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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.
Spark SQL can automatically infer the schema of a JSON dataset and load it as a `Dataset[Row]`. This conversion can be done using `SparkSession.read.json()` on either a `Dataset[String]`, or a JSON file.

Note that the file that is offered as a json file is not a typical JSON file. Each line must contain a separate, self-contained valid JSON object. For more information, please see JSON Lines text format, also called newline-delimited JSON.

For a regular multi-line JSON file, set the multiLine option to true.

{% include_example json_dataset scala/org/apache/spark/examples/sql/SQLDataSourceExample.scala %}

Spark SQL can automatically infer the schema of a JSON dataset and load it as a `Dataset`. This conversion can be done using `SparkSession.read().json()` on either a `Dataset`, or a JSON file.

Note that the file that is offered as a json file is not a typical JSON file. Each line must contain a separate, self-contained valid JSON object. For more information, please see JSON Lines text format, also called newline-delimited JSON.

For a regular multi-line JSON file, set the multiLine option to true.

{% include_example json_dataset java/org/apache/spark/examples/sql/JavaSQLDataSourceExample.java %}

Spark SQL can automatically infer the schema of a JSON dataset and load it as a DataFrame. This conversion can be done using `SparkSession.read.json` on a JSON file.

Note that the file that is offered as a json file is not a typical JSON file. Each line must contain a separate, self-contained valid JSON object. For more information, please see JSON Lines text format, also called newline-delimited JSON.

For a regular multi-line JSON file, set the multiLine parameter to True.

{% include_example json_dataset python/sql/datasource.py %}

Spark SQL can automatically infer the schema of a JSON dataset and load it as a DataFrame. using the `read.json()` function, which loads data from a directory of JSON files where each line of the files is a JSON object.

Note that the file that is offered as a json file is not a typical JSON file. Each line must contain a separate, self-contained valid JSON object. For more information, please see JSON Lines text format, also called newline-delimited JSON.

For a regular multi-line JSON file, set a named parameter multiLine to TRUE.

{% include_example json_dataset r/RSparkSQLExample.R %}

{% highlight sql %}

CREATE TEMPORARY VIEW jsonTable USING org.apache.spark.sql.json OPTIONS ( path "examples/src/main/resources/people.json" )

SELECT * FROM jsonTable

{% endhighlight %}