[DOP-13840] - add JSON.parse_column and JSON.serialize_column methods

docs/changelog/next_release/257.feature.rst

@@ -0,0 +1 @@
+Add ``JSON.parse_column`` and ``JSON.serialize_column`` methods to facilitate direct parsing of JSON strings into Spark DataFrame columns and serialization of structured DataFrame columns back into JSON strings.

docs/connection/db_connection/clickhouse/types.rst

@@ -336,13 +336,17 @@ Explicit type cast
 ~~~~~~~~~~~~
 
 Use ``CAST`` or ``toJSONString`` to get column data as string in JSON format,
-and then cast string column in resulting dataframe to proper type using `from_json <https://spark.apache.org/docs/latest/api/python/reference/pyspark.sql/api/pyspark.sql.functions.from_json.html>`_:
 
-.. code:: python
+For parsing JSON columns in ClickHouse, :obj:`JSON.parse_column <onetl.file.format.json.JSON.parse_column>` method.
+
+.. code-block:: python
 
-    from pyspark.sql.functions import from_json
     from pyspark.sql.types import ArrayType, IntegerType
 
+    from onetl.file.format import JSON
+    from onetl.connection import ClickHouse
+    from onetl.db import DBReader
+
     reader = DBReader(
         connection=clickhouse,
         columns=[
@@ -357,16 +361,22 @@ and then cast string column in resulting dataframe to proper type using `from_js
 
     df = df.select(
         df.id,
-        from_json(df.array_column, column_type).alias("array_column"),
+        JSON().parse_column("array_column", column_type),
     )
 
 ``DBWriter``
 ~~~~~~~~~~~~
 
-Convert dataframe column to JSON using `to_json <https://spark.apache.org/docs/latest/api/python/reference/pyspark.sql/api/pyspark.sql.functions.to_json.html>`_,
-and write it as ``String`` column in Clickhouse:
+For writing JSON data to ClickHouse, use the :obj:`JSON.serialize_column <onetl.file.format.json.JSON.serialize_column>` method to convert a DataFrame column to JSON format efficiently and write it as a ``String`` column in Clickhouse.
+
 
-.. code:: python
+.. code-block:: python
+
+    from onetl.file.format import JSON
+    from onetl.connection import ClickHouse
+    from onetl.db import DBWriter
+
+    clickhouse = ClickHouse(...)
 
     clickhouse.execute(
         """
@@ -379,11 +389,9 @@ and write it as ``String`` column in Clickhouse:
         """,
     )
 
-    from pyspark.sql.functions import to_json
-
     df = df.select(
         df.id,
-        to_json(df.array_column).alias("array_column_json"),
+        JSON().serialize_column(df.array_column).alias("array_column_json"),
     )
 
     writer.run(df)

docs/connection/db_connection/greenplum/types.rst

@@ -301,35 +301,29 @@ Explicit type cast
 ``DBReader``
 ~~~~~~~~~~~~
 
-Unfortunately, it is not possible to cast unsupported column to some supported type on ``DBReader`` side:
+Direct casting of Greenplum types is not supported by DBReader due to the connector’s implementation specifics.
 
 .. code-block:: python
 
-    DBReader(
+    reader = DBReader(
         connection=greenplum,
         # will fail
-        columns=["CAST(column AS text)"],
+        columns=["CAST(unsupported_column AS text)"],
     )
 
-This is related to Greenplum connector implementation. Instead of passing this ``CAST`` expression to ``SELECT`` query
-as is, it performs type cast on Spark side, so this syntax is not supported.
-
-But there is a workaround - create a view with casting unsupported column to ``text`` (or any other supported type).
+But there is a workaround - create a view with casting unsupported column to text (or any other supported type).
+For example, you can use `to_json <https://www.postgresql.org/docs/current/functions-json.html>`_ Postgres function to convert column of any type to string representation and then parse this column on Spark side using :obj:`JSON.parse_column <onetl.file.format.json.JSON.parse_column>` method.
 
-For example, you can use ``to_json`` Postgres function for convert column of any type to string representation.
-You can then parse this column on Spark side using `from_json <https://spark.apache.org/docs/latest/api/python/reference/pyspark.sql/api/pyspark.sql.functions.from_json.html>`_:
-
-.. code:: python
+.. code-block:: python
 
-    from pyspark.sql.functions import from_json
     from pyspark.sql.types import ArrayType, IntegerType
 
     from onetl.connection import Greenplum
     from onetl.db import DBReader
+    from onetl.file.format import JSON
 
     greenplum = Greenplum(...)
 
-    # create view with proper type cast
     greenplum.execute(
         """
         CREATE VIEW schema.view_with_json_column AS
@@ -350,29 +344,26 @@ You can then parse this column on Spark side using `from_json <https://spark.apa
     )
     df = reader.run()
 
-    # Spark requires all columns to have some type, describe it
-    column_type = ArrayType(IntegerType())
+    # Define the schema for the JSON data
+    json_scheme = ArrayType(IntegerType())
 
-    # cast column content to proper Spark type
     df = df.select(
         df.id,
         df.supported_column,
-        from_json(df.array_column_as_json, schema).alias("array_column"),
+        JSON().parse_column(df.array_column_as_json, json_scheme).alias("array_column"),
     )
 
 ``DBWriter``
 ~~~~~~~~~~~~
 
-It is always possible to convert data on Spark side to string, and then write it to ``text`` column in Greenplum table.
+To write data to a ``text`` or ``json`` column in a Greenplum table, use :obj:`JSON.serialize_column <onetl.file.format.json.JSON.serialize_column>` method.
 
-For example, you can convert data using `to_json <https://spark.apache.org/docs/latest/api/python/reference/pyspark.sql/api/pyspark.sql.functions.to_json.html>`_ function.
-
-.. code:: python
+.. code-block:: python
 
-    from pyspark.sql.functions import to_json
 
     from onetl.connection import Greenplum
-    from onetl.db import DBReader
+    from onetl.db import DBWriter
+    from onetl.file.format import JSON
 
     greenplum = Greenplum(...)
 
@@ -390,7 +381,7 @@ For example, you can convert data using `to_json <https://spark.apache.org/docs/
     write_df = df.select(
         df.id,
         df.supported_column,
-        to_json(df.array_column).alias("array_column_json"),
+        JSON().serialize_column(df.array_column).alias("array_column_json"),
     )
 
     writer = DBWriter(

docs/connection/db_connection/mysql/types.rst

@@ -289,16 +289,15 @@ It is possible to explicitly cast column type using ``DBReader(columns=...)`` sy
 
 For example, you can use ``CAST(column AS text)`` to convert data to string representation on MySQL side, and so it will be read as Spark's ``StringType()``.
 
-It is also possible to use `JSON_OBJECT <https://dev.mysql.com/doc/refman/en/json.html>`_ MySQL function
-to convert column of any type to string representation, and then parse this column on Spark side using
-`from_json <https://spark.apache.org/docs/latest/api/python/reference/pyspark.sql/api/pyspark.sql.functions.from_json.html>`_:
+It is also possible to use `JSON_OBJECT <https://dev.mysql.com/doc/refman/en/json.html>`_ MySQL function and parse JSON columns in MySQL with the :obj:`JSON.parse_column <onetl.file.format.json.JSON.parse_column>` method.
 
 .. code-block:: python
 
-    from pyspark.sql.types import IntegerType, StructField, StructType
+    from pyspark.sql.types import IntegerType, StructType, StructField
 
     from onetl.connection import MySQL
     from onetl.db import DBReader
+    from onetl.file.format import JSON
 
     mysql = MySQL(...)
 
@@ -314,42 +313,40 @@ to convert column of any type to string representation, and then parse this colu
     )
     df = reader.run()
 
-    # Spark requires all columns to have some type, describe it
-    column_type = StructType([StructField("key", IntegerType())])
+    json_scheme = StructType([StructField("key", IntegerType())])
 
-    # cast column content to proper Spark type
     df = df.select(
         df.id,
         df.supported_column,
         # explicit cast
         df.unsupported_column_str.cast("integer").alias("parsed_integer"),
-        # or explicit json parsing
-        from_json(df.json_column, schema).alias("struct_column"),
+        JSON().parse_column("json_column", json_scheme).alias("struct_column"),
     )
 
 ``DBWriter``
 ~~~~~~~~~~~~
 
-Convert dataframe column to JSON using `to_json <https://spark.apache.org/docs/latest/api/python/reference/pyspark.sql/api/pyspark.sql.functions.to_json.html>`_,
-and write it as ``text`` column in MySQL:
+To write JSON data to a ``json`` or ``text`` column in a MySQL table, use the :obj:`JSON.serialize_column <onetl.file.format.json.JSON.serialize_column>` method.
 
-.. code:: python
+.. code-block:: python
+
+    from onetl.connection import MySQL
+    from onetl.db import DBWriter
+    from onetl.file.format import JSON
 
     mysql.execute(
         """
-        CREATE TABLE schema.target_tbl AS (
+        CREATE TABLE schema.target_tbl (
             id bigint,
             array_column_json json -- any string type, actually
         )
         ENGINE = InnoDB
         """,
     )
 
-    from pyspark.sql.functions import to_json
-
     df = df.select(
         df.id,
-        to_json(df.array_column).alias("array_column_json"),
+        JSON().serialize_column(df.array_column).alias("array_column_json"),
     )
 
     writer.run(df)

docs/connection/db_connection/oracle/types.rst

@@ -301,12 +301,12 @@ For example, you can use ``CAST(column AS CLOB)`` to convert data to string repr
 
 It is also possible to use `JSON_ARRAY <https://docs.oracle.com/en/database/oracle/oracle-database/23/sqlrf/JSON_ARRAY.html>`_
 or `JSON_OBJECT <https://docs.oracle.com/en/database/oracle/oracle-database/23/sqlrf/JSON_OBJECT.html>`_ Oracle functions
-to convert column of any type to string representation, and then parse this column on Spark side using
-`from_json <https://spark.apache.org/docs/latest/api/python/reference/pyspark.sql/api/pyspark.sql.functions.from_json.html>`_:
+to convert column of any type to string representation. Then this JSON string can then be effectively parsed using the :obj:`JSON.parse_column <onetl.file.format.json.JSON.parse_column>` method.
 
 .. code-block:: python
 
-    from pyspark.sql.types import IntegerType
+    from onetl.file.format import JSON
+    from pyspark.sql.types import IntegerType, StructType, StructField
 
     from onetl.connection import Oracle
     from onetl.db import DBReader
@@ -325,32 +325,27 @@ to convert column of any type to string representation, and then parse this colu
     )
     df = reader.run()
 
-    # Spark requires all columns to have some type, describe it
-    column_type = IntegerType()
+    json_scheme = StructType([StructField("key", IntegerType())])
 
-    # cast column content to proper Spark type
     df = df.select(
         df.id,
         df.supported_column,
-        # explicit cast
         df.unsupported_column_str.cast("integer").alias("parsed_integer"),
-        # or explicit json parsing
-        from_json(df.array_column_json, schema).alias("array_column"),
+        JSON().parse_column("array_column_json", json_scheme).alias("array_column"),
     )
 
 ``DBWriter``
 ~~~~~~~~~~~~
 
-It is always possible to convert data on Spark side to string, and then write it to ``text`` column in Oracle table.
+It is always possible to convert data on Spark side to string, and then write it to text column in Oracle table.
 
-For example, you can convert data using `to_json <https://spark.apache.org/docs/latest/api/python/reference/pyspark.sql/api/pyspark.sql.functions.to_json.html>`_ function.
+To serialize and write JSON data to a ``text`` or ``json`` column in an Oracle table use the :obj:`JSON.serialize_column <onetl.file.format.json.JSON.serialize_column>` method.
 
-.. code:: python
-
-    from pyspark.sql.functions import to_json
+.. code-block:: python
 
     from onetl.connection import Oracle
-    from onetl.db import DBReader
+    from onetl.db import DBWriter
+    from onetl.file.format import JSON
 
     oracle = Oracle(...)
 
@@ -367,7 +362,7 @@ For example, you can convert data using `to_json <https://spark.apache.org/docs/
     write_df = df.select(
         df.id,
         df.supported_column,
-        to_json(df.unsupported_column).alias("array_column_json"),
+        JSON().serialize_column(df.unsupported_column).alias("array_column_json"),
     )
 
     writer = DBWriter(

docs/connection/db_connection/postgres/types.rst

@@ -348,17 +348,15 @@ It is possible to explicitly cast column of unsupported type using ``DBReader(co
 
 For example, you can use ``CAST(column AS text)`` to convert data to string representation on Postgres side, and so it will be read as Spark's ``StringType()``.
 
-It is also possible to use `to_json <https://www.postgresql.org/docs/current/functions-json.html>`_ Postgres function
-to convert column of any type to string representation, and then parse this column on Spark side using
-`from_json <https://spark.apache.org/docs/latest/api/python/reference/pyspark.sql/api/pyspark.sql.functions.from_json.html>`_:
+It is also possible to use `to_json <https://www.postgresql.org/docs/current/functions-json.html>`_ Postgres function to convert column of any type to string representation, and then parse this column on Spark side you can use the :obj:`JSON.parse_column <onetl.file.format.json.JSON.parse_column>` method:
 
 .. code-block:: python
 
-    from pyspark.sql.functions import from_json
     from pyspark.sql.types import IntegerType
 
     from onetl.connection import Postgres
     from onetl.db import DBReader
+    from onetl.file.format import JSON
 
     postgres = Postgres(...)
 
@@ -374,35 +372,37 @@ to convert column of any type to string representation, and then parse this colu
     )
     df = reader.run()
 
-    # Spark requires all columns to have some type, describe it
-    column_type = IntegerType()
-
-    # cast column content to proper Spark type
+    json_schema = StructType(
+        [
+            StructField("id", IntegerType(), nullable=True),
+            StructField("name", StringType(), nullable=True),
+            ...,
+        ]
+    )
     df = df.select(
         df.id,
         df.supported_column,
         # explicit cast
         df.unsupported_column_str.cast("integer").alias("parsed_integer"),
-        # or explicit json parsing
-        from_json(df.array_column_json, schema).alias("array_column"),
+        JSON().parse_column("array_column_json", json_schema).alias("json_string"),
     )
 
 ``DBWriter``
 ~~~~~~~~~~~~
 
-It is always possible to convert data on Spark side to string, and then write it to ``text`` column in Postgres table.
-
-Using ``to_json``
-^^^^^^^^^^^^^^^^^
+It is always possible to convert data on the Spark side to a string, and then write it to a text column in a Postgres table.
 
-For example, you can convert data using `to_json <https://spark.apache.org/docs/latest/api/python/reference/pyspark.sql/api/pyspark.sql.functions.to_json.html>`_ function.
+Using JSON.serialize_column
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+You can use the :obj:`JSON.serialize_column <onetl.file.format.json.JSON.serialize_column>` method for data serialization:
 
-.. code:: python
+.. code-block:: python
 
-    from pyspark.sql.functions import to_json
+    from onetl.file.format import JSON
+    from pyspark.sql.functions import col
 
     from onetl.connection import Postgres
-    from onetl.db import DBReader
+    from onetl.db import DBWriter
 
     postgres = Postgres(...)
 
@@ -419,7 +419,7 @@ For example, you can convert data using `to_json <https://spark.apache.org/docs/
     write_df = df.select(
         df.id,
         df.supported_column,
-        to_json(df.unsupported_column).alias("array_column_json"),
+        JSON().serialize_column(df.unsupported_column).alias("array_column_json"),
     )
 
     writer = DBWriter(
@@ -428,7 +428,7 @@ For example, you can convert data using `to_json <https://spark.apache.org/docs/
     )
     writer.run(write_df)
 
-Then you can parse this column on Postgres side (for example, by creating a view):
+Then you can parse this column on the Postgres side (for example, by creating a view):
 
 .. code-block:: sql
 

docs/file_df/file_formats/json.rst

@@ -6,4 +6,4 @@ JSON
 .. currentmodule:: onetl.file.format.json
 
 .. autoclass:: JSON
-    :members: __init__
+    :members: __init__, parse_column, serialize_column