---
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title: JSON Files
displayTitle: JSON Files
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<div class="codetabs">

<div data-lang="python"  markdown="1">
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](http://jsonlines.org/).

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

{% include_example json_dataset python/sql/datasource.py %}
</div>

<div data-lang="scala"  markdown="1">
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](http://jsonlines.org/).

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 %}
</div>

<div data-lang="java"  markdown="1">
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](http://jsonlines.org/).

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 %}
</div>

<div data-lang="r"  markdown="1">
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](http://jsonlines.org/).

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

{% include_example json_dataset r/RSparkSQLExample.R %}

</div>

<div data-lang="SQL"  markdown="1">

{% highlight sql %}

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

SELECT * FROM jsonTable

{% endhighlight %}

</div>

</div>

## Data Source Option

Data source options of JSON can be set via:
* the `.option`/`.options` methods of
  * `DataFrameReader`
  * `DataFrameWriter`
  * `DataStreamReader`
  * `DataStreamWriter`
* the built-in functions below
  * `from_json`
  * `to_json`
  * `schema_of_json`
* `OPTIONS` clause at [CREATE TABLE USING DATA_SOURCE](sql-ref-syntax-ddl-create-table-datasource.html)

<table>
  <thead><tr><th><b>Property Name</b></th><th><b>Default</b></th><th><b>Meaning</b></th><th><b>Scope</b></th></tr></thead>
  <tr>
    <td><code>timeZone</code></td>
    <td>(value of <code>spark.sql.session.timeZone</code> configuration)</td>
    <td>Sets the string that indicates a time zone ID to be used to format timestamps in the JSON datasources or partition values. The following formats of <code>timeZone</code> are supported:<br>
    <ul>
      <li>Region-based zone ID: It should have the form 'area/city', such as 'America/Los_Angeles'.</li>
      <li>Zone offset: It should be in the format '(+|-)HH:mm', for example '-08:00' or '+01:00'. Also 'UTC' and 'Z' are supported as aliases of '+00:00'.</li>
    </ul>
    Other short names like 'CST' are not recommended to use because they can be ambiguous.
    </td>
    <td>read/write</td>
  </tr>
  <tr>
    <td><code>primitivesAsString</code></td>
    <td><code>false</code></td>
    <td>Infers all primitive values as a string type.</td>
    <td>read</td>
  </tr>
  <tr>
    <td><code>prefersDecimal</code></td>
    <td><code>false</code></td>
    <td>Infers all floating-point values as a decimal type. If the values do not fit in decimal, then it infers them as doubles.</td>
    <td>read</td>
  </tr>
  <tr>
    <td><code>allowComments</code></td>
    <td><code>false</code></td>
    <td>Ignores Java/C++ style comment in JSON records.</td>
    <td>read</td>
  </tr>
  <tr>
    <td><code>allowUnquotedFieldNames</code></td>
    <td><code>false</code></td>
    <td>Allows unquoted JSON field names.</td>
    <td>read</td>
  </tr>
  <tr>
    <td><code>allowSingleQuotes</code></td>
    <td><code>true</code></td>
    <td>Allows single quotes in addition to double quotes.</td>
    <td>read</td>
  </tr>
  <tr>
    <td><code>allowNumericLeadingZeros</code></td>
    <td><code>false</code></td>
    <td>Allows leading zeros in numbers (e.g. 00012).</td>
    <td>read</td>
  </tr>
  <tr>
    <td><code>allowBackslashEscapingAnyCharacter</code></td>
    <td><code>false</code></td>
    <td>Allows accepting quoting of all character using backslash quoting mechanism.</td>
    <td>read</td>
  </tr>
  <tr>
    <td><code>mode</code></td>
    <td><code>PERMISSIVE</code></td>
    <td>Allows a mode for dealing with corrupt records during parsing.<br>
    <ul>
      <li><code>PERMISSIVE</code>: when it meets a corrupted record, puts the malformed string into a field configured by <code>columnNameOfCorruptRecord</code>, and sets malformed fields to <code>null</code>. To keep corrupt records, an user can set a string type field named <code>columnNameOfCorruptRecord</code> in an user-defined schema. If a schema does not have the field, it drops corrupt records during parsing. When inferring a schema, it implicitly adds a <code>columnNameOfCorruptRecord</code> field in an output schema.</li>
      <li><code>DROPMALFORMED</code>: ignores the whole corrupted records. This mode is unsupported in the JSON built-in functions.</li>
      <li><code>FAILFAST</code>: throws an exception when it meets corrupted records.</li>
    </ul>
    </td>
    <td>read</td>
  </tr>
  <tr>
    <td><code>columnNameOfCorruptRecord</code></td>
    <td>(value of <code>spark.sql.columnNameOfCorruptRecord</code> configuration)</td>
    <td>Allows renaming the new field having malformed string created by <code>PERMISSIVE</code> mode. This overrides spark.sql.columnNameOfCorruptRecord.</td>
    <td>read</td>
  </tr>
  <tr>
    <td><code>dateFormat</code></td>
    <td><code>yyyy-MM-dd</code></td>
    <td>Sets the string that indicates a date format. Custom date formats follow the formats at <a href="https://spark.apache.org/docs/latest/sql-ref-datetime-pattern.html"> datetime pattern</a>. This applies to date type.</td>
    <td>read/write</td>
  </tr>
  <tr>
    <td><code>timestampFormat</code></td>
    <td><code>yyyy-MM-dd'T'HH:mm:ss[.SSS][XXX]</code></td>
    <td>Sets the string that indicates a timestamp format. Custom date formats follow the formats at <a href="https://spark.apache.org/docs/latest/sql-ref-datetime-pattern.html"> datetime pattern</a>. This applies to timestamp type.</td>
    <td>read/write</td>
  </tr>
  <tr>
    <td><code>timestampNTZFormat</code></td>
    <td>yyyy-MM-dd'T'HH:mm:ss[.SSS]</td>
    <td>Sets the string that indicates a timestamp without timezone format. Custom date formats follow the formats at <a href="https://spark.apache.org/docs/latest/sql-ref-datetime-pattern.html">Datetime Patterns</a>. This applies to timestamp without timezone type, note that zone-offset and time-zone components are not supported when writing or reading this data type.</td>
    <td>read/write</td>
  </tr>
  <tr>
    <td><code>enableDateTimeParsingFallback</code></td>
    <td>Enabled if the time parser policy has legacy settings or if no custom date or timestamp pattern was provided.</td>
    <td>Allows falling back to the backward compatible (Spark 1.x and 2.0) behavior of parsing dates and timestamps if values do not match the set patterns.</td>
    <td>read</td>
  </tr>
  <tr>
    <td><code>multiLine</code></td>
    <td><code>false</code></td>
    <td>Parse one record, which may span multiple lines, per file. JSON built-in functions ignore this option.</td>
    <td>read</td>
  </tr>
  <tr>
    <td><code>allowUnquotedControlChars</code></td>
    <td><code>false</code></td>
    <td>Allows JSON Strings to contain unquoted control characters (ASCII characters with value less than 32, including tab and line feed characters) or not.</td>
    <td>read</td>
  </tr>
  <tr>
    <td><code>encoding</code></td>
    <td>Detected automatically when <code>multiLine</code> is set to <code>true</code> (for reading), <code>UTF-8</code> (for writing)</td>
    <td>For reading, allows to forcibly set one of standard basic or extended encoding for the JSON files. For example UTF-16BE, UTF-32LE. For writing, Specifies encoding (charset) of saved json files. JSON built-in functions ignore this option.</td>
    <td>read/write</td>
  </tr>
  <tr>
    <td><code>lineSep</code></td>
    <td><code>\r</code>, <code>\r\n</code>, <code>\n</code> (for reading), <code>\n</code> (for writing)</td>
    <td>Defines the line separator that should be used for parsing. JSON built-in functions ignore this option.</td>
    <td>read/write</td>
  </tr>
  <tr>
    <td><code>samplingRatio</code></td>
    <td><code>1.0</code></td>
    <td>Defines fraction of input JSON objects used for schema inferring.</td>
    <td>read</td>
  </tr>
  <tr>
    <td><code>dropFieldIfAllNull</code></td>
    <td><code>false</code></td>
    <td>Whether to ignore column of all null values or empty array during schema inference.</td>
    <td>read</td>
  </tr>
  <tr>
    <td><code>locale</code></td>
    <td><code>en-US</code></td>
    <td>Sets a locale as language tag in IETF BCP 47 format. For instance, <code>locale</code> is used while parsing dates and timestamps.</td>
    <td>read</td>
  </tr>
  <tr>
    <td><code>allowNonNumericNumbers</code></td>
    <td><code>true</code></td>
    <td>Allows JSON parser to recognize set of “Not-a-Number” (NaN) tokens as legal floating number values.<br>
    <ul>
      <li><code>+INF</code>: for positive infinity, as well as alias of <code>+Infinity</code> and <code>Infinity</code>.</li>
      <li><code>-INF</code>: for negative infinity, alias <code>-Infinity</code>.</li>
      <li><code>NaN</code>: for other not-a-numbers, like result of division by zero.</li>
    </ul>
    </td>
    <td>read</td>
  </tr>
  <tr>
    <td><code>compression</code></td>
    <td>(none)</td>
    <td>Compression codec to use when saving to file. This can be one of the known case-insensitive shorten names (none, bzip2, gzip, lz4, snappy and deflate). JSON built-in functions ignore this option.</td>
    <td>write</td>
  </tr>
  <tr>
    <td><code>ignoreNullFields</code></td>
    <td>(value of <code>spark.sql.jsonGenerator.ignoreNullFields</code> configuration)</td>
    <td>Whether to ignore null fields when generating JSON objects.</td>
    <td>write</td>
  </tr>
  <tr>
    <td><code>useUnsafeRow</code></td>
    <td>(value of <code>spark.sql.json.useUnsafeRow</code> configuration)</td>
    <td>Whether to use UnsafeRow to represent struct result in the JSON parser.</td>
    <td>read</td>
  </tr>
</table>
Other generic options can be found in <a href="https://spark.apache.org/docs/latest/sql-data-sources-generic-options.html"> Generic File Source Options</a>.
