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...earch/src/test/scala/com/stratio/crossdata/connector/elasticsearch/ElasticDataTypes.scala
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| /* | ||
| * Copyright (C) 2015 Stratio (http://stratio.com) | ||
| * | ||
| * 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 com.stratio.crossdata.connector.elasticsearch | ||
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| import java.util.{GregorianCalendar, UUID} | ||
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| import com.sksamuel.elastic4s.ElasticDsl._ | ||
| import com.sksamuel.elastic4s.mappings.FieldType._ | ||
| import com.sksamuel.elastic4s.mappings.{MappingDefinition, TypedFieldDefinition} | ||
| import com.stratio.common.utils.components.logger.impl.SparkLoggerComponent | ||
| import com.typesafe.config.ConfigFactory | ||
| import org.apache.spark.sql.Row | ||
| import org.apache.spark.sql.catalyst.expressions.GenericRowWithSchema | ||
| import org.apache.spark.sql.crossdata.test.SharedXDContextTypesTest | ||
| import org.apache.spark.sql.crossdata.test.SharedXDContextTypesTest.SparkSQLColDef | ||
| import org.joda.time.DateTime | ||
|
|
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| trait ElasticDataTypes extends ElasticWithSharedContext | ||
| with SharedXDContextTypesTest | ||
| with ElasticSearchDataTypesDefaultConstants | ||
| with SparkLoggerComponent { | ||
|
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| override val dataTypesSparkOptions = Map( | ||
| "resource" -> s"$Index/$Type", | ||
| "es.nodes" -> s"$ElasticHost", | ||
| "es.port" -> s"$ElasticRestPort", | ||
| "es.nativePort" -> s"$ElasticNativePort", | ||
| "es.cluster" -> s"$ElasticClusterName", | ||
| "es.nodes.wan.only" -> "true", | ||
| "es.read.field.as.array.include" -> Seq( | ||
| "arrayint" | ||
| ).mkString(",") | ||
| ) | ||
|
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| protected case class ESColumnData(elasticType: Option[TypedFieldDefinition], data: () => Any) | ||
| protected object ESColumnData { | ||
| def apply(data: () => Any): ESColumnData = ESColumnData(None, data) | ||
| def apply(elasticType: TypedFieldDefinition, data: () => Any): ESColumnData = ESColumnData(Some(elasticType), data) | ||
| } | ||
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|
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| override val arrayFlattenTestColumn: String = "arraystruct" | ||
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| protected val dataTest: Seq[(SparkSQLColDef, ESColumnData)] = Seq( | ||
| (SparkSQLColDef("id", "INT", _ shouldBe a[java.lang.Integer]), ESColumnData("id" typed IntegerType, () => 1)), | ||
| (SparkSQLColDef("age", "LONG", _ shouldBe a[java.lang.Long]), ESColumnData("age" typed LongType, () => 1)), | ||
| ( | ||
| SparkSQLColDef("description", "STRING", _ shouldBe a[java.lang.String]), | ||
| ESColumnData("description" typed StringType, () => "1") | ||
| ), | ||
| ( | ||
| SparkSQLColDef("name", "STRING", _ shouldBe a[java.lang.String]), | ||
| ESColumnData( "name" typed StringType index NotAnalyzed, () => "1") | ||
| ), | ||
| ( | ||
| SparkSQLColDef("enrolled", "BOOLEAN", _ shouldBe a[java.lang.Boolean]), | ||
| ESColumnData("enrolled" typed BooleanType, () => false) | ||
| ), | ||
| ( | ||
| SparkSQLColDef("birthday", "DATE", _ shouldBe a [java.sql.Date]), | ||
| ESColumnData("birthday" typed DateType, () => DateTime.parse(1980 + "-01-01T10:00:00-00:00").toDate) | ||
| ), | ||
| ( | ||
| SparkSQLColDef("salary", "DOUBLE", _ shouldBe a[java.lang.Double]), | ||
| ESColumnData("salary" typed DoubleType, () => 0.15) | ||
| ), | ||
| ( | ||
| SparkSQLColDef("timecol", "TIMESTAMP", _ shouldBe a[java.sql.Timestamp]), | ||
| ESColumnData( | ||
| "timecol" typed DateType, | ||
| () => new java.sql.Timestamp(new GregorianCalendar(1970, 0, 1, 0, 0, 0).getTimeInMillis) | ||
| ) | ||
| ), | ||
| ( | ||
| SparkSQLColDef("float", "FLOAT", _ shouldBe a[java.lang.Float]), | ||
| ESColumnData("float" typed FloatType, () => 0.15) | ||
| ), | ||
| ( | ||
| SparkSQLColDef("binary", "BINARY", x => x.isInstanceOf[Array[Byte]] shouldBe true), | ||
| ESColumnData("binary" typed BinaryType, () => "YWE=".getBytes) | ||
| ), | ||
| ( | ||
| SparkSQLColDef("tinyint", "TINYINT", _ shouldBe a[java.lang.Byte]), | ||
| ESColumnData("tinyint" typed ByteType, () => Byte.MinValue) | ||
| ), | ||
| ( | ||
| SparkSQLColDef("smallint", "SMALLINT", _ shouldBe a[java.lang.Short]), | ||
| ESColumnData("smallint" typed ShortType, () => Short.MaxValue) | ||
| ), | ||
| ( | ||
| SparkSQLColDef("subdocument", "STRUCT<field1: INT>", _ shouldBe a [Row]), | ||
| ESColumnData("subdocument" inner ("field1" typed IntegerType), () => Map( "field1" -> 15)) | ||
| ), | ||
| ( | ||
| SparkSQLColDef( | ||
| "structofstruct", | ||
| "STRUCT<field1: INT, struct1: STRUCT<structField1: INT>>", | ||
| { res => | ||
| res shouldBe a[GenericRowWithSchema] | ||
| res.asInstanceOf[GenericRowWithSchema].get(1) shouldBe a[GenericRowWithSchema] | ||
| } | ||
| ), | ||
| ESColumnData( | ||
| "structofstruct" inner ("field1" typed IntegerType, "struct1" inner("structField1" typed IntegerType)), | ||
| () => Map("field1" -> 15, "struct1" -> Map("structField1" -> 42)) | ||
| ) | ||
| ), | ||
| ( | ||
| SparkSQLColDef("arrayint", "ARRAY<INT>", _ shouldBe a[Seq[_]]), | ||
| ESColumnData(() => Seq(1,2,3,4)) | ||
| ), | ||
| ( | ||
| SparkSQLColDef("arraystruct", "ARRAY<STRUCT<field1: LONG, field2: LONG>>", _ shouldBe a[Seq[_]]), | ||
| ESColumnData( | ||
| "arraystruct" nested( | ||
| "field1" typed LongType, | ||
| "field2" typed LongType | ||
| ), | ||
| () => | ||
| Array( | ||
| Map( | ||
| "field1" -> 11, | ||
| "field2" -> 12 | ||
| ), | ||
| Map( | ||
| "field1" -> 21, | ||
| "field2" -> 22 | ||
| ), | ||
| Map( | ||
| "field1" -> 31, | ||
| "field2" -> 32 | ||
| ) | ||
| ) | ||
| ) | ||
| )/*, | ||
| ( | ||
| SparkSQLColDef( | ||
| "arraystructarraystruct", | ||
| "ARRAY<STRUCT<stringfield: STRING, arrayfield: ARRAY<STRUCT<field1: INT, field2: INT>>>>", | ||
| { res => | ||
| res shouldBe a[Seq[_]] | ||
| res.asInstanceOf[Seq[_]].head shouldBe a[Row] | ||
| res.asInstanceOf[Seq[_]].head.asInstanceOf[Row].get(1) shouldBe a[Seq[_]] | ||
| res.asInstanceOf[Seq[_]].head.asInstanceOf[Row].get(1).asInstanceOf[Seq[_]].head shouldBe a[Row] | ||
| } | ||
| ), | ||
| ESColumnData( | ||
| "arraystructarraystruct" nested ( | ||
| "stringfield" typed StringType, | ||
| "arrayfield" nested ( | ||
| "field1" typed IntegerType, | ||
| "field2" typed IntegerType | ||
| ) | ||
| ), | ||
| () => Array( | ||
| Map( | ||
| "stringfield" -> "hello", | ||
| "arrayfield" -> Array( | ||
| Map( | ||
| "field1" -> 10, | ||
| "field2" -> 20 | ||
| ) | ||
| ) | ||
| ) | ||
| ) | ||
| ) | ||
| )*/ | ||
| ) | ||
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| override protected def typesSet: Seq[SparkSQLColDef] = dataTest.map(_._1) | ||
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| abstract override def saveTestData: Unit = { | ||
| require(saveTypesData > 0, emptyTypesSetError) | ||
| } | ||
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| override def saveTypesData: Int = { | ||
| client.get.execute { | ||
| val fieldsData = dataTest map { | ||
| case (SparkSQLColDef(fieldName, _, _), ESColumnData(_, data)) => (fieldName, data()) | ||
| } | ||
| index into Index / Type fields (fieldsData: _*) | ||
| }.await | ||
| client.get.execute { | ||
| flush index Index | ||
| }.await | ||
| 1 | ||
| } | ||
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| override def typeMapping(): MappingDefinition = | ||
| Type fields ( | ||
| dataTest collect { | ||
| case (_, ESColumnData(Some(mapping), _)) => mapping | ||
| }: _* | ||
| ) | ||
|
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| override val emptyTypesSetError: String = "Couldn't insert Elasticsearch types test data" | ||
|
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| } | ||
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| trait ElasticSearchDataTypesDefaultConstants extends ElasticSearchDefaultConstants{ | ||
| private lazy val config = ConfigFactory.load() | ||
| override val Index = s"idxname${UUID.randomUUID.toString.replaceAll("-", "")}" | ||
| override val Type = s"typename${UUID.randomUUID.toString.replaceAll("-", "")}" | ||
|
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| } |
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