Spark Connect server - SQL / DataFrame

Completed Queries: 29
Failed Queries: 8

Completed Queries (29)

Page:

Spark Connect - session_id: "968efe1c-80d2-4c9c-8f88-84933a24d214" user_context { user_id: "iqran" } plan { root { common { pla... +details

Spark Connect - session_id: "968efe1c-80d2-4c9c-8f88-84933a24d214"
user_context {
  user_id: "iqran"
}
plan {
  root {
    common {
      pla...

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"

2026/05/11 04:22:29

0.9 s

[0]

Spark Connect - session_id: "968efe1c-80d2-4c9c-8f88-84933a24d214" user_context { user_id: "iqran" } plan { command { sql_command { ... +details

Spark Connect - session_id: "968efe1c-80d2-4c9c-8f88-84933a24d214"
user_context {
  user_id: "iqran"
}
plan {
  command {
    sql_command {
 ...

session_id: "968efe1c-80d2-4c9c-8f88-84933a24d214"
user_context {
  user_id: "iqran"
}
plan {
  command {
    sql_command {
      input {
        common {
          plan_id: 3
        }
        sql {
          query: "CREATE NAMESPACE IF NOT EXISTS bronze.test"
        }
      }
    }
  }
}
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: "6c53ae0d-7fb6-4eea-a202-88ef9df086f3"
client_observed_server_side_session_id: "96aa90ce-5e9e-4fc3-9fb4-4c8f91f72a9e"

2026/05/11 04:22:50

39 ms

Spark Connect - session_id: "968efe1c-80d2-4c9c-8f88-84933a24d214" user_context { user_id: "iqran" } plan { command { sql_command { ... +details

Spark Connect - session_id: "968efe1c-80d2-4c9c-8f88-84933a24d214"
user_context {
  user_id: "iqran"
}
plan {
  command {
    sql_command {
 ...

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"

2026/05/11 04:22:54

4 s

[3]

+details

Description

Submitted

Duration

Job IDs

Spark Connect - session_id: "968efe1c-80d2-4c9c-8f88-84933a24d214" user_context { user_id: "iqran" } plan { command { sql_command { ... +details

Spark Connect - session_id: "968efe1c-80d2-4c9c-8f88-84933a24d214"
user_context {
  user_id: "iqran"
}
plan {
  command {
    sql_command {
 ...

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"

2026/05/11 04:22:55

4 s

[1][2]

Spark Connect - session_id: "968efe1c-80d2-4c9c-8f88-84933a24d214" user_context { user_id: "iqran" } plan { command { sql_command { ... +details

Spark Connect - session_id: "968efe1c-80d2-4c9c-8f88-84933a24d214"
user_context {
  user_id: "iqran"
}
plan {
  command {
    sql_command {
 ...

session_id: "968efe1c-80d2-4c9c-8f88-84933a24d214"
user_context {
  user_id: "iqran"
}
plan {
  command {
    sql_command {
      input {
        common {
          plan_id: 9
        }
        sql {
          query: "CREATE NAMESPACE IF NOT EXISTS bronze.test"
        }
      }
    }
  }
}
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: "aeb95485-a5c4-46a9-8291-907172ec8e8c"
client_observed_server_side_session_id: "96aa90ce-5e9e-4fc3-9fb4-4c8f91f72a9e"

2026/05/11 04:25:36

18 ms

Spark Connect - session_id: "f15d8ea2-f375-4431-9a62-15dfcf3e3fdd" user_context { user_id: "iqran" } plan { root { common { pla... +details

Spark Connect - session_id: "f15d8ea2-f375-4431-9a62-15dfcf3e3fdd"
user_context {
  user_id: "iqran"
}
plan {
  root {
    common {
      pla...

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"

2026/05/11 04:26:52

79 ms

[3]

Spark Connect - session_id: "8c0de3c6-0219-44ff-b69c-319d15b20457" user_context { user_id: "iqran" } plan { root { common { pla... +details

Spark Connect - session_id: "8c0de3c6-0219-44ff-b69c-319d15b20457"
user_context {
  user_id: "iqran"
}
plan {
  root {
    common {
      pla...

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"

2026/05/11 04:30:02

52 ms

[4]

Spark Connect - session_id: "c72cf9f2-3595-4d09-adfc-629a083c596a" user_context { user_id: "iqran" } plan { root { common { pla... +details

Spark Connect - session_id: "c72cf9f2-3595-4d09-adfc-629a083c596a"
user_context {
  user_id: "iqran"
}
plan {
  root {
    common {
      pla...

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"

2026/05/11 04:31:36

74 ms

[5]

Spark Connect - session_id: "4ab3976b-dcbe-4020-b06b-15c63f8fd1c7" user_context { user_id: "iqran" } plan { root { common { pla... +details

Spark Connect - session_id: "4ab3976b-dcbe-4020-b06b-15c63f8fd1c7"
user_context {
  user_id: "iqran"
}
plan {
  root {
    common {
      pla...

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"

2026/05/11 04:34:17

49 ms

[6]

Spark Connect - session_id: "4ab3976b-dcbe-4020-b06b-15c63f8fd1c7" user_context { user_id: "iqran" } plan { command { sql_command { ... +details

Spark Connect - session_id: "4ab3976b-dcbe-4020-b06b-15c63f8fd1c7"
user_context {
  user_id: "iqran"
}
plan {
  command {
    sql_command {
 ...

session_id: "4ab3976b-dcbe-4020-b06b-15c63f8fd1c7"
user_context {
  user_id: "iqran"
}
plan {
  command {
    sql_command {
      input {
        common {
          plan_id: 3
        }
        sql {
          query: "CREATE NAMESPACE IF NOT EXISTS bronze.test"
        }
      }
    }
  }
}
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: "875eb492-e7be-4e66-b9a6-30330230f4a4"
client_observed_server_side_session_id: "442b3d64-e5ae-4f9b-b202-9128f7c20e39"

2026/05/11 04:34:35

97 ms

Spark Connect - session_id: "4ab3976b-dcbe-4020-b06b-15c63f8fd1c7" user_context { user_id: "iqran" } plan { command { sql_command { ... +details

Spark Connect - session_id: "4ab3976b-dcbe-4020-b06b-15c63f8fd1c7"
user_context {
  user_id: "iqran"
}
plan {
  command {
    sql_command {
 ...

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"

2026/05/11 04:34:40

2 s

[15]

+details

Description

Submitted

Duration

Job IDs

Spark Connect - session_id: "4ab3976b-dcbe-4020-b06b-15c63f8fd1c7" user_context { user_id: "iqran" } plan { command { sql_command { ... +details

Spark Connect - session_id: "4ab3976b-dcbe-4020-b06b-15c63f8fd1c7"
user_context {
  user_id: "iqran"
}
plan {
  command {
    sql_command {
 ...

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"

2026/05/11 04:34:40

1 s

[7][8]

Spark Connect - session_id: "61ec31aa-ec60-4847-8384-fe1f77644a71" user_context { user_id: "iqran" } plan { root { common { pla... +details

Spark Connect - session_id: "61ec31aa-ec60-4847-8384-fe1f77644a71"
user_context {
  user_id: "iqran"
}
plan {
  root {
    common {
      pla...

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"

2026/05/11 04:36:10

65 ms

[9]

Spark Connect - session_id: "61ec31aa-ec60-4847-8384-fe1f77644a71" user_context { user_id: "iqran" } plan { command { sql_command { ... +details

Spark Connect - session_id: "61ec31aa-ec60-4847-8384-fe1f77644a71"
user_context {
  user_id: "iqran"
}
plan {
  command {
    sql_command {
 ...

session_id: "61ec31aa-ec60-4847-8384-fe1f77644a71"
user_context {
  user_id: "iqran"
}
plan {
  command {
    sql_command {
      input {
        common {
          plan_id: 3
        }
        sql {
          query: "CREATE NAMESPACE IF NOT EXISTS bronze.test"
        }
      }
    }
  }
}
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: "069a403d-e9ba-47a0-b5c7-f904d468030a"
client_observed_server_side_session_id: "6d58c63b-fd77-4e70-ac9c-f6d1a7db4baf"

2026/05/11 04:36:22

78 ms

Spark Connect - session_id: "61ec31aa-ec60-4847-8384-fe1f77644a71" user_context { user_id: "iqran" } plan { command { sql_command { ... +details

Spark Connect - session_id: "61ec31aa-ec60-4847-8384-fe1f77644a71"
user_context {
  user_id: "iqran"
}
plan {
  command {
    sql_command {
 ...

session_id: "61ec31aa-ec60-4847-8384-fe1f77644a71"
user_context {
  user_id: "iqran"
}
plan {
  command {
    sql_command {
      input {
        common {
          plan_id: 5
        }
        sql {
          query: "CREATE TABLE IF NOT EXISTS bronze.test.verify_sts (id INT) USING iceberg"
        }
      }
    }
  }
}
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: "e6980855-92ea-41b1-a0e1-92864d671d0c"
client_observed_server_side_session_id: "6d58c63b-fd77-4e70-ac9c-f6d1a7db4baf"

2026/05/11 04:36:24

0.5 s

Spark Connect - session_id: "61ec31aa-ec60-4847-8384-fe1f77644a71" user_context { user_id: "iqran" } plan { command { sql_command { ... +details

Spark Connect - session_id: "61ec31aa-ec60-4847-8384-fe1f77644a71"
user_context {
  user_id: "iqran"
}
plan {
  command {
    sql_command {
 ...

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"

2026/05/11 04:36:26

1.0 s

[20]

+details

Description

Submitted

Duration

Job IDs

Spark Connect - session_id: "61ec31aa-ec60-4847-8384-fe1f77644a71" user_context { user_id: "iqran" } plan { command { sql_command { ... +details

Spark Connect - session_id: "61ec31aa-ec60-4847-8384-fe1f77644a71"
user_context {
  user_id: "iqran"
}
plan {
  command {
    sql_command {
 ...

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"

2026/05/11 04:36:26

0.9 s

[10][11]

Spark Connect - session_id: "61ec31aa-ec60-4847-8384-fe1f77644a71" user_context { user_id: "iqran" } plan { root { common { pla... +details

Spark Connect - session_id: "61ec31aa-ec60-4847-8384-fe1f77644a71"
user_context {
  user_id: "iqran"
}
plan {
  root {
    common {
      pla...

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"

2026/05/11 04:36:30

1.0 s

[12][13]

Spark Connect - session_id: "61ec31aa-ec60-4847-8384-fe1f77644a71" user_context { user_id: "iqran" } plan { command { sql_command { ... +details

Spark Connect - session_id: "61ec31aa-ec60-4847-8384-fe1f77644a71"
user_context {
  user_id: "iqran"
}
plan {
  command {
    sql_command {
 ...

session_id: "61ec31aa-ec60-4847-8384-fe1f77644a71"
user_context {
  user_id: "iqran"
}
plan {
  command {
    sql_command {
      input {
        common {
          plan_id: 12
        }
        sql {
          query: "drop table bronze.test.verify_sts"
        }
      }
    }
  }
}
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: "336a58a4-820d-42c2-9ba6-34532cff4a4f"
client_observed_server_side_session_id: "6d58c63b-fd77-4e70-ac9c-f6d1a7db4baf"

2026/05/11 04:38:04

71 ms

Spark Connect - session_id: "61ec31aa-ec60-4847-8384-fe1f77644a71" user_context { user_id: "iqran" } plan { command { sql_command { ... +details

Spark Connect - session_id: "61ec31aa-ec60-4847-8384-fe1f77644a71"
user_context {
  user_id: "iqran"
}
plan {
  command {
    sql_command {
 ...

session_id: "61ec31aa-ec60-4847-8384-fe1f77644a71"
user_context {
  user_id: "iqran"
}
plan {
  command {
    sql_command {
      input {
        common {
          plan_id: 14
        }
        sql {
          query: "drop table bronze.test.test"
        }
      }
    }
  }
}
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: "1d196cb7-8201-4a1e-818b-9fa406562ae3"
client_observed_server_side_session_id: "6d58c63b-fd77-4e70-ac9c-f6d1a7db4baf"

2026/05/11 04:38:39

49 ms

Spark Connect - session_id: "61ec31aa-ec60-4847-8384-fe1f77644a71" user_context { user_id: "iqran" } plan { root { common { pla... +details

Spark Connect - session_id: "61ec31aa-ec60-4847-8384-fe1f77644a71"
user_context {
  user_id: "iqran"
}
plan {
  root {
    common {
      pla...

session_id: "61ec31aa-ec60-4847-8384-fe1f77644a71"
user_context {
  user_id: "iqran"
}
plan {
  root {
    common {
      plan_id: 16
    }
    show_string {
      input {
        common {
          plan_id: 15
        }
        local_relation {
          data: "\377\377\377\377@\000\000\000[truncated(size=152)]"
        }
      }
      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: "9176afd4-a030-4e74-8972-7ae029562688"
client_observed_server_side_session_id: "6d58c63b-fd77-4e70-ac9c-f6d1a7db4baf"

2026/05/11 04:38:39

2 ms

Spark Connect - session_id: "61ec31aa-ec60-4847-8384-fe1f77644a71" user_context { user_id: "iqran" } plan { command { sql_command { ... +details

Spark Connect - session_id: "61ec31aa-ec60-4847-8384-fe1f77644a71"
user_context {
  user_id: "iqran"
}
plan {
  command {
    sql_command {
 ...

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"

2026/05/11 04:41:33

1 s

[27]

+details

Description

Submitted

Duration

Job IDs

Spark Connect - session_id: "61ec31aa-ec60-4847-8384-fe1f77644a71" user_context { user_id: "iqran" } plan { command { sql_command { ... +details

Spark Connect - session_id: "61ec31aa-ec60-4847-8384-fe1f77644a71"
user_context {
  user_id: "iqran"
}
plan {
  command {
    sql_command {
 ...

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"

2026/05/11 04:41:33

1 s

[14][15]

Spark Connect - session_id: "61ec31aa-ec60-4847-8384-fe1f77644a71" user_context { user_id: "iqran" } plan { command { sql_command { ... +details

Spark Connect - session_id: "61ec31aa-ec60-4847-8384-fe1f77644a71"
user_context {
  user_id: "iqran"
}
plan {
  command {
    sql_command {
 ...

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"

2026/05/11 04:44:54

1.0 s

[31]

+details

Description

Submitted

Duration

Job IDs

Spark Connect - session_id: "61ec31aa-ec60-4847-8384-fe1f77644a71" user_context { user_id: "iqran" } plan { command { sql_command { ... +details

Spark Connect - session_id: "61ec31aa-ec60-4847-8384-fe1f77644a71"
user_context {
  user_id: "iqran"
}
plan {
  command {
    sql_command {
 ...

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"

2026/05/11 04:44:54

0.9 s

[16][17]

Spark Connect - session_id: "61ec31aa-ec60-4847-8384-fe1f77644a71" user_context { user_id: "iqran" } plan { root { common { pla... +details

Spark Connect - session_id: "61ec31aa-ec60-4847-8384-fe1f77644a71"
user_context {
  user_id: "iqran"
}
plan {
  root {
    common {
      pla...

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"

2026/05/11 04:44:57

0.3 s

[18]

Spark Connect - session_id: "61ec31aa-ec60-4847-8384-fe1f77644a71" user_context { user_id: "iqran" } plan { command { sql_command { ... +details

Spark Connect - session_id: "61ec31aa-ec60-4847-8384-fe1f77644a71"
user_context {
  user_id: "iqran"
}
plan {
  command {
    sql_command {
 ...

session_id: "61ec31aa-ec60-4847-8384-fe1f77644a71"
user_context {
  user_id: "iqran"
}
plan {
  command {
    sql_command {
      input {
        common {
          plan_id: 28
        }
        sql {
          query: "DROP table bronze.test.test"
        }
      }
    }
  }
}
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: "93e8a089-d82d-4d7d-b857-75a41570e454"
client_observed_server_side_session_id: "6d58c63b-fd77-4e70-ac9c-f6d1a7db4baf"

2026/05/11 04:45:25

20 ms

Spark Connect - session_id: "61ec31aa-ec60-4847-8384-fe1f77644a71" user_context { user_id: "iqran" } plan { root { common { pla... +details

Spark Connect - session_id: "61ec31aa-ec60-4847-8384-fe1f77644a71"
user_context {
  user_id: "iqran"
}
plan {
  root {
    common {
      pla...

session_id: "61ec31aa-ec60-4847-8384-fe1f77644a71"
user_context {
  user_id: "iqran"
}
plan {
  root {
    common {
      plan_id: 30
    }
    show_string {
      input {
        common {
          plan_id: 29
        }
        local_relation {
          data: "\377\377\377\377@\000\000\000[truncated(size=152)]"
        }
      }
      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: "27dd2a78-2fa4-44ee-8a10-25fb6a9ca278"
client_observed_server_side_session_id: "6d58c63b-fd77-4e70-ac9c-f6d1a7db4baf"

2026/05/11 04:45:25

2 ms

Spark Connect - session_id: "61ec31aa-ec60-4847-8384-fe1f77644a71" user_context { user_id: "iqran" } plan { command { sql_command { ... +details

Spark Connect - session_id: "61ec31aa-ec60-4847-8384-fe1f77644a71"
user_context {
  user_id: "iqran"
}
plan {
  command {
    sql_command {
 ...

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"

2026/05/11 04:45:41

1 s

[37]

+details

Description

Submitted

Duration

Job IDs

Spark Connect - session_id: "61ec31aa-ec60-4847-8384-fe1f77644a71" user_context { user_id: "iqran" } plan { command { sql_command { ... +details

Spark Connect - session_id: "61ec31aa-ec60-4847-8384-fe1f77644a71"
user_context {
  user_id: "iqran"
}
plan {
  command {
    sql_command {
 ...

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"

2026/05/11 04:45:41

1 s

[19][20]

Spark Connect - session_id: "61ec31aa-ec60-4847-8384-fe1f77644a71" user_context { user_id: "iqran" } plan { command { sql_command { ... +details

Spark Connect - session_id: "61ec31aa-ec60-4847-8384-fe1f77644a71"
user_context {
  user_id: "iqran"
}
plan {
  command {
    sql_command {
 ...

session_id: "61ec31aa-ec60-4847-8384-fe1f77644a71"
user_context {
  user_id: "iqran"
}
plan {
  command {
    sql_command {
      input {
        common {
          plan_id: 34
        }
        sql {
          query: "TRUNCATE table bronze.test.test"
        }
      }
    }
  }
}
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: "2b0d76d7-75dc-4917-86c0-9253b4e5e80c"
client_observed_server_side_session_id: "6d58c63b-fd77-4e70-ac9c-f6d1a7db4baf"

2026/05/11 04:45:57

0.2 s

Spark Connect - session_id: "61ec31aa-ec60-4847-8384-fe1f77644a71" user_context { user_id: "iqran" } plan { root { common { pla... +details

Spark Connect - session_id: "61ec31aa-ec60-4847-8384-fe1f77644a71"
user_context {
  user_id: "iqran"
}
plan {
  root {
    common {
      pla...

session_id: "61ec31aa-ec60-4847-8384-fe1f77644a71"
user_context {
  user_id: "iqran"
}
plan {
  root {
    common {
      plan_id: 36
    }
    show_string {
      input {
        common {
          plan_id: 35
        }
        local_relation {
          data: "\377\377\377\377@\000\000\000[truncated(size=152)]"
        }
      }
      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: "4adff194-29c4-40e7-8722-4faf3d4be733"
client_observed_server_side_session_id: "6d58c63b-fd77-4e70-ac9c-f6d1a7db4baf"

2026/05/11 04:45:57

2 ms

Spark Connect - session_id: "61ec31aa-ec60-4847-8384-fe1f77644a71" user_context { user_id: "iqran" } plan { root { common { pla... +details

Spark Connect - session_id: "61ec31aa-ec60-4847-8384-fe1f77644a71"
user_context {
  user_id: "iqran"
}
plan {
  root {
    common {
      pla...

session_id: "61ec31aa-ec60-4847-8384-fe1f77644a71"
user_context {
  user_id: "iqran"
}
plan {
  root {
    common {
      plan_id: 39
    }
    show_string {
      input {
        common {
          plan_id: 38
        }
        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: "2248dcd4-cdd8-4587-9d70-46153ea93a3c"
client_observed_server_side_session_id: "6d58c63b-fd77-4e70-ac9c-f6d1a7db4baf"

2026/05/11 04:46:02

48 ms

Spark Connect - session_id: "61ec31aa-ec60-4847-8384-fe1f77644a71" user_context { user_id: "iqran" } plan { command { sql_command { ... +details

Spark Connect - session_id: "61ec31aa-ec60-4847-8384-fe1f77644a71"
user_context {
  user_id: "iqran"
}
plan {
  command {
    sql_command {
 ...

session_id: "61ec31aa-ec60-4847-8384-fe1f77644a71"
user_context {
  user_id: "iqran"
}
plan {
  command {
    sql_command {
      input {
        common {
          plan_id: 40
        }
        sql {
          query: "drop table bronze.test.test"
        }
      }
    }
  }
}
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: "d8f4d58f-4887-4245-8286-25017dea280d"
client_observed_server_side_session_id: "6d58c63b-fd77-4e70-ac9c-f6d1a7db4baf"

2026/05/11 04:46:19

18 ms

Spark Connect - session_id: "61ec31aa-ec60-4847-8384-fe1f77644a71" user_context { user_id: "iqran" } plan { root { common { pla... +details

Spark Connect - session_id: "61ec31aa-ec60-4847-8384-fe1f77644a71"
user_context {
  user_id: "iqran"
}
plan {
  root {
    common {
      pla...

session_id: "61ec31aa-ec60-4847-8384-fe1f77644a71"
user_context {
  user_id: "iqran"
}
plan {
  root {
    common {
      plan_id: 42
    }
    show_string {
      input {
        common {
          plan_id: 41
        }
        local_relation {
          data: "\377\377\377\377@\000\000\000[truncated(size=152)]"
        }
      }
      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: "9ea1da6f-1c79-4ccf-a929-919d1c218b33"
client_observed_server_side_session_id: "6d58c63b-fd77-4e70-ac9c-f6d1a7db4baf"

2026/05/11 04:46:19

2 ms

Page:

Failed Queries (8)

Page:

ID ▾	Description	Submitted	Duration	Error Message
35	Spark Connect - session_id: "61ec31aa-ec60-4847-8384-fe1f77644a71" user_context { user_id: "iqran" } plan { command { sql_command { ... +details Spark Connect - session_id: "61ec31aa-ec60-4847-8384-fe1f77644a71" user_context { user_id: "iqran" } plan { command { sql_command { ... session_id: "61ec31aa-ec60-4847-8384-fe1f77644a71" user_context { user_id: "iqran" } plan { command { sql_command { input { common { plan_id: 31 } sql { query: "SELECT * FROM bronze.test.test" } } } } } 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: "cdbf3eb8-f538-4e6a-94f9-8ee8afb03c96" client_observed_server_side_session_id: "6d58c63b-fd77-4e70-ac9c-f6d1a7db4baf"	2026/05/11 04:45:28	1 ms	TABLE_OR_VIEW_NOT_FOUND +details [TABLE_OR_VIEW_NOT_FOUND] The table or view `bronze`.`test`.`test` cannot be found. Verify the spelling and correctness of the schema and catalog. If you did not qualify the name with a schema, verify the current_schema() output, or qualify the name with the correct schema and catalog. To tolerate the error on drop use DROP VIEW IF EXISTS or DROP TABLE IF EXISTS. SQLSTATE: 42P01; line 1 pos 14; 'Project [*] +- 'UnresolvedRelation [bronze, test, test], [], false
29	Spark Connect - session_id: "61ec31aa-ec60-4847-8384-fe1f77644a71" user_context { user_id: "iqran" } plan { command { sql_command { ... +details Spark Connect - session_id: "61ec31aa-ec60-4847-8384-fe1f77644a71" user_context { user_id: "iqran" } plan { command { sql_command { ... session_id: "61ec31aa-ec60-4847-8384-fe1f77644a71" user_context { user_id: "iqran" } plan { command { sql_command { input { common { plan_id: 22 } sql { query: "SELECT * FROM bronze.test.test" } } } } } 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: "00649230-f1e5-4619-ad25-54cf6b021889" client_observed_server_side_session_id: "6d58c63b-fd77-4e70-ac9c-f6d1a7db4baf"	2026/05/11 04:44:40	1 ms	TABLE_OR_VIEW_NOT_FOUND +details [TABLE_OR_VIEW_NOT_FOUND] The table or view `bronze`.`test`.`test` cannot be found. Verify the spelling and correctness of the schema and catalog. If you did not qualify the name with a schema, verify the current_schema() output, or qualify the name with the correct schema and catalog. To tolerate the error on drop use DROP VIEW IF EXISTS or DROP TABLE IF EXISTS. SQLSTATE: 42P01; line 1 pos 14; 'Project [*] +- 'UnresolvedRelation [bronze, test, test], [], false
28	Spark Connect - session_id: "61ec31aa-ec60-4847-8384-fe1f77644a71" user_context { user_id: "iqran" } plan { command { sql_command { ... +details Spark Connect - session_id: "61ec31aa-ec60-4847-8384-fe1f77644a71" user_context { user_id: "iqran" } plan { command { sql_command { ... session_id: "61ec31aa-ec60-4847-8384-fe1f77644a71" user_context { user_id: "iqran" } plan { command { sql_command { input { common { plan_id: 20 } sql { query: "drop table bronze.test.test purge" } } } } } 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: "1306fe18-22e2-4ea4-adbb-b56356a8a9dc" client_observed_server_side_session_id: "6d58c63b-fd77-4e70-ac9c-f6d1a7db4baf"	2026/05/11 04:41:50	69 ms	java.lang.NoSuchMethodError: 'org.apache.spark.sql.execution.datasources.v2.DataSourceV2Relation org.apache.spark.sql.execution.datasources.v2.DataSourceV2Relation.create(org.apache.spark.sql.connector.catalog.Table, scala.Option, scala.Option, org.apache.spark.sql.util.CaseInsensitiveStringMap)' +details java.lang.NoSuchMethodError: 'org.apache.spark.sql.execution.datasources.v2.DataSourceV2Relation org.apache.spark.sql.execution.datasources.v2.DataSourceV2Relation.create(org.apache.spark.sql.connector.catalog.Table, scala.Option, scala.Option, org.apache.spark.sql.util.CaseInsensitiveStringMap)' at org.apache.iceberg.spark.SparkTableUtil.createRelation(SparkTableUtil.java:1000) at org.apache.iceberg.spark.SparkTableUtil.loadMetadataTable(SparkTableUtil.java:987) at org.apache.iceberg.spark.SparkTableUtil.loadMetadataTable(SparkTableUtil.java:980) at org.apache.iceberg.spark.actions.BaseSparkAction.loadMetadataTable(BaseSparkAction.java:224) at org.apache.iceberg.spark.actions.BaseSparkAction.manifestDF(BaseSparkAction.java:183) at org.apache.iceberg.spark.actions.BaseSparkAction.contentFileDS(BaseSparkAction.java:157) at org.apache.iceberg.spark.actions.BaseSparkAction.contentFileDS(BaseSparkAction.java:148) at org.apache.iceberg.spark.actions.DeleteReachableFilesSparkAction.reachableFileDS(DeleteReachableFilesSparkAction.java:121) at org.apache.iceberg.spark.actions.DeleteReachableFilesSparkAction.doExecute(DeleteReachableFilesSparkAction.java:106) at org.apache.iceberg.spark.JobGroupUtils.withJobGroupInfo(JobGroupUtils.java:59) at org.apache.iceberg.spark.JobGroupUtils.withJobGroupInfo(JobGroupUtils.java:51) at org.apache.iceberg.spark.actions.BaseSparkAction.withJobGroupInfo(BaseSparkAction.java:130) at org.apache.iceberg.spark.actions.DeleteReachableFilesSparkAction.execute(DeleteReachableFilesSparkAction.java:96) at org.apache.iceberg.spark.SparkCatalog.purgeTable(SparkCatalog.java:378) at org.apache.spark.sql.execution.datasources.v2.DropTableExec.run(DropTableExec.scala:39) at org.apache.spark.sql.execution.datasources.v2.V2CommandExec.result$lzycompute(V2CommandExec.scala:43) at org.apache.spark.sql.execution.datasources.v2.V2CommandExec.result(V2CommandExec.scala:43) at org.apache.spark.sql.execution.datasources.v2.V2CommandExec.executeCollect(V2CommandExec.scala:49) at org.apache.spark.sql.execution.QueryExecution.$anonfun$eagerlyExecuteCommands$2(QueryExecution.scala:185) at org.apache.spark.sql.execution.SQLExecution$.$anonfun$withNewExecutionId0$8(SQLExecution.scala:177) at org.apache.spark.sql.execution.SQLExecution$.withSessionTagsApplied(SQLExecution.scala:285) at org.apache.spark.sql.execution.SQLExecution$.$anonfun$withNewExecutionId0$7(SQLExecution.scala:139) at org.apache.spark.JobArtifactSet$.withActiveJobArtifactState(JobArtifactSet.scala:94) at org.apache.spark.sql.artifact.ArtifactManager.$anonfun$withResources$1(ArtifactManager.scala:112) at org.apache.spark.util.Utils$.withContextClassLoader(Utils.scala:185) at org.apache.spark.sql.artifact.ArtifactManager.withClassLoaderIfNeeded(ArtifactManager.scala:102) at org.apache.spark.sql.artifact.ArtifactManager.withResources(ArtifactManager.scala:111) at org.apache.spark.sql.execution.SQLExecution$.$anonfun$withNewExecutionId0$6(SQLExecution.scala:139) at org.apache.spark.sql.execution.SQLExecution$.withSQLConfPropagated(SQLExecution.scala:308) at org.apache.spark.sql.execution.SQLExecution$.$anonfun$withNewExecutionId0$1(SQLExecution.scala:138) at org.apache.spark.sql.SparkSession.withActive(SparkSession.scala:804) at org.apache.spark.sql.execution.SQLExecution$.withNewExecutionId0(SQLExecution.scala:92) at org.apache.spark.sql.execution.SQLExecution$.withNewExecutionId(SQLExecution.scala:250) at org.apache.spark.sql.execution.QueryExecution.$anonfun$eagerlyExecuteCommands$1(QueryExecution.scala:185) at org.apache.spark.sql.execution.QueryExecution$.withInternalError(QueryExecution.scala:717) at org.apache.spark.sql.execution.QueryExecution.org$apache$spark$sql$execution$QueryExecution$$eagerlyExecute$1(QueryExecution.scala:184) at org.apache.spark.sql.execution.QueryExecution$$anonfun$eagerlyExecuteCommands$3.applyOrElse(QueryExecution.scala:201) at org.apache.spark.sql.execution.QueryExecution$$anonfun$eagerlyExecuteCommands$3.applyOrElse(QueryExecution.scala:194) at org.apache.spark.sql.catalyst.trees.TreeNode.$anonfun$transformDownWithPruning$1(TreeNode.scala:491) at org.apache.spark.sql.catalyst.trees.CurrentOrigin$.withOrigin(origin.scala:107) at org.apache.spark.sql.catalyst.trees.TreeNode.transformDownWithPruning(TreeNode.scala:491) at org.apache.spark.sql.catalyst.plans.logical.LogicalPlan.org$apache$spark$sql$catalyst$plans$logical$AnalysisHelper$$super$transformDownWithPruning(LogicalPlan.scala:37) at org.apache.spark.sql.catalyst.plans.logical.AnalysisHelper.transformDownWithPruning(AnalysisHelper.scala:360) at org.apache.spark.sql.catalyst.plans.logical.AnalysisHelper.transformDownWithPruning$(AnalysisHelper.scala:356) at org.apache.spark.sql.catalyst.plans.logical.LogicalPlan.transformDownWithPruning(LogicalPlan.scala:37) at org.apache.spark.sql.catalyst.plans.logical.LogicalPlan.transformDownWithPruning(LogicalPlan.scala:37) at org.apache.spark.sql.catalyst.trees.TreeNode.transformDown(TreeNode.scala:467) at org.apache.spark.sql.execution.QueryExecution.eagerlyExecuteCommands(QueryExecution.scala:194) at org.apache.spark.sql.execution.QueryExecution.$anonfun$lazyCommandExecuted$1(QueryExecution.scala:155) at scala.util.Try$.apply(Try.scala:217) at org.apache.spark.util.Utils$.doTryWithCallerStacktrace(Utils.scala:1392) at org.apache.spark.util.LazyTry.tryT$lzycompute(LazyTry.scala:46) at org.apache.spark.util.LazyTry.tryT(LazyTry.scala:46) at org.apache.spark.util.LazyTry.get(LazyTry.scala:58) at org.apache.spark.sql.execution.QueryExecution.commandExecuted(QueryExecution.scala:160) at org.apache.spark.sql.classic.Dataset.<init>(Dataset.scala:276) at org.apache.spark.sql.classic.Dataset$.$anonfun$ofRows$5(Dataset.scala:139) at org.apache.spark.sql.SparkSession.withActive(SparkSession.scala:804) at org.apache.spark.sql.classic.Dataset$.ofRows(Dataset.scala:135) at org.apache.spark.sql.classic.SparkSession.$anonfun$sql$4(SparkSession.scala:584) at org.apache.spark.sql.SparkSession.withActive(SparkSession.scala:804) at org.apache.spark.sql.classic.SparkSession.sql(SparkSession.scala:561) at org.apache.spark.sql.connect.planner.SparkConnectPlanner.executeSQL(SparkConnectPlanner.scala:3148) at org.apache.spark.sql.connect.planner.SparkConnectPlanner.handleSqlCommand(SparkConnectPlanner.scala:2996) at org.apache.spark.sql.connect.planner.SparkConnectPlanner.process(SparkConnectPlanner.scala:2830) at org.apache.spark.sql.connect.execution.SparkConnectPlanExecution.handlePlan(SparkConnectPlanExecution.scala:96) at org.apache.spark.sql.connect.execution.ExecuteThreadRunner.$anonfun$executeInternal$1(ExecuteThreadRunner.scala:225) at org.apache.spark.sql.connect.execution.ExecuteThreadRunner.$anonfun$executeInternal$1$adapted(ExecuteThreadRunner.scala:197) at org.apache.spark.sql.connect.service.SessionHolder.$anonfun$withSession$2(SessionHolder.scala:396) at org.apache.spark.sql.SparkSession.withActive(SparkSession.scala:804) at org.apache.spark.sql.connect.service.SessionHolder.$anonfun$withSession$1(SessionHolder.scala:396) at org.apache.spark.JobArtifactSet$.withActiveJobArtifactState(JobArtifactSet.scala:94) at org.apache.spark.sql.artifact.ArtifactManager.$anonfun$withResources$1(ArtifactManager.scala:112) at org.apache.spark.util.Utils$.withContextClassLoader(Utils.scala:185) at org.apache.spark.sql.artifact.ArtifactManager.withClassLoaderIfNeeded(ArtifactManager.scala:102) at org.apache.spark.sql.artifact.ArtifactManager.withResources(ArtifactManager.scala:111) at org.apache.spark.sql.connect.service.SessionHolder.withSession(SessionHolder.scala:395) at org.apache.spark.sql.connect.execution.ExecuteThreadRunner.executeInternal(ExecuteThreadRunner.scala:197) at org.apache.spark.sql.connect.execution.ExecuteThreadRunner.org$apache$spark$sql$connect$execution$ExecuteThreadRunner$$execute(ExecuteThreadRunner.scala:126) at org.apache.spark.sql.connect.execution.ExecuteThreadRunner$ExecutionThread.run(ExecuteThreadRunner.scala:334)
25	Spark Connect - session_id: "61ec31aa-ec60-4847-8384-fe1f77644a71" user_context { user_id: "iqran" } plan { command { sql_command { ... +details Spark Connect - session_id: "61ec31aa-ec60-4847-8384-fe1f77644a71" user_context { user_id: "iqran" } plan { command { sql_command { ... session_id: "61ec31aa-ec60-4847-8384-fe1f77644a71" user_context { user_id: "iqran" } plan { command { sql_command { input { common { plan_id: 17 } sql { query: "drop table bronze.test.verify_sts purge" } } } } } 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: "7624a99b-a113-4452-b3f3-41edd7051320" client_observed_server_side_session_id: "6d58c63b-fd77-4e70-ac9c-f6d1a7db4baf"	2026/05/11 04:40:37	20 ms	TABLE_OR_VIEW_NOT_FOUND +details [TABLE_OR_VIEW_NOT_FOUND] The table or view `bronze`.`test`.`verify_sts` cannot be found. Verify the spelling and correctness of the schema and catalog. If you did not qualify the name with a schema, verify the current_schema() output, or qualify the name with the correct schema and catalog. To tolerate the error on drop use DROP VIEW IF EXISTS or DROP TABLE IF EXISTS. SQLSTATE: 42P01
9	Spark Connect - session_id: "f15d8ea2-f375-4431-9a62-15dfcf3e3fdd" user_context { user_id: "iqran" } plan { command { sql_command { ... +details Spark Connect - session_id: "f15d8ea2-f375-4431-9a62-15dfcf3e3fdd" user_context { user_id: "iqran" } plan { command { sql_command { ... session_id: "f15d8ea2-f375-4431-9a62-15dfcf3e3fdd" user_context { user_id: "iqran" } plan { command { sql_command { input { common { plan_id: 3 } sql { query: "CREATE NAMESPACE IF NOT EXISTS landing.test" } } } } } 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: "cd935f37-8522-48b7-8d03-2bda77c96374" client_observed_server_side_session_id: "23bd2d34-9233-41ce-883b-aa45af2684e2"	2026/05/11 04:27:17	0 ms	REQUIRES_SINGLE_PART_NAMESPACE +details [REQUIRES_SINGLE_PART_NAMESPACE] spark_catalog requires a single-part namespace, but got `landing`.`test`. SQLSTATE: 42K05
7	Spark Connect - session_id: "968efe1c-80d2-4c9c-8f88-84933a24d214" user_context { user_id: "iqran" } plan { command { sql_command { ... +details Spark Connect - session_id: "968efe1c-80d2-4c9c-8f88-84933a24d214" user_context { user_id: "iqran" } plan { command { sql_command { ... session_id: "968efe1c-80d2-4c9c-8f88-84933a24d214" user_context { user_id: "iqran" } plan { command { sql_command { input { common { plan_id: 12 } sql { query: "CREATE NAMESPACE IF NOT EXISTS landing.test" } } } } } 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: "192e7946-571a-421b-8185-f7af435bcc05" client_observed_server_side_session_id: "96aa90ce-5e9e-4fc3-9fb4-4c8f91f72a9e"	2026/05/11 04:26:32	0 ms	REQUIRES_SINGLE_PART_NAMESPACE +details [REQUIRES_SINGLE_PART_NAMESPACE] spark_catalog requires a single-part namespace, but got `landing`.`test`. SQLSTATE: 42K05
6	Spark Connect - session_id: "968efe1c-80d2-4c9c-8f88-84933a24d214" user_context { user_id: "iqran" } plan { command { sql_command { ... +details Spark Connect - session_id: "968efe1c-80d2-4c9c-8f88-84933a24d214" user_context { user_id: "iqran" } plan { command { sql_command { ... session_id: "968efe1c-80d2-4c9c-8f88-84933a24d214" user_context { user_id: "iqran" } plan { command { sql_command { input { common { plan_id: 11 } sql { query: "CREATE NAMESPACE IF NOT EXISTS test.test" } } } } } 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: "2c82d225-07f2-4001-b6d6-0d3f85166fbc" client_observed_server_side_session_id: "96aa90ce-5e9e-4fc3-9fb4-4c8f91f72a9e"	2026/05/11 04:25:42	0 ms	REQUIRES_SINGLE_PART_NAMESPACE +details [REQUIRES_SINGLE_PART_NAMESPACE] spark_catalog requires a single-part namespace, but got `test`.`test`. SQLSTATE: 42K05
4	Spark Connect - session_id: "968efe1c-80d2-4c9c-8f88-84933a24d214" user_context { user_id: "iqran" } plan { command { sql_command { ... +details Spark Connect - session_id: "968efe1c-80d2-4c9c-8f88-84933a24d214" user_context { user_id: "iqran" } plan { command { sql_command { ... session_id: "968efe1c-80d2-4c9c-8f88-84933a24d214" user_context { user_id: "iqran" } plan { command { sql_command { input { common { plan_id: 8 } sql { query: "CREATE NAMESPACE IF NOT EXISTS test.test" } } } } } 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: "cc2c67c1-c00d-4966-aabd-a4d18f0910d5" client_observed_server_side_session_id: "96aa90ce-5e9e-4fc3-9fb4-4c8f91f72a9e"	2026/05/11 04:24:55	0 ms	REQUIRES_SINGLE_PART_NAMESPACE +details [REQUIRES_SINGLE_PART_NAMESPACE] spark_catalog requires a single-part namespace, but got `test`.`test`. SQLSTATE: 42K05

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