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Thoughts on strings, patterns, and friends.

I've come to the realization that systems administration has big need of data mining. Heck, there are even huge conferences dedicated to log analysis. I do it almost every day at work. None of the tools I have output exactly the data I want exactly when I want it. Some tools output lots of data I don't need, and some of the data I do need. I want only the data I need, so I have to pipe it to grep, awk, sed, perl, cut, etc, just to get the data I want. Then I sort it, run uniq, or perhaps awk again, trying to find an anomaly.

I like pattern matching and data mining. It's the reason I wrote grok. Grok lets you tag data and extract it with less brainwork and effort. I'd like to see what other kinds of things I can do with patterns and sequences. To that end, I've come up with a few random ideas about finding patterns in logs.

There are lots of algorithms for finding the "longest common substring" and such, but I don't want the "longest common substring" for finding patterns. The key factor here is plural "patterns" not "pattern". There are books dedicated to string and sequence algorithms. Unfortunately, my background has not had me do much statistics or fancy algorithms. What better time than now to start? ;)

Standard algorithms for subsequence/substring discovery seems to take K strings as input and output a single string as a result. I don't want that. I want to input K strings, and output N "common" substrings. Seems like a simple subset of the problem, right? If you know the longest, why can't you find the second longest? And why does "common" have to mean "common among 100% of the strings"? And, why can't I find any tools to do this for me, already! (Granted, I haven't looked for that long)

Basically, the input will be a log file, and the output should be a set of substrings that appear often. This output will then be used to generate patterns that grok can use to match your log data.

What do I mean? Here's an example of something from /var/log/auth.log we would use as input:

May 14 13:26:13 kenya sshd[41244]: Invalid user louis from
May 14 13:26:16 kenya sshd[41246]: Invalid user louis from
May 15 04:00:58 kenya sshd[50672]: Did not receive identification string from
May 15 04:04:04 kenya sshd[50699]: Invalid user test from
May 15 04:04:06 kenya sshd[50701]: Invalid user test from
May 15 04:04:08 kenya sshd[50705]: Invalid user test from
LCS (In my test I used Tree::Suffix which uses libstree) finds 'kenya sshd[' as the longest common subsequence. A human will say that that is not the most obvious pattern in the data. The useful "common" patterns would be (the bold parts):

May 14 13:26:16 kenya sshd[41246]: Invalid user louis from 

If you were to have a reasonably clued human try to generate a "pattern" that would match any derivative of this string, he or she would come up with something like this:

<date> <hostname> <program>[<pid>] Invalid user <some user> from <some ip>

I see no reason why we can't make a program generate this pattern simply by analyzing a few logs. It seems like these "simple" *cough* steps.:

  1. Look for common token sequences, such as "Invalid user".
    Clearly something is worth checking out if it appears several times in a logfile, right?
  2. Find lines matching a subset of the "common token sequence" set.
    Uncommon token sequences will analyzed for the kind of pattern they most-often match. An example of an uncommon token sequence (or a field?) is the IP from the above log. The IP in the log entry above will match grok's %IP% pattern as well as other regex patterns, right? We can map an "unknown token sequence" to a set of patterns we know will match.

    The correct pattern might be chosen automatically with help of priorities. Example: %IP% should rank higher than %DATA% (aka /.*?/), because it's a more specific match.

  3. Output a list of most-specific patterns that will match one element in the log, where one element is a single type of log entry, such as the 'invalid user' log entries above. Anomalous (uncommon) lines will be noted separately.
The end goal is to have a system which takes a log, or any line-delimited data, and outputs a series of patterns that will match future instances of similar data.

The worst case is that the tool provides you a list of "probably good" patterns. You turn a billion lines of logs into a few "probably good" patterns using a tool and then use your human smarts to make small corrections producing a set of very good patterns. Once that happens, your previously hard-to-query log data becomes easy to query, because you'll have a way to parse it.

You might ask, why? Querying. If you can tokenize a given piece of generally unknown data into a dictionary of accessible tokens, then you can query it. Awk, XPath, Perl, SQL, and other things all exist somewhat to give you extended power in searching and processing text. XPath is a great example of a system that aids in querying and processing. Since XML often consists of content and semantics, you can easily query for the "kind" of data you want with a simple XPath expression. Logs and other data are not so simple.

When you know a token is a date, you turn it translate it into other formats and use it in other tools. When you know an token is an IP, you can track it in your apache logs, block it on your firewall, etc. When you know a number is an error code, you can cross reference the error code from the source code or documentation describing that error code. Once you map a token sequence to a namable pattern, you can apply it all sorts of places.

It's not exactly simple to use grep or awk to pull out any IP address from a log generated by sshd, is it? Grok lets you do it, but what if you don't have the time to generate a pattern? What if you wanted to bucket logs by type? What if you wanted to mark certain log patterns as normal but email you for others?

My day-to-day work is increasingly slanted towards "rapid creation" over "rapid execution". Software engineers often focus on speed of execution. Optimize this loop, etc. I need tools that let me query and process data with less typing and less thinking. You make yourself think less by having tools are powerful enough to let you express what you need easily.

Generating patterns would let me preemptively find things about my data that I didn't know, and when shit hits the fan, and your shoe is on fire, I'd like to have the power to query the data easily rather than having to visually pour over thousands of lines of logs trying to correlate events and find patterns.

One less step can be solved atleast partially through data mining, and you'll find patterns you didn't even know existed.