[Python] Deleting files by date from directory with a few million files

Recent problem: Single directory with 5.3 mio files. Using find did not work because it was filling memory up. I tried anything I can think of but in the end had to go with python to iterate over the directory. I used https://github.com/benhoyt/scandir 
Following Code will do the job

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import scandir import time import os old = time.time() - 28 * 24 * 60 * 60 #28 days for entry in scandir.scandir('/tmp/test'): #print(os.path.getctime(os.path.join('/tmp/test',item))) try: if (os.path.getctime(os.path.join('/tmp/test',entry.name)) < old): os.unlink(os.path.join('/tmp/test',entry.name)) except: pass

This new scandir version is implemented since python 3.5. If you don't have it just download the scandir.py and put it in the same directory as this code.

[Zabbix] Simple Anomaly / Outlier Detection with Tukey's Range Test

We are using Tukey's Range test to define lower and upper value borders to find outliers in our data. We'll be using a trigger function with those values to get a dynamic trigger that adapts to the data.

The range is defined as [Q1-k(Q3-Q1),Q3+k(Q3-Q1)] . Q3-Q1 = Interquartile Range. We are using the default k factor of 1.5, but you can adjust as wanted, the bigger k the further out the borders.

Example how it can look. In this case the monitored data is the blue graph, the borders red and green. There are 3 outlier events which could trigger an alarm.

Dependencies: datamash, jq, curl

Place following script in /usr/lib/zabbix/externalscripts/ on your zabbix server:

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#!/bin/bash #use like this: anomalydetection.sh 23432 testhost bupper blower #script requires datamash, curl and jq ITEMID=$1 #to be analyzed item HOST=$2 TARGETITEM1=$3 #upper limit TARGETITEM2=$4 #lower limit LIMIT='36' #item refresh every 5min, 36*5=3hour time period #we want the data from 7 days ago, but the data with an offset defined by the LIMIT*refreshtime DATE=$(date +%s --date="7 days ago 90 minutes ago") DATE2=$(date +%s --date="7 days ago 90 minutes") # CONSTANT VARIABLES ERROR='0' ZABBIX_USER='APIUSER' #Make user with API access and put name here ZABBIX_PASS='xxxxxxxxx' #Make user with API access and put password here API='https://domain.tld/api_jsonrpc.php' # Authenticate with Zabbix API #curl -s -H -k authenticate() { echo `curl -k -s -H 'Content-Type: application/json-rpc' -d "{\"jsonrpc\": \"2.0\",\"method\":\"user.login\",\"params\":{\"user\":\""${ZABBIX_USER}"\",\"password\":\""${ZABBIX_PASS}"\"},\"auth\": null,\"id\":0}" $API` } AUTH_TOKEN=`echo $(authenticate)|jq -r .result` echo `curl -k -s -H 'Content-Type: application/json-rpc' -d "{\"jsonrpc\":\"2.0\",\"method\":\"history.get\",\"params\": {\"output\":\"extend\",\"history\":\"3\",\"itemids\":\"$1\",\"time_from\":\"$DATE\",\"time_till\":\"$DATE2\",\"sortfield\": \"clock\",\"sortorder\": \"DESC\",\"limit\":\"$LIMIT\"},\"auth\":\"$AUTH_TOKEN\",\"id\":1}" $API` | jq -r .result[].value > /tmp/bandvalues filecontent=( `cat "/tmp/bandvalues" `) iqr=$(cat /tmp/bandvalues | /usr/bin/datamash iqr 1) q1=$(cat /tmp/bandvalues | /usr/bin/datamash q1 1) q3=$(cat /tmp/bandvalues | /usr/bin/datamash q3 1) k=2.0 #1.5 for standard outliers, 3.0 for far out, adjust as needed lower=$(echo $q1-$k*$iqr|bc) upper=$(echo $q3+$k*$iqr|bc) zabbix_sender -z 127.0.0.1 -p 10051 -s $HOST -k $TARGETITEM1 -o $upper zabbix_sender -z 127.0.0.1 -p 10051 -s $HOST -k $TARGETITEM2 -o $lower

You need to create 3 items (all of type numeric float!):
1 External Check which calls the script with a key looking like this:

anomalydetection.sh["36014","examplehost","b.upper","b.lower"]  anomalydetection.sh["itemidofmonitoreditem","hostnameofitem","trapperitemupperlimit","trapperitemlowerlimit"]

And 2 items of type Trapper which in my example are upper border and lower border

respective with key b.upper/b.lower

Your trigger definition has to look like this:

{examplehost:itemtobemonitored.last()}<{examplehost:b.lower.last()} or {examplehost:itemtobemonitored.last()}>{examplehost:b.upper.last()}

Fair warning: this only works well with not too volatile data streams