CAST_INVALID_INPUT kelas kesalahan

  • Artikel

SQLSTATE: 22018

Nilai <expression> jenis <sourceType> tidak dapat ditransmisikan ke <targetType> karena salah bentuk. Koreksi nilai sesuai sintaks, atau ubah jenis targetnya. Gunakan try_cast untuk mentolerir input yang salah bentuk dan mengembalikan NULL sebagai gantinya. Jika perlu diatur <ansiConfig> ke "false" untuk melewati kesalahan ini.

Parameter

  • ekspresi: Ekspresi yang perlu dilemparkan targettype
  • sourceType: Jenis expressiondata .
  • targetType: Jenis target operasi transmisi.
  • ansiConfig: Pengaturan konfigurasi untuk mengubah mode ANSI.

Penjelasan

expression tidak dapat dilemparkan ke targetType karena salah satu alasan berikut:

  • expression terlalu besar untuk domain jenis . Misalnya angka 1000 tidak dapat ditransmisian ke TINYINT karena domain tersebut hanya berkisar dari -128 hingga +127.
  • expression berisi karakter yang bukan bagian dari tipe. Misalnya a tidak dapat ditransmisikan ke jenis numerik apa pun.
  • expression diformat dengan cara operasi transmisi tidak dapat mengurai. Misalnya 1.0 dan 1e1 tidak dapat ditransmisikan ke jenis numerik integral apa pun.

Pemeran mungkin belum ditentukan secara eksplisit, tetapi mungkin telah disuntikkan secara implisit oleh Azure Databricks.

Informasi konteks yang diberikan dengan kesalahan ini mengisolasi objek dan ekspresi tempat kesalahan terjadi.

Untuk definisi domain dan format harfiah yang diterima, lihat definisi untuk jenistyopeNamedata .

Mitigasi

Mitigasi untuk kesalahan ini tergantung pada penyebabnya:

  • value Apakah diharapkan untuk mematuhi domain dan format dari yang ditentukan typeName?

    Verifikasi nilai produksi input dan koreksi sumber data.

  • Apakah target pemeran terlalu sempit?

    Perluas jenis dengan memindahkan, misalnya, dari DATE ke TIMESTAMP, INT ke BIGINT atau DOUBLE.

  • Apakah format salah value ?

    Pertimbangkan untuk menggunakan:

    Fungsi-fungsi ini memungkinkan berbagai format yang dapat Anda tentukan.

    Saat mentransmisikan harfiah numerik dengan titik desimal (misalnya 1.0 atau notasi ilmiah (misalnya 1e0) pertimbangkan transmisi ganda terlebih dahulu ke DECIMAL atau DOUBLE kemudian ke angka yang tepat.

  • Apakah data dengan nilai yang salah diharapkan, dan harus ditoleransi dengan memproduksi NULL?

    Ubah penggunaan ekspresi atau injeksi try_cast(value AS typeName). Fungsi ini mengembalikan NULL ketika diteruskan tanpa value yang memenuhi jenis .

    Jika Anda tidak dapat mengubah ekspresi, sebagai upaya terakhir, Anda dapat menonaktifkan mode ANSI untuk sementara menggunakan ansiConfig.

Contoh

-- A view with a cast and string literals outside the domain of the target type
> CREATE OR REPLACE TEMPORARY VIEW v(c1) AS SELECT CAST(a AS SMALLINT) FROM VALUES('100'), ('50000') AS t(a);
> SELECT c1 FROM v;
 [CAST_INVALID_INPUT] The value '50000' of the type "STRING" cannot be cast to "SMALLINT" because it is malformed.
 Correct the value as per the syntax, or change its target type. Use `try_cast` to tolerate malformed input and return NULL instead.
 If necessary set "spark.sql.ansi.enabled" to "false" to bypass this error.
 == SQL of VIEW v(line 1, position 8) ==
 SELECT CAST(a AS SMALLINT) FROM VALUES('100'), ('50000') A...
        ^^^^^^^^^^^^^^^^^^^

-- Widen the target type to match the domain of the input
> CREATE OR REPLACE TEMPORARY VIEW v(c1) AS SELECT cast(a AS INTEGER) FROM VALUES('100'), ('50000') AS t(a);
> SELECT c1 FROM v;
 100
 50000

-- The input data format does not match the target type
> SELECT cast(a AS INTEGER) FROM VALUES('1.0'), ('1e0') AS t(a);
 [CAST_INVALID_INPUT] The value '1.0' of the type "STRING" cannot be cast to "INT" because it is malformed.
 Correct the value as per the syntax, or change its target type. Use `try_cast` to tolerate malformed input and return NULL instead.
 If necessary set "spark.sql.ansi.enabled" to "false" to bypass this error.
 == SQL(line 1, position 8) ==
 SELECT cast(a AS INTEGER) FROM VALUES('1.0'), ('1e0') AS ...
        ^^^^^^^^^^^^^^^^^^

-- Adjust the target type to the match the format if the format is indicative of the domain.
> SELECT cast(a AS DOUBLE) FROM VALUES('1.0'), ('1e0') AS t(a);
 1.0
 1.0

-- ALternatively double cast to preserver the target type
> SELECT cast(cast(a AS DOUBLE) AS INTEGER) FROM VALUES('1.0'), ('1e0') AS t(a);
 1
 1

-- The format of the numeric input contains display artifacts
> SELECT cast(a AS DECIMAL(10, 3)) FROM VALUES('12,345.30-'), ('12+') AS t(a);
 [CAST_INVALID_INPUT] The value '12,345.30-' of the type "STRING" cannot be cast to "DECIMAL(10,3)" because it is malformed.
 Correct the value as per the syntax, or change its target type. Use `try_cast` to tolerate malformed input and return NULL instead.
 If necessary set "spark.sql.ansi.enabled" to "false" to bypass this error.
 == SQL(line 1, position 8) ==
 SELECT cast(a AS DECIMAL(10, 3)) FROM VALUES('$<123,45.30>'), ('...
        ^^^^^^^^^^^^^^^^^^^^^^^^^

-- Use to_number() to parse formatted values
> SELECT to_number(a, '9,999,999.999S') FROM VALUES('123,45.30-'), ('12+') AS t(a);
 -12345.300
 12.000

-- The format of a date input does not match the default format
> SELECT cast(geburtsdatum AS DATE) FROM VALUES('6.6.2000'), ('31.10.1970') AS t(geburtsdatum);
 [CAST_INVALID_INPUT] The value '6.6.2000' of the type "STRING" cannot be cast to "DATE" because it is malformed.
 Correct the value as per the syntax, or change its target type. Use `try_cast` to tolerate malformed input and return NULL instead.
 If necessary set "spark.sql.ansi.enabled" to "false" to bypass this error.
 == SQL(line 1, position 8) ==
 SELECT cast(geburtsdatum AS DATE) FROM VALUES('6.6.2000'), ('31.1...
        ^^^^^^^^^^^^^^^^^^^^^^^^^^

-- Use to_date to parse the correct input format for a date
> SELECT to_date(geburtsdatum, 'dd.MM.yyyy') FROM VALUES('6.6.2000'), ('31.10.1970') AS t(geburtsdatum);
  2000-06-06
  1970-10-31

-- The type resolution of Databricks did not derive a sufficiently wide type, failing an implicit cast
> SELECT 12 * monthly AS yearly FROM VALUES ('1200'), ('1520.56') AS t(monthly);
 [CAST_INVALID_INPUT] The value '1520.56' of the type "STRING" cannot be cast to "BIGINT" because it is malformed.
 Correct the value as per the syntax, or change its target type. Use `try_cast` to tolerate malformed input and return NULL instead.
 If necessary set "spark.sql.ansi.enabled" to "false" to bypass this error.
 == SQL(line 1, position 8) ==
 SELECT 12 * monthly AS yearly FROM VALUES ('1200'),...
        ^^^^^^^^^^^^

-- Explicitly declare the expected type
> SELECT 12 * cast(monthly AS DECIMAL(8, 2)) AS yearly FROM VALUES ('1200'), ('1520.56') AS t(monthly);
 14400.00
 18246.72

-- The input data is occasionally expected to incorrect
> SELECT cast(salary AS DECIMAL(9, 2)) FROM VALUES('30000'), ('prefer not to say') AS t(salary);
 [CAST_INVALID_INPUT] The value 'prefer not to say' of the type "STRING" cannot be cast to "DECIMAL(9,2)" because it is malformed.
 Correct the value as per the syntax, or change its target type. Use `try_cast` to tolerate malformed input and return NULL instead.
 If necessary set "spark.sql.ansi.enabled" to "false" to bypass this error.
== SQL(line 1, position 8) ==
SELECT cast(salary AS DECIMAL(9, 2)) FROM VALUES('30000'), ('prefer ...
       ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

-- Use try_cast to tolerate incorrect input
> SELECT try_cast(salary AS DECIMAL(9, 2)) FROM VALUES('30000'), ('prefer not to say') AS t(salary);
 30000.00
 NULL

-- In Databricks SQL temporarily disable ANSI mode to tolerate incorrect input.
> SET ANSI_MODE = false;
> SELECT cast(salary AS DECIMAL(9, 2)) FROM VALUES('30000'), ('prefer not to say') AS t(salary);
 30000.00
 NULL
> SET ANSI_MODE = true;

-- In Databricks Runtime temporarily disable ANSI mode to tolerate incorrect input.
> SET spark.sql.ansi.enabled = false;
> SELECT cast(salary AS DECIMAL(9, 2)) FROM VALUES('30000'), ('prefer not to say') AS t(salary);
 30000.00
 NULL
> SET spark.sql.ansi.enabled = true;