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.
# spark is from the previous example.
sc = spark.sparkContext
# A JSON dataset is pointed to by path.
# The path can be either a single text file or a directory storing text files
path = "examples/src/main/resources/people.json"
peopleDF = spark.read.json(path)
# The inferred schema can be visualized using the printSchema() method
peopleDF.printSchema()
# root
# |-- age: long (nullable = true)
# |-- name: string (nullable = true)
# Creates a temporary view using the DataFrame
peopleDF.createOrReplaceTempView("people")
# SQL statements can be run by using the sql methods provided by spark
teenagerNamesDF = spark.sql("SELECT name FROM people WHERE age BETWEEN 13 AND 19")
teenagerNamesDF.show()
# +------+
# | name|
# +------+
# |Justin|
# +------+
# Alternatively, a DataFrame can be created for a JSON dataset represented by
# an RDD[String] storing one JSON object per string
jsonStrings = ['{"name":"Yin","address":{"city":"Columbus","state":"Ohio"}}']
otherPeopleRDD = sc.parallelize(jsonStrings)
otherPeople = spark.read.json(otherPeopleRDD)
otherPeople.show()
# +---------------+----+
# | address|name|
# +---------------+----+
# |[Columbus,Ohio]| Yin|
# +---------------+----+Find full example code at "examples/src/main/python/sql/datasource.py" in the Spark repo.
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.
// Primitive types (Int, String, etc) and Product types (case classes) encoders are
// supported by importing this when creating a Dataset.
import spark.implicits._
// A JSON dataset is pointed to by path.
// The path can be either a single text file or a directory storing text files
val path = "examples/src/main/resources/people.json"
val peopleDF = spark.read.json(path)
// The inferred schema can be visualized using the printSchema() method
peopleDF.printSchema()
// root
// |-- age: long (nullable = true)
// |-- name: string (nullable = true)
// Creates a temporary view using the DataFrame
peopleDF.createOrReplaceTempView("people")
// SQL statements can be run by using the sql methods provided by spark
val teenagerNamesDF = spark.sql("SELECT name FROM people WHERE age BETWEEN 13 AND 19")
teenagerNamesDF.show()
// +------+
// | name|
// +------+
// |Justin|
// +------+
// Alternatively, a DataFrame can be created for a JSON dataset represented by
// a Dataset[String] storing one JSON object per string
val otherPeopleDataset = spark.createDataset(
"""{"name":"Yin","address":{"city":"Columbus","state":"Ohio"}}""" :: Nil)
val otherPeople = spark.read.json(otherPeopleDataset)
otherPeople.show()
// +---------------+----+
// | address|name|
// +---------------+----+
// |[Columbus,Ohio]| Yin|
// +---------------+----+Find full example code at "examples/src/main/scala/org/apache/spark/examples/sql/SQLDataSourceExample.scala" in the Spark repo.
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.
import org.apache.spark.sql.Dataset;
import org.apache.spark.sql.Row;
// A JSON dataset is pointed to by path.
// The path can be either a single text file or a directory storing text files
Dataset<Row> people = spark.read().json("examples/src/main/resources/people.json");
// The inferred schema can be visualized using the printSchema() method
people.printSchema();
// root
// |-- age: long (nullable = true)
// |-- name: string (nullable = true)
// Creates a temporary view using the DataFrame
people.createOrReplaceTempView("people");
// SQL statements can be run by using the sql methods provided by spark
Dataset<Row> namesDF = spark.sql("SELECT name FROM people WHERE age BETWEEN 13 AND 19");
namesDF.show();
// +------+
// | name|
// +------+
// |Justin|
// +------+
// Alternatively, a DataFrame can be created for a JSON dataset represented by
// a Dataset<String> storing one JSON object per string.
List<String> jsonData = Arrays.asList(
"{\"name\":\"Yin\",\"address\":{\"city\":\"Columbus\",\"state\":\"Ohio\"}}");
Dataset<String> anotherPeopleDataset = spark.createDataset(jsonData, Encoders.STRING());
Dataset<Row> anotherPeople = spark.read().json(anotherPeopleDataset);
anotherPeople.show();
// +---------------+----+
// | address|name|
// +---------------+----+
// |[Columbus,Ohio]| Yin|
// +---------------+----+Find full example code at "examples/src/main/java/org/apache/spark/examples/sql/JavaSQLDataSourceExample.java" in the Spark repo.
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.
# A JSON dataset is pointed to by path.
# The path can be either a single text file or a directory storing text files.
path <- "examples/src/main/resources/people.json"
# Create a DataFrame from the file(s) pointed to by path
people <- read.json(path)
# The inferred schema can be visualized using the printSchema() method.
printSchema(people)
## root
## |-- age: long (nullable = true)
## |-- name: string (nullable = true)
# Register this DataFrame as a table.
createOrReplaceTempView(people, "people")
# SQL statements can be run by using the sql methods.
teenagers <- sql("SELECT name FROM people WHERE age >= 13 AND age <= 19")
head(teenagers)
## name
## 1 JustinFind full example code at "examples/src/main/r/RSparkSQLExample.R" in the Spark repo.
CREATE TEMPORARY VIEW jsonTable
USING org.apache.spark.sql.json
OPTIONS (
path "examples/src/main/resources/people.json"
)
SELECT * FROM jsonTableData source options of JSON can be set via:
.option/.options methods of
DataFrameReaderDataFrameWriterDataStreamReaderDataStreamWriterfrom_jsonto_jsonschema_of_jsonOPTIONS clause at CREATE TABLE USING DATA_SOURCEtimeZone (value of spark.sql.session.timeZone configuration) 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 timeZone are supported:
primitivesAsString false Infers all primitive values as a string type. read prefersDecimal false Infers all floating-point values as a decimal type. If the values do not fit in decimal, then it infers them as doubles. read allowComments false Ignores Java/C++ style comment in JSON records. read allowUnquotedFieldNames false Allows unquoted JSON field names. read allowSingleQuotes true Allows single quotes in addition to double quotes. read allowNumericLeadingZeros false Allows leading zeros in numbers (e.g. 00012). read allowBackslashEscapingAnyCharacter false Allows accepting quoting of all character using backslash quoting mechanism. read mode PERMISSIVE Allows a mode for dealing with corrupt records during parsing.
PERMISSIVE: when it meets a corrupted record, puts the malformed string into a field configured by columnNameOfCorruptRecord, and sets malformed fields to null. To keep corrupt records, an user can set a string type field named columnNameOfCorruptRecord 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 columnNameOfCorruptRecord field in an output schema.DROPMALFORMED: ignores the whole corrupted records. This mode is unsupported in the JSON built-in functions.FAILFAST: throws an exception when it meets corrupted records.columnNameOfCorruptRecord (value of spark.sql.columnNameOfCorruptRecord configuration) Allows renaming the new field having malformed string created by PERMISSIVE mode. This overrides spark.sql.columnNameOfCorruptRecord. read dateFormat yyyy-MM-dd Sets the string that indicates a date format. Custom date formats follow the formats at datetime pattern. This applies to date type. read/write timestampFormat yyyy-MM-dd'T'HH:mm:ss[.SSS][XXX] Sets the string that indicates a timestamp format. Custom date formats follow the formats at datetime pattern. This applies to timestamp type. read/write timestampNTZFormat yyyy-MM-dd'T'HH:mm:ss[.SSS] Sets the string that indicates a timestamp without timezone format. Custom date formats follow the formats at Datetime Patterns. 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. read/write enableDateTimeParsingFallback Enabled if the time parser policy has legacy settings or if no custom date or timestamp pattern was provided. 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. read multiLine false Parse one record, which may span multiple lines, per file. JSON built-in functions ignore this option. read allowUnquotedControlChars false Allows JSON Strings to contain unquoted control characters (ASCII characters with value less than 32, including tab and line feed characters) or not. read encoding Detected automatically when multiLine is set to true (for reading), UTF-8 (for writing) 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. read/write lineSep \r, \r\n, \n (for reading), \n (for writing) Defines the line separator that should be used for parsing. JSON built-in functions ignore this option. read/write samplingRatio 1.0 Defines fraction of input JSON objects used for schema inferring. read dropFieldIfAllNull false Whether to ignore column of all null values or empty array during schema inference. read locale en-US Sets a locale as language tag in IETF BCP 47 format. For instance, locale is used while parsing dates and timestamps. read allowNonNumericNumbers true Allows JSON parser to recognize set of âNot-a-Numberâ (NaN) tokens as legal floating number values.
+INF: for positive infinity, as well as alias of +Infinity and Infinity.-INF: for negative infinity, alias -Infinity.NaN: for other not-a-numbers, like result of division by zero.compression (none) 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. write ignoreNullFields (value of spark.sql.jsonGenerator.ignoreNullFields configuration) Whether to ignore null fields when generating JSON objects. write useUnsafeRow (value of spark.sql.json.useUnsafeRow configuration) Whether to use UnsafeRow to represent struct result in the JSON parser. read
Other generic options can be found in Generic File Source Options.
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