session_id: "61ec31aa-ec60-4847-8384-fe1f77644a71"
user_context {
  user_id: "iqran"
}
plan {
  command {
    sql_command {
      input {
        common {
          plan_id: 32
        }
        sql {
          query: "\nCREATE TABLE bronze.test.test \nUSING iceberg \nAS\nWITH base_data AS (\n    -- Generates 1,000 initial rows\n    SELECT \n        id as raw_id,\n        uuid() as session_id,\n        CAST(rand() * 10000 AS INT) as category_id,\n        CASE WHEN rand() > 0.5 THEN \'ACTIVE\' ELSE \'INACTIVE\' END as status,\n        current_timestamp() as ts\n    FROM range(1000)\n),\nexploded_data AS (\n    -- Cross join to explode 1,000 rows to 1,000,000 rows\n    -- Increase the range(1000) to 10000 if you want 10M rows\n    SELECT a.* \n    FROM base_data a\n    CROSS JOIN (SELECT id FROM range(1000)) b\n)\nSELECT \n    raw_id,\n    session_id,\n    -- Complex hashing to stress CPU/Executors\n    sha2(concat(CAST(raw_id AS STRING), session_id), 256) as record_hash,\n    sha2(reverse(session_id), 512) as security_token,\n    -- Analytical transformations\n    category_id,\n    status,\n    ts,\n    -- Add some junk data to increase file size (S3 stress)\n    repeat(sha2(CAST(raw_id AS STRING), 256), 5) as padding_data\nFROM exploded_data;\n"
        }
      }
    }
  }
}
client_type: "_SPARK_CONNECT_PYTHON spark/4.1.1 os/darwin python/3.10.18"
request_options {
  result_chunking_options {
    allow_arrow_batch_chunking: true
  }
}
request_options {
  reattach_options {
    reattachable: true
  }
}
operation_id: "db4165ef-afeb-44ab-99fc-76c6de712efd"
client_observed_server_side_session_id: "6d58c63b-fd77-4e70-ac9c-f6d1a7db4baf"

session_id: "61ec31aa-ec60-4847-8384-fe1f77644a71"
user_context {
  user_id: "iqran"
}
plan {
  command {
    sql_command {
      input {
        common {
          plan_id: 32
        }
        sql {
          query: "\nCREATE TABLE bronze.test.test \nUSING iceberg \nAS\nWITH base_data AS (\n    -- Generates 1,000 initial rows\n    SELECT \n        id as raw_id,\n        uuid() as session_id,\n        CAST(rand() * 10000 AS INT) as category_id,\n        CASE WHEN rand() > 0.5 THEN \'ACTIVE\' ELSE \'INACTIVE\' END as status,\n        current_timestamp() as ts\n    FROM range(1000)\n),\nexploded_data AS (\n    -- Cross join to explode 1,000 rows to 1,000,000 rows\n    -- Increase the range(1000) to 10000 if you want 10M rows\n    SELECT a.* \n    FROM base_data a\n    CROSS JOIN (SELECT id FROM range(1000)) b\n)\nSELECT \n    raw_id,\n    session_id,\n    -- Complex hashing to stress CPU/Executors\n    sha2(concat(CAST(raw_id AS STRING), session_id), 256) as record_hash,\n    sha2(reverse(session_id), 512) as security_token,\n    -- Analytical transformations\n    category_id,\n    status,\n    ts,\n    -- Add some junk data to increase file size (S3 stress)\n    repeat(sha2(CAST(raw_id AS STRING), 256), 5) as padding_data\nFROM exploded_data;\n"
        }
      }
    }
  }
}
client_type: "_SPARK_CONNECT_PYTHON spark/4.1.1 os/darwin python/3.10.18"
request_options {
  result_chunking_options {
    allow_arrow_batch_chunking: true
  }
}
request_options {
  reattach_options {
    reattachable: true
  }
}
operation_id: "db4165ef-afeb-44ab-99fc-76c6de712efd"
client_observed_server_side_session_id: "6d58c63b-fd77-4e70-ac9c-f6d1a7db4baf"

session_id: "61ec31aa-ec60-4847-8384-fe1f77644a71"
user_context {
  user_id: "iqran"
}
plan {
  root {
    common {
      plan_id: 27
    }
    show_string {
      input {
        common {
          plan_id: 26
        }
        sql {
          query: "SELECT * FROM bronze.test.test"
        }
      }
      num_rows: 20
      truncate: 20
    }
  }
}
client_type: "_SPARK_CONNECT_PYTHON spark/4.1.1 os/darwin python/3.10.18"
request_options {
  result_chunking_options {
    allow_arrow_batch_chunking: true
  }
}
request_options {
  reattach_options {
    reattachable: true
  }
}
operation_id: "ec340348-af94-45c8-9987-018a67ac1f24"
client_observed_server_side_session_id: "6d58c63b-fd77-4e70-ac9c-f6d1a7db4baf"

session_id: "61ec31aa-ec60-4847-8384-fe1f77644a71"
user_context {
  user_id: "iqran"
}
plan {
  command {
    sql_command {
      input {
        common {
          plan_id: 23
        }
        sql {
          query: "\nCREATE TABLE bronze.test.test \nUSING iceberg \nAS\nWITH base_data AS (\n    -- Generates 1,000 initial rows\n    SELECT \n        id as raw_id,\n        uuid() as session_id,\n        CAST(rand() * 10000 AS INT) as category_id,\n        CASE WHEN rand() > 0.5 THEN \'ACTIVE\' ELSE \'INACTIVE\' END as status,\n        current_timestamp() as ts\n    FROM range(1000)\n),\nexploded_data AS (\n    -- Cross join to explode 1,000 rows to 1,000,000 rows\n    -- Increase the range(1000) to 10000 if you want 10M rows\n    SELECT a.* \n    FROM base_data a\n    CROSS JOIN (SELECT id FROM range(1000)) b\n)\nSELECT \n    raw_id,\n    session_id,\n    -- Complex hashing to stress CPU/Executors\n    sha2(concat(CAST(raw_id AS STRING), session_id), 256) as record_hash,\n    sha2(reverse(session_id), 512) as security_token,\n    -- Analytical transformations\n    category_id,\n    status,\n    ts,\n    -- Add some junk data to increase file size (S3 stress)\n    repeat(sha2(CAST(raw_id AS STRING), 256), 5) as padding_data\nFROM exploded_data;\n"
        }
      }
    }
  }
}
client_type: "_SPARK_CONNECT_PYTHON spark/4.1.1 os/darwin python/3.10.18"
request_options {
  result_chunking_options {
    allow_arrow_batch_chunking: true
  }
}
request_options {
  reattach_options {
    reattachable: true
  }
}
operation_id: "02e1d862-d2a1-47eb-8234-735f3dc89c10"
client_observed_server_side_session_id: "6d58c63b-fd77-4e70-ac9c-f6d1a7db4baf"

session_id: "61ec31aa-ec60-4847-8384-fe1f77644a71"
user_context {
  user_id: "iqran"
}
plan {
  command {
    sql_command {
      input {
        common {
          plan_id: 23
        }
        sql {
          query: "\nCREATE TABLE bronze.test.test \nUSING iceberg \nAS\nWITH base_data AS (\n    -- Generates 1,000 initial rows\n    SELECT \n        id as raw_id,\n        uuid() as session_id,\n        CAST(rand() * 10000 AS INT) as category_id,\n        CASE WHEN rand() > 0.5 THEN \'ACTIVE\' ELSE \'INACTIVE\' END as status,\n        current_timestamp() as ts\n    FROM range(1000)\n),\nexploded_data AS (\n    -- Cross join to explode 1,000 rows to 1,000,000 rows\n    -- Increase the range(1000) to 10000 if you want 10M rows\n    SELECT a.* \n    FROM base_data a\n    CROSS JOIN (SELECT id FROM range(1000)) b\n)\nSELECT \n    raw_id,\n    session_id,\n    -- Complex hashing to stress CPU/Executors\n    sha2(concat(CAST(raw_id AS STRING), session_id), 256) as record_hash,\n    sha2(reverse(session_id), 512) as security_token,\n    -- Analytical transformations\n    category_id,\n    status,\n    ts,\n    -- Add some junk data to increase file size (S3 stress)\n    repeat(sha2(CAST(raw_id AS STRING), 256), 5) as padding_data\nFROM exploded_data;\n"
        }
      }
    }
  }
}
client_type: "_SPARK_CONNECT_PYTHON spark/4.1.1 os/darwin python/3.10.18"
request_options {
  result_chunking_options {
    allow_arrow_batch_chunking: true
  }
}
request_options {
  reattach_options {
    reattachable: true
  }
}
operation_id: "02e1d862-d2a1-47eb-8234-735f3dc89c10"
client_observed_server_side_session_id: "6d58c63b-fd77-4e70-ac9c-f6d1a7db4baf"

session_id: "61ec31aa-ec60-4847-8384-fe1f77644a71"
user_context {
  user_id: "iqran"
}
plan {
  command {
    sql_command {
      input {
        common {
          plan_id: 18
        }
        sql {
          query: "\nCREATE TABLE bronze.test.test \nUSING iceberg \nAS\nWITH base_data AS (\n    -- Generates 1,000 initial rows\n    SELECT \n        id as raw_id,\n        uuid() as session_id,\n        CAST(rand() * 10000 AS INT) as category_id,\n        CASE WHEN rand() > 0.5 THEN \'ACTIVE\' ELSE \'INACTIVE\' END as status,\n        current_timestamp() as ts\n    FROM range(1000)\n),\nexploded_data AS (\n    -- Cross join to explode 1,000 rows to 1,000,000 rows\n    -- Increase the range(1000) to 10000 if you want 10M rows\n    SELECT a.* \n    FROM base_data a\n    CROSS JOIN (SELECT id FROM range(1000)) b\n)\nSELECT \n    raw_id,\n    session_id,\n    -- Complex hashing to stress CPU/Executors\n    sha2(concat(CAST(raw_id AS STRING), session_id), 256) as record_hash,\n    sha2(reverse(session_id), 512) as security_token,\n    -- Analytical transformations\n    category_id,\n    status,\n    ts,\n    -- Add some junk data to increase file size (S3 stress)\n    repeat(sha2(CAST(raw_id AS STRING), 256), 5) as padding_data\nFROM exploded_data;\n"
        }
      }
    }
  }
}
client_type: "_SPARK_CONNECT_PYTHON spark/4.1.1 os/darwin python/3.10.18"
request_options {
  result_chunking_options {
    allow_arrow_batch_chunking: true
  }
}
request_options {
  reattach_options {
    reattachable: true
  }
}
operation_id: "d9777848-021c-45ac-acb9-b2575596c989"
client_observed_server_side_session_id: "6d58c63b-fd77-4e70-ac9c-f6d1a7db4baf"

session_id: "61ec31aa-ec60-4847-8384-fe1f77644a71"
user_context {
  user_id: "iqran"
}
plan {
  command {
    sql_command {
      input {
        common {
          plan_id: 18
        }
        sql {
          query: "\nCREATE TABLE bronze.test.test \nUSING iceberg \nAS\nWITH base_data AS (\n    -- Generates 1,000 initial rows\n    SELECT \n        id as raw_id,\n        uuid() as session_id,\n        CAST(rand() * 10000 AS INT) as category_id,\n        CASE WHEN rand() > 0.5 THEN \'ACTIVE\' ELSE \'INACTIVE\' END as status,\n        current_timestamp() as ts\n    FROM range(1000)\n),\nexploded_data AS (\n    -- Cross join to explode 1,000 rows to 1,000,000 rows\n    -- Increase the range(1000) to 10000 if you want 10M rows\n    SELECT a.* \n    FROM base_data a\n    CROSS JOIN (SELECT id FROM range(1000)) b\n)\nSELECT \n    raw_id,\n    session_id,\n    -- Complex hashing to stress CPU/Executors\n    sha2(concat(CAST(raw_id AS STRING), session_id), 256) as record_hash,\n    sha2(reverse(session_id), 512) as security_token,\n    -- Analytical transformations\n    category_id,\n    status,\n    ts,\n    -- Add some junk data to increase file size (S3 stress)\n    repeat(sha2(CAST(raw_id AS STRING), 256), 5) as padding_data\nFROM exploded_data;\n"
        }
      }
    }
  }
}
client_type: "_SPARK_CONNECT_PYTHON spark/4.1.1 os/darwin python/3.10.18"
request_options {
  result_chunking_options {
    allow_arrow_batch_chunking: true
  }
}
request_options {
  reattach_options {
    reattachable: true
  }
}
operation_id: "d9777848-021c-45ac-acb9-b2575596c989"
client_observed_server_side_session_id: "6d58c63b-fd77-4e70-ac9c-f6d1a7db4baf"

session_id: "61ec31aa-ec60-4847-8384-fe1f77644a71"
user_context {
  user_id: "iqran"
}
plan {
  root {
    common {
      plan_id: 11
    }
    show_string {
      input {
        common {
          plan_id: 10
        }
        sql {
          query: "SELECT count(*), status FROM bronze.test.test GROUP BY status"
        }
      }
      num_rows: 20
      truncate: 20
    }
  }
}
client_type: "_SPARK_CONNECT_PYTHON spark/4.1.1 os/darwin python/3.10.18"
request_options {
  result_chunking_options {
    allow_arrow_batch_chunking: true
  }
}
request_options {
  reattach_options {
    reattachable: true
  }
}
operation_id: "c6b17976-e5fd-4d12-864f-e1d674bf95b7"
client_observed_server_side_session_id: "6d58c63b-fd77-4e70-ac9c-f6d1a7db4baf"

session_id: "61ec31aa-ec60-4847-8384-fe1f77644a71"
user_context {
  user_id: "iqran"
}
plan {
  root {
    common {
      plan_id: 11
    }
    show_string {
      input {
        common {
          plan_id: 10
        }
        sql {
          query: "SELECT count(*), status FROM bronze.test.test GROUP BY status"
        }
      }
      num_rows: 20
      truncate: 20
    }
  }
}
client_type: "_SPARK_CONNECT_PYTHON spark/4.1.1 os/darwin python/3.10.18"
request_options {
  result_chunking_options {
    allow_arrow_batch_chunking: true
  }
}
request_options {
  reattach_options {
    reattachable: true
  }
}
operation_id: "c6b17976-e5fd-4d12-864f-e1d674bf95b7"
client_observed_server_side_session_id: "6d58c63b-fd77-4e70-ac9c-f6d1a7db4baf"

session_id: "61ec31aa-ec60-4847-8384-fe1f77644a71"
user_context {
  user_id: "iqran"
}
plan {
  command {
    sql_command {
      input {
        common {
          plan_id: 7
        }
        sql {
          query: "\nCREATE TABLE bronze.test.test \nUSING iceberg \nAS\nWITH base_data AS (\n    -- Generates 1,000 initial rows\n    SELECT \n        id as raw_id,\n        uuid() as session_id,\n        CAST(rand() * 10000 AS INT) as category_id,\n        CASE WHEN rand() > 0.5 THEN \'ACTIVE\' ELSE \'INACTIVE\' END as status,\n        current_timestamp() as ts\n    FROM range(1000)\n),\nexploded_data AS (\n    -- Cross join to explode 1,000 rows to 1,000,000 rows\n    -- Increase the range(1000) to 10000 if you want 10M rows\n    SELECT a.* \n    FROM base_data a\n    CROSS JOIN (SELECT id FROM range(1000)) b\n)\nSELECT \n    raw_id,\n    session_id,\n    -- Complex hashing to stress CPU/Executors\n    sha2(concat(CAST(raw_id AS STRING), session_id), 256) as record_hash,\n    sha2(reverse(session_id), 512) as security_token,\n    -- Analytical transformations\n    category_id,\n    status,\n    ts,\n    -- Add some junk data to increase file size (S3 stress)\n    repeat(sha2(CAST(raw_id AS STRING), 256), 5) as padding_data\nFROM exploded_data;\n"
        }
      }
    }
  }
}
client_type: "_SPARK_CONNECT_PYTHON spark/4.1.1 os/darwin python/3.10.18"
request_options {
  result_chunking_options {
    allow_arrow_batch_chunking: true
  }
}
request_options {
  reattach_options {
    reattachable: true
  }
}
operation_id: "533d10ac-4b22-4337-a0b9-3050b7fb6181"
client_observed_server_side_session_id: "6d58c63b-fd77-4e70-ac9c-f6d1a7db4baf"

session_id: "61ec31aa-ec60-4847-8384-fe1f77644a71"
user_context {
  user_id: "iqran"
}
plan {
  command {
    sql_command {
      input {
        common {
          plan_id: 7
        }
        sql {
          query: "\nCREATE TABLE bronze.test.test \nUSING iceberg \nAS\nWITH base_data AS (\n    -- Generates 1,000 initial rows\n    SELECT \n        id as raw_id,\n        uuid() as session_id,\n        CAST(rand() * 10000 AS INT) as category_id,\n        CASE WHEN rand() > 0.5 THEN \'ACTIVE\' ELSE \'INACTIVE\' END as status,\n        current_timestamp() as ts\n    FROM range(1000)\n),\nexploded_data AS (\n    -- Cross join to explode 1,000 rows to 1,000,000 rows\n    -- Increase the range(1000) to 10000 if you want 10M rows\n    SELECT a.* \n    FROM base_data a\n    CROSS JOIN (SELECT id FROM range(1000)) b\n)\nSELECT \n    raw_id,\n    session_id,\n    -- Complex hashing to stress CPU/Executors\n    sha2(concat(CAST(raw_id AS STRING), session_id), 256) as record_hash,\n    sha2(reverse(session_id), 512) as security_token,\n    -- Analytical transformations\n    category_id,\n    status,\n    ts,\n    -- Add some junk data to increase file size (S3 stress)\n    repeat(sha2(CAST(raw_id AS STRING), 256), 5) as padding_data\nFROM exploded_data;\n"
        }
      }
    }
  }
}
client_type: "_SPARK_CONNECT_PYTHON spark/4.1.1 os/darwin python/3.10.18"
request_options {
  result_chunking_options {
    allow_arrow_batch_chunking: true
  }
}
request_options {
  reattach_options {
    reattachable: true
  }
}
operation_id: "533d10ac-4b22-4337-a0b9-3050b7fb6181"
client_observed_server_side_session_id: "6d58c63b-fd77-4e70-ac9c-f6d1a7db4baf"

session_id: "61ec31aa-ec60-4847-8384-fe1f77644a71"
user_context {
  user_id: "iqran"
}
plan {
  root {
    common {
      plan_id: 2
    }
    show_string {
      input {
        common {
          plan_id: 1
        }
        sql {
          query: "SELECT \'READY\' AS STATUS"
        }
      }
      num_rows: 20
      truncate: 20
    }
  }
}
client_type: "_SPARK_CONNECT_PYTHON spark/4.1.1 os/darwin python/3.10.18"
request_options {
  result_chunking_options {
    allow_arrow_batch_chunking: true
  }
}
request_options {
  reattach_options {
    reattachable: true
  }
}
operation_id: "f5df0598-1b71-4764-af9b-501ae2aece94"
client_observed_server_side_session_id: "6d58c63b-fd77-4e70-ac9c-f6d1a7db4baf"

session_id: "4ab3976b-dcbe-4020-b06b-15c63f8fd1c7"
user_context {
  user_id: "iqran"
}
plan {
  command {
    sql_command {
      input {
        common {
          plan_id: 5
        }
        sql {
          query: "\nCREATE TABLE bronze.test.test \nUSING iceberg \nAS\nWITH base_data AS (\n    -- Generates 1,000 initial rows\n    SELECT \n        id as raw_id,\n        uuid() as session_id,\n        CAST(rand() * 10000 AS INT) as category_id,\n        CASE WHEN rand() > 0.5 THEN \'ACTIVE\' ELSE \'INACTIVE\' END as status,\n        current_timestamp() as ts\n    FROM range(1000)\n),\nexploded_data AS (\n    -- Cross join to explode 1,000 rows to 1,000,000 rows\n    -- Increase the range(1000) to 10000 if you want 10M rows\n    SELECT a.* \n    FROM base_data a\n    CROSS JOIN (SELECT id FROM range(1000)) b\n)\nSELECT \n    raw_id,\n    session_id,\n    -- Complex hashing to stress CPU/Executors\n    sha2(concat(CAST(raw_id AS STRING), session_id), 256) as record_hash,\n    sha2(reverse(session_id), 512) as security_token,\n    -- Analytical transformations\n    category_id,\n    status,\n    ts,\n    -- Add some junk data to increase file size (S3 stress)\n    repeat(sha2(CAST(raw_id AS STRING), 256), 5) as padding_data\nFROM exploded_data;\n"
        }
      }
    }
  }
}
client_type: "_SPARK_CONNECT_PYTHON spark/4.1.1 os/darwin python/3.10.18"
request_options {
  result_chunking_options {
    allow_arrow_batch_chunking: true
  }
}
request_options {
  reattach_options {
    reattachable: true
  }
}
operation_id: "99c0fa8d-d144-4286-9401-592967857868"
client_observed_server_side_session_id: "442b3d64-e5ae-4f9b-b202-9128f7c20e39"

session_id: "4ab3976b-dcbe-4020-b06b-15c63f8fd1c7"
user_context {
  user_id: "iqran"
}
plan {
  command {
    sql_command {
      input {
        common {
          plan_id: 5
        }
        sql {
          query: "\nCREATE TABLE bronze.test.test \nUSING iceberg \nAS\nWITH base_data AS (\n    -- Generates 1,000 initial rows\n    SELECT \n        id as raw_id,\n        uuid() as session_id,\n        CAST(rand() * 10000 AS INT) as category_id,\n        CASE WHEN rand() > 0.5 THEN \'ACTIVE\' ELSE \'INACTIVE\' END as status,\n        current_timestamp() as ts\n    FROM range(1000)\n),\nexploded_data AS (\n    -- Cross join to explode 1,000 rows to 1,000,000 rows\n    -- Increase the range(1000) to 10000 if you want 10M rows\n    SELECT a.* \n    FROM base_data a\n    CROSS JOIN (SELECT id FROM range(1000)) b\n)\nSELECT \n    raw_id,\n    session_id,\n    -- Complex hashing to stress CPU/Executors\n    sha2(concat(CAST(raw_id AS STRING), session_id), 256) as record_hash,\n    sha2(reverse(session_id), 512) as security_token,\n    -- Analytical transformations\n    category_id,\n    status,\n    ts,\n    -- Add some junk data to increase file size (S3 stress)\n    repeat(sha2(CAST(raw_id AS STRING), 256), 5) as padding_data\nFROM exploded_data;\n"
        }
      }
    }
  }
}
client_type: "_SPARK_CONNECT_PYTHON spark/4.1.1 os/darwin python/3.10.18"
request_options {
  result_chunking_options {
    allow_arrow_batch_chunking: true
  }
}
request_options {
  reattach_options {
    reattachable: true
  }
}
operation_id: "99c0fa8d-d144-4286-9401-592967857868"
client_observed_server_side_session_id: "442b3d64-e5ae-4f9b-b202-9128f7c20e39"

session_id: "4ab3976b-dcbe-4020-b06b-15c63f8fd1c7"
user_context {
  user_id: "iqran"
}
plan {
  root {
    common {
      plan_id: 2
    }
    show_string {
      input {
        common {
          plan_id: 1
        }
        sql {
          query: "SELECT \'READY\' AS STATUS"
        }
      }
      num_rows: 20
      truncate: 20
    }
  }
}
client_type: "_SPARK_CONNECT_PYTHON spark/4.1.1 os/darwin python/3.10.18"
request_options {
  result_chunking_options {
    allow_arrow_batch_chunking: true
  }
}
request_options {
  reattach_options {
    reattachable: true
  }
}
operation_id: "ab7f8859-b372-4654-9343-a053ef0091e3"
client_observed_server_side_session_id: "442b3d64-e5ae-4f9b-b202-9128f7c20e39"

session_id: "c72cf9f2-3595-4d09-adfc-629a083c596a"
user_context {
  user_id: "iqran"
}
plan {
  root {
    common {
      plan_id: 2
    }
    show_string {
      input {
        common {
          plan_id: 1
        }
        sql {
          query: "SELECT \'READY\' AS STATUS"
        }
      }
      num_rows: 20
      truncate: 20
    }
  }
}
client_type: "_SPARK_CONNECT_PYTHON spark/4.1.1 os/darwin python/3.10.18"
request_options {
  result_chunking_options {
    allow_arrow_batch_chunking: true
  }
}
request_options {
  reattach_options {
    reattachable: true
  }
}
operation_id: "def49f09-1be4-46d3-82b9-d335721f6052"
client_observed_server_side_session_id: "d88c5c99-077f-462f-971c-f11c7e9be82e"

session_id: "8c0de3c6-0219-44ff-b69c-319d15b20457"
user_context {
  user_id: "iqran"
}
plan {
  root {
    common {
      plan_id: 2
    }
    show_string {
      input {
        common {
          plan_id: 1
        }
        sql {
          query: "SELECT \'READY\' AS STATUS"
        }
      }
      num_rows: 20
      truncate: 20
    }
  }
}
client_type: "_SPARK_CONNECT_PYTHON spark/4.1.1 os/darwin python/3.10.18"
request_options {
  result_chunking_options {
    allow_arrow_batch_chunking: true
  }
}
request_options {
  reattach_options {
    reattachable: true
  }
}
operation_id: "5f32c8e2-abc0-4fce-ac15-2e36e070dfcb"
client_observed_server_side_session_id: "086a972b-c33f-4f80-ba0b-8c72fc6cda66"

session_id: "f15d8ea2-f375-4431-9a62-15dfcf3e3fdd"
user_context {
  user_id: "iqran"
}
plan {
  root {
    common {
      plan_id: 2
    }
    show_string {
      input {
        common {
          plan_id: 1
        }
        sql {
          query: "SELECT \'READY\' AS STATUS"
        }
      }
      num_rows: 20
      truncate: 20
    }
  }
}
client_type: "_SPARK_CONNECT_PYTHON spark/4.1.1 os/darwin python/3.10.18"
request_options {
  result_chunking_options {
    allow_arrow_batch_chunking: true
  }
}
request_options {
  reattach_options {
    reattachable: true
  }
}
operation_id: "4ed01114-7e92-476a-ae13-ffe8e29fa58e"
client_observed_server_side_session_id: "23bd2d34-9233-41ce-883b-aa45af2684e2"

session_id: "968efe1c-80d2-4c9c-8f88-84933a24d214"
user_context {
  user_id: "iqran"
}
plan {
  command {
    sql_command {
      input {
        common {
          plan_id: 5
        }
        sql {
          query: "\nCREATE TABLE bronze.test.test \nUSING iceberg \nAS\nWITH base_data AS (\n    -- Generates 1,000 initial rows\n    SELECT \n        id as raw_id,\n        uuid() as session_id,\n        CAST(rand() * 10000 AS INT) as category_id,\n        CASE WHEN rand() > 0.5 THEN \'ACTIVE\' ELSE \'INACTIVE\' END as status,\n        current_timestamp() as ts\n    FROM range(1000)\n),\nexploded_data AS (\n    -- Cross join to explode 1,000 rows to 1,000,000 rows\n    -- Increase the range(1000) to 10000 if you want 10M rows\n    SELECT a.* \n    FROM base_data a\n    CROSS JOIN (SELECT id FROM range(1000)) b\n)\nSELECT \n    raw_id,\n    session_id,\n    -- Complex hashing to stress CPU/Executors\n    sha2(concat(CAST(raw_id AS STRING), session_id), 256) as record_hash,\n    sha2(reverse(session_id), 512) as security_token,\n    -- Analytical transformations\n    category_id,\n    status,\n    ts,\n    -- Add some junk data to increase file size (S3 stress)\n    repeat(sha2(CAST(raw_id AS STRING), 256), 5) as padding_data\nFROM exploded_data;\n"
        }
      }
    }
  }
}
client_type: "_SPARK_CONNECT_PYTHON spark/4.1.1 os/darwin python/3.10.18"
request_options {
  result_chunking_options {
    allow_arrow_batch_chunking: true
  }
}
request_options {
  reattach_options {
    reattachable: true
  }
}
operation_id: "b788f1b9-91f2-49b1-ad43-edc15f0064f4"
client_observed_server_side_session_id: "96aa90ce-5e9e-4fc3-9fb4-4c8f91f72a9e"

session_id: "968efe1c-80d2-4c9c-8f88-84933a24d214"
user_context {
  user_id: "iqran"
}
plan {
  command {
    sql_command {
      input {
        common {
          plan_id: 5
        }
        sql {
          query: "\nCREATE TABLE bronze.test.test \nUSING iceberg \nAS\nWITH base_data AS (\n    -- Generates 1,000 initial rows\n    SELECT \n        id as raw_id,\n        uuid() as session_id,\n        CAST(rand() * 10000 AS INT) as category_id,\n        CASE WHEN rand() > 0.5 THEN \'ACTIVE\' ELSE \'INACTIVE\' END as status,\n        current_timestamp() as ts\n    FROM range(1000)\n),\nexploded_data AS (\n    -- Cross join to explode 1,000 rows to 1,000,000 rows\n    -- Increase the range(1000) to 10000 if you want 10M rows\n    SELECT a.* \n    FROM base_data a\n    CROSS JOIN (SELECT id FROM range(1000)) b\n)\nSELECT \n    raw_id,\n    session_id,\n    -- Complex hashing to stress CPU/Executors\n    sha2(concat(CAST(raw_id AS STRING), session_id), 256) as record_hash,\n    sha2(reverse(session_id), 512) as security_token,\n    -- Analytical transformations\n    category_id,\n    status,\n    ts,\n    -- Add some junk data to increase file size (S3 stress)\n    repeat(sha2(CAST(raw_id AS STRING), 256), 5) as padding_data\nFROM exploded_data;\n"
        }
      }
    }
  }
}
client_type: "_SPARK_CONNECT_PYTHON spark/4.1.1 os/darwin python/3.10.18"
request_options {
  result_chunking_options {
    allow_arrow_batch_chunking: true
  }
}
request_options {
  reattach_options {
    reattachable: true
  }
}
operation_id: "b788f1b9-91f2-49b1-ad43-edc15f0064f4"
client_observed_server_side_session_id: "96aa90ce-5e9e-4fc3-9fb4-4c8f91f72a9e"

session_id: "968efe1c-80d2-4c9c-8f88-84933a24d214"
user_context {
  user_id: "iqran"
}
plan {
  root {
    common {
      plan_id: 2
    }
    show_string {
      input {
        common {
          plan_id: 1
        }
        sql {
          query: "SELECT \'READY\' AS STATUS"
        }
      }
      num_rows: 20
      truncate: 20
    }
  }
}
client_type: "_SPARK_CONNECT_PYTHON spark/4.1.1 os/darwin python/3.10.18"
request_options {
  result_chunking_options {
    allow_arrow_batch_chunking: true
  }
}
request_options {
  reattach_options {
    reattachable: true
  }
}
operation_id: "68fd2298-5078-401c-8551-f42b834e6a31"
client_observed_server_side_session_id: "96aa90ce-5e9e-4fc3-9fb4-4c8f91f72a9e"