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/*
 * Copyright (c) 2017 Uber Technologies, Inc. ([email protected])
 *
 * 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.uber.hoodie.table;

import com.uber.hoodie.common.model.HoodieRecord;
import com.uber.hoodie.common.model.HoodieRecordPayload;
import org.apache.spark.api.java.JavaRDD;

/**
 * Repartition input records into at least expected number of output spark partitions. It should give
 * below guarantees
 * - Output spark partition will have records from only one hoodie partition.
 * - Average records per output spark partitions should be almost equal to (#inputRecords / #outputSparkPartitions)
 * to avoid possible skews.
 */
public interface UserDefinedBulkInsertPartitioner {

    JavaRDD> repartitionRecords(JavaRDD> records, int outputSparkPartitions);
}