spark RequiresDistributionAndOrdering 源码
spark RequiresDistributionAndOrdering 代码
文件路径:/sql/catalyst/src/main/java/org/apache/spark/sql/connector/write/RequiresDistributionAndOrdering.java
/*
* 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.spark.sql.connector.write;
import org.apache.spark.annotation.Experimental;
import org.apache.spark.sql.connector.distributions.Distribution;
import org.apache.spark.sql.connector.distributions.UnspecifiedDistribution;
import org.apache.spark.sql.connector.expressions.SortOrder;
/**
* A write that requires a specific distribution and ordering of data.
*
* @since 3.2.0
*/
@Experimental
public interface RequiresDistributionAndOrdering extends Write {
/**
* Returns the distribution required by this write.
* <p>
* Spark will distribute incoming records across partitions to satisfy the required distribution
* before passing the records to the data source table on write.
* <p>
* Batch and micro-batch writes can request a particular data distribution.
* If a distribution is requested in the micro-batch context, incoming records in each micro batch
* will satisfy the required distribution (but not across micro batches). The continuous execution
* mode continuously processes streaming data and does not support distribution requirements.
* <p>
* Implementations may return {@link UnspecifiedDistribution} if they don't require any specific
* distribution of data on write.
*
* @return the required distribution
*/
Distribution requiredDistribution();
/**
* Returns if the distribution required by this write is strictly required or best effort only.
* <p>
* If true, Spark will strictly distribute incoming records across partitions to satisfy
* the required distribution before passing the records to the data source table on write.
* Otherwise, Spark may apply certain optimizations to speed up the query but break
* the distribution requirement.
*
* @return true if the distribution required by this write is strictly required; false otherwise.
*/
default boolean distributionStrictlyRequired() { return true; }
/**
* Returns the number of partitions required by this write.
* <p>
* Implementations may override this to require a specific number of input partitions.
* <p>
* Note that Spark doesn't support the number of partitions on {@link UnspecifiedDistribution},
* the query will fail if the number of partitions are provided but the distribution is
* unspecified.
*
* @return the required number of partitions, any value less than 1 mean no requirement.
*/
default int requiredNumPartitions() { return 0; }
/**
* Returns the ordering required by this write.
* <p>
* Spark will order incoming records within partitions to satisfy the required ordering
* before passing those records to the data source table on write.
* <p>
* Batch and micro-batch writes can request a particular data ordering.
* If an ordering is requested in the micro-batch context, incoming records in each micro batch
* will satisfy the required ordering (but not across micro batches). The continuous execution
* mode continuously processes streaming data and does not support ordering requirements.
* <p>
* Implementations may return an empty array if they don't require any specific ordering of data
* on write.
*
* @return the required ordering
*/
SortOrder[] requiredOrdering();
}
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