IBM Operations Analytics Predictive Insights Mediation ... · zabbix.sh needs bash shell to run,...

Operations Analytics Predictive Insights

IBM Operations Analytics Predictive Insights Mediation Pack for Zabbix

Version 1.2.0

Operations Analytics Predictive Insights

Edition notice This edition applies to IBM Operations Analytics Predictive Insights version 1.3.3 and to all subsequent releases. Please refer to the IBM developerWorks terms of use for more information. Also reference the IBM Operations Analytics Development Community (https://developer.ibm.com/itoa/) for additional information on Predictive Insights.

Table of Contents

1 Overview ................................................................................................................ 4

2 Prerequisite Information ........................................................................................ 5

3 Installing and Configuring ...................................................................................... 6

3.1 Mediation Pack Contents ........................................................................................... 6

3.2 Configuring the Mediation Pack ............................................................................... 11 3.2.1 Scheduling zabbix.sh on the Predictive Insights server ........................................................... 13 3.2.2 Output .CSV files ...................................................................................................................... 14 3.2.3 Output .JSON files .................................................................................................................... 14

3.3 Configuring Data Modeling ....................................................................................... 15

3.4 Setup the Predictive Insights Topic ........................................................................... 19

3.5 Deploy the Defined Model in Predictive Insights ...................................................... 20

3.6 Test Mediation Pack and Data Training Process ........................................................ 22

3.7 Review Results and Anomalies ................................................................................. 23

4 Support ................................................................................................................ 25

Operations Analytics Predictive Insights Zabbix Mediation Pack

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1 Overview

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IBM Operations Analytics Predictive Insights analyzes data and learns the normal behavior of a system through KPI metric data streams. This includes data from various sources including metrics gathered by industry performance and network monitoring solutions. A mediation pack helps users quickly integrate the KPI data streams by providing a default model to integrate a particular performance manager.

IBM Operations Analytics Predictive Insights Mediation Pack for Zabbix provides a pre-built model for data ingestion. Predictive Insights creates a performance model that it uses to detect or forecast behavior outside the modeled range, including generating an alarm when an anomalous behavior occurs. Doing so enables organizations to pro-actively avoid system and solution outages before users are impacted.

The Mediation Pack for Zabbix provides the capability to ingest performance data and metrics from Zabbix into IBM Operations Analytics Predictive Insights to perform analysis. This document will cover the versions of the Zabbix and Predictive Insights support, along with steps to setup the Mediation Pack and adjust the data stream for deployment in a customer environment.

Zabbix Mediation Pack architecture (CSV & JSON)

Mediation Pack development considers all possible integration options to determine the optimal configuration. Options are reviewed based on the source systems ability to present data using multiple methods. The selected methods are derived based on many factors including but not limited to flexibility, scalability, and availability of KPI’s (Key Performance Indicators). Additional considerations are based on the source systems majority configuration and maturity of such methods at the time of

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development. Requests to enhance mediation packs utilizing additional methods supported by the source system can be reviewed with our professional services team.

2 Prerequisite Information Item Description

Name Mediation Pack for Zabbix

Predictive Insights supported versions

V1.3.3, V1.3.5 and 1.3.6 (REST mediation is only supported with v1.3.6)

Predictive Insights Information IBM Operations Analytics (demo, trial, resources) https://www.ibm.com/us-en/marketplace/it-operations-analytics IBM Operations Analytics Community (docs, videos, mediation packs) https://developer.ibm.com/itoa/

IBM Operations Analytics Predictive Insights documentation http://www.ibm.com/support/knowledgecenter/SSJQQ3

Zabbix supported versions Zabbix Version 4.4.3 REST API v4.4.3 (Refer link for more information: https://www.zabbix.com/documentation/4.4/manual/api/reference

Zabbix Information Zabbix Product Information https://www.zabbix.com

Filename Zabbix_MediationPack_v1.2.0.zip

Data source The pack will retrieve performance data of Zabbix by using REST API.

Additional Information • The pack will poll for data points during the interval, if the configuration allows, it is recommended to configure Zabbix to collect the data every 5 minutes.


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3 Installing and Configuring The following section provides information for installing and configuring the Mediation Pack on your IBM Operations Analytics Predictive Insights system. Note: The pack can produce CSV file per interval with metric names in rows (in Predictive Insights terminology, this is ‘Skinny format’).

3.1 Mediation Pack Contents The following directories are contained in the Mediation Pack: Zabbix_MediationPack_v1.2.0.zip

Pack organization – /config

– zabbix_props.py – all_metrics.py – filtered_metric_groups.py

– /docs – Zabbix_MediationPack_UserGuide_v1.2.0pdf – Zabbix_MediationPack_README_v1.2.0.txt

– /license – /binaries

– zabbix.sh – zabbixGenerator – encrypt – zabbixGeneratorREST – JSON_push

– /pamodel – zabbix.pamodel

– /etc – /sample


/config Directory for configuration files. 1. zabbix_props.py – File stores information for calling Zabbix API

zabbix_props.py fields: *Following field values should be enclosed in double quotes and edited on Predictive Insights server (CSV), for REST it could be any server on the same network.

• user – username to access the Zabbix REST API • protocol – Protocol to use (e.g. http) • zabbixIP – IP address of Zabbix • frequency_interval – time interval for polling data (e.g. if the value is 5, the data

will be polled every 5-minute). This value should be multiple of 5. It is not recommended to configure this value to more than 15. Note: This will not automatically schedule the script to execute every five minutes. User needs to schedule the script with scheduling interval matching this field (refer section 3.2.1 for scheduling)

• Latency – In certain cases, the data from the API may not be available right away, use this filed to set offset minutes while querying the data. E.g. If the current time is 1 PM and latency is set for 30 minutes, the pack will query for the data between 12:25 PM to 12:30 PM (assuming frequency_interval is 5 minutes).

• outputPath – Directory to store the output CSV files Note: Following properties are only required for REST version, comment otherwise

• PI_protocol – Protocol for Predictive Insights server (usually “http”) • PI_serverIP = Predictive Insights server IP for REST mode• PI_serverPort -- Predictive Insights server port for REST mode • PI_tenantID – Predictive Insights server tenant ID for REST mode # #Always use

capital letters; 2 to 50 characters

Illustration: user = "admin" protocol = "http" zabbixIP = "1.1.1.10"

frequency_interval = '5' latency = '10'

outputPath = "/opt/home/scadmin/statOutputFiles" #Following properties should be commented for CSV mode. PI_protocol="http"

PI_serverIP="10.x.x.xxx" PI_serverPort="9081" PI_tenantID="zabbix_REST"


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2. all_metrics.py

This file holds metric information to get started. The pack is tested with these metrics and thus this set of metric groups are provided as a guidance. This file is static and NOT actively used by the pack for querying the data. For configuring the pack for certain metrics, please configure user required metrics in filtered_metric_groups.py file (refer section for filtered_metric_groups.py). This file holds data in a JSON dictionary format.

3. filtered_metric_groups.py

This file holds the metric list that user wants to query from Zabbix. The list can be a subset of metrics from all_metrics.py, or with the presence of any new metric that user wants and is part of Zabbix environment. Though the pack is designed to mediate any metric that is part of local Zabbix environment, note that it is tested with the metric list given in all_metrics.py file. The metric list is stored in group name key inside square ‘[]’ brackets. For readability an escape character may be used to enter the metric information on a new line. Illustration: all_groups = {"cpu_group": ["metric_1", "metric_2"], "memory_group": ["metric_3", "metric_4"], "system_group": ["metric_5", "metric_6",\

“metric_7”] }

Note: If the filtered_metric_groups.py is changed from the base pack, user may have to update the mediation model reflecting the changes (model is only required for CSV files). Refer ‘Configuration and Administration Guide’ of IBM Operations Analytics Predictive Insights for model configuration (chapter 2) OR https://www.ibm.com/support/knowledgecenter/SSJQQ3_1.3.6/com.ibm.scapi.doc/admin_guide/t_tasp_syncwithdatasource.html

/docs Users Guide .PDF and README .txt for Mediation Pack of Zabbix for deploying and configuring the Mediation Pack.

/license Text license files for governing the use of the Mediation pack.


/binaries 1. zabbix.sh – This is the parent script of the pack for CSV files, JSON files, and encryption. It

should run with either of the argument, --encrypt for encrypting the password, --REST for sending data to Predictive Insights through JSON format, or without any argument for running the data extraction in CSV files. Refer respective sections of these arguments for more information. zabbix.sh needs bash shell to run, default location is /bin/bash. If the bash shell is located elsewhere, modify the path on the first line of zabbix.sh

2. zabbixGenerator – File generator of the mediation pack for .CSV files. When zabbix.sh is run, internally, it will query the data and generate .CSV file by executing this file.

3. encrypt – Executable to encrypt the password for the first run or when the password is

updated. After running this executable with ./zabbix.sh --encrypt, it will prompt for Zabbix REST API password, after user enters the password, it will store it in encrypted binary file cipher. Do not edit or remove this file, without password in cipher file, the pack will fail to mediate.

4. zabbixGeneratorREST – File generator for the .JSON files. When zabbix.sh run with --REST argument, this binary will be executed internally. It will query the data using REST APIs from the Zabbix server and generate JSON data for sending to Predictive Insights. If for any reason, the data is not pushed during that interval, this binary will store it in a JSON file with a timestamp and metric group information in its name.

5. JSON_push – Binary for pushing historical saved .JSON files from outputPath directory to

Predictive Insights server. When zabbix.sh is run with --push mode, it will be handled by this executable. Note: Every execution of zabbix.sh script with --REST mode, will also execute JSON_push script to push any residual file to Predictive Insights server. However, if the user has any files that need to be pushed to Predictive Insights server manually, employing a --push mode is an option.

/pamodel Core files used in the Mediation Pack representing the Predictive Insights model consisting of all attributes and metric definitions Zabbix.pamodel The Zabbix.pamodel file is a Predictive Insights model file which consists of all attributes and metric definitions. This file consists of all metric group definitions. Import this file into mediation tool. Make sure to update this file if filtered_metric_groups.py file is changed from the base pack.


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/sample Contains a sample .CSV/JSON files representing the captured data streams.

File naming format: The filename format can be described using the following regular expression: ([a-zA-Z]\w*)_(\d{4}-\d{2}-\d{2}_\d{2}_\d{2}_\d{2}).*\.csv/.JSON Example: system_group_2017-12-06_17_30_00.csv/JSON Which is composed of two regular expressions capturing groups:

1. The first is the name of the group containing only characters: a-zA-Z_0-9 2. The second is the start time of the interval in UTC.


3.2 Configuring the Mediation Pack Use the following steps to add the Mediation Pack to your Operations Analytics Predictive Insights system and configure the required views, modeling and data stream training.

A. Pre-requisite – Anaconda Package:

Download the anaconda package on the Predictive Insights server (Tested on RHEL 6.0 +):

1. Download the installer in a directory of your choice (login using scadmin):

$ wget https://repo.continuum.io/archive/Anaconda3-5.0.1-Linux-x86_64.sh

2. Make the .sh file executable if it is not already.

3. Execute the downloaded .sh file and follow the prompt, it will ask usual questions – agreement etc.

4. When prompted, keep the default installation location or change per user’s preference. Make a note of installation location.

*This path is required in step 2 of section B

5. When asked, “Do you wish the installer to prepend the Anaconda3 install location to PATH in your “/home/scadmin/.bashrc?”, Say No.

6. Done.

If the server has bash installed in other location, edit the path of the bash shell on the first line.

B. Upload the pack on Predictive Insights server in a new directory of user’s choice, unzip it.

1. The zabbix.sh script (in /binaries directory of the unzipped directory) uses bash shell. The default path on the first line given is: /bin/bash. If the server has bash installed in other location, edit the path of the bash shell on the first line

Illustration (edit first line only):

#!/path/to/bash

2. Create a file .zabbixrc in mediation pack binaries directory and make sure the ANACONDA_HOME variable in . zabbixrc is pointing to the directory where anaconda3 is installed. Illustration: Execute the following command while in mediation pack binaries directory on Predictive Insights server, assuming anaconda3 is in /opt/home/scadmin/anaconda3 directory;

$ echo 'ANACONDA3_HOME=/opt/home/scadmin/anaconda3' > .zabbixrc


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Alternatively, user can create a file .zabbixrc in mediation pack binaries directory and enter the path of anaconda3 directory in ANACONDA3_HOME variable. The first line should be:

ANACONDA3_HOME=/path/to/anaconda3/directory

3. Encrypt the password by running ./zabbix.sh --encrypt

4. Enter the password when prompted, verify the displayed password is correct. Verify that the cipher file is been placed in the directory after entering the password.

5. Create a directory to store the output .CSV/JSON files. Make a note of directory

path.

6. Edit the zabbix_props.py for user, protocol, zabbixIP, frequency_interval, latency, outputPath fields. Make sure the field values are enclosed in double quotes. Enter the properties for REST mode if using Predictive Insights REST mediation. *outputPath is path of the directory created in last step

Illustration: user = "admin" protocol = "http" zabbixIP = "1.1.1.10"

frequency_interval = '5' latency = '10'

outputPath = "/opt/home/scadmin/OutputFiles"

7. Edit filtered_metric_groups.py per the metric requirements. If user is not sure of full metric list at this point, just enter a few metric groups that user has in their environment, user may refer all_metrics.py for examples.

8. Before proceeding, make sure metrics given in the filtered_metric_groups.py file is available in the environment. Though pack is designed to handle metrics as an exception, providing invalid metrics can affect the performance.

9. Run the files extractor using ./zabbix.sh (for CSV) or ./zabbix.sh --REST (for JSON)

• Try couple of manual runs • Observe if the execution is completed within the interval the user intends to

query the data, (i.e. frequency_interval field in zabbix_props.py file) • If the time required to complete the script is more than specified by

frequency_interval field, increase the interval value (Note: It is not recommended to configure frequency_interval value to more than 15 minutes). This is required to validate that the current execution instance is completed before the next instance begins. Note: Though user can enter different values for frequency_interval to query the data, the pack will always produce 5-minute interval files. E.g. If the frequency_interval value is 10, the pack will produce 2 set of files for that execution, if the value is 15, 3 set of files will be produced.


• After ensuring that run time is less than value in frequency_interval field, schedule the script with that interval (with cronjob or any other scheduling mechanism).

• It is not recommended to have Interval value more than 15 minutes (though the pack will run at values greater than 15).

10. Schedule the script ./zabbix.sh

The pack will produce files in Zabbix Time Zone (GMT, by default). The model comes configured with GMT time zone, if the user’s Zabbix provides the data in different time zone, please change the time zone in the model with the help of mediation tool. In such a case, to avoid any potential discrepancies due to time zone conflict, it is recommended that Predictive Insights server and Zabbix server be in same time zone.

3.2.1 Scheduling zabbix.sh on the Predictive Insights server 1. Open a Terminal Window (Command Line). 2. Give the script correct permissions. For example, if the script is called “zabbix.sh”, set permission as follows: *(If not already) $ chmod 755 zabbix.sh 3. Add new cron job to crontab: $ crontab -e This opens the vi editor for you if your default editor is vi. You can set a different editor using the following: export EDITOR=emacs Create the cron command using the following syntax: For CSV */15 * * * * /path_to_zabbix.sh/zabbix.sh For REST */15 * * * * /path_to_zabbix.sh/zabbix.sh --REST This will execute zabbix.sh on Predictive Insights server at 15 minutes interval and will produce a set of files (one file per metric group) for every five-minute interval. Thus for 15 minutes, there will be 3 set of files. User can schedule this frequency to 5 minutes instead of 15 if the running time for each execution is less than 5 minutes.


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3.2.2 Output .CSV files The file naming convention of produced .csv files is as follows:

<system_group><start_time_of_the_interval_string>.csv <cpu_group><start_time_of_the_interval_string>.csv

Each file will have four headers and metric name information will be in rows (skinny format): • timestamp – consists timestamps for each row • host_name – column consists of Device names/IPs. • metric_name – column consists of Metric Name of the particular entry • value – this column will have corresponding metric value

Note: Each execution will generate one .csv file per metric group. This in turn will store data more efficiently on Predictive Insights side.

3.2.3 Output .JSON files The file naming convention of produced .csv files is as follows:

<system_group>_<start_time_of_the_interval_string>.json <cpu_group>_<start_time_of_the_interval_string>.json The file schema per the IBM Predictive Insights requirement. Note: Each execution will generate one .JSON file per metric group. This in turn will store data more efficiently on Predictive Insights side.


3.3 Configuring Data Modeling Use the following steps to configure the data modeling. Note: Screenshots are for illustration purpose only.

1. Copy the Zabbix.pamodel file to the Predictive Insight server where the mediation tool is

installed. 2. Run $PI_HOME/bin/mediationtool.sh to launch mediation tool 3. Go to File menu and click on the import button.

Note: The model is pre-defined with some selected metrics. Make sure the mediation model is updated with all metrics and/or metric groups that are present in pack_filetered_metrics.py file before deployment. Refer ‘Configuration and Administration Guide’ of IBM Operations Analytics Predictive Insights for model configuration (chapter 2).

4. Select the Predictive Insights Model in the menu and click on Next


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5. Select the Zabbix.pamodel file and click on ok

6. Click on finish to complete the import process.



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7. Enter the Zabbix File System details as shown below. Note make sure to set the Time Zone to GMT. For further information please refer to the following url: https://www.ibm.com/support/knowledgecenter/SSJQQ3_1.3.6/com.ibm.scapi.doc/admin_guide/t_tasp_admin_createfilesystemdatasource.html

To preview the data in the mediation tool click on the icon shown circled in red. The data appears at the bottom of the screen.


3.4 Setup the Predictive Insights Topic Use the following steps to define a topic for handling the data flow in the Mediation Pack.

1. Login into the Predictive Insight server as scadmin 2. Change to the $PI_HOME/bin directory 3. Create the topic using the ./admin.sh tool as shown below.

./admin.sh create_topic Zabbix

4. Run command below to set the topic options.

./admin.sh set -t= Zabbix system.aggregation.interval 5.

For more information on properties for the admin.sh refer to the following: http://www.ibm.com/support/knowledgecenter/SSJQQ3_1.3.6/com.ibm.scapi.doc/admin_guide/r_tsaa_adminguide_cli_show_instance_properties.html

Refer to the following for the set parameter: http://www.ibm.com/support/knowledgecenter/SSJQQ3_1.3.6/com.ibm.scapi.doc/admin_guide/r_tsaa_adminguide_cli_set_property.html

Refer to the following on the properties that can be set:

http://www.ibm.com/support/knowledgecenter/SSJQQ3_1.3.6/com.ibm.scapi.doc/admin_guide/c_tsaa_adminguide_config_properties.html


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3.5 Deploy the Defined Model in Predictive Insights Follow these steps to deploy the defined model for representing the data flow in Predictive Insights.

1. Click on the model(s) to be deployed and multi-select if multiple models are to be

deployed to the same topic. Then Right click on the selection and select the Deploy Model option.

2. Gather the hostname, database and port number of Predictive Insights database. Tip: grep connection. $PI_HOME/config/tasp.connection.properties

Deploy model Zabbix.pamodel to Predictive Insights system


3. Enter the password for scadmin and update the JDBC URL with the hostname and port

number.

4. Click the box for counting KPI counts. Click on Yes to deploy the model.

hostnamesss

Deploy model Zabbix.pamodel to Predictive Insights system


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3.6 Test Mediation Pack and Data Training Process Follow these steps to start the training process and test the mediation pack

1. Login into the Predictive Insight server as scadmin 2. Change to the $PI_HOME/bin directory 3. Run ./start.sh -t=Zabbix (to start the topic) 4. Start the training using the command below. Note: -s start time is based on your

datasource collection. Mention the latency based on the observations regarding execution time while configuring the pack.

./admin.sh run_extractor_instance –t=Zabbix –s=20170909-1703 –l=15

Review the Training Process

1. Once the training has started you will see the events below in the Active event list screen capture.

2. Login in to the console using the following url: http://hostname:16310/ibm/console 3. User name : ncoadmin 4. Enter the ncoadmin password 5. You can see list events showing status about training process in Active event list 6. Illustration -


3.7 Review Results and Anomalies Follow these steps to review the detected anomalies

1. Login in to the console using the following url: http://hostname:16310/ibm/console 2. User name: ncoadmin 3. Enter the ncoadmin password 4. Navigate to the “Detected Anomalies” page

5. Review the Anomalies (illustration) -


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6. Right click on an anomaly and select ServiceDiagnosis tool (illustration) –

7. Review the affected metrics and behavior of the metrics for the time period (illustration) -


4 Support To get assistance from the IBM support team, you may open a Problem Management Record (PMR). The PMR needs to be opened for IBM Operations Analytics Predictive Insights or IBM Operations Analytics Advanced Insights indicating the “Mediation Pack for Zabbix”. Describe the problem encountered providing the following information that must be attached to the PMR: Product Name: IBM Operations Analytics Predictive Insights or IBM Operations Analytics Advanced Insights Product Version: 1.3.3 or higher Mediation Pack: Mediation Pack for Zabbix Mediation Pack Version: 1.2.0 Include the following:

• Sample extracted CSV/JSON files, which are being ingested into Predictive Insights. Attach the output of the command $PI_HOME/bin/collect.sh -t=<topicname>. Found in folder $PI_HOME/var/spool/topics/<topicname>/extracted/

• Data source connectivity status screenshot • Predictive Insights log files ($PI_HOME/log/topicname/) when the problem occurred while using

the mediation pack. Use the command $PI_HOME/bin/collect.sh -t=<topicname> • A screen shot of the error message that is generated while using the mediation pack. • A specific use case scenario(s) where problem occurred while using the mediation pack.


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