適用於:Databricks SQL Databricks Runtime將現有的 Parquet 資料表就地轉換為 Delta 資料表。 此命令會列出目錄中的所有檔案、建立 Delta Lake 事務歷史記錄來追蹤這些檔案,並藉由讀取所有 Parquet 檔案的頁尾自動推斷數據架構。 轉換程式會收集統計數據,以改善已轉換之 Delta 數據表的查詢效能。 如果您提供數據表名稱...
CONVERT TO DELTANovember 14, 2024 Applies to: Databricks SQL Databricks RuntimeConverts an existing Parquet table to a Delta table in-place. This command lists all the files in the directory, creates a Delta Lake transaction log that tracks these files, and automatically infers the data schema...
In Databricks Runtime 11.1 and below,PARTITIONED BYis a required argument for all partitioned data. Examples Note You do not need to provide partitioning information for Iceberg tables or tables registered to the metastore. sql CONVERT TO DELTA database_name.table_name;-- only for Parquet table...
In Databricks Runtime 13.3 LTS and above, you can work with truncated columns of types string, long, or int. Azure Databricks does not support working with truncated columns of type decimal. You can convert a directory of Parquet data files to a Delta Lake table as long as you have write...
In Databricks Runtime 13.3 LTS and above, you can work with truncated columns of types string, long, or int. Azure Databricks does not support working with truncated columns of type decimal.You can convert a directory of Parquet data files to a Delta Lake table as long as you have write ...
In-place migration The goal of this approach is to repurpose the files from the previous format into the new format to reduce the amount of data that needs to be rewritten. This approach results in taking the existing Parquet files from a Hive, Hudi, or Delta Lake table and registering th...
Add the JSON string as a collection type and pass it as an input tospark.createDataset. This converts it to a DataFrame. The JSON reader infers the schema automatically from the JSON string. This sample code uses a list collection type, which is represented asjson :: Nil. You can also...
skip_profile("databricks_sql_endpoint", "spark_session") class TestInsertOverwriteOnSchemaChange(IncrementalOnSchemaChangeIgnoreFail): @pytest.fixture(scope="class") def project_config_update(self): return { "models": { "+file_format": "parquet", "+partition_by": "id", "+incremental_...
In Databricks Runtime 13.3 LTS and above, you can work with truncated columns of typesstring,long, orint. Azure Databricks does not support working with truncated columns of typedecimal. You can convert a directory of Parquet data files to a Delta Lake table as long as you have write access...
In Databricks Runtime 13.3 LTS and above, you can work with truncated columns of types string, long, or int. Azure Databricks does not support working with truncated columns of type decimal.You can convert a directory of Parquet data files to a Delta Lake table as long as you have write ...