feat: Add tell() to OutputStream writers#2998
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kevinjqliu merged 1 commit intoapache:mainfrom Feb 2, 2026
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Currently `Table.append(df)` and `Table.overwrite(df)` only accept a materialised `pa.Table`, which forces callers to load the entire dataset into memory before writing. This makes pyiceberg unusable for large or unbounded inputs and has been a recurring complaint (apache#1004, apache#2152, dlt-hub#3753). Allow `pa.RecordBatchReader` as an alternative input. When a reader is provided, batches are streamed and microbatched into target-sized Parquet files via the new `bin_pack_record_batches` helper, then committed in a single snapshot via the existing fast_append path. Memory is bounded by `write.target-file-size-bytes` (default 512 MiB) per worker rather than the full input size. Scope of this PR — unpartitioned tables only. Streaming into partitioned tables raises NotImplementedError pointing back to apache#2152; partitioned support needs additional design (high-cardinality partition handling, per-partition rolling writers) and is tracked as a follow-up. Mirrors iceberg-go#369's staging — that project shipped unpartitioned streaming first. Internal note: the implementation buffers up to `target_file_size` of in- memory RecordBatches before flushing to a Parquet file. A more memory- efficient rolling-ParquetWriter approach is a planned follow-up that will benefit from the `OutputStream.tell()` API added in apache#2998.
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Rationale for this change
Currently, PyIceberg writes one manifest per snapshot operation regardless of manifest size. In order to eventually support this we need to be able to track written bytes without closing the file, so that we can roll to a new file once we hit target size.
We had some of this work done in #650, but we can keep this simple and add writers as a follow up. The nice thing is that the underlying streams we support already have a tell() method and we just need to expose it.
With this change in the follow up we can do:
Are these changes tested?
Yes, added a test :)
Are there any user-facing changes?
No