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Snowflake Transformation

• Limits
• Aborting Transformation Execution
• Example
• Best Practices
  • Case Sensitivity
  • Working With Data Types
  • Timestamp Columns

Snowflake has many advantages:

• No database administration
• No indexes, sort keys, distribution styles, or column compressions
• Easy scaling
• Simple data types
• Amazing processing power and data throughput

Limits

• Snowflake queries are limited to 900 seconds by default.
• Queries containing comments longer than 8,192 characters will segfault.
• Constraints (like PRIMARY KEY or UNIQUE) are defined but not enforced.

Snowflake is a cloud database and, as such, brings continuous updates and behavioral changes. If you are interested in those changes, please follow the official Snowflake change log.

When loading data to a Snowflake transformation, beware that there are two different methods: copy and clone.

Aborting Transformation Execution

In some cases, you may need to abort the transformation execution and exit with an error message. To abort the execution, set the ABORT_TRANSFORMATION variable to any nonempty string value.

SET ABORT_TRANSFORMATION = (
  SELECT 
      CASE
          WHEN COUNT = 0 THEN ''
          ELSE 'Integrity check failed'
      END
  FROM (
    SELECT COUNT(*) AS COUNT FROM INTEGRITY_CHECK WHERE RESULT = 'failed'
  )   
);

This example will set the ABORT_TRANSFORMATION variable value to 'Integrity check failed' if the INTEGRITY_CHECK table contains one or more records with the RESULT column equal to the value 'failed'.

The transformation engine checks the ABORT_TRANSFORMATION after each successfully executed query and returns the value of the variable as a user error, Transformation aborted: Integrity check failed. in this case.

Example

To create a simple Snowflake transformation, follow these steps:

• Create a table in Storage by uploading the sample CSV file.
• Create an input mapping from that table, setting its destination to source (as expected by the Snowflake script).
• Create an output mapping, setting its destination to a new table in your Storage.
• Copy & paste the below script into the transformation code.
• Save and run the transformation.

CREATE OR REPLACE TABLE "result" AS
	SELECT "first", "second" * 42 AS "larger_second" FROM "source";

You can organize the script into blocks.

Best Practices

Case Sensitivity

Snowflake is case sensitive. All unquoted table/column names are converted to upper case while quoted names keep their case.

So, if you want to create the following table,

-- creates table FOOTABLE
CREATE TABLE footable (...);

all of these commands will work

SELECT * FROM FOOTABLE;
SELECT * FROM "FOOTABLE";
SELECT * FROM footable;

while this one will not:

-- table footable not found!
SELECT * FROM "footable";

Be especially careful when setting up input and output mappings.

When writing your transformation script, quoting all table and column names is highly recommended. Snowflake converts all unquoted table/column identifiers to uppercase, which won’t match table/column identifiers created by Keboola Connection (unless they happen to be all uppercase).

SELECT "barcolumn" FROM "footable";

Working With Data Types

Storage tables store data in character types. When you create a table used on output mapping, you can rely on implicit casting to char:

CREATE OR REPLACE TABLE "test" (ID VARCHAR, TM TIMESTAMP, NUM NUMERIC);

INSERT INTO "test" (ID, TM, NUM)
SELECT 'first', CURRENT_TIMESTAMP, 12.5;

Or, you can create the table directly with character columns (and rely on implicit casting to char):

CREATE OR REPLACE TABLE "test" (ID VARCHAR, TM VARCHAR, NUM VARCHAR);

INSERT INTO "test" (ID, TM, NUM)
SELECT 'first', CURRENT_TIMESTAMP, 12.5;

You can also explicitly cast the columns to char:

CREATE OR REPLACE TABLE "test" (ID VARCHAR, TM VARCHAR, NUM VARCHAR);

INSERT INTO "test" (ID, TM, NUM)
SELECT 
    TO_CHAR('first'), 
    TO_CHAR(CURRENT_TIMESTAMP), 
    TO_CHAR(12.5)
;

When using an unstructured data type, you always have to use the explicit cast:

CREATE OR REPLACE TABLE "test" (ID VARCHAR, TM VARCHAR, NUM VARCHAR, OBJ VARCHAR);

INSERT INTO "test" (ID, TM, NUM, OBJ)
SELECT
    'first',
    CURRENT_TIMESTAMP,
    12.5,
    TO_CHAR( --  <- required!
        OBJECT_CONSTRUCT( 
            'NAME','name',
            'CIN','123'
        )
    )
;

The implicit cast does not work for the ARRAY, OBJECT and VARIANT types, so the following code:

CREATE OR REPLACE TABLE "test" (ID VARCHAR, TM TIMESTAMP, NUM NUMERIC, OBJ OBJECT);

INSERT INTO "test" (ID, TM, NUM, OBJ)
SELECT
    'first',
    CURRENT_TIMESTAMP,
    12.5,
    OBJECT_CONSTRUCT(
        'NAME','name',
        'CIN','123'
    )
;

will lead to an error:

Expression type does not match column data type, expecting VARCHAR(16777216) but got OBJECT for column OBJ, SQL state 22000

Timestamp Columns

By default, Snowflake uses the YYYY-MM-DD HH24:MI:SS.FF3 format when converting the timestamp column to a character string.

This means that if you create a table in a transformation that uses a timestamp column,

CREATE TABLE "ts_test" AS (SELECT CURRENT_TIMESTAMP AS "ts");

the table value will come out as 2018-04-09 06:43:57.866 -0700 in Storage. If you want to output it in a different format, you have to cast the column to a string first, for example:

CREATE TABLE "out" AS
    (SELECT TO_CHAR("ts", 'YYYY-MM-DD HH:MI:SS') AS "ts" FROM "ts_test");

Do not use ALTER SESSION queries to modify the default timestamp format, as the loading and unloading sessions are separate from your transformation/sandbox session and the format may change unexpectedly.

Important: In the AWS US Keboola Connection region (connection.keboola.com), the following Snowflake default parameters are overridden:

• TIMESTAMP_OUTPUT_FORMAT – DY, DD MON YYYY HH24:MI:SS TZHTZM
• TIMESTAMP_TYPE_MAPPING – TIMESTAMP_LTZ
• TIMESTAMP_DAY_IS_ALWAYS_24H – yes

Important: Snowflake works with time zones (and Daylight Savings Time), requiring you to distinguish between various conversion functions:

SELECT
    -- yields 2013-03-10 02:12:00.000 +0000
    TO_TIMESTAMP_NTZ('10.3.2013 2:12', 'DD.MM.YYYY HH:MI'),
    -- yields 2013-03-10 03:12:00.000 -0700
    TO_TIMESTAMP_TZ('10.3.2013 2:12', 'DD.MM.YYYY HH:MI'),
    -- yields 2013-03-10 03:12:00.000 -0700
    TO_TIMESTAMP('10.3.2013 2:12', 'DD.MM.YYYY HH:MI');