Apache Arrow Explained: Columnar Memory and Flight RPC

Apache Arrow is a language-independent columnar memory format and a set of libraries for working with analytical data across programming languages. Data engineers, analytics developers, and platform teams use it to move tables between tools without repeatedly translating every value into a different in-memory layout. This explainer covers Arrow’s memory model, its relationship to persistent file formats, and how Arrow Flight RPC streams record batches between services.

Why Apache Arrow Is Being Talked About

Analytical systems increasingly pass large tables between query engines, programming languages, and storage tools. A pipeline might read Parquet, transform data in Python, and hand the result to a database or another service. If every boundary converts values into a private representation and then serializes them again, the conversions consume CPU and memory bandwidth even when the data itself has not changed.

Apache Arrow provides a shared data layout and implementations in many languages. That gives compatible tools a common way to interpret columnar data in memory. Arrow Flight extends the same idea to network services, defining RPC methods for discovering, uploading, and downloading streams of Arrow data.

What Is Apache Arrow?

Apache Arrow is an open-source project whose central format specifies how typed columnar data is represented in memory. Its libraries implement that format for languages including C++, Java, Python, Rust, and JavaScript. The Apache Arrow project describes the format and the language-specific libraries built around it.

Rather than storing each row as one sequence of values, Arrow lays out values by column. A table with event_type, timestamp, and duration columns has corresponding typed arrays. An engine that aggregates durations can scan that column without loading every field from each row. The Arrow columnar format specification describes the buffers and metadata that make this layout interoperable.

Arrow itself is not a database, query engine, or general-purpose on-disk table format. Its memory layout is used by libraries and applications; Arrow IPC can serialize Arrow data, while persistent formats such as Parquet store files for longer-term use. Arrow Flight is a separate RPC protocol for moving streams of Arrow record batches between services.

The Problem Apache Arrow Solves

Different tools often represent the same logical table in incompatible ways. A conversion between them can require allocating new buffers, copying values, and translating types. Repeating that work across a pipeline increases latency and memory pressure, and it can introduce subtle differences in null handling, timestamps, or nested values.

Arrow provides a shared physical representation so compatible libraries can exchange arrays and record batches with less conversion. Some operations can share buffers without copying, but zero-copy is conditional: the producer and consumer must agree on the representation and memory ownership, and a process boundary or network transfer still requires transport and often new buffers.

Network transfer has another problem: a large result should not need to be assembled as one enormous response before a client can start processing it. Flight uses gRPC and Arrow’s inter-process communication format to stream record batches. It can describe data locations and split a result across endpoints, leaving applications to fetch those endpoints in parallel where appropriate.

How Apache Arrow Works

An Arrow array combines a data type and a set of buffers. A primitive integer array, for example, has a contiguous values buffer and may have a validity bitmap that marks null entries. Variable-length values also need offsets into a data buffer. Nested arrays use child arrays and metadata to describe their structure. This representation supports efficient sequential scans, vectorized operations, and typed access.

An Arrow record batch groups equal-length arrays under one schema, which makes the columns correspond to the same set of rows. A table may consist of several record batches, so it can represent chunked data without forcing all columns into one allocation. Libraries can process a batch at a time rather than materializing an entire dataset.

Arrow components address different boundaries:

These are complementary layers, not competing versions of the same thing. A pipeline can read Parquet from object storage, decode it into Arrow arrays, process record batches, and use Flight to send results to a remote consumer. For table transactions, snapshots, and schema evolution over files, see Apache Iceberg’s table format and snapshot architecture.

Flight RPC Request Flow

Flight builds on gRPC and Arrow IPC. A client identifies data with a FlightDescriptor, such as a path or an application-defined command. It asks the service for FlightInfo, which includes the schema and one or more endpoints. Each endpoint contains a ticket and may identify a server location. The client presents that ticket with DoGet to receive record batches.

For uploads, DoPut streams batches to a service. DoExchange supports a bidirectional stream in which both sides can send data. Other methods can list available flights or expose application-specific actions. The Arrow Flight RPC specification defines these messages and common request patterns. The server still decides what a descriptor means, who may access it, and how data is produced.

Key Components and Concepts

Schema and arrays. A schema names fields and assigns each a type and optional metadata. Arrays hold the corresponding values in buffers. Validity bitmaps, offsets, and child arrays represent nulls, strings, lists, and other nested data without changing the table’s row-oriented meaning to the caller.

Record batches and tables. A record batch is a schema plus a group of equal-length arrays. A table is a higher-level collection of columns that can be chunked into batches. Batching bounds working memory and gives streaming operations a unit they can consume incrementally.

Arrow IPC. The IPC format serializes schemas and batches as messages or as a file. It is not the same as Arrow’s in-memory layout: IPC defines how to encode that data for a transfer or file boundary. Reading an IPC stream reconstructs Arrow objects for the receiving process.

Flight descriptors, endpoints, and tickets. Descriptors identify a requested data set according to the service’s application. FlightInfo advertises its schema and endpoints; an endpoint’s opaque ticket lets the client request a particular stream. A ticket is not an authorization mechanism by itself. Services need explicit access controls and should avoid placing sensitive data in descriptors or tickets.

Flight SQL. Flight SQL adds a standardized interface for SQL-oriented database operations over Flight. It is useful when clients need database metadata and query workflows, while base Flight can transport application-defined streams without requiring a SQL engine.

Real-World Uses

Arrow is useful inside analytical applications that connect multiple libraries or languages. A Python data preparation step can hand Arrow tables to a C++ query engine, avoiding an application-specific row-by-row conversion. DataFrame libraries, query engines, and database clients can also use Arrow as an interchange layer.

Flight fits services that return large query results, distribute data across workers, or accept batches from ingestion clients. A query service can return multiple endpoints so a client can retrieve partitions concurrently. This is most valuable when both sides support Arrow and the workload benefits from streaming; it does not remove network bandwidth limits, server-side query costs, or the need to handle retries and partial failures.

Arrow is also relevant to data-lake and columnar database architectures, but it does not replace their storage or compute layers. For context on storage and processing choices, read building a data lake, Apache Spark and big data processing, and ClickHouse for analytics.

Getting Started with PyArrow and Flight

Install PyArrow in a virtual environment. The PyArrow installation guide documents supported installation methods and package variants.

python -m venv .venv
source .venv/bin/activate
python -m pip install pyarrow

In Windows PowerShell, activate with .\.venv\Scripts\Activate.ps1. Save this as arrow_example.py to build a table, serialize it as an IPC stream, and verify that the round trip preserves the data:

import pyarrow as pa

table = pa.table(
    {
        "event_type": ["open", "click", "close"],
        "duration_ms": [12, 31, 8],
    }
)

sink = pa.BufferOutputStream()
with pa.ipc.new_stream(sink, table.schema) as writer:
    writer.write_table(table)

restored = pa.ipc.open_stream(sink.getvalue()).read_all()
assert restored.equals(table)
print(restored.schema)
print(f"Rows: {restored.num_rows}; bytes: {restored.nbytes}")

Run python arrow_example.py; the output should show the two typed columns and three rows. This example keeps the IPC payload in process memory. To write a durable Arrow IPC file, use pa.ipc.new_file with a file sink; for interoperable data-lake storage, choose a file format and table design that match the consumers’ needs.

For a minimal Flight service, save the following as flight_server.py. It exposes one in-memory table on the local loopback interface and serves it in response to a DoGet ticket:

import pyarrow as pa
import pyarrow.flight as flight


class ExampleServer(flight.FlightServerBase):
    def __init__(self):
        super().__init__("grpc://127.0.0.1:8815")
        self.table = pa.table(
            {"event": ["open", "click"], "latency_ms": [12, 31]}
        )

    def get_flight_info(self, context, descriptor):
        endpoint = flight.FlightEndpoint(flight.Ticket(b"events"), [])
        return flight.FlightInfo(
            self.table.schema,
            descriptor,
            [endpoint],
            self.table.num_rows,
            self.table.nbytes,
        )

    def do_get(self, context, ticket):
        if ticket.ticket != b"events":
            raise ValueError("Unknown ticket")
        return flight.RecordBatchStream(self.table)


ExampleServer().serve()

In a second terminal, save and run this client as flight_client.py:

import pyarrow.flight as flight

client = flight.connect("grpc://127.0.0.1:8815")
descriptor = flight.FlightDescriptor.for_path("events")
info = client.get_flight_info(descriptor)
reader = client.do_get(info.endpoints[0].ticket)
print(reader.read_all().to_pydict())

Start the server with python flight_server.py, then run python flight_client.py. The client should print the two rows. This is a local demonstration, not a production configuration: it has no authentication or TLS, stores all rows in server memory, and should not be exposed to an untrusted network. For deployment, configure TLS and authentication, authorize each requested dataset, limit resource use, and decide how clients should retry failed or expired endpoints. If a Python environment uses a reduced Conda package variant, confirm that its PyArrow installation includes the Flight component.

Common Misconceptions

“Arrow is a database or a storage format.” Arrow defines an in-memory layout and related interchange protocols. It does not provide table transactions, a catalog, durable storage management, or a query engine.

“Zero-copy means data never moves.” It means a compatible operation may reuse an existing buffer rather than copy it. Data crossing a network must be transmitted, and a receiver generally allocates memory to read it. Ownership, alignment, conversions, and language bindings also determine whether a particular operation can share memory.

“Flight makes any service fast automatically.” Flight can reduce serialization overhead and stream data, but it cannot fix a slow query, inadequate network, inefficient batching, or poor authorization design. Benchmark the complete path and secure the service.

Related Articles

• Understand the persistent table layer in Apache Iceberg’s table format and snapshot architecture.
• Explore storage, processing, and query components in building a data lake.
• Review a distributed processing engine in Apache Spark and big data processing.
• Compare Arrow’s interchange layer with a columnar analytics database in ClickHouse for analytics.

Changelog and Last Updated

• Changelog: Initial publication.
• Last updated: Reviewed against the Apache Arrow format and Flight RPC specifications and PyArrow documentation.