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GraphStatisticsSnapshotTest.scala
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GraphStatisticsSnapshotTest.scala
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/*
* Copyright (c) 2002-2016 "Neo Technology,"
* Network Engine for Objects in Lund AB [http://neotechnology.com]
*
* This file is part of Neo4j.
*
* Neo4j is free software: you can redistribute it and/or modify
* it under the terms of the GNU General Public License as published by
* the Free Software Foundation, either version 3 of the License, or
* (at your option) any later version.
*
* This program is distributed in the hope that it will be useful,
* but WITHOUT ANY WARRANTY; without even the implied warranty of
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
* GNU General Public License for more details.
*
* You should have received a copy of the GNU General Public License
* along with this program. If not, see <http://www.gnu.org/licenses/>.
*/
package org.neo4j.cypher.internal.compiler.v3_1.spi
import org.neo4j.cypher.internal.compiler.v3_1.planner.logical.{Cardinality, Selectivity}
import org.neo4j.cypher.internal.frontend.v3_1.test_helpers.CypherFunSuite
import org.neo4j.cypher.internal.frontend.v3_1.{LabelId, PropertyKeyId, RelTypeId}
import scala.language.reflectiveCalls
class GraphStatisticsSnapshotTest extends CypherFunSuite {
def graphStatistics() = new GraphStatistics {
private var _factor = 1L
def nodesWithLabelCardinality(labelId: Option[LabelId]): Cardinality =
Cardinality(labelId.fold(500)(_.id * 10) * _factor)
def cardinalityByLabelsAndRelationshipType(fromLabel: Option[LabelId], relTypeId: Option[RelTypeId], toLabel: Option[LabelId]): Cardinality =
Cardinality(relTypeId.fold(5000)(_.id * 10) * _factor)
def indexSelectivity(label: LabelId, property: PropertyKeyId): Option[Selectivity] =
Selectivity.of(1.0 / ((property.id + 1) * _factor))
def indexPropertyExistsSelectivity(label: LabelId, property: PropertyKeyId): Option[Selectivity] =
Selectivity.of(1.0 / ((property.id + 1) * _factor))
def factor(factor: Long): Unit = {
_factor = factor
}
}
test("records queries and its observed values") {
val snapshot = MutableGraphStatisticsSnapshot()
val instrumentedStatistics = InstrumentedGraphStatistics(graphStatistics(), snapshot)
instrumentedStatistics.nodesWithLabelCardinality(None)
instrumentedStatistics.indexSelectivity(LabelId(0), PropertyKeyId(3))
instrumentedStatistics.nodesWithLabelCardinality(Some(LabelId(4)))
instrumentedStatistics.cardinalityByLabelsAndRelationshipType(Some(LabelId(2)), None, None)
instrumentedStatistics.cardinalityByLabelsAndRelationshipType(None, Some(RelTypeId(1)), Some(LabelId(2)))
snapshot.freeze.map should equal(Map(
NodesWithLabelCardinality(None) -> 500,
IndexSelectivity(LabelId(0), PropertyKeyId(3)) -> 0.25,
NodesWithLabelCardinality(Some(LabelId(4))) -> 40,
CardinalityByLabelsAndRelationshipType(Some(LabelId(2)), None, None) -> 5000,
CardinalityByLabelsAndRelationshipType(None, Some(RelTypeId(1)), Some(LabelId(2))) -> 10
))
}
test("a snapshot shouldn't diverge from itself") {
val snapshot = MutableGraphStatisticsSnapshot()
val instrumentedStatistics = InstrumentedGraphStatistics(graphStatistics(), snapshot)
instrumentedStatistics.nodesWithLabelCardinality(None)
instrumentedStatistics.indexSelectivity(LabelId(0), PropertyKeyId(3))
instrumentedStatistics.nodesWithLabelCardinality(Some(LabelId(4)))
instrumentedStatistics.cardinalityByLabelsAndRelationshipType(Some(LabelId(2)), None, None)
instrumentedStatistics.cardinalityByLabelsAndRelationshipType(None, Some(RelTypeId(1)), Some(LabelId(2)))
val frozenSnapshot = snapshot.freeze
val smallNumber: Double = 1e-10
frozenSnapshot.diverges(frozenSnapshot, smallNumber) should equal(false)
}
test("a snapshot should pick up divergences") {
val snapshot1 = MutableGraphStatisticsSnapshot()
val statistics = graphStatistics()
val instrumentedStatistics1 = InstrumentedGraphStatistics(statistics, snapshot1)
instrumentedStatistics1.nodesWithLabelCardinality(None)
instrumentedStatistics1.indexSelectivity(LabelId(0), PropertyKeyId(3))
instrumentedStatistics1.nodesWithLabelCardinality(Some(LabelId(4)))
val snapshot2 = MutableGraphStatisticsSnapshot()
val instrumentedStatistics2 = InstrumentedGraphStatistics(statistics, snapshot2)
instrumentedStatistics2.nodesWithLabelCardinality(None)
instrumentedStatistics2.nodesWithLabelCardinality(Some(LabelId(4)))
statistics.factor(2)
instrumentedStatistics2.indexSelectivity(LabelId(0), PropertyKeyId(3))
val frozen1 = snapshot1.freeze
val frozen2 = snapshot2.freeze
val smallNumber = 0.1
val bigNumber = 0.6
frozen1.diverges(frozen2, smallNumber) should equal(true)
frozen1.diverges(frozen2, bigNumber) should equal(false)
}
test("if threshold is 1.0 nothing diverges") {
val snapshot1 = MutableGraphStatisticsSnapshot()
val statistics = graphStatistics()
val instrumentedStatistics1 = InstrumentedGraphStatistics(statistics, snapshot1)
instrumentedStatistics1.nodesWithLabelCardinality(None)
instrumentedStatistics1.indexSelectivity(LabelId(0), PropertyKeyId(3))
instrumentedStatistics1.nodesWithLabelCardinality(Some(LabelId(4)))
val snapshot2 = MutableGraphStatisticsSnapshot()
val instrumentedStatistics2 = InstrumentedGraphStatistics(statistics, snapshot2)
instrumentedStatistics2.nodesWithLabelCardinality(None)
instrumentedStatistics2.nodesWithLabelCardinality(Some(LabelId(4)))
statistics.factor(Long.MaxValue)
instrumentedStatistics2.indexSelectivity(LabelId(0), PropertyKeyId(3))
val frozen1 = snapshot1.freeze
val frozen2 = snapshot2.freeze
frozen1.diverges(frozen2, 1.0) should equal(false)
}
}