SnowConvert AI – BigQuery – Funktionale Unterschiede

Bemerkung

Konvertierungsbereich

SnowConvert AI für Google BigQuery unterstützt derzeit die Bewertung und Übersetzung vonTABLES und VIEWS. Obwohl SnowConvert AI andere Arten von Anweisungen erkennen kann, werden diese nicht vollständig unterstützt.

SSC-FDM-BQ0001

Der Zugriff auf Arrays erzeugt NULL anstelle eines Fehlers für positive Out-of-Bounds-Indizes in Snowflake.

Beschreibung

Wenn in Snowflake auf ein ARRAY-Objekt per Index zugegriffen wird und der angegebene Index größer als die Größe des Arrays ist, wird der Wert NULL zurückgegeben. Dies unterscheidet sich vom Verhalten in BigQuery, wo der Zugriff auf ein ARRAY mit einem ungültigen Index einen Fehler erzeugt – es sei denn, die Funktionen SAFE_OFFSET oder SAFE_ORDINAL werden verwendet.

This FDM is added to any ARRAY access that is not safe.

Codebeispiel

Eingabecode:
BigQuery
 SELECT ([40, 12, 30])[8];

SELECT ([40, 12, 30])[SAFE_OFFSET(8)];
Generierter Code:
Snowflake
 SELECT
--** SSC-FDM-BQ0001 - ACCESSING ARRAYS PRODUCES NULL INSTEAD OF AN ERROR FOR POSITIVE OUT OF BOUNDS INDEXES IN SNOWFLAKE **
([40, 12, 30])[8];

SELECT
PUBLIC.SAFE_OFFSET_UDF( ([40, 12, 30]), 8);

Best Practices

  • Analyze the uses of array access in the code. If there was never the risk of getting an out of bounds error in the original code, no difference will be observed and this FDM can be safely ignored.

  • If the original code relies on out-of-bounds access raising an error (e.g., for flow control), add explicit bounds checking in Snowflake using ARRAY_SIZE before accessing the array.

SSC-FDM-BQ0002

Ausnahme-Systemvariablen werden in Snowflake nicht unterstützt.

Beschreibung

BigQuery’s exception system variables (@@error.message, @@error.stack_trace, @@error.statement_text, @@error.formatted_stack_trace) have no direct equivalent in Snowflake. SnowConvert AI replaces exception variable references with OBJECT_CONSTRUCT('SQLERRM', SQLERRM, 'SQLCODE', SQLCODE, 'SQLSTATE', SQLSTATE) as a workaround. This workaround provides basic error information but does not include stack trace or statement text details available in BigQuery. For more information, see Handling Exceptions in Snowflake.

Codebeispiel

Eingabecode:
BigQuery
 CREATE OR REPLACE PROCEDURE test.proc1()
BEGIN
  SELECT 1/0;
EXCEPTION WHEN ERROR THEN
  SELECT
    @@error.message as message,
    @@error.stack_trace as stack_trace,
    @@error.statement_text as statement_text,
    @@error.formatted_stack_trace as formatted_stack_trace;
END;
Ergebnis
 [{
  "message": "Query error: division by zero: 1 / 0 at [snowflake-snowconvert-team.test.proc1:2:3]",
  "stack_trace": [{
    "line": "2",
    "column": "3",
    "filename": null,
    "location": "snowflake-snowconvert-team.test.proc1"
  }, {
    "line": "1",
    "column": "1",
    "filename": null,
    "location": null
  }],
  "statement_text": "SELECT 1/0",
  "formatted_stack_trace": "At snowflake-snowconvert-team.test.proc1[2:3]\nAt [1:1]\n"
}]
Generierter Code:
Snowflake
 CREATE OR REPLACE PROCEDURE test.proc1 ()
RETURNS VARCHAR
LANGUAGE SQL
EXECUTE AS CALLER
COMMENT = '{ "origin": "sf_sc", "name": "snowconvert", "version": {  "major": 0,  "minor": 0,  "patch": "0" }, "attributes": {  "component": "bigquery",  "convertedOn": "04/09/2025",  "domain": "test" }}'
AS
$$
    BEGIN
    SELECT 1/0;
  EXCEPTION WHEN OTHER THEN
--      --** SSC-FDM-BQ0002 - EXCEPTION SYSTEM VARIABLES ARE NOT SUPPORTED IN SNOWFLAKE. **
--    SELECT
--      @@error.message as message,
--      @@error.stack_trace as stack_trace,
--      @@error.statement_text as statement_text,
--      @@error.formatted_stack_trace as formatted_stack_trace;
      RETURN OBJECT_CONSTRUCT('SQLERRM', SQLERRM, 'SQLCODE', SQLCODE, 'SQLSTATE', SQLSTATE);
    END;
$$;
Ergebnis
 {
  "SQLCODE": 100051,
  "SQLERRM": "Division by zero",
  "SQLSTATE": "22012"
}

Best Practices

  • Snowflake provides three built-in exception variables as an alternative to BigQuery’s @@error system variables:

    BigQuery Variable

    Snowflake Equivalent

    Notes

    @@error.message

    SQLERRM

    Error message text

    @@error.statement_text

    N/A

    No direct equivalent in Snowflake

    @@error.stack_trace

    N/A

    No direct equivalent in Snowflake

    @@error.formatted_stack_trace

    N/A

    No direct equivalent in Snowflake

    N/A

    SQLSTATE

    5-character ANSI SQL state code

    N/A

    SQLCODE

    5-digit signed integer error code

  • Review the generated OBJECT_CONSTRUCT('SQLERRM', SQLERRM, 'SQLCODE', SQLCODE, 'SQLSTATE', SQLSTATE) workaround and adjust it based on your specific error-handling requirements.

  • For more information, see Handling Exceptions in Snowflake.

SSC-FDM-BQ0003

Nicht in der Lage, korrekte Klausel für die Rückgabetabelle zu generieren, da Informationen über abhängige Objekte fehlen.

Bemerkung

This issue is deprecated and no longer generated by SnowConvert AI. Check SSC-EWI-BQ0009 for the issue now generated for this scenario

Beschreibung

Snowflake erfordert eine gültige RETURNS TABLE-Klausel für CREATE TABLE FUNCTION-Anweisungen.

If the original BigQuery source code does not have a RETURNS TABLE clause, SnowConvert AI must build one. To do this, an analysis is made to the CREATE TABLE FUNCTION query to properly infer the types of the columns of the resulting table. When SnowConvert AI cannot gather the required information, this EWI is added.

Codebeispiel

Eingabecode:
BigQuery
 CREATE OR REPLACE TABLE FUNCTION function_name_noreturns_asterisk_join (parameter_name INTEGER)
AS
  SELECT * 
  FROM unknownTable1 t1 
  JOIN unknownTable2 t2 ON t1.col1 = t2.fk_col1;
Generierter Code:
Snowflake
 --** SSC-FDM-0007 - MISSING DEPENDENT OBJECTS "unknownTable1", "unknownTable2" **

CREATE OR REPLACE FUNCTION function_name_noreturns_asterisk_join (parameter_name INTEGER)
----** SSC-FDM-BQ0003 - UNABLE TO GENERATE CORRECT RETURNS TABLE CLAUSE DUE TO MISSING DEPENDENT OBJECT INFORMATION. **
--RETURNS TABLE (
--)
AS
    $$
      SELECT *
      FROM
      unknownTable1 t1
      JOIN
          unknownTable2 t2 ON t1.col1 = t2.fk_col1
    $$;

Best Practices

  • Versuchen Sie immer, alle abhängigen Objektdefinitionen in den Eingabecode einzubeziehen, sodass SnowConvert AI Zugriff auf wichtige Informationen hat.

  • Wenn Sie weitere Unterstützung benötigen, können Sie uns eine E-Mail an snowconvert-support@snowflake.com senden.

SSC-FDM-BQ0004

Die INFER_SCHEMA-Funktion erfordert einen Dateipfad ohne Platzhalter, um die Tabellenvorlage zu generieren und den FILE_PATH-Platzhalter damit zu ersetzen.

Warnung

Dieses FDM ist veraltet. Die neueste Version für dieses FDM finden Sie unter SSC-FDM-0035.

Beschreibung

Die Funktion INFER_SCHEMA-wird in Snowflake verwendet, um die Spaltendefinition einer Tabelle basierend auf der Struktur einer Datei zu generieren. Sie erfordert einen LOCATION-Parameter, der den Pfad zu einer Datei oder einem Ordner angibt, aus dem die Tabellenspalten erstellt werden. Dieser Pfad unterstützt jedoch keine regulären Ausdrücke, das heißt, das Platzhalterzeichen * wird nicht unterstützt.

When the table has no columns, SnowConvert AI will check all URIS to find one that does not use wildcards and use it in the INFER_SCHEMA function. When no URI meets such criteria, this FDM and a FILE_PATH placeholder is generated, and the placeholder has to be replaced with the path of one of the files referenced by the external table to generate the table columns.

Codebeispiel

Eingabecode:
BigQuery
 CREATE OR REPLACE EXTERNAL TABLE test.my_external_table_json2
OPTIONS(
  FORMAT='JSON',
  URIS=['gs://sc_external_table_bucket/folder_with_json/*']
);
Generierter Code:
Snowflake
 CREATE OR REPLACE TEMPORARY FILE FORMAT SC_TEST_MY_EXTERNAL_TABLE_JSON2_FORMAT
TYPE = JSON;


CREATE OR REPLACE EXTERNAL TABLE test.my_external_table_json2 USING TEMPLATE (
SELECT
  ARRAY_AGG(OBJECT_CONSTRUCT('COLUMN_NAME', COLUMN_NAME, 'TYPE', TYPE, 'NULLABLE', NULLABLE, 'EXPRESSION', EXPRESSION))
FROM
  --** SSC-FDM-BQ0004 - THE INFER_SCHEMA FUNCTION REQUIRES A FILE PATH WITHOUT WILDCARDS TO GENERATE THE TABLE TEMPLATE, REPLACE THE FILE_PATH PLACEHOLDER WITH IT **
  TABLE(INFER_SCHEMA(LOCATION => '@EXTERNAL_STAGE/FILE_PATH', FILE_FORMAT => 'SC_TEST_MY_EXTERNAL_TABLE_JSON2_FORMAT'))
)
!!!RESOLVE EWI!!! /*** SSC-EWI-BQ0015 - EXTERNAL TABLE REQUIRES AN EXTERNAL STAGE TO ACCESS gs://sc_external_table_bucket, DEFINE AND REPLACE THE EXTERNAL_STAGE PLACEHOLDER ***/!!!
LOCATION = @EXTERNAL_STAGE
AUTO_REFRESH = false
PATTERN = 'folder_with_json/.*'
FILE_FORMAT = (TYPE = JSON);

Best Practices

SSC-FDM-BQ0005

Parsen des CSV-Headers wird in externen Tabellen nicht unterstützt. Spalten müssen umbenannt werden, damit sie den ursprünglichen Namen entsprechen.

Beschreibung

Snowflake external tables do not support parsing the header of CSV files. SKIP_HEADER is used as a workaround to avoid runtime errors, but the resulting table column names will have auto-generated names (c1, c2, …, cN) instead of the original header names.

When SnowConvert AI detects an external table with CSV file format and no explicit column list, it adds the SKIP_HEADER = 1 file format option. The columns must be manually renamed to match the original names from the CSV header.

Codebeispiel

Eingabecode:
BigQuery
 CREATE OR REPLACE EXTERNAL TABLE test.my_external_table_csv
OPTIONS(
  FORMAT='CSV',
  URIS=['gs://sc_external_table_bucket/folder_with_csv/Employees.csv']
);
Generierter Code:
Snowflake
 CREATE OR REPLACE TEMPORARY FILE FORMAT SC_TEST_MY_EXTERNAL_TABLE_CSV_FORMAT
TYPE = CSV
SKIP_HEADER = 1;

CREATE OR REPLACE EXTERNAL TABLE test.my_external_table_csv
--** SSC-FDM-BQ0005 - PARSING THE CSV HEADER IS NOT SUPPORTED IN EXTERNAL TABLES, COLUMNS MUST BE RENAMED TO MATCH THE ORIGINAL NAMES **
USING TEMPLATE (
SELECT
  ARRAY_AGG(OBJECT_CONSTRUCT('COLUMN_NAME', COLUMN_NAME, 'TYPE', TYPE, 'NULLABLE', NULLABLE, 'EXPRESSION', EXPRESSION))
FROM
  TABLE(INFER_SCHEMA(LOCATION => '@EXTERNAL_STAGE/folder_with_csv/Employees.csv', FILE_FORMAT => 'SC_TEST_MY_EXTERNAL_TABLE_CSV_FORMAT'))
)
!!!RESOLVE EWI!!! /*** SSC-EWI-0032 - EXTERNAL TABLE REQUIRES AN EXTERNAL STAGE TO ACCESS gs://sc_external_table_bucket, DEFINE AND REPLACE THE EXTERNAL_STAGE PLACEHOLDER ***/!!!
LOCATION = @EXTERNAL_STAGE
AUTO_REFRESH = false
PATTERN = 'folder_with_csv/Employees.csv'
FILE_FORMAT = (TYPE = CSV SKIP_HEADER = 1);

Best Practices

  • Rename the auto-generated column names (c1, c2, …, cN) back to the original column names from the CSV file header.

  • If the original column names are known, use ALTER TABLE ... RENAME COLUMN or recreate the external table with explicit column definitions.

  • For non-external-table loading scenarios, consider using MATCH_BY_COLUMN_NAME with PARSE_HEADER = TRUE in the file format to automatically match columns by header names.

SSC-FDM-BQ0006

Das Lesen von Google Drive wird in Snowflake nicht unterstützt. Laden Sie die Dateien an den externen Speicherort hoch, und ersetzen Sie die FILE_PATH-Platzhalter.

Beschreibung

Snowflake unterstützt das Lesen von Daten aus Dateien nicht, die in Google Drive gehostet werden. Dieses FDM wird generiert, um eine Benachrichtigung zu erstellen. Laden Sie die Google Drive-Dateien in den externen Speicherort hoch, damit sie über den externen Stagingbereich auf sie zugreifen können.

Die PATTERN-Klausel enthält automatisch generierte Platzhalter FILE_PATH0, FILE_PATH1, …, FILE_PATHN, die nach dem Verschieben der Dateien an den externen Speicherort durch die tatsächlichen Datei- oder Ordnerpfade ersetzt werden müssen.

Codebeispiel

Eingabecode:
BigQuery
 CREATE OR REPLACE EXTERNAL TABLE test.my_external_table_drive_test
OPTIONS(
  FORMAT='JSON',
  URIS=['https://drive.google.com/open?id=someFileId']
);
Generierter Code:
Snowflake
 CREATE OR REPLACE TEMPORARY FILE FORMAT SC_TEST_MY_EXTERNAL_TABLE_DRIVE_TEST_FORMAT
TYPE = JSON;

CREATE OR REPLACE EXTERNAL TABLE test.my_external_table_drive_test USING TEMPLATE (
SELECT
  ARRAY_AGG(OBJECT_CONSTRUCT('COLUMN_NAME', COLUMN_NAME, 'TYPE', TYPE, 'NULLABLE', NULLABLE, 'EXPRESSION', EXPRESSION))
FROM
  --** SSC-FDM-0035 - THE INFER_SCHEMA FUNCTION REQUIRES A FILE PATH WITHOUT WILDCARDS TO GENERATE THE TABLE TEMPLATE, REPLACE THE FILE_PATH PLACEHOLDER WITH IT **
  TABLE(INFER_SCHEMA(LOCATION => '@EXTERNAL_STAGE/FILE_PATH', FILE_FORMAT => 'SC_TEST_MY_EXTERNAL_TABLE_DRIVE_TEST_FORMAT'))
)
!!!RESOLVE EWI!!! /*** SSC-EWI-0032 - EXTERNAL TABLE REQUIRES AN EXTERNAL STAGE TO ACCESS AN EXTERNAL LOCATION, DEFINE AND REPLACE THE EXTERNAL_STAGE PLACEHOLDER ***/!!!
LOCATION = @EXTERNAL_STAGE
AUTO_REFRESH = false
--** SSC-FDM-BQ0006 - READING FROM GOOGLE DRIVE IS NOT SUPPORTED IN SNOWFLAKE, UPLOAD THE FILES TO THE EXTERNAL LOCATION AND REPLACE THE FILE_PATH PLACEHOLDERS **
PATTERN = 'FILE_PATH0'
FILE_FORMAT = (TYPE = JSON);

Best Practices

  • Download the files from Google Drive and upload them to a cloud storage location accessible by Snowflake (e.g., Amazon S3, Azure Blob Storage, or Google Cloud Storage).

  • Create or configure an external stage in Snowflake pointing to the cloud storage location.

  • Replace the FILE_PATH placeholders in the PATTERN clause with the actual file or folder paths relative to the external stage.

SSC-FDM-BQ0007

The GOOGLE_SHEETS format is not supported in Snowflake. CSV file type is used as a workaround.

Beschreibung

The GOOGLE_SHEETS format is not supported in Snowflake. CSV file type is used as a workaround because the structure of Google Sheets data is similar to CSV.

When SnowConvert AI detects an external table using the GOOGLE_SHEETS format, it produces an external table with the CSV file format instead. The resulting table expects a CSV file rather than a Google Sheets source.

Codebeispiel

Eingabecode:
BigQuery
 CREATE OR REPLACE EXTERNAL TABLE test.spreadsheetTable
(
  Name STRING,
  Code INTEGER,
  Price INTEGER,
  Expiration_date DATE
)
OPTIONS(
  format="GOOGLE_SHEETS",
  skip_leading_rows = 1,
  uris=['https://docs.google.com/spreadsheets/d/someFileId/edit?usp=sharing']
);
Generierter Code:
Snowflake
--** SSC-FDM-BQ0007 - THE GOOGLE_SHEETS FORMAT IS NOT SUPPORTED IN SNOWFLAKE. CSV FILE TYPE IS USED AS A WORKAROUND. **
CREATE OR REPLACE EXTERNAL TABLE test.spreadsheetTable
(
  Name STRING AS CAST(GET_IGNORE_CASE($1, 'c1') AS STRING),
  Code INTEGER AS CAST(GET_IGNORE_CASE($1, 'c2') AS INTEGER),
  Price INTEGER AS CAST(GET_IGNORE_CASE($1, 'c3') AS INTEGER),
  Expiration_date DATE AS CAST(GET_IGNORE_CASE($1, 'c4') AS DATE)
)
!!!RESOLVE EWI!!! /*** SSC-EWI-0032 - EXTERNAL TABLE REQUIRES AN EXTERNAL STAGE TO ACCESS AN EXTERNAL LOCATION, DEFINE AND REPLACE THE EXTERNAL_STAGE PLACEHOLDER ***/!!!
LOCATION = @EXTERNAL_STAGE
AUTO_REFRESH = false
--** SSC-FDM-BQ0006 - READING FROM GOOGLE DRIVE IS NOT SUPPORTED IN SNOWFLAKE, UPLOAD THE FILES TO THE EXTERNAL LOCATION AND REPLACE THE FILE_PATH PLACEHOLDERS **
PATTERN = 'FILE_PATH0'
FILE_FORMAT = (TYPE = CSV SKIP_HEADER = 1)
COMMENT = '{ "origin": "sf_sc", "name": "snowconvert", "version": {  "major": 0,  "minor": 0,  "patch": "0" }, "attributes": {  "component": "bigquery",  "convertedOn": "07/16/2025",  "domain": "no-domain-provided" }}';

Best Practices

  • Export the Google Sheets data as CSV files and upload them to a cloud storage location accessible by Snowflake.

  • Verify that the CSV export preserves the expected data types and formatting, especially for dates, numbers, and text fields with commas.

  • If the external table also references Google Drive URIs, see SSC-FDM-BQ0006 for instructions on migrating the files to an external stage.

SSC-FDM-BQ0008

Where clause references a column of STRUCT type. Comparison operations may produce different results in Snowflake.

Beschreibung

BigQuery STRUCT types have no direct equivalent in Snowflake. VARIANT is used as a workaround (see SSC-FDM-0034). When a comparison involves a Snowflake VARIANT created from a BigQuery STRUCT, the results may differ because Snowflake compares both keys and values, whereas BigQuery compares only values regardless of field names.

This FDM is added when a WHERE clause comparison involves a column of STRUCT type that was converted to VARIANT.

Codebeispiel

Eingabecode:
BigQuery
 CREATE OR REPLACE TABLE test.compExprTable
(
  COL1 STRUCT<sc1 INT64>,
  COL2 STRUCT<sc2 INT64>  
);

SELECT * FROM test.compExprTable WHERE COL1 <> (COL2);
Output Code:
Snowflake
 CREATE OR REPLACE TABLE test.compExprTable
(
  COL1 VARIANT /*** SSC-FDM-0034 - STRUCT<INT64> CONVERTED TO VARIANT. SOME OF ITS USAGES MIGHT HAVE FUNCTIONAL DIFFERENCES. ***/,
  COL2 VARIANT /*** SSC-FDM-0034 - STRUCT<INT64> CONVERTED TO VARIANT. SOME OF ITS USAGES MIGHT HAVE FUNCTIONAL DIFFERENCES. ***/
)
COMMENT = '{ "origin": "sf_sc", "name": "snowconvert", "version": {  "major": 0,  "minor": 0,  "patch": "0" }, "attributes": {  "component": "bigquery",  "convertedOn": "07/02/2025",  "domain": "no-domain-provided" }}';

SELECT * FROM
  test.compExprTable
--** SSC-FDM-BQ0008 - WHERE CLAUSE REFERENCES A COLUMN OF STRUCT TYPE. COMPARISON OPERATIONS MAY PRODUCE DIFFERENT RESULTS IN SNOWFLAKE. **
WHERE COL1 <> (COL2);

Best Practices

  • Review WHERE clause comparisons involving STRUCT-derived VARIANT columns. If the original BigQuery query compared STRUCTs by value only, extract and compare individual fields explicitly in Snowflake.

  • For example, replace WHERE col1 <> col2 with WHERE col1:sc1 <> col2:sc2 to compare specific field values instead of the entire VARIANT object.

  • For more information on VARIANT comparison behavior, see the Snowflake VARIANT documentation.

SSC-FDM-BQ0010

Die Geography-Funktion ist in Snowflake nicht erforderlich.

Beschreibung

Snowflake automatically detects GEOGRAPHY data from WGS 84 formatted strings (WKT, WKB, GeoJSON), so explicit geography conversion functions like ST_GEOGFROMTEXT are not required in VALUES clause inserts. SnowConvert AI removes the function call and passes the string literal directly. This FDM is added to notify that the geography function was removed.

Codebeispiel

Eingabecode:
BigQuery
 CREATE OR REPLACE TABLE test.geographyType
(
  COL1 GEOGRAPHY
);

INSERT INTO test.geographyType VALUES
(ST_GEOGFROMTEXT('POINT(-122.35 37.55)')), 
(ST_GEOGFROMTEXT('LINESTRING(-124.20 42.00, -120.01 41.99)'));

SELECT * FROM test.geographyType;
Output Code:
Snowflake
 CREATE OR REPLACE TABLE test.geographyType
(
  COL1 GEOGRAPHY
);

INSERT INTO test.geographyType
VALUES
    (
     --** SSC-FDM-BQ0010 - THE FUNCTION 'ST_GEOGFROMTEXT' IS NOT REQUIRED IN SNOWFLAKE. **
     'POINT(-122.35 37.55)'), (
     --** SSC-FDM-BQ0010 - THE FUNCTION 'ST_GEOGFROMTEXT' IS NOT REQUIRED IN SNOWFLAKE. **
     'LINESTRING(-124.20 42.00, -120.01 41.99)');

ALTER SESSION SET GEOGRAPHY_OUTPUT_FORMAT = 'WKT';
SELECT * FROM
test.geographyType;

Best Practices

  • This FDM can be safely ignored in most cases. Snowflake natively supports GEOGRAPHY data from WKT, WKB, and GeoJSON string formats without requiring explicit conversion functions.

  • If the removed function performed validation or transformation beyond simple type casting, verify that the inserted data is valid GEOGRAPHY data in Snowflake.

  • For more information, see the Snowflake GEOGRAPHY data type documentation.

SSC-FDM-BQ0011

Benannte Parameter in diesem Skript wurden in Snowflake-CLI-Variablen umgewandelt.

Beschreibung

BigQuery supports named parameters using the @parameter_name syntax in queries. SnowConvert AI transforms these named parameters to Snowflake CLI variables using the <% parameter_name %> syntax.

To execute the transformed .sql scripts containing named parameters, use Snowflake CLI with variable substitution.

For more information on how to set up and use Snowflake CLI, see What is Snowflake CLI?

Codebeispiel

Eingabecode:
BigQuery
SELECT column1 FROM test.parametersExample WHERE column2 = @searchValue;
Ausführung des Beispiels (mit dem Befehl bq query)
bq query \
  --use_legacy_sql=false \
  --parameter=searchValue:Int64:80 \
  'SELECT column1 FROM test.parametersExample WHERE column2 = @searchValue'
Output Code:
Snowflake
--** SSC-FDM-BQ0011 - NAMED PARAMETERS IN THIS SCRIPT WERE TRANSFORMED TO SNOWFLAKE CLI VARIABLES. **
SELECT column1 FROM
test.parametersExample
WHERE column2 = <% searchValue %>;
Beispielausführung (Snowflake CLI)
snow sql -f output_file_path -D "searchValue=80"

Best Practices

  • Install and configure Snowflake CLI to execute the transformed scripts with variable substitution using the -D flag (e.g., snow sql -f script.sql -D "param=value").

  • Review each transformed <% parameter_name %> variable to ensure the parameter name and intended value match the original BigQuery @parameter_name usage.

  • If the transformed script will be executed outside of Snowflake CLI (e.g., in a Snowflake worksheet), replace <% parameter_name %> variables with literal values or session variables as appropriate.

SSC-FDM-BQ0012

Select * with multiple UNNEST operators will produce column ambiguity in Snowflake

Beschreibung

As part of the SnowConvert transformation for the UNNEST operator, the FLATTEN function is used, this function generates multiple columns not required to emulate the UNNEST operator functionality like the THIS or PATH columns.

When a SELECT * with the UNNEST operator is found, SnowConvert will remove the unnecessary columns using the EXCLUDE keyword, however, when multiple UNNEST operators are used in the same statement, the columns can not be removed due to ambiguity problems, this FDM will be generated to mark these cases.

It is recommended to expand the SELECT expression list in order to specify only the expected columns and solve this issue.

Codebeispiel

Eingabecode:
BigQuery
SELECT * FROM UNNEST ([10,20,30]);

SELECT * FROM UNNEST ([10,20,30]) AS numbers, UNNEST(['Hi', 'Hello', 'Bye']) AS words;
Generierter Code:
Snowflake
SELECT
* EXCLUDE(SEQ, KEY, PATH, THIS, INDEX)
FROM
TABLE(FLATTEN(INPUT => [10,20,30])) AS F0_ (
SEQ,
KEY,
PATH,
INDEX,
F0_,
THIS
);

SELECT
--** SSC-FDM-BQ0012 - SELECT * WITH MULTIPLE UNNEST OPERATORS WILL RESULT IN COLUMN AMBIGUITY IN SNOWFLAKE **
 * FROM
TABLE(FLATTEN(INPUT => [10,20,30])) AS numbers (
SEQ,
KEY,
PATH,
INDEX,
numbers,
THIS
),
TABLE(FLATTEN(INPUT => ['Hi', 'Hello', 'Bye'])) AS words (
SEQ,
KEY,
PATH,
INDEX,
words,
THIS
);

Empfehlungen

  1. Expand the SELECT list: Replace SELECT * with an explicit column list specifying only the columns you need from each UNNEST/FLATTEN result. This eliminates the ambiguity caused by duplicate metadata columns.

  2. Use table aliases: Qualify each column reference with the corresponding table alias to avoid ambiguity between the FLATTEN results.