Pyarrow serialize

materializationengine/blueprints/client/api2.py

@@ -84,6 +84,14 @@
         "(faster), false returns json"
     ),
 )
+query_parser.add_argument(
+    "arrow_format",
+    type=inputs.boolean,
+    default=False,
+    required=False,
+    location="args",
+    help=("whether to convert dataframe to pyarrow ipc batch format"),
+)
 query_parser.add_argument(
     "split_positions",
     type=inputs.boolean,
@@ -121,6 +129,7 @@
 not be relevant and the user might not be getting data back that they expect, but it will not error.",
 )
 
+
 @cached(cache=TTLCache(maxsize=64, ttl=600))
 def get_relevant_datastack_info(datastack_name):
     ds_info = get_datastack_info(datastack_name=datastack_name)
@@ -1004,7 +1013,10 @@ def post(self, datastack_name: str):
             user_data["desired_resolution"] = des_res
 
         modified_user_data, query_map, remap_warnings = remap_query(
-            user_data, chosen_timestamp, cg_client, allow_invalid_root_ids,
+            user_data,
+            chosen_timestamp,
+            cg_client,
+            allow_invalid_root_ids,
         )
 
         mat_df, column_names, mat_warnings = execute_materialized_query(
@@ -1044,6 +1056,7 @@ def post(self, datastack_name: str):
             column_names=column_names,
             desired_resolution=user_data["desired_resolution"],
             return_pyarrow=args["return_pyarrow"],
+            arrow_format=args["arrow_format"],
         )
 
 
@@ -1273,6 +1286,7 @@ def post(
             column_names=column_names,
             desired_resolution=data["desired_resolution"],
             return_pyarrow=args["return_pyarrow"],
+            arrow_format=args["arrow_format"],
         )
 
 

materializationengine/blueprints/client/common.py

@@ -240,6 +240,7 @@ def handle_simple_query(
         column_names=column_names,
         desired_resolution=data["desired_resolution"],
         return_pyarrow=args["return_pyarrow"],
+        arrow_format=args['arrow_format']
     )
 
 
@@ -393,4 +394,5 @@ def handle_complex_query(
         column_names=column_names,
         desired_resolution=data["desired_resolution"],
         return_pyarrow=args["return_pyarrow"],
+        arrow_format=args['arrow_format']
     )

materializationengine/blueprints/client/utils.py

@@ -1,6 +1,7 @@
 import pyarrow as pa
-from flask import Response, request
+from flask import Response, request, send_file
 from cloudfiles import compression
+from io import BytesIO
 
 
 def collect_crud_columns(column_names):
@@ -61,17 +62,45 @@ def update_notice_text_warnings(ann_md, warnings, table_name):
 
 
 def create_query_response(
-    df, warnings, desired_resolution, column_names, return_pyarrow=True
+    df,
+    warnings,
+    desired_resolution,
+    column_names,
+    return_pyarrow=True,
+    arrow_format=False,
 ):
+    accept_encoding = request.headers.get("Accept-Encoding", "")
 
     headers = add_warnings_to_headers({}, warnings)
     if desired_resolution is not None:
         headers["dataframe_resolution"] = desired_resolution
     headers["column_names"] = column_names
     if return_pyarrow:
+        if arrow_format:
+            batch = pa.RecordBatch.from_pandas(df)
+            sink = pa.BufferOutputStream()
+            if "lz4" in accept_encoding:
+                compression = "LZ4_FRAME"
+            elif "zstd" in accept_encoding:
+                compression = "ZSTD"
+            else:
+                compression = None
+            opt = pa.ipc.IpcWriteOptions(compression=compression)
+            with pa.ipc.new_stream(sink, batch.schema, options=opt) as writer:
+                writer.write_batch(batch)
+            response = send_file(BytesIO(sink.getvalue().to_pybytes()), "data.arrow")
+            response.headers.update(headers)
+            return after_request(response)
+        # headers = add_warnings_to_headers(
+        #     headers,
+        #     [
+        #         "Using deprecated pyarrow serialization method, please upgrade CAVEClient with pip install --upgrade caveclient"
+        #     ],
+        # )
         context = pa.default_serialization_context()
         serialized = context.serialize(df).to_buffer().to_pybytes()
-        return Response(serialized, headers=headers, mimetype="x-application/pyarrow")
+        response = Response(serialized, headers=headers, mimetype="x-application/pyarrow")
+        return after_request(response)
     else:
         dfjson = df.to_json(orient="records")
         response = Response(dfjson, headers=headers, mimetype="application/json")