fix: Fix OpenGraph batching and prevent duplicate DLT delivery

[ktstrader]

Summary

Fix OpenGraph batching for DLT 1.26.x. Relationships now span source rows in a DLT page, and the destination bypasses the get_batches defect that could duplicate non-aligned load-file data.

Motivation

Resolved: BED-9372

Changes

• Batch relationships across rows and flush at the configured size/page end.
• Keep node and relationship output separate, with state per resource.
• Read normalized JSONL directly and make retries idempotent.
• Add DLT integration, restart, boundary, and multi-resource tests.
• Add a manual benchmark/replay tool; artifacts go in .benchmark-results/.

Testing

• Focused unit and DLT integration tests passed, including cold restart and Ruff validation.
• Raw replay parity passed for Okta application_users (10,489 rows; 4,120 legacy wrappers vs. 63 batched) and GitHub org_role_members (3 vs. 1). Sequence and multiset hashes matched.

Synthetic benchmark findings from benchmarks/opengraph_batching_results.json (Windows 11, Python 3.14.2, DLT 1.26.0; 150 relationships per source wrapper):

Rows	Per-row / batched wrappers	Parts	Wall time	Peak RSS
100k	100,000 / 700	100 / 1	6.78 / 5.16 s	109.5 / 212.8 MiB
1m	1,000,000 / 7,000	1,000 / 7	92.15 / 58.90 s	270.7 / 292.2 MiB

Batching reduced wrappers and parts by about 143x, with lower CPU/wall time but higher peak memory. These are local synthetic results, not customer performance.

Real-DLT pipeline measurements were also collected with the OpenGraph file destination. Each load created two package files (graph data and DLT state) and made one destination callback:

Rows	Callback items	Callback bytes	Output parts
100k	700	565,874	1
1m	7,000	5,643,496	7

The retained measurements are in benchmarks/opengraph_batching_results.json.

Manual BloodHound validation passed for the recreated 1m real-DLT output: all seven ~26 MiB graph files were accepted. Ingest job 8 completed with zero failed and zero partially failed files (2m 4s ingest duration).

Customer-shaped Okta replay (application_users) also ran against one saved raw collection file and lookup.duckdb: 10,489 rows produced 8,577 relationships in 13.68s wall time (13.20s CPU; 144.2 MiB peak RSS). Page batching emitted 63 wrappers versus 4,120 legacy wrappers; sequence and multiset hashes matched. This includes model conversion and lookup work, but not live collection API calls or DLT package creation.

Summary by CodeRabbit

• New Features

  • OpenGraph processing now batches records in pages and stages destination output into manageable parts.
  • Added retry-safe publishing to prevent duplicate or incomplete graph data.
  • Added benchmarks covering throughput, memory usage, output equivalence, and pipeline performance across synthetic and real inputs.

• Documentation

  • Added setup, usage, comparison, output, and metrics guidance for OpenGraph batching benchmarks.

• Bug Fixes

  • Improved handling of retries, page boundaries, empty inputs, variable edge counts, and non-aligned JSONL records.

[coderabbitai]

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• benchmarks/opengraph_dlt_pipeline_metrics.py

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Walkthrough

The change adds paged OpenGraph conversion, destination-managed JSONL batching, atomic retry-safe publication, end-to-end tests, and benchmark tooling with recorded results and documentation.

Changes

OpenGraph batching pipeline

Layer / File(s)	Summary
Paged source graph generation src/openhound/sources/opengraph/source.py, tests/test_opengraph_batching.py	The source processes JSONL pages of 1,000 records, batches edges, preserves ordering, isolates retries, and validates positive batch sizes.
Load-file batching and retry publication src/openhound/destinations/opengraph/destination.py, tests/test_opengraph_destination.py, tests/test_opengraph_destination_retry.py, tests/test_opengraph_dlt_integration.py	The destination reads load-job files, writes staged parts, publishes completion markers atomically, and removes failed-attempt artifacts before retry publication.
Batching benchmark and measurement tooling benchmarks/opengraph_batching.py, benchmarks/opengraph_dlt_pipeline_metrics.py, benchmarks/opengraph_batching_results.json, benchmarks/README.md, .gitignore	The benchmark compares conversion modes, replays graph and raw inputs, records resource and output metrics, documents usage, and ignores generated benchmark artifacts.

Estimated code review effort: 4 (Complex) | ~45 minutes

Merge Risk: ⚪ Minimal · up to 6b2e0

The PR changes OpenGraph batching and retry handling, with focused integration and restart tests reported as passing; no actionable merge-blocking risk remains beyond normal checks and review.

Sequence Diagram(s)

sequenceDiagram
  participant JSONLSource
  participant OpenGraphTransformer
  participant DLTLoadFile
  participant OpenGraphDestination
  participant OutputDirectory
  JSONLSource->>OpenGraphTransformer: read source pages
  OpenGraphTransformer->>DLTLoadFile: emit normalized JSONL items
  DLTLoadFile->>OpenGraphDestination: provide load-job file
  OpenGraphDestination->>OutputDirectory: publish numbered parts and completion marker
  OpenGraphDestination->>OutputDirectory: remove staged retry artifacts

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Poem

A rabbit sorts the edges bright
Through pages measured, batch by batch
Retry paths leave the staging light
Metrics bloom in files at last
The graph hops whole and clean tonight 🐇

✨ Finishing Touches

📝 Generate docstrings

• Create stacked PR
• Commit on current branch

🧪 Generate unit tests (beta)

• Create PR with unit tests
• Commit unit tests in branch fix/BED-9372-fix-opengraph-batching

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[coderabbitai]

Actionable comments posted: 1

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Inline comments:
In `@benchmarks/opengraph_dlt_pipeline_metrics.py`:
- Around line 53-57: In main, create args.output_dir with parents=True before
calling tempfile.mkdtemp, so the default .benchmark-results path works on a
fresh checkout; preserve the existing run_dir and raw_dir setup.

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ℹ️ Review info

⚙️ Run configuration

Configuration used: Organization UI

Review profile: CHILL

Plan: Pro

Run ID: ea64144b-4772-4633-a07a-c7bfca234340

📥 Commits

Reviewing files that changed from the base of the PR and between e343a98 and 452f9d2.

📒 Files selected for processing (11)

• .gitignore
• benchmarks/README.md
• benchmarks/opengraph_batching.py
• benchmarks/opengraph_batching_results.json
• benchmarks/opengraph_dlt_pipeline_metrics.py
• src/openhound/destinations/opengraph/destination.py
• src/openhound/sources/opengraph/source.py
• tests/test_opengraph_batching.py
• tests/test_opengraph_destination.py
• tests/test_opengraph_destination_retry.py
• tests/test_opengraph_dlt_integration.py

Included review availability: 4 reviews are currently available. Your included PR review attempts over the past 7 days set your current allowance at 5 reviews per hour.