Snowpark Migration Accelerator: Unterstützte Plattformen¶

SQL-Dialekte¶

Der Snowpark Migration Accelerator (SMA) kann Codedateien analysieren, um SQL-Elemente zu identifizieren. Derzeit kann SMA SQL-Code erkennen, der in den folgenden Formaten geschrieben wurde:

• Spark SQL

• Hive QL

• Databricks SQL

SQL Assessment and Conversion Guidelines¶

Obwohl Spark SQL und Snowflake SQL in hohem Maße kompatibel sind, kann es vorkommen, dass SQL-Code nicht perfekt konvertiert wird.

SQL analysis is only possible when the SQL is received in the following ways:

• A SQL cell within a supported notebook file

• A .sql or .hql file

• A complete string passed to a spark.sql statement.

  Some variable substitutions are not supported. Here are a few examples:

  • Parsed:

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    spark.sql("select * from TableA")
    

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    New SMA scenarios supported include the following:

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    # explicit concatenation
    spark.sql("select * from TableA" + ' where col1 = 1')
    
    # implicit concatenation (juxtaposition)
    spark.sql("select * from TableA" ' where col1 = 1')
    
    # var initialized with sql in previous lines before execution on same scope
    sql = "select * from TableA"
    spark.sql(sql)
    
    # f-string interpolation:
    spark.sql(f"select * from {varTableA}")
    
    # format kindof interpolation
    spark.sql("select * from {}".format(varTableA))
    
    # mix var with concat and f-string interpolation
    sql = f"select * from {varTableA} " + f'where {varCol1} = 1'
    spark.sql(sql)
    

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  • Not Parsed:

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    some_variable = "TableA"
    spark.sql("select * from" + some_variable)
    

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  SQL elements are accounted for in the object inventories, and a readiness score is generated specifically for SQL.