Monitoring rsyslog’s impstats with Kibana and SPM

Original post: Monitoring rsyslog with Kibana and SPM by @Sematext

A while ago we published this post where we explained how you can get stats about rsyslog, such as the number of messages enqueued, the number of output errors and so on. The point was to send them to Elasticsearch (or Logsene, our logging SaaS, which exposes the Elasticsearch API) in order to analyze them.

This is part 2 of that story, where we share how we process these stats in production. We’ll cover:

  • an updated config, working with Elasticsearch 2.x
  • what Kibana dashboards we have in Logsene to get an overview of what rsyslog is doing
  • how we send some of these metrics to SPM as well, in order to set up alerts on their values: both threshold-based alerts and anomaly detection

Continue reading “Monitoring rsyslog’s impstats with Kibana and SPM”

Recipe: Apache Logs + rsyslog (parsing) + Elasticsearch

Original post: Recipe: Apache Logs + rsyslog (parsing) + Elasticsearch by @Sematext

This recipe is about tailing Apache HTTPD logs with rsyslog, parsing them into structured JSON documents, and forwarding them to Elasticsearch (or a log analytics SaaS, like Logsene, which exposes the Elasticsearch API). Having them indexed in a structured way will allow you to do better analytics with tools like Kibana:


We’ll also cover pushing logs coming from the syslog socket and kernel, and how to buffer all of them properly. So this is quite a complete recipe for your centralized logging needs.

Getting the ingredients

Even though most distros already have rsyslog installed, it’s highly recommended to get the latest stable from the rsyslog repositories. The packages you’ll need are:

With the ingredients in place, let’s start cooking a configuration. The configuration needs to do the following:

  • load the required modules
  • configure inputs: tailing Apache logs and system logs
  • configure the main queue to buffer your messages. This is also the place to define the number of worker threads and batch sizes (which will also be Elasticsearch bulk sizes)
  • parse common Apache logs into JSON
  • define a template where you’d specify how JSON messages would look like. You’d use this template to send logs to Logsene/Elasticsearch via the Elasticsearch output

Loading modules

Here, we’ll need imfile to tail files, mmnormalize to parse them, and omelasticsearch to send them. If you want to tail the system logs, you’d also need to include imuxsock and imklog (for kernel logs).

# system logs
# file
# parser
# sender

Configure inputs

For system logs, you typically don’t need any special configuration (unless you want to listen to a non-default Unix Socket). For Apache logs, you’d point to the file(s) you want to monitor. You can use wildcards for file names as well. You also need to specify a syslog tag for each input. You can use this tag later for filtering.


NOTE: By default, rsyslog will not poll for file changes every N seconds. Instead, it will rely on the kernel (via inotify) to poke it when files get changed. This makes the process quite realtime and scales well, especially if you have many files changing rarely. Inotify is also less prone to bugs when it comes to file rotation and other events that would otherwise happen between two “polls”. You can still use the legacy mode=”polling” by specifying it in imfile’s module parameters.

Queue and workers

By default, all incoming messages go into a main queue. You can also separate flows (e.g. files and system logs) by using different rulesets but let’s keep it simple for now.

For tailing files, this kind of queue would work well:


This would be a small in-memory queue of 10K messages, which works well if Elasticsearch goes down, because the data is still in the file and rsyslog can stop tailing when the queue becomes full, and then resume tailing. 4 worker threads will pick batches of up to 1000 messages from the queue, parse them (see below) and send the resulting JSONs to Elasticsearch.

If you need a larger queue (e.g. if you have lots of system logs and want to make sure they’re not lost), I would recommend using a disk-assisted memory queue, that will spill to disk whenever it uses too much memory:

  queue.highWatermark="500000"    # max no. of events to hold in memory
  queue.lowWatermark="200000"     # use memory queue again, when it's back to this level
  queue.spoolDirectory="/var/run/rsyslog/queues"  # where to write on disk
  queue.maxDiskSpace="5g"        # it will stop at this much disk space
  queue.size="5000000"           # or this many messages
  queue.saveOnShutdown="on"      # save memory queue contents to disk when rsyslog is exiting

Parsing with mmnormalize

The message normalization module uses liblognorm to do the parsing. So in the configuration you’d simply point rsyslog to the liblognorm rulebase:


where apache.rb will contain rules for parsing apache logs, that can look like this:


rule=:%clientip:word% %ident:word% %auth:word% [%timestamp:char-to:]%] "%verb:word% %request:word% HTTP/%httpversion:float%" %response:number% %bytes:number% "%referrer:char-to:"%" "%agent:char-to:"%"%blob:rest%

Where version=2 indicates that rsyslog should use liblognorm’s v2 engine (which is was introduced in rsyslog 8.13) and then you have the actual rule for parsing logs. You can find more details about configuring those rules in the liblognorm documentation.

Besides parsing Apache logs, creating new rules typically requires a lot of trial and error. To check your rules without messing with rsyslog, you can use the lognormalizer binary like:

head -1 /path/to/log.file | /usr/lib/lognorm/lognormalizer -r /path/to/rulebase.rb -e json

NOTE: If you’re used to Logstash’s grok, this kind of parsing rules will look very familiar. However, things are quite different under the hood. Grok is a nice abstraction over regular expressions, while liblognorm builds parse trees out of specialized parsers. This makes liblognorm much faster, especially as you add more rules. In fact, it scales so well, that for all practical purposes, performance depends on the length of the log lines and not on the number of rules. This post explains the theory behind this assuption, and this is actually proven by various tests. The downside is that you’ll lose some of the flexibility offered by regular expressions. You can still use regular expressions with liblognorm (you’d need to set allow_regex to on when loading mmnormalize) but then you’d lose a lot of the benefits that come with the parse tree approach.

Template for parsed logs

Since we want to push logs to Elasticsearch as JSON, we’d need to use templates to format them. For Apache logs, by the time parsing ended, you already have all the relevant fields in the $!all-json variable, that you’ll use as a template:

template(name="all-json" type="list"){

Template for time-based indices

For the logging use-case, you’d probably want to use time-based indices (e.g. if you keep your logs for 7 days, you can have one index per day). Such a design will give your cluster a lot more capacity due to the way Elasticsearch merges data in the background (you can learn the details in our presentations at GeeCON and Berlin Buzzwords).

To make rsyslog use daily or other time-based indices, you need to define a template that builds an index name off the timestamp of each log. This is one that names them logstash-YYYY.MM.DD, like Logstash does by default:

  type="list") {
    property(name="timereported" dateFormat="rfc3339" position.from="1""4")
    property(name="timereported" dateFormat="rfc3339" position.from="6""7")
    property(name="timereported" dateFormat="rfc3339" position.from="9""10")

And then you’d use this template in the Elasticsearch output:


Putting both Apache and system logs together

If you use the same rsyslog to parse system logs, mmnormalize won’t parse them (because they don’t match Apache’s common log format). In this case, you’ll need to pick the rsyslog properties you want and build an additional JSON template:

  type="list") {
      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")

Then you can make rsyslog decide: if a log was parsed successfully, use the all-json template. If not, use the plain-syslog one:

if $parsesuccess == "OK" then {
} else {

And that’s it! Now you can restart rsyslog and get both your system and Apache logs parsed, buffered and indexed into Elasticsearch. If you’re a Logsene user, the recipe is a bit simpler: you’d follow the same steps, except that you’ll skip the logstash-index template (Logsene does that for you) and your Elasticsearch actions will look like this:

  template="all-json or plain-syslog"

rsyslog 8.13.0 (v8-stable) released

We have released rsyslog 8.13.0.

This release sports a big number of changes. While most are bugfixes, there are also some additions to existing functionality, most notably the enhancements for ZeroMQ and Redis modules.

For more details, please take a look at the Changelog.


As always, feedback is appreciated.

Best regards,
Florian Riedl

rsyslog 8.7.0 (v8-stable) released

We have released rsyslog 8.7.0.

Version 8.7.0 contains various improvements and additions to a wide array of modules, like imfile, imptcp, improvements to RainerScript and mmnormalize (thanks to Singh Janmejay) and a couple of other improvements. But, the biggest addition is the new omkafka module that now allows direct writing to Apache Kafka.

This release also contains important bug fixes.

This is a recommended upgrade for all users.




As always, feedback is appreciated.

Best regards,
Florian Riedl

Changelog for 8.7.0 (v8-stable)

Version 8.7.0 [v8-stable] 2015-01-13

  • add message metadata “system” to msg object
    this permits to store metadata alongside the message
  • imfile: add support for “filename” metadata
    this is useful in cases where wildcards are used
  • imptcp: make stats counter names consistent with what imudp, imtcp uses
  • added new module “omkafka” to support writing to Apache Kafka
  • omfwd: add new “udp.senddelay” parameter
  • mmnormalize enhancements
    Thanks to Janmejay Singh for the patch.
  • RainerScript “foreach” iterator and array reading support
    Thanks to Janmejay Singh for the patch.
  • now requires liblognorm >= 1.0.2
  • add support for systemd >= 209 library names
  • BSD “ntp” facility (value 12) is now also supported in filter
    Thanks to Douglas K. Rand of Iteris, Inc. for the patch.
    Note: this patch was released under ASL 2.0 (see email-conversation).
  • bugfix: global(localHostName=”xxx”) was not respected in all modules
  • bugfix: emit correct error message on config-file-not-found
  • bugfix: impstats emitted invalid JSON format (if JSON was selected)
  • bugfix: (small) memory leak in omfile’s outchannel code
    Thanks to Koral Ilgun for reporting this issue.
  • bugfix: imuxsock did not deactivate some code not supported by platform
    Among potential other problemns, this caused build failure under Solaris.
    Note that this build problem just made a broader problem appear that so
    far always existed but was not visible.

rsyslog 7.5.5 (v7-devel) released

This release offers some important bug fixes and exciting new features. Most importantly, imudp can now use multiple input threads for even higher UDP input throughput. And imfile now longer has a hardcoded limit on the number of files that can be monitored. You can now monitor as many files as you like. Support for setting default timezones has been added to imptcp and imudp, but is still in experimental state (but feel free to use it if it suits you!). More detailed information is available in the changelog.



As always, feedback is appreciated.

Best regards, Florian Riedl

Changelog for 7.5.5 (v7-devel)

Version 7.5.5 [devel] 2013-10-16

  • imfile: permit to monitor an unlimited number of files
  • imptcp: add “defaultTZ” input parameter
  • imudp: support for multiple receiver threads added
  • imudp: add “dfltTZ” input config parameter
  • bugfix: memory leak in mmnormalize
  • bugfix: mmutf8fix did not properly handle invalid UTF-8 at END of message if the very last character sequence was too long, this was not detected
    Thanks to Risto Vaarandi for reporting this problem.
  • mmanon: removed the check for specific “terminator characters” after  last octet. As it turned out, this didn’t work in practice as there was an enormous set of potential terminator chars — so removing them was the best thing to do. Note that this may change behaviour of existing installations. Yet, we still consider this an important bugfix, that should be applied to the stable branch.
    Thanks to Muri Cicanor for initiating the discussion
  • now requires libestr 0.1.7 as early versions had a nasty bug in string comparisons
  • bugfix: mmanon did not detect all IP addresses in rewrite mode
    The problem occured if two IPs were close to each other and the first one was shrunk.
    Thanks to for reporting this bug
  • bugfix: mmanon sometimes used invalid replacement char in simple mode depending on configuration sequence, the replacement character was set to ‘s’ instead of the correct value. Most importantly, it was set to ‘s’ if simple mode was selected and no replacement char set.
    Thanks to for reporting this bug
  • bugfix: memory leak in mmnormalize
  • bugfix: array-based ==/!= comparisions lead to invalid results
    This was a regression introduced in 7.3.5 bei the PRI optimizer

Normalizing Cisco ASA messages

This time, we want to parse log messages received from a Cisco ASA for specific parts. The log messages will be parsed by liblognorm and then they will be written into a specific file resembling the sent code.

This guide has been tested with v7.3.4 of rsyslog.

Things to think about

We basically need a receiver, the normalizer module mmnormalize, some templates and several actions. To keep the logs seperated from other log messages, we also will need a filter. But that is just valid for the rsyslog configuration.

We also need liblognorm installed and a rulebase to work with. The rulebase is needed for liblognorm to know what to do with the messages, so it knows, which part should be fed into a custom property and which to ignore.

Config Statements

We now have basically two parts to configure. The first part should be the rulebase which holds format representations of the expected log messages. For this example we will work with the following log line:

2012-11-23T10:47:42+01:00 : %ASA-3-313001: Denied ICMP type=8, code=0 from on interface outside

The rulebase entry for this log line looks like this:

rule=: %date:word% %host:ipv4% : \x25ASA-%ddd:char-to:-%-%eee:number%: Denied ICMP type=%number2:number%, code=%number3:number% from %origin:ipv4% on interface outside

And here is the rsyslog configuration:

module (load="immark")
module (load="imuxsock")
module (load="imklog")
module (load="mmnormalize")
module (load="imudp")
input(type="imudp" port="514")
template(name="filename" type="string" string="/var/log/%$!ddd%-%$!eee%")
if $msg contains '%ASA' then{
 action(type="mmnormalize" userawmsg="off" rulebase="/path/to/rulebase.rb")
 action(type="omfile" DynaFile="filename")

How it works

The rulebase holds currently one rule. The rule is a representation of the above log message. For this case, we are only interested in the ASA code. The code identifies a specific message, thus, the message itself is not interesting. Though, we have to reflect complete message and put temporary variables, where the same message of a kind still has different values. These would be the timestamp, source, type and code the the IP which triggered the message. The problem is here, that we really only need two parts to be filled into a property, but other parts can be variable. The message format can be the same, but i.e. the IP from the source can vary. If you fill in a concrete value into the rule where such parts are, the message will only be parsed correctly if the message originates from the exact same IP.

The configuration itself isn’t very complicated. It loads several standard modules, which are mainly used to ensure local logging, as well as the UDP input module and the precious mmnormalize. The mmnormalize module will be later used as a output module. It handles the loading of the rulebase, as well as implements the normalization features of liblognorm.

After loading the modules, we have our single UDP input configured, which is necessary to receive the log messages from the Cisco ASA. Nothing fancy  here, but a standard syslog receiver for UDP.

The next step is to create a template. The template is not for a message output format, but a file name format. It will help writing the messages into log files that reflect the ASA number in the filename.

Finally we will filter for “%ASA” in the message. That will ensure, that only ASA messages are processed by the following actions. The first action runs the message through mmnormalize, while using our rulebase. The messages will be parsed by liblognorm. If the messages do fit the rules in the rulebase, the parts we need will be put into properties. These properties come into play in the second action where we simply write the messages via Dynafile to disk. The template and our new properties will be used to define the filename.


This configuration works or fails with the rulebase. All messages, that should be run through the normalizer must fit to a rule. If you are in doubt, that the rules are correct, follow the linked guide. It will show you how to create a output format that shows if a message has been parsed correctly or if and where the parsing broke.

Log Normalization and special characters

When trying to normalize log messages via liblognorm and mmnormalize, you need to create a rulebase first. The rulebase is usually a representation of message formats.

Due to the format of these rules, it is necessary to be cautious. Some messages and rule necessities could possibly cause confusion to the configuration interpreter. This mainly applies to clear text passages in single rules.

For example, if you have a log message from a Cisco ASA, the message looks like this:

2012-11-23T10:47:42+01:00 : %ASA-3-313001: ...

The only interesting parts are the IP and the numerical code to identify the message. We are not interested in the timestamp or “%ASA”. But when making the rule, the trouble starts there. The percent character is also used to define variables and their values in a rule. Thus it needs to be escaped. This is done with the ASCII code representation of the percent character. The rule would look like this:

rule=: %date:word% %host:ipv4% : \x25ASA-%char1:char-to:-%-%char2:number%: ...

If you write “%ASA” into the rule, the interpreter will think, that a new variable starts there. This will cause confusion to the rest of the rule and render it not working correctly. This needs to be avoided.

The same applies to “:”. But this time, it needs to be escaped when using it as delimiter vor variables. Example:


This will fill “variable” with everything until the next “:” occurs. If you just put a “:” here as a delimiter, the rule will not work anymore.

Log normalization and the leading space

Log normalization is simple, but has its quirks. A common pitfall is syslog message format as induced by RFC3164. Let’s look at a common case:  A log message has been sent to rsyslog. The message itself had no irregular characters. But, the message that should have been parsed by mmnormalize now has a leading space character. Basically, the message that should be parsed looks like this:

This is a test

Usually, one would think, that a simple parser can be used here. You might be correct, but there is a small caveat about this. The rulebase entry we currently have looks something like this:

rule=:%word1:word% %word2:word% %word3:word% %word4%

But strangely, rsyslog responds the following:

mmnormalize generated: {“originalmsg”: ” This is a test″, “unparsed-data”: ” This is a test″}

How comes, that rsyslog cannot parse the message? Why is there a leading space character in from of the message? The answer is, that messages are processed as RFC3164. In this RFC it is defined, that everything after the “:” of the syslog header is to be considered as the message. Thus, the message has a leading space now.

How is this to be solved? Simply insert the space to your rules in the rulebase. This will lead to a rule like this:

rule=: %word1:word% %word2:word% %word3:word% %word4%

Please note, that there has just the space character been added. Further, this is really only a example. The rule will fit to all messages that are 4 words long, so it is really not very suitable to be adopted to your configuration.

Scroll to top