Snapchat/GiGL
Apr 2025 – Dec 2025
9 Months active
Languages Used
C++PythonMakefileYAMLJinjaJupyter NotebookSQLMarkdown
Technical Skills
Data EngineeringData PartitioningDistributed SystemsGraph Neural NetworksMachine LearningPyTorch
mkolodner-sc
PROFILE
Mkolodner-sc
Over nine months, Michael Kolodner engineered distributed data processing and machine learning infrastructure for the Snapchat/GiGL repository, focusing on scalable graph neural network workflows. He developed modular data loaders, partitioners, and end-to-end training pipelines using Python and PyTorch, introducing features like range-based partitioning, asynchronous loading, and robust configuration validation. His work included refactoring APIs for maintainability, implementing feature flags, and enhancing export pipelines to Google Cloud Platform and BigQuery. By addressing data integrity, test coverage, and distributed system reliability, Michael delivered production-ready solutions that improved scalability, reproducibility, and onboarding for heterogeneous and homogeneous graph learning tasks.
Overall Statistics
Feature vs Bugs
84%Features
Repository Contributions
55Total
Bugs
5
Commits
55
Features
27
Lines of code
22,631
Activity Months9
Your Network
24 peopleSame Organization
@snapchat.com16
Work History
December 2025
2 Commits • 2 Features
Dec 1, 2025December 2025: Delivered two high-impact updates for Snapchat/GiGL that improve configuration correctness, backend adaptability, and maintainability of embedding exports. Implemented Resource Configuration Validation for Online Subgraph Sampling with new validation rules and backend-aware logic, accompanied by comprehensive unit tests to ensure robustness. Cleaned up and deprecated legacy GiGL embedding export API, replacing flush_embeddings() with flush_records() and removing deprecated classes to simplify maintenance and future backend integration. No critical defects reported this month; primary focus was on feature delivery, test coverage, and API cleanup to accelerate safe backend changes and onboarding.
2 Commits • 2 Features
Dec 1, 2025December 2025: Delivered two high-impact updates for Snapchat/GiGL that improve configuration correctness, backend adaptability, and maintainability of embedding exports. Implemented Resource Configuration Validation for Online Subgraph Sampling with new validation rules and backend-aware logic, accompanied by comprehensive unit tests to ensure robustness. Cleaned up and deprecated legacy GiGL embedding export API, replacing flush_embeddings() with flush_records() and removing deprecated classes to simplify maintenance and future backend integration. No critical defects reported this month; primary focus was on feature delivery, test coverage, and API cleanup to accelerate safe backend changes and onboarding.
December 2025
November 2025
1 Commits • 1 Features
Nov 1, 2025Month: 2025-11 — Snapchat/GiGL delivered API modernization for the Data Splitter by introducing Dist-prefixed splitters and deprecating Hashed splitters. This refactor improves maintainability and positions the codebase for future distributed processing capabilities, while aligning with the roadmap. No explicit bug fixes were recorded for this repo this month; the focus was on refactoring and groundwork for upcoming releases that unlock scalability and cross-team collaboration.
November 2025
1 Commits • 1 Features
Nov 1, 2025Month: 2025-11 — Snapchat/GiGL delivered API modernization for the Data Splitter by introducing Dist-prefixed splitters and deprecating Hashed splitters. This refactor improves maintainability and positions the codebase for future distributed processing capabilities, while aligning with the roadmap. No explicit bug fixes were recorded for this repo this month; the focus was on refactoring and groundwork for upcoming releases that unlock scalability and cross-team collaboration.
October 2025
3 Commits • 3 Features
Oct 1, 2025October 2025 (Snapchat/GiGL): Delivered scalable data processing and end-to-end export capabilities, driven by a new feature flag for embedding population, sharded read enhancements for BigQuery data processing, and a refactored exporter suite to enable predictions export to GCS and loading into BigQuery. The work improves data quality, reduces processing payloads, and strengthens the end-to-end data pipeline with expanded test coverage.
3 Commits • 3 Features
Oct 1, 2025October 2025 (Snapchat/GiGL): Delivered scalable data processing and end-to-end export capabilities, driven by a new feature flag for embedding population, sharded read enhancements for BigQuery data processing, and a refactored exporter suite to enable predictions export to GCS and loading into BigQuery. The work improves data quality, reduces processing payloads, and strengthens the end-to-end data pipeline with expanded test coverage.
October 2025
September 2025
7 Commits • 2 Features
Sep 1, 2025In September 2025 (Month: 2025-09), Snapchat/GiGL focused on stabilizing distributed data processing, improving data handling, and updating testing coverage. Key delivered work included: 1) Fix for training failures caused by sorting feature keys in preprocessed metadata, restoring correct functionality for distributed training (DDP) with a dedicated commit. 2) Distributed dataset loading improvements: TFRecordDataLoader now returns labels separately; DistDataset migrated to its own file; dataset build simplified; partitioning handling unified, enabling more scalable data processing across workers. 3) Bug fix for link prediction examples: ensure model saving occurs on the primary process and timing is measured accurately, improving reproducibility of results. 4) GLT upgrade: bumped GraphLearn for PyTorch (GLT) and added unit tests for distributed neighbor loaders around isolated nodes in both heterogeneous and homogeneous graphs, improving test coverage and reliability. Overall impact: more reliable distributed training, clearer data pipelines, improved evaluation rigor, and better alignment with production workflows. Technologies/skills demonstrated: PyTorch DDP, TFRecordDataLoader, DistDataset architecture, dataset partitioning strategies, GLT integration, unit testing, and container/docker updates.
September 2025
7 Commits • 2 Features
Sep 1, 2025In September 2025 (Month: 2025-09), Snapchat/GiGL focused on stabilizing distributed data processing, improving data handling, and updating testing coverage. Key delivered work included: 1) Fix for training failures caused by sorting feature keys in preprocessed metadata, restoring correct functionality for distributed training (DDP) with a dedicated commit. 2) Distributed dataset loading improvements: TFRecordDataLoader now returns labels separately; DistDataset migrated to its own file; dataset build simplified; partitioning handling unified, enabling more scalable data processing across workers. 3) Bug fix for link prediction examples: ensure model saving occurs on the primary process and timing is measured accurately, improving reproducibility of results. 4) GLT upgrade: bumped GraphLearn for PyTorch (GLT) and added unit tests for distributed neighbor loaders around isolated nodes in both heterogeneous and homogeneous graphs, improving test coverage and reliability. Overall impact: more reliable distributed training, clearer data pipelines, improved evaluation rigor, and better alignment with production workflows. Technologies/skills demonstrated: PyTorch DDP, TFRecordDataLoader, DistDataset architecture, dataset partitioning strategies, GLT integration, unit testing, and container/docker updates.
August 2025
5 Commits • 1 Features
Aug 1, 2025August 2025 performance summary for Snapchat/GiGL: Implemented scalable node-based data partitioning with HashedNodeSplitter, expanded loading to support node-labels, and introduced node-label separation with features; added Node Classification support in Dataset and Dataloaders; clarified TFRecord integer feature handling to prevent precision loss; introduced early-fail on invalid Node IDs to improve data quality. These changes collectively improve distribution reliability, data integrity, and developer productivity in distributed training pipelines.
5 Commits • 1 Features
Aug 1, 2025August 2025 performance summary for Snapchat/GiGL: Implemented scalable node-based data partitioning with HashedNodeSplitter, expanded loading to support node-labels, and introduced node-label separation with features; added Node Classification support in Dataset and Dataloaders; clarified TFRecord integer feature handling to prevent precision loss; introduced early-fail on invalid Node IDs to improve data quality. These changes collectively improve distribution reliability, data integrity, and developer productivity in distributed training pipelines.
August 2025
July 2025
18 Commits • 6 Features
Jul 1, 2025July 2025 GiGL monthly summary: Delivered end-to-end distributed training for homogeneous graphs in the E2E framework, including refactored inference and new training/testing modules to enable scalable pipelines. Extended E2E to heterogeneous graphs with updated configuration and data loading/model initialization. Implemented cross-graph link prediction with hid_dim/out_dim parameterization across homogeneous and heterogeneous graphs. Improved distributed data loading, sampling, and instrumentation with enhanced fanout handling, logging, and robustness. Strengthened reliability with HGT edge-type ordering validation and unit tests to prevent indexing errors.
July 2025
18 Commits • 6 Features
Jul 1, 2025July 2025 GiGL monthly summary: Delivered end-to-end distributed training for homogeneous graphs in the E2E framework, including refactored inference and new training/testing modules to enable scalable pipelines. Extended E2E to heterogeneous graphs with updated configuration and data loading/model initialization. Implemented cross-graph link prediction with hid_dim/out_dim parameterization across homogeneous and heterogeneous graphs. Improved distributed data loading, sampling, and instrumentation with enhanced fanout handling, logging, and robustness. Strengthened reliability with HGT edge-type ordering validation and unit tests to prevent indexing errors.
June 2025
9 Commits • 7 Features
Jun 1, 2025June 2025 performance summary for Snapchat/GiGL: Delivered major data-loading, dataset-building, and modularity improvements across GiGL, enabling more scalable experiments and robust data handling. Implemented ABLP DataLoader enhancements with DistABLPLoader, extended sampling for heterogeneous graphs, and corrected batch handling; added configurable label-to-edge conversion; optimized distributed partitioning with edge-feature awareness; introduced InfiniteIterator for cyclic data iteration; and advanced modular retrieval loss and link prediction components to improve experimentation flexibility.
9 Commits • 7 Features
Jun 1, 2025June 2025 performance summary for Snapchat/GiGL: Delivered major data-loading, dataset-building, and modularity improvements across GiGL, enabling more scalable experiments and robust data handling. Implemented ABLP DataLoader enhancements with DistABLPLoader, extended sampling for heterogeneous graphs, and corrected batch handling; added configurable label-to-edge conversion; optimized distributed partitioning with edge-feature awareness; introduced InfiniteIterator for cyclic data iteration; and advanced modular retrieval loss and link prediction components to improve experimentation flexibility.
June 2025
May 2025
8 Commits • 3 Features
May 1, 2025May 2025 monthly summary for Snapchat/GiGL focusing on delivering production-ready end-to-end graph inference and performance improvements, stabilizing CI, and expanding test coverage. This period delivered four main threads: end-to-end GLT-enabled GraphLearn inference, data partitioning and asynchronous loading optimizations, targeted bug work to stabilize CI, and improved reliability through concurrency-focused tests. The work advances production readiness for heterogeneous graph workloads, improves data throughput and memory efficiency, and strengthens CI stability and test coverage across distributed loaders.
May 2025
8 Commits • 3 Features
May 1, 2025May 2025 monthly summary for Snapchat/GiGL focusing on delivering production-ready end-to-end graph inference and performance improvements, stabilizing CI, and expanding test coverage. This period delivered four main threads: end-to-end GLT-enabled GraphLearn inference, data partitioning and asynchronous loading optimizations, targeted bug work to stabilize CI, and improved reliability through concurrency-focused tests. The work advances production readiness for heterogeneous graph workloads, improves data throughput and memory efficiency, and strengthens CI stability and test coverage across distributed loaders.
April 2025
2 Commits • 2 Features
Apr 1, 2025April 2025 — Snapchat/GiGL: Delivered two high-impact features to strengthen distributed training/inference scalability and reproducibility. Implemented a memory-conscious range-based partitioner for distributed link prediction and extended the dataset factory with URI-based loading. No major bugs reported this period; changes provide clear API surfaces and traceable commits to support larger-scale experiments.
2 Commits • 2 Features
Apr 1, 2025April 2025 — Snapchat/GiGL: Delivered two high-impact features to strengthen distributed training/inference scalability and reproducibility. Implemented a memory-conscious range-based partitioner for distributed link prediction and extended the dataset factory with URI-based loading. No major bugs reported this period; changes provide clear API surfaces and traceable commits to support larger-scale experiments.
April 2025
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Quality Metrics
Correctness90.4%
Maintainability86.0%
Architecture87.0%
Performance77.8%
AI Usage20.4%
Skills & Technologies
Programming Languages
C++JinjaJupyter NotebookMakefileMarkdownPythonSQLShellTypeScriptYAML
Technical Skills
Algorithm OptimizationApache AvroApache BeamAsynchronous ProgrammingBackend DevelopmentBigQueryCI/CDCloud ComputingCode OrganizationCode RefactoringConfiguration ManagementData EngineeringData LoadingData PartitioningData Preprocessing
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