| /* |
| Licensed to the Apache Software Foundation (ASF) under one |
| or more contributor license agreements. See the NOTICE file |
| distributed with this work for additional information |
| regarding copyright ownership. The ASF licenses this file |
| to you 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 org.apache.griffin.measure.sink |
| |
| import org.apache.spark.sql.DataFrame |
| import org.apache.spark.sql.Row |
| import org.apache.spark.sql.types._ |
| import org.scalatest.FlatSpec |
| import org.scalatest.Matchers |
| |
| import org.apache.griffin.measure.Loggable |
| import org.apache.griffin.measure.configuration.dqdefinition.SinkParam |
| import org.apache.griffin.measure.configuration.enums.BatchProcessType |
| import org.apache.griffin.measure.context.{ContextId, DQContext} |
| import org.apache.griffin.measure.SparkSuiteBase |
| |
| trait SinkTestBase extends FlatSpec with Matchers with SparkSuiteBase with Loggable { |
| |
| var sinkParams: Seq[SinkParam] |
| |
| def getDqContext(name: String = "test-context"): DQContext = { |
| DQContext( |
| ContextId(System.currentTimeMillis), |
| name, |
| Nil, |
| sinkParams, |
| BatchProcessType |
| )(spark) |
| } |
| |
| |
| def createDataFrame(arr: Seq[Int]): DataFrame = { |
| val schema = StructType(Array( |
| StructField("id", LongType), |
| StructField("name", StringType), |
| StructField("sex", StringType), |
| StructField("age", IntegerType) |
| )) |
| val rows = arr.map { i => |
| Row(i.toLong, s"name_$i", if (i % 2 == 0) "man" else "women", i + 15) |
| } |
| val rowRdd = spark.sparkContext.parallelize(rows) |
| spark.createDataFrame(rowRdd, schema) |
| } |
| } |