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Diffstat (limited to 'sqlglot/dataframe/README.md')
| -rw-r--r-- | sqlglot/dataframe/README.md | 18 |
1 files changed, 11 insertions, 7 deletions
diff --git a/sqlglot/dataframe/README.md b/sqlglot/dataframe/README.md index 02179f4..86fdc4b 100644 --- a/sqlglot/dataframe/README.md +++ b/sqlglot/dataframe/README.md @@ -9,7 +9,7 @@ Currently many of the common operations are covered and more functionality will ## Instructions * [Install SQLGlot](https://github.com/tobymao/sqlglot/blob/main/README.md#install) and that is all that is required to just generate SQL. [The examples](#examples) show generating SQL and then executing that SQL on a specific engine and that will require that engine's client library. * Find/replace all `from pyspark.sql` with `from sqlglot.dataframe`. -* Prior to any `spark.read.table` or `spark.table` run `sqlglot.schema.add_table('<table_name>', <column_structure>)`. +* Prior to any `spark.read.table` or `spark.table` run `sqlglot.schema.add_table('<table_name>', <column_structure>, dialect="spark")`. * The column structure can be defined the following ways: * Dictionary where the keys are column names and values are string of the Spark SQL type name. * Ex: `{'cola': 'string', 'colb': 'int'}` @@ -33,12 +33,16 @@ import sqlglot from sqlglot.dataframe.sql.session import SparkSession from sqlglot.dataframe.sql import functions as F -sqlglot.schema.add_table('employee', { - 'employee_id': 'INT', - 'fname': 'STRING', - 'lname': 'STRING', - 'age': 'INT', -}) # Register the table structure prior to reading from the table +sqlglot.schema.add_table( + 'employee', + { + 'employee_id': 'INT', + 'fname': 'STRING', + 'lname': 'STRING', + 'age': 'INT', + }, + dialect="spark", +) # Register the table structure prior to reading from the table spark = SparkSession() |