from_json function
Applies to: Databricks SQL Databricks Runtime
Returns a struct value with the jsonStr and schema.
from_json(jsonStr, schema [, options])
jsonStr: A STRING expression specifying a json document.schema: A STRING expression or invocation of schema_of_json function.options: An optional MAP<STRING,STRING> literal specifying directives.jsonStr should be well-formed with respect to schema and options.
The schema must be defined as comma-separated column names and data type pairs, similar to the format used in CREATE TABLE. Prior to Databricks Runtime 12.2 schema must be a literal.
Alternatively, you can use from_json with Lakeflow Declarative Pipelines to automatically infer and evolve the schema by setting schema to NULL and specifying a schemaLocationKey. For examples, see Infer and evolve the schema using from_json in Lakeflow Declarative Pipelines.
note
The column and field names in schema are case-sensitive and must match the names in jsonStr exactly. To map JSON fields which differ only in case, you can cast the resulting struct to distinct field names. See Examples for more details.
options, if provided, can be any of the following:
primitivesAsString (default false): infers all primitive values as a string type.prefersDecimal (default false): infers all floating-point values as a decimal type. If the values do not fit in decimal, then it infers them as doubles.allowComments (default false): ignores Java and C++ style comment in JSON records.allowUnquotedFieldNames (default false): allows unquoted JSON field names.allowSingleQuotes (default true): allows single quotes in addition to double quotes.allowNumericLeadingZeros (default false): allows leading zeros in numbers (for example, 00012).allowBackslashEscapingAnyCharacter (default false): allows accepting quoting of all character using backslash quoting mechanism.allowUnquotedControlChars (default false): allows JSON Strings to contain unquoted control characters (ASCII characters with value less than 32, including tab and line feed characters) or not.mode (default 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, you 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.FAILFAST: throws an exception when it meets corrupted records.columnNameOfCorruptRecord (default is the value specified in spark.sql.columnNameOfCorruptRecord): allows renaming the new field having malformed string created by PERMISSIVE mode. This overrides spark.sql.columnNameOfCorruptRecord.dateFormat (default yyyy-MM-dd): sets the string that indicates a date format. Custom date formats follow the formats at Datetime patterns. This applies to date type.timestampFormat (default 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 patterns. This applies to timestamp type.multiLine (default false): parses one record, which may span multiple lines, per file.encoding (by default it is not set): allows to forcibly set one of standard basic or extended encoding for the JSON files. For example UTF-16BE, UTF-32LE. If the encoding is not specified and multiLine is set to true, it is detected automatically.lineSep (default covers all \r, \r\n and \n): defines the line separator that should be used for parsing.samplingRatio (default 1.0): defines fraction of input JSON objects used for schema inferring.dropFieldIfAllNull (default false): whether to ignore column of all null values or empty array/struct during schema inference.locale (default is en-US): sets a locale as language tag in IETF BCP 47 format. For instance, this is used while parsing dates and timestamps.allowNonNumericNumbers (default true): allows JSON parser to recognize set of not-a-number (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.readerCaseSensitive (default true): specifies the case sensitivity behavior when rescuedDataColumn is enabled. If true, rescue the data columns whose names differ by case from the schema; otherwise, read the data in a case-insensitive manner. Available in Databricks SQL and Databricks Runtime 13.3 LTS and above.A struct with field names and types matching the schema definition.
ExamplesâSQL
> SELECT from_json('{"a":1, "b":0.8}', 'a INT, b DOUBLE');
{"a":1,"b":0.8}
-- The column name must to match the case of the JSON field
> SELECT from_json('{"a":1}', 'A INT');
{"A":null}
> SELECT from_json('{"datetime":"26/08/2015"}', 'datetime Timestamp', map('timestampFormat', 'dd/MM/yyyy'));
{"datetime":2015-08-26 00:00:00}
-- Disambiguate field names with different cases
> SELECT cast(from_json('{"a":1, "A":0.8}', 'a INT, A DOUBLE') AS STRUCT<a: INT, b: DOUBLE>);
{"a":1, "b":0.8}
from_csv functionschema_of_json functionschema_of_csv functionto_csv functionto_json functionjson_object_keys functionjson_array_length functionjson_tuple table-valued generator functionfrom_json functionget_json_object functionRetroSearch is an open source project built by @garambo | Open a GitHub Issue
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