output {
    elasticsearch {
      hosts => ["http://es-cn-***.elasticsearch.aliyuncs.com:9200"]
      index => "myindex"
      user => "elastic"
      password => "changeme"
    }
  }

• Test procedure
  1. Use a Java producer to send 2 MB, 4 MB, 8 MB, 16 MB, and 32 MB of data to the Logstore every second.

     Each log entry contains 10 key-value pairs and is about 500 bytes in length.

  2. Start Logstash to consume data in the Logstore. Make sure that the consumption latency does not increase and the consumption speed can keep pace with the production speed.
• Test results

  Traffic (MB/s)	CPU utilization (%)	Memory usage (GB)
  32	170.3	1.3
  16	83.3	1.3
  8	41.5	1.3
  4	21.0	1.3
  2	11.3	1.3