risingwavelabs/risingwave · error · SinkError::BigQuery

VECTOR is not supported for BigQuery sink.

Error message

VECTOR is not supported for BigQuery sink.

What it means

The BigQuery sink's type mapping function `get_string_and_check_support_from_datatype` encountered `DataType::Vector`, which has no mapping to a BigQuery column type. VECTOR (embedding/vector-index) columns are not supported by this sink, so it raises `SinkError::BigQuery` and halts.

Solutions

  1. Exclude the vector column from the sink by selecting only supported columns.
  2. Serialize the vector to JSONB or VARCHAR (e.g. cast to a string representation) before sinking.
  3. Store the vector as an ARRAY<FLOAT64>-compatible representation (e.g. float array) if downstream BigQuery usage allows.

Example fix

// before
CREATE SINK s FROM mv WITH (...) TYPE bigquery; -- mv.embedding VECTOR(3)

// after: expose vector as JSON string
CREATE MATERIALIZED VIEW mv2 AS SELECT cast_to_json(embedding) AS embedding FROM mv;
CREATE SINK s FROM mv2 WITH (...) TYPE bigquery;
Defensive patterns

Strategy: validation

Validate before calling

-- Verify no VECTOR columns before sinking:
SELECT column_name, data_type
FROM rw_catalog.rw_columns
WHERE relation_id = ('<schema>.<mv_name>')::regclass
  AND data_type ILIKE 'vector%';

Type guard

fn is_bigquery_supported(dt: &DataType) -> bool {
    !matches!(dt, DataType::Vector(_) | DataType::Map(_) | DataType::Int256 | DataType::Variant)
}

Prevention

When it happens

Trigger: Creating or validating a BigQuery sink whose source schema includes a VECTOR-typed column (e.g. from a vector index or embedding column).

Common situations: Users embedding ML vectors in RisingWave tables and attempting to replicate them to BigQuery; vector types are a newer RisingWave feature and most sinks predate their support.

Understand the failure class

Background: UnsupportedOperationException and "is not supported" errors: when a library deliberately refuses a call — this error's family across 30 libraries.

Related errors

AI-assisted analysis of risingwavelabs/risingwave@6469eb736d (2026-09-11). Data as JSON: /api/errors/12583e875f33419d. Report an issue: GitHub.

Appendix: source

Thrown at src/connector/src/sink/big_query.rs:433

                Ok(format!("STRUCT<{}>", elements_vec.join(", ")))
            }
            DataType::List(l) => {
                let element_string = Self::get_string_and_check_support_from_datatype(l.elem())?;
                Ok(format!("ARRAY<{}>", element_string))
            }
            DataType::Bytea => Ok("BYTES".to_owned()),
            DataType::Jsonb => Ok("JSON".to_owned()),
            DataType::Variant => Err(SinkError::BigQuery(anyhow::anyhow!(
                "VARIANT is not supported for BigQuery sink."
            ))),
            DataType::Serial => Ok("INT64".to_owned()),
            DataType::Int256 => Err(SinkError::BigQuery(anyhow::anyhow!(
                "INT256 is not supported for BigQuery sink."
            ))),
            DataType::Map(_) => Err(SinkError::BigQuery(anyhow::anyhow!(
                "MAP is not supported for BigQuery sink."
            ))),
            DataType::Vector(_) => Err(SinkError::BigQuery(anyhow::anyhow!(
                "VECTOR is not supported for BigQuery sink."
            ))),
        }
    }

    fn map_field(rw_field: &Field) -> Result<TableFieldSchema> {
        let tfs = match &rw_field.data_type {
            DataType::Boolean => TableFieldSchema::bool(&rw_field.name),
            DataType::Int16 | DataType::Int32 | DataType::Int64 | DataType::Serial => {
                TableFieldSchema::integer(&rw_field.name)
            }
            DataType::Float32 => {
                return Err(SinkError::BigQuery(anyhow::anyhow!(
                    "REAL is not supported for BigQuery sink. Please convert to FLOAT64 or other supported types."
                )));
            }
            DataType::Float64 => TableFieldSchema::float(&rw_field.name),
            DataType::Decimal => TableFieldSchema::numeric(&rw_field.name),

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