SSIS数据转换失败报错求助：存在潜在数据丢失风险

Hey there, let's unpack these SSIS errors and get your pipeline back up and running—this is a super common issue rooted in data type mismatches, so we'll break it down and fix it step by step.

First, Let's Understand the Error Chain #

These three errors are linked, with a single root cause:

错误#1
[OLE DB Source [1525]] 错误：输出“OLE DB Source Output”(1535)上的输出列“ABC”(1545)出现错误。返回的列状态为：“因存在潜在数据丢失风险，无法转换该值。”
错误#2
[OLE DB Source [1525]] 错误：SSIS错误代码DTS_E_INDUCEDTRANSFORMFAILUREONERROR。“输出列‘ABC’(1545)”执行失败，原因是发生错误代码0xC0209072，且“输出列‘ABC’(1545)”的错误行处置指定出错即失败。指定组件的指定对象发生错误。此前可能有更多关于该失败的错误信息。
错误#3
[SSIS.Pipeline] 错误：SSIS错误代码DTS_E_PRIMEOUTPUTFAILED。组件“OLE DB Source”(1525)上的PrimeOutput方法返回错误代码0xC0209029。当管道引擎调用PrimeOutput()时，该组件返回失败代码。失败代码的含义由组件定义，但该错误为致命错误，管道已停止执行。此前可能有更多关于该失败的错误信息。

1. Error #1 is the root cause: The column ABC from your OLE DB Source can't be converted without risking data loss (e.g., a large numeric value being forced into a smaller integer type, or a long string being squeezed into a shorter varchar column).
2. Error #2 follows: Your SSIS package is set to fail immediately when this conversion error happens (the default "Fail on error" setting for the column).
3. Error #3 is the final crash: The pipeline engine stops entirely because the source component can't successfully output data due to the conversion failure.

Step-by-Step Fixes #

1. Check Data Type Mismatches (Most Critical)

• Open your OLE DB Source editor, go to the Columns tab, and note the data type of the source ABC column.
• Compare this to the data type expected by the next component in your pipeline (or the target destination column). Common issues here include:
  • Source is numeric(18,6) but target is int (decimal values would be truncated)
  • Source is varchar(255) but target is varchar(50) (long strings get cut off)
  • Source is datetime2 but target is datetime (precision loss)
• Adjust the target/output column's data type to match or accommodate the source (e.g., switch from int to bigint or numeric, increase varchar length).

2. Adjust Error Handling (For Debugging)

If you need to identify exactly which rows are causing the issue before fixing the data types:

• Right-click the OLE DB Source component → Show Advanced Editor.
• Go to the Input and Output Properties tab, expand OLE DB Source Output → Output Columns → select ABC.
• Find the Error Row Disposition property and change it to:
  • Redirect row: Sends bad rows to an error output, which you can connect to a destination (like a flat file or table) to inspect problematic data.
  • Ignore failure: Skips the error and continues execution (use this temporarily only—don't leave it long-term, as you'll lose data).

3. Add Explicit Data Conversion (If Safe)

If you confirm the data can be safely converted without losing critical information:

• Add a Derived Column component right after the OLE DB Source.
• Create a new derived column with an explicit conversion. Examples:
  • Convert a large numeric to an integer (only if all values fit): (DT_I4)ABC
  • Truncate a long string to fit the target length: SUBSTRING(ABC, 1, 50)
• Use this new derived column instead of the original ABC column in your pipeline.

4. Clean Up Source Data

Sometimes the issue is dirty data in your source system:

• Run a query against your source database to check for outliers in the ABC column:

  -- Check for numeric values outside int range
  SELECT ABC FROM YourSourceTable WHERE ABC > 2147483647 OR ABC < -2147483648;
  
  -- Check for strings longer than 50 characters
  SELECT ABC FROM YourSourceTable WHERE LEN(ABC) > 50;
  

• Clean up these outliers (e.g., update invalid values, remove extra-long strings) at the source if possible.

内容的提问来源于stack exchange，提问作者Adachew Workneh