[2026-01-07T18:05:03] OpenAI Codex v0.0.0 (research preview)
[2026-01-07T18:05:03] binary: /home/volker/.cache/just-every/code/0.6.40/code-aarch64-unknown-linux-musl
--------
workdir: /home/volker/coding/fsspeckit
model: gpt-5.1-codex-mini
provider: openai
approval: never
sandbox: read-only
reasoning effort: medium
reasoning summaries: auto
--------
[2026-01-07T18:05:03] User instructions:
Context: Repo: /home/volker/coding/fsspeckit. We updated getting_started examples with dependency checks and README install guidance. Ran examples via uv and they succeeded. There may be API drift in examples (e.g., 04_pyarrow_merges mentions fs.insert_dataset etc which are removed). Identify worthwhile follow-ups that improve reliability and user experience.

Agent: Propose meaningful and useful follow-ups after getting_started examples now run successfully. Consider docs/README improvements, example accuracy vs current APIs, testing, CI checks, and optional refactors. Provide a concise plan with steps and rationale.

Files to consider: /home/volker/coding/fsspeckit/examples/datasets/getting_started/README.md, /home/volker/coding/fsspeckit/examples/datasets/getting_started/04_pyarrow_merges.py, /home/volker/coding/fsspeckit/src/fsspeckit/datasets/pyarrow/io.py

[Running in read-only mode - no modifications allowed]

[2026-01-07T18:05:04] thinking

**Preparing to read openspec AGENTS.md**
[2026-01-07T18:05:05] exec /bin/sh -c ls in /home/volker/coding/fsspeckit
[2026-01-07T18:05:05] /bin/sh -c ls succeeded in 7ms:
AGENTS.md
CLAUDE.md
CONTRIBUTING.md
LICENSE
README.md
cache
cleanup_decision.md
code_review
coverage.xml
dev
docs
examples
htmlcov
mkdocs.yml
openspec
pyproject.toml
scripts
src
test_adaptive_tracker.py
test_adaptive_tracker_basic.py
[2026-01-07T18:05:05] tokens used: 591

[2026-01-07T18:05:06] thinking

**Opening openspec AGENTS**
[2026-01-07T18:05:06] exec /bin/sh -c 'cat openspec/AGENTS.md' in /home/volker/coding/fsspeckit
[2026-01-07T18:05:06] /bin/sh -c 'cat openspec/AGENTS.md' succeeded in 5ms:
# 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)
[2026-01-07T18:05:06] tokens used: 901

[2026-01-07T18:05:07] thinking

**Opening plan skill file**
[2026-01-07T18:05:07] exec /bin/sh -c 'cat /home/volker/.code/skills/.system/plan/SKILL.md' in /home/volker/coding/fsspeckit
[2026-01-07T18:05:07] /bin/sh -c 'cat /home/volker/.code/skills/.system/plan/SKILL.md' succeeded in 4ms:
---
name: plan
description: Generate a plan for how an agent should accomplish a complex coding task. Use when a user asks for a plan, and optionally when they want to save, find, read, update, or delete plan files in $CODEX_HOME/plans (default ~/.codex/plans).
metadata:
  short-description: Generate a plan for a complex task
---

# Plan

## Overview

Draft structured plans that clarify intent, scope, requirements, action items, testing/validation, and risks.

Optionally, save plans to disk as markdown files with YAML frontmatter and free-form content. When drafting in chat, output only the plan body without frontmatter; add frontmatter only when saving to disk. Only write to the plans folder; do not modify the repository codebase.

This skill can also be used to draft codebase or system overviews.

## Core rules

- Resolve the plans directory as `$CODEX_HOME/plans` or `~/.codex/plans` when `CODEX_HOME` is not set.
[2026-01-07T18:05:07] tokens used: 5,686

[2026-01-07T18:05:10] thinking

**Preparing to generate plan with rationale**
[2026-01-07T18:05:10] exec /bin/sh -c 'cat examples/datasets/getting_started/README.md' in /home/volker/coding/fsspeckit
[2026-01-07T18:05:10] /bin/sh -c 'cat examples/datasets/getting_started/README.md' succeeded in 6ms:
# 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
- Master essential data merging techniques
- Build a foundation for more advanced workflows

## Learning Path

We recommend following the examples in numerical order:

### 1. `01_duckdb_basics.py` - DuckDB Fundamentals
**Prerequisites**: Basic Python knowledge
**Time**: 15-20 minutes

[2026-01-07T18:05:10] tokens used: 12,451

[2026-01-07T18:05:11] thinking

**Inspecting pyarrow merges file**
[2026-01-07T18:05:12] exec /bin/sh -c 'cat examples/datasets/getting_started/04_pyarrow_merges.py' in /home/volker/coding/fsspeckit
[2026-01-07T18:05:12] /bin/sh -c 'cat examples/datasets/getting_started/04_pyarrow_merges.py' succeeded in 4ms:
"""
PyArrow Merge-Aware Writes - Getting Started

This example introduces PyArrow's merge-aware write functionality for efficient dataset operations.

The example covers:
1. Basic merge-aware write concepts
2. Strategy selection (insert, upsert, update, etc.)
3. Key column configuration
4. Convenience helper functions
5. Performance benefits over traditional approaches
"""

import argparse
import tempfile
from pathlib import Path
from typing import Dict, Any

try:
    import pyarrow as pa
[2026-01-07T18:05:12] tokens used: 29,066

[2026-01-07T18:05:13] thinking

**Inspecting API presence in src file**
[2026-01-07T18:05:13] exec /bin/sh -c 'rg -n "insert_dataset" -n src/fsspeckit/datasets/pyarrow -g"*.py"' in /home/volker/coding/fsspeckit
[2026-01-07T18:05:13] /bin/sh -c 'rg -n "insert_dataset" -n src/fsspeckit/datasets/pyarrow -g"*.py"' succeeded in 8ms:
src/fsspeckit/datasets/pyarrow/io.py:1146:    def insert_dataset(
src/fsspeckit/datasets/pyarrow/io.py:1167:            "insert_dataset has been removed. Use merge(..., strategy='insert') instead."
[2026-01-07T18:05:13] tokens used: 33,792

[2026-01-07T18:05:18] thinking

**Extracting function references from io file**
[2026-01-07T18:05:18] exec /bin/sh -c "sed -n '1130,1235p' src/fsspeckit/datasets/pyarrow/io.py" in /home/volker/coding/fsspeckit
[2026-01-07T18:05:18] /bin/sh -c "sed -n '1130,1235p' src/fsspeckit/datasets/pyarrow/io.py" succeeded in 6ms:
            ```
        """
        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,
            target_rows_per_file=target_rows_per_file,
            partition_filter=partition_filter,
            compression=compression,
            filesystem=self._filesystem,
            verbose=verbose,
        )

    def insert_dataset(
        self,
        data: pa.Table | list[pa.Table],
        path: str,
[2026-01-07T18:05:18] tokens used: 34,082

[2026-01-07T18:05:21] thinking

**Planning documentation and example updates**
[2026-01-07T18:05:22] exec /bin/sh -c 'rg -n "def merge" -n src/fsspeckit/datasets/pyarrow/io.py' in /home/volker/coding/fsspeckit
[2026-01-07T18:05:22] /bin/sh -c 'rg -n "def merge" -n src/fsspeckit/datasets/pyarrow/io.py' succeeded in 6ms:
349:    def merge(
1010:    def merge_parquet_dataset(
[2026-01-07T18:05:22] tokens used: 35,175

[2026-01-07T18:05:23] thinking

**Checking file start for usage**
[2026-01-07T18:05:24] exec /bin/sh -c "sed -n '300,420p' src/fsspeckit/datasets/pyarrow/io.py" in /home/volker/coding/fsspeckit
[2026-01-07T18:05:24] /bin/sh -c "sed -n '300,420p' src/fsspeckit/datasets/pyarrow/io.py" succeeded in 15ms:
        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
[2026-01-07T18:05:24] tokens used: 35,389

[2026-01-07T18:05:29] thinking

**Planning updates for example and docs**

I’m organizing a clear plan to update example 04 and docs to align with the current `fs.merge` API, propose adding tests to catch API drift, and suggest verifying usage consistency around `fs` and dataset handling functions to improve reliability and user experience.
[2026-01-07T18:05:31] exec /bin/sh -c "rg -n \"\\.merge\\(\" -n src/fsspeckit -g\"*.py\" | head" in /home/volker/coding/fsspeckit
[2026-01-07T18:05:31] /bin/sh -c "rg -n \"\\.merge\\(\" -n src/fsspeckit -g\"*.py\" | head" succeeded in 10ms:
src/fsspeckit/datasets/pyarrow/dataset.py:1433:        "Use PyarrowDatasetIO.merge() instead.",
src/fsspeckit/datasets/pyarrow/io.py:63:        io.merge(table, "/path/to/dataset/", strategy="upsert", key_columns=["id"])
src/fsspeckit/datasets/pyarrow/io.py:1686:        handler.merge(table, "/path/to/dataset/", strategy="upsert", key_columns=["id"])
src/fsspeckit/core/ext/dataset.py:534:        "insert_dataset has been removed. Use PyarrowDatasetIO.merge(strategy='insert') instead."
src/fsspeckit/core/ext/dataset.py:576:        "upsert_dataset has been removed. Use PyarrowDatasetIO.merge(strategy='upsert') instead."
src/fsspeckit/core/ext/dataset.py:618:        "update_dataset has been removed. Use PyarrowDatasetIO.merge(strategy='update') instead."
[2026-01-07T18:05:31] tokens used: 37,357

[2026-01-07T18:05:33] thinking

**Inspecting dataset.py usage examples**
[2026-01-07T18:05:33] exec /bin/sh -c "sed -n '1,140p' src/fsspeckit/datasets/pyarrow/io.py" in /home/volker/coding/fsspeckit
[2026-01-07T18:05:33] /bin/sh -c "sed -n '1,140p' src/fsspeckit/datasets/pyarrow/io.py" succeeded in 5ms:
"""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
[2026-01-07T18:05:33] tokens used: 53,232

[2026-01-07T18:05:40] thinking

**Outlining detailed follow-up plan**

I'm putting together a structured plan to update the example code with the current API, improve README dependency instructions, and add CI/testing for automatic example validation. The plan will explain why each step matters for reliability and user experience, include file references with line numbers, and propose actions with checkboxes to help track progress. I'll mention using the plan skill explicitly as well.
[2026-01-07T18:05:42] exec /bin/sh -c "nl -ba examples/datasets/getting_started/README.md | sed -n '70,160p'" in /home/volker/coding/fsspeckit
[2026-01-07T18:05:42] /bin/sh -c "nl -ba examples/datasets/getting_started/README.md | sed -n '70,160p'" succeeded in 5ms:
    70	- You need complex SQL queries
    71	- You have analytical workloads
    72	- You prefer database-style operations
    73	- You need advanced aggregations
    74	- Memory is abundant and you want convenience
    75	
    76	### Use PyArrow When:
    77	- You need in-memory performance
    78	- You have simple filtering and transformations
    79	- Memory is constrained
    80	- You want to avoid database overhead
    81	- You need fine-grained control over operations
    82	
    83	## Code Patterns
    84	
    85	### Basic DuckDB Pattern
    86	```python
    87	from fsspeckit.datasets import DuckDBParquetHandler
    88	
    89	with DuckDBParquetHandler() as handler:
[2026-01-07T18:05:42] tokens used: 65,507

[2026-01-07T18:05:43] thinking

**Extending installation section**
[2026-01-07T18:05:43] exec /bin/sh -c "nl -ba examples/datasets/getting_started/README.md | sed -n '158,260p'" in /home/volker/coding/fsspeckit
[2026-01-07T18:05:43] /bin/sh -c "nl -ba examples/datasets/getting_started/README.md | sed -n '158,260p'" succeeded in 5ms:
   158	## Prerequisites Installation
   159	
   160	```bash
   161	# Recommended (from repo root)
   162	python -m venv .venv
   163	source .venv/bin/activate
   164	pip install -e ".[datasets]"
   165	
   166	# If you use uv instead of pip
   167	uv sync
   168	
   169	# Development dependencies (optional)
   170	pip install jupyter matplotlib seaborn
   171	```
   172	
   173	This installs the required runtime dependencies for the examples, including
   174	`pyarrow`, `duckdb`, `pandas`, and `polars`.
   175	
   176	When using uv, run the examples with `uv run python examples/datasets/getting_started/01_duckdb_basics.py`
   177	to ensure the managed environment is used.
[2026-01-07T18:05:43] tokens used: 66,773

[2026-01-07T18:05:46] thinking

**Adding line numbers for clarity**
[2026-01-07T18:05:46] exec /bin/sh -c "nl -ba examples/datasets/getting_started/04_pyarrow_merges.py | sed -n '310,520p'" in /home/volker/coding/fsspeckit
[2026-01-07T18:05:46] /bin/sh -c "nl -ba examples/datasets/getting_started/04_pyarrow_merges.py | sed -n '310,520p'" succeeded in 5ms:
   310	    print("   • Need to add NEW records only? → Use INSERT")
   311	    print("   • Need to add NEW + update EXISTING? → Use UPSERT")
   312	    print("   • Need to update EXISTING records only? → Use UPDATE")
   313	    print("   • Need to replace EVERYTHING? → Use FULL_MERGE")
   314	    print("   • Need to remove duplicates? → Use DEDUPLICATE")
   315	
   316	
   317	def demonstrate_key_columns():
   318	    """Explain key column concepts and best practices."""
   319	    print("\n🔑 Key Columns - Best Practices")
   320	    print("=" * 50)
   321	
   322	    print("\n📋 What are Key Columns?")
   323	    print("   • Columns that uniquely identify each record")
   324	    print("   • Used to match new data with existing data")
   325	    print("   • Critical for INSERT, UPSERT, UPDATE strategies")
   326	
   327	    print("\n✅ Good Key Column Examples:")
   328	    print("   • customer_id - Unique customer identifier")
   329	    print("   • transaction_id - Unique transaction number")
[2026-01-07T18:05:46] tokens used: 87,685

[2026-01-07T18:05:49] thinking

**Clarifying line referencing conventions**
[2026-01-07T18:05:50] exec /bin/sh -c "nl -ba src/fsspeckit/datasets/pyarrow/io.py | sed -n '40,120p'" in /home/volker/coding/fsspeckit
[2026-01-07T18:05:50] /bin/sh -c "nl -ba src/fsspeckit/datasets/pyarrow/io.py | sed -n '40,120p'" succeeded in 5ms:
    40	
    41	    This class provides methods for reading and writing parquet files and datasets
    42	    using PyArrow's high-performance parquet engine.
    43	
    44	    The class inherits from BaseDatasetHandler to leverage shared implementations
    45	    while providing PyArrow-specific optimizations.
    46	
    47	    Args:
    48	        filesystem: Optional fsspec filesystem instance. If None, uses local filesystem.
    49	
    50	    Example:
    51	        ```python
    52	        from fsspeckit.datasets.pyarrow import PyarrowDatasetIO
    53	
    54	        io = PyarrowDatasetIO()
    55	
    56	        # Read parquet
    57	        table = io.read_parquet("/path/to/data.parquet")
    58	
    59	        # Write dataset
[2026-01-07T18:05:50] tokens used: 89,888

[2026-01-07T18:06:02] thinking

**Drafting detailed follow-up plan**

I'm creating a plan that addresses API drift in examples by updating code and docs with specific file and line references, adding tests, and proposing clear action items with rationale; I'll also incorporate usage of the plan skill and suggest next steps while following the user's instructions closely.
[2026-01-07T18:06:03] codex

Using the plan skill to outline follow-up work for the getting-started examples.

# Plan

The getting-started examples now run again, but `04_pyarrow_merges.py` still teaches APIs that no longer exist, and the surrounding docs don’t surface the newest installation and validation workflow. Let’s update the example, strengthen the README guidance, and add automation so future API drift is caught before it reaches readers.

## Requirements
- Re-sync the 04 example narrative and “Quick Reference” output with today’s merge API (`src/fsspeckit/datasets/pyarrow/io.py:63` shows the supported `PyarrowDatasetIO.merge(...)` call) so users aren’t guided toward removed helpers.
- Keep the README’s install/run instructions (`examples/datasets/getting_started/README.md:158`) aligned with the environments that actually worked during the recent uv run (venv + `uv sync`/`uv run`), making the onboarding flow reproducible.
- Prevent future regressions by automating a runnable smoke test for the getting-started scripts (e.g., `04_pyarrow_merges.py`) and surface failures in CI before documentation is published.

## Scope
- **In:** 04_pyarrow_merges.py narrative, quick-reference text, README install instructions, and a lightweight check/test.
- **Out:** The DuckDB-based examples and broader docs unless we spot direct API mismatches while working on the above.

## Files and entry points
- `examples/datasets/getting_started/04_pyarrow_merges.py:406` (quick reference still mentions `fs.insert_dataset`/`fs.upsert_dataset`, which no longer exist).
- `examples/datasets/getting_started/README.md:158` (installation block that should highlight the working uv workflow and any dependency checks).
- `src/fsspeckit/datasets/pyarrow/io.py:41`–`63` (current `PyarrowDatasetIO` example showing how to call `merge`, our source of truth for the API).

## Action items
[ ] Rewrite the 04 example so that every demo section uses `PyarrowDatasetIO`/`merge(...)` (or the latest helpers) instead of `fs.insert_dataset` et al., update the narrative text to explain the supported `strategy` argument, and refresh the “Quick Reference” at `examples/datasets/getting_started/04_pyarrow_merges.py:406` to point readers at the actual merge-based functions.
[ ] Expand the getting-started README’s install and troubleshooting guidance (`examples/datasets/getting_started/README.md:158`) with explicit notes about activating the venv, running `uv sync`, and invoking the examples via `uv run python ...` so users re-create the tested workflow.
[ ] Add a scripted smoke check (e.g., `scripts/verify-getting-started.py` or a lightweight pytest) that runs `python examples/datasets/getting_started/04_pyarrow_merges.py` inside the managed environment; wire that check into the existing CI or a new “getting-started examples” check so future API removals are detected automatically.

## Testing and validation
- Run `uv run python examples/datasets/getting_started/04_pyarrow_merges.py` after each change to ensure the updated example executes without referencing removed APIs.
- Execute the new smoke-test script as part of CI to verify the tutorial still works with real dependencies.

## Risks and edge cases
- PyArrow memory demands might cause the smoke test to fail on the smallest CI runners; keep the dataset size tiny and rely on streaming/`merge_chunk_size_rows`.
- If the README’s uv instructions change based on future tooling updates, we’ll need to revisit the guidance—consider flagging the section for review when uv is updated.

## Open questions
- Should the new automation cover more examples (e.g., the DuckDB tutorials), or keep the scope limited to the merge-focused script to avoid long-running CI jobs?

If you’d like me to start implementing one of the above actions, just reply with the number you’d like me to tackle first (1–3).
[2026-01-07T18:06:07] tokens used: 100,528
