[[["容易理解","easyToUnderstand","thumb-up"],["確實解決了我的問題","solvedMyProblem","thumb-up"],["其他","otherUp","thumb-up"]],[["難以理解","hardToUnderstand","thumb-down"],["資訊或程式碼範例有誤","incorrectInformationOrSampleCode","thumb-down"],["缺少我需要的資訊/範例","missingTheInformationSamplesINeed","thumb-down"],["翻譯問題","translationIssue","thumb-down"],["其他","otherDown","thumb-down"]],["上次更新時間：2025-09-04 (世界標準時間)。"],[[["\u003cp\u003eWrangler allows you to parse files before loading them, inferring data types and mapping columns automatically, similar to file source plugins in the Pipeline Studio.\u003c/p\u003e\n"],["\u003cp\u003eFile parsing in Wrangler requires the use of a file connection such as File, Cloud Storage, or Amazon S3, and offers the ability to manage these connections.\u003c/p\u003e\n"],["\u003cp\u003eThe parsing options dialog lets you specify the file format, delimiter (if applicable), enable quoted values, use the first row as a header, and select the file encoding type.\u003c/p\u003e\n"],["\u003cp\u003eYou can optionally import a schema, which is necessary for file formats like JSON where schema inference is not possible.\u003c/p\u003e\n"],["\u003cp\u003eWrangler supports parsing various file formats, including Avro, Blob, CSV, Delimited, JSON, Parquet, Text, and TSV, each with its own specific requirements.\u003c/p\u003e\n"]]],[],null,["# Parse files\n\nThis page explains how to parse files when you prepare data in the Wrangler\nworkspace of the Cloud Data Fusion Studio. Wrangler lets you parse a file before\nloading it into the Wrangler workspace:\n\n- Wrangler infers data types and maps each column to the inferred data type in the same way file source plugins do in the Pipeline Studio.\n- When schema inference isn't possible, you can import the schema for a file format, such as JSON.\n- The recipe doesn't include the parse directive, which reduces transformation logic during pipeline runs.\n- When you create a pipeline from Wrangler, the source plugin includes all the same parsing properties and values that you set in Wrangler.\n\nCreate a file connection\n------------------------\n\nTo parse a file before loading it into Wrangler, you must use a file connection,\nsuch as File, Cloud Storage, or Amazon S3.\n\n1. [Go to the Wrangler workspace in Cloud Data Fusion](/data-fusion/docs/concepts/wrangler-overview#navigate-to-wrangler).\n2. Click the **Select data** expander arrow to view the available connections.\n3. Add a connection for File, Cloud Storage, or S3. For more information, see [Create and manage connections](/data-fusion/docs/how-to/managing-connections).\n4. To open the parsing options dialog, go to the **Select data** panel and click the name of the file.\n5. In the **Parsing options** dialog, enter the following information:\n\n 1. In the **Format** field, choose the file format of the data being\n read---for example, **csv** . For more information, see [Supported\n formats](#supported-formats).\n\n - If you choose the delimiter format, in the **Delimiter** field that appears, enter the delimiter information.\n - If you choose CSV, TSV, or delimiter format, an **Enable quoted\n values** field appears. If your data is wrapped in quotation marks, select **True** . This setting trims quotation marks from the parsed output. For example, the following input, `1, \"a, b, c\"`, parses into two fields. The first field has the value: `1`. The second field has the value: `a, b, c`. The newline delimiter cannot be within quotes.\n - If you chose text, CSV, TSV, or delimiter format, a **Use first row\n as header** field appears. To use the first line of each file as a column header, select **True**.\n 2. In the **File encoding** field, choose the file encoding type of the\n source file---for example, **UTF-8**.\n\n 3. Optional: to import the schema or override the inferred schema for the\n file, click **Import Schema**. You import the schema for formats, such\n as JSON and some Avro files, where schema inference isn't possible. The\n schema must be in the Avro format.\n\n 4. Click **Confirm**. The parsed file appears in the Wrangler workspace.\n\nSupported formats\n-----------------\n\nThe following formats are supported for file parsing:\n\n- Avro\n- Blob (the blob format requires a schema that contains a field named `body` of type `bytes`)\n- CSV\n- Delimited\n- JSON\n- Parquet\n- Text (the text format requires a schema that contains a field named `body` of type `string`)\n- TSV\n\nWhat's next\n-----------\n\n- Learn more about [Wrangler directives](/data-fusion/docs/concepts/wrangler-overview#apply_directives)."]]
