[2026-01-07T17:46:58] OpenAI Codex v0.0.0 (research preview)
[2026-01-07T17:46:58] binary: /home/volker/.cache/just-every/code/0.6.40/code-aarch64-unknown-linux-musl
--------
workdir: /home/volker/coding/fsspeckit
model: gpt-5.2
provider: openai
approval: never
sandbox: read-only
reasoning effort: medium
reasoning summaries: auto
--------
[2026-01-07T17:46:58] User instructions:
Context: Repo: /home/volker/coding/fsspeckit. README in examples/datasets/workflows/README.md. Getting started scripts: examples/datasets/getting_started/01_duckdb_basics.py through 05_duckdb_upserts.py. I already ran them and got ModuleNotFoundError: No module named 'pyarrow' for all scripts. Check if other issues likely once deps installed and propose plan. Use current APIs (DuckDBConnection, PyarrowDatasetIO.merge etc.).

Agent: Read examples/datasets/workflows/README.md and run examples in examples/datasets/getting_started. Use run outputs (errors) to propose a fix plan for getting_started examples. Focus on identifying missing deps, environment setup, or code fixes needed. Provide a concrete, ordered plan.

Files to consider: /home/volker/coding/fsspeckit/examples/datasets/workflows/README.md, /home/volker/coding/fsspeckit/examples/datasets/getting_started/README.md, /home/volker/coding/fsspeckit/examples/datasets/getting_started/01_duckdb_basics.py, /home/volker/coding/fsspeckit/examples/datasets/getting_started/02_pyarrow_basics.py, /home/volker/coding/fsspeckit/examples/datasets/getting_started/03_simple_merges.py, /home/volker/coding/fsspeckit/examples/datasets/getting_started/04_pyarrow_merges.py, /home/volker/coding/fsspeckit/examples/datasets/getting_started/05_duckdb_upserts.py

[Running in read-only mode - no modifications allowed]
[2026-01-07T17:47:04] exec sh -lc "ls -la examples/datasets/getting_started && echo '---' && sed -n '1,200p' examples/datasets/getting_started/README.md" in /home/volker/coding/fsspeckit
[2026-01-07T17:47:04] sh -lc "ls -la examples/datasets/getting_started && echo '---' && sed -n '1,200p' examples/datasets/getting_started/README.md" succeeded in 19ms:
total 120
drwxrwxr-x 3 volker volker  4096 Jan  7 16:16 .
drwxrwxr-x 5 volker volker  4096 Dec 13 20:22 ..
-rw-rw-r-- 1 volker volker 14696 Dec 13 20:36 01_duckdb_basics.py
-rw-rw-r-- 1 volker volker 18943 Dec 13 20:40 02_pyarrow_basics.py
-rw-rw-r-- 1 volker volker 21857 Dec 13 20:40 03_simple_merges.py
-rw-rw-r-- 1 volker volker 16026 Dec 13 21:10 04_pyarrow_merges.py
-rw------- 1 volker volker 23753 Jan  7 16:16 05_duckdb_upserts.py
-rw-rw-r-- 1 volker volker  6548 Dec  8 08:52 README.md
drwxr-xr-x 2 volker volker  4096 Dec 13 21:19 __pycache__
---
# Getting Started with Datasets

This directory contains beginner-friendly examples that introduce the fundamental concepts of working with datasets using fsspeckit.

## Overview

The examples in this directory are designed to help you:
- Understand basic dataset operations
- Learn when to use DuckDB vs PyArrow approaches
[2026-01-07T17:47:04] exec sh -lc "sed -n '1,200p' examples/datasets/workflows/README.md" in /home/volker/coding/fsspeckit
[2026-01-07T17:47:04] sh -lc "sed -n '1,200p' examples/datasets/workflows/README.md" succeeded in 9ms:
# Workflow Examples

This directory contains intermediate-level examples that demonstrate real-world workflows and advanced dataset operations using fsspeckit.

## Overview

The workflow examples build on the foundational knowledge from the getting started section and show how to apply fsspeckit in practical, production scenarios.

## Prerequisites

Before diving into these examples, you should have completed:
- Basic DuckDB and PyArrow operations
- Understanding of fundamental dataset merging
- Familiarity with SQL filtering and schema management
- Basic understanding of cloud storage concepts

## Available Workflows

### 1. `cloud_datasets.py` - Cloud Storage Workflows
**Prerequisites**: Getting Started completion
[2026-01-07T17:47:04] exec sh -lc "for f in examples/datasets/getting_started/0*_*.py; do echo '====' "'$f; sed -n '"'1,220p' "'$f; done' in /home/volker/coding/fsspeckit
[2026-01-07T17:47:04] sh -lc "for f in examples/datasets/getting_started/0*_*.py; do echo '====' "'$f; sed -n '"'1,220p' "'$f; done' succeeded in 42ms:
==== examples/datasets/getting_started/01_duckdb_basics.py
"""
DuckDB Basics - Getting Started with Datasets

This beginner-friendly example introduces the fundamental concepts of using
DuckDBParquetHandler for efficient parquet dataset operations.

The example covers:
1. Basic setup and configuration
2. Creating and reading parquet files
3. Simple queries and filtering
4. Writing data back to parquet
5. Context manager usage for resource management

This is your starting point for learning how to use fsspeckit's
dataset capabilities with DuckDB for high-performance data operations.
"""

from __future__ import annotations

[2026-01-07T17:47:05] exec sh -lc 'rg -n "pyarrow|duckdb" pyproject.toml setup.cfg setup.py requirements*.txt examples/datasets/getting_started/README.md examples/datasets/workflows/README.md || true' in /home/volker/coding/fsspeckit
[2026-01-07T17:47:05] sh -lc 'rg -n "pyarrow|duckdb" pyproject.toml setup.cfg setup.py requirements*.txt examples/datasets/getting_started/README.md examples/datasets/workflows/README.md || true' succeeded in 14ms:
pyproject.toml:50:    "pyarrow>=22.0.0",
pyproject.toml:64:    "pyarrow>=20.0.0",
pyproject.toml:65:    "duckdb>=1.4.0",
pyproject.toml:72:    "duckdb>=1.4.0",
pyproject.toml:142:[mypy-pyarrow]
pyproject.toml:151:[mypy-duckdb]
examples/datasets/getting_started/README.md:17:### 1. `01_duckdb_basics.py` - DuckDB Fundamentals
examples/datasets/getting_started/README.md:33:### 2. `02_pyarrow_basics.py` - PyArrow Dataset Operations
examples/datasets/getting_started/README.md:100:import pyarrow.parquet as pq
examples/datasets/getting_started/README.md:101:from fsspeckit.datasets import optimize_parquet_dataset_pyarrow
examples/datasets/getting_started/README.md:108:optimize_parquet_dataset_pyarrow("dataset_path", zorder_columns=["col1", "col2"])
examples/datasets/getting_started/README.md:113:import pyarrow as pa
examples/datasets/getting_started/README.md:162:pip install fsspeckit[datasets] pyarrow
examples/datasets/getting_started/README.md:165:pip install pandas polars duckdb
examples/datasets/workflows/README.md:110:from fsspeckit.datasets import optimize_parquet_dataset_pyarrow
examples/datasets/workflows/README.md:113:optimize_parquet_dataset_pyarrow(
examples/datasets/workflows/README.md:344:pip install fsspeckit[datasets] pyarrow
examples/datasets/workflows/README.md:353:pip install duckdb pandas polars
[2026-01-07T17:47:05] tokens used: 3,315
[2026-01-07T17:47:07] exec sh -lc "sed -n '1,220p' pyproject.toml" in /home/volker/coding/fsspeckit
[2026-01-07T17:47:07] sh -lc "sed -n '1,220p' pyproject.toml" succeeded in 9ms:
[build-system]
requires = ["hatchling"]
build-backend = "hatchling.build"

[project]
name = "fsspeckit"
version = "0.9.0"
description = "Enhanced utilities and extensions for fsspec, storage_options and obstore with multi-format I/O support."
readme = "README.md"
license = "MIT"
authors = [{ name = "legout", email = "ligno.blades@gmail.com" }]
keywords = [
    "fsspec",
    "filesystem",
    "cloud-storage",
    "object-storage",
    "obstore",
    "data-io",
    "parquet",
    "json",
[2026-01-07T17:47:07] tokens used: 16,248

[2026-01-07T17:47:13] thinking

**Exploring dependencies and installation issues**

I see that the package has a dependency on pyarrow, which is missing. This means it’s likely not installed or in a different environment. The README suggests running `pip install fsspeckit[datasets] pyarrow`, which might mean extras are needed. There’s also a potential issue with the numpy version being too high.

If the user ran scripts from the repo without proper installation, that could lead to import problems. I should plan to help the user install dependencies via pip, but I need to consider offline restrictions. I’ll bring up other dependencies like pandas and duckdb that may also cause issues.
**Checking dataset imports**

I’m realizing that the scripts import `fsspeckit.datasets`, which might require additional extras. It’s essential to check the packaging of `fsspeckit.datasets`. I need to inspect the source code, particularly looking at the dataset modules and their imports. There’s a possibility that `pyarrow` is imported during module initialization, so I’ll focus on examining the structure in `src/fsspeckit/datasets` to clarify what dependencies are involved.
[2026-01-07T17:47:19] codex
**Checking dataset imports**

I’m realizing that the scripts import `fsspeckit.datasets`, which might require additional extras. It’s essential to check the packaging of `fsspeckit.datasets`. I need to inspect the source code, particularly looking at the dataset modules and their imports. There’s a possibility that `pyarrow` is imported during module initialization, so I’ll focus on examining the structure in `src/fsspeckit/datasets` to clarify what dependencies are involved.
[2026-01-07T17:47:19] exec sh -lc "ls -ლა src/fsspeckit && ls -la src/fsspeckit/datasets && rg -n \"class DuckDBConnection|DuckDBParquetHandler|PyarrowDatasetIO|merge\\(\" -S src/fsspeckit/datasets src/fsspeckit | head -n 80" in /home/volker/coding/fsspeckit
[2026-01-07T17:47:19] sh -lc "ls -ლა src/fsspeckit && ls -la src/fsspeckit/datasets && rg -n \"class DuckDBConnection|DuckDBParquetHandler|PyarrowDatasetIO|merge\\(\" -S src/fsspeckit/datasets src/fsspeckit | head -n 80" exited 2 in 9ms:
ERROR
ls: invalid option -- 'á'
Try 'ls --help' for more information.
[2026-01-07T17:47:19] tokens used: 18,444
[2026-01-07T17:47:22] exec sh -lc "ls -la src/fsspeckit && echo '---' && ls -la src/fsspeckit/datasets && echo '---' && rg -n \"class DuckDBConnection|DuckDBParquetHandler|PyarrowDatasetIO|\\.merge\\(\" -S src/fsspeckit/datasets src/fsspeckit | head -n 120" in /home/volker/coding/fsspeckit
[2026-01-07T17:47:22] sh -lc "ls -la src/fsspeckit && echo '---' && ls -la src/fsspeckit/datasets && echo '---' && rg -n \"class DuckDBConnection|DuckDBParquetHandler|PyarrowDatasetIO|\\.merge\\(\" -S src/fsspeckit/datasets src/fsspeckit | head -n 120" succeeded in 22ms:
total 44
drwxrwxr-x 9 volker volker 4096 Dec 17 21:24 .
drwxrwxr-x 3 volker volker 4096 Dec  8 08:52 ..
-rw-rw-r-- 1 volker volker 1335 Dec 17 21:24 __init__.py
drwxrwxr-x 2 volker volker 4096 Jan  7 16:10 __pycache__
drwxrwxr-x 4 volker volker 4096 Dec 17 21:24 common
drwxrwxr-x 5 volker volker 4096 Dec 27 17:30 core
drwxrwxr-x 5 volker volker 4096 Jan  7 17:18 datasets
-rw-rw-r-- 1 volker volker   95 Dec  8 08:52 py.typed
drwxrwxr-x 4 volker volker 4096 Dec  8 15:34 sql
drwxrwxr-x 3 volker volker 4096 Dec  9 09:39 storage_options
drwxrwxr-x 3 volker volker 4096 Dec 17 21:24 utils
---
total 68
drwxrwxr-x 5 volker volker  4096 Jan  7 17:18 .
drwxrwxr-x 9 volker volker  4096 Dec 17 21:24 ..
-rw-rw-r-- 1 volker volker  2733 Jan  7 17:38 __init__.py
drwxrwxr-x 2 volker volker  4096 Jan  7 17:39 __pycache__
-rw-rw-r-- 1 volker volker 16810 Dec 27 17:30 base.py
drwxrwxr-x 3 volker volker  4096 Jan  7 17:02 duckdb
[2026-01-07T17:47:22] tokens used: 18,888

[2026-01-07T17:47:27] thinking

**Examining import dependencies**

I need to look at the `__init__` of datasets because it may need duckdb, which could require pyarrow. If pyarrow is missing, importing fsspeckit.datasets might fail, especially if it relies on pyarrow for type hints. I plan to check `src/fsspeckit/datasets/__init__.py` and the duckdb module for import-time requirements. It seems that the getting_started scripts directly import pyarrow too, making it a critical dependency, so we should ensure everything is installed properly.
**Inspecting API compatibility**

I need to check the code for API compatibility. There's a chance that script 05 references a private API, but the user mentioned we should use current public APIs. I'll plan to update scripts to tap into stable attributes instead. In read-only mode, I can't modify anything, but I can still run the scripts to see if errors occur beyond the missing pyarrow import. I might simulate it by adding a minimal stub, but I can't install it, so I'll use try/except and inspect all the scripts for potential issues.
[2026-01-07T17:47:32] codex
**Inspecting API compatibility**

I need to check the code for API compatibility. There's a chance that script 05 references a private API, but the user mentioned we should use current public APIs. I'll plan to update scripts to tap into stable attributes instead. In read-only mode, I can't modify anything, but I can still run the scripts to see if errors occur beyond the missing pyarrow import. I might simulate it by adding a minimal stub, but I can't install it, so I'll use try/except and inspect all the scripts for potential issues.
[2026-01-07T17:47:32] exec sh -lc "for f in examples/datasets/getting_started/03_simple_merges.py examples/datasets/getting_started/04_pyarrow_merges.py examples/datasets/getting_started/05_duckdb_upserts.py; do echo '====' "'$f; sed -n '"'1,260p' "'$f; echo '"'---- tail'; tail -n 80 "'$f; done' in /home/volker/coding/fsspeckit
[2026-01-07T17:47:32] sh -lc "for f in examples/datasets/getting_started/03_simple_merges.py examples/datasets/getting_started/04_pyarrow_merges.py examples/datasets/getting_started/05_duckdb_upserts.py; do echo '====' "'$f; sed -n '"'1,260p' "'$f; echo '"'---- tail'; tail -n 80 "'$f; done' succeeded in 15ms:
==== examples/datasets/getting_started/03_simple_merges.py
"""
Simple Dataset Merges - Getting Started

This beginner-friendly example introduces fundamental dataset merging concepts
using fsspeckit's dataset utilities.

The example covers:
1. Basic dataset merging concepts
2. Simple append operations
3. Schema handling during merges
4. Duplicate detection and handling
5. Performance considerations for merging

This example helps you understand how to combine multiple datasets
efficiently and safely.
"""

from __future__ import annotations

[2026-01-07T17:47:32] tokens used: 22,222

[2026-01-07T17:47:36] thinking

**Evaluating file system methods**

I’m noticing that the 04_pyarrow_merges script uses methods like `insert_dataset` from `LocalFileSystem`, which are now outdated. It seems these should now call `PyarrowDatasetIO.merge`. This script likely won't work even if `pyarrow` is installed. The `fs.insert_dataset` method doesn’t exist in the current `LocalFileSystem` unless it has been modified by `fsspeckit`. I should check `core/ext/dataset.py` to see what methods were removed and understand how to adapt this.
[2026-01-07T17:47:40] exec sh -lc "sed -n '1,260p' src/fsspeckit/core/ext/dataset.py" in /home/volker/coding/fsspeckit
[2026-01-07T17:47:40] sh -lc "sed -n '1,260p' src/fsspeckit/core/ext/dataset.py" succeeded in 10ms:
"""Dataset creation helpers for fsspec filesystems.

This module contains functions for creating PyArrow datasets with support for:
- Schema enforcement
- Partitioning
- Format-specific optimizations
- Predicate pushdown
- Merge-aware writes

The functions in this module implement the DatasetHandler protocol to provide
a consistent interface across different backend implementations (DuckDB, PyArrow, etc.).
"""

from __future__ import annotations

import posixpath
import tempfile
from pathlib import Path
from typing import TYPE_CHECKING, Any

[2026-01-07T17:47:40] tokens used: 31,923
[2026-01-07T17:47:42] exec sh -lc "rg -n \"has been removed\" -n src/fsspeckit/core/ext/dataset.py | head -n 50 && sed -n '240,720p' src/fsspeckit/core/ext/dataset.py" in /home/volker/coding/fsspeckit
[2026-01-07T17:47:42] sh -lc "rg -n \"has been removed\" -n src/fsspeckit/core/ext/dataset.py | head -n 50 && sed -n '240,720p' src/fsspeckit/core/ext/dataset.py" succeeded in 16ms:
266:        "write_pyarrow_dataset has been removed. Use PyarrowDatasetIO.write_dataset "
534:        "insert_dataset has been removed. Use PyarrowDatasetIO.merge(strategy='insert') instead."
576:        "upsert_dataset has been removed. Use PyarrowDatasetIO.merge(strategy='upsert') instead."
618:        "update_dataset has been removed. Use PyarrowDatasetIO.merge(strategy='update') instead."
659:        "deduplicate_dataset has been removed. Use a dedicated dataset maintenance API instead."
        fs = LocalFileSystem()

        # Standard dataset write
        fs.write_pyarrow_dataset(
            data=table,
            path="dataset/",
            partition_by=["year", "month"]
        )

        # Merge-aware write with upsert
        fs.write_pyarrow_dataset(
            data=new_data,
            path="dataset/",
            strategy="upsert",
            key_columns=["id"]
[2026-01-07T17:47:42] tokens used: 34,327

[2026-01-07T17:47:45] thinking

**Proposing updates for ext methods**

I’m noticing that ext methods have been removed, which means using `fs.write_pyarrow_dataset` will raise a `NotImplementedError` and break those examples. I wonder if they were updated to use `PyarrowDatasetIO.merge`, as suggested by the README. The 04 version seems outdated, so I need to propose a plan to update it. The version 03 only uses pyarrow functions and should be fine. I'll look into the DuckDBParquetHandler in version 05 and check the relevant source files.
[2026-01-07T17:47:48] exec sh -lc "sed -n '1,220p' src/fsspeckit/datasets/duckdb/__init__.py && echo '---' && sed -n '1,260p' src/fsspeckit/datasets/duckdb/connection.py && echo '---' && sed -n '1,260p' src/fsspeckit/datasets/duckdb/io.py" in /home/volker/coding/fsspeckit
[2026-01-07T17:47:48] sh -lc "sed -n '1,220p' src/fsspeckit/datasets/duckdb/__init__.py && echo '---' && sed -n '1,260p' src/fsspeckit/datasets/duckdb/connection.py && echo '---' && sed -n '1,260p' src/fsspeckit/datasets/duckdb/io.py" exited 2 in 11ms:
"""DuckDB dataset integration for fsspeckit.

This package contains focused submodules for DuckDB functionality:
- dataset: Dataset I/O and maintenance operations
- connection: Connection management and filesystem registration
- helpers: Utility functions for DuckDB operations

All public APIs are re-exported here for convenient access.
"""

from typing import Any, Literal

from fsspec import AbstractFileSystem
from fsspeckit.storage_options.base import BaseStorageOptions

# Re-export connection management
from .connection import (
    DuckDBConnection,
    create_duckdb_connection,
)

ERROR
sed: can't read src/fsspeckit/datasets/duckdb/io.py: No such file or directory
[2026-01-07T17:47:48] tokens used: 38,564

[2026-01-07T17:47:49] thinking

**Inspecting the dataset module**

I see there's no duckdb/io.py, so I need to check the dataset module instead. It seems like I’m trying to troubleshoot something, but I'm not sure what specific datasets I'm looking for. I'll dig into it to see what I can find and how it connects to my task. It's always interesting to investigate how modules work together! Let's see what I can uncover!
[2026-01-07T17:47:52] exec sh -lc "ls -la src/fsspeckit/datasets/duckdb && echo '---' && sed -n '1,260p' src/fsspeckit/datasets/duckdb/dataset.py | head -n 260" in /home/volker/coding/fsspeckit
[2026-01-07T17:47:52] sh -lc "ls -la src/fsspeckit/datasets/duckdb && echo '---' && sed -n '1,260p' src/fsspeckit/datasets/duckdb/dataset.py | head -n 260" succeeded in 12ms:
total 116
drwxrwxr-x 3 volker volker  4096 Jan  7 17:02 .
drwxrwxr-x 5 volker volker  4096 Jan  7 17:18 ..
-rw-rw-r-- 1 volker volker  2934 Jan  7 16:13 __init__.py
drwxrwxr-x 2 volker volker  4096 Jan  7 17:35 __pycache__
-rw-rw-r-- 1 volker volker  4465 Jan  7 17:03 connection.py
-rw-rw-r-- 1 volker volker 84342 Jan  7 17:35 dataset.py
-rw-rw-r-- 1 volker volker  2026 Jan  7 17:02 exceptions.py
-rw-rw-r-- 1 volker volker  1215 Jan  7 17:02 helpers.py
---
"""DuckDB dataset I/O and maintenance operations.

This module contains functions for reading, writing, and maintaining
parquet datasets using DuckDB.
"""

from __future__ import annotations

import uuid
from pathlib import Path
[2026-01-07T17:47:52] tokens used: 41,098

[2026-01-07T17:47:57] thinking

**Assessing DuckDB and dependencies**

The `DuckDBParquetHandler` has a `_connection` property that's of type `DuckDBConnection`, meaning that using `handler._connection.connection` should work for script 05, though it's internal. It might be better to use `DuckDBConnection` directly in the example. Another consideration is that if `pyarrow` isn't installed, it could lead to failures. Since scripts depend on `pandas` for certain functions, we need to check if `pandas` is installed in the environment. I'll verify that quickly.
[2026-01-07T17:48:01] exec sh -lc "python3 -c \"import sys; print(sys.version)\" && python3 -c \"import fsspeckit, pkgutil; print('fsspeckit', fsspeckit.__version__ if hasattr(fsspeckit,'__version__') else 'n/a')\" && python3 -c \"import importlib; mods=['pyarrow','duckdb','pandas','polars','sqlglot','psutil'];
for m in mods:
    try:
        importlib.import_module(m)
        print(m,'OK')
    except Exception as e:
        print(m,'FAIL',type(e).__name__, e)\"" in /home/volker/coding/fsspeckit
[2026-01-07T17:48:01] sh -lc "python3 -c \"import sys; print(sys.version)\" && python3 -c \"import fsspeckit, pkgutil; print('fsspeckit', fsspeckit.__version__ if hasattr(fsspeckit,'__version__') else 'n/a')\" && python3 -c \"import importlib; mods=['pyarrow','duckdb','pandas','polars','sqlglot','psutil'];
for m in mods:
    try:
        importlib.import_module(m)
        print(m,'OK')
    except Exception as e:
        print(m,'FAIL',type(e).__name__, e)\"" exited 1 in 173ms:
3.12.3 (main, Nov  6 2025, 13:44:16) [GCC 13.3.0]

ERROR
Traceback (most recent call last):
  File "<string>", line 1, in <module>
ModuleNotFoundError: No module named 'fsspeckit'
[2026-01-07T17:48:01] tokens used: 44,385
[2026-01-07T17:48:07] exec sh -lc "set -euo pipefail
for f in examples/datasets/getting_started/0*_*.py; do echo \"\\n===== RUN "'$f"; python3 $f || true; done' in /home/volker/coding/fsspeckit
[2026-01-07T17:48:07] sh -lc "set -euo pipefail
for f in examples/datasets/getting_started/0*_*.py; do echo \"\\n===== RUN "'$f"; python3 $f || true; done' exited 2 in 7ms:
ERROR
sh: 1: set: Illegal option -o pipefail
[2026-01-07T17:48:07] tokens used: 45,121
[2026-01-07T17:48:09] exec bash -lc "for f in examples/datasets/getting_started/0*_*.py; do echo -e \"\\n===== RUN "'$f"; python3 $f || true; done' in /home/volker/coding/fsspeckit
[2026-01-07T17:48:10] bash -lc "for f in examples/datasets/getting_started/0*_*.py; do echo -e \"\\n===== RUN "'$f"; python3 $f || true; done' succeeded in 776ms:

===== RUN examples/datasets/getting_started/01_duckdb_basics.py

===== RUN examples/datasets/getting_started/02_pyarrow_basics.py

===== RUN examples/datasets/getting_started/03_simple_merges.py

===== RUN examples/datasets/getting_started/04_pyarrow_merges.py

===== RUN examples/datasets/getting_started/05_duckdb_upserts.py
[2026-01-07T17:48:10] tokens used: 45,841
[2026-01-07T17:48:17] exec sh -lc "sed -n '1,220p' openspec/AGENTS.md" in /home/volker/coding/fsspeckit
[2026-01-07T17:48:17] sh -lc "sed -n '1,220p' openspec/AGENTS.md" succeeded in 9ms:
# OpenSpec Instructions

Instructions for AI coding assistants using OpenSpec for spec-driven development.

## TL;DR Quick Checklist

- Search existing work: `openspec spec list --long`, `openspec list` (use `rg` only for full-text search)
- Decide scope: new capability vs modify existing capability
- Pick a unique `change-id`: kebab-case, verb-led (`add-`, `update-`, `remove-`, `refactor-`)
- Scaffold: `proposal.md`, `tasks.md`, `design.md` (only if needed), and delta specs per affected capability
- Write deltas: use `## ADDED|MODIFIED|REMOVED|RENAMED Requirements`; include at least one `#### Scenario:` per requirement
- Validate: `openspec validate [change-id] --strict` and fix issues
- Request approval: Do not start implementation until proposal is approved

## Three-Stage Workflow

### Stage 1: Creating Changes
Create proposal when you need to:
- Add features or functionality
- Make breaking changes (API, schema)
[2026-01-07T17:48:17] tokens used: 46,775

[2026-01-07T17:48:25] thinking

**Analyzing installation issues**

I’m noticing that if the user runs `pip install -e .` and no pyarrow is installed, there could be a problem. It might be due to pip not installing dependencies because of the `--no-deps` flag or because they’re using system Python without a virtual environment. I want to clarify this. I’m considering suggesting using `uv` or pip with constraints. However, since I can't run the necessary commands in this sandbox, I need to outline those steps for them to solve their issues.
[2026-01-07T17:48:27] exec sh -lc "sed -n '1,220p' src/fsspeckit/datasets/pyarrow/io.py" in /home/volker/coding/fsspeckit
[2026-01-07T17:48:27] sh -lc "sed -n '1,220p' src/fsspeckit/datasets/pyarrow/io.py" succeeded in 11ms:
"""PyArrow dataset I/O and maintenance operations.

This module contains the PyarrowDatasetIO class for reading, writing, and
maintaining parquet datasets using PyArrow's high-performance engine.
"""

from __future__ import annotations

from typing import TYPE_CHECKING, Any, Literal, Callable, Iterable

if TYPE_CHECKING:
    import pyarrow as pa
    import pyarrow.dataset as ds
    from fsspec import AbstractFileSystem

    from fsspeckit.core.incremental import MergeResult
    from fsspeckit.core.merge import MergeStats
    from fsspeckit.datasets.pyarrow.memory import MemoryMonitor
    from fsspeckit.datasets.pyarrow.adaptive_tracker import AdaptiveKeyTracker
    from fsspeckit.datasets.write_result import WriteDatasetResult
[2026-01-07T17:48:27] tokens used: 49,445
[2026-01-07T17:48:29] exec sh -lc "rg -n \"def merge\\(\" -n src/fsspeckit/datasets/pyarrow/io.py && sed -n '1120,1400p' src/fsspeckit/datasets/pyarrow/io.py" in /home/volker/coding/fsspeckit
[2026-01-07T17:48:29] sh -lc "rg -n \"def merge\\(\" -n src/fsspeckit/datasets/pyarrow/io.py && sed -n '1120,1400p' src/fsspeckit/datasets/pyarrow/io.py" succeeded in 12ms:
349:    def merge(
            Optimization statistics

        Example:
            ```python
            io = PyarrowDatasetIO()
            stats = io.optimize_parquet_dataset(
                "dataset/",
                target_mb_per_file=64,
                compression="zstd",
            )
            ```
        """
        from fsspeckit.datasets.pyarrow.dataset import optimize_parquet_dataset_pyarrow

        path = self._normalize_path(path, operation="optimize")

        return optimize_parquet_dataset_pyarrow(
            path=path,
            target_mb_per_file=target_mb_per_file,
[2026-01-07T17:48:29] tokens used: 52,178
[2026-01-07T17:48:31] exec sh -lc "sed -n '300,460p' src/fsspeckit/datasets/pyarrow/io.py" in /home/volker/coding/fsspeckit
[2026-01-07T17:48:31] sh -lc "sed -n '300,460p' src/fsspeckit/datasets/pyarrow/io.py" succeeded in 9ms:
        files: list[FileWriteMetadata] = []
        for wf in written:
            row_count = 0
            if wf.metadata is not None:
                row_count = int(wf.metadata.num_rows)
            else:
                try:
                    row_count = int(
                        pq.read_metadata(wf.path, filesystem=self._filesystem).num_rows
                    )
                except (IOError, RuntimeError, ValueError) as e:
                    logger.warning(
                        "failed_to_read_metadata",
                        path=wf.path,
                        error=str(e),
                        operation="write_dataset",
                    )
                    row_count = 0

            size_bytes = None
[2026-01-07T17:48:31] tokens used: 54,742

[2026-01-07T17:48:37] thinking

**Evaluating merge signature and dependencies**

I'm considering that the merge signature uses partition_columns and only supports insert, update, or upsert functions. That's useful! I should definitely update Example 04 to use this instead of filesystem methods. I also need to keep in mind that core/ext/dataset.py has some dead code after raise NotImplementedError, which isn't relevant. Now, I want to check what the datasets/__init__ exports are, as they import duckdb and pyarrow modules early, which might require specific dependencies.
[2026-01-07T17:48:39] exec sh -lc "sed -n '1,140p' src/fsspeckit/datasets/__init__.py" in /home/volker/coding/fsspeckit
[2026-01-07T17:48:39] sh -lc "sed -n '1,140p' src/fsspeckit/datasets/__init__.py" succeeded in 11ms:
"""Dataset-level operations for fsspeckit.

This package contains dataset-specific functionality including:
- DuckDB parquet handlers for high-performance dataset operations
- PyArrow handlers for dataset I/O operations
- PyArrow utilities for schema management and type conversion
- Dataset merging and optimization tools
"""

import warnings
from typing import Any

from .duckdb import DuckDBParquetHandler, MergeStrategy
# ... existing imports ...

_DEPRECATED_IMPORTS = {
    "duckdb_dataset": ("fsspeckit.datasets.duckdb.dataset", None),
    "duckdb_connection": ("fsspeckit.datasets.duckdb.connection", "DuckDBConnection"),
    "duckdb_helpers": ("fsspeckit.datasets.duckdb.helpers", None),
    "_duckdb_helpers": ("fsspeckit.datasets.duckdb.helpers", None),
[2026-01-07T17:48:39] tokens used: 56,425

[2026-01-07T17:48:45] thinking

**Planning setup for fsspeckit**

I'm looking into how fsspeckit.datasets imports DuckDBParquetHandler, which seems to involve optional imports without raising errors. I need a clear plan for the user to set up quickly and verify everything. We should also note any dependencies in the pyproject file that may need a network connection. Additionally, we need to edit any outdated references in the script and ensure fsspeckit is installed for performance comparisons. Let’s check the examples directory for installation mentions too!
**Updating README and planning**

I’m suggesting an update to the README to include installation commands like `pip install -e ".[datasets]"` or `uv pip install -e '.[datasets]'`. We can’t patch directly, but I’ll make a plan for it. I need to check that the file permissions are set correctly, aiming for 600 and 664. The user asked to identify other potential issues post-dependencies installation and propose a plan using the latest APIs. We must also ensure we include missing dependencies and check for a lock file in the repository.
[2026-01-07T17:48:50] codex
**Updating README and planning**

I’m suggesting an update to the README to include installation commands like `pip install -e ".[datasets]"` or `uv pip install -e '.[datasets]'`. We can’t patch directly, but I’ll make a plan for it. I need to check that the file permissions are set correctly, aiming for 600 and 664. The user asked to identify other potential issues post-dependencies installation and propose a plan using the latest APIs. We must also ensure we include missing dependencies and check for a lock file in the repository.
[2026-01-07T17:48:50] exec sh -lc "ls -la && rg -n \"uv|pip install|\\[datasets\\]\" README.md pyproject.toml examples/datasets/getting_started/README.md examples/datasets/workflows/README.md | head -n 80" in /home/volker/coding/fsspeckit
[2026-01-07T17:48:50] sh -lc "ls -la && rg -n \"uv|pip install|\\[datasets\\]\" README.md pyproject.toml examples/datasets/getting_started/README.md examples/datasets/workflows/README.md | head -n 80" succeeded in 15ms:
total 1276
drwxrwxr-x 21 volker volker   4096 Jan  7 17:43 .
drwxrwxr-x 26 volker volker   4096 Jan  3 12:02 ..
-rw-rw-r--  1 volker volker   8196 Dec  8 08:52 .DS_Store
drwxrwxr-x  3 volker volker   4096 Jan  7 16:06 .claude
drwxrwxr-x  3 volker volker   4096 Jan  7 17:26 .code
-rw-r--r--  1 volker volker  69632 Jan  7 17:43 .coverage
drwxrwxr-x  8 volker volker   4096 Jan  7 17:46 .git
drwxrwxr-x  3 volker volker   4096 Dec 17 21:24 .github
-rw-rw-r--  1 volker volker    471 Dec 27 17:30 .gitignore
drwxrwxr-x  3 volker volker   4096 Dec  9 09:10 .mypy_cache
-rw-rw-r--  1 volker volker      0 Dec  8 08:52 .nojekyll
drwxrwxr-x  4 volker volker   4096 Jan  7 16:21 .opencode
drwxrwxr-x  3 volker volker   4096 Dec  8 11:16 .pytest_cache
drwxrwxr-x  4 volker volker   4096 Dec  8 10:53 .ruff_cache
drwxrwxr-x  5 volker volker   4096 Dec  8 10:44 .venv
-rw-rw-r--  1 volker volker    660 Jan  7 16:04 AGENTS.md
-rw-rw-r--  1 volker volker    660 Jan  7 16:04 CLAUDE.md
-rw-------  1 volker volker   7484 Dec  8 15:33 CONTRIBUTING.md
-rw-rw-r--  1 volker volker   1072 Dec  8 08:52 LICENSE
[2026-01-07T17:48:50] tokens used: 57,772

[2026-01-07T17:48:52] thinking

**Exploring the development environment**

I see there's a `.venv` and an `uv.lock` file, which likely indicates a use of `uv`. I think it would be helpful to check the README for further details about the development environment. By reviewing it, I can find the relevant installation instructions that might clarify how everything is set up. I want to ensure I fully understand the context before moving ahead!
[2026-01-07T17:48:54] exec sh -lc "sed -n '1,120p' README.md" in /home/volker/coding/fsspeckit
[2026-01-07T17:48:54] sh -lc "sed -n '1,120p' README.md" succeeded in 11ms:
# fsspeckit

Enhanced utilities and extensions for fsspec filesystems with multi-format I/O support.

## Overview

`fsspeckit` is a comprehensive toolkit that extends [fsspec](https://filesystem-spec.readthedocs.io/) with:

- **Multi-cloud storage configuration** - Easy setup for AWS S3, Google Cloud Storage, Azure Storage, GitHub, and GitLab
- **Enhanced caching** - Improved caching filesystem with monitoring and path preservation
- **Extended I/O operations** - Read/write operations for JSON, CSV, Parquet with Polars/PyArrow integration
- **Domain-specific packages** - Organized into logical packages for better discoverability

## Package Structure

`fsspeckit` is organized into domain-specific packages:

- **`fsspeckit.core`** - Core filesystem APIs and backend-neutral planning logic
- **`fsspeckit.storage_options`** - Multi-cloud storage configuration classes
- **`fsspeckit.datasets`** - Dataset-level operations (DuckDB & PyArrow helpers)
[2026-01-07T17:48:54] tokens used: 59,721

[2026-01-07T17:48:57] thinking

**Verifying virtual environment setup**

It looks like the repo already has a virtual environment set up, likely using uv, but currently, python3 doesn’t recognize `fsspec` because the virtual environment isn't activated. My plan is to use `.venv/bin/python`. I should confirm whether `pyarrow` is inside the virtual environment too. Let me check quickly to ensure everything is in order!
[2026-01-07T17:48:59] exec sh -lc ". .venv/bin/activate 2>/dev/null || true; .venv/bin/python -c \"import importlib; mods=['fsspeckit','pyarrow','duckdb','pandas','polars'];
for m in mods:
    try:
        importlib.import_module(m)
        print(m,'OK')
    except Exception as e:
        print(m,'FAIL',type(e).__name__, e)\"" in /home/volker/coding/fsspeckit
[2026-01-07T17:49:00] sh -lc ". .venv/bin/activate 2>/dev/null || true; .venv/bin/python -c \"import importlib; mods=['fsspeckit','pyarrow','duckdb','pandas','polars'];
for m in mods:
    try:
        importlib.import_module(m)
        print(m,'OK')
    except Exception as e:
        print(m,'FAIL',type(e).__name__, e)\"" succeeded in 1s:
fsspeckit OK
pyarrow OK
duckdb OK
pandas OK
polars OK
[2026-01-07T17:49:00] tokens used: 61,116

[2026-01-07T17:49:02] thinking

**Identifying project issues**

I noticed there's already a virtual environment (venv) in the repository with dependencies. It seems like the user might have run scripts using the system Python instead of the venv, which could be the root cause of some issues. The solution is simple: use the Python executable from the venv located at `.venv/bin/python`. By doing this, I can run the scripts in the appropriate environment and check for any other errors that might pop up.
[2026-01-07T17:49:05] exec bash -lc "for f in examples/datasets/getting_started/0*_*.py; do echo -e \"\\n===== RUN (venv) "'$f"; .venv/bin/python $f || true; done' in /home/volker/coding/fsspeckit
[2026-01-07T17:49:10] bash -lc "for f in examples/datasets/getting_started/0*_*.py; do echo -e \"\\n===== RUN (venv) "'$f"; .venv/bin/python $f || true; done' succeeded in 4s:

===== RUN (venv) examples/datasets/getting_started/01_duckdb_basics.py
🐥 DuckDB Basics - Getting Started
==================================================
This example introduces fundamental DuckDBParquetHandler usage
for efficient parquet dataset operations.

🚀 Basic DuckDBParquetHandler Usage

❌ Example failed: [Errno 2] No usable temporary directory found in ['/tmp', '/var/tmp', '/usr/tmp', '/home/volker/coding/fsspeckit']

===== RUN (venv) examples/datasets/getting_started/02_pyarrow_basics.py
➡️ PyArrow Basics - Dataset Optimization
============================================================
This example introduces PyArrow dataset optimization techniques
for high-performance data processing without requiring a database.

🔹 Basic PyArrow Table Operations
📦 Creating sample inventory data...
Created inventory dataset with 200 records
[2026-01-07T17:49:10] tokens used: 61,923

[2026-01-07T17:49:16] thinking

**Addressing execution issues**

I’m noticing a problem with the execution environment — it seems there's no writable temporary directory like /tmp available, likely due to the read-only sandbox. This is explaining why the user received a pyarrow error previously. However, I see we already got some outputs. 

To move forward, I'll focus on the user’s actual environment, which might be missing pyarrow instead of a temp directory issue. I could suggest running commands using `.venv/bin/python` or `uv run`. Also, I’ll mention that temporary directories could work if writable.
**Identifying potential issues**

I’m predicting some issues after dependencies are installed. If the user runs with system Python, they might miss pyarrow and its package. In a read-only environment, using /tmp could fail, which is not typical but still possible. They should set `TMPDIR` to a writable directory like `./.tmp` to avoid this.

Also, I noticed that version 04 uses deprecated methods, but we didn't reach that point because of the temp directory error. Confirming by running the script with a writable temp directory would help, but since we can't, we can still check for potential issues ahead, like handler.write_parquet and read_parquet.
**Examining code dependencies**

I'm considering how the code uses pandas, and it seems like it's supposed to use it correctly. In line 02, functions like optimize_parquet_dataset_pyarrow and compact_parquet_dataset_pyarrow are present, which is good. I need to check if optimize is called with zorder—I'll inspect that later. It might call optimize_parquet_dataset_pyarrow directly, possibly requiring a pyarrow.dataset writer, which should generally be fine.

In line 03, it uses pa.concat tables and possibly DuckDBParquetHandler for merging. I think I should search the file for instances of DuckDBParquetHandler to confirm how it's used.
[2026-01-07T17:49:27] codex
**Identifying potential issues**

I’m predicting some issues after dependencies are installed. If the user runs with system Python, they might miss pyarrow and its package. In a read-only environment, using /tmp could fail, which is not typical but still possible. They should set `TMPDIR` to a writable directory like `./.tmp` to avoid this.

Also, I noticed that version 04 uses deprecated methods, but we didn't reach that point because of the temp directory error. Confirming by running the script with a writable temp directory would help, but since we can't, we can still check for potential issues ahead, like handler.write_parquet and read_parquet.
[2026-01-07T17:49:27] codex
**Examining code dependencies**

I'm considering how the code uses pandas, and it seems like it's supposed to use it correctly. In line 02, functions like optimize_parquet_dataset_pyarrow and compact_parquet_dataset_pyarrow are present, which is good. I need to check if optimize is called with zorder—I'll inspect that later. It might call optimize_parquet_dataset_pyarrow directly, possibly requiring a pyarrow.dataset writer, which should generally be fine.

In line 03, it uses pa.concat tables and possibly DuckDBParquetHandler for merging. I think I should search the file for instances of DuckDBParquetHandler to confirm how it's used.
[2026-01-07T17:49:27] exec sh -lc 'rg -n "DuckDBParquetHandler|execute_sql|register_dataset|_connection" examples/datasets/getting_started/03_simple_merges.py examples/datasets/getting_started/02_pyarrow_basics.py examples/datasets/getting_started/01_duckdb_basics.py | head -n 80' in /home/volker/coding/fsspeckit
[2026-01-07T17:49:27] sh -lc 'rg -n "DuckDBParquetHandler|execute_sql|register_dataset|_connection" examples/datasets/getting_started/03_simple_merges.py examples/datasets/getting_started/02_pyarrow_basics.py examples/datasets/getting_started/01_duckdb_basics.py | head -n 80' succeeded in 12ms:
examples/datasets/getting_started/03_simple_merges.py:30:from fsspeckit.datasets import DuckDBParquetHandler
examples/datasets/getting_started/03_simple_merges.py:389:        with DuckDBParquetHandler() as handler:
examples/datasets/getting_started/03_simple_merges.py:392:            duplicate_analysis = handler.execute_sql(f"""
examples/datasets/getting_started/03_simple_merges.py:427:            deduplicated_data = handler.execute_sql(deduplication_query).fetchdf()
examples/datasets/getting_started/03_simple_merges.py:516:        with DuckDBParquetHandler() as handler:
examples/datasets/getting_started/03_simple_merges.py:524:            duckdb_result = handler.execute_sql(
examples/datasets/getting_started/01_duckdb_basics.py:5:DuckDBParquetHandler for efficient parquet dataset operations.
examples/datasets/getting_started/01_duckdb_basics.py:27:from fsspeckit.datasets import DuckDBParquetHandler
examples/datasets/getting_started/01_duckdb_basics.py:117:    """Demonstrate basic DuckDBParquetHandler usage."""
examples/datasets/getting_started/01_duckdb_basics.py:119:    print("\n🚀 Basic DuckDBParquetHandler Usage")
examples/datasets/getting_started/01_duckdb_basics.py:126:        # Write data using DuckDBParquetHandler
examples/datasets/getting_started/01_duckdb_basics.py:129:        with DuckDBParquetHandler() as handler:
examples/datasets/getting_started/01_duckdb_basics.py:167:    """Demonstrate basic SQL queries with DuckDBParquetHandler."""
examples/datasets/getting_started/01_duckdb_basics.py:169:    print("\n🔍 SQL Queries with DuckDBParquetHandler")
examples/datasets/getting_started/01_duckdb_basics.py:177:        with DuckDBParquetHandler() as handler:
examples/datasets/getting_started/01_duckdb_basics.py:184:            result1 = handler.execute_sql(
examples/datasets/getting_started/01_duckdb_basics.py:191:            result2 = handler.execute_sql(f"""
examples/datasets/getting_started/01_duckdb_basics.py:201:            result3 = handler.execute_sql(f"""
examples/datasets/getting_started/01_duckdb_basics.py:216:            result4 = handler.execute_sql(f"""
examples/datasets/getting_started/01_duckdb_basics.py:252:        with DuckDBParquetHandler() as handler:
[2026-01-07T17:49:27] tokens used: 66,468

[2026-01-07T17:49:33] thinking

**Updating code dependencies**

I’m considering the code's usage of `fetchdf`, which returns a pandas DataFrame. If pandas is missing, it causes an error — so that’s something to keep in mind. User requested I focus on current APIs, so I plan to update scripts 01, 03, and 05 to use `DuckDBConnection` and `DuckDBDatasetIO`, ensuring it’s mentioned in the documentation. For scripts 02 and 03, I'll verify the use of parquet dataset functions and suggest using `PyarrowDatasetIO` functions where applicable for optimization and compacting.
**Checking virtual environment usage**

I’m looking into the usage of `.venv` in this repo, which utilizes `uv` and has an `uv.lock` file. It seems like the intention might be to run `uv sync` to create the virtual environment, but the user may not have `uv` installed. The `.venv` directory does exist, so typically, I’d expect to see `uv sync` or `pip install -e .` being used. I’ll check the AGENTS.md documentation for any specific instructions provided by the developer.
[2026-01-07T17:49:38] codex
**Checking virtual environment usage**

I’m looking into the usage of `.venv` in this repo, which utilizes `uv` and has an `uv.lock` file. It seems like the intention might be to run `uv sync` to create the virtual environment, but the user may not have `uv` installed. The `.venv` directory does exist, so typically, I’d expect to see `uv sync` or `pip install -e .` being used. I’ll check the AGENTS.md documentation for any specific instructions provided by the developer.
[2026-01-07T17:49:39] exec sh -lc 'cat AGENTS.md' in /home/volker/coding/fsspeckit
[2026-01-07T17:49:39] sh -lc 'cat AGENTS.md' succeeded in 9ms:
<!-- OPENSPEC:START -->
# OpenSpec Instructions

These instructions are for AI assistants working in this project.

Always open `@/openspec/AGENTS.md` when the request:
- Mentions planning or proposals (words like proposal, spec, change, plan)
- Introduces new capabilities, breaking changes, architecture shifts, or big performance/security work
- Sounds ambiguous and you need the authoritative spec before coding

Use `@/openspec/AGENTS.md` to learn:
- How to create and apply change proposals
- Spec format and conventions
- Project structure and guidelines

Keep this managed block so 'openspec update' can refresh the instructions.

<!-- OPENSPEC:END -->
[2026-01-07T17:49:39] tokens used: 68,019

[2026-01-07T17:49:51] thinking

**Creating a project plan**

I’m laying out a clear plan now. First, I'll set up or create a virtual environment, making sure to run with the correct Python version and dependencies. If I don't have a venv, I’ll use `uv` or pip to install necessary packages like `pyarrow`, `duckdb`, and `pandas`. I’ll verify that the temporary directory is writable and update scripts to minimize optional requirements for pandas. Finally, I need to fix the 04 script by removing certain calls and providing specific mapping for dataset operations.
[2026-01-07T17:49:55] exec sh -lc 'rg -n "def deduplicate|deduplicate_parquet_dataset" -S src/fsspeckit/datasets/pyarrow src/fsspeckit/core | head -n 60' in /home/volker/coding/fsspeckit
[2026-01-07T17:49:55] sh -lc 'rg -n "def deduplicate|deduplicate_parquet_dataset" -S src/fsspeckit/datasets/pyarrow src/fsspeckit/core | head -n 60' succeeded in 21ms:
src/fsspeckit/datasets/pyarrow/dataset.py:650:        deduplicate_parquet_dataset_pyarrow(
src/fsspeckit/datasets/pyarrow/dataset.py:677:def deduplicate_parquet_dataset_pyarrow(
src/fsspeckit/datasets/pyarrow/io.py:1218:    def deduplicate_dataset(
src/fsspeckit/core/ext/dataset.py:636:def deduplicate_dataset(
src/fsspeckit/core/ext/dataset.py:671:def deduplicate_parquet_dataset(
src/fsspeckit/core/ext/dataset.py:712:        stats = fs.deduplicate_parquet_dataset(
src/fsspeckit/core/ext/dataset.py:720:        stats = fs.deduplicate_parquet_dataset("/tmp/dataset/")
src/fsspeckit/core/ext/dataset.py:732:        return io.deduplicate_parquet_dataset(
src/fsspeckit/core/ext/dataset.py:745:            deduplicate_parquet_dataset_pyarrow,
src/fsspeckit/core/ext/dataset.py:748:        return deduplicate_parquet_dataset_pyarrow(
[2026-01-07T17:49:55] tokens used: 68,724
[2026-01-07T17:49:57] exec sh -lc "sed -n '620,760p' src/fsspeckit/datasets/pyarrow/dataset.py" in /home/volker/coding/fsspeckit
[2026-01-07T17:49:57] sh -lc "sed -n '620,760p' src/fsspeckit/datasets/pyarrow/dataset.py" succeeded in 13ms:

    Args:
        path: Dataset root directory
        target_mb_per_file: Target size per file in MB
        target_rows_per_file: Target rows per file
        partition_filter: Optional partition filters
        compression: Compression codec to use
        deduplicate_key_columns: Optional key columns for deduplication before optimization
        dedup_order_by: Columns to order by for deduplication
        filesystem: Optional filesystem instance
        verbose: Print progress information

    Returns:
        Optimization statistics

    Example:
        ```python
        # Optimization with deduplication
        stats = optimize_parquet_dataset_pyarrow(
            "dataset/",
[2026-01-07T17:49:57] tokens used: 69,237
[2026-01-07T17:50:01] exec sh -lc "rg -n \"def deduplicate_parquet_dataset\\(\" -S src/fsspeckit/datasets/pyarrow/io.py src/fsspeckit/datasets/pyarrow/__init__.py src/fsspeckit/datasets/__init__.py src/fsspeckit/core/ext/dataset.py | head -n 80" in /home/volker/coding/fsspeckit
[2026-01-07T17:50:01] sh -lc "rg -n \"def deduplicate_parquet_dataset\\(\" -S src/fsspeckit/datasets/pyarrow/io.py src/fsspeckit/datasets/pyarrow/__init__.py src/fsspeckit/datasets/__init__.py src/fsspeckit/core/ext/dataset.py | head -n 80" succeeded in 15ms:
src/fsspeckit/core/ext/dataset.py:671:def deduplicate_parquet_dataset(
[2026-01-07T17:50:01] tokens used: 70,768
[2026-01-07T17:50:04] exec sh -lc "sed -n '660,820p' src/fsspeckit/core/ext/dataset.py" in /home/volker/coding/fsspeckit
[2026-01-07T17:50:04] sh -lc "sed -n '660,820p' src/fsspeckit/core/ext/dataset.py" succeeded in 9ms:
    )
    return self.write_pyarrow_dataset(
        data=data,
        path=path,
        strategy="deduplicate",
        key_columns=key_columns,
        dedup_order_by=dedup_order_by,
        **kwargs,
    )


def deduplicate_parquet_dataset(
    self: AbstractFileSystem,
    path: str,
    *,
    key_columns: list[str] | str | None = None,
    dedup_order_by: list[str] | str | None = None,
    partition_filter: list[str] | None = None,
    compression: str | None = None,
    dry_run: bool = False,
[2026-01-07T17:50:04] tokens used: 71,533
[2026-01-07T17:50:13] exec sh -lc "rg -n \"deduplicate_parquet_dataset\\(|write_pyarrow_dataset\\(|pyarrow_dataset\\(|extend\\(\" src/fsspeckit | head -n 120" in /home/volker/coding/fsspeckit
[2026-01-07T17:50:13] sh -lc "rg -n \"deduplicate_parquet_dataset\\(|write_pyarrow_dataset\\(|pyarrow_dataset\\(|extend\\(\" src/fsspeckit | head -n 120" succeeded in 21ms:
src/fsspeckit/datasets/duckdb/dataset.py:1881:            dedup_stats = self.deduplicate_parquet_dataset(
src/fsspeckit/datasets/duckdb/dataset.py:2062:    def deduplicate_parquet_dataset(
src/fsspeckit/datasets/duckdb/dataset.py:2106:            stats = io.deduplicate_parquet_dataset(
src/fsspeckit/datasets/duckdb/dataset.py:2114:            stats = io.deduplicate_parquet_dataset("/tmp/dataset/")
src/fsspeckit/datasets/pyarrow/dataset.py:405:                result_batches.extend(chunk_unique.to_batches())
src/fsspeckit/datasets/pyarrow/dataset.py:409:                result_batches.extend(new_rows.to_batches())
src/fsspeckit/core/filesystem/gitlab.py:235:                all_files.extend(files)
src/fsspeckit/core/ext/dataset.py:34:def pyarrow_dataset(
src/fsspeckit/core/ext/dataset.py:72:        ds = fs.pyarrow_dataset("data/")
src/fsspeckit/core/ext/dataset.py:76:        ds = fs.pyarrow_dataset(
src/fsspeckit/core/ext/dataset.py:84:        ds = fs.pyarrow_dataset(
src/fsspeckit/core/ext/dataset.py:169:def write_pyarrow_dataset(
src/fsspeckit/core/ext/dataset.py:243:        fs.write_pyarrow_dataset(
src/fsspeckit/core/ext/dataset.py:250:        fs.write_pyarrow_dataset(
src/fsspeckit/core/ext/dataset.py:543:    return self.write_pyarrow_dataset(
src/fsspeckit/core/ext/dataset.py:585:    return self.write_pyarrow_dataset(
src/fsspeckit/core/ext/dataset.py:627:    return self.write_pyarrow_dataset(
src/fsspeckit/core/ext/dataset.py:661:    return self.write_pyarrow_dataset(
src/fsspeckit/core/ext/dataset.py:671:def deduplicate_parquet_dataset(
src/fsspeckit/core/ext/dataset.py:712:        stats = fs.deduplicate_parquet_dataset(
[2026-01-07T17:50:13] tokens used: 72,696

[2026-01-07T17:50:15] thinking

**Inspecting fsspec patching**

I think `fsspeckit/__init__.py` is likely responsible for patching the AbstractFileSystem within fsspec by adding some functions. It sounds like a good idea to take a closer look at the file and see what's going on. I should check for how the functions are implemented and their effects on the AbstractFileSystem. This could provide insight into the functionality of fsspec and its extensibility. Let's dig deeper into that!
[2026-01-07T17:50:18] exec sh -lc "sed -n '1,220p' src/fsspeckit/__init__.py && echo '---' && rg -n \"core.ext.dataset\" -S src/fsspeckit/__init__.py src/fsspeckit/core/__init__.py src/fsspeckit/core/ext/__init__.py 2>/dev/null || true" in /home/volker/coding/fsspeckit
[2026-01-07T17:50:18] sh -lc "sed -n '1,220p' src/fsspeckit/__init__.py && echo '---' && rg -n \"core.ext.dataset\" -S src/fsspeckit/__init__.py src/fsspeckit/core/__init__.py src/fsspeckit/core/ext/__init__.py 2>/dev/null || true" succeeded in 16ms:
"""fsspeckit: Enhanced utilities and extensions for fsspec filesystems.

This package provides enhanced filesystem utilities built on top of fsspec,
including:
- Multi-format data I/O (JSON, CSV, Parquet)
- Cloud storage configuration utilities
- Enhanced caching and monitoring
- Batch processing and parallel operations
"""

import importlib.metadata

try:
    __version__ = importlib.metadata.version("fsspeckit")
except importlib.metadata.PackageNotFoundError:
    __version__ = "0.5.0-dev"
except Exception:
    # Fallback for any other import issues during development
    __version__ = "0.5.0-dev"

[2026-01-07T17:50:18] tokens used: 73,546
[2026-01-07T17:50:21] exec sh -lc "sed -n '1,260p' src/fsspeckit/core/__init__.py" in /home/volker/coding/fsspeckit
[2026-01-07T17:50:21] sh -lc "sed -n '1,260p' src/fsspeckit/core/__init__.py" succeeded in 10ms:
"""Core filesystem functionality for fsspeckit."""

from .base import (
    GitLabFileSystem,
    MonitoredSimpleCacheFileSystem,
    filesystem,
    get_filesystem,
    DirFileSystem,
)

# Conditional imports for extended functionality
try:
    from .ext import AbstractFileSystem
except ImportError:
    from fsspec import AbstractFileSystem

__all__ = [
    "GitLabFileSystem",
    "MonitoredSimpleCacheFileSystem",
    "DirFileSystem",
[2026-01-07T17:50:22] tokens used: 74,286
[2026-01-07T17:50:25] exec sh -lc "ls -la src/fsspeckit/core/ext && sed -n '1,220p' src/fsspeckit/core/ext/__init__.py && echo '---' && sed -n '1,240p' src/fsspeckit/core/ext/filesystem.py" in /home/volker/coding/fsspeckit
[2026-01-07T17:50:25] sh -lc "ls -la src/fsspeckit/core/ext && sed -n '1,220p' src/fsspeckit/core/ext/__init__.py && echo '---' && sed -n '1,240p' src/fsspeckit/core/ext/filesystem.py" exited 2 in 12ms:
total 116
drwxrwxr-x 3 volker volker  4096 Dec 17 21:24 .
drwxrwxr-x 5 volker volker  4096 Dec 27 17:30 ..
-rw-rw-r-- 1 volker volker  1526 Dec 17 21:24 __init__.py
drwxrwxr-x 2 volker volker  4096 Jan  7 16:10 __pycache__
-rw-rw-r-- 1 volker volker 17907 Dec 17 21:24 csv.py
-rw-rw-r-- 1 volker volker 23679 Dec 17 21:24 dataset.py
-rw-rw-r-- 1 volker volker 11589 Dec  8 12:48 io.py
-rw-rw-r-- 1 volker volker 19044 Dec 17 21:24 json.py
-rw-rw-r-- 1 volker volker 19013 Dec  8 13:25 parquet.py
-rw-rw-r-- 1 volker volker  2090 Dec 17 21:24 register.py
"""Core extension I/O helpers for fsspec filesystems.

This package contains focused submodules for different file formats and operations:
- csv: CSV file I/O helpers
- json: JSON/JSONL file I/O helpers
- parquet: Parquet file I/O helpers
- dataset: PyArrow dataset creation helpers
- io: Universal I/O interfaces
- register: Registration layer for AbstractFileSystem

ERROR
sed: can't read src/fsspeckit/core/ext/filesystem.py: No such file or directory
[2026-01-07T17:50:25] tokens used: 75,574

[2026-01-07T17:50:27] thinking

I'm looking into the register module to examine how methods have been added. It's a good idea to understand its structure, as that will help me grasp how things are functioning. I wonder what kind of methods are being implemented and how they interact with the rest of the module. Inspecting this will hopefully give me some clarity and insight into its design. Alright, let's see what I can uncover!
[2026-01-07T17:50:29] exec sh -lc "sed -n '1,260p' src/fsspeckit/core/ext/register.py" in /home/volker/coding/fsspeckit
[2026-01-07T17:50:29] sh -lc "sed -n '1,260p' src/fsspeckit/core/ext/register.py" succeeded in 8ms:
"""Registration layer for extending AbstractFileSystem with format-specific methods.

This module provides the wiring layer that attaches format-specific helpers
(JSON, CSV, Parquet) to the AbstractFileSystem class through monkey-patching.
This ensures that all filesystem instances have access to the enhanced I/O methods.
"""

from fsspec import AbstractFileSystem

from fsspeckit.core.ext.csv import (
    read_csv,
    read_csv_file,
    write_csv,
)
from fsspeckit.core.ext.dataset import (
    pyarrow_dataset,
    pyarrow_parquet_dataset,
)

# Import universal I/O helpers
[2026-01-07T17:50:29] tokens used: 76,660
[2026-01-07T17:50:43] exec sh -lc "rg -n \"AbstractFileSystem\\.(write_pyarrow_dataset|upsert_dataset|insert_dataset|update_dataset|deduplicate_dataset|deduplicate_parquet_dataset)\\s*=\" -S src/fsspeckit | head -n 80" in /home/volker/coding/fsspeckit
[2026-01-07T17:50:43] sh -lc "rg -n \"AbstractFileSystem\\.(write_pyarrow_dataset|upsert_dataset|insert_dataset|update_dataset|deduplicate_dataset|deduplicate_parquet_dataset)\\s*=\" -S src/fsspeckit | head -n 80" succeeded in 20ms:
[2026-01-07T17:50:45] tokens used: 77,848

[2026-01-07T17:50:51] thinking

**Fixing environment and scripts**

It seems that LocalFileSystem doesn't have certain methods needed for my script, specifically regarding dataset operations. Instead, I should update the script to use `PyarrowDatasetIO` as per user instruction. The current version references fsspeckit docs, which might be fine. I noticed that the Python version in the .venv is 3.13.6, while the system version is 3.12.3. Since the user may not have permissions for certain actions, I’ll need to provide a clear plan to fix the environment and scripts.
**Planning environment setup and code fixes**

I need to outline a clear plan for the user to fix their environment and code. First, verify they're using the correct interpreter by checking with `which python` and testing PyArrow import. They can either use the `.venv/bin/python` or run `uv`. Alternatively, creating a new virtualenv with `uv sync --extra datasets` is an option. If `/tmp` isn't writable, they should create a `./.tmp` directory and set the TMPDIR environment variable. 

Next, I’ll address specific code updates, including modifying `04_pyarrow_merges` to utilize `PyarrowDatasetIO.merge`, along with some reference updates. Lastly, I'll suggest adding notes in the README about using the virtual environment.
**Finalizing setup instructions**

Since I can't modify the environment directly, my plan involves updating the scripts and documentation. I'll include a list of dependencies like PyArrow, DuckDB, Pandas, Polars, SQLGlot, Orjson, and Psutil. While Polars isn't used in the current scripts, it's still part of the core. In the docs, I'll mention using `pip install fsspeckit[datasets]` for PyPI installs, but for development within the repo, it's better to use `pip install -e ".[datasets]"` or `uv sync --extra datasets`. If `fsspeckit` isn't installed, users should set `PYTHONPATH=src` for source execution. I'll confirm that `.venv` has `fsspeckit` included due to `uv sync`. Finally, I'll instruct users to run the scripts with `uv run python examples/...` or `.venv/bin/python ...`, ensuring they replace `python3` with `.venv/bin/python` to avoid missing PyArrow.
**Creating a fix plan**

This is more about crafting a fix plan than a coding plan. I need to present it in a bullet list format that's ordered. I’ll reference specific file positions, such as file 04 and core/ext/dataset. Including line numbers is essential for clarity, so I should use `nl -ba` to find those line numbers for important instances. I'll focus on gathering line numbers from 04 where `fs.upsert_dataset` is mentioned and take note of the bottom quick reference in 04 as well.  I'll use `nl -ba ... | sed` for extracting those.
[2026-01-07T17:51:06] codex
**Planning environment setup and code fixes**

I need to outline a clear plan for the user to fix their environment and code. First, verify they're using the correct interpreter by checking with `which python` and testing PyArrow import. They can either use the `.venv/bin/python` or run `uv`. Alternatively, creating a new virtualenv with `uv sync --extra datasets` is an option. If `/tmp` isn't writable, they should create a `./.tmp` directory and set the TMPDIR environment variable. 

Next, I’ll address specific code updates, including modifying `04_pyarrow_merges` to utilize `PyarrowDatasetIO.merge`, along with some reference updates. Lastly, I'll suggest adding notes in the README about using the virtual environment.
[2026-01-07T17:51:06] codex
**Finalizing setup instructions**

Since I can't modify the environment directly, my plan involves updating the scripts and documentation. I'll include a list of dependencies like PyArrow, DuckDB, Pandas, Polars, SQLGlot, Orjson, and Psutil. While Polars isn't used in the current scripts, it's still part of the core. In the docs, I'll mention using `pip install fsspeckit[datasets]` for PyPI installs, but for development within the repo, it's better to use `pip install -e ".[datasets]"` or `uv sync --extra datasets`. If `fsspeckit` isn't installed, users should set `PYTHONPATH=src` for source execution. I'll confirm that `.venv` has `fsspeckit` included due to `uv sync`. Finally, I'll instruct users to run the scripts with `uv run python examples/...` or `.venv/bin/python ...`, ensuring they replace `python3` with `.venv/bin/python` to avoid missing PyArrow.
[2026-01-07T17:51:06] codex
**Creating a fix plan**

This is more about crafting a fix plan than a coding plan. I need to present it in a bullet list format that's ordered. I’ll reference specific file positions, such as file 04 and core/ext/dataset. Including line numbers is essential for clarity, so I should use `nl -ba` to find those line numbers for important instances. I'll focus on gathering line numbers from 04 where `fs.upsert_dataset` is mentioned and take note of the bottom quick reference in 04 as well.  I'll use `nl -ba ... | sed` for extracting those.
[2026-01-07T17:51:07] exec sh -lc "nl -ba examples/datasets/getting_started/04_pyarrow_merges.py | sed -n '120,220p' && echo '---' && nl -ba examples/datasets/getting_started/04_pyarrow_merges.py | sed -n '340,430p' && echo '---' && nl -ba src/fsspeckit/datasets/pyarrow/io.py | sed -n '330,420p' && echo '---' && nl -ba src/fsspeckit/datasets/duckdb/connection.py | sed -n '1,140p'" in /home/volker/coding/fsspeckit
[2026-01-07T17:51:07] sh -lc "nl -ba examples/datasets/getting_started/04_pyarrow_merges.py | sed -n '120,220p' && echo '---' && nl -ba examples/datasets/getting_started/04_pyarrow_merges.py | sed -n '340,430p' && echo '---' && nl -ba src/fsspeckit/datasets/pyarrow/io.py | sed -n '330,420p' && echo '---' && nl -ba src/fsspeckit/datasets/duckdb/connection.py | sed -n '1,140p'" succeeded in 17ms:
   120	
   121	    if interactive:
   122	        input("\nPress Enter to continue...")
   123	
   124	
   125	def demonstrate_upsert_basics():
   126	    """Demonstrate basic UPSERT functionality."""
   127	    print("\n🔄 UPSERT Strategy - Insert or Update")
   128	    print("=" * 50)
   129	
   130	    print("\n📋 Use Case: Customer data synchronization")
   131	    print("   • New customers get added")
   132	    print("   • Existing customers get updated")
   133	    print("   • Most common CDC (Change Data Capture) pattern")
   134	
   135	    fs = LocalFileSystem()
   136	    data = create_simple_customer_data()
   137	
   138	    with tempfile.TemporaryDirectory() as temp_dir:
   139	        customer_path = Path(temp_dir) / "customers"
[2026-01-07T17:51:07] tokens used: 79,013
[2026-01-07T17:51:16] codex

Your `ModuleNotFoundError: No module named 'pyarrow'` is an interpreter/env mismatch, not a code bug in the examples.

- Running with system `python3` fails immediately at `import pyarrow` in all scripts.
- This repo already has a populated venv: `.venv/bin/python` can import `fsspeckit`, `pyarrow`, `duckdb`, `pandas`, `polars` successfully (so the deps *are* available, just not in the interpreter you used).

Once deps are installed/used correctly, there’s one real **example-code issue** likely to bite you next: `04_pyarrow_merges.py` still documents/calls old filesystem helper APIs (`fs.upsert_dataset`, `fs.write_pyarrow_dataset`, etc.) that are not current. The current API you asked for is `PyarrowDatasetIO.merge(...)` (`src/fsspeckit/datasets/pyarrow/io.py:349`) and DuckDB uses `DuckDBConnection` (`src/fsspeckit/datasets/duckdb/connection.py:31`).

## Concrete fix plan (ordered)

1. **Use the repo’s venv interpreter for all examples**
   - Verify: `.venv/bin/python -c "import pyarrow, duckdb, pandas; import fsspeckit; print('ok')"`
   - Run examples with: `.venv/bin/python examples/datasets/getting_started/01_duckdb_basics.py` (repeat for 02–05)

2. **If you don’t want to rely on the committed `.venv`, recreate it explicitly**
   - With uv (recommended since `uv.lock` exists): `uv sync --extra datasets` then `uv run python examples/datasets/getting_started/01_duckdb_basics.py`
   - With pip: `python -m venv .venv && . .venv/bin/activate && pip install -e ".[datasets]"`

3. **Environment sanity checks before debugging code**
   - Ensure a writable temp dir exists (these scripts use `tempfile.*` heavily). If you’re in a restricted environment, run with e.g. `TMPDIR=./.tmp mkdir -p .tmp && TMPDIR=./.tmp .venv/bin/python ...`.

4. **Update `04_pyarrow_merges.py` to the current merge API**
   - The script currently references old helpers and “pretends” upsert is not implemented (`examples/datasets/getting_started/04_pyarrow_merges.py:159-163`, `examples/datasets/getting_started/04_pyarrow_merges.py:186-190`, `examples/datasets/getting_started/04_pyarrow_merges.py:393-400`).
   - Replace those calls/docs with `from fsspeckit.datasets.pyarrow import PyarrowDatasetIO` and calls like:
     - `io = PyarrowDatasetIO()` then `io.merge(table, str(path), strategy="upsert", key_columns=["customer_id"])` (see `src/fsspeckit/datasets/pyarrow/io.py:349`).

5. **(Optional but aligns with “current APIs”) Modernize DuckDB examples**
   - `01_duckdb_basics.py` / `05_duckdb_upserts.py` can keep `DuckDBParquetHandler`, but if you want “current API” usage, switch to `DuckDBConnection` + `DuckDBDatasetIO` (connection entry point is `src/fsspeckit/datasets/duckdb/connection.py:31`).

6. **Re-run scripts in order and capture the *next* failure**
   - After steps 1–4, you should expect 01/02/03/05 to run in a normal writable environment; 04 will stop failing once it uses `PyarrowDatasetIO.merge`.

If you want, I can do a quick “predicted diff” for `04_pyarrow_merges.py` (what to replace with what) based on its current structure, but I can’t apply patches in this session’s read-only mode.
[2026-01-07T17:51:33] tokens used: 86,478
