Category: Technology

Python Logging to Logstash Server

Since we are having a problem with some of our filebeat servers actually delivering data over to logstash, I put together a really quick python script that connects to the logstash server and sends a log record. I can then run tcpdump on the logstash server and hopefully see what is going wrong. 

import logging
import logstash
import sys

strHost = 'logstash.example.com'
iPort = 5048

test_logger = logging.getLogger('python-logstash-logger')
test_logger.setLevel(logging.INFO)
test_logger.addHandler(logstash.TCPLogstashHandler(host=strHost,port=iPort))

test_logger.info('May 22 23:34:13 ABCDOHEFG66SC03 sipd[3863cc60] CRITICAL One or more Dns Servers are currently unreachable!')
test_logger.warning('May 22 23:34:13 ABCDOHEFG66SC03 sipd[3863cc60] CRITICAL One or more Dns Servers are currently unreachable!')
test_logger.error('May 22 23:34:13 ABCDOHEFG66SC03 sipd[3863cc60] CRITICAL One or more Dns Servers are currently unreachable!')

Using urandom to Generate Password

Frequently, I’ll use password generator websites to create some pseudo-random string of characters for system accounts, database replication,etc. But sometimes the Internet isn’t readily available … and you can create a decent password right from the Linux command line using urandom.

If you want pretty much any “normal” character, use tr to pull out all of the other characters:

'\11\12\40-\176'

Or remove anything outside of upper case, lower case, and number characters using

a-zA-Z0-9

Pass the output to head to grab however many characters you actually want. Voila — a quick password.

ElasticSearch Analyzer

Analyzer Components

Character filters are the first component of an analyzer. They can remove unwanted characters – this could be html tags (“char_filter”: [“html_strip”]) or some custom replacement – or change character(s) into other character(s). Output from the character filter is passed to the tokenizer.

The tokenizer breaks the string out into individual components (tokens). A commonly used tokenizer is the whitespace tokenizer which uses whitespace characters as the token delimiter. For CSV data, you could build a custom pattern tokenizer with “,” as the delimiter.

Then token filters removes anything deemed unnecessary. The standard token filter applies a lower-case function too – so NOW, Now, and now all produce the same token.

Testing an analyzer

You can one-off analyze a string using any of the

curl -u “admin:admin” -k -X GET https://localhost:9200/_analyze –header ‘Content-Type: application/json’ –data ‘

“analyzer”:”standard”,

“text”: “THE QUICK BROWN FOX JUMPED OVER THE LAZY DOG’\”S BACK 1234567890″

}’

Specifying different analyzers produces different tokens

It’s even possible to define a custom analyzer in an index – you’ll see this in the index configuration. Adding character mappings to a custom filter – the example used in Elastic’s documentation maps Arabic numbers to their European counterparts – might be a useful tool in our implementation. One of the examples is turning ASCII emoticons into emotional descriptors (_happy_, _sad_, _crying_, _raspberry_, etc) that would be useful in analyzing customer communications. In log processing, we might want to map phrases into commonly used abbreviations (not a real-world example, but if programmatic input spelled out “self-contained breathing apparatus”, I expect most people would still search for SCBA if they wanted to see how frequently SCBA tanks were used for call-outs). It will be interesting to see how frequently programmatic input doesn’t line up with user expectations to see if character mappings will be beneficial.

In addition to testing individual analyzers, you can test the analyzer associated to an index – instead of using the /_analyze endpoint, use the /indexname/_analyze endpoint.

 

Resetting Lost/Forgotten ElasticSearch Admin Passwords

There are a few ways to reset the password on an individual account … but they require you to have a known password. But what about when you don’t have any good passwords? (You might be able to read your kibana.yml and get a known good password, so that would be a good place to check). Provided you have OS access, just create another superuser account using the elasticsearch-users binary:

/usr/share/elasticsearch/bin/elasticsearch-users useradd ljradmin -p S0m3pA5sw0Rd -r superuser

You can then use curl to the ElasticSearch API to reset the elastic account password

curl -s --user ljradmin:S0m3pA5sw0Rd -XPUT "http://127.0.0.1:9200/_xpack/security/user/elastic/_password" -H 'Content-Type: application/json' -d'
{
"password" : "N3wPa5sw0Rd4ElasticU53r"
}
'

 

ElasticSearch ILM – Data Lifecycle

The following defines a simple data lifecycle policy we use for event log data.

Immediately, the data is in the “hot” phase.

After one day, it is moved to the “warm” phase where the number of segments is compressed to 1 (lots-o-segments are good for writing, but since we’re dealing with timescale stats & log data [i.e. something that’s not being written to the next day], there is no need to optimize write performance. The index will be read only, thus can be optimized for read performance). After seven days, the index is frozen (mostly moved out of memory) as in this use case, data generally isn’t used after a week. Thus, there is no need to fill up the server’s memory to speed up access to unused data elements. Since freeze is deprecated in a future version (due to improvements in memory utilization that should obsolete freezing indices), we’ll need to watch our memory usage after upgrading to ES8.

Finally, after fourteen days, the data is deleted.

To use the policy, set it as the template on an index:

Upon creating a new index (ljrlogs-5), the ILM policy has been applied: