hvanhovell commented on code in PR #40783: URL: https://github.com/apache/spark/pull/40783#discussion_r1169022344
########## connector/connect/client/jvm/src/main/scala/org/apache/spark/sql/streaming/DataStreamReader.scala: ########## @@ -0,0 +1,275 @@ +/* + * 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) + } + + /** + * 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) + + /** + * Loads text file(s) and returns a `Dataset` of String. The underlying schema of the Dataset + * contains a single string column named "value". The text files must be encoded as UTF-8. + * + * If the directory structure of the text files contains partitioning information, those are + * ignored in the resulting Dataset. To include partitioning information as columns, use `text`. + * + * By default, each line in the text file is a new element in the resulting Dataset. For + * example: + * {{{ + * // Scala: + * spark.readStream.textFile("/path/to/spark/README.md") + * + * // Java: + * spark.readStream().textFile("/path/to/spark/README.md") + * }}} + * + * You can set the text-specific options as specified in `DataStreamReader.text`. + * + * @param path + * input path + * @since 3.5.0 + */ + def textFile(path: String): Dataset[String] = { + text(path).select("value").as[String](sparkSession.implicits.newStringEncoder) Review Comment: Or just `StringEncoder`... No need to drag in the `sparkSession` for this. -- This is an automated message from the Apache Git Service. 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