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Add schema to columnStats --------- Co-authored-by: jiawei <jiaweihu08@gmail.com>
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149
core/src/main/scala/io/qbeast/core/model/QbeastColumnStats.scala
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| /* | ||
| * Copyright 2021 Qbeast Analytics, S.L. | ||
| * | ||
| * Licensed under the Apache License, Version 2.0 (the "License"); | ||
| * you may not use this file except in compliance with the License. | ||
| * You may obtain a copy of the License at | ||
| * | ||
| * http://www.apache.org/licenses/LICENSE-2.0 | ||
| * | ||
| * Unless required by applicable law or agreed to in writing, software | ||
| * distributed under the License is distributed on an "AS IS" BASIS, | ||
| * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | ||
| * See the License for the specific language governing permissions and | ||
| * limitations under the License. | ||
| */ | ||
| package io.qbeast.core.model | ||
|
|
||
| import io.qbeast.core.transform._ | ||
| import io.qbeast.spark.utils.SparkToQTypesUtils | ||
| import org.apache.spark.internal.Logging | ||
| import org.apache.spark.sql.types._ | ||
| import org.apache.spark.sql.AnalysisExceptionFactory | ||
| import org.apache.spark.sql.Row | ||
| import org.apache.spark.sql.SparkSession | ||
|
|
||
| /** | ||
| * Container for Qbeast Column Stats | ||
| * | ||
| * @param schema | ||
| * the column stats schema | ||
| * @param rowOption | ||
| * the column stats row | ||
| */ | ||
| case class QbeastColumnStats(schema: StructType, rowOption: Option[Row]) extends Logging { | ||
|
|
||
| /** | ||
| * Creates a transformation for the given transformer. If the stats are not available, it | ||
| * returns None. An IllegalArgumentException is thrown if the output is an | ||
| * IdentityTransformation. Otherwise, the provided stats will be used to create and returns the | ||
| * transformation. | ||
| * | ||
| * @param transformer | ||
| * the transformer | ||
| * @return | ||
| */ | ||
| def createTransformation(transformer: Transformer): Option[Transformation] = rowOption match { | ||
| case Some(row) => | ||
| val hasStats = transformer.stats.statsNames.exists(row.getAs[Object](_) != null) | ||
| if (hasStats) { | ||
| val transformation = transformer.makeTransformation(row.getAs[Object]) | ||
| (transformer, transformation) match { | ||
| case (_: LinearTransformer, _: IdentityTransformation) => | ||
| // If the transformation is IdentityTransformation, it means: | ||
| // 1. Either columnName_min or columnName_max is missing or | ||
| // 2. columnName_min and columnName_max are the same | ||
| throw new IllegalArgumentException( | ||
| s"The provided columnStats for column ${transformer.columnName} are not valid. " + | ||
| s"Make sure both min and max values are provided and satisfy the condition: min < max.") | ||
| case _ => Some(transformation) | ||
| } | ||
| } else None | ||
| case None => None | ||
| } | ||
|
|
||
| } | ||
|
|
||
| /** | ||
| * Companion object for QbeastColumnStats | ||
| */ | ||
| object QbeastColumnStats { | ||
|
|
||
| /** | ||
| * Builds the column stats schema | ||
| * | ||
| * For each column transformer, create the sequence StructField for its column stats | ||
| * @param columnTransformers | ||
| * the column transformers | ||
| * @return | ||
| */ | ||
| private[model] def buildColumnStatsSchema(columnTransformers: Seq[Transformer]): StructType = { | ||
| val builder = Seq.newBuilder[StructField] | ||
| columnTransformers.foreach { t => | ||
| val fields = t match { | ||
| case lt: LinearTransformer => | ||
| val sparkDataType = SparkToQTypesUtils.convertToSparkDataType(lt.dataType) | ||
| lt.stats.statsNames.map(StructField(_, sparkDataType, nullable = true)) | ||
| case nq: CDFNumericQuantilesTransformer => | ||
| nq.stats.statsNames.map(StructField(_, ArrayType(DoubleType), nullable = true)) | ||
| case sq: CDFStringQuantilesTransformer => | ||
| sq.stats.statsNames.map(StructField(_, ArrayType(StringType), nullable = true)) | ||
| case sh: StringHistogramTransformer => | ||
| sh.stats.statsNames.map(StructField(_, ArrayType(StringType), nullable = true)) | ||
| case _ => Seq.empty | ||
| // TODO: Add support for other transformers | ||
| } | ||
| builder ++= fields | ||
| } | ||
| StructType(builder.result()) | ||
| } | ||
|
|
||
| /** | ||
| * Builds the column stats row | ||
| * | ||
| * @param stats | ||
| * the stats in a JSON string | ||
| * @param columnStatsSchema | ||
| * the column stats schema | ||
| * @return | ||
| */ | ||
| private[model] def buildColumnStatsRow( | ||
| stats: String, | ||
| columnStatsSchema: StructType): Option[Row] = { | ||
| if (stats.isEmpty) None // No stats are provided | ||
| else { | ||
| val spark = SparkSession.active | ||
| import spark.implicits._ | ||
| val columnStatsJSON = Seq(stats).toDS() | ||
| val row = spark.read | ||
| .option("inferTimestamp", "true") | ||
| .option("timestampFormat", "yyyy-MM-dd HH:mm:ss.SSSSSS'Z'") | ||
| .schema(columnStatsSchema) | ||
| .json(columnStatsJSON) | ||
| .first() | ||
| // All values will be Null is the input JSON is invalid | ||
| val isInvalidJSON = row.toSeq.forall(_ == null) | ||
| if (isInvalidJSON) { | ||
| throw AnalysisExceptionFactory.create( | ||
| s"The columnStats provided is not a valid JSON: $stats") | ||
| } | ||
| Some(row) | ||
| } | ||
| } | ||
|
|
||
| /** | ||
| * Builds the QbeastColumnStats | ||
| * | ||
| * @param statsString | ||
| * the stats in a JSON string | ||
| * @param columnTransformers | ||
| * the set of columnTransformers to build the Stats from | ||
| * @return | ||
| */ | ||
| def apply(statsString: String, columnTransformers: Seq[Transformer]): QbeastColumnStats = { | ||
| val statsSchema = buildColumnStatsSchema(columnTransformers) | ||
| val statsRowOption = buildColumnStatsRow(statsString, statsSchema) | ||
| QbeastColumnStats(statsSchema, statsRowOption) | ||
| } | ||
|
|
||
| } |
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