From c54b5c542ce5d24f1f4fb375312eab3e46c60ea3 Mon Sep 17 00:00:00 2001 From: f41gh7 Date: Fri, 15 Nov 2024 19:21:51 +0100 Subject: [PATCH] make vmui-update --- app/vmselect/vmui/asset-manifest.json | 4 ++-- app/vmselect/vmui/index.html | 2 +- .../vmui/static/js/{main.7ec4e6eb.js => main.a7037969.js} | 4 ++-- ...n.7ec4e6eb.js.LICENSE.txt => main.a7037969.js.LICENSE.txt} | 0 4 files changed, 5 insertions(+), 5 deletions(-) rename app/vmselect/vmui/static/js/{main.7ec4e6eb.js => main.a7037969.js} (99%) rename app/vmselect/vmui/static/js/{main.7ec4e6eb.js.LICENSE.txt => main.a7037969.js.LICENSE.txt} (100%) diff --git a/app/vmselect/vmui/asset-manifest.json b/app/vmselect/vmui/asset-manifest.json index a5692f74b..11aee8cd2 100644 --- a/app/vmselect/vmui/asset-manifest.json +++ b/app/vmselect/vmui/asset-manifest.json @@ -1,13 +1,13 @@ { "files": { "main.css": "./static/css/main.d781989c.css", - "main.js": "./static/js/main.7ec4e6eb.js", + "main.js": "./static/js/main.a7037969.js", "static/js/685.f772060c.chunk.js": "./static/js/685.f772060c.chunk.js", "static/media/MetricsQL.md": "./static/media/MetricsQL.a00044c91d9781cf8557.md", "index.html": "./index.html" }, "entrypoints": [ "static/css/main.d781989c.css", - "static/js/main.7ec4e6eb.js" + "static/js/main.a7037969.js" ] } \ No newline at end of file diff --git a/app/vmselect/vmui/index.html b/app/vmselect/vmui/index.html index 6fdd68e56..b61bbca54 100644 --- a/app/vmselect/vmui/index.html +++ b/app/vmselect/vmui/index.html @@ -1 +1 @@ -vmui
\ No newline at end of file +vmui
\ No newline at end of file diff --git a/app/vmselect/vmui/static/js/main.7ec4e6eb.js b/app/vmselect/vmui/static/js/main.a7037969.js similarity index 99% rename from app/vmselect/vmui/static/js/main.7ec4e6eb.js rename to app/vmselect/vmui/static/js/main.a7037969.js index 342fcc85e..f78e3e9c0 100644 --- a/app/vmselect/vmui/static/js/main.7ec4e6eb.js +++ b/app/vmselect/vmui/static/js/main.a7037969.js @@ -1,2 +1,2 @@ -/*! 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Mr("keydown",(e=>{const{code:t,ctrlKey:n,altKey:r}=e;"Space"===t&&(n||r)&&(e.preventDefault(),a({type:"SET_AUTOCOMPLETE_QUICK",payload:!0}))})),Nt("div",{className:Er()({"vm-additional-settings":!0,"vm-additional-settings_mobile":t}),children:[!(null!==n&&void 0!==n&&n.autocomplete)&&Nt(ba,{title:Nt(Ct.FK,{children:["Quick tip: ",Na]}),children:Nt(Ti,{label:"Autocomplete",value:r,onChange:()=>{a({type:"TOGGLE_AUTOCOMPLETE"})},fullWidth:t})}),Nt(Ti,{label:"Disable cache",value:i,onChange:()=>{l({type:"TOGGLE_NO_CACHE"})},fullWidth:t}),!(null!==n&&void 0!==n&&n.traceQuery)&&Nt(Ti,{label:"Trace query",value:o,onChange:()=>{l({type:"TOGGLE_QUERY_TRACING"})},fullWidth:t})]})},ml=e=>{const{isMobile:t}=ta(),n=(0,r.useRef)(null),{value:a,toggle:i,setFalse:o}=ma(!1);return t?Nt(Ct.FK,{children:[Nt("div",{ref:n,children:Nt(da,{variant:"outlined",startIcon:Nt(dr,{}),onClick:i,ariaLabel:"additional the query settings"})}),Nt(ha,{open:a,buttonRef:n,placement:"bottom-left",onClose:o,title:"Query 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    \n

    \n When looking to reduce the number of active series in your data source,\n you can start by inspecting individual metrics with high cardinality\n (i.e. that have lots of active time series associated with them),\n since that single metric contributes a large fraction of the series that make up your total series count.\n

    ",seriesCountByLabelName:"\n

    \n This table returns a list of the labels with the highest number of series.\n

    \n

    \n Use this table to identify labels that are storing dimensions with high cardinality\n (many different label values).\n

    \n

    \n It is recommended to choose labels such that they have a finite set of values,\n since every unique combination of key-value label pairs creates a new time series\n and therefore can dramatically increase the number of time series in your system.\n

    ",seriesCountByFocusLabelValue:"\n

    \n This table returns a list of unique label values per selected label.\n

    \n

    \n Use this table to identify label values that are storing per each selected series.\n

    ",labelValueCountByLabelName:"\n

    \n This table returns a list of labels with the highest number of the unique values.\n

    \n ",seriesCountByLabelValuePair:"\n

    \n This table returns a list of the label values pairs with the highest number of series.\n

    \n

    \n Use this table to identify unique label values pairs. This helps to identify same labels \n is applied to count timeseries in your system, since every unique combination of key-value label pairs \n creates a new time series and therefore can dramatically increase the number of time series in your system\n

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WITH expressions may improve this:"]}),Nt(pp,{code:'WITH (\n my_resource_utilization(free, limit, filters) = (limit{filters} - free{filters}) / limit{filters} * 100\n)\nmy_resource_utilization(\n node_memory_MemFree_bytes,\n node_memory_MemTotal_bytes,\n {instance=~"$node:$port",job=~"$job"},\n)'}),Nt("p",{className:"vm-with-template-tutorial-section__text",children:"Now the template function my_resource_utilization() may be used for monitoring arbitrary resources - memory, CPU, network, storage, you name it."})]}),Nt("div",{className:"vm-with-template-tutorial-section",children:[Nt("p",{className:"vm-with-template-tutorial-section__text",children:["Let's take another nice query from ",Nt(vp,{})," dashboard:"]}),Nt(pp,{code:'(\n (\n (\n count(\n count(node_cpu_seconds_total{instance=~"$node:$port",job=~"$job"}) by (cpu)\n )\n )\n -\n avg(\n sum by (mode) (rate(node_cpu_seconds_total{mode=\'idle\',instance=~"$node:$port",job=~"$job"}[5m]))\n )\n )\n *\n 100\n)\n /\ncount(\n count(node_cpu_seconds_total{instance=~"$node:$port",job=~"$job"}) by (cpu)\n)'}),Nt("p",{className:"vm-with-template-tutorial-section__text",children:"Do you understand what does this mess do? 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    \n

    \n When looking to reduce the number of active series in your data source,\n you can start by inspecting individual metrics with high cardinality\n (i.e. that have lots of active time series associated with them),\n since that single metric contributes a large fraction of the series that make up your total series count.\n

    ",seriesCountByLabelName:"\n

    \n This table returns a list of the labels with the highest number of series.\n

    \n

    \n Use this table to identify labels that are storing dimensions with high cardinality\n (many different label values).\n

    \n

    \n It is recommended to choose labels such that they have a finite set of values,\n since every unique combination of key-value label pairs creates a new time series\n and therefore can dramatically increase the number of time series in your system.\n

    ",seriesCountByFocusLabelValue:"\n

    \n This table returns a list of unique label values per selected label.\n

    \n

    \n Use this table to identify label values that are storing per each selected series.\n

    ",labelValueCountByLabelName:"\n

    \n This table returns a list of labels with the highest number of the unique values.\n

    \n ",seriesCountByLabelValuePair:"\n

    \n This table returns a list of the label values pairs with the highest number of series.\n

    \n

    \n Use this table to identify unique label values pairs. This helps to identify same labels \n is applied to count timeseries in your system, since every unique combination of key-value label pairs \n creates a new time series and therefore can dramatically increase the number of time series in your system\n

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WITH expressions may improve this:"]}),Nt(pp,{code:'WITH (\n my_resource_utilization(free, limit, filters) = (limit{filters} - free{filters}) / limit{filters} * 100\n)\nmy_resource_utilization(\n node_memory_MemFree_bytes,\n node_memory_MemTotal_bytes,\n {instance=~"$node:$port",job=~"$job"},\n)'}),Nt("p",{className:"vm-with-template-tutorial-section__text",children:"Now the template function my_resource_utilization() may be used for monitoring arbitrary resources - memory, CPU, network, storage, you name it."})]}),Nt("div",{className:"vm-with-template-tutorial-section",children:[Nt("p",{className:"vm-with-template-tutorial-section__text",children:["Let's take another nice query from ",Nt(vp,{})," dashboard:"]}),Nt(pp,{code:'(\n (\n (\n count(\n count(node_cpu_seconds_total{instance=~"$node:$port",job=~"$job"}) by (cpu)\n )\n )\n -\n avg(\n sum by (mode) (rate(node_cpu_seconds_total{mode=\'idle\',instance=~"$node:$port",job=~"$job"}[5m]))\n )\n )\n *\n 100\n)\n /\ncount(\n count(node_cpu_seconds_total{instance=~"$node:$port",job=~"$job"}) by (cpu)\n)'}),Nt("p",{className:"vm-with-template-tutorial-section__text",children:"Do you understand what does this mess do? 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