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You can keep data in Arrow’s columnar form from the moment ClickHouse Connect returns it, and you can pass that data to other Python libraries without rewriting it. What you cannot promise is a copy-free path all the way from the ClickHouse server into your application. Zero-copy holds at specific in-process boundaries. The network transfer and any conversion into Python objects are where copies can happen.
What Arrow can share without copying
Apache Arrow is a columnar in-memory model and interchange toolkit. In Python, PyArrow exposes typed arrays, record batches, tables, and buffers. A table is a set of columns, and each column is a chunked array built from typed memory buffers. Arrow data is immutable. The Apache Arrow documentation on data types and the in-memory model puts it this way: “Arrow data is immutable, so values can be selected but not assigned.” (No individual author is named on that page.) Because values cannot be overwritten in place, a slice can reference the existing buffers instead of rewriting the values.
Buffer handling is where the zero-copy behavior is most concrete. A PyArrow buffer can wrap memory that already implements Python’s buffer protocol, without allocating a second buffer. Converting a buffer to a memoryview is documented as zero-copy. Converting it to Python bytes is not: Buffer.to_pybytes() materializes a new bytes object and copies the data.
The C Data Interface and its boundary
The Arrow C Data Interface is the low-level mechanism that lets compatible implementations share Arrow structures through pointers instead of serializing them. The producer supplies a release callback, and the consumer calls it when it has finished, so the memory lifetime is coordinated across the two sides. The Apache Arrow specification states the scope plainly. Sharing data between independent runtimes or components in the same process is a goal. Sharing across processes and persistence are non-goals.
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That scope settles the most common confusion. The C Data Interface is an in-process handoff. If data must cross a process or machine boundary, or be stored, Arrow IPC is the appropriate format. IPC is a serialized format, so it does not share the producer’s live buffers the way the C Data Interface does.
The PyCapsule protocol between Python libraries
For Python libraries, PyArrow’s PyCapsule interface exposes the handoff through the methods __arrow_c_schema__, __arrow_c_array__, and __arrow_c_stream__. PyArrow constructors can consume these protocols for schemas, arrays, tables, and streams. The documentation says these conversions can be zero-copy when the participating structures and implementations support the interface. It does not mean every conversion or every dtype qualifies, so check the types in your own workload.
Retrieving results with ClickHouse Connect
ClickHouse Connect is the Python client covered by the current ClickHouse documentation for this workflow. Its Arrow methods request ClickHouse’s Arrow output format, so results come back as Arrow structures rather than as Python row objects. Two methods return PyArrow data directly.
query_arrow() for a bounded result
Use query_arrow() when the result is small enough to hold as one table. It returns a pyarrow.Table.
import clickhouse_connect
client = clickhouse_connect.get_client(host="localhost", username="default", password="")
table = client.query_arrow("SELECT event_date, count() AS n FROM events GROUP BY event_date")
print(type(table)) # pyarrow.Table
print(table.num_rows)query_arrow_stream() for incremental processing
query_arrow_stream() is the better choice when you want to process a large result batch by batch. It returns a stream context that yields pyarrow.RecordBatch objects. The ClickHouse documentation requires the stream to be opened in a with block.
total = 0
with client.query_arrow_stream("SELECT * FROM events") as stream:
for batch in stream:
total += batch.num_rowsBecause each batch is handled and then released by your code, you do not need to hold the complete result in one table. Do all batch work inside the with block, because the stream is closed when the block exits.
Arrow-backed pandas and Polars output
If downstream code expects a DataFrame, the client’s DataFrame methods wrap the Arrow result. pandas output uses Arrow-backed dtypes and requires pandas 2.x. Polars can be built from the Arrow table. ClickHouse Connect documents these conversions as zero-copy “where possible.” That phrase is conditional. Whether a given column converts without copying depends on its type and on the library versions, so confirm the dtypes you get back rather than assuming them.
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Sending Arrow data into ClickHouse
The insert direction needs more caution than the query direction. A translated copy of the ClickHouse documentation describes an insert_arrow method that accepts a PyArrow Table. The official English page for that method was not confirmed when this article was prepared, so check its exact signature in the ClickHouse Connect release you install. The standard client API documentation covers the general insert methods and points to the specialized Arrow methods for Arrow and DataFrame input.
No documentation establishes that the insert path avoids copies on the client or the server side. Until you have measured it in your environment, describe the insert as “accepts Arrow tables” and nothing stronger.
Where “zero-copy” stops being accurate
The phrase is accurate for a specific handoff, and it is inaccurate when it is applied to the whole pipeline. These are the points where copies or non-zero-copy behavior enter:
- The network path. A ClickHouse query crosses a client/server transport boundary. The documentation establishes Arrow output and possible zero-copy DataFrame conversion, but it does not guarantee that the server-to-client path avoids copies.
- Conversion to Python bytes.
Buffer.to_pybytes()copies the buffer. - Row-wise Python objects. Turning rows into lists or dictionaries creates new Python objects for each value, which discards the columnar layout.
- Unsupported conversions. Arrow-backed pandas and Polars conversion is zero-copy only where possible, so some columns may be converted by copying.
- Other processes and machines. The C Data Interface does not cover inter-process sharing. Use Arrow IPC, which serializes the data.
- Persistence. Stored or cached data is outside the C Data Interface’s scope.
Choosing an approach
Compare the options on five axes: result size and streaming needs, whether the boundary is in-process or remote, whether downstream code accepts Arrow types, dtype compatibility, and how long the Arrow buffers must stay alive.
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Random freezes, missing sound and display glitches usually trace back to one bad driver. Find and replace yours safely.Free scan · under a minute| Choice | Use when | Copy consideration |
|---|---|---|
query_arrow() returning a PyArrow Table |
The result fits in one bounded table | Arrow output avoids a row-oriented Python representation. The documentation does not promise zero copies across the network or client path. |
query_arrow_stream() yielding record batches |
Results should be processed batch by batch | You do not need to retain the complete result as one table. The stream must be used inside a with block. Transport copies are not guaranteed to be avoided. |
| Arrow-backed pandas or Polars output | Existing analysis code expects a DataFrame | Zero-copy “where possible.” pandas output requires pandas 2.x. Type support is conditional, so check dtypes. |
| Arrow C Data or PyCapsule handoff | Two compatible libraries share data in one process | Buffers can be shared without copying. Lifetime management, type compatibility, and protocol support determine whether it works. |
| Arrow IPC | Data crosses a process or machine boundary, or is persisted | Serialized transfer. Not a live buffer share, and outside the C Data Interface’s scope by design. |
Keep Arrow objects across library boundaries when the consumer supports the C Data or PyCapsule protocols. Avoid to_pybytes() and row materialization when reducing copies is the goal. Keep the Arrow memory alive for as long as any consumer references its buffers, because the release callback exists to coordinate that lifetime.
Versions and checks before you rely on it
The result of this approach depends on library versions, and the official ClickHouse Connect documentation is published from the moving main branch, so method signatures and supported types can change between releases.
Quick Recap
- Pin
clickhouse-connectandpyarrowin your requirements file, and record the exact versions you tested. - At the time of writing, the PyArrow Python documentation lists version 25.0.1 as current. Compare against your installed version.
- Check pandas is 2.x if you use Arrow-backed pandas output.
- Confirm method signatures in your installed release, for example with
help(client.query_arrow_stream). - Inspect the dtypes of returned columns to see whether a DataFrame conversion is zero-copy for your data.
- Measure before claiming savings. This article gives no throughput, latency, or memory figures, because none has been established for this path. Any benchmark you run should state its hardware, software versions, dataset, and method.
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