2d:null 2e:["$","title",null,{"children":"Performance Analysis | Kasi Viswanath | Observable"}] 33:T652,c = { //charts division basically for quantitative // init + query // Details of the experiment in the paper. describe methods such a way any grad student can replicate // software for running the benchmarking // Research contributions // 1. experiment results // 2. conclusions // 3. benchmarking framework const line = vl.markLine({ tooltip: true, point: true }).encode( vl .y() .fieldQ("filterTime") .aggregate("median") .scale({ type: "pow", exponent: "0.2" }) .title("Filter time(ms)"), vl.color().fieldN("library"), vl .x() .fieldO("experimentName") .title("record Count") .axis({ labelExpr: "datum.label + 'k'", labelAngle: "0" }) // vl // .row() // .fieldN("attrComb") // .title("Avg Filter Time") // .sort(["Categorical + Quantitative", "Quantitative", "Categorical"]), // vl // .column() // .fieldN("valComb") // .title("values selected") // .sort([ // "No", // "Random", // "All", // "Random-No", // "All-No", // "All-Random", // "All-No-Random" // ]) ); const band = line.markErrorband({ extent: "iqr" }); return vl .layer(band, line) .height(100) .width(250) .facet({ column: vl.fieldN("Selected Values"), row: vl.fieldN("Attribute Type") }) .data( transformData({ data: selectedData }).filter( (d) => datasets.includes(d["dataset"]) && !["All", "Random", "No"].includes(d["Selected Values"]) ) ) .render(); }34:T7f7,table = { let filtersTime = [ "vanillaJS-filter_time", "arquero-filter_time", "duckDB-filter_time" ]; let combs = Array.from(d3.group(selectedData, (d) => d["comb"]).keys()); let avgTimeMap = new Map(); for (let filter of filtersTime) { let avg_time = d3.rollup( selectedData.filter((d) => datasets.includes(d.dataset)), (v) => Math.round(d3.sum(v, (d) => d[filter]) / v.length), (d) => d["comb"] ); for (let comb of combs) { let prevValue = avgTimeMap.get(comb) || {}; Object.assign(prevValue, { [`${filter.replace("-", "_")}_avg`]: avg_time.get(comb) }); avgTimeMap.set(comb, prevValue); } } let mapToTable = Array.from(avgTimeMap.entries()).map(([key, val]) => { val["attributeType"] = checkAttributes(key); val["valuesSelected"] = checkValues(key); return val; }); let updatedCols = filtersTime.map((d) => `${d.replace("-", "_")}_avg`); return htl.html`${formatTableRow(mapToTable, { columns: [...updatedCols, "attributeType", "valuesSelected"], cmpValueFn: (row) => { let median = d3.median([ row.vanillaJS_filter_time_avg, row.arquero_filter_time_avg, row.duckDB_filter_time_avg ]); return median; }, formatColumns: { vanillaJS_filter_time_avg: { condition: (cmpValue, d) => d["vanillaJS_filter_time_avg"] <= cmpValue, trueFormatting: { color: "green" }, falseFormatting: { color: "red" } }, arquero_filter_time_avg: { condition: (cmpValue, d) => d["arquero_filter_time_avg"] <= cmpValue, trueFormatting: { color: "green" }, falseFormatting: { color: "red" } }, duckDB_filter_time_avg: { condition: (cmpValue, d) => d["duckDB_filter_time_avg"] <= cmpValue, falseFormatting: { color: "red" }, trueFormatting: { color: "green" } } } })}`; // return Inputs.table(mapToTable, { // columns: ["comb", updatedCols], // maxHeight: 800 // }); }35:T440,function transformData({ data }) { if (!data || data.length === 0) { return []; } let removeColumns = Object.keys(data[0]).reduce((prev, d) => { d.includes("-") && prev.push(d); return prev; }, []); let tempData = aq .from(data) .fold( ["vanillaJS-filter_time", "arquero-filter_time", "duckDB-filter_time"], { as: ["library", "filterTime"] } ) .derive({ filterTime: (d) => +d.filterTime, library: (d) => op.split(d["library"], "-")[0], experimentName: (d) => +op.split(d["experimentName"], "_")[2] / 1000, initTime: aq.escape((d) => d[`${d["library"]}-init_time`]), "Attribute Type": aq.escape((d) => checkAttributes(d["comb"])), "Selected Values": aq.escape((d) => checkValues(d["comb"])) }) .select(aq.not(removeColumns)); return tempData.objects().map((d) => { d["library"] === "duckDB" ? (d["library"] = "DuckDB-WASM") : ""; d["library"] === "arquero" ? (d["library"] = "Arquero") : "", d["library"] === "vanillaJS" ? (d["library"] = "VanillaJS") : ""; return d; }); 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31:{"id":"b7bf851309e08dbc","slug":"performance-analysis","title":"Performance Analysis","thumbnail":"5fbf381948c4e99a0183ee1732792a4bf20fad5909bad84127f7c58ef6321a02","access_level":"public","create_time":"$D2023-04-09T23:56:11.499Z","update_time":"$D2024-04-29T16:43:56.574Z","view_time":null,"first_public_time":"$D2023-04-09T23:56:11.499Z","has_unpublished_edits":null,"fork":false,"stars":2,"starred":false,"owner":{"type":"team","id":"b760ea566a896d7d","login":"kasivisu4","name":"Kasi Viswanath","avatar_url":"https://avatars-next.observableusercontent.com/48e36723913e7e5b18b906e1d7cde12fad4146715e78bbf74d3db7969a248b56","tier":"starter_2024"},"version":2024,"publish_version":null,"role":null,"authored":false,"authors":[{"type":"individual","id":"fcd94e1c1ca12ae9","login":"kasivisu4","name":"Kasi Viswanath","avatar_url":"https://avatars-next.observableusercontent.com/48e36723913e7e5b18b906e1d7cde12fad4146715e78bbf74d3db7969a248b56","tier":null}],"members":[{"type":"individual","id":"be0076fec73aa550","login":"john-guerra","name":"John Alexis Guerra Gómez","avatar_url":"https://avatars-next.observableusercontent.com/21638cee44bb0ea70798da725b57111a5d3e2acebcd14524565992f7d1594aed","tier":null,"role":"editor"},{"type":"individual","id":"fcd94e1c1ca12ae9","login":"kasivisu4","name":"Kasi Viswanath","avatar_url":"https://avatars-next.observableusercontent.com/48e36723913e7e5b18b906e1d7cde12fad4146715e78bbf74d3db7969a248b56","tier":null,"role":"editor"}],"comments":[],"custom_thumbnail":false,"trashed":false,"lapsed":null,"subscribed":null,"forks":5,"collection":{"id":"fb28ec1f4856bd04","slug":"benchmark-tools","title":"Benchmark tools","owner":{"id":"b760ea566a896d7d","login":"kasivisu4","name":"Kasi Viswanath"}},"type":"notebook","body":{"title":"Performance Analysis","cells":[{"id":0,"value":"# Performance Analysis\n\nLet's analyze how much time each query had taken to execute on Arquero, DuckDB-WASM, and Vanilla Javascript","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":1037,"value":"### Select the experiments to Analyze.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":1551,"value":"viewof selectedExperiments = searchCheckbox(experimentNames, {\n value: experimentNames\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":290,"value":"### select the combinations","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":1565,"value":"viewof selectCombinations = scentedCheckbox(\n rawData.filter((d) => selectedExperiments.includes(d.experimentName)),\n (d) => d.comb\n)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1963,"value":"{\n //statistacially difference\n // order by blank charts.\n // categorical\n // Why do have u\n //Report -> worker ->\n // landing notebook -> results and then details of the experiment\n const line = vl\n .markLine({ tooltip: true, point: true })\n .encode(\n vl\n .y()\n .fieldQ(\"filterTime\")\n .aggregate(\"median\")\n .scale({ type: \"pow\", exponent: \"0.2\" })\n .title(\"Filter Time (ms)\"),\n vl.color().fieldN(\"library\"),\n vl\n .x()\n .fieldO(\"experimentName\")\n .title(\"record Count\")\n .axis({ labelExpr: \"datum.label + 'k'\", labelAngle: \"0\" })\n );\n\n const band = line.markErrorband({ extent: \"iqr\" });\n\n return vl\n .layer(band, line)\n .height(200)\n .width(800)\n .facet({\n column: vl.fieldN(\"dataset\")\n // row: vl.fieldN(\"Attribute Type\")\n })\n .data(\n transformData({ data: selectedData }).filter((d) =>\n datasets.includes(d[\"dataset\"])\n )\n )\n\n .render();\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1789,"value":"$33","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1971,"value":"transformData({ data: selectedData }).filter((d) => d[\"filterTime\"] > 50000)","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":293,"value":"vl\n .markBoxplot({ tooltip: true })\n .encode(\n vl\n .x()\n .fieldQ(\"filterTime\")\n .scale({ type: \"pow\", exponent: 0.2 })\n .title(\"Filter Time(in ms)\"),\n vl.y().fieldN(\"library\"),\n vl.color().fieldN(\"library\")\n )\n .data(transformData({ data: selectedData }))\n .width(800)\n .height(100)\n .render()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1427,"value":"vl\n .markBoxplot()\n .encode(\n vl.x().fieldQ(\"filterTime\").scale({ type: \"pow\", exponent: 0.2 }),\n vl.y().fieldN(\"library\"),\n vl.color().fieldN(\"library\"),\n vl.column().fieldN(\"attrComb\"),\n vl.row().fieldN(\"valComb\")\n )\n .data(transformData({ data: selectedData }))\n .width(300)\n .render()","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1999,"value":"transformData({ data: selectedData })","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1740,"value":"viewof datasets = searchCheckbox(\n new Set(transformData({ data: selectedData }).map((d) => d.dataset)),\n {\n label: \"Select Datasets\",\n value: new Set(transformData({ data: selectedData }).map((d) => d.dataset))\n }\n)","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1782,"value":"d3.median([36, 181, 181])","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1458,"value":"$34","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1514,"value":"import { formatTableRow } from \"@kasivisu4/format-table-row\";","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1874,"value":"{//statistacially difference \n // order by blank charts.\n // categorical \n // Why do have u\n //Report -> worker -> \n // landing notebook -> results and then details of the experiment\n const line = vl\n .markLine({ tooltip: true, point: true })\n .encode(\n vl\n .y()\n .fieldQ(\"filterTime\")\n .aggregate(\"median\")\n .scale({ type: \"pow\", exponent: \"0.2\" })\n .title(\"Filter Time (ms)\"),\n vl.color().fieldN(\"library\"),\n vl\n .x()\n .fieldO(\"experimentName\")\n .title(\"record Count\")\n .axis({ labelExpr: \"datum.label + 'k'\", labelAngle: \"0\" })\n );\n\n const band = line.markErrorband({ extent: \"iqr\" });\n\n return vl\n .layer(band, line)\n .height(100)\n .width(150)\n .facet({\n column: vl.fieldN(\"dataset\"),\n row: vl.fieldN(\"Attribute Type\")\n })\n .data(\n transformData({ data: selectedData }).filter((d) =>\n datasets.includes(d[\"dataset\"])\n )\n )\n\n .render();\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1679,"value":"d3.flatRollup(\n transformData({ data: selectedData }).filter(\n (d) =>\n d.attrComb === \"Quantitative\" &&\n d.valComb === \"All\" &&\n d.dataset === \"Weather\"\n ),\n (v) => d3.mean(v, (d) => d.filterTime),\n (d) => d.experimentName,\n (d) => d.library\n)","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1683,"value":"transformData({ data: selectedData })","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1605,"value":"vl\n .markBoxplot({ tooltip: true })\n .encode(\n vl.y().fieldQ(\"filterTime\").scale({ type: \"pow\", exponent: 0.2 }),\n vl.color().fieldN(\"library\"),\n vl.x().fieldN(\"experimentName\").title(\"record Count\"),\n vl.row().fieldN(\"attrComb\"),\n vl.column().fieldN(\"valComb\")\n )\n .data(transformData({ data: selectedData }))\n .height(100)\n .width(90)\n .render()","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1621,"value":"transformData({ data: selectedData })","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1949,"value":"d3.rollup(\n selectedData,\n (v) => v.length,\n (d) => d[\"experimentName\"],\n (d) => d.comb,\n (d) => d.dataset\n)","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1951,"value":"selectedData","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":885,"value":"## How many times the techniques is faster across queries?","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":1015,"value":"vl\n .markBar({ tooltip: true })\n .encode(\n vl.y().fieldQ(\"count\"),\n vl.x().fieldN(\"fastLibrary\"),\n vl.row().fieldN(\"attrComb\"),\n vl.column().fieldN(\"valComb\"),\n vl.color().fieldN(\"fastLibrary\")\n )\n .width(100)\n .height(100)\n .data(vegaData)\n .render()","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":890,"value":"chart1 = {\n async function markBar(data) {\n let plot = await vl\n .markBar()\n .encode(\n vl.y().fieldN(\"fastLibrary\"),\n vl.color().fieldN(\"fastLibrary\").legend(null),\n vl.x().fieldQ(\"count\")\n )\n .width(100)\n .height(80)\n .data(data)\n .render();\n console.log(data);\n return html`

${plot}

`;\n }\n\n const valCombinations = [\"@\", \"R\", \"N\", \"@-R\", \"@-N\", \"@-N-R\"];\n\n const table = await Promise.all(\n data.map(\n async (row) =>\n await Promise.all(\n valCombinations.map(async (c) => await markBar(row[c]))\n )\n )\n );\n\n return htl.html`\n ${table.map(\n (r) => htl.html`\n

\n ${r.map((c) => htl.html`

${c}

`)}\n

`\n )}\n`;\n}","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":721,"value":"{\n function markBar(data) {\n // let plot = await vl\n // .markBar()\n // .encode(vl.x().field(\"fastLibrary\"), vl.y().field(\"count\"))\n // .data(data)\n // .render();\n // console.log(plot);\n // return Promise.resolve(plot);\n data.sort((a, b) => b.count - a.count);\n return html`${data.map(\n (d) => d[\"fastLibrary\"] + \" : \" + d[\"count\"] + \"
\"\n )}`;\n }\n\n // let valCombinations = [\"@\", \"R\", \"N\", \"@-R\", \"@-N\", \"@-N-R\"];\n // let plots = data.map(async (d) => {\n // let plot = [(\"@\", \"R\", \"N\", \"@-R\", \"@-N\", \"@-N-R\")].map(async (e) => {\n // let res = await markBar(d[e]);\n // return { [e]: Promise.all(res) };\n // });\n // await Promise.all(plot);\n // return plot;\n // });\n // return Promise.all(plots);\n\n return Inputs.table(data, {\n format: {\n \"@\": (d) => markBar(d),\n R: (d) => markBar(d),\n N: (d) => markBar(d),\n \"@-R\": (d) => markBar(d),\n \"@-N\": (d) => markBar(d),\n \"@-N-R\": (d) => markBar(d)\n }\n });\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":334,"value":"## By dataset","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":336,"value":"vl\n .markBoxplot({ tooltip: true })\n .encode(\n vl.x().fieldQ(\"filterTime\").scale({ type: \"pow\", exponent: 0.2 }),\n vl.y().fieldN(\"library\"),\n vl.color().fieldN(\"library\"),\n vl.row().fieldN(\"comb\"),\n vl.column().fieldN(\"dataset\")\n )\n .data(transformData({ data: selectedData }))\n .width(200)\n .render()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":900,"value":"// tidyResultsData = formatData({ data: findFastLibrary({ data: selectedData }) });\ntidyResultsData = transformData({ data: selectedData });","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":976,"value":"Plot.plot({\n marks: [\n Plot.auto(null, {})\n ]\n})","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":903,"value":"vl\n .markBoxplot()\n .encode(\n vl.x().fieldQ(\"filterTime\"),\n vl.y().fieldN(\"library\"),\n vl.color().fieldN(\"library\"),\n vl.column().fieldN(\"attrComb\"),\n vl.row().fieldN(\"valComb\"),\n )\n .width(200)\n .data(tidyResultsData)\n .render()","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":673,"value":"data = {\n let t = formatData({ data: findFastLibrary({ data: selectedData }) });\n t = aq.from(t).groupby(\"attrComb\", \"valComb\", \"fastLibrary\").count();\n t = t.objects();\n let res = [];\n let valCombinations = [\"@\", \"R\", \"N\", \"@-R\", \"@-N\", \"@-N-R\"];\n let attrCombinations = [\"C\", \"Q\", \"C+Q\"];\n\n for (let attrComb of attrCombinations) {\n let comb = {};\n for (let valComb of valCombinations) {\n let filterdData = t.filter(\n (d) => d[\"attrComb\"] === attrComb && d[\"valComb\"] == valComb\n );\n comb[valComb] = filterdData;\n }\n res.push({ attrComb, ...comb });\n }\n\n return res;\n}","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":754,"value":"vegaData = {\n let t = formatData({ data: findFastLibrary({ data: selectedData }) });\n t = aq.from(t).groupby(\"attrComb\", \"valComb\", \"fastLibrary\").count();\n // .groupby(\"attrComb\")\n // .pivot(\"valComb\", [\"count\", \"fastLibrary\"]);\n return t.objects();\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":309,"value":"chart = ViolinPlot(transformData({ data: selectedData }), {\n groupBy: (d) => d.library,\n value: (d) => +d.filterTime,\n yLabel: \"↑ Filter Time\",\n width,\n height: 600,\n drawBars: false,\n drawPoints: true,\n drawHistogram: false\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":196,"value":"$35","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":2001,"value":"transformData({ data: selectedData })","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":435,"value":"function findFastLibrary({ data }) {\n function checkFastLibrary(query) {\n if (\n query[\"vanillaJS-filter_time\"] <= query[\"arquero-filter_time\"] &&\n query[\"vanillaJS-filter_time\"] <= query[\"duckDB-filter_time\"]\n ) {\n return \"vanillaJS\";\n }\n\n if (\n query[\"arquero-filter_time\"] <= query[\"vanillaJS-filter_time\"] &&\n query[\"arquero-filter_time\"] <= query[\"duckDB-filter_time\"]\n ) {\n return \"arquero\";\n }\n\n if (\n query[\"duckDB-filter_time\"] <= query[\"arquero-filter_time\"] &&\n query[\"duckDB-filter_time\"] <= query[\"vanillaJS-filter_time\"]\n ) {\n return \"duckDB\";\n }\n }\n\n data = data.map((d) => ({ ...d }));\n\n for (let query of data) {\n query[\"fastLibrary\"] = checkFastLibrary(query);\n }\n\n return data;\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":912,"value":"function checkAttributes(comb) {\n const categoricalRegex = /C+/gi,\n quantitativeRegex = /Q+/gi;\n if (comb.match(categoricalRegex) && comb.match(quantitativeRegex)) {\n return \"Categorical + Quantitative\";\n }\n if (comb.match(categoricalRegex)) {\n return \"Categorical\";\n }\n if (comb.match(quantitativeRegex)) {\n return \"Quantitative\";\n }\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":911,"value":"function checkValues(comb) {\n const allValuesRegex = /@+/g,\n noValuesRegex = /N+/gi,\n randomValuesRegex = /R+/gi;\n\n if (\n comb.match(allValuesRegex) &&\n comb.match(noValuesRegex) &&\n comb.match(randomValuesRegex)\n ) {\n return \"All-No-Random\";\n }\n if (comb.match(allValuesRegex) && comb.match(randomValuesRegex)) {\n return \"All-Random\";\n }\n if (comb.match(allValuesRegex) && comb.match(noValuesRegex)) {\n return \"All-No\";\n }\n if (comb.match(noValuesRegex) && comb.match(randomValuesRegex)) {\n return \"Random-No\";\n }\n\n if (comb.match(allValuesRegex)) {\n return \"All\";\n }\n\n if (comb.match(randomValuesRegex)) {\n return \"Random\";\n }\n\n if (comb.match(noValuesRegex)) {\n return \"No\";\n }\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":373,"value":"function formatData({ data }) {\n data = data.map((d) => ({ ...d }));\n\n for (let query of data) {\n query[\"attrComb\"] = checkAttributes(query[\"comb\"]);\n query[\"valComb\"] = checkValues(query[\"comb\"]);\n // query[\"valComb\"] =\n // checkValues(query[\"comb\"]) + checkAttributes(query[\"comb\"]);\n }\n\n return data;\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":214,"value":"import { aq, op } from \"@uwdata/arquero\"","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":260,"value":"import { mongodbClient } from \"@kasivisu4/mongodb-local\"\n","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":342,"value":"import { vl } from \"@vega/vega-lite-api-v5\";\n","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1539,"value":"experimentNames = {\n let experiments;\n try {\n experiments = (\n await mongodbClient({\n db: \"benchmark-all\",\n collection: \"queryExecutor\",\n operation: \"aggregate\",\n query: [\n {\n $group: {\n _id: \"$experimentName\"\n }\n }\n ]\n })\n ).map((d) => d._id);\n } catch {\n experiments = Array.from(d3.group(file, (d) => d[\"experimentName\"]).keys());\n } finally {\n return experiments;\n }\n}","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":2010,"value":"rawData.filter((d) => d[\"experimentName\"].indexOf(\"All\") != -1)","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":366,"value":"// // To fetch Queries results from Mongodb\nrawData = {\n let data;\n try {\n data = await mongodbClient({\n db: \"benchmark-all\",\n collection: \"queryExecutor\",\n operation: \"find\",\n query: {\n // experimentName: {\n // $in: selectedExperiments.length > 0 ? selectedExperiments : [-1]\n // }\n }\n });\n\n data = aq\n .from(data)\n .groupby(\"experimentName\", \"comb\", \"dataset\")\n .pivot(\"filter\", \"filter_time\")\n .objects();\n } catch (err) {\n data = file;\n } finally {\n return data;\n }\n}","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1544,"value":"file = (\n await FileAttachment(\"experiment_results_replica_1_replica_10.json\").json()\n).filter(\n (d) =>\n d[\"experimentName\"].indexOf(\"replica_10\") != -1 &&\n d[\"experimentName\"] != \"Exp_datasize_30000_tmstp_1700088500435_replica_10\"\n)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":2016,"value":"experiment_results_replica_1_replica_10 = FileAttachment(\"experiment_results_replica_1_replica_10.json\").json()","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":310,"value":"import {ViolinPlot} from \"@john-guerra/violin-plot\"","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":298,"value":"import {navio} from \"@john-guerra/navio\"","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1047,"value":"import { scentedCheckbox } from \"@john-guerra/scented-checkbox\"","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1059,"value":"selectedData = {\n let selectColumns = [\n \"comb\",\n \"dataset\",\n \"experimentName\",\n \"arquero-init_time\",\n \"duckDB-init_time\",\n \"vanillaJS-init_time\",\n \"arquero-filter_time\",\n \"duckDB-filter_time\",\n \"vanillaJS-filter_time\"\n ];\n return rawData\n .filter(\n (d) =>\n selectedExperiments.includes(d[\"experimentName\"]) &&\n selectCombinations.includes(d[\"comb\"])\n )\n .map((d) => {\n return selectColumns.reduce((prev, attr) => {\n prev[attr] = d[attr];\n return prev;\n }, {});\n });\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1223,"value":"import { searchCheckbox } from \"@john-guerra/search-checkbox\"","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"}],"files":"$W36","stdlib":"1","titling":"auto"}} 32:"$31:body"