Create and Store output in hive table of JSON data

Using hive

-- Create the target table
CREATE TABLE combined_json_table (
  id INT,
  column1 STRING,
  column2 INT,
  json_data STRING
)
ROW FORMAT DELIMITED
FIELDS TERMINATED BY ','
STORED AS TEXTFILE;

-- Insert the data into the target table
INSERT INTO TABLE combined_json_table
SELECT
  id,
  column1,
  column2,
  CONCAT(
    '{',
    CONCAT(
      '"column1": "', column1, '",',
      '"column2": ', column2,
    )
  ) AS json_data
FROM your_table;

Here’s an example in PySpark:

from pyspark.sql import SparkSession
from pyspark.sql.functions import to_json, struct

# Create SparkSession
spark = SparkSession.builder \
    .appName("ColumnToJson") \
    .getOrCreate()

# Read data from Hive table
hive_table = spark.table("your_table")

# Convert columns to JSON format
json_data = hive_table.select(to_json(struct(hive_table.columns)).alias("json_data"))

# Save JSON data to a file
output_path = "/path/to/output.json"
json_data.write.json(output_path)

# Stop SparkSession
spark.stop()

Store multiple table outputs in one json table

-- Create the target JSON table
CREATE TABLE combined_json_table
ROW FORMAT SERDE 'org.apache.hive.hcatalog.data.JsonSerDe'
STORED AS TEXTFILE
AS SELECT NULL AS id, NULL AS json_data;

-- Insert data from table1
INSERT INTO TABLE combined_json_table
SELECT 1, concat('{', '"column1": "', column1, '", "column2": "', column2, '", ... }') AS json_data
FROM table1;

-- Insert data from table2
INSERT INTO TABLE combined_json_table
SELECT 2, concat('{', '"column1": "', column1, '", "column2": "', column2, '", ... }') AS json_data
FROM table2;

-- Insert data from table3
INSERT INTO TABLE combined_json_table
SELECT 3, concat('{', '"column1": "', column1, '", "column2": "', column2, '", ... }') AS json_data
FROM table3;

Using spark to combine tables

from pyspark.sql import SparkSession
from pyspark.sql.functions import concat, lit, to_json

# Create SparkSession
spark = SparkSession.builder \
    .appName("CombineTablesToJson") \
    .getOrCreate()

# Read data from table1
table1 = spark.table("table1")

# Read data from table2
table2 = spark.table("table2")

# Read data from table3
table3 = spark.table("table3")

# Select JSON columns and convert to JSON
json_columns_table1 = [col for col in table1.columns if col.startswith("json_")]
json_data_table1 = table1.select(concat(lit("table1"), to_json(table1[json_columns_table1])).alias("json_data"))

json_columns_table2 = [col for col in table2.columns if col.startswith("json_")]
json_data_table2 = table2.select(concat(lit("table2"), to_json(table2[json_columns_table2])).alias("json_data"))

json_columns_table3 = [col for col in table3.columns if col.startswith("json_")]
json_data_table3 = table3.select(concat(lit("table3"), to_json(table3[json_columns_table3])).alias("json_data"))

# Select non-JSON columns
non_json_columns_table1 = [col for col in table1.columns if col not in json_columns_table1]
non_json_data_table1 = table1.select(non_json_columns_table1)

non_json_columns_table2 = [col for col in table2.columns if col not in json_columns_table2]
non_json_data_table2 = table2.select(non_json_columns_table2)

non_json_columns_table3 = [col for col in table3.columns if col not in json_columns_table3]
non_json_data_table3 = table3.select(non_json_columns_table3)

# Union JSON and non-JSON columns
combined_data = json_data_table1.union(non_json_data_table1) \
    .union(json_data_table2).union(non_json_data_table2) \
    .union(json_data_table3).union(non_json_data_table3)

# Write JSON data to a file
output_path = "/path/to/output.json"
combined_data.write.json(output_path)

# Stop SparkSession
spark.stop()

import org.apache.spark.sql.{SparkSession, DataFrame}
import org.apache.spark.sql.functions._

val spark = SparkSession.builder()
  .appName("HiveTableWithJsonColumn")
  .enableHiveSupport()
  .getOrCreate()

val hiveTableDF = spark.table("your_existing_hive_table_name")

val selectedColumns = hiveTableDF.select("column1", "column2", "column3")
val jsonDF = selectedColumns.withColumn("json", to_json(struct(selectedColumns.columns.map(col): _*)))

val newTableDF = hiveTableDF.select("existing_column1", "existing_column2", "existing_column3")
  .join(jsonDF, Seq("common_column"))

newTableDF.write
  .mode("overwrite") // Specify the write mode as per your requirement
  .saveAsTable("your_new_hive_table_name")

import org.apache.spark.sql.{SparkSession, DataFrame}
import org.apache.spark.sql.functions._

val spark = SparkSession.builder()
  .appName("HiveTableWithJsonColumn")
  .enableHiveSupport()
  .getOrCreate()

val hiveTableDF = spark.table("your_existing_hive_table_name")

val selectedColumns = hiveTableDF.select("column1", "column2", "column3")
val jsonDF = selectedColumns.withColumn("json", to_json(struct(selectedColumns.columns.map(col): _*)))

val tableWithJsonColumnDF = hiveTableDF.withColumn("json", jsonDF("json"))

tableWithJsonColumnDF.write
  .mode("overwrite") // Specify the write mode as per your requirement
  .saveAsTable("your_new_hive_table_name")