sea-results: SeaOperationBackend wires kernel result-stream → JS rows

Implements IOperationBackend over the napi binding's Statement.
fetchChunk decodes Arrow IPC bytes → apache-arrow RecordBatch →
ArrowResultConverter (Phase 1+2 reused unchanged) → JS rows.

All M0 datatypes round-trip via the same converter the thrift path
uses (BOOL, INT8/16/32/64, FLOAT, DOUBLE, DECIMAL, STRING, BINARY,
DATE, TIMESTAMP, INTERVAL, ARRAY, MAP, STRUCT). Unit tests
construct synthetic IPC batches; e2e test against pecotesting
confirms byte-identical parity vs thrift.

No new dependencies. ArrowResultConverter / ResultSlicer /
OperationIterator all reused unchanged (DRY).

Author: msrathore-db

lib/sea/SeaArrowIpc.ts

@@ -0,0 +1,217 @@
+// Copyright (c) 2026 Databricks, Inc.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+import { RecordBatchReader, Schema, Field, DataType, TypeMap } from 'apache-arrow';
+import { TTableSchema, TTypeId, TPrimitiveTypeEntry } from '../../thrift/TCLIService_types';
+
+/**
+ * Field metadata key used by the kernel to attach the original Databricks
+ * SQL type name to each Arrow field. See `databricks-sql-kernel/src/reader/mod.rs`.
+ */
+const DATABRICKS_TYPE_NAME = 'databricks.type_name';
+
+/**
+ * Decode an Arrow IPC stream payload (schema header + zero-or-more
+ * record-batch messages) into its row count.
+ *
+ * Returns `{ schema, rowCount }`. The schema is left intact as the
+ * apache-arrow Schema object so callers can reuse it; the rowCount is
+ * the sum of `RecordBatch.numRows` across every record-batch message
+ * in the stream.
+ *
+ * Why we parse upfront: `ArrowResultConverter` consumes `ArrowBatch`
+ * objects which carry an explicit `rowCount`. The kernel's IPC payload
+ * does not carry a separate count — only per-RecordBatch numRows. We
+ * walk the messages once to sum them so the converter sees the same
+ * shape as the thrift path (`ArrowResultHandler.fetchNext` at
+ * `lib/result/ArrowResultHandler.ts:55`).
+ *
+ * Re-parsing inside the converter is unavoidable because `RecordBatch`
+ * instances created here cannot be passed across the converter's
+ * `Buffer[]` boundary without rewriting the converter. The IPC bytes
+ * themselves are small enough (one record batch per call) that the
+ * double-parse cost is negligible for M0.
+ */
+export function decodeIpcBatch(ipcBytes: Buffer): { schema: Schema<TypeMap>; rowCount: number } {
+  const reader = RecordBatchReader.from<TypeMap>(ipcBytes);
+  // Eagerly open so `schema` is populated.
+  reader.open();
+  const { schema } = reader;
+
+  let rowCount = 0;
+  // Iterate all record batches in the stream and sum row counts.
+  for (const batch of reader) {
+    rowCount += batch.numRows;
+  }
+  return { schema, rowCount };
+}
+
+/**
+ * Decode an Arrow IPC schema payload (no record batches) into the
+ * apache-arrow Schema object.
+ */
+export function decodeIpcSchema(ipcBytes: Buffer): Schema<TypeMap> {
+  const reader = RecordBatchReader.from<TypeMap>(ipcBytes);
+  reader.open();
+  return reader.schema;
+}
+
+/**
+ * Map an Arrow `DataType` (with optional `databricks.type_name`
+ * metadata) onto the closest Thrift `TTypeId`.
+ *
+ * This is the synthesis step that lets the existing
+ * `ArrowResultConverter` Phase-2 dispatch (`convertThriftValue` in
+ * `lib/result/utils.ts:61-98`) keep working unchanged for the SEA
+ * path. Phase-2 keys exclusively off `TPrimitiveTypeEntry.type` per
+ * column, so we synthesize a `TColumnDesc` whose `TTypeId` matches the
+ * server-emitted Arrow type as closely as possible.
+ *
+ * Resolution order:
+ *   1. The kernel attaches `databricks.type_name` (e.g. "DECIMAL",
+ *      "INTERVAL", "STRUCT") to each field's metadata. Prefer that when
+ *      present — it carries the original SQL semantic that the Arrow
+ *      type alone can lose (e.g. INTERVAL → Utf8 with metadata).
+ *   2. Fall back to the Arrow `DataType.typeId` for primitive types.
+ *
+ * This matches the JDBC and Python drivers' policy of trusting the
+ * server's logical type assignment over the wire-level Arrow encoding.
+ */
+function arrowTypeToTTypeId(field: Field<DataType>): TTypeId {
+  const typeName = field.metadata.get(DATABRICKS_TYPE_NAME)?.toUpperCase();
+
+  switch (typeName) {
+    case 'BOOLEAN':
+      return TTypeId.BOOLEAN_TYPE;
+    case 'TINYINT':
+    case 'BYTE':
+      return TTypeId.TINYINT_TYPE;
+    case 'SMALLINT':
+    case 'SHORT':
+      return TTypeId.SMALLINT_TYPE;
+    case 'INT':
+    case 'INTEGER':
+      return TTypeId.INT_TYPE;
+    case 'BIGINT':
+    case 'LONG':
+      return TTypeId.BIGINT_TYPE;
+    case 'FLOAT':
+    case 'REAL':
+      return TTypeId.FLOAT_TYPE;
+    case 'DOUBLE':
+      return TTypeId.DOUBLE_TYPE;
+    case 'STRING':
+      return TTypeId.STRING_TYPE;
+    case 'VARCHAR':
+      return TTypeId.VARCHAR_TYPE;
+    case 'CHAR':
+      return TTypeId.CHAR_TYPE;
+    case 'BINARY':
+      return TTypeId.BINARY_TYPE;
+    case 'DATE':
+      return TTypeId.DATE_TYPE;
+    case 'TIMESTAMP':
+    case 'TIMESTAMP_NTZ':
+      return TTypeId.TIMESTAMP_TYPE;
+    case 'DECIMAL':
+      return TTypeId.DECIMAL_TYPE;
+    case 'INTERVAL':
+    case 'INTERVAL DAY':
+    case 'INTERVAL DAY TO HOUR':
+    case 'INTERVAL DAY TO MINUTE':
+    case 'INTERVAL DAY TO SECOND':
+    case 'INTERVAL HOUR':
+    case 'INTERVAL HOUR TO MINUTE':
+    case 'INTERVAL HOUR TO SECOND':
+    case 'INTERVAL MINUTE':
+    case 'INTERVAL MINUTE TO SECOND':
+    case 'INTERVAL SECOND':
+      return TTypeId.INTERVAL_DAY_TIME_TYPE;
+    case 'INTERVAL YEAR':
+    case 'INTERVAL YEAR TO MONTH':
+    case 'INTERVAL MONTH':
+      return TTypeId.INTERVAL_YEAR_MONTH_TYPE;
+    case 'ARRAY':
+      return TTypeId.ARRAY_TYPE;
+    case 'MAP':
+      return TTypeId.MAP_TYPE;
+    case 'STRUCT':
+      return TTypeId.STRUCT_TYPE;
+    case 'NULL':
+    case 'VOID':
+      return TTypeId.NULL_TYPE;
+    default:
+      break;
+  }
+
+  // Fall back to Arrow's own type id when no databricks metadata is set
+  // (e.g. unit tests constructing batches without metadata).
+  const arrowType = field.type;
+  if (DataType.isBool(arrowType)) return TTypeId.BOOLEAN_TYPE;
+  if (DataType.isInt(arrowType)) {
+    switch (arrowType.bitWidth) {
+      case 8:
+        return TTypeId.TINYINT_TYPE;
+      case 16:
+        return TTypeId.SMALLINT_TYPE;
+      case 32:
+        return TTypeId.INT_TYPE;
+      case 64:
+        return TTypeId.BIGINT_TYPE;
+      default:
+        return TTypeId.BIGINT_TYPE;
+    }
+  }
+  if (DataType.isFloat(arrowType)) {
+    // arrow Float precision: 16=HALF, 32=SINGLE, 64=DOUBLE
+    return arrowType.precision === 2 ? TTypeId.DOUBLE_TYPE : TTypeId.FLOAT_TYPE;
+  }
+  if (DataType.isDecimal(arrowType)) return TTypeId.DECIMAL_TYPE;
+  if (DataType.isUtf8(arrowType)) return TTypeId.STRING_TYPE;
+  if (DataType.isBinary(arrowType)) return TTypeId.BINARY_TYPE;
+  if (DataType.isDate(arrowType)) return TTypeId.DATE_TYPE;
+  if (DataType.isTimestamp(arrowType)) return TTypeId.TIMESTAMP_TYPE;
+  if (DataType.isList(arrowType)) return TTypeId.ARRAY_TYPE;
+  if (DataType.isMap(arrowType)) return TTypeId.MAP_TYPE;
+  if (DataType.isStruct(arrowType)) return TTypeId.STRUCT_TYPE;
+  if (DataType.isNull(arrowType)) return TTypeId.NULL_TYPE;
+
+  return TTypeId.STRING_TYPE;
+}
+
+/**
+ * Synthesize a Thrift `TTableSchema` from an Arrow schema decoded out
+ * of the kernel's IPC stream. Used by `SeaOperationBackend.getResultMetadata`
+ * to drive `ArrowResultConverter.convertThriftTypes` (Phase 2) without
+ * changing that code.
+ */
+export function arrowSchemaToThriftSchema(arrowSchema: Schema<TypeMap>): TTableSchema {
+  const columns = arrowSchema.fields.map((field, index) => {
+    const primitiveEntry: TPrimitiveTypeEntry = {
+      type: arrowTypeToTTypeId(field),
+    };
+    return {
+      columnName: field.name,
+      typeDesc: {
+        types: [
+          {
+            primitiveEntry,
+          },
+        ],
+      },
+      position: index + 1,
+    };
+  });
+  return { columns };
+}