logstash

Original post: Recipe: rsyslog + Kafka + Logstash by @Sematext

This recipe is similar to the previous rsyslog + Redis + Logstash one, except that we’ll use Kafka as a central buffer and connecting point instead of Redis. You’ll have more of the same advantages:

• rsyslog is light and crazy-fast, including when you want it to tail files and parse unstructured data (see the Apache logs + rsyslog + Elasticsearch recipe)
• Kafka is awesome at buffering things
• Logstash can transform your logs and connect them to N destinations with unmatched ease

There are a couple of differences to the Redis recipe, though:

• rsyslog already has Kafka output packages, so it’s easier to set up
• Kafka has a different set of features than Redis (trying to avoid flame wars here) when it comes to queues and scaling

As with the other recipes, I’ll show you how to install and configure the needed components. The end result would be that local syslog (and tailed files, if you want to tail them) will end up in Elasticsearch, or a logging SaaS like Logsene (which exposes the Elasticsearch API for both indexing and searching). Of course you can choose to change your rsyslog configuration to parse logs as well (as we’ve shown before), and change Logstash to do other things (like adding GeoIP info).

Getting the ingredients

First of all, you’ll probably need to update rsyslog. Most distros come with ancient versions and don’t have the plugins you need. From the official packages you can install:

• rsyslog. This will update the base package, including the file-tailing module
• rsyslog-kafka. This will get you the Kafka output module

If you don’t have Kafka already, you can set it up by downloading the binary tar. And then you can follow the quickstart guide. Basically you’ll have to start Zookeeper first (assuming you don’t have one already that you’d want to re-use):

bin/zookeeper-server-start.sh config/zookeeper.properties

And then start Kafka itself and create a simple 1-partition topic that we’ll use for pushing logs from rsyslog to Logstash. Let’s call it rsyslog_logstash:

bin/kafka-server-start.sh config/server.properties
bin/kafka-topics.sh --create --zookeeper localhost:2181 --replication-factor 1 --partitions 1 --topic rsyslog_logstash

Finally, you’ll have Logstash. At the time of writing this, we have a beta of 2.0, which comes with lots of improvements (including huge performance gains of the GeoIP filter I touched on earlier). After downloading and unpacking, you can start it via:

bin/logstash -f logstash.conf

Though you also have packages, in which case you’d put the configuration file in /etc/logstash/conf.d/ and start it with the init script.

Configuring rsyslog

With rsyslog, you’d need to load the needed modules first:

module(load="imuxsock")  # will listen to your local syslog
module(load="imfile")    # if you want to tail files
module(load="omkafka")   # lets you send to Kafka

If you want to tail files, you’d have to add definitions for each group of files like this:

input(type="imfile"
  File="/opt/logs/example*.log"
  Tag="examplelogs"
)

Then you’d need a template that will build JSON documents out of your logs. You would publish these JSON’s to Kafka and consume them with Logstash. Here’s one that works well for plain syslog and tailed files that aren’t parsed via mmnormalize:

template(name="json_lines" type="list" option.json="on") {
  constant(value="{")
  constant(value="\"timestamp\":\"")
  property(name="timereported" dateFormat="rfc3339")
  constant(value="\",\"message\":\"")
  property(name="msg")
  constant(value="\",\"host\":\"")
  property(name="hostname")
  constant(value="\",\"severity\":\"")
  property(name="syslogseverity-text")
  constant(value="\",\"facility\":\"")
  property(name="syslogfacility-text")
  constant(value="\",\"syslog-tag\":\"")
  property(name="syslogtag")
  constant(value="\"}")
}

By default, rsyslog has a memory queue of 10K messages and has a single thread that works with batches of up to 16 messages (you can find all queue parameters here). You may want to change:
– the batch size, which also controls the maximum number of messages to be sent to Kafka at once
– the number of threads, which would parallelize sending to Kafka as well
– the size of the queue and its nature: in-memory(default), disk or disk-assisted

In a rsyslog->Kafka->Logstash setup I assume you want to keep rsyslog light, so these numbers would be small, like:

main_queue(
  queue.workerthreads="1"      # threads to work on the queue
  queue.dequeueBatchSize="100" # max number of messages to process at once
  queue.size="10000"           # max queue size
)

Finally, to publish to Kafka you’d mainly specify the brokers to connect to (in this example we have one listening to localhost:9092) and the name of the topic we just created:

action(
  broker=["localhost:9092"]
  type="omkafka"
  topic="rsyslog_logstash"
  template="json"
)

Assuming Kafka is started, rsyslog will keep pushing to it.

Configuring Logstash

This is the part where we pick the JSON logs (as defined in the earlier template) and forward them to the preferred destinations. First, we have the input, which will use to the Kafka topic we created. To connect, we’ll point Logstash to Zookeeper, and it will fetch all the info about Kafka from there:

input {
  kafka {
    zk_connect => "localhost:2181"
    topic_id => "rsyslog_logstash"
  }
}

At this point, you may want to use various filters to change your logs before pushing to Logsene/Elasticsearch. For this last step, you’d use the Elasticsearch output:

output {
  elasticsearch {
    hosts => "localhost" # it used to be "host" pre-2.0
    port => 9200
    #ssl => "true"
    #protocol => "http" # removed in 2.0
  }
}

And that’s it! Now you can use Kibana (or, in the case of Logsene, either Kibana or Logsene’s own UI) to search your logs!