WweiL commented on code in PR #40783: URL: https://github.com/apache/spark/pull/40783#discussion_r1167203715
########## connector/connect/client/jvm/src/main/scala/org/apache/spark/sql/streaming/DataStreamReader.scala: ########## @@ -0,0 +1,305 @@ +/* + * 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.streaming + +import scala.collection.JavaConverters._ + +import org.apache.spark.annotation.Evolving +import org.apache.spark.connect.proto.Read.DataSource +import org.apache.spark.sql.DataFrame +import org.apache.spark.sql.Dataset +import org.apache.spark.sql.SparkSession +import org.apache.spark.sql.types.StructType + +/** + * Interface used to load a streaming `Dataset` from external storage systems (e.g. file systems, + * key-value stores, etc). Use `SparkSession.readStream` to access this. + * + * @since 3.5.0 + */ +@Evolving +final class DataStreamReader private[sql](sparkSession: SparkSession) { + + /** + * Specifies the input data source format. + * + * @since 3.5.0 + */ + def format(source: String): DataStreamReader = { + sourceBuilder.setFormat(source) + this + } + + + /** + * Specifies the input schema. Some data sources (e.g. JSON) can infer the input schema + * automatically from data. By specifying the schema here, the underlying data source can + * skip the schema inference step, and thus speed up data loading. + * + * @since 3.5.0 + */ + def schema(schema: StructType): DataStreamReader = { + if (schema != null) { + sourceBuilder.setSchema(schema.json) // Use json. DDL does not retail all the attributes. + } + this + } + + /** + * Specifies the schema by using the input DDL-formatted string. Some data sources (e.g. JSON) can + * infer the input schema automatically from data. By specifying the schema here, the underlying + * data source can skip the schema inference step, and thus speed up data loading. + * + * @since 3.5.0 + */ + def schema(schemaString: String): DataStreamReader = { + sourceBuilder.setSchema(schemaString) + this + } + + /** + * Adds an input option for the underlying data source. + * + * @since 3.5.0 + */ + def option(key: String, value: String): DataStreamReader = { + sourceBuilder.putOptions(key, value) + this + } + + /** + * Adds an input option for the underlying data source. + * + * @since 3.5.0 + */ + def option(key: String, value: Boolean): DataStreamReader = option(key, value.toString) + + /** + * Adds an input option for the underlying data source. + * + * @since 3.5.0 + */ + def option(key: String, value: Long): DataStreamReader = option(key, value.toString) + + /** + * Adds an input option for the underlying data source. + * + * @since 3.5.0 + */ + def option(key: String, value: Double): DataStreamReader = option(key, value.toString) + + /** + * (Scala-specific) Adds input options for the underlying data source. + * + * @since 3.5.0 + */ + def options(options: scala.collection.Map[String, String]): DataStreamReader = { + this.options(options.asJava) + this + } + + /** + * (Java-specific) Adds input options for the underlying data source. + * + * @since 3.5.0 + */ + def options(options: java.util.Map[String, String]): DataStreamReader = { + sourceBuilder.putAllOptions(options) + this + } + + + /** + * Loads input data stream in as a `DataFrame`, for data streams that don't require a path + * (e.g. external key-value stores). + * + * @since 3.5.0 + */ + def load(): DataFrame = { + sparkSession.newDataFrame { relationBuilder => + relationBuilder + .getReadBuilder + .setIsStreaming(true) + .setDataSource(sourceBuilder.build()) + } + } + + /** + * Loads input in as a `DataFrame`, for data streams that read from some path. + * + * @since 3.5.0 + */ + def load(path: String): DataFrame = { + sourceBuilder.clearPaths() + sourceBuilder.addPaths(path) + load() + } + + /** + * Loads a JSON file stream and returns the results as a `DataFrame`. + * + * <a href="http://jsonlines.org/";>JSON Lines</a> (newline-delimited JSON) is supported by + * default. For JSON (one record per file), set the `multiLine` option to true. + * + * This function goes through the input once to determine the input schema. If you know the + * schema in advance, use the version that specifies the schema to avoid the extra scan. + * + * You can set the following option(s): + * <ul> + * <li>`maxFilesPerTrigger` (default: no max limit): sets the maximum number of new files to be + * considered in every trigger.</li> + * </ul> + * + * You can find the JSON-specific options for reading JSON file stream in + * <a href="https://spark.apache.org/docs/latest/sql-data-sources-json.html#data-source-option";> + * Data Source Option</a> in the version you use. + * + * @since 3.5.0 + */ + def json(path: String): DataFrame = { + format("json").load(path) + } + + /** + * Loads a CSV file stream and returns the result as a `DataFrame`. + * + * This function will go through the input once to determine the input schema if `inferSchema` + * is enabled. To avoid going through the entire data once, disable `inferSchema` option or + * specify the schema explicitly using `schema`. + * + * You can set the following option(s): + * <ul> + * <li>`maxFilesPerTrigger` (default: no max limit): sets the maximum number of new files to be + * considered in every trigger.</li> + * </ul> + * + * You can find the CSV-specific options for reading CSV file stream in + * <a href="https://spark.apache.org/docs/latest/sql-data-sources-csv.html#data-source-option";> + * Data Source Option</a> in the version you use. + * + * @since 3.5.0 + */ + def csv(path: String): DataFrame = format("csv").load(path) Review Comment: Format should align with `json` and `orc`? ########## connector/connect/client/jvm/src/main/scala/org/apache/spark/sql/streaming/DataStreamWriter.scala: ########## @@ -0,0 +1,299 @@ +/* + * 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.streaming + +import java.util.Locale +import java.util.concurrent.TimeoutException + +import scala.collection.JavaConverters._ + +import org.apache.spark.annotation.Evolving +import org.apache.spark.api.java.function.VoidFunction2 +import org.apache.spark.connect.proto.Command +import org.apache.spark.connect.proto.WriteStreamOperationStart +import org.apache.spark.sql.Dataset +import org.apache.spark.sql.execution.streaming.AvailableNowTrigger +import org.apache.spark.sql.execution.streaming.ContinuousTrigger +import org.apache.spark.sql.execution.streaming.OneTimeTrigger +import org.apache.spark.sql.execution.streaming.ProcessingTimeTrigger + +/** + * Interface used to write a streaming `Dataset` to external storage systems (e.g. file systems, + * key-value stores, etc). Use `Dataset.writeStream` to access this. + * + * @since 3.5.0 + */ +@Evolving +final class DataStreamWriter[T] private[sql](ds: Dataset[T]) { + + /** + * Specifies how data of a streaming DataFrame/Dataset is written to a streaming sink. + * <ul> + * <li> `OutputMode.Append()`: only the new rows in the streaming DataFrame/Dataset will be + * written to the sink.</li> + * <li> `OutputMode.Complete()`: all the rows in the streaming DataFrame/Dataset will be written + * to the sink every time there are some updates.</li> + * <li> `OutputMode.Update()`: only the rows that were updated in the streaming + * DataFrame/Dataset will be written to the sink every time there are some updates. + * If the query doesn't contain aggregations, it will be equivalent to + * `OutputMode.Append()` mode.</li> + * </ul> + * + * @since 3.5.0 + */ + def outputMode(outputMode: OutputMode): DataStreamWriter[T] = { + sinkBuilder.setOutputMode(outputMode.toString.toLowerCase(Locale.ROOT)) + this + } + + /** + * Specifies how data of a streaming DataFrame/Dataset is written to a streaming sink. + * <ul> + * <li> `append`: only the new rows in the streaming DataFrame/Dataset will be written to + * the sink.</li> + * <li> `complete`: all the rows in the streaming DataFrame/Dataset will be written to the sink + * every time there are some updates.</li> + * <li> `update`: only the rows that were updated in the streaming DataFrame/Dataset will + * be written to the sink every time there are some updates. If the query doesn't + * contain aggregations, it will be equivalent to `append` mode.</li> + * </ul> + * + * @since 3.5.0 + */ + def outputMode(outputMode: String): DataStreamWriter[T] = { + sinkBuilder.setOutputMode(outputMode) + this + } + + /** + * Set the trigger for the stream query. The default value is `ProcessingTime(0)` and it will run + * the query as fast as possible. + * + * Scala Example: + * {{{ + * df.writeStream.trigger(ProcessingTime("10 seconds")) + * + * import scala.concurrent.duration._ + * df.writeStream.trigger(ProcessingTime(10.seconds)) + * }}} + * + * Java Example: + * {{{ + * df.writeStream().trigger(ProcessingTime.create("10 seconds")) + * + * import java.util.concurrent.TimeUnit + * df.writeStream().trigger(ProcessingTime.create(10, TimeUnit.SECONDS)) + * }}} + * + * @since 3.5.0 + */ + def trigger(trigger: Trigger): DataStreamWriter[T] = { + trigger match { + case ProcessingTimeTrigger(intervalMs) => + sinkBuilder.setProcessingTimeInterval(s"$intervalMs milliseconds") + case AvailableNowTrigger => + sinkBuilder.setAvailableNow(true) + case OneTimeTrigger => + sinkBuilder.setOnce(true) + case ContinuousTrigger(intervalMs) => + sinkBuilder.setContinuousCheckpointInterval(s"$intervalMs milliseconds") + } + this + } + + /** + * Specifies the name of the [[StreamingQuery]] that can be started with `start()`. + * This name must be unique among all the currently active queries in the associated SQLContext. + * + * @since 3.5.0 + */ + def queryName(queryName: String): DataStreamWriter[T] = { + sinkBuilder.setQueryName(queryName) + this + } + + /** + * Specifies the underlying output data source. + * + * @since 3.5.0 + */ + def format(source: String): DataStreamWriter[T] = { + sinkBuilder.setFormat(source) + this + } + + /** + * Partitions the output by the given columns on the file system. If specified, the output is + * laid out on the file system similar to Hive's partitioning scheme. As an example, when we + * partition a dataset by year and then month, the directory layout would look like: + * + * <ul> + * <li> year=2016/month=01/</li> + * <li> year=2016/month=02/</li> + * </ul> + * + * Partitioning is one of the most widely used techniques to optimize physical data layout. + * It provides a coarse-grained index for skipping unnecessary data reads when queries have + * predicates on the partitioned columns. In order for partitioning to work well, the number + * of distinct values in each column should typically be less than tens of thousands. + * + * @since 3.5.0 + */ + @scala.annotation.varargs + def partitionBy(colNames: String*): DataStreamWriter[T] = { + sinkBuilder.clearPartitioningColumnNames() + sinkBuilder.addAllPartitioningColumnNames(colNames.asJava) + this + } + + /** + * Adds an output option for the underlying data source. + * + * @since 3.5.0 + */ + def option(key: String, value: String): DataStreamWriter[T] = { + sinkBuilder.putOptions(key, value) + this + } + + /** + * Adds an output option for the underlying data source. + * + * @since 3.5.0 + */ + def option(key: String, value: Boolean): DataStreamWriter[T] = option(key, value.toString) + + /** + * Adds an output option for the underlying data source. + * + * @since 3.5.0 + */ + def option(key: String, value: Long): DataStreamWriter[T] = option(key, value.toString) + + /** + * Adds an output option for the underlying data source. + * + * @since 3.5.0 + */ + def option(key: String, value: Double): DataStreamWriter[T] = option(key, value.toString) + + /** + * (Scala-specific) Adds output options for the underlying data source. + * + * @since 3.5.0 + */ + def options(options: scala.collection.Map[String, String]): DataStreamWriter[T] = { + this.options(options.asJava) Review Comment: Should one of these be like `sinkBuilder.putAllOptions(options)`? ########## connector/connect/client/jvm/src/main/scala/org/apache/spark/sql/streaming/DataStreamReader.scala: ########## @@ -0,0 +1,305 @@ +/* + * 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.streaming + +import scala.collection.JavaConverters._ + +import org.apache.spark.annotation.Evolving +import org.apache.spark.connect.proto.Read.DataSource +import org.apache.spark.sql.DataFrame +import org.apache.spark.sql.Dataset +import org.apache.spark.sql.SparkSession +import org.apache.spark.sql.types.StructType + +/** + * Interface used to load a streaming `Dataset` from external storage systems (e.g. file systems, + * key-value stores, etc). Use `SparkSession.readStream` to access this. + * + * @since 3.5.0 + */ +@Evolving +final class DataStreamReader private[sql](sparkSession: SparkSession) { + + /** + * Specifies the input data source format. + * + * @since 3.5.0 + */ + def format(source: String): DataStreamReader = { + sourceBuilder.setFormat(source) + this + } + + + /** + * Specifies the input schema. Some data sources (e.g. JSON) can infer the input schema + * automatically from data. By specifying the schema here, the underlying data source can + * skip the schema inference step, and thus speed up data loading. + * + * @since 3.5.0 + */ + def schema(schema: StructType): DataStreamReader = { + if (schema != null) { + sourceBuilder.setSchema(schema.json) // Use json. DDL does not retail all the attributes. + } + this + } + + /** + * Specifies the schema by using the input DDL-formatted string. Some data sources (e.g. JSON) can + * infer the input schema automatically from data. By specifying the schema here, the underlying + * data source can skip the schema inference step, and thus speed up data loading. + * + * @since 3.5.0 + */ + def schema(schemaString: String): DataStreamReader = { + sourceBuilder.setSchema(schemaString) + this + } + + /** + * Adds an input option for the underlying data source. + * + * @since 3.5.0 + */ + def option(key: String, value: String): DataStreamReader = { + sourceBuilder.putOptions(key, value) + this + } + + /** + * Adds an input option for the underlying data source. + * + * @since 3.5.0 + */ + def option(key: String, value: Boolean): DataStreamReader = option(key, value.toString) + + /** + * Adds an input option for the underlying data source. + * + * @since 3.5.0 + */ + def option(key: String, value: Long): DataStreamReader = option(key, value.toString) + + /** + * Adds an input option for the underlying data source. + * + * @since 3.5.0 + */ + def option(key: String, value: Double): DataStreamReader = option(key, value.toString) + + /** + * (Scala-specific) Adds input options for the underlying data source. + * + * @since 3.5.0 + */ + def options(options: scala.collection.Map[String, String]): DataStreamReader = { + this.options(options.asJava) + this + } + + /** + * (Java-specific) Adds input options for the underlying data source. + * + * @since 3.5.0 + */ + def options(options: java.util.Map[String, String]): DataStreamReader = { + sourceBuilder.putAllOptions(options) + this + } + + + /** + * Loads input data stream in as a `DataFrame`, for data streams that don't require a path + * (e.g. external key-value stores). + * + * @since 3.5.0 + */ + def load(): DataFrame = { + sparkSession.newDataFrame { relationBuilder => + relationBuilder + .getReadBuilder + .setIsStreaming(true) + .setDataSource(sourceBuilder.build()) + } + } + + /** + * Loads input in as a `DataFrame`, for data streams that read from some path. + * + * @since 3.5.0 + */ + def load(path: String): DataFrame = { + sourceBuilder.clearPaths() + sourceBuilder.addPaths(path) + load() + } + + /** + * Loads a JSON file stream and returns the results as a `DataFrame`. + * + * <a href="http://jsonlines.org/";>JSON Lines</a> (newline-delimited JSON) is supported by + * default. For JSON (one record per file), set the `multiLine` option to true. + * + * This function goes through the input once to determine the input schema. If you know the + * schema in advance, use the version that specifies the schema to avoid the extra scan. + * + * You can set the following option(s): + * <ul> + * <li>`maxFilesPerTrigger` (default: no max limit): sets the maximum number of new files to be + * considered in every trigger.</li> + * </ul> + * + * You can find the JSON-specific options for reading JSON file stream in + * <a href="https://spark.apache.org/docs/latest/sql-data-sources-json.html#data-source-option";> + * Data Source Option</a> in the version you use. + * + * @since 3.5.0 + */ + def json(path: String): DataFrame = { + format("json").load(path) + } + + /** + * Loads a CSV file stream and returns the result as a `DataFrame`. + * + * This function will go through the input once to determine the input schema if `inferSchema` + * is enabled. To avoid going through the entire data once, disable `inferSchema` option or + * specify the schema explicitly using `schema`. + * + * You can set the following option(s): + * <ul> + * <li>`maxFilesPerTrigger` (default: no max limit): sets the maximum number of new files to be + * considered in every trigger.</li> + * </ul> + * + * You can find the CSV-specific options for reading CSV file stream in + * <a href="https://spark.apache.org/docs/latest/sql-data-sources-csv.html#data-source-option";> + * Data Source Option</a> in the version you use. + * + * @since 3.5.0 + */ + def csv(path: String): DataFrame = format("csv").load(path) + + /** + * Loads a ORC file stream, returning the result as a `DataFrame`. + * + * You can set the following option(s): + * <ul> + * <li>`maxFilesPerTrigger` (default: no max limit): sets the maximum number of new files to be + * considered in every trigger.</li> + * </ul> + * + * ORC-specific option(s) for reading ORC file stream can be found in + * <a href= + * "https://spark.apache.org/docs/latest/sql-data-sources-orc.html#data-source-option";> + * Data Source Option</a> in the version you use. + * + * @since 3.5.0 + */ + def orc(path: String): DataFrame = { + format("orc").load(path) + } + + /** + * Loads a Parquet file stream, returning the result as a `DataFrame`. + * + * You can set the following option(s): + * <ul> + * <li>`maxFilesPerTrigger` (default: no max limit): sets the maximum number of new files to be + * considered in every trigger.</li> + * </ul> + * + * Parquet-specific option(s) for reading Parquet file stream can be found in + * <a href= + * "https://spark.apache.org/docs/latest/sql-data-sources-parquet.html#data-source-option";> + * Data Source Option</a> in the version you use. + * + * @since 3.5.0 + */ + def parquet(path: String): DataFrame = { + format("parquet").load(path) + } + + /** + * Define a Streaming DataFrame on a Table. The DataSource corresponding to the table should + * support streaming mode. + * @param tableName The name of the table + * @since 3.5.0 + */ + def table(tableName: String): DataFrame = { + require(tableName != null, "The table name can't be null") + throw new UnsupportedOperationException("table() is not yet implemented") + } + + /** + * Loads text files and returns a `DataFrame` whose schema starts with a string column named + * "value", and followed by partitioned columns if there are any. + * The text files must be encoded as UTF-8. + * + * By default, each line in the text files is a new row in the resulting DataFrame. For example: + * {{{ + * // Scala: + * spark.readStream.text("/path/to/directory/") + * + * // Java: + * spark.readStream().text("/path/to/directory/") + * }}} + * + * You can set the following option(s): + * <ul> + * <li>`maxFilesPerTrigger` (default: no max limit): sets the maximum number of new files to be + * considered in every trigger.</li> + * </ul> + * + * You can find the text-specific options for reading text files in + * <a href="https://spark.apache.org/docs/latest/sql-data-sources-text.html#data-source-option";> + * Data Source Option</a> in the version you use. + * + * @since 3.5.0 + */ + def text(path: String): DataFrame = format("text").load(path) Review Comment: ditto -- This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. 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