Examples with MatchCandidate com.apple.foundationdb.record.query.plan.temp.MatchCandidate used on opensource projects

Example 1 with MatchCandidate

use of com.apple.foundationdb.record.query.plan.temp.MatchCandidate in project fdb-record-layer by FoundationDB.

the class PlannerGraphProperty method show.

/**
 * Show the planner expression that and all the match candidates rendered in your default browser. This also
 * shows {@link PartialMatch}es between references if they exist.
 * @param renderSingleGroups iff true group references with just one member are not rendered
 * @param queryPlanRootReference the planner expression to be rendered.
 * @param matchCandidates a set of candidates for matching which should also be shown
 * @return the word "done" (IntelliJ really likes a return of String).
 */
@Nonnull
public static String show(final boolean renderSingleGroups, @Nonnull final GroupExpressionRef<? extends RelationalExpression> queryPlanRootReference, @Nonnull final Set<MatchCandidate> matchCandidates) {
    final PlannerGraph queryPlannerGraph = Objects.requireNonNull(queryPlanRootReference.acceptPropertyVisitor(forInternalShow(renderSingleGroups, true)));
    final PlannerGraph.InternalPlannerGraphBuilder graphBuilder = queryPlannerGraph.derived();
    final Map<MatchCandidate, PlannerGraph> matchCandidateMap = matchCandidates.stream().collect(ImmutableMap.toImmutableMap(Function.identity(), matchCandidate -> Objects.requireNonNull(matchCandidate.getTraversal().getRootReference().acceptPropertyVisitor(forInternalShow(renderSingleGroups)))));
    matchCandidateMap.forEach((matchCandidate, matchCandidateGraph) -> graphBuilder.addGraph(matchCandidateGraph));
    final ExpressionRefTraversal queryGraphTraversal = ExpressionRefTraversal.withRoot(queryPlanRootReference);
    final Set<ExpressionRef<? extends RelationalExpression>> queryGraphRefs = queryGraphTraversal.getRefs();
    queryGraphRefs.forEach(queryGraphRef -> {
        for (final MatchCandidate matchCandidate : Sets.intersection(matchCandidates, queryGraphRef.getMatchCandidates())) {
            final Set<PartialMatch> partialMatchesForCandidate = queryGraphRef.getPartialMatchesForCandidate(matchCandidate);
            final PlannerGraph matchCandidatePlannerGraph = Objects.requireNonNull(matchCandidateMap.get(matchCandidate));
            final Node queryRefNode = Objects.requireNonNull(queryPlannerGraph.getNodeForIdentity(queryGraphRef));
            for (final PartialMatch partialMatchForCandidate : partialMatchesForCandidate) {
                @Nullable final Node matchCandidateNode = matchCandidatePlannerGraph.getNodeForIdentity(partialMatchForCandidate.getCandidateRef());
                // should always be true but we don't want to bail out for corrupt graphs
                if (matchCandidateNode != null) {
                    graphBuilder.addEdge(queryRefNode, matchCandidateNode, new PartialMatchEdge());
                }
            }
        }
    });
    final String dotString = exportToDot(graphBuilder.build(), queryPlannerGraph.getNetwork().nodes(), nestedClusterProvider -> matchCandidateMap.entrySet().stream().map(entry -> new NamedCluster(entry.getKey().getName(), entry.getValue().getNetwork().nodes(), nestedClusterProvider)).collect(Collectors.toList()));
    return show(dotString);
}

Example 2 with MatchCandidate

use of com.apple.foundationdb.record.query.plan.temp.MatchCandidate in project fdb-record-layer by FoundationDB.

the class AdjustMatchRule method onMatch.

@Override
@SuppressWarnings("java:S135")
public void onMatch(@Nonnull PlannerRuleCall call) {
    final PlannerBindings bindings = call.getBindings();
    final PartialMatch incompleteMatch = bindings.get(rootMatcher);
    final ExpressionRef<? extends RelationalExpression> queryReference = incompleteMatch.getQueryRef();
    final MatchCandidate matchCandidate = incompleteMatch.getMatchCandidate();
    final SetMultimap<ExpressionRef<? extends RelationalExpression>, RelationalExpression> refToExpressionMap = matchCandidate.findReferencingExpressions(ImmutableList.of(queryReference));
    for (final Map.Entry<ExpressionRef<? extends RelationalExpression>, RelationalExpression> entry : refToExpressionMap.entries()) {
        final ExpressionRef<? extends RelationalExpression> candidateReference = entry.getKey();
        final RelationalExpression candidateExpression = entry.getValue();
        matchWithCandidate(incompleteMatch, candidateExpression).ifPresent(matchInfo -> call.yieldPartialMatch(incompleteMatch.getBoundAliasMap(), matchCandidate, incompleteMatch.getQueryExpression(), candidateReference, matchInfo));
    }
}

Example 3 with MatchCandidate

use of com.apple.foundationdb.record.query.plan.temp.MatchCandidate in project fdb-record-layer by FoundationDB.

the class AbstractDataAccessRule method onMatch.

/**
 * Method that does the leg work to create the appropriate expression dag for data access using value indexes or
 * value index-like scans (primary scans).
 *
 * Conceptually we do the following work:
 *
 * <ul>
 * <li> This method yields a scan plan for each matching primary candidate ({@link PrimaryScanMatchCandidate}).
 *      There is only ever going to be exactly one {@link PrimaryScanMatchCandidate} for a primary key. Due to the
 *      candidate being solely based upon a primary key, the match structure is somewhat limited. In essence, there
 *      is an implicit guarantee that we can always create a primary scan for a data source.
 * </li>
 * <li> This method yields an index scan plan for each matching value index candidate
 *      ({@link ValueIndexScanMatchCandidate}).
 * </li>
 * <li> This method yields the combinatorial expansion of intersections of distinct-ed index scan plans.
 * </li>
 * </ul>
 *
 * The work described above is semantically correct in a sense that it creates a search space that can be explored
 * and pruned in suitable ways that will eventually converge into an optimal data access plan.
 *
 * We can choose to create an index scan for every index that is available regardless what the coverage
 * of an index is. The coverage of an index is a measurement that tells us how well an index can answer what a
 * filter (or by extension a query) asks for. For instance, a high number of search arguments used in the index scan
 * can be associated with high coverage (as in the index scan covers more of the query) and vice versa.
 *
 * Similarly, we can choose to create the intersection of all possible combinations of suitable scans over indexes
 * (that we have matches for). Since we create a logical intersection of these access plans we can leave it up to
 * the respective implementation rules (e.g., {@link ImplementIntersectionRule}) to do the right thing and implement
 * the physical plan for the intersection if possible (e.g. ensuring compatibly ordered legs, etc.).
 *
 * In fact, the two before-mentioned approaches are completely valid with respect to correctness of the plan and
 * the guaranteed creation of the optimal plan. However, in reality using this approach, although valid and probably
 * the conceptually better and more orthogonal approach, will result in a ballooning of the search space very quickly.
 * While that may be acceptable for group-by engines and only few index access paths, in an OLTP world where there
 * are potentially dozens of indexes, memory footprint and the sheer number of tasks that would be created for
 * subsequent exploration and implementation of all these alternatives make the purist approach to planning these
 * indexes infeasible.
 *
 * Thus we would like to eliminate unnecessary exploration by avoiding variations we know can never be successful
 * either in creating a successful executable plan (e.g. logical expression may not ever be able to produce a
 * compatible ordering) or cannot ever create an optimal plan. In a nutshell, we try to utilize additional
 * information that is available in addition to the matching partition in order to make decisions about which
 * expression variation to create and which to avoid:
 *
 * <ul>
 * <li> For a matching primary scan candidate ({@link PrimaryScanMatchCandidate})
 *      we will not create a primary scan if the scan is incompatible with an interesting order that has been
 *      communicated downwards in the graph.
 * </li>
 * <li> For a matching index scan candidate ({@link ValueIndexScanMatchCandidate})
 *      we will not create an index scan if the scan is incompatible with an interesting order that has been
 *      communicated downwards in the graph.
 * </li>
 * <li> We will only create a scan if there is no other index scan with a greater coverage (think of coverage
 *      as the assumed amount of filtering or currently the number of bound predicates) for the search arguments
 *      which are bound by the query.
 *      For instance, an index scan {@code INDEX SCAN(i1, a = [5, 5], b = [10, 10])} is still planned along
 *      {@code INDEX SCAN(i2, x = ["hello", "hello"], y = ["world", "world"], z = [10, inf])} even though
 *      the latter utilizes three search arguments while the former one only uses two. However, an index scan
 *      {@code INDEX SCAN(i1, a = [5, 5], b = [10, 10])} is not created (and yielded) if there we also
 *      have a choice to plan {@code INDEX SCAN(i2, b = [10, 10], a = [5, 5], c = ["Guten", "Morgen"])} as that
 *      index {@code i2} has a higher coverage compared to {@code i1} <em>and</em> all bound arguments in the scan
 *      over {@code i2} are also bound in the scan over {@code i1}.
 * <li>
 *      We will only create intersections of scans if we can already establish that the logical intersection
 *      can be implemented by a {@link com.apple.foundationdb.record.query.plan.plans.RecordQueryIntersectionPlan}.
 *      That requires that the legs of the intersection are compatibly ordered <em>and</em> that that ordering follows
 *      a potentially required ordering.
 * </li>
 * </ul>
 *
 * @param call the call associated with this planner rule execution
 */
@Override
@SuppressWarnings("java:S135")
public void onMatch(@Nonnull PlannerRuleCall call) {
    final PlannerBindings bindings = call.getBindings();
    final List<? extends PartialMatch> completeMatches = bindings.getAll(getCompleteMatchMatcher());
    final R expression = bindings.get(getExpressionMatcher());
    // 
    if (completeMatches.isEmpty()) {
        return;
    }
    // 
    // return if there is no pre-determined interesting ordering
    // 
    final Optional<Set<RequestedOrdering>> requestedOrderingsOptional = call.getInterestingProperty(OrderingAttribute.ORDERING);
    if (requestedOrderingsOptional.isEmpty()) {
        return;
    }
    final Set<RequestedOrdering> requestedOrderings = requestedOrderingsOptional.get();
    // 
    // group matches by candidates
    // 
    final LinkedHashMap<MatchCandidate, ? extends ImmutableList<? extends PartialMatch>> completeMatchMap = completeMatches.stream().collect(Collectors.groupingBy(PartialMatch::getMatchCandidate, LinkedHashMap::new, ImmutableList.toImmutableList()));
    // find the best match for a candidate as there may be more than one due to partial matching
    final ImmutableSet<PartialMatch> maximumCoverageMatchPerCandidate = completeMatchMap.entrySet().stream().flatMap(entry -> {
        final List<? extends PartialMatch> completeMatchesForCandidate = entry.getValue();
        final Optional<? extends PartialMatch> bestMatchForCandidateOptional = completeMatchesForCandidate.stream().max(Comparator.comparing(PartialMatch::getNumBoundParameterPrefix));
        return bestMatchForCandidateOptional.map(Stream::of).orElse(Stream.empty());
    }).collect(ImmutableSet.toImmutableSet());
    final List<PartialMatch> bestMaximumCoverageMatches = maximumCoverageMatches(maximumCoverageMatchPerCandidate, requestedOrderings);
    if (bestMaximumCoverageMatches.isEmpty()) {
        return;
    }
    // create scans for all best matches
    final Map<PartialMatch, RelationalExpression> bestMatchToExpressionMap = createScansForMatches(bestMaximumCoverageMatches);
    final ExpressionRef<RelationalExpression> toBeInjectedReference = GroupExpressionRef.empty();
    // create single scan accesses
    for (final PartialMatch bestMatch : bestMaximumCoverageMatches) {
        final RelationalExpression dataAccessAndCompensationExpression = compensateSingleDataAccess(bestMatch, bestMatchToExpressionMap.get(bestMatch));
        toBeInjectedReference.insert(dataAccessAndCompensationExpression);
    }
    final Map<PartialMatch, RelationalExpression> bestMatchToDistinctExpressionMap = distinctMatchToScanMap(bestMatchToExpressionMap);
    @Nullable final KeyExpression commonPrimaryKey = call.getContext().getCommonPrimaryKey();
    if (commonPrimaryKey != null) {
        final var commonPrimaryKeyParts = commonPrimaryKey.normalizeKeyForPositions();
        final var boundPartitions = Lists.<List<PartialMatch>>newArrayList();
        // create intersections for all n choose k partitions from k = 2 .. n
        IntStream.range(2, bestMaximumCoverageMatches.size() + 1).mapToObj(k -> ChooseK.chooseK(bestMaximumCoverageMatches, k)).flatMap(iterable -> StreamSupport.stream(iterable.spliterator(), false)).forEach(boundPartitions::add);
        boundPartitions.stream().flatMap(partition -> createIntersectionAndCompensation(commonPrimaryKeyParts, bestMatchToDistinctExpressionMap, partition, requestedOrderings).stream()).forEach(toBeInjectedReference::insert);
    }
    call.yield(inject(expression, completeMatches, toBeInjectedReference));
}

Example 4 with MatchCandidate

use of com.apple.foundationdb.record.query.plan.temp.MatchCandidate in project fdb-record-layer by FoundationDB.

the class MatchableSortExpression method forPartialMatch.

/**
 * This synthesizes a list of {@link BoundKeyPart}s from the current partial match and the ordering information
 * contained in this expression. Using the list of parameter ids, each {@link BoundKeyPart} links together the
 * (1) normalized key expression that originally produced the key (from index, or common primary key)
 * (2) a comparison range for this parameter which is contained in the already existent partial match
 * (3) the predicate on the query part that participated and bound this parameter (and implicitly was used to
 *     synthesize the comparison range in (2)
 * (4) the candidate predicate on the candidate side that is the {@link Placeholder} for the parameter
 * @param partialMatch the pre-existing partial match on {@code (expression, this)} that the caller wants to adjust.
 * @return a list of bound key parts that express the order of the outgoing data stream and their respective mappings
 *         between query and match candidate
 */
@Nonnull
private List<BoundKeyPart> forPartialMatch(@Nonnull PartialMatch partialMatch) {
    final MatchCandidate matchCandidate = partialMatch.getMatchCandidate();
    final MatchInfo matchInfo = partialMatch.getMatchInfo();
    final Map<CorrelationIdentifier, ComparisonRange> parameterBindingMap = matchInfo.getParameterBindingMap();
    final PredicateMap accumulatedPredicateMap = matchInfo.getAccumulatedPredicateMap();
    final ImmutableMap<CorrelationIdentifier, QueryPredicate> parameterBindingPredicateMap = accumulatedPredicateMap.entries().stream().filter(entry -> {
        final PredicateMapping predicateMapping = entry.getValue();
        return predicateMapping.getParameterAliasOptional().isPresent();
    }).collect(ImmutableMap.toImmutableMap(entry -> {
        final PredicateMapping predicateMapping = entry.getValue();
        return Objects.requireNonNull(predicateMapping.getParameterAliasOptional().orElseThrow(() -> new RecordCoreException("parameter alias should have been set")));
    }, entry -> Objects.requireNonNull(entry.getKey())));
    final List<KeyExpression> normalizedKeys = matchCandidate.getAlternativeKeyExpression().normalizeKeyForPositions();
    final ImmutableList.Builder<BoundKeyPart> builder = ImmutableList.builder();
    final List<CorrelationIdentifier> candidateParameterIds = matchCandidate.getParameters();
    for (final CorrelationIdentifier parameterId : sortParameterIds) {
        final int ordinalInCandidate = candidateParameterIds.indexOf(parameterId);
        Verify.verify(ordinalInCandidate >= 0);
        final KeyExpression normalizedKey = normalizedKeys.get(ordinalInCandidate);
        Objects.requireNonNull(parameterId);
        Objects.requireNonNull(normalizedKey);
        @Nullable final ComparisonRange comparisonRange = parameterBindingMap.get(parameterId);
        @Nullable final QueryPredicate queryPredicate = parameterBindingPredicateMap.get(parameterId);
        Verify.verify(comparisonRange == null || comparisonRange.getRangeType() == ComparisonRange.Type.EMPTY || queryPredicate != null);
        builder.add(BoundKeyPart.of(normalizedKey, comparisonRange == null ? ComparisonRange.Type.EMPTY : comparisonRange.getRangeType(), queryPredicate, isReverse));
    }
    return builder.build();
}

Example 5 with MatchCandidate

use of com.apple.foundationdb.record.query.plan.temp.MatchCandidate in project fdb-record-layer by FoundationDB.

the class MatchIntermediateRule method onMatch.

@Override
public void onMatch(@Nonnull PlannerRuleCall call) {
    final PlannerBindings bindings = call.getBindings();
    final RelationalExpression expression = bindings.get(root);
    final List<? extends Quantifier> quantifiers = bindings.getAll(quantifierMatcher);
    final ImmutableList<? extends ExpressionRef<? extends RelationalExpression>> rangesOverRefs = quantifiers.stream().map(Quantifier::getRangesOver).collect(ImmutableList.toImmutableList());
    // form union of all possible match candidates that this rule application should look at
    final Set<MatchCandidate> childMatchCandidates = new LinkedIdentitySet<>();
    for (int i = 0; i < rangesOverRefs.size(); i++) {
        final ExpressionRef<? extends RelationalExpression> rangesOverGroup = rangesOverRefs.get(i);
        childMatchCandidates.addAll(rangesOverGroup.getMatchCandidates());
    }
    // go through all match candidates
    for (final MatchCandidate matchCandidate : childMatchCandidates) {
        final SetMultimap<ExpressionRef<? extends RelationalExpression>, RelationalExpression> refToExpressionMap = matchCandidate.findReferencingExpressions(rangesOverRefs);
        // go through all reference paths, i.e., (ref, expression) pairs
        for (final Map.Entry<ExpressionRef<? extends RelationalExpression>, RelationalExpression> entry : refToExpressionMap.entries()) {
            final ExpressionRef<? extends RelationalExpression> candidateReference = entry.getKey();
            final RelationalExpression candidateExpression = entry.getValue();
            // match this expression with the candidate expression and yield zero to n new partial matches
            final Iterable<BoundMatch<MatchInfo>> boundMatchInfos = matchWithCandidate(expression, matchCandidate, candidateExpression);
            boundMatchInfos.forEach(boundMatchInfo -> call.yieldPartialMatch(boundMatchInfo.getAliasMap(), matchCandidate, expression, candidateReference, boundMatchInfo.getMatchResult()));
        }
    }
}