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SERVER-100611: Estimate seeks required for IXSCAN [reapply] (#53934)

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GitOrigin-RevId: 01087ce4ddfeee920e1ba99d04ca9f499a580821
This commit is contained in:
James H 2026-05-21 14:50:26 +01:00 committed by MongoDB Bot
parent 75388dc3c6
commit 2b1df07a97
28 changed files with 1790 additions and 1237 deletions
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35
jstests/noPassthrough/query/multiplanner/tie_breaking_index_prefix.js
View File

@ -4,7 +4,7 @@
"use strict";
import {getPlanStages, getWinningPlanFromExplain} from "jstests/libs/query/analyze_plan.js";
import {getEngine, getPlanStages, getWinningPlanFromExplain} from "jstests/libs/query/analyze_plan.js";
import {getPlanRankerMode, isPlanCosted} from "jstests/libs/query/cbr_utils.js";
import {checkSbeFullyEnabled} from "jstests/libs/query/sbe_util.js";
@ -96,7 +96,7 @@ function assertIndexScan(isTieBreakingHeuristicEnabled, filter, expectedIndexKey
assert.eq(expectedIndexKeyPatterns.length, indexScans.length);
for (let i = 0; i < expectedIndexKeyPatterns.length; ++i) {
assert.eq(indexScans[i]["keyPattern"], expectedIndexKeyPatterns[i], tojson(explain));
assert.eq(expectedIndexKeyPatterns[i], indexScans[i]["keyPattern"], tojson(explain));
}
}
@ -194,23 +194,34 @@ function preferShortestIndexWithComparisonsInFilter(indexPruningActive) {
const filter = {a: {$gt: 1}, b: "hello"};
assert.commandWorked(coll.createIndexes(indexes));
// Index pruning would have removed the a/b/c index for us already, so a/b would win.
if (indexPruningActive) {
// Index pruning removes the a/b/c index, so a/b wins regardless of heuristic.
assertIndexScan(false, filter, [{a: 1, b: 1}]);
assertIndexScan(true, filter, [{a: 1, b: 1}]);
} else {
const explain = setParamsAndRunCommand(false, filter);
// If we fall back to CBR, we will choose the smaller index regardless of whether index pruning is used or not.
const abIndex = [{a: 1, b: 1}];
const abcIndex = [{a: 1, b: 1, c: 1}];
// TODO SERVER-100611: re-enable these tests.
// const winningPlan = getWinningPlanFromExplain(explain);
// if (isPlanCosted(winningPlan) && !checkSbeFullyEnabled(db)) {
// assertIndexScan(false, filter, [{a: 1, b: 1}], explain);
// } else {
assertIndexScan(false, filter, [{a: 1, b: 1, c: 1}], explain);
// }
const isSBE = getEngine(explain) == "sbe";
const isCBR = getPlanRankerMode(db) !== "multiPlanning";
const [expectedWithoutTieBreaking, expectedWithTieBreaking] = (() => {
if (isSBE) {
// Planning for SBE selects longer index.
return [abcIndex, abcIndex];
}
if (isCBR) {
// CBR costs the plan using the shorter index lower, as fewer seeks will be required.
return [abIndex, abIndex];
}
return [abcIndex, abIndex];
})();
assertIndexScan(false, filter, expectedWithoutTieBreaking, explain);
assertIndexScan(true, filter, expectedWithTieBreaking, explain);
}
assertIndexScan(true, filter, [{a: 1, b: 1}]);
for (const index of indexes) {
assert.commandWorked(coll.dropIndex(index));

109
jstests/noPassthrough/query/plan_evaluation_coll_fraction.js
View File

@ -119,61 +119,60 @@ describe("MultiPlanner exit condition metrics get updated correctly", function (
if (planRankerMode === "automaticCE" && !isSBEEnabled) {
describe("automaticCE (CBR) mode", function () {
// TODO SERVER-100611: re-enable these tests.
// describe("fallback to CBR", function () {
// it("does not update multi-planner metrics when plan cache is disabled", function () {
// // We do not measure works for the chosen CBR plan, so MP metrics must not change.
// assert.commandWorked(db.adminCommand({setParameter: 1, internalQueryDisablePlanCache: true}));
//
// assert.docEq(
// {
// hitEof: 0,
// hitResultsLimit: 0,
// hitWorksLimit: 0,
// },
// getStoppingCondition(0.1),
// );
//
// assert.docEq(
// {
// hitEof: 0,
// hitResultsLimit: 0,
// hitWorksLimit: 0,
// },
// getStoppingCondition(20.0),
// );
// });
//
// it("records hitWorksLimit when CBR fallback is measured with low collFraction", function () {
// // With plan cache enabled, we measure works for the CBR-chosen plan.
// // At low collFraction, CBR evaluation stops due to works budget, so hitWorksLimit should increment.
// assert.commandWorked(db.adminCommand({setParameter: 1, internalQueryDisablePlanCache: false}));
//
// assert.docEq(
// {
// hitEof: 0,
// hitResultsLimit: 0,
// hitWorksLimit: 0,
// },
// getStoppingCondition(0.1),
// );
// });
//
// it("records hitEof when CBR fallback is measured with high collFraction", function () {
// // With a higher collFraction, CBR can run until EOF instead of hitting works limit.
// // With plan cache enabled, this should be reflected as hitEof.
// assert.commandWorked(db.adminCommand({setParameter: 1, internalQueryDisablePlanCache: false}));
//
// assert.docEq(
// {
// hitEof: 0,
// hitResultsLimit: 0,
// hitWorksLimit: 0,
// },
// getStoppingCondition(20.0),
// );
// });
// });
describe("fallback to CBR", function () {
it("does not update multi-planner metrics when plan cache is disabled", function () {
// We do not measure works for the chosen CBR plan, so MP metrics must not change.
assert.commandWorked(db.adminCommand({setParameter: 1, internalQueryDisablePlanCache: true}));
assert.docEq(
{
hitEof: 0,
hitResultsLimit: 0,
hitWorksLimit: 0,
},
getStoppingCondition(0.1),
);
assert.docEq(
{
hitEof: 0,
hitResultsLimit: 0,
hitWorksLimit: 0,
},
getStoppingCondition(20.0),
);
});
it("records hitWorksLimit when CBR fallback is measured with low collFraction", function () {
// With plan cache enabled, we measure works for the CBR-chosen plan.
// At low collFraction, CBR evaluation stops due to works budget, so hitWorksLimit should increment.
assert.commandWorked(db.adminCommand({setParameter: 1, internalQueryDisablePlanCache: false}));
assert.docEq(
{
hitEof: 0,
hitResultsLimit: 0,
hitWorksLimit: 0,
},
getStoppingCondition(0.1),
);
});
it("records hitEof when CBR fallback is measured with high collFraction", function () {
// With a higher collFraction, CBR can run until EOF instead of hitting works limit.
// With plan cache enabled, this should be reflected as hitEof.
assert.commandWorked(db.adminCommand({setParameter: 1, internalQueryDisablePlanCache: false}));
assert.docEq(
{
hitEof: 0,
hitResultsLimit: 0,
hitWorksLimit: 0,
},
getStoppingCondition(20.0),
);
});
});
describe("no fallback to CBR", function () {
it("records EOF when multiplanning produces a result before exhausting works", function () {

5
jstests/noPassthroughWithMongod/query/cbr/cbr_histograms.js
View File

@ -198,9 +198,8 @@ try {
{query: {a: {$gt: 20, $lt: 40}}, expectedCE: 18.8},
{query: {a: 20, b: 20}, expectedCE: 0.1},
{query: {a: 20, b: {$gt: 20}}, expectedCE: 0.9},
// TODO SERVER-100611: re-enable these tests.
// {query: {a: {$gt: 20}, b: 20}, expectedCE: 0.9},
// {query: {a: {$gt: 20}, b: {$gt: 20}}, expectedCE: 70.8},
{query: {a: {$gt: 20}, b: 20}, expectedCE: 0.9},
{query: {a: {$gt: 20}, b: {$gt: 20}}, expectedCE: 70.8},
];
testCases.forEach((tc) => assertQueryUsesHistograms(tc));
} finally {

70
jstests/noPassthroughWithMongod/query/cbr/cbr_infrastructure.js
View File

@ -86,10 +86,9 @@ assert.commandWorked(coll.runCommand({analyze: collName, key: "bool_field", numb
// Test queries
const queries = [
// TODO SERVER-100611: re-enable these tests.
// {a: {$gt: 10}, b: {$eq: 99}},
// {a: {$in: [5, 1]}, b: {$in: [7, 99]}},
// {a: {$gt: 90}, b: {$eq: 99}, c: {$lt: 5}},
{a: {$gt: 10}, b: {$eq: 99}},
{a: {$in: [5, 1]}, b: {$in: [7, 99]}},
{a: {$gt: 90}, b: {$eq: 99}, c: {$lt: 5}},
/*
The following query has 4 plans:
1. Filter: a in (1,5) AND b in (7, 99)
@ -127,8 +126,7 @@ const queries = [
{missing_10_percent: {$not: {$exists: false}}},
{missing_90_percent: {$exists: false}},
{missing_90_percent: {$not: {$exists: false}}},
// TODO SERVER-100611: re-enable this test.
// {a: {$not: {$lt: 130}}, b: 12, c: {$not: {$gt: 1200}}},
{a: {$not: {$lt: 130}}, b: 12, c: {$not: {$gt: 1200}}},
{a: {$not: {$in: [[100, 101, 102]]}}},
{$nor: [{$and: [{a: {$lt: 10}}, {b: {$gt: 19}}]}]},
{$nor: [{$or: [{a: {$lt: 10}}, {b: {$gt: 19}}]}]},
@ -141,8 +139,7 @@ const queries = [
{a: {$gt: 10}, b: {$in: []}},
{$nor: [{a: 1}]},
{$nor: [{a: 1}, {b: {$gt: 1000}}]},
// TODO SERVER-100611: re-enable these tests.
// {$and: [{$nor: [{a: 1}, {a: {$gt: 1000}}]}, {b: {$lt: 100}}]},
{$and: [{$nor: [{a: 1}, {a: {$gt: 1000}}]}, {b: {$lt: 100}}]},
{$and: [{$or: [{$nor: [{a: {$gt: 100}}, {b: {$gt: 50}}]}, {a: 1}]}, {b: {$lt: 100}}]},
// This query has an empty result, thus should estimate as 0
{$and: [{$or: [{a: 0}, {a: 1}]}, {$or: [{a: {$gt: 3}}, {a: {$lt: 0}}]}]},
@ -183,10 +180,9 @@ const queries = [
// {$or: [{a: 3}, {b: {$size: 9}}]},
];
// TODO SERVER-100611: re-enable these tests.
// queries.push({$or: [queries[0], queries[1]]});
queries.push({$or: [queries[0], queries[1]]});
function assertCbrExplain(plan) {
function assertCbrExplain(plan, isSamplingCE = false) {
assert(plan.hasOwnProperty("cardinalityEstimate"), plan);
if (plan.stage === "EOF") {
assert.eq(plan.cardinalityEstimate, 0, plan);
@ -197,10 +193,16 @@ function assertCbrExplain(plan) {
}
assert(plan.hasOwnProperty("costEstimate"), plan);
assert.gt(plan.costEstimate, 0, plan);
if (isSamplingCE && plan.stage === "IXSCAN") {
assert(
plan.hasOwnProperty("indexSeekEstimate"),
"IXSCAN stage must have indexSeekEstimate in sampling CE mode: " + tojson(plan),
);
}
if (plan.hasOwnProperty("inputStage")) {
assertCbrExplain(plan.inputStage);
assertCbrExplain(plan.inputStage, isSamplingCE);
} else if (plan.hasOwnProperty("inputStages")) {
plan.inputStages.forEach((p) => assertCbrExplain(p));
plan.inputStages.forEach((p) => assertCbrExplain(p, isSamplingCE));
} else {
assert(plan.hasOwnProperty("numKeysEstimate") || plan.hasOwnProperty("numDocsEstimate"), plan);
}
@ -236,24 +238,24 @@ function checkWinningPlan({query = {}, project = {}, order = {}}) {
r1.map((e) => assertCbrExplain(e));
// The CBR-chosen winning plan is wrapped in a MultiPlanStage (with a single candidate,
// namely the CBR winner) so we can reuse the classic multiplanner’s code to:
// namely the CBR winner) so we can reuse the classic multiplanner's code to:
//
// 1. Measure how many works() the CBR plan needs to reach a multiplanner exit
// condition (results limit / EOF / works limit), and
// 2. Cache that plan together with the measured works value.
//
// This wrapping can introduce a small off‑by‑one difference in the "works" metric for
// This wrapping can introduce a small off-by-one difference in the "works" metric for
// the winning plan in the specific case where the MultiPlan trial hits EOF:
//
// * During the trial, the child plan reaches EOF once.
// * Later, when we call doWork() on the MultiPlanStage in normal execution, it first
// drains any buffered results and then calls work() on the child again, observing
// EOF a second time.
// * Both EOF probes increment the child plan’s works counter, even though no extra
// * Both EOF probes increment the child plan's works counter, even though no extra
// keys or documents are examined.
//
// As a result, the CBR path can have exactly one more work() call than the pure plan without the
// MultiPlan stage at its root. 'cbrWorksEpsilon' captures this off‑by‑one.
// MultiPlan stage at its root. 'cbrWorksEpsilon' captures this off-by-one.
// TODO SERVER-117425: Remove 'cbrWorksEpsilon' once we no longer do a second EOF probe.
const cbrWorksEpsilon = 1;
@ -316,6 +318,39 @@ function verifyHeuristicEstimateSource() {
assert.eq(w1.estimatesMetadata.ceSource, "Heuristics", w1);
}
function verifySamplingCEIndexSeekEstimate() {
coll.drop();
const docs = [];
for (let i = 0; i < 1000; i++) {
docs.push({a: i, b: i % 10});
}
assert.commandWorked(coll.insertMany(docs));
assert.commandWorked(coll.createIndex({a: 1}));
assert.commandWorked(coll.createIndex({b: 1}));
assert.commandWorked(
db.adminCommand({
setParameter: 1,
featureFlagCostBasedRanker: true,
internalQueryCBRCEMode: "samplingCE",
}),
);
// A query with a range predicate produces a FETCH + IXSCAN plan. Under sampling CE every
// IXSCAN stage must expose indexSeekEstimate in explain output.
const explain = coll.find({a: {$gt: 100}}).explain();
const winningPlan = getWinningPlanFromExplain(explain);
assertCbrExplain(winningPlan, true /* isSamplingCE */);
getRejectedPlans(explain).forEach((p) => assertCbrExplain(p, true /* isSamplingCE */));
// A point query also produces a single-seek IXSCAN; indexSeekEstimate should equal 1.
const explainPoint = coll.find({a: 42}).hint({a: 1}).explain();
const winningPoint = getWinningPlanFromExplain(explainPoint);
assertCbrExplain(winningPoint, true /* isSamplingCE */);
const ixscanPoint = getPlanStage(winningPoint, "IXSCAN");
assert.eq(ixscanPoint.indexSeekEstimate, 1, "Point query IXSCAN should have indexSeekEstimate = 1");
}
function verifyFetchOverFetchDoesNotAssert() {
coll.drop();
assert.commandWorked(coll.insert({_id: 1}));
@ -363,6 +398,7 @@ try {
verifyCollectionCardinalityEstimate();
verifyHeuristicEstimateSource();
verifySamplingCEIndexSeekEstimate();
verifyFetchOverFetchDoesNotAssert();
/**

3
jstests/noPassthroughWithMongod/query/cbr/cbr_plan_selection.js
View File

@ -115,8 +115,7 @@ try {
// In the presence of a compound index, it is chosen regardless of the selectivity of the
// predicates.
assert.contains(winningIndex({unique1: 1, unique2: 1}), ["unique1_1_unique2_1", "unique2_1_unique1_1"]);
// TODO SERVER-100611: re-enable these tests.
// assert.eq(winningIndex({unique1: {$gte: 0}, unique2: {$gte: 0}}), "unique1_1_unique2_1");
assert.eq(winningIndex({unique1: {$gte: 0}, unique2: {$gte: 0}}), "unique1_1_unique2_1");
assert.eq(winningIndex({unique1: 1, unique2: {$gte: 0}}), "unique1_1_unique2_1");
// TODO(SERVER-97933): assert.eq(winningIndex({unique1: {$lt: 0}, unique2: {$lt: 0}}),

5
jstests/noPassthroughWithMongod/query/cbr/cbr_sampling.js
View File

@ -103,9 +103,8 @@ try {
assert.commandWorked(coll.createIndex({c: 1}));
assertAllPlansUseSampling({a: {$lt: 100}});
assertAllPlansUseSampling({b: {$lt: 100}});
// TODO SERVER-100611: re-enable these tests.
// assertAllPlansUseSampling({a: {$lt: 100}, b: {$lt: 500}});
// assertAllPlansUseSampling({a: {$lt: 100}, b: {$lt: 500}, c: {$exists: true}});
assertAllPlansUseSampling({a: {$lt: 100}, b: {$lt: 500}});
assertAllPlansUseSampling({a: {$lt: 100}, b: {$lt: 500}, c: {$exists: true}});
// Test that invalid query does not error during Sampling CE. Every sampled document throws
// during evaluation, so the effective sample size is 0 and the estimate is reported with

122
jstests/noPassthroughWithMongod/query/cbr/cbr_stage_costing.js
View File

@ -44,6 +44,21 @@ const collZeroRows = db[collZeroRowsName];
collZeroRows.createIndex({a: 1});
collZeroRows.runCommand({analyze: collZeroRows, key: "a"});
// Collection used to test NDV-based seek estimation for skip-scan index scans.
// field 'unique' has 2000 distinct values; 'binary' has 2 values (0 or 1, half each).
const collSkipScanName = collName + "_skip_scan";
const collSkipScan = db[collSkipScanName];
collSkipScan.drop();
{
const docs = [];
for (let i = 0; i < 2000; i++) {
docs.push({unique: i, binary: i % 2});
}
collSkipScan.insert(docs);
collSkipScan.createIndex({unique: 1, binary: 1});
collSkipScan.createIndex({binary: 1});
}
/**
* Extracts the complete cost of the winning plan
*/
@ -168,10 +183,27 @@ function runTest(planRankerMode) {
// IXSCAN of same number of keys over one or more than one interval should have similar
// cost.
assert.close(
ixscanCost({predicate: {a: {$lte: 3}}, hint: {a: 1}}),
ixscanCost({predicate: {$or: [{a: 0}, {a: 1}, {a: 2}, {a: 3}]}, hint: {a: 1}}),
);
if (planRankerMode == "samplingCE") {
// IXSCAN of same number of keys over multiple intervals will estimate a higher than
// expected cost; estimation of seeks doesn't predict that contiguous intervals will be
// provided.
assert.lt(
ixscanCost({predicate: {a: {$lte: 3}}, hint: {a: 1}}),
ixscanCost({predicate: {$or: [{a: 0}, {a: 1}, {a: 2}, {a: 3}]}, hint: {a: 1}}),
);
// IXSCAN of same number of keys over multiple non-consecutive intervals will *correctly*
// estimate a higher cost than one interval.
assert.lt(
ixscanCost({predicate: {a: {$lte: 3}}, hint: {a: 1}}),
ixscanCost({predicate: {$or: [{a: 0}, {a: 2}, {a: 4}, {a: 6}]}, hint: {a: 1}}),
);
} else {
assert.close(
ixscanCost({predicate: {a: {$lte: 3}}, hint: {a: 1}}),
ixscanCost({predicate: {$or: [{a: 0}, {a: 1}, {a: 2}, {a: 3}]}, hint: {a: 1}}),
);
}
// IXSCAN over $or should have similar cost as an IXSCAN over an equivalent $in.
assert.close(
@ -204,11 +236,83 @@ function runTest(planRankerMode) {
// Use a predicate that actually matches (in the dataset a == b == i, so a=5 AND b>4 matches
// one document, i=5) to avoid the 1.0 inflation that kicks in for zero CEs.
const predicateOverAandB = {a: 5, b: {$gt: 4}};
// TODO SERVER-100611: re-enable these tests.
// assert.lt(
// ixscanCost({predicate: predicateOverAandB, hint: {a: 1, b: 1}}),
// ixscanCost({predicate: predicateOverAandB, hint: {b: 1, a: 1}}),
// );
assert.lt(
ixscanCost({predicate: predicateOverAandB, hint: {a: 1, b: 1}}),
ixscanCost({predicate: predicateOverAandB, hint: {b: 1, a: 1}}),
);
if (planRankerMode === "samplingCE") {
// Specifically validate that NDV-based seek count estimation produces correct plan
// selection for skip-scan patterns.
{
const skipScanExplain = collSkipScan.find({unique: {$gte: 0}, binary: 1}).explain();
const skipScanWinningPlan = getWinningPlanFromExplain(skipScanExplain);
const winningIxScan = getPlanStage(skipScanWinningPlan, "IXSCAN");
assert.eq(
winningIxScan.indexName,
"binary_1",
"CBR should prefer {binary:1} over skip-scan {unique:1,binary:1}",
);
}
{
// This test fails with the equality-prefix heuristic.
//
// With the equality-prefix heuristic:
//
// {unique:1, binary:1} estimates
// * numKeys ~1200
// * cardinality ~600
//
// and {binary:1}
// * numKeys ~1000
// * cardinality ~1000
//
// And the heuristic wrongly prefers the skip-scan, as the lower cardinality leads
// to a lower overall plan cost (including a FETCH).
//
// With NDV-based seek estimation, the skip-scan must seek ~NDV(unique<=1200)=1200 times,
// making it more expensive than a single-seek scan over {binary:1}.
const skipScanExplain = collSkipScan.find({unique: {$lte: 1200}, binary: 1}).explain();
const skipScanWinningPlan = getWinningPlanFromExplain(skipScanExplain);
const winningIxScan = getPlanStage(skipScanWinningPlan, "IXSCAN");
assert.eq(
winningIxScan.indexName,
"binary_1",
"CBR with NDV based seek estimation should prefer {binary:1} over skip-scan {unique:1,binary:1}",
);
}
// Verify that indexSeekEstimate is present in explain for IXSCAN stages. It is only
// populated in sampling CE mode.
{
// A point query uses a single seek, so indexSeekEstimate should be 1.
const explainPoint = coll.find({a: 1}).hint({a: 1}).explain();
const ixscanPoint = getPlanStage(explainPoint, "IXSCAN");
assert(
ixscanPoint.hasOwnProperty("indexSeekEstimate"),
"IXSCAN should have indexSeekEstimate in sampling CE mode: " + tojson(ixscanPoint),
);
assert.eq(ixscanPoint.indexSeekEstimate, 1, "Point query should require exactly 1 seek");
// A skip-scan over {unique:1, binary:1} with a range on 'unique' requires seeking
// for each distinct value of 'unique', so indexSeekEstimate should be >> 1.
const explainSkipScan = collSkipScan
.find({unique: {$gte: 0}, binary: 1})
.hint({unique: 1, binary: 1})
.explain();
const ixscanSkipScan = getPlanStage(explainSkipScan, "IXSCAN");
assert(
ixscanSkipScan.hasOwnProperty("indexSeekEstimate"),
"Skip-scan IXSCAN should have indexSeekEstimate in sampling CE mode: " + tojson(ixscanSkipScan),
);
assert.gt(
ixscanSkipScan.indexSeekEstimate,
1,
"Skip-scan should require multiple seeks: " + tojson(ixscanSkipScan),
);
}
}
// IXSCAN over an index that matches all predicates should produce a lower-cost
// plan than any of the alternatives.

3
jstests/noPassthroughWithMongod/query/cbr/histogramCE_accuracy.js
View File

@ -129,7 +129,6 @@ for (const indexes of [[], [{a: 1}]]) {
// Multi-field predicates
// TODO SERVER-100611: re-enable these tests.
for (const indexes of [/*[{a: 1, b: 1}], */ [{a: 1}, {b: 1}]]) {
for (const indexes of [[{a: 1, b: 1}], [{a: 1}, {b: 1}]]) {
runOneTest({dataset: new TwoFieldDataset(), indexes: indexes, analyze: ["a", "b"]});
}

38
jstests/noPassthroughWithMongod/query/plan_ranking/cost_based_choice_cbr_mp.js
View File

@ -285,16 +285,15 @@ try {
mpEndCond: mpEndConditions.kMaxWorks,
ranker: rankerStrategies.kCBR,
});
// TODO SERVER-100611: re-enable these tests.
// checkRanker({
// qID: "3.2",
// cName: "20k",
// query: {f1: {$lt: 505}, f2: {$gt: 100}},
// order: {x1: 1},
// limit: batchSize + 1,
// mpEndCond: mpEndConditions.kMaxWorks,
// ranker: rankerStrategies.kCBR,
// });
checkRanker({
qID: "3.2",
cName: "20k",
query: {f1: {$lt: 505}, f2: {$gt: 100}},
order: {x1: 1},
limit: batchSize + 1,
mpEndCond: mpEndConditions.kMaxWorks,
ranker: rankerStrategies.kCBR,
});
// This is an interesting case with productivity = 0.0052 which is < 0.0101
checkRanker({
qID: "3.3",
@ -338,16 +337,15 @@ try {
mpEndCond: mpEndConditions.kMaxWorks,
ranker: rankerStrategies.kCBR,
});
// TODO SERVER-100611: re-enable these tests.
// checkRanker({
// qID: "5.2",
// cName: "20k",
// query: {f1: {$lt: 505}, f2: {$gt: 100}},
// order: {f3: 1},
// limit: batchSize + 1,
// mpEndCond: mpEndConditions.kFullBatch,
// ranker: rankerStrategies.kCBR,
// });
checkRanker({
qID: "5.2",
cName: "20k",
query: {f1: {$lt: 505}, f2: {$gt: 100}},
order: {f3: 1},
limit: batchSize + 1,
mpEndCond: mpEndConditions.kFullBatch,
ranker: rankerStrategies.kCBR,
});
} finally {
setCBRConfig(db, prevCBRConfig);
}

170
jstests/query_golden/expected_output/automaticCE/CBRCostBasedRankerChoice/plan_stability
View File

@ -92,9 +92,9 @@
"rows" : 138},
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184
jstests/query_golden/expected_output/automaticCE/CBRCostBasedRankerChoice/plan_stability2
View File

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252
jstests/query_golden/expected_output/automaticCE/CBRForNoMultiplanningResults/plan_stability
View File

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190
jstests/query_golden/expected_output/automaticCE/CBRForNoMultiplanningResults/plan_stability2
View File

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250
jstests/query_golden/expected_output/automaticCE/HistogramCEWithHeuristicFallback/plan_stability
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372
jstests/query_golden/expected_output/automaticCE/HistogramCEWithHeuristicFallback/plan_stability2
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48
jstests/query_golden/expected_output/histogramCE/plan_stability
View File

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68
jstests/query_golden/expected_output/histogramCE/plan_stability2
View File

@ -7,7 +7,7 @@
[jsTest] ----
[jsTest] Found python 3.13 by default. Likely this is because we are using a virtual environment.
[jsTest] Using Python from RESMOKE_PYTHON: /home/ubuntu/mongo/python3-venv/bin/python3
[jsTest] ----
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260
jstests/query_golden/expected_output/samplingCE/plan_stability
View File

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308
jstests/query_golden/expected_output/samplingCE/plan_stability2
View File

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@ -6524,9 +6524,9 @@
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{">>>pipeline": [{"$match":{"$nor":[{"field40_list":{"$gte":21}},{"field30_Decimal128_idx":{"$ne":{"$numberDecimal":"8.1"}},"field27_bool_idx":{"$eq":false},"field19_datetime_idx":{"$ne":"2024-02-09T00:00:00.000Z"}}],"$or":[{"$and":[{"field9_bool_idx":{"$gt":true},"field22_list_idx":{"$exists":false}},{"field24_mixed_idx":{"$size":17},"field21_Decimal128_idx":{"$exists":false},"field22_list_idx":{"$size":8},"field13_list_idx":{"$all":[46]}}]},{"field27_bool_idx":{"$lte":false},"field23_dict_idx":{"$gt":{"c":2}}},{"field41_dict":{"$type":"objectId"},"field14_dict_idx":{"$gt":{"i":1,"b":1,"d":1}}}],"field30_Decimal128_idx":{"$lt":{"$numberDecimal":"100.3"}},"field26_int_idx":{"$lt":7},"field35_int_idx":{"$ne":336}}}],
"winningPlan": {"stage":"FETCH","filter":true,"inputStage":{"stage":"OR","inputStages":[{"stage":"FETCH","filter":true,"inputStage":{"stage":"IXSCAN","indexName":"field9_bool_idx_-1_field35_int_idx_1","indexBounds":{"field9_bool_idx":[],"field35_int_idx":["[MinKey, 336.0)","(336.0, MaxKey]"]}}},{"stage":"FETCH","filter":true,"inputStage":{"stage":"IXSCAN","indexName":"field14_dict_idx_-1_field34_str_idx_-1","indexBounds":{"field14_dict_idx":["([], { i: 1.0, b: 1.0, d: 1.0 })"],"field34_str_idx":["[MaxKey, MinKey]"]}}},{"stage":"FETCH","filter":true,"inputStage":{"stage":"IXSCAN","indexName":"field23_dict_idx_1","indexBounds":{"field23_dict_idx":["({ c: 2.0 }, [])"]}}}]}},
"keys" : 14301,
"docs" : 27302,
"winningPlan": {"stage":"FETCH","filter":true,"inputStage":{"stage":"IXSCAN","indexName":"field26_int_idx_-1_field21_Decimal128_idx_-1","indexBounds":{"field26_int_idx":["(7.0, -inf]"],"field21_Decimal128_idx":["[MaxKey, MinKey]"]}}},
"keys" : 15429,
"docs" : 15429,
"sorts": 0,
"plans": 9,
"rows" : 2},
@ -6540,9 +6540,9 @@
"rows" : 126},
{">>>pipeline": [{"$match":{"field45_bool":{"$lte":true},"field25_str_idx":{"$exists":true},"$or":[{"field33_mixed_idx":{"$ne":9},"field31_list_idx":{"$lt":20}},{"field19_datetime_idx":{"$type":"date"},"field27_bool_idx":{"$gt":false}},{"$or":[{"field44_int":{"$type":"int"},"field24_mixed_idx":{"$gt":205}},{"field13_list_idx":{"$type":"int"},"field47_Timestamp":{"$eq":{"$timestamp":{"t":1756993998,"i":0}}}}]},{"field22_list_idx":{"$nin":[21,69]},"field6_mixed_idx":{"$in":[23940,94274,1,79745,38712,5576]},"field9_bool_idx":{"$lt":true},"field4_list_idx":[53,2]},{"field6_mixed_idx":{"$ne":"x"},"field43_str":{"$gt":"f"}}]}},{"$sort":{"field5_dict_idx":-1,"field21_Decimal128_idx":-1,"field18_bool_idx":1}},{"$project":{"field27_bool_idx":1,"field34_str_idx":1,"_id":0}}],
"winningPlan": {"stage":"PROJECTION_SIMPLE","inputStage":{"stage":"SORT","inputStage":{"stage":"FETCH","filter":true,"inputStage":{"stage":"OR","inputStages":[{"stage":"FETCH","filter":true,"inputStage":{"stage":"IXSCAN","indexName":"field4_list_idx_-1_field9_bool_idx_1","indexBounds":{"field4_list_idx":["[[ 53.0, 2.0 ], [ 53.0, 2.0 ]]","[53.0, 53.0]"],"field9_bool_idx":["[false, true)"]}}},{"stage":"FETCH","filter":true,"inputStage":{"stage":"IXSCAN","indexName":"field6_mixed_idx_1","indexBounds":{"field6_mixed_idx":["[MinKey, \"x\")","(\"x\", MaxKey]"]}}},{"stage":"FETCH","filter":true,"inputStage":{"stage":"IXSCAN","indexName":"field13_list_idx_1_field28_datetime_idx_-1","indexBounds":{"field13_list_idx":["[nan, inf]"],"field28_datetime_idx":["[MaxKey, MinKey]"]}}},{"stage":"FETCH","filter":true,"inputStage":{"stage":"IXSCAN","indexName":"field19_datetime_idx_1","indexBounds":{"field19_datetime_idx":["[new Date(-9223372036854775808), new Date(9223372036854775807)]"]}}},{"stage":"FETCH","filter":true,"inputStage":{"stage":"IXSCAN","indexName":"field24_mixed_idx_-1_field28_datetime_idx_1","indexBounds":{"field24_mixed_idx":["[inf, 205.0)"],"field28_datetime_idx":["[MinKey, MaxKey]"]}}},{"stage":"FETCH","filter":true,"inputStage":{"stage":"IXSCAN","indexName":"field31_list_idx_-1_field7_str_idx_-1","indexBounds":{"field31_list_idx":["(20.0, -inf]"],"field7_str_idx":["[MaxKey, MinKey]"]}}}]}}}},
"keys" : 429192,
"docs" : 495086,
"winningPlan": {"stage":"PROJECTION_SIMPLE","inputStage":{"stage":"SORT","inputStage":{"stage":"FETCH","filter":true,"inputStage":{"stage":"IXSCAN","indexName":"field25_str_idx_1","indexBounds":{"field25_str_idx":["[MinKey, MaxKey]"]}}}}},
"keys" : 100000,
"docs" : 100000,
"sorts": 1221602,
"plans": 5,
"rows" : 97801},
@ -6604,8 +6604,8 @@
"rows" : 15601},
{">>>pipeline": [{"$match":{"field42_mixed":{"$gte":false},"field29_Timestamp_idx":{"$lte":{"$timestamp":{"t":1763090090,"i":0}}},"field4_list_idx":{"$in":["b","t"]},"$and":[{"field37_datetime":{"$eq":"2024-01-09T00:00:00.000Z"},"field11_Timestamp_idx":{"$lt":{"$timestamp":{"t":1760168870,"i":0}}}},{"field8_int_idx":{"$type":"int"},"field2_Timestamp_idx":{"$ne":{"$timestamp":{"t":1717978667,"i":0}}}},{"$or":[{"field4_list_idx":{"$lte":"y"},"field43_str":{"$gt":"a"}},{"field12_Decimal128_idx":{"$ne":{"$numberDecimal":"7.2"}},"field20_Timestamp_idx":{"$gte":{"$timestamp":{"t":1743534449,"i":0}}}},{"$and":[{"field23_dict_idx":{"$eq":{"b":1,"g":1}},"field36_bool":{"$ne":false},"field11_Timestamp_idx":{"$lte":{"$timestamp":{"t":1722798875,"i":0}}}},{"field15_mixed_idx":{"$ne":"2024-02-02T00:00:00.000Z"},"field1_datetime_idx":{"$eq":"2024-03-09T00:00:00.000Z"}}]},{"field6_mixed_idx":{"$size":15},"field4_list_idx":["b","h"]}]}]}}],
"winningPlan": {"stage":"FETCH","filter":true,"inputStage":{"stage":"IXSCAN","indexName":"field4_list_idx_-1_field9_bool_idx_1","indexBounds":{"field4_list_idx":["[\"t\", \"t\"]","[\"b\", \"b\"]"],"field9_bool_idx":["[MinKey, MaxKey]"]}}},
"keys" : 16810,
"winningPlan": {"stage":"FETCH","filter":true,"inputStage":{"stage":"IXSCAN","indexName":"field2_Timestamp_idx_-1_field4_list_idx_1","indexBounds":{"field2_Timestamp_idx":["[MaxKey, Timestamp(1717978667, 0))","(Timestamp(1717978667, 0), MinKey]"],"field4_list_idx":["[\"b\", \"b\"]","[\"t\", \"t\"]"]}}},
"keys" : 21333,
"docs" : 16802,
"sorts": 0,
"plans": 7,
@ -6836,9 +6836,9 @@
"rows" : 9033},
{">>>pipeline": [{"$match":{"$or":[{"field7_str_idx":{"$regex":{"$regex":"jnqK","$options":""}},"field21_Decimal128_idx":{"$ne":{"$numberDecimal":"2.4"}},"field10_datetime_idx":{"$exists":false},"field4_list_idx":{"$exists":false}},{"field13_list_idx":{"$in":[40]},"field35_int_idx":{"$type":"int"},"field45_bool":{"$gte":true},"field36_bool":{"$gt":false},"field4_list_idx":{"$regex":{"$regex":"p","$options":""}}},{"field0_bool_idx":{"$type":"bool"},"field13_list_idx":{"$nin":[38,54,55,84,5,95,21,20,12]}}],"field35_int_idx":{"$lte":8086},"field41_dict":{"$lte":{"b":2,"h":1}}}},{"$project":{"field35_int_idx":1,"field12_Decimal128_idx":1}}],
"winningPlan": {"stage":"PROJECTION_SIMPLE","inputStage":{"stage":"FETCH","filter":true,"inputStage":{"stage":"OR","inputStages":[{"stage":"FETCH","filter":true,"inputStage":{"stage":"IXSCAN","indexName":"field0_bool_idx_1","indexBounds":{"field0_bool_idx":["[false, true]"]}}},{"stage":"FETCH","filter":true,"inputStage":{"stage":"IXSCAN","indexName":"field13_list_idx_1_field28_datetime_idx_-1","indexBounds":{"field13_list_idx":["[40.0, 40.0]"],"field28_datetime_idx":["[MaxKey, MinKey]"]}}},{"stage":"FETCH","filter":true,"inputStage":{"stage":"IXSCAN","indexName":"field35_int_idx_-1_field10_datetime_idx_1","indexBounds":{"field35_int_idx":["[8086.0, -inf]"],"field10_datetime_idx":["[null, null]"]}}}]}}},
"keys" : 106540,
"docs" : 198054,
"winningPlan": {"stage":"PROJECTION_SIMPLE","inputStage":{"stage":"FETCH","filter":true,"inputStage":{"stage":"IXSCAN","indexName":"field35_int_idx_-1_field10_datetime_idx_1","indexBounds":{"field35_int_idx":["[8086.0, -inf]"],"field10_datetime_idx":["[MinKey, MaxKey]"]}}}},
"keys" : 97084,
"docs" : 97084,
"sorts": 0,
"plans": 6,
"rows" : 63703},
@ -6908,9 +6908,9 @@
"rows" : 1},
{">>>pipeline": [{"$match":{"$or":[{"$or":[{"field24_mixed_idx":{"$regex":{"$regex":"b","$options":""}},"field42_mixed":{"$gt":{"b":7}}},{"field7_str_idx":{"$lt":"Wgd"},"field24_mixed_idx":{"$regex":{"$regex":"^x","$options":""}},"field14_dict_idx":{"$gte":{"c":1}},"field1_datetime_idx":{"$eq":"2024-05-29T00:00:00.000Z"}}]},{"field22_list_idx":[27,52,26,43],"field40_list":{"$eq":"o"}},{"field28_datetime_idx":{"$type":"date"},"field13_list_idx":{"$lte":11},"field11_Timestamp_idx":{"$lte":{"$timestamp":{"t":1762715599,"i":0}}}}],"field36_bool":{"$type":"bool"},"field14_dict_idx":{"$lt":{"b":4,"d":1}}}}],
"winningPlan": {"stage":"FETCH","filter":true,"inputStage":{"stage":"OR","inputStages":[{"stage":"FETCH","filter":true,"inputStage":{"stage":"IXSCAN","indexName":"field13_list_idx_1_field28_datetime_idx_-1","indexBounds":{"field13_list_idx":["[-inf, 11.0]"],"field28_datetime_idx":["[new Date(9223372036854775807), new Date(-9223372036854775808)]"]}}},{"stage":"FETCH","filter":true,"inputStage":{"stage":"IXSCAN","indexName":"field14_dict_idx_-1_field34_str_idx_-1","indexBounds":{"field14_dict_idx":[],"field34_str_idx":["[MaxKey, MinKey]"]}}},{"stage":"FETCH","filter":true,"inputStage":{"stage":"IXSCAN","indexName":"field22_list_idx_1","indexBounds":{"field22_list_idx":["[27.0, 27.0]","[[ 27.0, 52.0, 26.0, 43.0 ], [ 27.0, 52.0, 26.0, 43.0 ]]"]}}},{"stage":"FETCH","filter":true,"inputStage":{"stage":"IXSCAN","indexName":"field24_mixed_idx_-1_field28_datetime_idx_1","indexBounds":{"field24_mixed_idx":["[/b/, /b/]","({}, \"\"]"],"field28_datetime_idx":["[MinKey, MaxKey]"]}}}]}},
"keys" : 24993,
"docs" : 27190,
"winningPlan": {"stage":"FETCH","filter":true,"inputStage":{"stage":"OR","inputStages":[{"stage":"FETCH","filter":true,"inputStage":{"stage":"IXSCAN","indexName":"field1_datetime_idx_1","indexBounds":{"field1_datetime_idx":["[new Date(1716940800000), new Date(1716940800000)]"]}}},{"stage":"FETCH","filter":true,"inputStage":{"stage":"IXSCAN","indexName":"field22_list_idx_1","indexBounds":{"field22_list_idx":["[27.0, 27.0]","[[ 27.0, 52.0, 26.0, 43.0 ], [ 27.0, 52.0, 26.0, 43.0 ]]"]}}},{"stage":"FETCH","filter":true,"inputStage":{"stage":"IXSCAN","indexName":"field24_mixed_idx_-1_field28_datetime_idx_1","indexBounds":{"field24_mixed_idx":["[/b/, /b/]","({}, \"\"]"],"field28_datetime_idx":["[MinKey, MaxKey]"]}}},{"stage":"FETCH","filter":true,"inputStage":{"stage":"IXSCAN","indexName":"field28_datetime_idx_1_field23_dict_idx_1","indexBounds":{"field28_datetime_idx":["[new Date(-9223372036854775808), new Date(9223372036854775807)]"],"field23_dict_idx":["[MinKey, MaxKey]"]}}}]}},
"keys" : 24326,
"docs" : 27228,
"sorts": 0,
"plans": 7,
"rows" : 125},
@ -7540,9 +7540,9 @@
"rows" : 1},
{">>>pipeline": [{"$match":{"$or":[{"$and":[{"field26_int_idx":{"$lte":71},"field16_str_idx":{"$lte":"Si"}},{"field6_mixed_idx":{"$nin":[]},"field40_list":{"$all":[51,45,161,31,27,19,158,625]},"field33_mixed_idx":{"$lt":85}},{"field21_Decimal128_idx":{"$ne":{"$numberDecimal":"6.3"}},"field31_list_idx":[70,112,12,26,39],"field32_dict_idx":{"$gte":{"b":19}},"field22_list_idx":91},{"field40_list":[29,11,20,22],"field35_int_idx":{"$ne":4334},"field31_list_idx":{"$ne":50},"field4_list_idx":{"$size":7}}]},{"field37_datetime":{"$type":"date"},"field7_str_idx":{"$lt":"mmf"}}],"field20_Timestamp_idx":{"$gte":{"$timestamp":{"t":1753113733,"i":0}}},"field36_bool":{"$ne":false}}}],
"winningPlan": {"stage":"FETCH","filter":true,"inputStage":{"stage":"OR","inputStages":[{"stage":"FETCH","filter":true,"inputStage":{"stage":"IXSCAN","indexName":"field7_str_idx_1_field20_Timestamp_idx_-1_field6_mixed_idx_-1","indexBounds":{"field7_str_idx":["[\"\", \"mmf\")"],"field20_Timestamp_idx":["[Timestamp(4294967295, 4294967295), Timestamp(1753113733, 0)]"],"field6_mixed_idx":["[MaxKey, MinKey]"]}}},{"stage":"FETCH","filter":true,"inputStage":{"stage":"IXSCAN","indexName":"field22_list_idx_1","indexBounds":{"field22_list_idx":["[91.0, 91.0]"]}}}]}},
"keys" : 52894,
"docs" : 59533,
"winningPlan": {"stage":"FETCH","filter":true,"inputStage":{"stage":"IXSCAN","indexName":"field20_Timestamp_idx_-1","indexBounds":{"field20_Timestamp_idx":["[Timestamp(4294967295, 4294967295), Timestamp(1753113733, 0)]"]}}},
"keys" : 39890,
"docs" : 39890,
"sorts": 0,
"plans": 10,
"rows" : 29048},
@ -7948,8 +7948,8 @@
"rows" : 362},
{">>>pipeline": [{"$match":{"field4_list_idx":{"$exists":true},"field9_bool_idx":{"$lte":false},"$or":[{"$or":[{"field40_list":{"$elemMatch":{"$in":["s"]}},"field7_str_idx":{"$ne":"oWO"}},{"field19_datetime_idx":{"$gte":"2024-01-17T00:00:00.000Z"},"field4_list_idx":{"$regex":{"$regex":"e","$options":""}},"field5_dict_idx":{"$exists":false}}]},{"$and":[{"field5_dict_idx":{"$type":"objectId"},"field28_datetime_idx":{"$lte":"2024-01-03T00:00:00.000Z"}},{"field1_datetime_idx":{"$type":"date"},"field30_Decimal128_idx":{"$lt":{"$numberDecimal":"7.5"}},"field45_bool":{"$lte":true}},{"field42_mixed":{"$type":"bool"},"field26_int_idx":{"$gt":70}}]},{"field14_dict_idx":{"$ne":{"f":6}},"field5_dict_idx":{"$exists":false},"field43_str":{"$ne":"b"},"field39_Decimal128":{"$gte":{"$numberDecimal":"9.2"}}}]}},{"$sort":{"field18_bool_idx":-1,"field23_dict_idx":1,"field3_Decimal128_idx":-1}}],
"winningPlan": {"stage":"SORT","inputStage":{"stage":"FETCH","filter":true,"inputStage":{"stage":"IXSCAN","indexName":"field4_list_idx_-1_field9_bool_idx_1","indexBounds":{"field4_list_idx":["[MaxKey, MinKey]"],"field9_bool_idx":["[false, false]"]}}}},
"keys" : 102100,
"winningPlan": {"stage":"SORT","inputStage":{"stage":"FETCH","filter":true,"inputStage":{"stage":"IXSCAN","indexName":"field9_bool_idx_-1_field35_int_idx_1","indexBounds":{"field9_bool_idx":["[false, false]"],"field35_int_idx":["[MinKey, MaxKey]"]}}}},
"keys" : 88249,
"docs" : 88249,
"sorts": 3,
"plans": 8,
@ -8268,7 +8268,7 @@
"rows" : 96224}
],
">>>totals": {"pipelines": 1032, "plans": 7958, "keys": 29209608, "docs": 28822050, "sorts": 9788519, "rows": 10011323, "errors": 0},
">>>totals": {"pipelines": 1032, "plans": 7958, "keys": 27831709, "docs": 27037373, "sorts": 9788519, "rows": 10011323, "errors": 0},
">>>parameters": {"featureFlagCostBasedRanker":{"value":true,"fcv_gated":false,"currentlyEnabled":true},"internalQueryCBRCEMode":"samplingCE","samplingMarginOfError":5,"samplingConfidenceInterval":"95","internalQuerySamplingCEMethod":"chunk","internalQuerySamplingBySequentialScan":true}}

2
jstests/query_golden/test_inputs/plan_stability_pipelines.js
View File

@ -13814,7 +13814,7 @@ export const pipelines = [
{"$limit": 93},
{"$project": {"a_idx": 1}},
],
// TODO SERVER-100611: [CE Issue] Impacts samplingCE/histogramCE/automaticCE plan choice
// [CE Issue] Impacts samplingCE/histogramCE/automaticCE plan choice
// Once we estimate the number of seeks, we should be able to choose the better plan here which
// does an IXSCAN on 'z_compound_1' instead of 'i_compound_1_z_compound_1'. It should also
// drastically reduce the number of examined keys.

1
src/mongo/db/query/compiler/optimizer/cost_based_ranker/BUILD.bazel
View File

@ -20,6 +20,7 @@ mongo_cc_library(
"ce_utils",
"estimates",
"heuristic_estimator",
"//src/mongo/db/query:query_knobs",
"//src/mongo/db/query/compiler/ce/histogram:histogram_estimator",
"//src/mongo/db/query/compiler/ce/sampling:sampling_estimator_interface",
"//src/mongo/db/query/compiler/optimizer/index_bounds_builder",

173
src/mongo/db/query/compiler/optimizer/cost_based_ranker/cardinality_estimator.cpp
View File

@ -36,6 +36,7 @@
#include "mongo/db/query/compiler/optimizer/cost_based_ranker/heuristic_estimator.h"
#include "mongo/db/query/compiler/optimizer/index_bounds_builder/index_bounds_builder.h"
#include "mongo/db/query/compiler/physical_model/query_solution/stage_types.h"
#include "mongo/db/query/query_optimization_knobs_gen.h"
#include <absl/container/flat_hash_map.h>
@ -483,8 +484,6 @@ OrderedIntervalList makeOpenOil(std::string fieldName) {
struct EqualityPrefix {
std::unique_ptr<IndexBounds> eqPrefixPtr;
bool isEqPrefix;
// TODO SERVER-100611: Remove this flag, and all references to it.
bool isIndexSkipScan;
};
/**
@ -495,21 +494,138 @@ struct EqualityPrefix {
EqualityPrefix equalityPrefix(const IndexBounds* node) {
auto eqPrefix = std::make_unique<IndexBounds>();
bool isEqPrefix = true;
bool isIndexSkipScan = false;
for (auto&& oil : node->fields) {
if (isEqPrefix) {
eqPrefix->fields.push_back(oil);
isEqPrefix = isEqPrefix && oil.isPoint();
} else {
// We set the 'isIndexSkipScan' flag to true if at any point after 'isEqPrefix' is false
// we encounter a non-fully open interval.
if (!oil.isFullyOpen() && oil.intervals.size() >= 1) {
isIndexSkipScan = true;
}
eqPrefix->fields.push_back(makeOpenOil(oil.name));
}
}
return EqualityPrefix{std::move(eqPrefix), isEqPrefix, isIndexSkipScan};
return EqualityPrefix{std::move(eqPrefix), isEqPrefix};
}
CEResult CardinalityEstimator::estimateIndexSeeks(const IndexBounds& bounds, bool multiKey) {
auto oils = bounds.fields;
double seekEstimate = 1;
/*
* For some example set of bounds:
*
* a: [[0, 9]], b: [[0, 9]], c: [[0, 9]], d: [[0, 9], [100, 200]]
*
* An index will seek once initially to {a:0, b:0, c:0, d:0}, and will read
* keys until one which falls outside the current intervals is encountered.
*
* As indexes are sorted lexicographically, assuming for simplicity documents exist inside and
* outside every interval, the pattern of seeks will follow:
*
* {a:0, b:0, c:0, d:0}
* {a:0, b:0, c:0, d:100}
* {a:0, b:0, c:1, d:0}
* {a:0, b:0, c:1, d:100}
* {a:0, b:0, c:2, d:0}
* {a:0, b:0, c:2, d:100}
* ...
* {a:0, b:1, c:0, d:0}
* ... etc
*
* The *worst case* seek count would be:
*
* NDV(a) * NDV(b) * NDV(c) * NumIntervals(d)
*
* However, in reality, some combinations of (a, b, c) may not exist in the dataset.
*
* In reality, the scan only needs to seek once per interval of d, per unique value of the
* prefix (a, b, c).
*
* Given the sample, we have the information to find a better estimate for the seeks required,
* by combining the NDV estimations. The above example changes to:
*
* NDV((a, b, c)) * NumIntervals(d)
*
* This is a closer reflection of the underlying scan, and is less of a pessimisation.
*
* *TRAILING FULLY OPEN INTERVALS*
*
* If trailing fields have interval [MinKey, MaxKey], a document will never be seen which does
* not match this, so those fields don't directly cause additional seeks - the index scan can
* continue reading and will fall into the next value of the _preceding_ field.
*
* a: [[0, 9]], b: [[0, 9], [100, 200]], c: [[MinKey, MaxKey]], d: [[MinKey, MaxKey]]
*
* will seek
*
* {a:0, b:0, c:MinKey, d:MinKey}
* {a:0, b:100, c:MinKey, d:MinKey}
* {a:1, b:0, c:MinKey, d:MinKey}
* {a:1, b:100, c:MinKey, d:MinKey}
* {a:2, b:0, c:MinKey, d:MinKey}
* {a:2, b:100, c:MinKey, d:MinKey}
*
* Note that now b does not need to seek for every *distinct* value, just for every *interval*.
*
* The seek *count* will be equivalent to that of a scan over a shorter index with just the
* preceding fields.
*
* * NDV(a) * NumIntervals(b) * 1 * 1
*/
// Trailing fully open intervals do not change the number of seeks - a cursor will only need
// to seek after encountering a key which does not match the current bounds.
while (!oils.empty() && oils.back().isFullyOpen()) {
oils.pop_back();
}
if (!oils.empty()) {
// This is the last (rightmost) field with a non-fully-open interval.
// A single seek will be required per interval for this field.
if (oils.back().intervals.empty()) {
// This field has no intervals; there will be no key for the
// ixscan to seek to to start, and no documents returned.
return zeroCE;
}
seekEstimate *= oils.back().intervals.size();
oils.pop_back();
}
// Remove point OILs (NDV=1, contribute a factor of 1 to seeks) and empty OILs in a single
// pass. Track whether any empty OIL is encountered - an empty OIL means no index keys can
// match.
bool isEmptyInterval = false;
std::erase_if(oils, [&](const OrderedIntervalList& oil) {
if (oil.intervals.empty()) {
isEmptyInterval = true;
}
return oil.intervals.empty() || oil.isPoint();
});
if (isEmptyInterval) {
return zeroCE;
}
if (oils.empty()) {
return CardinalityEstimate{CardinalityType{seekEstimate}, EstimationSource::Sampling};
}
std::vector<ce::FieldPathAndEqSemantics> fieldAndEqs;
for (const auto& oil : oils) {
// For index bounds, using the default null == missing semantics is fine.
fieldAndEqs.emplace_back(oil.name);
}
auto oilSpan = std::span<const OrderedIntervalList>(oils);
CardinalityEstimate ndv = multiKey
? _samplingEstimator->estimateNDVMultiKey(fieldAndEqs, oilSpan)
: _samplingEstimator->estimateNDV(fieldAndEqs, oilSpan);
ndv = std::max(oneCE, ndv);
seekEstimate *= ndv.toDouble();
tassert(10061109, "IndexBounds should always have >=1 seek estimate", seekEstimate >= 1.0);
auto seekCE = CardinalityEstimate{CardinalityType{seekEstimate}, EstimationSource::Sampling};
return seekCE;
}
CEResult CardinalityEstimator::estimate(const IndexScanNode* node) {
@ -580,19 +696,20 @@ CEResult CardinalityEstimator::estimate(const IndexScanNode* node) {
// We can avoid computing input selectivity for sampling CE queries over
// non-multikey fields without residual filter.
auto prefix = equalityPrefix(&node->bounds);
if (prefix.isIndexSkipScan) {
return Status(ErrorCodes::UnsupportedCbrNode, "encountered index skip scan case");
}
if (prefix.isEqPrefix) {
// If the prefix is equal to all the bounds, the number of keys scanned is equal to
// the resulting docs.
est.inCE = est.outCE;
} else {
// Evaluate only the equality prefix against the sample.
est.inCE = ridsEstFunct(*prefix.eqPrefixPtr, nullptr);
est.inCE = ridsEstFunct(node->bounds, nullptr);
}
}
auto seekCE = estimateIndexSeeks(node->bounds, node->index.multikey);
if (seekCE.isOK()) {
est.indexSeekCE = seekCE.getValue();
}
CardinalityEstimate outCE{est.outCE};
_qsnEstimates.emplace(node, std::make_unique<QSNEstimate>(std::move(est)));
return outCE;
@ -1238,23 +1355,15 @@ CEResult CardinalityEstimator::estimate(
* Intervals
*/
CEResult CardinalityEstimator::estimate(const IndexBounds* node) {
if (node->isSimpleRange) {
CEResult CardinalityEstimator::estimate(const IndexBounds* bounds) {
if (bounds->isSimpleRange) {
// TODO SERVER-96816: Implement support for estimation of simple ranges
return Status(ErrorCodes::UnsupportedCbrNode, "simple ranges unsupported");
}
if (_rankerMode == QueryPlanRankerModeEnum::kSamplingCE) {
// TODO SERVER-122572: avoid copies to construct the equality prefix. We could do this by
// teaching SamplingEstimator or IndexBounds about the equality prefix concept.
auto eqPrefix = equalityPrefix(node);
if (eqPrefix.isIndexSkipScan) {
return Status(ErrorCodes::UnsupportedCbrNode, "encountered index skip scan case");
}
const auto eqPrefixPtr = eqPrefix.eqPrefixPtr.get();
return _ceCache.getOrCompute(std::move(eqPrefix.eqPrefixPtr), [&] {
return _samplingEstimator->estimateKeysScanned(*eqPrefixPtr);
});
return _ceCache.getOrCompute(
bounds, [&] { return _samplingEstimator->estimateKeysScanned(*bounds); });
}
// Iterate over all intervals over individual index fields (OILs). These intervals are
@ -1276,7 +1385,7 @@ CEResult CardinalityEstimator::estimate(const IndexBounds* node) {
bool isEqPrefix = true; // Tracks if an OIL is part of an equality prefix
// Ignore selectivities pushed by other operators up to this point.
size_t selOffset = _conjSels.size();
for (const auto& field : node->fields) {
for (const auto& field : bounds->fields) {
const OrderedIntervalList* oil = &field;
// Notice that OILs are considered leaves from CE perspective.
auto ceRes = estimate(oil);
@ -1287,10 +1396,6 @@ CEResult CardinalityEstimator::estimate(const IndexBounds* node) {
if (isEqPrefix) {
_conjSels.emplace_back(sel);
} else {
// TODO SERVER-100611: Remove this code.
if (!oil->isFullyOpen() && oil->intervals.size() >= 1) {
return Status(ErrorCodes::UnsupportedCbrNode, "encountered index skip scan case");
}
residualSels.emplace_back(sel);
}
isEqPrefix = isEqPrefix && oil->isPoint();
@ -1403,6 +1508,12 @@ void CardinalityEstimator::propagateLimit(const QuerySolutionNode* node, size_t
}
applyLimitToSelf();
inCE = limitFraction * inCE;
// If we have an estimate for index seeks, naively scale this
// down by the limit fraction.
if (auto& indexSeekEst = _qsnEstimates[node]->indexSeekCE) {
*indexSeekEst = std::max(*indexSeekEst * limitFraction, oneCE);
}
break;
}
case STAGE_FETCH: {

4
src/mongo/db/query/compiler/optimizer/cost_based_ranker/cardinality_estimator.h
View File

@ -86,7 +86,7 @@ public:
CEResult estimatePlan(const QuerySolution& plan);
private:
protected:
// QuerySolutionNodes
CEResult estimate(const QuerySolutionNode* node);
CEResult estimate(const CollectionScanNode* node);
@ -146,6 +146,8 @@ private:
*/
void clampZeroEstimates();
CEResult estimateIndexSeeks(const IndexBounds& bounds, bool multiKey);
// Internal helper functions
/**

335
src/mongo/db/query/compiler/optimizer/cost_based_ranker/cardinality_estimator_test.cpp
View File

@ -43,6 +43,23 @@
namespace mongo::cost_based_ranker {
namespace {
// Subclass of CardinalityEstimator that exposes protected methods for unit tests.
class CardinalityEstimatorForTest : public CardinalityEstimator {
public:
using CardinalityEstimator::CardinalityEstimator;
using CardinalityEstimator::estimateIndexSeeks;
};
CEResult estimateIndexSeeks(ce::SamplingEstimator& samplingEstimator,
const IndexBounds& bounds,
bool multiKey) {
auto collInfo = buildCollectionInfo({}, makeCollStatsWithHistograms({}, 1000.0));
EstimateMap qsnEstimates;
CardinalityEstimatorForTest estimator{
collInfo, &samplingEstimator, qsnEstimates, QueryPlanRankerModeEnum::kSamplingCE};
return estimator.estimateIndexSeeks(bounds, multiKey);
}
TEST(CardinalityEstimator, PointInterval) {
auto testNss = NamespaceString::createNamespaceString_forTest("testdb.coll");
std::vector<std::string> indexFields = {"a"};
@ -64,21 +81,20 @@ TEST(CardinalityEstimator, ManyPointIntervals) {
ASSERT_EQ(getPlanHeuristicCE(*plan, 100.0), makeCard(50.0));
}
// TODO SERVER-100611: re-enable this test.
// TEST(CardinalityEstimator, CompoundIndex) {
// auto testNss = NamespaceString::createNamespaceString_forTest("testdb.coll");
// IndexBounds bounds;
// std::vector<std::string> indexFields = {"a", "b", "c", "d", "e"};
// for (size_t i = 0; i < indexFields.size(); ++i) {
// OrderedIntervalList oil(indexFields[i]);
// for (size_t j = 0; j < 7; ++j) {
// oil.intervals.push_back(IndexBoundsBuilder::makePointInterval(i * j));
// }
// bounds.fields.push_back(oil);
// }
// auto plan = makeIndexScanFetchPlan(testNss, std::move(bounds), indexFields);
// ASSERT_EQ(getPlanHeuristicCE(*plan, 100.0), makeCard(51.2341));
// }
TEST(CardinalityEstimator, CompoundIndex) {
auto testNss = NamespaceString::createNamespaceString_forTest("testdb.coll");
IndexBounds bounds;
std::vector<std::string> indexFields = {"a", "b", "c", "d", "e"};
for (size_t i = 0; i < indexFields.size(); ++i) {
OrderedIntervalList oil(indexFields[i]);
for (size_t j = 0; j < 7; ++j) {
oil.intervals.push_back(IndexBoundsBuilder::makePointInterval(i * j));
}
bounds.fields.push_back(oil);
}
auto plan = makeIndexScanFetchPlan(testNss, std::move(bounds), indexFields);
ASSERT_EQ(getPlanHeuristicCE(*plan, 100.0), makeCard(51.2341));
}
TEST(CardinalityEstimator, PointMoreSelectiveThanRange) {
auto testNss = NamespaceString::createNamespaceString_forTest("testdb.coll");
@ -1124,7 +1140,7 @@ OrderedIntervalList makePointOil(const std::string& fieldName) {
return makePointInterval(5.0, fieldName);
}
// An empty OIL represents an unsatisfiable predicate — no values can match.
// An empty OIL represents an unsatisfiable predicate -- no values can match.
OrderedIntervalList makeEmptyOil(const std::string& fieldName) {
return OrderedIntervalList(fieldName);
}
@ -1148,13 +1164,13 @@ std::unique_ptr<QuerySolution> makeEqualityPrefixPlan(IndexBounds bounds) {
}
} // namespace
// Fixture for testing the index skip scan detection cases via heuristic CE.
// Fixture for testing index bounds patterns that involve skip scans via heuristic CE.
class EqualityPrefixHeuristicCETest : public unittest::Test {
protected:
void assertSkipScan(IndexBounds bounds) {
auto plan = makeEqualityPrefixPlan(std::move(bounds));
const auto ceRes = getPlanCE(*plan, _collInfo, QueryPlanRankerModeEnum::kHeuristicCE);
ASSERT(!ceRes.isOK() && ceRes.getStatus().code() == ErrorCodes::UnsupportedCbrNode);
ASSERT(ceRes.isOK());
}
void assertNotSkipScan(IndexBounds bounds) {
@ -1168,9 +1184,7 @@ private:
buildCollectionInfo({}, makeCollStatsWithHistograms({}, 100.0));
};
// Fixture for testing the index skip scan detection cases via sampling CE.
// The skip scan check in the sampling path goes through equalityPrefix() directly (unlike the
// heuristic path which has its own inline check).
// Fixture for testing index bounds patterns that involve skip scans via sampling CE.
class EqualityPrefixSamplingCETest : public ce::SamplingEstimatorTest {
protected:
void setUp() override {
@ -1185,7 +1199,7 @@ protected:
CardinalityEstimator estimator{
collInfo, &*_samplingEstimator, qsnEstimates, QueryPlanRankerModeEnum::kSamplingCE};
const auto ceRes = estimator.estimatePlan(*plan);
ASSERT(!ceRes.isOK() && ceRes.getStatus().code() == ErrorCodes::UnsupportedCbrNode);
ASSERT(ceRes.isOK());
}
void assertNotSkipScan(IndexBounds bounds) {
@ -1394,5 +1408,282 @@ TYPED_TEST(EqualityPrefixTypedTest, NonFullyOpen_FullyOpen_FullyOpen_MultiInterv
this->assertSkipScan(std::move(bounds));
}
// Tests for CardinalityEstimator::estimateIndexSeeks.
//
// estimateIndexSeeks computes the number of index seeks for a set of IndexBounds. The algorithm
// works backwards through the OILs:
// 1. Trailing fully-open OILs are stripped (they never cause a seek).
// 2. The first non-open OIL contributes numIntervals seeks.
// 3. Each remaining (leading) OIL contributes NDV seeks; point OILs contribute NDV=1.
// 4. An empty OIL anywhere yields zeroCE (no seeks, no docs).
// A minimal SamplingEstimator mock. Only estimateNDV and estimateNDVMultiKey are implemented;
// all other methods are unimplemented and will throw if called.
class MockSamplingEstimator : public ce::SamplingEstimator {
public:
explicit MockSamplingEstimator(double ndv,
boost::optional<double> ndvMultiKey = boost::none,
boost::optional<double> ndvBounded = boost::none,
boost::optional<double> ndvMultiKeyBounded = boost::none)
: _ndv(ndv),
_ndvMultiKey(ndvMultiKey.value_or(ndv)),
_ndvBounded(ndvBounded.value_or(ndv)),
_ndvMultiKeyBounded(ndvMultiKeyBounded.value_or(_ndvMultiKey)) {}
CardinalityEstimate estimateCardinality(const MatchExpression*) const override {
MONGO_UNIMPLEMENTED;
}
std::vector<CardinalityEstimate> estimateCardinality(
const std::vector<const MatchExpression*>&) const override {
MONGO_UNIMPLEMENTED;
}
CardinalityEstimate estimateKeysScanned(const IndexBounds&) const override {
MONGO_UNIMPLEMENTED;
}
std::vector<CardinalityEstimate> estimateKeysScanned(
const std::vector<const IndexBounds*>&) const override {
MONGO_UNIMPLEMENTED;
}
CardinalityEstimate estimateRIDs(const IndexBounds&, const MatchExpression*) const override {
MONGO_UNIMPLEMENTED;
}
std::vector<CardinalityEstimate> estimateRIDs(
const std::vector<const IndexBounds*>&,
const std::vector<const MatchExpression*>&) const override {
MONGO_UNIMPLEMENTED;
}
void generateSample(ce::ProjectionParams) override {
MONGO_UNIMPLEMENTED;
}
CardinalityEstimate estimateNDV(
const std::vector<ce::FieldPathAndEqSemantics>&,
boost::optional<std::span<const OrderedIntervalList>> bounds) const override {
return makeCard(bounds ? _ndvBounded : _ndv);
}
CardinalityEstimate estimateNDVMultiKey(
const std::vector<ce::FieldPathAndEqSemantics>&,
boost::optional<std::span<const OrderedIntervalList>> bounds) const override {
return makeCard(bounds ? _ndvMultiKeyBounded : _ndvMultiKey);
}
double getCollCard() const override {
MONGO_UNIMPLEMENTED;
}
size_t getSampleSize() const override {
MONGO_UNIMPLEMENTED;
}
private:
double _ndv;
double _ndvMultiKey;
double _ndvBounded;
double _ndvMultiKeyBounded;
};
// Single-field, single point interval: one seek to position the cursor.
TEST(CardinalityEstimator, IndexSeeks_SingleFieldPointInterval) {
MockSamplingEstimator mock{/*ndv=*/5.0};
IndexBounds bounds;
bounds.fields.push_back(makePointOil("a"));
auto result = estimateIndexSeeks(mock, bounds, /*multiKey=*/false);
ASSERT_OK(result);
ASSERT_EQ(result.getValue(), makeCard(1.0));
}
// Single-field, multiple intervals (e.g. $in with 3 values): one seek per interval.
TEST(CardinalityEstimator, IndexSeeks_SingleFieldMultipleIntervals) {
MockSamplingEstimator mock{/*ndv=*/5.0};
IndexBounds bounds;
bounds.fields.push_back(makeMultiIntervalOil("a"));
auto result = estimateIndexSeeks(mock, bounds, /*multiKey=*/false);
ASSERT_OK(result);
ASSERT_EQ(result.getValue(), makeCard(3.0));
}
// Single-field, single range interval: one seek (the trailing field contributes numIntervals=1).
TEST(CardinalityEstimator, IndexSeeks_SingleFieldRangeInterval) {
MockSamplingEstimator mock{/*ndv=*/5.0};
IndexBounds bounds;
bounds.fields.push_back(makeRangeOil("a"));
auto result = estimateIndexSeeks(mock, bounds, /*multiKey=*/false);
ASSERT_OK(result);
// Only one field, the trailing non-open field: seekEstimate = numIntervals = 1.
// The NDV mock is not consulted since there are no preceding fields.
ASSERT_EQ(result.getValue(), makeCard(1.0));
}
// Single-field, empty OIL: no possible keys to seek to, so the result is zeroCE.
TEST(CardinalityEstimator, IndexSeeks_SingleFieldEmptyInterval) {
MockSamplingEstimator mock{/*ndv=*/5.0};
IndexBounds bounds;
bounds.fields.emplace_back(OrderedIntervalList("a"));
auto result = estimateIndexSeeks(mock, bounds, /*multiKey=*/false);
ASSERT_OK(result);
ASSERT_EQ(result.getValue(), zeroCE);
}
// Trailing fully-open OIL is stripped; the preceding field determines seek count.
TEST(CardinalityEstimator, IndexSeeks_TrailingFullyOpenDropped) {
MockSamplingEstimator mock{/*ndv=*/5.0};
// a: single range interval; b: fully open (MinKey..MaxKey)
IndexBounds bounds;
bounds.fields.push_back(makeRangeOil("a"));
bounds.fields.push_back(makeFullyOpenOil("b"));
auto result = estimateIndexSeeks(mock, bounds, /*multiKey=*/false);
ASSERT_OK(result);
// b is stripped; a is the first non-open field with 1 interval; no preceding fields.
ASSERT_EQ(result.getValue(), makeCard(1.0));
}
// Multiple trailing fully-open OILs are all stripped; only the first non-open field counts.
TEST(CardinalityEstimator, IndexSeeks_MultipleTrailingFullyOpenDropped) {
MockSamplingEstimator mock{/*ndv=*/5.0};
// a: 3 point intervals; b: fully open; c: fully open
IndexBounds bounds;
bounds.fields.push_back(makeMultiIntervalOil("a"));
bounds.fields.push_back(makeFullyOpenOil("b"));
bounds.fields.push_back(makeFullyOpenOil("c"));
auto result = estimateIndexSeeks(mock, bounds, /*multiKey=*/false);
ASSERT_OK(result);
// b and c stripped; a is the first non-open field with 3 intervals; no preceding fields.
ASSERT_EQ(result.getValue(), makeCard(3.0));
}
// Point interval in leading position contributes NDV=1 (no multiplier effect).
TEST(CardinalityEstimator, IndexSeeks_PointLeadingFieldContributesNDVOne) {
MockSamplingEstimator mock{/*ndv=*/5.0};
// a: point; b: 3 point intervals
IndexBounds bounds;
bounds.fields.push_back(makePointOil("a"));
bounds.fields.push_back(makeMultiIntervalOil("b"));
auto result = estimateIndexSeeks(mock, bounds, /*multiKey=*/false);
ASSERT_OK(result);
// b is the first non-open trailing field: seekEstimate = 3.
// a is point: NDV=1, no multiplier. seek = 3.
ASSERT_EQ(result.getValue(), makeCard(3.0));
}
// Range interval in leading position multiplies seeks by NDV from the sampling estimator.
TEST(CardinalityEstimator, IndexSeeks_RangeLeadingFieldMultipliesByNDV) {
MockSamplingEstimator mock{/*ndv=*/5.0};
// a: range; b: single range interval
IndexBounds bounds;
bounds.fields.push_back(makeRangeOil("a"));
bounds.fields.push_back(makeRangeOil("b"));
auto result = estimateIndexSeeks(mock, bounds, /*multiKey=*/false);
ASSERT_OK(result);
// b is the first non-open trailing field: seekEstimate = 1.
// a is range (non-point): seekEstimate *= NDV(a) = 5. seek = 5.
ASSERT_EQ(result.getValue(), makeCard(5.0));
}
// NDV of zero is clamped to 1 to avoid discarding information from other fields.
TEST(CardinalityEstimator, IndexSeeks_NDVClampedToOne) {
MockSamplingEstimator mock{/*ndv=*/0.0};
IndexBounds bounds;
bounds.fields.push_back(makeRangeOil("a"));
bounds.fields.push_back(makeRangeOil("b"));
auto result = estimateIndexSeeks(mock, bounds, /*multiKey=*/false);
ASSERT_OK(result);
// b contributes 1; a's NDV=0 is clamped to 1. seek = 1.
ASSERT_EQ(result.getValue(), makeCard(1.0));
}
// Empty OIL in a leading position (preceding the last non-open field) returns zeroCE.
TEST(CardinalityEstimator, IndexSeeks_EmptyLeadingFieldReturnsZero) {
MockSamplingEstimator mock{/*ndv=*/5.0};
// a: empty; b: range interval
IndexBounds bounds;
bounds.fields.emplace_back(OrderedIntervalList("a"));
bounds.fields.push_back(makeRangeOil("b"));
auto result = estimateIndexSeeks(mock, bounds, /*multiKey=*/false);
ASSERT_OK(result);
// b contributes 1 seek; then a is empty -> return zeroCE.
ASSERT_EQ(result.getValue(), zeroCE);
}
// Empty OIL at the trailing (last) position returns zeroCE.
TEST(CardinalityEstimator, IndexSeeks_EmptyTrailingFieldReturnsZero) {
MockSamplingEstimator mock{/*ndv=*/5.0};
// a: point; b: empty OIL
IndexBounds bounds;
bounds.fields.push_back(makePointOil("a"));
bounds.fields.emplace_back(OrderedIntervalList("b"));
auto result = estimateIndexSeeks(mock, bounds, /*multiKey=*/false);
ASSERT_OK(result);
// b is the first non-open trailing field; b.intervals is empty -> return zeroCE.
ASSERT_EQ(result.getValue(), zeroCE);
}
// Trailing fully-open OIL after a range plus a leading point: only the range is counted.
TEST(CardinalityEstimator, IndexSeeks_PointRangeTrailingOpen) {
MockSamplingEstimator mock{/*ndv=*/5.0};
// a: point; b: range; c: fully open
IndexBounds bounds;
bounds.fields.push_back(makePointOil("a"));
bounds.fields.push_back(makeRangeOil("b"));
bounds.fields.push_back(makeFullyOpenOil("c"));
auto result = estimateIndexSeeks(mock, bounds, /*multiKey=*/false);
ASSERT_OK(result);
// c stripped; b is first non-open, 1 interval; a is point -> no change. seek = 1.
ASSERT_EQ(result.getValue(), makeCard(1.0));
}
// Multikey index uses estimateNDVMultiKey rather than estimateNDV, producing a different estimate.
TEST(CardinalityEstimator, IndexSeeks_MultikeyUsesNDVMultiKey) {
// Non-multikey mock returns NDV=3, multikey mock returns NDVMultiKey=7.
MockSamplingEstimator mock{/*ndv=*/3.0, /*ndvMultiKey=*/7.0};
// a: range; b: single range interval
IndexBounds bounds;
bounds.fields.push_back(makeRangeOil("a"));
bounds.fields.push_back(makeRangeOil("b"));
auto nonMultikeyResult = estimateIndexSeeks(mock, bounds, /*multiKey=*/false);
ASSERT_OK(nonMultikeyResult);
ASSERT_EQ(nonMultikeyResult.getValue(), makeCard(3.0));
auto multikeyResult = estimateIndexSeeks(mock, bounds, /*multiKey=*/true);
ASSERT_OK(multikeyResult);
ASSERT_EQ(multikeyResult.getValue(), makeCard(7.0));
}
// estimateIndexSeeks must pass the OIL to estimateNDV (not boost::none), so the estimator
// can constrain its sample to values within the interval.
TEST(CardinalityEstimator, IndexSeeks_IntervalsPassedToNDV) {
// ndvBounded=3.0 is what the mock returns when bounds are supplied;
// _ndv=10.0 would be returned if boost::none were passed instead.
MockSamplingEstimator mock{/*ndv=*/10.0,
/*ndvMultiKey=*/boost::none,
/*ndvBounded=*/3.0};
// a: range [leading]; b: range [trailing, determines numIntervals=1]
IndexBounds bounds;
bounds.fields.push_back(makeRangeOil("a"));
bounds.fields.push_back(makeRangeOil("b"));
auto result = estimateIndexSeeks(mock, bounds, /*multiKey=*/false);
ASSERT_OK(result);
// b contributes 1 seek; a is leading range -> seekEstimate *= NDV(a, bounds).
// If intervals are passed correctly, NDV == 3.0 -> seek = 3.0.
// If intervals were omitted (boost::none), NDV == 10.0 -> seek = 10.0.
ASSERT_EQ(result.getValue(), makeCard(3.0));
}
// Same check for the multikey path: estimateNDVMultiKey must receive the OIL.
TEST(CardinalityEstimator, IndexSeeks_IntervalsPassedToNDVMultiKey) {
// ndvMultiKeyBounded=4.0 is returned when bounds are supplied to estimateNDVMultiKey;
// ndvMultiKey=10.0 would be returned for boost::none.
MockSamplingEstimator mock{/*ndv=*/10.0,
/*ndvMultiKey=*/10.0,
/*ndvBounded=*/10.0,
/*ndvMultiKeyBounded=*/4.0};
IndexBounds bounds;
bounds.fields.push_back(makeRangeOil("a"));
bounds.fields.push_back(makeRangeOil("b"));
auto result = estimateIndexSeeks(mock, bounds, /*multiKey=*/true);
ASSERT_OK(result);
// b contributes 1 seek; a is leading range -> seekEstimate *= NDVMultiKey(a, bounds).
// Correct: NDVMultiKey with bounds == 4.0 -> seek = 4.0.
// Wrong (no intervals): NDVMultiKey without bounds == 10.0 -> seek = 10.0.
ASSERT_EQ(result.getValue(), makeCard(4.0));
}
} // unnamed namespace
} // namespace mongo::cost_based_ranker

8
src/mongo/db/query/compiler/optimizer/cost_based_ranker/cost_estimator.cpp
View File

@ -108,15 +108,13 @@ void CostEstimator::computeAndSetNodeCost(const QuerySolutionNode* node,
const auto totalIncrementalFieldCost =
incrementalFieldCost * (numIncrementalFields * inCE);
// TODO SERVER-100611: Incorporate index seek cost (coefficient 'indexSeek') by adding
// 'indexSeek * estimatedNumSeeks' to 'nodeCost'. In the meantime, we know there will
// always be at least one seek.
const auto& seekEst = qsnEst.indexSeekCE.value_or(oneCE);
if (ixscanNode->direction == 1) {
nodeCost += indexScanForwardExamineKey * inCE + totalIncrementalFieldCost +
indexForwardSeek * oneCE;
indexForwardSeek * seekEst;
} else {
nodeCost += indexScanBackwardExamineKey * inCE + totalIncrementalFieldCost +
indexBackwardSeek * oneCE;
indexBackwardSeek * seekEst;
}
if (filter) {

4
src/mongo/db/query/compiler/optimizer/cost_based_ranker/estimates.h
View File

@ -41,6 +41,7 @@
#include <limits>
#include <boost/functional/hash.hpp>
#include <boost/optional/optional.hpp>
namespace mongo::cost_based_ranker {
@ -582,6 +583,7 @@ struct QSNEstimate {
// A QSN may have three estimates:
// - the number of processed data items (docs or keys): 'inCE'
// - the number of produced data items: 'outCE'
// - the number of index seeks: 'indexSeekCE'
// Only leaf QSN nodes have both 'inCE' and 'outCE' estimates. All other nodes have an
// 'out' CE since their input size is equal to the number of produced items by their child.
// For instance:
@ -589,8 +591,10 @@ struct QSNEstimate {
// and 'outCE' is the number of documents after applying the filter.
// - For an IndexScan node 'inCE' is the number of scanned keys, 'outCE' is the number of
// keys after applying a possible filter expression to the matching keys.
// Only IndexScanNodes will have a corresponding indexSeekCE
boost::optional<CardinalityEstimate> inCE;
CardinalityEstimate outCE{CardinalityType{0}, EstimationSource::Code};
boost::optional<CardinalityEstimate> indexSeekCE;
CostEstimate cost{CostType::maxValue(), EstimationSource::Code};
QSNEstimate() = default;

3
src/mongo/db/query/plan_explainer_impl.cpp
View File

@ -281,6 +281,9 @@ void statsToBSON(const stage_builder::PlanStageToQsnMap& planStageQsnMap,
bob->append("numDocsEstimate", ce);
}
}
if (est.indexSeekCE.has_value()) {
bob->append("indexSeekEstimate", est.indexSeekCE->toDouble());
}
}
// Display the BSON representation of the filter, if there is one.

5
src/mongo/dbtests/plan_ranking.cpp
View File

@ -697,9 +697,8 @@ public:
const std::string bestPlan("{fetch: {node: {ixscan: {pattern: {a: 1}}}}}");
auto soln = pickBestPlan(cq.get());
ASSERT(QueryPlannerTestLib::solutionMatches(bestPlan, soln->root()).isOK());
// TODO SERVER-100611: re-enable these tests.
// auto cbrSoln = bestCBRPlan(cq.get(), static_cast<size_t>(docCount));
// ASSERT(QueryPlannerTestLib::solutionMatches(bestPlan, cbrSoln->root()).isOK());
auto cbrSoln = bestCBRPlan(cq.get(), static_cast<size_t>(docCount));
ASSERT(QueryPlannerTestLib::solutionMatches(bestPlan, cbrSoln->root()).isOK());
}
};