Claude Code for Arrow Flight Workflow (2026)

Last updated: April 17, 2026

Apache Arrow Flight is a high-performance protocol for transferring Arrow data between processes, machines, and cloud regions. When combined with Claude Code’s AI-assisted development capabilities, you can rapidly build, debug, and optimize Arrow Flight workflows. This tutorial walks you through practical patterns for integrating Claude Code into your Arrow Flight projects, from a minimal server and client up through production-ready authentication, streaming, and comprehensive test suites.

Why Arrow Flight?

Before diving into code, it is worth understanding why you would choose Arrow Flight over more familiar alternatives like REST or gRPC with Protobuf. Arrow Flight is built specifically for columnar data movement. It transmits data in the native Arrow columnar binary format, which means no serialization or deserialization overhead on the hot path. A REST API serving a 500 MB CSV must parse that CSV on every request; a Flight server serving the same data as an Arrow table can stream it directly from memory to the network buffer.

Transfer Method	Format	Serialization	Throughput (typical)
REST + JSON	Row-oriented text	High (parse every field)	50-200 MB/s
REST + Parquet	Columnar binary	Medium (decode Parquet)	200-500 MB/s
gRPC + Protobuf	Row-oriented binary	Medium (schema mapping)	300-700 MB/s
Arrow Flight	Columnar binary (native)	Near zero (zero-copy)	1-10+ GB/s

Flight also integrates naturally with the broader Arrow ecosystem, pandas, Polars, DuckDB, Spark, and Snowflake all support Arrow IPC, so data flows between systems without format conversion.

Setting Up Your Arrow Flight Environment

Before building workflows, ensure your development environment has the necessary dependencies. Arrow Flight requires both the Arrow C++ libraries and the Python bindings. Use Claude Code to help you set up and verify your installation:

Install Arrow Flight dependencies
pip install pyarrow pandas polars
Verify the installation
python -c "import pyarrow.flight as flight; print(flight.__version__)"

Claude Code can generate a complete environment setup script tailored to your project. Simply describe your requirements, whether you’re working with large-scale data pipelines or embedded systems, and let Claude Code produce the appropriate configuration.

If you are working in a team environment, pin your PyArrow version in requirements.txt to avoid schema compatibility issues across different Arrow minor releases:

pyarrow==15.0.2
pandas>=2.0.0
polars>=0.20.0

The key components you’ll work with include the Flight client for sending requests, the Flight server for receiving data, and PyArrow for data serialization. Understanding how these pieces interact is essential for building solid workflows.

Building a Basic Flight Server

A Flight server exposes Arrow data through the Flight protocol, enabling efficient binary transfer without serialization overhead. Here’s a practical server implementation that Claude Code can help you create:

import pyarrow as pa
import pyarrow.flight as flight
class ExampleFlightServer(flight.FlightServerBase):
 def __init__(self, location="grpc://0.0.0.0:8815"):
 super().__init__(location)
 self.flights = {}
 def list_flights(self, context, criteria):
 """List all available datasets."""
 for key, table in self.flights.items():
 descriptor = flight.FlightDescriptor.for_path(key)
 endpoint = flight.FlightEndpoint(
 ticket=flight.Ticket(key.encode()),
 locations=[self._location]
 )
 yield flight.FlightInfo(
 schema=table.schema,
 descriptor=descriptor,
 endpoints=[endpoint],
 total_records=table.num_rows,
 total_bytes=table.nbytes
 )
 def do_get(self, context, ticket):
 """Handle data retrieval requests."""
 key = ticket.ticket.decode()
 table = self.flights.get(key)
 if table is None:
 raise flight.FlightServerError(f"Dataset '{key}' not found")
 return flight.RecordBatchStream(table)
 def do_put(self, context, descriptor, reader, writer):
 """Handle data upload requests."""
 key = descriptor.path[0].decode()
 table = reader.read_all()
 self.flights[key] = table
 return
if __name__ == "__main__":
 server = ExampleFlightServer("grpc://0.0.0.0:8815")
 print("Flight server running on localhost:8815")
 server.serve()

Note the use of FlightServerBase (not the deprecated ServerBase). The list_flights method is important for discoverability, clients can call it to enumerate available datasets without knowing their names in advance. The do_get method raises a proper FlightServerError with an informative message rather than silently returning an empty table.

Claude Code can help you extend this with authentication middleware, SSL/TLS encryption, and custom ticket handling for more complex scenarios. A prompt like “add basic token authentication to this Flight server” will produce a working FlightServerAuthHandler subclass in seconds.

Creating a Flight Client Workflow

The client side connects to Flight servers and performs data operations. Building a solid client involves proper error handling, connection pooling, and efficient data transformation:

import pyarrow.flight as flight
import pyarrow as pa
def create_flight_client(host="localhost", port=8815, tls=False):
 """Initialize Flight client with connection options."""
 if tls:
 location = flight.Location.for_grpc_tls(host, port)
 else:
 location = flight.Location.for_grpc_tcp(host, port)
 client = flight.FlightClient(location)
 return client
def fetch_dataset(client, dataset_name, timeout=30.0):
 """Retrieve Arrow table from Flight server."""
 options = flight.FlightCallOptions(timeout=timeout)
 ticket = flight.Ticket(dataset_name.encode())
 reader = client.do_get(ticket, options)
 return reader.read_all()
def upload_dataset(client, name, table, timeout=60.0):
 """Upload Arrow table to Flight server."""
 options = flight.FlightCallOptions(timeout=timeout)
 descriptor = flight.FlightDescriptor.for_path(name)
 writer, _ = client.do_put(descriptor, table.schema, options)
 writer.write_table(table)
 writer.close()
def list_datasets(client):
 """List all datasets available on the server."""
 return [
 info.descriptor.path[0].decode()
 for info in client.list_flights()
 ]

Claude Code excels at expanding this foundation. You can ask it to add retry logic, implement circuit breakers, or create async variants for improved performance in high-throughput scenarios. The timeout parameters are exposed as arguments here so callers can tune them based on dataset size, a 30-second timeout is reasonable for small tables but inadequate for a multi-gigabyte transfer.

Integrating with Data Pipelines

Arrow Flight becomes powerful when integrated into larger data workflows. Claude Code can help you connect Flight operations with pandas, Polars, or other data processing libraries:

import pandas as pd
import polars as pl
--- pandas integration ---
def flight_to_pandas(client, dataset_name):
 """Convert Flight data directly to pandas DataFrame."""
 table = fetch_dataset(client, dataset_name)
 return table.to_pandas()
def pandas_to_flight(client, name, df):
 """Convert pandas DataFrame to Flight data."""
 # Use preserve_index=False unless you need the index as a column
 table = pa.Table.from_pandas(df, preserve_index=False)
 upload_dataset(client, name, table)
--- Polars integration (zero-copy where possible) ---
def flight_to_polars(client, dataset_name):
 """Convert Flight data to Polars DataFrame."""
 table = fetch_dataset(client, dataset_name)
 return pl.from_arrow(table)
def polars_to_flight(client, name, df):
 """Convert Polars DataFrame to Flight data."""
 table = df.to_arrow()
 upload_dataset(client, name, table)
Example ETL workflow
client = create_flight_client()
Read CSV, process with pandas, push to Flight
raw_df = pd.read_csv("large_dataset.csv", parse_dates=["timestamp"])
processed_df = raw_df.dropna().query("value > 0")
pandas_to_flight(client, "processed_data", processed_df)
Pull the processed data back as Polars for fast aggregation
result_df = flight_to_polars(client, "processed_data")
summary = result_df.group_by("category").agg(pl.col("value").mean())
print(summary)

This pattern is particularly useful for ETL pipelines where data needs to be transferred between services efficiently. Polars is worth adding to your toolbox here: its Arrow-native internals mean that pl.from_arrow() is essentially free, there is no data copying, just a schema handshake.

Error Handling and Retry Patterns

Production workflows require solid error handling. Claude Code can generate comprehensive retry logic with exponential backoff, which is essential when dealing with transient network errors in long-running pipelines:

import time
import logging
from functools import wraps
logger = logging.getLogger(__name__)
def retry_on_failure(max_retries=3, base_delay=1.0, backoff=2.0,
 retryable_errors=(flight.FlightUnavailableError,
 flight.FlightTimedOutError)):
 """Decorator for retrying Flight operations with exponential backoff."""
 def decorator(func):
 @wraps(func)
 def wrapper(*args, kwargs):
 last_exception = None
 for attempt in range(max_retries):
 try:
 return func(*args, kwargs)
 except retryable_errors as e:
 last_exception = e
 wait = base_delay * (backoff attempt)
 logger.warning(
 "Attempt %d/%d failed: %s. Retrying in %.1fs",
 attempt + 1, max_retries, e, wait
 )
 if attempt < max_retries - 1:
 time.sleep(wait)
 raise last_exception
 return wrapper
 return decorator
@retry_on_failure(max_retries=5, base_delay=0.5)
def robust_fetch(client, dataset_name):
 return fetch_dataset(client, dataset_name)

Beyond retries, add explicit error classification to distinguish between recoverable and non-recoverable failures:

def safe_fetch(client, dataset_name):
 """Fetch with structured error handling."""
 try:
 return fetch_dataset(client, dataset_name)
 except flight.FlightServerError as e:
 # Server-side error (e.g., dataset not found). do not retry
 logger.error("Server returned error for '%s': %s", dataset_name, e)
 raise
 except flight.FlightUnavailableError:
 # Server is down. retry is appropriate upstream
 logger.warning("Flight server unavailable, check server health")
 raise
 except flight.FlightTimedOutError:
 # Operation exceeded timeout. may want to retry with longer timeout
 logger.warning("Fetch timed out for dataset '%s'", dataset_name)
 raise
 except Exception as e:
 # Unexpected error. log with full traceback
 logger.exception("Unexpected error fetching '%s'", dataset_name)
 raise

Optimizing Performance

Arrow Flight’s efficiency comes from columnar data representation and zero-copy transfers. Maximize performance with these techniques:

Batch your data: Instead of sending individual rows, batch them into Arrow RecordBatches. A batch size of 64K-256K rows typically strikes the best balance between memory pressure and network usage.
Use compression: Enable LZ4 or Zstandard compression for network transfers, both decompress faster than gzip while achieving comparable ratios for columnar data.
Pre-define schemas: Define schemas upfront rather than inferring them on each call. Schema inference requires a full scan of the data and produces less precise types.
Reuse client connections: Instantiate FlightClient once and reuse it. Creating a new client per request pays the gRPC handshake cost on every call.
Stream large results: Use RecordBatchReader iteration instead of read_all() for datasets that do not fit in memory.

def optimized_batch_upload(client, name, df, batch_size=65536):
 """Upload large DataFrames in optimal batches with compression."""
 # Define schema explicitly for best performance
 schema = pa.Schema.from_pandas(df, preserve_index=False)
 table = pa.Table.from_pandas(df, schema=schema, preserve_index=False)
 batches = table.to_batches(max_chunksize=batch_size)
 descriptor = flight.FlightDescriptor.for_path(name)
 write_options = pa.ipc.IpcWriteOptions(
 compression='lz4' # Fast compression, good for columnar data
 )
 writer, _ = client.do_put(descriptor, schema)
 for batch in batches:
 writer.write_batch(batch)
 writer.close()
def streaming_fetch(client, dataset_name):
 """Iterate over a large dataset without loading it all into memory."""
 ticket = flight.Ticket(dataset_name.encode())
 reader = client.do_get(ticket)
 for batch in reader:
 # Process one RecordBatch at a time
 df_chunk = batch.to_pandas()
 yield df_chunk

Using streaming_fetch in your pipeline means you can process a 100 GB dataset on a machine with 8 GB of RAM, as long as each batch fits in memory individually.

Authentication and TLS

Exposing a Flight server on a network without authentication is only appropriate for local development. For production, implement token-based authentication:

class TokenAuthServerMiddleware(flight.ServerMiddleware):
 def __init__(self, token):
 self.token = token
 def call_completed(self, exception):
 pass
class TokenAuthServerMiddlewareFactory(flight.ServerMiddlewareFactory):
 VALID_TOKENS = {"my-secret-token"}
 def start_call(self, info, headers):
 auth_header = dict(headers).get("authorization", [None])[0]
 if not auth_header or not auth_header.startswith("Bearer "):
 raise flight.FlightUnauthenticatedError("Missing Bearer token")
 token = auth_header[len("Bearer "):]
 if token not in self.VALID_TOKENS:
 raise flight.FlightUnauthenticatedError("Invalid token")
 return TokenAuthServerMiddleware(token)
class SecureFlightServer(ExampleFlightServer):
 def __init__(self, location):
 super().__init__(location)
Start server with auth middleware
server = SecureFlightServer("grpc://0.0.0.0:8815")
server._middleware = {"auth": TokenAuthServerMiddlewareFactory()}
Client sends the token in call headers
def create_authenticated_client(host, port, token):
 location = flight.Location.for_grpc_tcp(host, port)
 client = flight.FlightClient(location)
 options = flight.FlightCallOptions(
 headers=[("authorization", f"Bearer {token}")]
 )
 return client, options

For TLS, pass tls_root_certs to the client and provide cert_chain and private_key to the server. Claude Code can generate a complete TLS setup script including certificate generation for development.

Testing Your Workflows

Claude Code can generate comprehensive tests for your Flight workflows. The key insight for testing Flight is to spin up an in-process server during test setup, this avoids mocking the transport layer and gives you full integration coverage without needing a separate server process:

import pytest
import threading
import pyarrow as pa
import pyarrow.flight as flight
import pandas as pd
@pytest.fixture(scope="module")
def flight_server():
 """Start an in-process Flight server for tests."""
 server = ExampleFlightServer("grpc://localhost:0") # port 0 = OS assigns
 # Start in background thread
 t = threading.Thread(target=server.serve, daemon=True)
 t.start()
 yield server
 server.shutdown()
@pytest.fixture(scope="module")
def flight_client(flight_server):
 """Create a client connected to the test server."""
 location = flight_server._location
 return flight.FlightClient(location)
def test_upload_and_fetch(flight_client):
 """Round-trip: upload a table, then fetch it back."""
 original = pa.table({
 "id": pa.array([1, 2, 3], type=pa.int64()),
 "name": pa.array(["alice", "bob", "carol"])
 })
 upload_dataset(flight_client, "test_roundtrip", original)
 fetched = fetch_dataset(flight_client, "test_roundtrip")
 assert fetched.num_rows == original.num_rows
 assert fetched.column_names == original.column_names
 assert fetched.equals(original)
def test_pandas_roundtrip(flight_client):
 """Test pandas DataFrame survives a Flight roundtrip."""
 df = pd.DataFrame({"a": [1.0, 2.0, 3.0], "b": ["x", "y", "z"]})
 pandas_to_flight(flight_client, "test_pandas", df)
 result = flight_to_pandas(flight_client, "test_pandas")
 pd.testing.assert_frame_equal(df, result)
def test_dataset_not_found_raises(flight_client):
 """Missing dataset should raise FlightServerError."""
 with pytest.raises(flight.FlightServerError, match="not found"):
 fetch_dataset(flight_client, "nonexistent_dataset")
def test_list_datasets(flight_client):
 """list_datasets should include uploaded datasets."""
 upload_dataset(flight_client, "visible_dataset",
 pa.table({"v": [42]}))
 available = list_datasets(flight_client)
 assert "visible_dataset" in available

Pair these integration tests with fast unit tests for pure data transformation logic. Keep the in-process server fixture at scope="module" so it starts once per test file, Flight servers take a moment to bind their port and you do not want to pay that cost per test.

Practical Workflow: Serving ML Features

A common real-world pattern is using Arrow Flight as a feature store transport. Model training jobs fetch feature tables from a central Flight server; inference services push prediction results back. Claude Code can scaffold this entire pattern from a short description:

class FeatureStoreServer(flight.FlightServerBase):
 """Flight server backed by Parquet files on disk."""
 def __init__(self, location, data_dir):
 super().__init__(location)
 self.data_dir = data_dir
 def do_get(self, context, ticket):
 dataset_name = ticket.ticket.decode()
 parquet_path = f"{self.data_dir}/{dataset_name}.parquet"
 table = pa.parquet.read_table(parquet_path)
 return flight.RecordBatchStream(table)
 def do_put(self, context, descriptor, reader, writer):
 dataset_name = descriptor.path[0].decode()
 table = reader.read_all()
 pa.parquet.write_table(table, f"{self.data_dir}/{dataset_name}.parquet")

This server is entirely stateless with respect to in-memory storage, all data lives on disk as Parquet. Restart the server and all datasets are immediately available again. You can scale this horizontally by placing the data directory on shared NFS or an S3-compatible store mounted via s3fs.

Conclusion

Building Arrow Flight workflows with Claude Code combines high-performance data transfer with AI-assisted development. Start with the basic server and client patterns shown here, then progressively add authentication, TLS, compression, and retry logic as your workflow matures. Claude Code’s ability to generate boilerplate, suggest improvements, and help debug type mismatches and schema errors makes this process significantly faster than working from documentation alone.

For next steps, explore integrating Flight with DuckDB’s read_parquet over a Flight endpoint for interactive SQL on streaming data, or look into the Arrow Flight SQL extension for drop-in compatibility with JDBC and ODBC clients. Both directions open up Arrow Flight as a universal data interchange layer across your entire data infrastructure.

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