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Setting Up an Advanced Logstash Pipeline

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Setting Up an Advanced Logstash Pipeline

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A Logstash pipeline in most use cases has one or more input, filter, and output plugins. The scenarios in this section build Logstash configuration files to specify these plugins and discuss what each plugin is doing.

The Logstash configuration file defines your Logstash pipeline. When you start a Logstash instance, use the -f <path/to/file> option to specify the configuration file that defines that instance’s pipeline.

A Logstash pipeline has two required elements, input and output, and one optional element, filter. The input plugins consume data from a source, the filter plugins modify the data as you specify, and the output plugins write the data to a destination.

The following text represents the skeleton of a configuration pipeline:

# The # character at the beginning of a line indicates a comment. Use
# comments to describe your configuration.
input {
}
# The filter part of this file is commented out to indicate that it is
# optional.
# filter {
#
# }
output {
}

This skeleton is non-functional, because the input and output sections don’t have any valid options defined. The examples in this tutorial build configuration files to address specific use cases.

Paste the skeleton into a file named first-pipeline.conf in your home Logstash directory.

Parsing Apache Logs into Elasticsearch

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This example creates a Logstash pipeline that takes Apache web logs as input, parses those logs to create specific, named fields from the logs, and writes the parsed data to an Elasticsearch cluster.

Configuring Logstash for File Input

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To start your Logstash pipeline, configure the Logstash instance to read from a file using the file input plugin.

Edit the first-pipeline.conf file to add the following text:

input {
    file {
        path => "/path/to/groksample.log"
        start_position => beginning 
    }
}

The default behavior of the file input plugin is to monitor a file for new information, in a manner similar to the UNIX tail -f command. To change this default behavior and process the entire file, we need to specify the position where Logstash starts processing the file.

Replace /path/to/ with the actual path to the location of groksample.log in your file system.

Parsing Web Logs with the Grok Filter Plugin

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The grok filter plugin is one of several plugins that are available by default in Logstash. For details on how to manage Logstash plugins, see the reference documentation for the plugin manager.

Because the grok filter plugin looks for patterns in the incoming log data, configuration requires you to make decisions about how to identify the patterns that are of interest to your use case. A representative line from the web server log sample looks like this:

83.149.9.216 - - [04/Jan/2015:05:13:42 +0000] "GET /presentations/logstash-monitorama-2013/images/kibana-search.png
HTTP/1.1" 200 203023 "http://semicomplete.com/presentations/logstash-monitorama-2013/" "Mozilla/5.0 (Macintosh; Intel
Mac OS X 10_9_1) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/32.0.1700.77 Safari/537.36"

The IP address at the beginning of the line is easy to identify, as is the timestamp in brackets. In this tutorial, use the %{COMBINEDAPACHELOG} grok pattern, which structures lines from the Apache log using the following schema:

Information	Field Name
IP Address	`clientip`
User ID	`ident`
User Authentication	`auth`
timestamp	`timestamp`
HTTP Verb	`verb`
Request body	`request`
HTTP Version	`httpversion`
HTTP Status Code	`response`
Bytes served	`bytes`
Referrer URL	`referrer`
User agent	`agent`

Edit the first-pipeline.conf file to add the following text:

filter {
    grok {
        match => { "message" => "%{COMBINEDAPACHELOG}"}
    }
}

After processing, the sample line has the following JSON representation:

{
"clientip" : "83.149.9.216",
"ident" : ,
"auth" : ,
"timestamp" : "04/Jan/2015:05:13:42 +0000",
"verb" : "GET",
"request" : "/presentations/logstash-monitorama-2013/images/kibana-search.png",
"httpversion" : "HTTP/1.1",
"response" : "200",
"bytes" : "203023",
"referrer" : "http://semicomplete.com/presentations/logstash-monitorama-2013/",
"agent" : "Mozilla/5.0 (Macintosh; Intel Mac OS X 10_9_1) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/32.0.1700.77 Safari/537.36"
}

Indexing Parsed Data into Elasticsearch

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Now that the web logs are broken down into specific fields, the Logstash pipeline can index the data into an Elasticsearch cluster. Edit the first-pipeline.conf file to add the following text after the input section:

output {
    elasticsearch {
        protocol => "http"
    }
}

With this configuration, Logstash uses multicast discovery to connect to Elasticsearch.

Multicast discovery is acceptable for development work, but unsuited for production environments. For the purposes of this example, however, the default behavior is sufficient.

Enhancing Your Data with the Geoip Filter Plugin

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In addition to parsing log data for better searches, filter plugins can derive supplementary information from existing data. As an example, the geoip plugin looks up IP addresses, derives geographic location information from the addresses, and adds that location information to the logs.

Configure your Logstash instance to use the geoip filter plugin by adding the following lines to the filter section of the first-pipeline.conf file:

geoip {
    source => "clientip"
}

The geoip plugin configuration requires data that is already defined as separate fields. Make sure that the geoip section is after the grok section of the configuration file.

Specify the name of the field that contains the IP address to look up. In this tutorial, the field name is clientip.

Testing Your Initial Pipeline

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At this point, your first-pipeline.conf file has input, filter, and output sections properly configured, and looks like this:

input {
    file {
        path => "/Users/palecur/logstash-1.5.2/logstash-tutorial-dataset"
        start_position => beginning
    }
}
filter {
    grok {
        match => { "message" => "%{COMBINEDAPACHELOG}"}
    }
    geoip {
        source => "clientip"
    }
}
output {
    elasticsearch {
        protocol => "http"
    }
    stdout {}
}

To verify your configuration, run the following command:

bin/logstash -f first-pipeline.conf --configtest

The --configtest option parses your configuration file and reports any errors. When the configuration file passes the configuration test, start Logstash with the following command:

bin/logstash -f first-pipeline.conf

Try a test query to Elasticsearch based on the fields created by the grok filter plugin:

curl -XGET 'localhost:9200/logstash-$DATE/_search?q=response=401'

Replace $DATE with the current date, in YYYY.MM.DD format.

Since our sample has just one 401 HTTP response, we get one hit back:

{"took":2,
"timed_out":false,
"_shards":{"total":5,
  "successful":5,
  "failed":0},
"hits":{"total":1,
  "max_score":1.5351382,
  "hits":[{"_index":"logstash-2015.07.30",
    "_type":"logs",
    "_id":"AU7gqOky1um3U6ZomFaF",
    "_score":1.5351382,
    "_source":{"message":"83.149.9.216 - - [04/Jan/2015:05:13:45 +0000] \"GET /presentations/logstash-monitorama-2013/images/frontend-response-codes.png HTTP/1.1\" 200 52878 \"http://semicomplete.com/presentations/logstash-monitorama-2013/\" \"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_9_1) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/32.0.1700.77 Safari/537.36\"",
      "@version":"1",
      "@timestamp":"2015-07-30T20:30:41.265Z",
      "host":"localhost",
      "path":"/path/to/logstash-tutorial-dataset",
      "clientip":"83.149.9.216",
      "ident":"-",
      "auth":"-",
      "timestamp":"04/Jan/2015:05:13:45 +0000",
      "verb":"GET",
      "request":"/presentations/logstash-monitorama-2013/images/frontend-response-codes.png",
      "httpversion":"1.1",
      "response":"200",
      "bytes":"52878",
      "referrer":"\"http://semicomplete.com/presentations/logstash-monitorama-2013/\"",
      "agent":"\"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_9_1) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/32.0.1700.77 Safari/537.36\""
      }
    }]
  }
}

Try another search for the geographic information derived from the IP address:

curl -XGET 'localhost:9200/logstash-$DATE/_search?q=geoip.city_name=Buffalo'

Replace $DATE with the current date, in YYYY.MM.DD format.

Only one of the log entries comes from Buffalo, so the query produces a single response:

{"took":3,
"timed_out":false,
"_shards":{
  "total":5,
  "successful":5,
  "failed":0},
"hits":{"total":1,
  "max_score":1.03399,
  "hits":[{"_index":"logstash-2015.07.31",
    "_type":"logs",
    "_id":"AU7mK3CVSiMeBsJ0b_EP",
    "_score":1.03399,
    "_source":{
      "message":"108.174.55.234 - - [04/Jan/2015:05:27:45 +0000] \"GET /?flav=rss20 HTTP/1.1\" 200 29941 \"-\" \"-\"",
      "@version":"1",
      "@timestamp":"2015-07-31T22:11:22.347Z",
      "host":"localhost",
      "path":"/path/to/logstash-tutorial-dataset",
      "clientip":"108.174.55.234",
      "ident":"-",
      "auth":"-",
      "timestamp":"04/Jan/2015:05:27:45 +0000",
      "verb":"GET",
      "request":"/?flav=rss20",
      "httpversion":"1.1",
      "response":"200",
      "bytes":"29941",
      "referrer":"\"-\"",
      "agent":"\"-\"",
      "geoip":{
        "ip":"108.174.55.234",
        "country_code2":"US",
        "country_code3":"USA",
        "country_name":"United States",
        "continent_code":"NA",
        "region_name":"NY",
        "city_name":"Buffalo",
        "postal_code":"14221",
        "latitude":42.9864,
        "longitude":-78.7279,
        "dma_code":514,
        "area_code":716,
        "timezone":"America/New_York",
        "real_region_name":"New York",
        "location":[-78.7279,42.9864]
      }
    }
  }]
 }
}

Multiple Input and Output Plugins

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The information you need to manage often comes from several disparate sources, and use cases can require multiple destinations for your data. Your Logstash pipeline can use multiple input and output plugins to handle these requirements.

This example creates a Logstash pipeline that takes input from a Twitter feed and the Logstash Forwarder client, then sends the information to an Elasticsearch cluster as well as writing the information directly to a file.

Reading from a Twitter feed

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To add a Twitter feed, you need several pieces of information:

A consumer key, which uniquely identifies your Twitter app, which is Logstash in this case.
A consumer secret, which serves as the password for your Twitter app.
One or more keywords to search in the incoming feed.
An oauth token, which identifies the Twitter account using this app.
An oauth token secret, which serves as the password of the Twitter account.

Visit https://dev.twitter.com/apps to set up a Twitter account and generate your consumer key and secret, as well as your OAuth token and secret.

Use this information to add the following lines to the input section of the first-pipeline.conf file:

twitter {
    consumer_key =>
    consumer_secret =>
    keywords =>
    oauth_token =>
    oauth_token_secret =>
}

The Logstash Forwarder

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The Logstash Forwarder is a lightweight, resource-friendly tool that collects logs from files on the server and forwards these logs to your Logstash instance for processing. The Logstash Forwarder uses a secure protocol called lumberjack to communicate with your Logstash instance. The lumberjack protocol is designed for reliability and low latency. The Logstash Forwarder uses the computing resources of the machine hosting the source data, and the Lumberjack input plugin minimizes the resource demands on the Logstash instance.

In a typical use case, the Logstash Forwarder client runs on a separate machine from the machine running your Logstash instance. For the purposes of this tutorial, both Logstash and the Logstash Forwarder will be running on the same machine.

Default Logstash configuration includes the Lumberjack input plugin, which is designed to be resource-friendly. To install the Logstash Forwarder on your data source machine, install the appropriate package from the main Logstash product page.

Create a configuration file for the Logstash Forwarder similar to the following example:

{
    "network": {
        "servers": [ "localhost:5043" ],
        "ssl ca": "/path/to/localhost.crt", 
        "timeout": 15
    },
    "files": [
        {
            "paths": [
                "/path/to/sample-log" 
            ],
            "fields": { "type": "apache" }
        }
    ]
}

	Path to the SSL certificate for the Logstash instance.
	Path to the file or files that the Logstash Forwarder processes.

Save this configuration file as logstash-forwarder.conf.

Configure your Logstash instance to use the Lumberjack input plugin by adding the following lines to the input section of the first-pipeline.conf file:

lumberjack {
    port => "5043"
    ssl_certificate => "/path/to/ssl-cert" 
    ssl_key => "/path/to/ssl-key" 
}

	Path to the SSL certificate that the Logstash instance uses to authenticate itself to Logstash Forwarder.
	Path to the key for the SSL certificate.

Writing Logstash Data to a File

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You can configure your Logstash pipeline to write data directly to a file with the file output plugin.

Configure your Logstash instance to use the file output plugin by adding the following lines to the output section of the first-pipeline.conf file:

file {
    path => /path/to/target/file
}

Writing to multiple Elasticsearch nodes

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Writing to multiple Elasticsearch nodes lightens the resource demands on a given Elasticsearch node, as well as providing redundant points of entry into the cluster when a particular node is unavailable.

To configure your Logstash instance to write to multiple Elasticsearch nodes, edit the output section of the first-pipeline.conf file to read:

output {
    elasticsearch {
        protocol => "http"
        host => ["IP Address 1", "IP Address 2", "IP Address 3"]
    }
}

Use the IP addresses of three non-master nodes in your Elasticsearch cluster in the host line. When the host parameter lists multiple IP addresses, Logstash load-balances requests across the list of addresses.

Testing the Pipeline

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At this point, your first-pipeline.conf file looks like this:

input {
    twitter {
        consumer_key =>
        consumer_secret =>
        keywords =>
        oauth_token =>
        oauth_token_secret =>
    }
    lumberjack {
        port => "5043"
        ssl_certificate => "/path/to/ssl-cert"
        ssl_key => "/path/to/ssl-key"
    }
}
output {
    elasticsearch {
        protocol => "http"
        host => ["IP Address 1", "IP Address 2", "IP Address 3"]
    }
    file {
        path => /path/to/target/file
    }
}

Logstash is consuming data from the Twitter feed you configured, receiving data from the Logstash Forwarder, and indexing this information to three nodes in an Elasticsearch cluster as well as writing to a file.

At the data source machine, run the Logstash Forwarder with the following command:

logstash-forwarder -config logstash-forwarder.conf

Logstash Forwarder will attempt to connect on port 5403. Until Logstash starts with an active Lumberjack plugin, there won’t be any answer on that port, so any messages you see regarding failure to connect on that port are normal for now.

To verify your configuration, run the following command:

bin/logstash -f first-pipeline.conf --configtest

The --configtest option parses your configuration file and reports any errors. When the configuration file passes the configuration test, start Logstash with the following command:

bin/logstash -f first-pipeline.conf

Use the grep utility to search in the target file to verify that information is present:

grep Mozilla /path/to/target/file

Run an Elasticsearch query to find the same information in the Elasticsearch cluster:

curl -XGET 'localhost:9200/logstash-2015.07.30/_search?q=agent=Mozilla'

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