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SERVER-98749 Basic jstest for histogramCE estimation accuracy (#31955)
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‎jstests/noPassthroughWithMongod/query/cbr/histogramCE_accuracy.js

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/* Check the cardinality estimation of very simple predicates using histograms by running
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the predicate itself and comparing the number of documents matched to the estimate.
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In this test, we use distributions that allow for "perfect" histograms, that is,
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histograms where, even with the information loss, perfect estimates can be made.
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Simularily, the predicates used are those that can be estimated perfectly
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(except for the occasional off-by-one errors)
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*/
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import {
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getAllPlans,
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} from "jstests/libs/query/analyze_plan.js";
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import {checkSbeFullyEnabled} from "jstests/libs/query/sbe_util.js";
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import {ArrayDataset} from 'jstests/noPassthroughWithMongod/query/cbr/lib/datasets/array.js';
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import {BooleanDataset} from 'jstests/noPassthroughWithMongod/query/cbr/lib/datasets/boolean.js';
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import {
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DateDataset,
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TimestampDataset
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} from 'jstests/noPassthroughWithMongod/query/cbr/lib/datasets/date_time.js';
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import {
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OneHoleDataset,
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OnePeakDataset,
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SkewedDataset,
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ThreePeakDataset,
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UniformDataset
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} from "jstests/noPassthroughWithMongod/query/cbr/lib/datasets/distributions.js";
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import {
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MixedNumbersDataset,
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MixedTypesDataset
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} from "jstests/noPassthroughWithMongod/query/cbr/lib/datasets/mixed_types.js";
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import {
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TwoFieldDataset
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} from "jstests/noPassthroughWithMongod/query/cbr/lib/datasets/multifield.js";
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import {NumberDataset} from "jstests/noPassthroughWithMongod/query/cbr/lib/datasets/number.js";
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import {StringDataset} from "jstests/noPassthroughWithMongod/query/cbr/lib/datasets/string.js";
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// TODO SERVER-92589: Remove this exemption
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if (checkSbeFullyEnabled(db)) {
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jsTestLog(`Skipping ${jsTestName()} as SBE executor is not supported yet`);
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quit();
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}
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const collName = jsTestName();
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const coll = db[collName];
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function runOneTest({dataset, indexes, analyze, numberBuckets = 1000}) {
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try {
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assert.commandWorked(db.adminCommand({setParameter: 1, planRankerMode: "histogramCE"}));
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coll.drop();
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assert.commandWorked(coll.insertMany(dataset.docs()));
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for (const index of indexes ? indexes : []) {
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assert.commandWorked(coll.createIndex(index));
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}
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for (const analyze_key of analyze ? analyze : ["a"]) {
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var analyze_cmd = {analyze: collName, key: analyze_key, numberBuckets: numberBuckets};
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assert.commandWorked(coll.runCommand(analyze_cmd));
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}
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for (const predicate of dataset.predicates()) {
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var cursor = coll.find(predicate);
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const actualDocuments = cursor.count();
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const explain = cursor.explain();
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const plans = getAllPlans(explain);
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for (const plan of plans) {
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assert(plan.hasOwnProperty("cardinalityEstimate"));
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const cardinalityEstimate = plan.cardinalityEstimate;
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// 'Histogram', 'Code' and 'Metadata' all imply a confident estimate,
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// so we accept all of them.
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assert(plan.estimatesMetadata.ceSource === "Histogram" ||
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plan.estimatesMetadata.ceSource === "Code" ||
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plan.estimatesMetadata.ceSource === "Metadata",
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predicate);
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printjsononeline(predicate);
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print(`actualDocuments: ${actualDocuments}; cardinalityEstimate: ${
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cardinalityEstimate}`);
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if (Math.abs(actualDocuments - cardinalityEstimate) > 1) {
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printjsononeline(plan);
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assert(
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false,
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`Got cardinalityEstimate = ${cardinalityEstimate} but actualDocuments = ${
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actualDocuments} for predicate: ${tojson(predicate)}; dataset: ${
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dataset.constructor.name}; indexes: ${indexes};`);
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}
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}
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}
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} finally {
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// Make sure that we restore the default no matter what
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assert.commandWorked(db.adminCommand({setParameter: 1, planRankerMode: "multiPlanning"}));
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}
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}
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for (const indexes of [[], [{a: 1}]]) {
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for (const dataset of [new ArrayDataset(),
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new BooleanDataset(),
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new DateDataset(),
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new TimestampDataset(),
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new SkewedDataset(),
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new MixedTypesDataset(),
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new MixedNumbersDataset(),
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new NumberDataset(),
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new StringDataset()]) {
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runOneTest({dataset: dataset, indexes: indexes});
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}
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/* Skewed datasets under a constrained number of buckets. We give each
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dataset just enough buckets for it can be estimated accurately.
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*/
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for (const test of [{dataset: new UniformDataset(), numberBuckets: 2},
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{dataset: new OnePeakDataset(), numberBuckets: 4},
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{dataset: new OneHoleDataset(), numberBuckets: 3},
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{dataset: new ThreePeakDataset(), numberBuckets: 8},
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{dataset: new SkewedDataset(), numberBuckets: 10}]) {
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test.indexes = indexes;
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runOneTest(test);
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}
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}
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// Multi-field predicates
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for (const indexes of [[{a: 1, b: 1}], [{a: 1}, {b: 1}]]) {
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runOneTest({dataset: new TwoFieldDataset(), indexes: indexes, analyze: ["a", "b"]});
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}

‎jstests/noPassthroughWithMongod/query/cbr/lib/datasets/array.js

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/*
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A dataset with an array column and a set of relevant predicates.
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*/
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export class ArrayDataset {
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docs() {
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let array_docs = [];
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let array = [];
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for (let i = 0; i < 100; i++) {
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array_docs.push({a: array.slice()});
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array.push(i);
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}
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return array_docs;
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}
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predicates() {
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return [
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// TODO(SERVER-99630): {a: null},
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{a: -1},
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{a: 50},
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// TODO(SERVER-99634): {a: {$all:[]}},
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{a: {$all: [-1]}},
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{a: {$all: [-1, 50]}},
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// TODO(SERVER-98085): {a: {$all:[50,75]}},
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// Not estimated via histograms: {a: {$size: 50}},
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// TODO(SERVER-99025): {a: {$gt: 900}},
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// TODO(SERVER-99025): {a: {$gt: 250, $lt: 750}},
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/* TODO(SERVER-100451): Not supported under histogramCE:
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{a: {$elemMatch: {$eq: 50}}},
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{a: {$elemMatch: {$gt: 50}}},
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{a: {$elemMatch: {$ne: 50}}}
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*/
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];
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}
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}

‎jstests/noPassthroughWithMongod/query/cbr/lib/datasets/boolean.js

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/*
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A dataset with a boolean column and relevant predicates for it
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*/
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export class BooleanDataset {
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docs() {
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let boolean_docs = [];
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for (let i = 0; i < 100; i++) {
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boolean_docs.push({a: 0});
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boolean_docs.push({a: 1});
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boolean_docs.push({a: 1.0});
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boolean_docs.push({a: ""});
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boolean_docs.push({a: true});
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boolean_docs.push({a: false});
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boolean_docs.push({a: null});
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boolean_docs.push({b: 123});
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}
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return boolean_docs;
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}
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predicates() {
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let boolean_predicates = [];
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for (let val of [true,
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false,
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// TODO(SERVER-98094): null
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]) {
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boolean_predicates.push({a: val});
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boolean_predicates.push({a: {$gt: val}});
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boolean_predicates.push({a: {$gte: val}});
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boolean_predicates.push({a: {$lt: val}});
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boolean_predicates.push({a: {$lte: val}});
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boolean_predicates.push({a: {$ne: val}});
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}
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return boolean_predicates;
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}
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}

‎jstests/noPassthroughWithMongod/query/cbr/lib/datasets/date_time.js

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/*
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Datasets and relevant predicates for Date and Timestamp
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*/
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export class DateDataset {
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docs() {
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const date_docs = [];
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// Vary the year component of the date
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for (let i = 0; i < 1000; i++) {
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i = String(i).padStart(3, '0');
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date_docs.push({a: ISODate(`2${i}-01-01T01:01:01.001`)});
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}
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// Vary the subsecond component of the date
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for (let i = 0; i < 1000; i++) {
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i = String(i).padStart(3, '0');
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date_docs.push({a: ISODate(`2050-05-05T05:05:05.${i}`)});
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}
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date_docs.push({a: ISODate(0)});
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return date_docs;
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}
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predicates() {
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// Each corresponds to one of the two batches of dates from docs()
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const date1 = ISODate("2010-01-01T01:01:01.001");
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const date2 = ISODate("2050-05-05T05:05:05.050");
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return [
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{a: date1},
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{a: {$gt: date1}},
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{a: {$gte: date1}},
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{a: {$lt: date1}},
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{a: {$lte: date1}},
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{a: {$ne: date1}},
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{a: date2},
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{a: {$lt: date2}},
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{a: {$gt: date2}},
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{a: ISODate(0)},
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{a: {$ne: ISODate(0)}},
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{a: {$gt: ISODate(0)}},
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];
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}
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}
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export class TimestampDataset {
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docs() {
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let timestamp_docs = [];
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for (let i = 0; i < 100; i++) {
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timestamp_docs.push({a: Timestamp(0, i)});
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timestamp_docs.push({a: Timestamp(i, 0)});
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}
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return timestamp_docs;
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}
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predicates() {
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return [
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{a: Timestamp(0, 50)},
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{a: {$gt: Timestamp(0, 50)}},
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{a: {$ne: Timestamp(0, 50)}},
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{a: Timestamp(50, 0)},
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{a: {$gt: Timestamp(50, 0)}},
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{a: {$ne: Timestamp(50, 0)}},
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];
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}
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}

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