Rsyslog to kafka

Rsyslog to kafka DEFAULT

omkafka: write to Apache Kafka

Action Parameters¶

Note that omkafka supports some Array-type parameters. While the parameter name can only be set once, it is possible to set multiple values with that single parameter. See the Using Array Type Parameter section for details.

Broker¶

typedefaultmandatory directive
arraylocalhost:9092nonone

Specifies the broker(s) to use.

Topic¶

typedefaultmandatory directive
stringnoneyesnone

Specifies the topic to produce to.

Key¶

typedefaultmandatory directive
wordnonenonone

Kafka key to be used for all messages.

If a key is provided and partitions.auto=”on” is set, then all messages will be assigned to a partition based on the key.

DynaKey¶

typedefaultmandatory directive
binaryoffnonone

If set, the key parameter becomes a template for the key to base the partitioning on.

DynaTopic¶

typedefaultmandatory directive
binaryoffnonone

If set, the topic parameter becomes a template for which topic to produce messages to. The cache is cleared on HUP.

DynaTopic.Cachesize¶

typedefaultmandatory directive
integer50nonone

If set, defines the number of topics that will be kept in the dynatopic cache.

Partitions.Auto¶

typedefaultmandatory directive
binaryoffnonone

Librdkafka provides an automatic partitioning function that will automatically distribute the produced messages into all partitions configured for that topic.

To use, set partitions.auto=”on”. This is instead of specifying the number of partitions on the producer side, where it would be easier to change the kafka configuration on the cluster for number of partitions/topic vs on every machine talking to Kafka via rsyslog.

If no key is set, messages will be distributed randomly across partitions. This results in a very even load on all partitions, but does not preserve ordering between the messages.

If a key is set, a partition will be chosen automatically based on it. All messages with the same key will be sorted into the same partition, preserving their ordering. For example, by setting the key to the hostname, messages from a specific host will be written to one partition and ordered, but messages from different nodes will be distributed across different partitions. This distribution is essentially random, but stable. If the number of different keys is much larger than the number of partitions on the topic, load will be distributed fairly evenly.

If set, it will override any other partitioning scheme configured.

Partitions.number¶

typedefaultmandatory directive
integernonenonone

If set, specifies how many partitions exists and activates load-balancing among them. Messages are distributed more or less evenly between the partitions. Note that the number specified must be correct. Otherwise, some errors may occur or some partitions may never receive data.

Partitions.useFixed¶

typedefaultmandatory directive
integernonenonone

If set, specifies the partition to which data is produced. All data goes to this partition, no other partition is ever involved for this action.

errorFile¶

typedefaultmandatory directive
wordnonenonone

If set, messages that could not be sent and caused an error messages are written to the file specified. This file is in JSON format, with a single record being written for each message in error. The entry contains the full message, as well as Kafka error number and reason string.

The idea behind the error file is that the admin can periodically run a script that reads the error file and reacts on it. Note that the error file is kept open from when the first error occurred up until rsyslog is terminated or received a HUP signal.

statsFile¶

typedefaultmandatory directive
wordnonenonone

If set, the contents of the JSON object containing the full librdkafka statistics will be written to the file specified. The file will be updated based on the statistics.interval.ms confparam value, which must also be set.

ConfParam¶

typedefaultmandatory directive
arraynonenonone

Permits to specify Kafka options. Rather than offering a myriad of config settings to match the Kafka parameters, we provide this setting here as a vehicle to set any Kafka parameter. This has the big advantage that Kafka parameters that come up in new releases can immediately be used.

Note that we use librdkafka for the Kafka connection, so the parameters are actually those that librdkafka supports. As of our understanding, this is a superset of the native Kafka parameters.

TopicConfParam¶

typedefaultmandatory directive
arraynonenonone

In essence the same as confParam, but for the Kafka topic.

Template¶

typedefaultmandatory directive
wordtemplate set via template module parameternonone

Sets the template to be used for this action.

closeTimeout¶

typedefaultmandatory directive
integer2000nonone

Sets the time to wait in ms (milliseconds) for draining messages submitted to kafka-handle (provided by librdkafka) before closing it.

The maximum value of closeTimeout used across all omkafka action instances is used as librdkafka unload-timeout while unloading the module (for shutdown, for instance).

resubmitOnFailure¶

typedefaultmandatory directive
binaryoffnonone

New in version 8.28.0.

If enabled, failed messages will be resubmit automatically when kafka is able to send messages again. To prevent message loss, this option should be enabled.

Note: Messages that are rejected by kafka due to exceeding the maximum configured message size, are automatically dropped. These errors are not retriable.

KeepFailedMessages¶

typedefaultmandatory directive
binaryoffnonone

If enabled, failed messages will be saved and loaded on shutdown/startup and resend after startup if the kafka server is able to receive messages again. This setting requires resubmitOnFailure to be enabled as well.

failedMsgFile¶

typedefaultmandatory directive
wordnonenonone

New in version 8.28.0.

Filename where the failed messages should be stored into. Needs to be set when keepFailedMessages is enabled, otherwise failed messages won’t be saved.

Sours: https://www.rsyslog.com/doc/master/configuration/modules/omkafka.html

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:

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):

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:

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:

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:

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

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:

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:

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:

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:

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

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!

Sours: https://dzone.com/articles/recipe-rsyslog-kafka-logstash-1
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Recipe: How to integrate rsyslog with Kafka and Logstash

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 for the logstash + kafka + rsyslog integration

rsyslog Kafka Output

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:

Setting up Kafka

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.

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.

rsyslog to Logstash via Kafka

rsyslog inputs, templates and queues

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 )

rsyslog Kafka Output

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.

Logstash Kafka Input

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 the Kafka topic we created. To connect, we’ll point Logstash to at least one Kafka broker, and it will fetch info about other Kafka brokers from there:

input {  kafka {    bootstrap_servers => ["localhost:9092"]    topics => ["rsyslog_logstash"]  }}

If you need Logstash to listen to multiple topics, you can add all of them in the topics array. A regular expression (topics_pattern) is also possible, if topics are dynamic and tend to follow a pattern.

Logstash Elasticsearch Output

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

output { elasticsearch { hosts => "logsene-receiver.sematext.com:443" # it used to be "host" and "port" pre-2.0 ssl => "true" index => "your Logsene app token goes here" manage_template => false #protocol => "http" # removed in 2.0 #port => "443" # 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!

You might also like

Sours: https://sematext.com/blog/recipe-rsyslog-apache-kafka-logstash/
DevOps \u0026 SysAdmins: Rsyslog. How to count messages sent to kafka

Rsyslog kafka and elk

Intro.

The elk stack is popular, but I have not found any suitable articles about connecting Rsyslog to Kafka and ELK. You can found somewhere about Kafka somewhere about Logstash or Rsyslog, but not altogether.

In this article, we will make a docker-compose file that will launch the entire system, and build an image that simulates an application with logs. We will also consider how you can check each system separately.

I don't want to create a detailed description of each application. It is just starting point for you to learn rsyslog and ELK

Github with full project: https://github.com/ArtemMe/rsyslog_kafka_elk

I split the project on release (tag). Every release is a new service like Rsyslog(tag 0.1) or Kibana(tag 0.4). You can switch to desire release and start project to test build

Below in the article, I give a description of each service. You can download the project and go to the root of the project and enter the command:


Rsyslog kafka logstash elasticsearch and kibana will be up. So you can go to kibana on and check launch.
Also, each section contains tips on how to check the service %)

Rsyslog. (tag 0.1)

We will need two configuration files: one with the basic settings , the second is optional with settings for our needs

We will not delve into the rsyslog settings because you can dig into them for a long time. Just remember basic instructions: module, template, action
Let's take a look at an example of the file :

Remember topic name: test_topic_1

You can find the full list of property names for templates there: https://www.rsyslog.com/doc/master/configuration/properties.html

Also note the main file contains a line like:
This is a directive that tells us where else to read the settings for rsyslog. It is useful to separate common settings from specific ones

Create an image for generating logs

The image will essentially just start rsyslog. In the future, we will be able to enter this container and generate logs.

You can find the Docker file in the folder. Let's look at the chunk of that file where on the first and second lines we copy our config. On the third line, we mount a folder for logs which will be generated

Building. Go to folder and execute

Launch the container to check the image.

To check that rsyslog are writing logs, go to our container and call the command:

Let's look at folder and you should find a string like this .

Congratulations! You have configured rsyslog in your docker container!

A bit about networking in docker containers.

Let's create our network in the docker-compose.yml file. In the future, each service can be launched to different machines. This is no problem.

Kafka (tag 0.2)

I took this repository as a basis:
The resulting service is:

Tips how to check kafka (you can do it after starting containers):

We will be able to see what is in the Kafka topic when we launch our containers. First, you need to download Kafka. Here is a cool tutorial but if it's short download it here and unpack it to folder.
Actually, we need scripts in the folder.

Now, we can connect to the container and execute a script to see if there are any entries inside the topic :

About the command itself: we connect to the rsyslog_kafka_elk_elk network, rsyslog_kafka_elk is the name of the folder where the docker-compose.yml file is located, and elk is the network that we specified. With the -v command, we mount scripts for Kafka into our container.

The result of command should be something like this:

Logstash (tag 0.3)

Configs are located in the folder. - here we specify parameters for connecting to Elasticsearch

In the config, there is a setting for Kafka as for an incoming stream and a setting for elasticsearch as for an outgoing stream

To monitor what goes into the Elastisearch and check the Logstesh is working properly, I created a file output stream so logs will be written to file. The main thing is does not forget to add volume to docker-compose.yml

When you start the service check file in your project. You should find logs from kafka

Elasticsearch (tag 0.3)

This is the simplest configuration. We will launch the trial version, but you can turn on the open source one if you wish. Configs as usual in

Tips how to check elasticsearch (you can do it after starting containers):

Let's check the indexes of the elastic. Take as a basis a cool image of and command curl:

As the result of command:

We can see that the indices are being created and our elastic is alive. Let's connect Kibana now

Kibana (tag 0.4)

This is what the service looks like

In the folder we have a docker file to build an image and also settings for kibana:

To enter the Kibana UI, you need to log in to the browser (login/password is elasctic/changeme)
In the left menu, find Discover, click on it and create an index. I suggest this

Create index pattern

Sours: https://dev.to/lbatters/rsyslog-kafka-and-elk-3kgk

To kafka rsyslog

# system logsmodule(load="imuxsock")module(load="imklog")# filemodule(load="imfile")# parsermodule(load="mmnormalize")# sendermodule(load="omkafka")input(type="imfile" File="/var/log/example.log.smaller" Tag="apache:")global( workDirectory="/var/run/")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)# try to parse logsaction(type="mmnormalize" rulebase="/etc/rsyslog_apache.rb")# template for successfully parsed logstemplate(name="all-json" type="list"){ property(name="$!all-json")}# template for plain (unparsed) syslogtemplate(name="plain-syslog" type="list") { constant(value="{") constant(value="\"timestamp\":\"") property(name="timereported" dateFormat="rfc3339") constant(value="\",\"host\":\"") property(name="hostname") constant(value="\",\"severity\":\"") property(name="syslogseverity-text") constant(value="\",\"facility\":\"") property(name="syslogfacility-text") constant(value="\",\"tag\":\"") property(name="syslogtag" format="json") constant(value="\",\"message\":\"") property(name="msg" format="json") constant(value="\"}")}# send to Kafkaif $parsesuccess == "OK" then { action(type="omkafka" broker=["localhost:9092"] topic="rsyslog_logstash" template="all-json" action.resumeRetryCount="-1" )} else { action(type="omkafka" broker=["localhost:9092"] topic="rsyslog_logstash" template="plain-syslog" action.resumeRetryCount="-1" )}
Sours: https://github.com/sematext/velocity/blob/master/rsyslog.conf.kafka
Stateless Fluentd with Kafka - Steven McDonald, Usabilla

omkafka: write to Apache Kafka¶

The omkafka plug-in implements an Apache Kafka producer, permitting rsyslog to write data to Kafka.

Configuration Parameters¶

Module Parameters¶

Currently none.

Action Parameters¶

Note that omkafka supports some Array-type parameters. While the parameter name can only be set once, it is possible to set multiple values with that single parameter.

For example, to select “snappy” compression, you can use

action(type="omkafka" topic="mytopic" confParam="compression.codec=snappy")

which is equivalent to

action(type="omkafka" topic="mytopic" confParam=["compression.codec=snappy"])

To specify multiple values, just use the bracket notation and create a comma-delimited list of values as shown here:

action(type="omkafka" topic="mytopic" confParam=["compression.codec=snappy", "socket.timeout.ms=5", "socket.keepalive.enable=true"] )

Type: Array

Default: “localhost:9092”

Specifies the broker(s) to use.

Mandatory

Specifies the topic to produce to.

Default: none

Kafka key to be used for all messages.

Default: off

If set, the topic parameter becomes a template for which topic to produce messages to. The cache is cleared on HUP.

Default: 50

If set, defines the number of topics that will be kept in the dynatopic cache.

Default: off

librdkafka provides an automatic partitioning function that will evenly split the produced messages into all partitions configured for that topic.

To use, set partitions.auto=”on”. This is instead of specifying the number of partitions on the producer side, where it would be easier to change the kafka configuration on the cluster for number of partitions/topic vs on every machine talking to Kafka via rsyslog.

If set, it will override any other partitioning scheme configured.

Default: none

If set, specifies how many partitions exists and activates load-balancing among them. Messages are distributed more or less evenly between the partitions. Note that the number specified must be correct. Otherwise, some errors may occur or some partitions may never receive data.

Default: none

If set, specifies the partition to which data is produced. All data goes to this partition, no other partition is ever involved for this action.

Default: none

If set, messages that could not be sent and caused an error messages are written to the file specified. This file is in JSON format, with a single record being written for each message in error. The entry contains the full message, as well as Kafka error number and reason string.

The idea behind the error file is that the admin can periodically run a script that reads the error file and reacts on it. Note that the error file is kept open from when the first error occured up until rsyslog is terminated or received a HUP signal.

Type: Array

Default: none

Permits to specify Kafka options. Rather than offering a myriad of config settings to match the Kafka parameters, we provide this setting here as a vehicle to set any Kafka parameter. This has the big advantage that Kafka parameters that come up in new releases can immediately be used.

Note that we use librdkafka for the Kafka connection, so the parameters are actually those that librdkafka supports. As of our understanding, this is a superset of the native Kafka parameters.

Type: Array

Default: none

In essence the same as confParam, but for the Kafka topic.

Default: template set via “template” module parameter

Sets the template to be used for this action.

Default: 2000

Sets the time to wait in ms (milliseconds) for draining messages submitted to kafka-handle (provided by librdkafka) before closing it.

The maximum value of closeTimeout used across all omkafka action instances is used as librdkafka unload-timeout while unloading the module (for shutdown, for instance).

Example¶

To be added, see intro to action parameters.


© Copyright 2008-2016, Rainer Gerhards and Adiscon. Revision .

Sours: https://rsyslog.readthedocs.io/en/latest/configuration/modules/omkafka.html

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