Convert your hive output to Json format

Using CONCAT and string manipulation functions:

SELECT CONCAT('{ "column1": "', column1, '", "column2": "', column2, '", "column3": "', column3, '" }') AS json_data
FROM your_table;

Using a custom UDF (User-Defined Function): If the built-in functions in Hive do not provide the required functionality, you can create a custom UDF to convert the data to JSON format. Here’s an example using Java:

import org.apache.hadoop.hive.ql.exec.UDF;
import com.google.gson.Gson;

public class ToJsonUDF extends UDF {
    public String evaluate(Object input) {
        Gson gson = new Gson();
        return gson.toJson(input);
    }
}

3. using spark

import org.apache.spark.sql.SparkSession

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

// Read the data from Hive into a DataFrame
val df = spark.sql("SELECT * FROM your_table")

// Convert the DataFrame to JSON format
val jsonDF = df.toJSON

// Show the JSON data
jsonDF.show(false)

4. Using Spark SQL

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

val spark = SparkSession.builder()
  .appName("ColumnToJson")
  .getOrCreate()

// Read the data from Hive into a DataFrame
val df = spark.sql("SELECT column_name FROM your_table")

// Convert the column to JSON format
val jsonDF = df.select(functions.to_json(functions.col("column_name")).alias("json_data"))

// Show the JSON data
jsonDF.show(false)

Using Hive Scripting: Hive provides a scripting interface that allows you to execute scripts written in scripting languages like Python or Bash. You can leverage this feature to write a script that fetches data from Hive and converts it to JSON format.

import subprocess
import json

# Execute Hive query and fetch the result as a string
hive_query = "SELECT * FROM your_table"
hive_output = subprocess.check_output(["hive", "-e", hive_query]).decode("utf-8")

# Process the Hive output and convert it to JSON format
hive_result = [line.split("\t") for line in hive_output.strip().split("\n")]
column_names = hive_result[0]
json_data = [dict(zip(column_names, row)) for row in hive_result[1:]]

# Convert the JSON data to a JSON string
json_string = json.dumps(json_data)

# Print or write the JSON string to a file as needed
print(json_string)

6: Using Hive Scripting with Batch Processing

#!/bin/bash

# Define the Hive query to fetch the data in batches
HIVE_QUERY="SELECT * FROM your_hive_table LIMIT 1000000"

# Fetch data in batches and convert to JSON
BATCH_SIZE=100000
OFFSET=0
TOTAL_RECORDS=$(hive -e "SELECT COUNT(*) FROM your_hive_table")

while [ $OFFSET -lt $TOTAL_RECORDS ]; do
  BATCH_QUERY="$HIVE_QUERY OFFSET $OFFSET LIMIT $BATCH_SIZE"
  OUTPUT_FILE="output_$OFFSET.json"
  
  # Execute Hive query and convert to JSON using custom script or UDFs
  hive -e "$BATCH_QUERY" | python convert_to_json.py > $OUTPUT_FILE
  
  # Increment offset for the next batch
  OFFSET=$((OFFSET + BATCH_SIZE))
done

INSERT OVERWRITE LOCAL DIRECTORY '/path/to/output'
ROW FORMAT DELIMITED
FIELDS TERMINATED BY ','
SELECT concat('{',
              '"column1": "', get_json_object(to_json(named_struct("column1", column1))), '",',
              '"column2": "', get_json_object(to_json(named_struct("column2", column2))), '",',
              ...
              '"columnN": "', get_json_object(to_json(named_struct("columnN", columnN))), '"',
              '}'
            ) AS json_data
FROM your_table;

PySpark code snippet that converts Hive table output to JSON format

from pyspark.sql import SparkSession

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

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

# Convert DataFrame to JSON format
json_data = hive_table.toJSON()

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

# Stop SparkSession
spark.stop()

INSERT OVERWRITE LOCAL DIRECTORY '/path/to/output'
ROW FORMAT DELIMITED
FIELDS TERMINATED BY ','
SELECT CONCAT(
         '{',
         CONCAT_WS(
             ',',
             CONCAT('"column1": "', column1, '"'),
             CONCAT('"column2": "', column2, '"'),
             ...
             CONCAT('"columnN": "', columnN, '"')
         ),
         '}'
       ) AS json_data
FROM your_table;

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

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

val hiveTableDF = spark.table("your_hive_table_name")

// Select some columns as normal columns and some columns as JSON format
val resultDF = hiveTableDF.select(
  col("column1"),
  col("column2"),
  col("column3"),
  to_json(struct(col("column4"))).alias("json_column1"),
  to_json(struct(col("column5"), col("column6"), col("column7"))).alias("json_column2")
)

// Show or save the resulting DataFrame as per your requirement
resultDF.show()