What causes " field was received -- type mismatch" in apache/beam?

Fix or filter malformed rows in the source CSV so each cell matches its field's type.

apache/beam · error · IllegalArgumentException

field was received -- type mismatch

Error message

 field  was received -- type mismatch

What it means

parseCell() wraps any IllegalArgumentException thrown while converting a CSV cell (e.g. Integer.parseInt failure) into a new IllegalArgumentException whose message is '<original message> field <name> was received -- type mismatch'. The reported message shows empty brackets because the underlying message and field name were null/empty at runtime, but the cause is always a bad cell value for the declared FieldType.

Source

Thrown at sdks/java/io/csv/src/main/java/org/apache/beam/sdk/io/csv/CsvIOParseHelpers.java:158

        case BOOLEAN:
          return Boolean.parseBoolean(cell);
        case BYTE:
          return Byte.parseByte(cell);
        case DECIMAL:
          return new BigDecimal(cell);
        case DOUBLE:
          return Double.parseDouble(cell);
        case FLOAT:
          return Float.parseFloat(cell);
        case DATETIME:
          return Instant.parse(cell);
        default:
          throw new UnsupportedOperationException(
              "Unsupported type: " + fieldType + ", consider using withCustomRecordParsing");
      }

    } catch (IllegalArgumentException e) {
      throw new IllegalArgumentException(
          e.getMessage() + " field " + field.getName() + " was received -- type mismatch");
    }
  }
}

View on GitHub (pinned to 12126d8942)

Solutions

Fix or filter malformed rows in the source CSV so each cell matches its field's type.
Mark the field nullable and supply a custom parser/empty-cell handling to tolerate blanks.
Validate data out-of-band (e.g. a dry-run parse) and log offending records before running the pipeline.

Example fix

// before: csv row '1,abc' with INTEGER field 'count' -> type mismatch
// after
CsvIO.read(path).withCustomRecordParsing(ParsingBuilder.of(schema)
    .setCustomParser("count", cell -> cell.isEmpty() ? 0L : Long.parseLong(cell.trim()))
    .build());

Defensive patterns

Strategy: try-catch

Validate before calling

// pre-validate sample rows against field types
long bad = rows.filter(r -> !matchesSchema(r)).count();

Try / catch

try { pipeline.apply(CsvIO.read(path)); } catch (IllegalArgumentException e) { LOG.error("CSV type mismatch: {}", e.getMessage()); /* route file to dead-letter */ }

Prevention

Validate cell formats upstream (regex/date checks) before pipeline ingestion.
Use custom parsers for blank or locale-formatted numeric/date columns.
Keep a dead-letter path for malformed rows instead of failing the whole job.

When it happens

Trigger: A CSV cell that cannot be parsed as the schema field's type, e.g. 'abc' in an INTEGER column, '2024-13-99' in a DATETIME column, or 'yes' in a BOOLEAN column.

Common situations: Dirty data files with empty or malformed cells; columns shifted so values land in the wrong fields; locale-formatted numbers ('1,5') in DOUBLE columns; Excel-exported dates in unexpected formats.

Understand the failure class

Background: Type mismatch errors: IllegalArgumentException, TypeError and type guards across 150 open-source libraries — this error's family across 150 libraries.

Related errors

AI-assisted analysis of apache/beam@12126d8942 (2026-09-13). Data as JSON: /api/errors/bd31f3798a6ec5f7. Report an issue: GitHub.