From 9a3f44e79cfc865f019c23714ebca3d864e27974 Mon Sep 17 00:00:00 2001 From: Aliaksandr Valialkin Date: Sat, 27 Jul 2024 13:50:31 +0200 Subject: [PATCH] app/{vmselect,vlselect}: run `make vmui-update vmui-logs-update` after efd70b2c52c5c5aec83baad3135d8c1f347f9c70 --- app/vlselect/vmui/asset-manifest.json | 6 +- app/vlselect/vmui/index.html | 2 +- .../js/{main.62a82f4a.js => main.8451d9b9.js} | 4 +- ...CENSE.txt => main.8451d9b9.js.LICENSE.txt} | 0 .../media/MetricsQL.d46c42c8e891f06298c4.md} | 3 - app/vmselect/vmui/asset-manifest.json | 6 +- app/vmselect/vmui/index.html | 2 +- .../js/{main.ff023df1.js => main.36983a8a.js} | 4 +- ...CENSE.txt => main.36983a8a.js.LICENSE.txt} | 0 .../media/MetricsQL.d46c42c8e891f06298c4.md} | 124 +++++++++++++++--- .../packages/vmui/src/assets/MetricsQL.md | 3 - 11 files changed, 117 insertions(+), 37 deletions(-) rename app/vlselect/vmui/static/js/{main.62a82f4a.js => main.8451d9b9.js} (58%) rename app/vlselect/vmui/static/js/{main.62a82f4a.js.LICENSE.txt => main.8451d9b9.js.LICENSE.txt} (100%) rename app/{vmselect/vmui/static/media/MetricsQL.3de5efa763ef6e05a579.md => vlselect/vmui/static/media/MetricsQL.d46c42c8e891f06298c4.md} (99%) rename app/vmselect/vmui/static/js/{main.ff023df1.js => main.36983a8a.js} (99%) rename app/vmselect/vmui/static/js/{main.ff023df1.js.LICENSE.txt => main.36983a8a.js.LICENSE.txt} (100%) rename app/{vlselect/vmui/static/media/MetricsQL.aaabf95f2c9bf356bde4.md => vmselect/vmui/static/media/MetricsQL.d46c42c8e891f06298c4.md} (95%) diff --git a/app/vlselect/vmui/asset-manifest.json b/app/vlselect/vmui/asset-manifest.json index 72f77501f..49b7e0579 100644 --- a/app/vlselect/vmui/asset-manifest.json +++ b/app/vlselect/vmui/asset-manifest.json @@ -1,13 +1,13 @@ { "files": { "main.css": "./static/css/main.1041c3d4.css", - "main.js": "./static/js/main.62a82f4a.js", + "main.js": "./static/js/main.8451d9b9.js", "static/js/685.bebe1265.chunk.js": "./static/js/685.bebe1265.chunk.js", - "static/media/MetricsQL.md": "./static/media/MetricsQL.aaabf95f2c9bf356bde4.md", + "static/media/MetricsQL.md": "./static/media/MetricsQL.d46c42c8e891f06298c4.md", "index.html": "./index.html" }, "entrypoints": [ "static/css/main.1041c3d4.css", - "static/js/main.62a82f4a.js" + "static/js/main.8451d9b9.js" ] } \ No newline at end of file diff --git a/app/vlselect/vmui/index.html b/app/vlselect/vmui/index.html index bce93a07c..1453de3a8 100644 --- a/app/vlselect/vmui/index.html +++ b/app/vlselect/vmui/index.html @@ -1 +1 @@ -VM UI
\ No newline at end of file +VM UI
\ No newline at end of file diff --git a/app/vlselect/vmui/static/js/main.62a82f4a.js b/app/vlselect/vmui/static/js/main.8451d9b9.js similarity index 58% rename from app/vlselect/vmui/static/js/main.62a82f4a.js rename to app/vlselect/vmui/static/js/main.8451d9b9.js index 0f4eb8778..1012cef30 100644 --- a/app/vlselect/vmui/static/js/main.62a82f4a.js +++ b/app/vlselect/vmui/static/js/main.8451d9b9.js @@ -1,2 +1,2 @@ -/*! 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83c\udde7\ud83c\uddf8",bhutan:"\ud83c\udde7\ud83c\uddf9",bouvet_island:"\ud83c\udde7\ud83c\uddfb",botswana:"\ud83c\udde7\ud83c\uddfc",belarus:"\u2b1c\ufe0f\ud83d\udfe5\u2b1c",belize:"\ud83c\udde7\ud83c\uddff",canada:"\ud83c\udde8\ud83c\udde6",cocos_islands:"\ud83c\udde8\ud83c\udde8",congo_kinshasa:"\ud83c\udde8\ud83c\udde9",central_african_republic:"\ud83c\udde8\ud83c\uddeb",congo_brazzaville:"\ud83c\udde8\ud83c\uddec",switzerland:"\ud83c\udde8\ud83c\udded",cote_divoire:"\ud83c\udde8\ud83c\uddee",cook_islands:"\ud83c\udde8\ud83c\uddf0",chile:"\ud83c\udde8\ud83c\uddf1",cameroon:"\ud83c\udde8\ud83c\uddf2",cn:"\ud83c\udde8\ud83c\uddf3",colombia:"\ud83c\udde8\ud83c\uddf4",clipperton_island:"\ud83c\udde8\ud83c\uddf5",costa_rica:"\ud83c\udde8\ud83c\uddf7",cuba:"\ud83c\udde8\ud83c\uddfa",cape_verde:"\ud83c\udde8\ud83c\uddfb",curacao:"\ud83c\udde8\ud83c\uddfc",christmas_island:"\ud83c\udde8\ud83c\uddfd",cyprus:"\ud83c\udde8\ud83c\uddfe",czech_republic:"\ud83c\udde8\ud83c\uddff",de:"\ud83c\udde9\ud83c\uddea",diego_garcia:"\ud83c\udde9\ud83c\uddec",djibouti:"\ud83c\udde9\ud83c\uddef",denmark:"\ud83c\udde9\ud83c\uddf0",dominica:"\ud83c\udde9\ud83c\uddf2",dominican_republic:"\ud83c\udde9\ud83c\uddf4",algeria:"\ud83c\udde9\ud83c\uddff",ceuta_melilla:"\ud83c\uddea\ud83c\udde6",ecuador:"\ud83c\uddea\ud83c\udde8",estonia:"\ud83c\uddea\ud83c\uddea",egypt:"\ud83c\uddea\ud83c\uddec",western_sahara:"\ud83c\uddea\ud83c\udded",eritrea:"\ud83c\uddea\ud83c\uddf7",es:"\ud83c\uddea\ud83c\uddf8",ethiopia:"\ud83c\uddea\ud83c\uddf9",eu:"\ud83c\uddea\ud83c\uddfa",european_union:"\ud83c\uddea\ud83c\uddfa",finland:"\ud83c\uddeb\ud83c\uddee",fiji:"\ud83c\uddeb\ud83c\uddef",falkland_islands:"\ud83c\uddeb\ud83c\uddf0",micronesia:"\ud83c\uddeb\ud83c\uddf2",faroe_islands:"\ud83c\uddeb\ud83c\uddf4",fr:"\ud83c\uddeb\ud83c\uddf7",gabon:"\ud83c\uddec\ud83c\udde6",gb:"\ud83c\uddec\ud83c\udde7",uk:"\ud83c\uddec\ud83c\udde7",grenada:"\ud83c\uddec\ud83c\udde9",georgia:"\ud83c\uddec\ud83c\uddea",french_guiana:"\ud83c\uddec\ud83c\uddeb",guernsey:"\ud83c\uddec\ud83c\uddec",ghana:"\ud83c\uddec\ud83c\udded",gibraltar:"\ud83c\uddec\ud83c\uddee",greenland:"\ud83c\uddec\ud83c\uddf1",gambia:"\ud83c\uddec\ud83c\uddf2",guinea:"\ud83c\uddec\ud83c\uddf3",guadeloupe:"\ud83c\uddec\ud83c\uddf5",equatorial_guinea:"\ud83c\uddec\ud83c\uddf6",greece:"\ud83c\uddec\ud83c\uddf7",south_georgia_south_sandwich_islands:"\ud83c\uddec\ud83c\uddf8",guatemala:"\ud83c\uddec\ud83c\uddf9",guam:"\ud83c\uddec\ud83c\uddfa",guinea_bissau:"\ud83c\uddec\ud83c\uddfc",guyana:"\ud83c\uddec\ud83c\uddfe",hong_kong:"\ud83c\udded\ud83c\uddf0",heard_mcdonald_islands:"\ud83c\udded\ud83c\uddf2",honduras:"\ud83c\udded\ud83c\uddf3",croatia:"\ud83c\udded\ud83c\uddf7",haiti:"\ud83c\udded\ud83c\uddf9",hungary:"\ud83c\udded\ud83c\uddfa",canary_islands:"\ud83c\uddee\ud83c\udde8",indonesia:"\ud83c\uddee\ud83c\udde9",ireland:"\ud83c\uddee\ud83c\uddea",israel:"\ud83c\uddee\ud83c\uddf1",isle_of_man:"\ud83c\uddee\ud83c\uddf2",india:"\ud83c\uddee\ud83c\uddf3",british_indian_ocean_territory:"\ud83c\uddee\ud83c\uddf4",iraq:"\ud83c\uddee\ud83c\uddf6",iran:"\ud83c\uddee\ud83c\uddf7",iceland:"\ud83c\uddee\ud83c\uddf8",it:"\ud83c\uddee\ud83c\uddf9",jersey:"\ud83c\uddef\ud83c\uddea",jamaica:"\ud83c\uddef\ud83c\uddf2",jordan:"\ud83c\uddef\ud83c\uddf4",jp:"\ud83c\uddef\ud83c\uddf5",kenya:"\ud83c\uddf0\ud83c\uddea",kyrgyzstan:"\ud83c\uddf0\ud83c\uddec",cambodia:"\ud83c\uddf0\ud83c\udded",kiribati:"\ud83c\uddf0\ud83c\uddee",comoros:"\ud83c\uddf0\ud83c\uddf2",st_kitts_nevis:"\ud83c\uddf0\ud83c\uddf3",north_korea:"\ud83c\uddf0\ud83c\uddf5",kr:"\ud83c\uddf0\ud83c\uddf7",kuwait:"\ud83c\uddf0\ud83c\uddfc",cayman_islands:"\ud83c\uddf0\ud83c\uddfe",kazakhstan:"\ud83c\uddf0\ud83c\uddff",laos:"\ud83c\uddf1\ud83c\udde6",lebanon:"\ud83c\uddf1\ud83c\udde7",st_lucia:"\ud83c\uddf1\ud83c\udde8",liechtenstein:"\ud83c\uddf1\ud83c\uddee",sri_lanka:"\ud83c\uddf1\ud83c\uddf0",liberia:"\ud83c\uddf1\ud83c\uddf7",lesotho:"\ud83c\uddf1\ud83c\uddf8",lithuania:"\ud83c\uddf1\ud83c\uddf9",luxembourg:"\ud83c\uddf1\ud83c\uddfa",latvia:"\ud83c\uddf1\ud83c\uddfb",libya:"\ud83c\uddf1\ud83c\uddfe",morocco:"\ud83c\uddf2\ud83c\udde6",monaco:"\ud83c\uddf2\ud83c\udde8",moldova:"\ud83c\uddf2\ud83c\udde9",montenegro:"\ud83c\uddf2\ud83c\uddea",st_martin:"\ud83c\uddf2\ud83c\uddeb",madagascar:"\ud83c\uddf2\ud83c\uddec",marshall_islands:"\ud83c\uddf2\ud83c\udded",macedonia:"\ud83c\uddf2\ud83c\uddf0",mali:"\ud83c\uddf2\ud83c\uddf1",myanmar:"\ud83c\uddf2\ud83c\uddf2",mongolia:"\ud83c\uddf2\ud83c\uddf3",macau:"\ud83c\uddf2\ud83c\uddf4",northern_mariana_islands:"\ud83c\uddf2\ud83c\uddf5",martinique:"\ud83c\uddf2\ud83c\uddf6",mauritania:"\ud83c\uddf2\ud83c\uddf7",montserrat:"\ud83c\uddf2\ud83c\uddf8",malta:"\ud83c\uddf2\ud83c\uddf9",mauritius:"\ud83c\uddf2\ud83c\uddfa",maldives:"\ud83c\uddf2\ud83c\uddfb",malawi:"\ud83c\uddf2\ud83c\uddfc",mexico:"\ud83c\uddf2\ud83c\uddfd",malaysia:"\ud83c\uddf2\ud83c\uddfe",mozambique:"\ud83c\uddf2\ud83c\uddff",namibia:"\ud83c\uddf3\ud83c\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    \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

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    \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

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    \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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Isn't it? :)"})]}),Nt("div",{className:"vm-with-template-tutorial-section",children:[Nt("p",{className:"vm-with-template-tutorial-section__text",children:"What's wrong with this query? Copy-pasted label filters for distinct timeseries which makes it easy to mistype these filters during modification. Let's simplify the query with WITH expressions:"}),Nt(hp,{code:'WITH (\n commonFilters = {instance=~"$node:$port",job=~"$job"}\n)\n(\n node_memory_MemTotal_bytes{commonFilters}\n -\n node_memory_MemFree_bytes{commonFilters}\n)\n /\nnode_memory_MemTotal_bytes{commonFilters} * 100'})]}),Nt("div",{className:"vm-with-template-tutorial-section",children:[Nt("p",{className:"vm-with-template-tutorial-section__text",children:["Now label filters are located in a single place instead of three distinct places. The query mentions node_memory_MemTotal_bytes metric twice and ","{commonFilters}"," three times. WITH expressions may improve this:"]}),Nt(hp,{code:'WITH (\n my_resource_utilization(free, limit, filters) = (limit{filters} - free{filters}) / limit{filters} * 100\n)\nmy_resource_utilization(\n node_memory_MemFree_bytes,\n node_memory_MemTotal_bytes,\n {instance=~"$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(pp,{})," dashboard:"]}),Nt(hp,{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=~"$job"}) by (cpu)\n)'}),Nt("p",{className:"vm-with-template-tutorial-section__text",children:"Do you understand what does this mess do? 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Extract rate(...) part into cpuIdle variable, since it is clear now that this part calculates the number of idle CPUs:"}),Nt(hp,{code:'WITH (\n commonFilters = {instance=~"$node:$port",job=~"$job"},\n cpuCount = count(count(node_cpu_seconds_total{commonFilters}) by (cpu)),\n cpuIdle = sum(rate(node_cpu_seconds_total{mode=\'idle\',commonFilters}[5m]))\n)\n((cpuCount - cpuIdle) * 100) / cpuCount'})]}),Nt("div",{className:"vm-with-template-tutorial-section",children:[Nt("p",{className:"vm-with-template-tutorial-section__text",children:["4. 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Try expanding the following query:"}),Nt(hp,{code:"WITH (\n f(a, b) = WITH (\n f1(x) = b-x,\n f2(x) = x+x\n ) f1(a)*f2(b)\n) f(foo, with(x=bar) x)"})]})]}),vp=()=>{const{serverUrl:e}=Mt(),[t,n]=He(),[a,o]=(0,r.useState)(""),[i,l]=(0,r.useState)(!1),[s,c]=(0,r.useState)();return{data:a,error:s,loading:i,expand:async r=>{t.set("expr",r),n(t);const a=((e,t)=>"".concat(e,"/expand-with-exprs?query=").concat(encodeURIComponent(t),"&format=json"))(e,r);l(!0);try{const e=await fetch(a),t=await e.json();o((null===t||void 0===t?void 0:t.expr)||""),c(String(t.error||""))}catch(Tp){Tp instanceof Error&&"AbortError"!==Tp.name&&c("".concat(Tp.name,": ").concat(Tp.message))}l(!1)}}},gp=()=>{const[e]=He(),{data:t,loading:n,error:a,expand:o}=vp(),[i,l]=(0,r.useState)(e.get("expr")||""),s=()=>{o(i)};return(0,r.useEffect)((()=>{i&&o(i)}),[]),Nt("section",{className:"vm-with-template",children:[n&&Nt(bl,{}),Nt("div",{className:"vm-with-template-body 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b/app/vmselect/vmui/static/js/main.36983a8a.js.LICENSE.txt similarity index 100% rename from app/vmselect/vmui/static/js/main.ff023df1.js.LICENSE.txt rename to app/vmselect/vmui/static/js/main.36983a8a.js.LICENSE.txt diff --git a/app/vlselect/vmui/static/media/MetricsQL.aaabf95f2c9bf356bde4.md b/app/vmselect/vmui/static/media/MetricsQL.d46c42c8e891f06298c4.md similarity index 95% rename from app/vlselect/vmui/static/media/MetricsQL.aaabf95f2c9bf356bde4.md rename to app/vmselect/vmui/static/media/MetricsQL.d46c42c8e891f06298c4.md index d49ccfb6e..64b343f53 100644 --- a/app/vlselect/vmui/static/media/MetricsQL.aaabf95f2c9bf356bde4.md +++ b/app/vmselect/vmui/static/media/MetricsQL.d46c42c8e891f06298c4.md @@ -10,9 +10,6 @@ aliases: - /ExtendedPromQL.html - /MetricsQL.html --- - -# MetricsQL - [VictoriaMetrics](https://github.com/VictoriaMetrics/VictoriaMetrics) implements MetricsQL - query language inspired by [PromQL](https://prometheus.io/docs/prometheus/latest/querying/basics/). MetricsQL is backwards-compatible with PromQL, so Grafana dashboards backed by Prometheus datasource should work @@ -220,9 +217,11 @@ See also [descent_over_time](#descent_over_time). over [raw samples](https://docs.victoriametrics.com/keyconcepts/#raw-samples) on the given lookbehind window `d` per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyconcepts/#filtering). +This function is usually applied to [gauges](https://docs.victoriametrics.com/keyconcepts/#gauge). + This function is supported by PromQL. -See also [median_over_time](#median_over_time). +See also [median_over_time](#median_over_time), [min_over_time](#min_over_time) and [max_over_time](#max_over_time). #### changes @@ -262,6 +261,8 @@ from the given [series_selector](https://docs.victoriametrics.com/keyconcepts/#f Metric names are stripped from the resulting rollups. Add [keep_metric_names](#keep_metric_names) modifier in order to keep metric names. +This function is usually applied to [gauges](https://docs.victoriametrics.com/keyconcepts/#gauge). + See also [count_over_time](#count_over_time), [share_eq_over_time](#share_eq_over_time) and [count_values_over_time](#count_values_over_time). #### count_gt_over_time @@ -272,6 +273,8 @@ from the given [series_selector](https://docs.victoriametrics.com/keyconcepts/#f Metric names are stripped from the resulting rollups. Add [keep_metric_names](#keep_metric_names) modifier in order to keep metric names. +This function is usually applied to [gauges](https://docs.victoriametrics.com/keyconcepts/#gauge). + See also [count_over_time](#count_over_time) and [share_gt_over_time](#share_gt_over_time). #### count_le_over_time @@ -282,6 +285,8 @@ from the given [series_selector](https://docs.victoriametrics.com/keyconcepts/#f Metric names are stripped from the resulting rollups. Add [keep_metric_names](#keep_metric_names) modifier in order to keep metric names. +This function is usually applied to [gauges](https://docs.victoriametrics.com/keyconcepts/#gauge). + See also [count_over_time](#count_over_time) and [share_le_over_time](#share_le_over_time). #### count_ne_over_time @@ -292,7 +297,9 @@ from the given [series_selector](https://docs.victoriametrics.com/keyconcepts/#f Metric names are stripped from the resulting rollups. Add [keep_metric_names](#keep_metric_names) modifier in order to keep metric names. -See also [count_over_time](#count_over_time). +This function is usually applied to [gauges](https://docs.victoriametrics.com/keyconcepts/#gauge). + +See also [count_over_time](#count_over_time) and [count_eq_over_time](#count_eq_over_time). #### count_over_time @@ -313,6 +320,8 @@ The results are calculated independently per each time series returned from the Metric names are stripped from the resulting rollups. Add [keep_metric_names](#keep_metric_names) modifier in order to keep metric names. +This function is usually applied to [gauges](https://docs.victoriametrics.com/keyconcepts/#gauge). + See also [count_eq_over_time](#count_eq_over_time), [count_values](#count_values) and [distinct_over_time](#distinct_over_time) and [label_match](#label_match). #### decreases_over_time @@ -432,6 +441,8 @@ from the given [series_selector](https://docs.victoriametrics.com/keyconcepts/#f Metric names are stripped from the resulting rollups. Add [keep_metric_names](#keep_metric_names) modifier in order to keep metric names. +This function is usually applied to [gauges](https://docs.victoriametrics.com/keyconcepts/#gauge). + #### histogram_over_time `histogram_over_time(series_selector[d])` is a [rollup function](#rollup-functions), which calculates @@ -443,11 +454,15 @@ For example, the following query calculates median temperature by country over t `histogram_quantile(0.5, sum(histogram_over_time(temperature[24h])) by (vmrange,country))`. +This function is usually applied to [gauges](https://docs.victoriametrics.com/keyconcepts/#gauge). + #### hoeffding_bound_lower `hoeffding_bound_lower(phi, series_selector[d])` is a [rollup function](#rollup-functions), which calculates lower [Hoeffding bound](https://en.wikipedia.org/wiki/Hoeffding%27s_inequality) for the given `phi` in the range `[0...1]`. +This function is usually applied to [gauges](https://docs.victoriametrics.com/keyconcepts/#gauge). + See also [hoeffding_bound_upper](#hoeffding_bound_upper). #### hoeffding_bound_upper @@ -455,6 +470,8 @@ See also [hoeffding_bound_upper](#hoeffding_bound_upper). `hoeffding_bound_upper(phi, series_selector[d])` is a [rollup function](#rollup-functions), which calculates upper [Hoeffding bound](https://en.wikipedia.org/wiki/Hoeffding%27s_inequality) for the given `phi` in the range `[0...1]`. +This function is usually applied to [gauges](https://docs.victoriametrics.com/keyconcepts/#gauge). + See also [hoeffding_bound_lower](#hoeffding_bound_lower). #### holt_winters @@ -462,8 +479,9 @@ See also [hoeffding_bound_lower](#hoeffding_bound_lower). `holt_winters(series_selector[d], sf, tf)` is a [rollup function](#rollup-functions), which calculates Holt-Winters value (aka [double exponential smoothing](https://en.wikipedia.org/wiki/Exponential_smoothing#Double_exponential_smoothing)) for [raw samples](https://docs.victoriametrics.com/keyconcepts/#raw-samples) over the given lookbehind window `d` using the given smoothing factor `sf` and the given trend factor `tf`. -Both `sf` and `tf` must be in the range `[0...1]`. It is expected that the [series_selector](https://docs.victoriametrics.com/keyconcepts/#filtering) -returns time series of [gauge type](https://docs.victoriametrics.com/keyconcepts/#gauge). +Both `sf` and `tf` must be in the range `[0...1]`. + +This function is usually applied to [gauges](https://docs.victoriametrics.com/keyconcepts/#gauge). This function is supported by PromQL. @@ -495,13 +513,14 @@ See also [deriv](#deriv). `increase(series_selector[d])` is a [rollup function](#rollup-functions), which calculates the increase over the given lookbehind window `d` per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyconcepts/#filtering). -It is expected that the `series_selector` returns time series of [counter type](https://docs.victoriametrics.com/keyconcepts/#counter). Unlike Prometheus, it takes into account the last sample before the given lookbehind window `d` when calculating the result. See [this article](https://medium.com/@romanhavronenko/victoriametrics-promql-compliance-d4318203f51e) for details. Metric names are stripped from the resulting rollups. Add [keep_metric_names](#keep_metric_names) modifier in order to keep metric names. +This function is usually applied to [counters](https://docs.victoriametrics.com/keyconcepts/#counter). + This function is supported by PromQL. See also [increase_pure](#increase_pure), [increase_prometheus](#increase_prometheus) and [delta](#delta). @@ -510,12 +529,13 @@ See also [increase_pure](#increase_pure), [increase_prometheus](#increase_promet `increase_prometheus(series_selector[d])` is a [rollup function](#rollup-functions), which calculates the increase over the given lookbehind window `d` per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyconcepts/#filtering). -It is expected that the `series_selector` returns time series of [counter type](https://docs.victoriametrics.com/keyconcepts/#counter). It doesn't take into account the last sample before the given lookbehind window `d` when calculating the result in the same way as Prometheus does. See [this article](https://medium.com/@romanhavronenko/victoriametrics-promql-compliance-d4318203f51e) for details. Metric names are stripped from the resulting rollups. Add [keep_metric_names](#keep_metric_names) modifier in order to keep metric names. +This function is usually applied to [counters](https://docs.victoriametrics.com/keyconcepts/#counter). + See also [increase_pure](#increase_pure) and [increase](#increase). #### increase_pure @@ -524,6 +544,10 @@ See also [increase_pure](#increase_pure) and [increase](#increase). of the following corner case - it assumes that [counters](https://docs.victoriametrics.com/keyconcepts/#counter) always start from 0, while [increase](#increase) ignores the first value in a series if it is too big. +This function is usually applied to [counters](https://docs.victoriametrics.com/keyconcepts/#counter). + +See also [increase](#increas) and [increase_prometheus](#increase_prometheus). + #### increases_over_time `increases_over_time(series_selector[d])` is a [rollup function](#rollup-functions), which calculates the number of [raw sample](https://docs.victoriametrics.com/keyconcepts/#raw-samples) @@ -540,15 +564,18 @@ on the given lookbehind window `d` per each time series returned from the given Metric names are stripped from the resulting rollups. Add [keep_metric_names](#keep_metric_names) modifier in order to keep metric names. +This function is usually applied to [gauges](https://docs.victoriametrics.com/keyconcepts/#gauge). + #### irate `irate(series_selector[d])` is a [rollup function](#rollup-functions), which calculates the "instant" per-second increase rate over the last two [raw samples](https://docs.victoriametrics.com/keyconcepts/#raw-samples) on the given lookbehind window `d` per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyconcepts/#filtering). -It is expected that the `series_selector` returns time series of [counter type](https://docs.victoriametrics.com/keyconcepts/#counter). Metric names are stripped from the resulting rollups. Add [keep_metric_names](#keep_metric_names) modifier in order to keep metric names. +This function is usually applied to [counters](https://docs.victoriametrics.com/keyconcepts/#counter). + This function is supported by PromQL. See also [rate](#rate) and [rollup_rate](#rollup_rate). @@ -587,6 +614,8 @@ See also [duration_over_time](#duration_over_time) and [lag](#lag). over [raw samples](https://docs.victoriametrics.com/keyconcepts/#raw-samples) on the given lookbehind window `d` per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyconcepts/#filtering). +This function is usually applied to [gauges](https://docs.victoriametrics.com/keyconcepts/#gauge). + See also [mad](#mad), [range_mad](#range_mad) and [outlier_iqr_over_time](#outlier_iqr_over_time). #### max_over_time @@ -594,9 +623,11 @@ See also [mad](#mad), [range_mad](#range_mad) and [outlier_iqr_over_time](#outli `max_over_time(series_selector[d])` is a [rollup function](#rollup-functions), which calculates the maximum value over [raw samples](https://docs.victoriametrics.com/keyconcepts/#raw-samples) on the given lookbehind window `d` per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyconcepts/#filtering). +This function is usually applied to [gauges](https://docs.victoriametrics.com/keyconcepts/#gauge). + This function is supported by PromQL. -See also [tmax_over_time](#tmax_over_time). +See also [tmax_over_time](#tmax_over_time) and [min_over_time](#min_over_time). #### median_over_time @@ -604,6 +635,8 @@ See also [tmax_over_time](#tmax_over_time). on the given lookbehind window `d` per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyconcepts/#filtering). +This function is usually applied to [gauges](https://docs.victoriametrics.com/keyconcepts/#gauge). + See also [avg_over_time](#avg_over_time). #### min_over_time @@ -611,9 +644,11 @@ See also [avg_over_time](#avg_over_time). `min_over_time(series_selector[d])` is a [rollup function](#rollup-functions), which calculates the minimum value over [raw samples](https://docs.victoriametrics.com/keyconcepts/#raw-samples) on the given lookbehind window `d` per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyconcepts/#filtering). +This function is usually applied to [gauges](https://docs.victoriametrics.com/keyconcepts/#gauge). + This function is supported by PromQL. -See also [tmin_over_time](#tmin_over_time). +See also [tmin_over_time](#tmin_over_time) and [max_over_time](#max_over_time). #### mode_over_time @@ -622,6 +657,8 @@ for [raw samples](https://docs.victoriametrics.com/keyconcepts/#raw-samples) on from the given [series_selector](https://docs.victoriametrics.com/keyconcepts/#filtering). It is expected that [raw sample](https://docs.victoriametrics.com/keyconcepts/#raw-samples) values are discrete. +This function is usually applied to [gauges](https://docs.victoriametrics.com/keyconcepts/#gauge). + #### outlier_iqr_over_time `outlier_iqr_over_time(series_selector[d])` is a [rollup function](#rollup-functions), which returns the last sample on the given lookbehind window `d` @@ -632,6 +669,8 @@ if its value is either smaller than the `q25-1.5*iqr` or bigger than `q75+1.5*iq The `outlier_iqr_over_time()` is useful for detecting anomalies in gauge values based on the previous history of values. For example, `outlier_iqr_over_time(memory_usage_bytes[1h])` triggers when `memory_usage_bytes` suddenly goes outside the usual value range for the last hour. +This function is usually applied to [gauges](https://docs.victoriametrics.com/keyconcepts/#gauge). + See also [outliers_iqr](#outliers_iqr). #### predict_linear @@ -659,6 +698,8 @@ This function is supported by PromQL. on the given lookbehind window `d` per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyconcepts/#filtering). The `phi` value must be in the range `[0...1]`. +This function is usually applied to [gauges](https://docs.victoriametrics.com/keyconcepts/#gauge). + This function is supported by PromQL. See also [quantiles_over_time](#quantiles_over_time). @@ -670,6 +711,8 @@ over [raw samples](https://docs.victoriametrics.com/keyconcepts/#raw-samples) on from the given [series_selector](https://docs.victoriametrics.com/keyconcepts/#filtering). The function returns individual series per each `phi*` with `{phiLabel="phi*"}` label. `phi*` values must be in the range `[0...1]`. +This function is usually applied to [gauges](https://docs.victoriametrics.com/keyconcepts/#gauge). + See also [quantile_over_time](#quantile_over_time). #### range_over_time @@ -680,11 +723,12 @@ E.g. it calculates `max_over_time(series_selector[d]) - min_over_time(series_sel Metric names are stripped from the resulting rollups. Add [keep_metric_names](#keep_metric_names) modifier in order to keep metric names. +This function is usually applied to [gauges](https://docs.victoriametrics.com/keyconcepts/#gauge). + #### rate `rate(series_selector[d])` is a [rollup function](#rollup-functions), which calculates the average per-second increase rate over the given lookbehind window `d` per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyconcepts/#filtering). -It is expected that the `series_selector` returns time series of [counter type](https://docs.victoriametrics.com/keyconcepts/#counter). If the lookbehind window is skipped in square brackets, then it is automatically calculated as `max(step, scrape_interval)`, where `step` is the query arg value passed to [/api/v1/query_range](https://docs.victoriametrics.com/keyconcepts/#range-query) or [/api/v1/query](https://docs.victoriametrics.com/keyconcepts/#instant-query), @@ -703,6 +747,8 @@ See also [irate](#irate) and [rollup_rate](#rollup_rate). on the given lookbehind window `d`. The calculations are performed individually per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyconcepts/#filtering). +This function is usually applied to [gauges](https://docs.victoriametrics.com/keyconcepts/#gauge). + Metric names are stripped from the resulting rollups. Add [keep_metric_names](#keep_metric_names) modifier in order to keep metric names. #### resets @@ -710,10 +756,11 @@ Metric names are stripped from the resulting rollups. Add [keep_metric_names](#k `resets(series_selector[d])` is a [rollup function](#rollup-functions), which returns the number of [counter](https://docs.victoriametrics.com/keyconcepts/#counter) resets over the given lookbehind window `d` per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyconcepts/#filtering). -It is expected that the `series_selector` returns time series of [counter type](https://docs.victoriametrics.com/keyconcepts/#counter). Metric names are stripped from the resulting rollups. Add [keep_metric_names](#keep_metric_names) modifier in order to keep metric names. +This function is usually applied to [counters](https://docs.victoriametrics.com/keyconcepts/#counter). + This function is supported by PromQL. #### rollup @@ -725,6 +772,10 @@ These values are calculated individually per each time series returned from the Optional 2nd argument `"min"`, `"max"` or `"avg"` can be passed to keep only one calculation result and without adding a label. See also [label_match](#label_match). +This function is usually applied to [gauges](https://docs.victoriametrics.com/keyconcepts/#gauge). + +See also [rollup_rate](#rollup_rate). + #### rollup_candlestick `rollup_candlestick(series_selector[d])` is a [rollup function](#rollup-functions), which calculates `open`, `high`, `low` and `close` values (aka OHLC) @@ -736,6 +787,8 @@ from the given [series_selector](https://docs.victoriametrics.com/keyconcepts/#f Optional 2nd argument `"open"`, `"high"` or `"low"` or `"close"` can be passed to keep only one calculation result and without adding a label. See also [label_match](#label_match). +This function is usually applied to [gauges](https://docs.victoriametrics.com/keyconcepts/#gauge). + #### rollup_delta `rollup_delta(series_selector[d])` is a [rollup function](#rollup-functions), which calculates differences between adjacent [raw samples](https://docs.victoriametrics.com/keyconcepts/#raw-samples) @@ -762,6 +815,8 @@ See also [label_match](#label_match). Metric names are stripped from the resulting rollups. Add [keep_metric_names](#keep_metric_names) modifier in order to keep metric names. +See also [rollup](#rollup) and [rollup_rate](#rollup_rate). + #### rollup_increase `rollup_increase(series_selector[d])` is a [rollup function](#rollup-functions), which calculates increases for adjacent [raw samples](https://docs.victoriametrics.com/keyconcepts/#raw-samples) @@ -774,12 +829,17 @@ See also [label_match](#label_match). Metric names are stripped from the resulting rollups. Add [keep_metric_names](#keep_metric_names) modifier in order to keep metric names. See also [rollup_delta](#rollup_delta). +This function is usually applied to [counters](https://docs.victoriametrics.com/keyconcepts/#counter). + +See also [rollup](#rollup) and [rollup_rate](#rollup_rate). + #### rollup_rate `rollup_rate(series_selector[d])` is a [rollup function](#rollup-functions), which calculates per-second change rates for adjacent [raw samples](https://docs.victoriametrics.com/keyconcepts/#raw-samples) on the given lookbehind window `d` and returns `min`, `max` and `avg` values for the calculated per-second change rates and returns them in time series with `rollup="min"`, `rollup="max"` and `rollup="avg"` additional labels. +The calculations are performed individually per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyconcepts/#filtering). See [this article](https://valyala.medium.com/why-irate-from-prometheus-doesnt-capture-spikes-45f9896d7832) in order to understand better when to use `rollup_rate()`. @@ -787,10 +847,12 @@ when to use `rollup_rate()`. Optional 2nd argument `"min"`, `"max"` or `"avg"` can be passed to keep only one calculation result and without adding a label. See also [label_match](#label_match). -The calculations are performed individually per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyconcepts/#filtering). - Metric names are stripped from the resulting rollups. Add [keep_metric_names](#keep_metric_names) modifier in order to keep metric names. +This function is usually applied to [counters](https://docs.victoriametrics.com/keyconcepts/#counter). + +See also [rollup](#rollup) and [rollup_increase](#rollup_increase). + #### rollup_scrape_interval `rollup_scrape_interval(series_selector[d])` is a [rollup function](#rollup-functions), which calculates the interval in seconds between @@ -824,6 +886,8 @@ This function is useful for calculating SLI and SLO. Example: `share_gt_over_tim Metric names are stripped from the resulting rollups. Add [keep_metric_names](#keep_metric_names) modifier in order to keep metric names. +This function is usually applied to [gauges](https://docs.victoriametrics.com/keyconcepts/#gauge). + See also [share_le_over_time](#share_le_over_time) and [count_gt_over_time](#count_gt_over_time). #### share_le_over_time @@ -838,6 +902,8 @@ the share of time series values for the last 24 hours when memory usage was belo Metric names are stripped from the resulting rollups. Add [keep_metric_names](#keep_metric_names) modifier in order to keep metric names. +This function is usually applied to [gauges](https://docs.victoriametrics.com/keyconcepts/#gauge). + See also [share_gt_over_time](#share_gt_over_time) and [count_le_over_time](#count_le_over_time). #### share_eq_over_time @@ -849,6 +915,8 @@ from the given [series_selector](https://docs.victoriametrics.com/keyconcepts/#f Metric names are stripped from the resulting rollups. Add [keep_metric_names](#keep_metric_names) modifier in order to keep metric names. +This function is usually applied to [gauges](https://docs.victoriametrics.com/keyconcepts/#gauge). + See also [count_eq_over_time](#count_eq_over_time). #### stale_samples_over_time @@ -866,6 +934,8 @@ on the given lookbehind window `d` per each time series returned from the given Metric names are stripped from the resulting rollups. Add [keep_metric_names](#keep_metric_names) modifier in order to keep metric names. +This function is usually applied to [gauges](https://docs.victoriametrics.com/keyconcepts/#gauge). + This function is supported by PromQL. See also [stdvar_over_time](#stdvar_over_time). @@ -877,6 +947,8 @@ on the given lookbehind window `d` per each time series returned from the given Metric names are stripped from the resulting rollups. Add [keep_metric_names](#keep_metric_names) modifier in order to keep metric names. +This function is usually applied to [gauges](https://docs.victoriametrics.com/keyconcepts/#gauge). + This function is supported by PromQL. See also [stddev_over_time](#stddev_over_time). @@ -888,6 +960,8 @@ values equal to `eq` on the given lookbehind window `d` per each time series ret Metric names are stripped from the resulting rollups. Add [keep_metric_names](#keep_metric_names) modifier in order to keep metric names. +This function is usually applied to [gauges](https://docs.victoriametrics.com/keyconcepts/#gauge). + See also [sum_over_time](#sum_over_time) and [count_eq_over_time](#count_eq_over_time). #### sum_gt_over_time @@ -897,6 +971,8 @@ values bigger than `gt` on the given lookbehind window `d` per each time series Metric names are stripped from the resulting rollups. Add [keep_metric_names](#keep_metric_names) modifier in order to keep metric names. +This function is usually applied to [gauges](https://docs.victoriametrics.com/keyconcepts/#gauge). + See also [sum_over_time](#sum_over_time) and [count_gt_over_time](#count_gt_over_time). #### sum_le_over_time @@ -906,6 +982,8 @@ values smaller or equal to `le` on the given lookbehind window `d` per each time Metric names are stripped from the resulting rollups. Add [keep_metric_names](#keep_metric_names) modifier in order to keep metric names. +This function is usually applied to [gauges](https://docs.victoriametrics.com/keyconcepts/#gauge). + See also [sum_over_time](#sum_over_time) and [count_le_over_time](#count_le_over_time). #### sum_over_time @@ -915,6 +993,8 @@ on the given lookbehind window `d` per each time series returned from the given Metric names are stripped from the resulting rollups. Add [keep_metric_names](#keep_metric_names) modifier in order to keep metric names. +This function is usually applied to [gauges](https://docs.victoriametrics.com/keyconcepts/#gauge). + This function is supported by PromQL. #### sum2_over_time @@ -924,6 +1004,8 @@ values on the given lookbehind window `d` per each time series returned from the Metric names are stripped from the resulting rollups. Add [keep_metric_names](#keep_metric_names) modifier in order to keep metric names. +This function is usually applied to [gauges](https://docs.victoriametrics.com/keyconcepts/#gauge). + #### timestamp `timestamp(series_selector[d])` is a [rollup function](#rollup-functions), which returns the timestamp in seconds with millisecond precision @@ -1001,6 +1083,8 @@ from the given [series_selector](https://docs.victoriametrics.com/keyconcepts/#f Metric names are stripped from the resulting rollups. Add [keep_metric_names](#keep_metric_names) modifier in order to keep metric names. +This function is usually applied to [gauges](https://docs.victoriametrics.com/keyconcepts/#gauge). + See also [zscore](#zscore), [range_trim_zscore](#range_trim_zscore) and [outlier_iqr_over_time](#outlier_iqr_over_time). @@ -2230,7 +2314,8 @@ Any [rollup function](#rollup-functions) for something other than [series select Nested rollup functions can be implicit thanks to the [implicit query conversions](#implicit-query-conversions). For example, `delta(sum(m))` is implicitly converted to `delta(sum(default_rollup(m))[1i:1i])`, so it becomes a subquery, since it contains [default_rollup](#default_rollup) nested into [delta](#delta). -This behavior can be disabled or logged via cmd-line flags `-search.disableImplicitConversion` and `-search.logImplicitConversion` since v1.101.0. +This behavior can be disabled or logged via `-search.disableImplicitConversion` and `-search.logImplicitConversion` command-line flags +starting from [`v1.101.0` release](https://docs.victoriametrics.com/changelog/). VictoriaMetrics performs subqueries in the following way: @@ -2263,5 +2348,6 @@ VictoriaMetrics performs the following implicit conversions for incoming queries For example, `avg_over_time(rate(http_requests_total[5m])[1h])` is automatically converted to `avg_over_time(rate(http_requests_total[5m])[1h:1i])`. * If something other than [series selector](https://docs.victoriametrics.com/keyconcepts/#filtering) is passed to [rollup function](#rollup-functions), then a [subquery](#subqueries) with `1i` lookbehind window and `1i` step is automatically formed. - For example, `rate(sum(up))` is automatically converted to `rate((sum(default_rollup(up)))[1i:1i])`. - This behavior can be disabled or logged via cmd-line flags `-search.disableImplicitConversion` and `-search.logImplicitConversion` since v1.101.0. + For example, `rate(sum(up))` is automatically converted to `rate((sum(default_rollup(up)))[1i:1i])`. + This behavior can be disabled or logged via `-search.disableImplicitConversion` and `-search.logImplicitConversion` command-line flags + starting from [`v1.101.0` release](https://docs.victoriametrics.com/changelog/). diff --git a/app/vmui/packages/vmui/src/assets/MetricsQL.md b/app/vmui/packages/vmui/src/assets/MetricsQL.md index ba795cefe..64b343f53 100644 --- a/app/vmui/packages/vmui/src/assets/MetricsQL.md +++ b/app/vmui/packages/vmui/src/assets/MetricsQL.md @@ -10,9 +10,6 @@ aliases: - /ExtendedPromQL.html - /MetricsQL.html --- - -# MetricsQL - [VictoriaMetrics](https://github.com/VictoriaMetrics/VictoriaMetrics) implements MetricsQL - query language inspired by [PromQL](https://prometheus.io/docs/prometheus/latest/querying/basics/). MetricsQL is backwards-compatible with PromQL, so Grafana dashboards backed by Prometheus datasource should work