in processing/src/main/java/org/apache/druid/segment/virtual/ExpressionPlanner.java [54:210]
public static ExpressionPlan plan(ColumnInspector inspector, Expr expression)
{
final Expr.BindingAnalysis analysis = expression.analyzeInputs();
EnumSet<ExpressionPlan.Trait> traits = EnumSet.noneOf(ExpressionPlan.Trait.class);
Set<String> noCapabilities = new HashSet<>();
Set<String> maybeMultiValued = new HashSet<>();
List<String> needsApplied = ImmutableList.of();
ColumnType singleInputType = null;
ExpressionType outputType = null;
final Set<String> columns = analysis.getRequiredBindings();
// check and set traits which allow optimized selectors to be created
if (columns.isEmpty()) {
traits.add(ExpressionPlan.Trait.CONSTANT);
} else if (expression.isIdentifier()) {
traits.add(ExpressionPlan.Trait.IDENTIFIER);
} else if (columns.size() == 1) {
final String column = Iterables.getOnlyElement(columns);
final ColumnCapabilities capabilities = inspector.getColumnCapabilities(column);
// These flags allow for selectors that wrap a single underlying column to be optimized, through caching results
// and via allowing deferred execution in the case of building dimension selectors.
// SINGLE_INPUT_SCALAR
// is set if an input is single valued, and the output is definitely single valued, with an additional requirement
// for strings that the column is dictionary encoded.
// SINGLE_INPUT_MAPPABLE
// is set when a single input string column, which can be multi-valued, but if so, it must be implicitly mappable
// (i.e. the expression is not treating its input as an array and not wanting to output an array)
if (capabilities != null && !analysis.hasInputArrays() && !analysis.isOutputArray()) {
boolean isSingleInputMappable = false;
boolean isSingleInputScalar = capabilities.hasMultipleValues().isFalse();
if (capabilities.is(ValueType.STRING)) {
isSingleInputScalar = isSingleInputScalar && capabilities.isDictionaryEncoded().isTrue();
isSingleInputMappable = capabilities.isDictionaryEncoded().isTrue() &&
!capabilities.hasMultipleValues().isUnknown();
}
// if satisfied, set single input output type and flags
if (isSingleInputScalar || isSingleInputMappable) {
singleInputType = capabilities.toColumnType();
if (isSingleInputScalar) {
traits.add(ExpressionPlan.Trait.SINGLE_INPUT_SCALAR);
}
if (isSingleInputMappable) {
traits.add(ExpressionPlan.Trait.SINGLE_INPUT_MAPPABLE);
}
}
}
}
// if we didn't eliminate this expression as a single input scalar or mappable expression, it might need
// automatic transformation to map across multi-valued inputs (or row by row detection in the worst case)
if (
ExpressionPlan.none(
traits,
ExpressionPlan.Trait.SINGLE_INPUT_SCALAR,
ExpressionPlan.Trait.CONSTANT,
ExpressionPlan.Trait.IDENTIFIER
)
) {
final Set<String> definitelyMultiValued = new HashSet<>();
final Set<String> definitelyArray = new HashSet<>();
for (String column : analysis.getRequiredBindings()) {
final ColumnCapabilities capabilities = inspector.getColumnCapabilities(column);
if (capabilities != null) {
if (capabilities.isArray()) {
definitelyArray.add(column);
} else if (capabilities.is(ValueType.STRING) && capabilities.hasMultipleValues().isTrue()) {
definitelyMultiValued.add(column);
} else if (capabilities.is(ValueType.STRING) &&
capabilities.hasMultipleValues().isMaybeTrue() &&
!analysis.getArrayBindings().contains(column)
) {
maybeMultiValued.add(column);
}
} else {
noCapabilities.add(column);
}
}
// find any inputs which will need implicitly mapped across multi-valued rows
needsApplied =
columns.stream()
.filter(
c -> !definitelyArray.contains(c)
&& definitelyMultiValued.contains(c)
&& !analysis.getArrayBindings().contains(c)
)
.collect(Collectors.toList());
// if any multi-value inputs, set flag for non-scalar inputs
if (analysis.hasInputArrays()) {
traits.add(ExpressionPlan.Trait.NON_SCALAR_INPUTS);
}
if (!noCapabilities.isEmpty()) {
traits.add(ExpressionPlan.Trait.UNKNOWN_INPUTS);
}
if (!maybeMultiValued.isEmpty()) {
traits.add(ExpressionPlan.Trait.INCOMPLETE_INPUTS);
}
// if expression needs transformed, lets do it
if (!needsApplied.isEmpty()) {
traits.add(ExpressionPlan.Trait.NEEDS_APPLIED);
}
}
// only set output type if we are pretty confident about input types
final boolean shouldComputeOutput = ExpressionPlan.none(
traits,
ExpressionPlan.Trait.UNKNOWN_INPUTS,
ExpressionPlan.Trait.INCOMPLETE_INPUTS
);
if (shouldComputeOutput) {
outputType = expression.getOutputType(inspector);
}
// if analysis predicts output, or inferred output type, is array, output will be arrays
if (analysis.isOutputArray() || (outputType != null && outputType.isArray())) {
traits.add(ExpressionPlan.Trait.NON_SCALAR_OUTPUT);
// single input mappable may not produce array output explicitly, only through implicit mapping
traits.remove(ExpressionPlan.Trait.SINGLE_INPUT_SCALAR);
traits.remove(ExpressionPlan.Trait.SINGLE_INPUT_MAPPABLE);
}
// vectorized expressions do not support unknown inputs, but they also do not currently have to deal with them, as
// missing capabilites is indicative of a non-existent column instead of an unknown schema. If this ever changes,
// this check should also change
boolean supportsVector = ExpressionPlan.none(
traits,
ExpressionPlan.Trait.INCOMPLETE_INPUTS
);
if (supportsVector && expression.canVectorize(inspector)) {
// make sure to compute the output type for a vector expression though, because we might have skipped it earlier
// due to unknown inputs, but that's ok here since it just means it doesnt exist
outputType = expression.getOutputType(inspector);
traits.add(ExpressionPlan.Trait.VECTORIZABLE);
}
return new ExpressionPlan(
inspector,
expression,
analysis,
traits,
outputType,
singleInputType,
Sets.union(noCapabilities, maybeMultiValued),
needsApplied
);
}