{"id":694,"date":"2021-02-07T17:52:51","date_gmt":"2021-02-07T16:52:51","guid":{"rendered":"http:\/\/thomas-kopton.de\/vblog\/?p=694"},"modified":"2021-02-07T17:56:00","modified_gmt":"2021-02-07T16:56:00","slug":"wavefront-by-vmware-and-log-ingestion","status":"publish","type":"post","link":"https:\/\/thomas-kopton.de\/vblog\/?p=694","title":{"rendered":"Wavefront by VMware and Log Ingestion"},"content":{"rendered":"\n<h4 class=\"wp-block-heading\">Motivation<\/h4>\n\n\n\n<p>In my last post, I have described how to ingest power consumption data provided by the vzlogger project into <strong><span class=\"has-inline-color has-vivid-cyan-blue-color\">vRealize Log Insight<\/span><\/strong> and how to extract the actual metrics from the log message.<\/p>\n\n\n\n<figure class=\"wp-block-embed is-type-wp-embed is-provider-tomsops wp-block-embed-tomsops\"><div class=\"wp-block-embed__wrapper\">\n<blockquote class=\"wp-embedded-content\" data-secret=\"cEhfU9e0VS\"><a href=\"https:\/\/thomas-kopton.de\/vblog\/?p=660\">Energy Consumption Monitoring using SML Data and vRealize Log Insight<\/a><\/blockquote><iframe loading=\"lazy\" class=\"wp-embedded-content\" sandbox=\"allow-scripts\" security=\"restricted\" style=\"position: absolute; clip: rect(1px, 1px, 1px, 1px);\" title=\"&#8220;Energy Consumption Monitoring using SML Data and vRealize Log Insight&#8221; &#8212; TOMsOps\" src=\"https:\/\/thomas-kopton.de\/vblog\/?p=660&#038;embed=true#?secret=VPqNggc8MV#?secret=cEhfU9e0VS\" data-secret=\"cEhfU9e0VS\" width=\"600\" height=\"338\" frameborder=\"0\" marginwidth=\"0\" marginheight=\"0\" scrolling=\"no\"><\/iframe>\n<\/div><\/figure>\n\n\n\n<p>That setup is working since days as expected but the primary use case for vRealize Log Insight is intelligent logging and analytics of structured, semi-structured and unstructured messages.<\/p>\n\n\n\n<p>In my electric power consumption use case I am interested in <strong><span class=\"has-inline-color has-vivid-purple-color\">collecting<\/span> <\/strong>and<strong> <\/strong><span class=\"has-inline-color has-vivid-purple-color\"><strong>analyzing time-series<\/strong> <\/span>data in <strong><span class=\"has-inline-color has-vivid-purple-color\">real-time<\/span><\/strong>.<\/p>\n\n\n\n<p>And this is exactly the use case for <strong><span class=\"has-inline-color has-vivid-cyan-blue-color\">Wavefront by VMware<\/span><\/strong>: <\/p>\n\n\n\n<p><a href=\"https:\/\/tanzu.vmware.com\/observability\">https:\/\/tanzu.vmware.com\/observability<\/a><\/p>\n\n\n\n<h4 class=\"wp-block-heading\">Logical Design<\/h4>\n\n\n\n<p>The logical design id fairly simple, only three components are needed:<\/p>\n\n\n\n<ul class=\"wp-block-list\"><li><strong><span class=\"has-inline-color has-vivid-purple-color\">Wavefront<\/span><\/strong> SaaS access<\/li><li><strong><span class=\"has-inline-color has-vivid-purple-color\">Wavefront Proxy<\/span><\/strong> &#8211; your on-premises part of the Wavefront solution<\/li><li>Log <span class=\"has-inline-color has-vivid-purple-color\"><strong>data<\/strong> <strong>source<\/strong><\/span> &#8211; in my scenario it is still by Raspbian providing the data via rsyslog<\/li><\/ul>\n\n\n\n<figure class=\"wp-block-image size-large\"><a href=\"https:\/\/thomas-kopton.de\/vblog\/wp-content\/uploads\/2021\/02\/figure_01.png\"><img loading=\"lazy\" decoding=\"async\" width=\"1024\" height=\"220\" src=\"https:\/\/thomas-kopton.de\/vblog\/wp-content\/uploads\/2021\/02\/figure_01-1024x220.png\" alt=\"\" class=\"wp-image-700\" srcset=\"https:\/\/thomas-kopton.de\/vblog\/wp-content\/uploads\/2021\/02\/figure_01-1024x220.png 1024w, https:\/\/thomas-kopton.de\/vblog\/wp-content\/uploads\/2021\/02\/figure_01-300x64.png 300w, https:\/\/thomas-kopton.de\/vblog\/wp-content\/uploads\/2021\/02\/figure_01-768x165.png 768w, https:\/\/thomas-kopton.de\/vblog\/wp-content\/uploads\/2021\/02\/figure_01-1536x330.png 1536w, https:\/\/thomas-kopton.de\/vblog\/wp-content\/uploads\/2021\/02\/figure_01.png 1816w\" sizes=\"auto, (max-width: 1024px) 100vw, 1024px\" \/><\/a><figcaption><em>Figure 01: Logical design<\/em><\/figcaption><\/figure>\n\n\n\n<p>In my setup is rsyslog sending the messages every 5 seconds to the proxy and the proxy is sending the extracted metric(s) to the SaaS Wavefront endpoint.<\/p>\n\n\n\n<p>The actual installation and configuration consists of three steps:<\/p>\n\n\n\n<ul class=\"wp-block-list\"><li>Wavefront Proxy installation<\/li><li>Wavefront Proxy configuration<\/li><li>rsyslog configuration<\/li><\/ul>\n\n\n\n<h4 class=\"wp-block-heading\">Wavefront Proxy<\/h4>\n\n\n\n<p>The installation of the proxy is basically one simple step. In the Wavefront UI a click on &#8220;<strong>ADD NEW PROXY<\/strong>&#8221; shows details on how to install the proxy as:<\/p>\n\n\n\n<ul class=\"wp-block-list\"><li>Linux installation<\/li><li>Windows installation<\/li><li>Mac installation<\/li><li>Docker image<\/li><\/ul>\n\n\n\n<figure class=\"wp-block-image size-large\"><a href=\"https:\/\/thomas-kopton.de\/vblog\/wp-content\/uploads\/2021\/02\/figure_02.png\"><img loading=\"lazy\" decoding=\"async\" width=\"1024\" height=\"484\" src=\"https:\/\/thomas-kopton.de\/vblog\/wp-content\/uploads\/2021\/02\/figure_02-1024x484.png\" alt=\"\" class=\"wp-image-705\" srcset=\"https:\/\/thomas-kopton.de\/vblog\/wp-content\/uploads\/2021\/02\/figure_02-1024x484.png 1024w, https:\/\/thomas-kopton.de\/vblog\/wp-content\/uploads\/2021\/02\/figure_02-300x142.png 300w, https:\/\/thomas-kopton.de\/vblog\/wp-content\/uploads\/2021\/02\/figure_02-768x363.png 768w, https:\/\/thomas-kopton.de\/vblog\/wp-content\/uploads\/2021\/02\/figure_02-1536x726.png 1536w, https:\/\/thomas-kopton.de\/vblog\/wp-content\/uploads\/2021\/02\/figure_02.png 1582w\" sizes=\"auto, (max-width: 1024px) 100vw, 1024px\" \/><\/a><figcaption><em>Figure 02: Proxy installation &#8211; &#8220;Add New Proxy&#8221;<\/em><\/figcaption><\/figure>\n\n\n\n<p>I have installed my proxy on a Ubuntu 18.04 server VM using the command provided by Wavefront UI which includes an auto-generated API token.<\/p>\n\n\n\n<figure class=\"wp-block-image size-large is-resized\"><a href=\"https:\/\/thomas-kopton.de\/vblog\/wp-content\/uploads\/2021\/02\/figure_03.png\"><img loading=\"lazy\" decoding=\"async\" src=\"https:\/\/thomas-kopton.de\/vblog\/wp-content\/uploads\/2021\/02\/figure_03-936x1024.png\" alt=\"\" class=\"wp-image-707\" width=\"468\" height=\"512\" srcset=\"https:\/\/thomas-kopton.de\/vblog\/wp-content\/uploads\/2021\/02\/figure_03-936x1024.png 936w, https:\/\/thomas-kopton.de\/vblog\/wp-content\/uploads\/2021\/02\/figure_03-274x300.png 274w, https:\/\/thomas-kopton.de\/vblog\/wp-content\/uploads\/2021\/02\/figure_03-768x841.png 768w, https:\/\/thomas-kopton.de\/vblog\/wp-content\/uploads\/2021\/02\/figure_03.png 1164w\" sizes=\"auto, (max-width: 468px) 100vw, 468px\" \/><\/a><figcaption><em>Figure 03: Proxy installation on Linux<\/em><\/figcaption><\/figure>\n\n\n\n<p>The proxy automatically connects to the Wavefront service and is ready for forwarding metrics to the cloud. <\/p>\n\n\n\n<p>What is missing now, is the proper configuration to receive log messages and extract the actual time series data I would like to have collected in Wavefront.<\/p>\n\n\n\n<h4 class=\"wp-block-heading\">Log Ingestion Config<\/h4>\n\n\n\n<p>Now it&#8217;s time to configure the required integration. Wavefront supports a large number of integrations. One of them is the Log Data Integration.<\/p>\n\n\n\n<figure class=\"wp-block-image size-large\"><a href=\"https:\/\/thomas-kopton.de\/vblog\/wp-content\/uploads\/2021\/02\/figure_04.png\"><img loading=\"lazy\" decoding=\"async\" width=\"1024\" height=\"568\" src=\"https:\/\/thomas-kopton.de\/vblog\/wp-content\/uploads\/2021\/02\/figure_04-1024x568.png\" alt=\"\" class=\"wp-image-711\" srcset=\"https:\/\/thomas-kopton.de\/vblog\/wp-content\/uploads\/2021\/02\/figure_04-1024x568.png 1024w, https:\/\/thomas-kopton.de\/vblog\/wp-content\/uploads\/2021\/02\/figure_04-300x166.png 300w, https:\/\/thomas-kopton.de\/vblog\/wp-content\/uploads\/2021\/02\/figure_04-768x426.png 768w, https:\/\/thomas-kopton.de\/vblog\/wp-content\/uploads\/2021\/02\/figure_04-1536x852.png 1536w, https:\/\/thomas-kopton.de\/vblog\/wp-content\/uploads\/2021\/02\/figure_04-2048x1136.png 2048w\" sizes=\"auto, (max-width: 1024px) 100vw, 1024px\" \/><\/a><figcaption><em>Figure 04: Log Data Integration<\/em><\/figcaption><\/figure>\n\n\n\n<p>If you have never worked with e.g. <strong>Logstash<\/strong> or any other solution using <strong><span class=\"has-inline-color has-vivid-purple-color\">grok patterns<\/span><\/strong>, that might be de most challenging part of the setup.<\/p>\n\n\n\n<p>First part of the proxy config is easy, we enable the proxy to listen for log messages coming in either via Filebeat or raw TCP. This is the corresponding part of my proxy config file<\/p>\n\n\n\n<p><code>\/etc\/wavefront\/wavefront-proxy\/wavefront.conf<\/code><\/p>\n\n\n\n<pre class=\"wp-block-code\"><code>#### LOGS TO METRICS SETTINGS #####\n## Port on which to listen for FileBeat data (Lumberjack protocol). Default: none\nfilebeatPort=5044\n## Port on which to listen for raw logs data (TCP and HTTP). Default: none\nrawLogsPort=5045\n## Maximum line length for received raw logs (Default: 4096)\nrawLogsMaxReceivedLength=4096\n## Maximum allowed request size (in bytes) for incoming HTTP requests with raw logs (Default: 16MB)\nrawLogsHttpBufferSize=16777216\n## Location of the `logsingestion.yaml` configuration file\nlogsIngestionConfigFile=\/etc\/wavefront\/wavefront-proxy\/logsingestion.yaml<\/code><\/pre>\n\n\n\n<p>The second part is to have a working log insgestion config file, in my case:<\/p>\n\n\n\n<p><code>\/etc\/wavefront\/wavefront-proxy\/logsingestion.yaml<\/code><\/p>\n\n\n\n<p>&#8220;Working&#8221; means not only accepted by the proxy but also and even more important, extracting the metric(s) we want to forward to Wavefront.<\/p>\n\n\n\n<p>At the moment I am interested in the current electric power consumption. This is the red highlighted value in the set of the vzlogger messages:<\/p>\n\n\n\n<figure class=\"wp-block-image size-large\"><a href=\"https:\/\/thomas-kopton.de\/vblog\/wp-content\/uploads\/2021\/02\/figure_05.png\"><img loading=\"lazy\" decoding=\"async\" width=\"1024\" height=\"266\" src=\"https:\/\/thomas-kopton.de\/vblog\/wp-content\/uploads\/2021\/02\/figure_05-1024x266.png\" alt=\"\" class=\"wp-image-715\" srcset=\"https:\/\/thomas-kopton.de\/vblog\/wp-content\/uploads\/2021\/02\/figure_05-1024x266.png 1024w, https:\/\/thomas-kopton.de\/vblog\/wp-content\/uploads\/2021\/02\/figure_05-300x78.png 300w, https:\/\/thomas-kopton.de\/vblog\/wp-content\/uploads\/2021\/02\/figure_05-768x200.png 768w, https:\/\/thomas-kopton.de\/vblog\/wp-content\/uploads\/2021\/02\/figure_05-1536x399.png 1536w, https:\/\/thomas-kopton.de\/vblog\/wp-content\/uploads\/2021\/02\/figure_05-2048x532.png 2048w\" sizes=\"auto, (max-width: 1024px) 100vw, 1024px\" \/><\/a><figcaption><em>Figure 05: vzlogger message containing the metric<\/em><\/figcaption><\/figure>\n\n\n\n<p>Time for the grok patterns. The task is to extract the electric power consumption value. To test grok patterns with my log messages as input I have used:<\/p>\n\n\n\n<p><a href=\"http:\/\/grokdebug.herokuapp.com\/\">http:\/\/grokdebug.herokuapp.com\/<\/a><\/p>\n\n\n\n<p>Finally I came up with this pattern and the log ingestion proxy configuration.<\/p>\n\n\n\n<p><strong>Pattern<\/strong>:<\/p>\n\n\n\n<p><code>[%{MONTH} %{MONTHDAY} %{TIME}][%{WORD}] %{WORD}: id=1-0:1.7.0%{GREEDYDATA} value=%{BASE16FLOAT:currConsumption} ts=%{NUMBER}<\/code><\/p>\n\n\n\n<p><strong>Resulting config<\/strong>,<code> \/etc\/wavefront\/wavefront-proxy\/logsingestion.yaml<\/code><\/p>\n\n\n\n<p><span class=\"has-inline-color has-vivid-red-color\">Please not, it is yaml, so you need to preserve the 2 spaces for indentation.<\/span><\/p>\n\n\n\n<pre class=\"wp-block-code\"><code>aggregationIntervalSeconds: 5  # Metrics are aggregated and sent at this interval\ngauges:\n  - pattern: '\\&#91;%{MONTH} %{MONTHDAY} %{TIME}\\]\\&#91;%{WORD}\\] %{WORD}: id=1-0:1.7.0%{GREEDYDATA} value\\=%{BASE16FLOAT:currConsumption} ts=%{NUMBER}'\n    metricName: 'myCurrConsumption'\n    valueLabel: 'currConsumption'<\/code><\/pre>\n\n\n\n<h4 class=\"wp-block-heading\">rsyslog Config<\/h4>\n\n\n\n<p>The last step is to re-configure <strong><span class=\"has-inline-color has-vivid-purple-color\">rsyslog<\/span><\/strong> to send the messages to the proxy or as I did to the Wavefront proxy and to vRealize Log Insight. This is my rsyslog config file after adding the second target.<\/p>\n\n\n\n<pre class=\"wp-block-code\"><code>$ModLoad imfile\n\n$InputFileName \/var\/log\/vzlogger\/vzlogger.log\n$InputFileTag vzlogger\n$InputFilePollInterval 10\n$InputFileSeverity info\n$InputFileFacility local3\n$InputRunFileMonitor\n# my local Wavefront proxy\nlocal3.* @@192.168.0.137:5045\n\n$InputFileName \/var\/log\/vzlogger\/vzlogger.log\n$InputFileTag vzlogger\n$InputFilePollInterval 10\n$InputFileSeverity info\n$InputFileFacility local3\n$InputRunFileMonitor\n# my vRealize Log Insight instance on VMC\nlocal3.* @@xxx.xxx.xxx.xxx:yyyy<\/code><\/pre>\n\n\n\n<h4 class=\"wp-block-heading\">Results<\/h4>\n\n\n\n<p>Wavefront UI displays the collected real-time data and offers a wide range of functions and transformation for data analytics.<\/p>\n\n\n\n<figure class=\"wp-block-image size-large\"><a href=\"https:\/\/thomas-kopton.de\/vblog\/wp-content\/uploads\/2021\/02\/figure_06.png\"><img loading=\"lazy\" decoding=\"async\" width=\"1024\" height=\"455\" src=\"https:\/\/thomas-kopton.de\/vblog\/wp-content\/uploads\/2021\/02\/figure_06-1024x455.png\" alt=\"\" class=\"wp-image-720\" srcset=\"https:\/\/thomas-kopton.de\/vblog\/wp-content\/uploads\/2021\/02\/figure_06-1024x455.png 1024w, https:\/\/thomas-kopton.de\/vblog\/wp-content\/uploads\/2021\/02\/figure_06-300x133.png 300w, https:\/\/thomas-kopton.de\/vblog\/wp-content\/uploads\/2021\/02\/figure_06-768x342.png 768w, https:\/\/thomas-kopton.de\/vblog\/wp-content\/uploads\/2021\/02\/figure_06-1536x683.png 1536w, https:\/\/thomas-kopton.de\/vblog\/wp-content\/uploads\/2021\/02\/figure_06-2048x911.png 2048w\" sizes=\"auto, (max-width: 1024px) 100vw, 1024px\" \/><\/a><figcaption><em>Figure 06: Real-time data in Wavefront UI<\/em><\/figcaption><\/figure>\n\n\n\n<p>As my proxy is running as a VM on my laptop I will need to move it to something more reliable:-) Once I have collected more data points and added another metrics provided by vzlogger I am going to play around with the <span class=\"has-inline-color has-vivid-cyan-blue-color\"><strong>WQL<\/strong><\/span> &#8211; the <strong><span class=\"has-inline-color has-vivid-cyan-blue-color\">Wavefront Query Language<\/span><\/strong>. <\/p>\n\n\n\n<h4 class=\"wp-block-heading\">Outlook<\/h4>\n\n\n\n<p>Next challange is to collect the data using <strong><span class=\"has-inline-color has-vivid-cyan-blue-color\">vRealize Operations<\/span><\/strong>.<\/p>\n\n\n\n<p><strong>Stay safe.<\/strong><\/p>\n\n\n\n<p>Thomas \u2013&nbsp;<a href=\"https:\/\/twitter.com\/ThomasKopton\">https:\/\/twitter.com\/ThomasKopton<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Motivation In my last post, I have described how to ingest power consumption data provided by the vzlogger project into vRealize Log Insight and how to extract the actual metrics from the log message. That setup is working since days as expected but the primary use case for vRealize Log Insight is intelligent logging and &#8230;<\/p>\n","protected":false},"author":1,"featured_media":720,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[46],"tags":[45],"class_list":["post-694","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-wavefront","tag-wavefront"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.0 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Wavefront by VMware and Log Ingestion - TOMsOps<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/thomas-kopton.de\/vblog\/?p=694\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Wavefront by VMware and Log Ingestion - TOMsOps\" \/>\n<meta property=\"og:description\" content=\"Motivation In my last post, I have described how to ingest power consumption data provided by the vzlogger project into vRealize Log Insight and how to extract the actual metrics from the log message. 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