Datasets:

Chitrapradha
/

syncrag-raw-logs

Error code:   FeaturesError
Exception:    ArrowInvalid
Message:      JSON parse error: Invalid value. in row 0
Traceback:    Traceback (most recent call last):
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 324, in _generate_tables
                  df = pandas_read_json(f)
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 38, in pandas_read_json
                  return pd.read_json(path_or_buf, **kwargs)
                         ~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/pandas/io/json/_json.py", line 815, in read_json
                  return json_reader.read()
                         ~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/pandas/io/json/_json.py", line 1014, in read
                  obj = self._get_object_parser(self.data)
                File "/usr/local/lib/python3.14/site-packages/pandas/io/json/_json.py", line 1040, in _get_object_parser
                  obj = FrameParser(json, **kwargs).parse()
                File "/usr/local/lib/python3.14/site-packages/pandas/io/json/_json.py", line 1176, in parse
                  self._parse()
                  ~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/pandas/io/json/_json.py", line 1392, in _parse
                  ujson_loads(json, precise_float=self.precise_float), dtype=None
                  ~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
              ValueError: Expected object or value
              
              During handling of the above exception, another exception occurred:
              
              Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/split/first_rows.py", line 244, in compute_first_rows_from_streaming_response
                  iterable_dataset = iterable_dataset._resolve_features()
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 4523, in _resolve_features
                  features = _infer_features_from_batch(self.with_format(None)._head())
                                                        ~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2768, in _head
                  return next(iter(self.iter(batch_size=n)))
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2972, in iter
                  for key, pa_table in ex_iterable.iter_arrow():
                                       ~~~~~~~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2483, in _iter_arrow
                  yield from self.ex_iterable._iter_arrow()
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 547, in _iter_arrow
                  for key, pa_table in iterator:
                                       ^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 430, in _iter_arrow
                  for key, pa_table in self.generate_tables_fn(**gen_kwags):
                                       ~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 327, in _generate_tables
                  raise e
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 290, in _generate_tables
                  pa_table = paj.read_json(
                      io.BytesIO(batch), read_options=paj.ReadOptions(block_size=block_size)
                  )
                File "pyarrow/_json.pyx", line 364, in pyarrow._json.read_json
                File "pyarrow/error.pxi", line 155, in pyarrow.lib.pyarrow_internal_check_status
                  return check_status(status)
                File "pyarrow/error.pxi", line 92, in pyarrow.lib.check_status
                  raise convert_status(status)
              pyarrow.lib.ArrowInvalid: JSON parse error: Invalid value. in row 0

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SyncRAG research artifact

Research artifact for the manuscript "SyncRAG: Snapshot-Consistent Publication and Recovery of Derived Vector Artifacts over Mutable Knowledge Repositories" by Chitrapradha Ganesan (Independent Researcher, Dallas, Texas, USA; ORCID 0009-0009-1305-1724). Status: prepared for submission to ICDE 2027 Research Track, Round 2; not accepted or published; no DOI exists for the manuscript or this dataset. This version matches the manuscript as of Amendment A34 (PDF SHA-256 ae89cec5ec768c861f2a9786ea42710260cfbe1e18dc85be476c4daf2b7c30fb).

Files

File	Bytes	SHA-256
`syncrag_record_A_code_data_processed.zip`	1,068,929,686	ed987c1ea1e512f2ec01525794f55c88ed2d56cfbf8aa7da71c1003268448978
`syncrag_record_B1_raw_sqlite_and_superseded.zip`	16,802,601,010	fcec9037de7e607824c24e4c8477fd50240be1204bab951bed1078e531d1b031
`syncrag_record_B2_raw_pg_and_performance.zip`	17,601,389,651	1584594374995f00990971c9fa343168ea162dc337324576b81e68583cc5342c
`syncrag_record_C_raw_followup_campaigns.zip`	2,906,924,598	4b4f3ecc04039641b599b7040a84b9bf3b321b16c64007179e94a90dd9b46441
`ZIP_SHA256.txt`		the same four checksums

Record A (rebuilt 2026-10-18, manuscript and protocol refreshed for A34 on 2026-10-19; 7,950 files): 02_protocol/ (preregistration with amendments A1-A34, formal invariants, threat model, all design files, freeze hashes), 03_data/ (the two revision-event streams, data card, checksums), 04_code/ (harness, baselines, fault injection, analysis, independent checker, coverage analysis, table and figure generation, audits, 76 tests), 05_results/ without raw logs (processed results for every campaign, including the independent checker and coverage outputs; tables; figures; generated macros; run manifest; claim-to-evidence ledgers; exclusions; negative and null results) and 06_manuscript/ (LaTeX sources, bibliography, PDF, AI-assistance disclosure, data and code availability statement). README.md and REPRODUCE.md inside explain the layout and the exact commands.

Records B1 and B2 (built 2026-10-11, unchanged): per-run ingestion logs, query logs and summaries of the main campaigns. B1: SQLite+hnswlib consistency campaign, the A10 scale check, and the outputs quarantined by amendments A1, A4, A6 and A8 (superseded_runs, never used in a table). B2: PostgreSQL+pgvector consistency campaign and the serialised performance campaign.

Record C (built 2026-10-18, 1,696 files): raw logs produced after B1/B2 were built — the A11 guarded-recovery-sweep baseline (its consistency and performance runs), the A14 opt-in backfill runs on pgvector, the A15/A18/A23 independent-checker campaigns (with poller logs) and the A17 full-length scale check — with the design files that produced them. Unzip B1, B2 and C into 05_results/ of Record A so their raw/ folders merge; together they hold every raw log the manuscript's numbers were derived from.

Integrity and redaction

Each record contains SHA256_MANIFEST.txt and MANIFEST_OF_INCLUDED_FILES.csv covering every file it holds. Absolute local filesystem paths of the machine the experiments ran on appeared only inside Python error tracebacks (the trace field of ingest.jsonl, which no analysis script reads); they were replaced by <project_root>/ in the published copies (B1/B2: 293 files in 2026-10-11; Record C: 24 files; Record A: one processed diagnostic file), listed in each record's REDACTION_LOG.txt (A and C). Records A and C were checked before upload: zip -T OK, no symlinks, no remaining local paths, no caches or virtual environments; the only secret-shaped string is a truncated example certificate in the upstream Kubernetes documentation (concepts/configuration/secret.md), not a usable key. The tests (76) pass and the manuscript builds from Record A's own files.

Reproduction (short form)

Unzip Record A; create a Python 3.12+ environment with 04_code/requirements.txt; from 04_code/ run python -m pytest -q tests and python make_tables.py --results ../05_results (about two minutes) to regenerate every table, figure and macro. REPRODUCE.md covers re-analysing the raw logs, re-executing the campaigns (needs PostgreSQL 17 with pgvector 0.8.7) and a clean-environment spot check.

Licences

Code, documents and processed results: MIT, (c) 2026 Chitrapradha Ganesan. The revision-event streams under 03_data/ and the raw logs (which contain chunks of the source documents) keep their upstream licences: Kubernetes documentation (kubernetes/website), Creative Commons Attribution 4.0 International, (c) The Kubernetes Authors; PostgreSQL manual sources (postgres/postgres, doc/src/sgml), PostgreSQL License, Portions Copyright (c) 1996-2026, PostgreSQL Global Development Group. Attribution text is in 03_data/DATA_CARD.md of Record A.

Citation

Until the manuscript is published, cite this dataset by its URL (https://huggingface.co/datasets/Chitrapradha/syncrag-raw-logs) and the manuscript title above.

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