ydb-platform/ydb
Jun 2025 – Feb 2026
9 Months active
Languages Used
C++PythonprotobufProto
Technical Skills
Actor ModelAlgorithm DevelopmentBackend DevelopmentC++Configuration ManagementDistributed Systems
Ivan
PROFILE
Ivan
Over a nine-month period, contributed to the ydb-platform/ydb repository by building and refining core backend systems focused on query scheduling, resource management, and reliability. Leveraging C++ and the actor model, delivered features such as a dynamic HDRF scheduler, configurable memory buffers, and advanced KQP executor refactoring to improve throughput and observability. Addressed concurrency and deadlock issues through targeted bug fixes and enhanced exception handling, while optimizing memory profiling and partition pruning for performance. The work emphasized maintainable code organization, cross-platform stability, and configuration-driven behavior, resulting in a more robust, efficient, and scalable distributed database platform for complex workloads.
Overall Statistics
Feature vs Bugs
73%Features
Repository Contributions
46Total
Bugs
8
Commits
46
Features
22
Lines of code
15,742
Activity Months9
Your Network
603 peopleSame Organization
@yandex-team.ru124
Shared Repositories
479
Aleksandr DuplishchevMember
3y3k0Member
a-dyuMember
Anton RomanovMember
Andrey MaslovMember
aaprokopyevMember
aavorotnikovMember
abityugovMember
abramov-alexMember
Work History
February 2026
2 Commits • 2 Features
Feb 1, 2026February 2026 — Delivered two performance-focused features in ydb-platform/ydb that improve query scheduling and data-partition handling, driving higher throughput and better resource utilization. Key outputs include enabling CPU Scheduler fair-share overlimit for equal-to-parent mode and refactoring partition pruning to be outside the TasksGraph, resulting in more efficient query execution. These changes reduce contention in multi-tenant workloads and simplify future optimizations.
2 Commits • 2 Features
Feb 1, 2026February 2026 — Delivered two performance-focused features in ydb-platform/ydb that improve query scheduling and data-partition handling, driving higher throughput and better resource utilization. Key outputs include enabling CPU Scheduler fair-share overlimit for equal-to-parent mode and refactoring partition pruning to be outside the TasksGraph, resulting in more efficient query execution. These changes reduce contention in multi-tenant workloads and simplify future optimizations.
February 2026
January 2026
5 Commits • 3 Features
Jan 1, 2026January 2026 | ydb-platform/ydb. Focused on delivering robust scheduling features, improving concurrency safety, and clarifying KQP graph operations, with an emphasis on business value and maintainability.
January 2026
5 Commits • 3 Features
Jan 1, 2026January 2026 | ydb-platform/ydb. Focused on delivering robust scheduling features, improving concurrency safety, and clarifying KQP graph operations, with an emphasis on business value and maintainability.
December 2025
3 Commits • 1 Features
Dec 1, 2025Monthly summary for 2025-12 focused on stability, concurrency, and resource management improvements in ydb-platform/ydb. Key work stabilized compute actor lifecycle, reduced deadlocks in the task scheduler, and enhanced resource pool classification to handle no-user and system-user scenarios, improving resource allocation consistency and reliability across the platform.
3 Commits • 1 Features
Dec 1, 2025Monthly summary for 2025-12 focused on stability, concurrency, and resource management improvements in ydb-platform/ydb. Key work stabilized compute actor lifecycle, reduced deadlocks in the task scheduler, and enhanced resource pool classification to handle no-user and system-user scenarios, improving resource allocation consistency and reliability across the platform.
December 2025
November 2025
4 Commits • 3 Features
Nov 1, 20252025-11 monthly summary for repository ydb-platform/ydb. Focused on delivering reliability, observability, and efficiency improvements across buffer management, query lifecycle, memory profiling, and node state handling. Implementations were delivered with clear business value: simplified configuration, reduced risk of runtime faults, enhanced monitoring, and better resource management.
November 2025
4 Commits • 3 Features
Nov 1, 20252025-11 monthly summary for repository ydb-platform/ydb. Focused on delivering reliability, observability, and efficiency improvements across buffer management, query lifecycle, memory profiling, and node state handling. Implementations were delivered with clear business value: simplified configuration, reduced risk of runtime faults, enhanced monitoring, and better resource management.
October 2025
7 Commits • 3 Features
Oct 1, 2025Oct 2025 focused on reliability, observability, and robustness in the KQP engine, Compute Scheduler, and actor error handling across the ydb platform. Delivered three major features with concrete improvements and contributed fixes, resulting in more stable execution, improved scheduling fairness, and broader exception handling. The work enhances diagnostics, reduces downtime, and supports more scalable runtime behavior.
7 Commits • 3 Features
Oct 1, 2025Oct 2025 focused on reliability, observability, and robustness in the KQP engine, Compute Scheduler, and actor error handling across the ydb platform. Delivered three major features with concrete improvements and contributed fixes, resulting in more stable execution, improved scheduling fairness, and broader exception handling. The work enhances diagnostics, reduces downtime, and supports more scalable runtime behavior.
October 2025
September 2025
5 Commits • 1 Features
Sep 1, 2025September 2025 performance summary for ydb KQP area: Delivered a core refactor and stability fixes that improve reliability, maintainability, and data processing correctness. Focus areas included: (1) Kqp Core Refactor: Task Graph and Executer Consolidation to a dedicated class, centralizing task-graph management and metadata and reducing duplication across execution paths; (2) major bugs fixed: HashShuffle propagation (prevent mixing of scalar and block connections), AddQuery response delivery (always send response to avoid deadlocks), and enforcement of ArrayBufferMinFillPercentage from configuration; (3) overall impact: more robust KQP optimizer data handling, elimination of potential deadlocks, and consistent configurability. Technologies/skills demonstrated: C++ refactoring, architecture consolidation, concurrency/dataflow correctness, and configuration-driven behavior.
September 2025
5 Commits • 1 Features
Sep 1, 2025September 2025 performance summary for ydb KQP area: Delivered a core refactor and stability fixes that improve reliability, maintainability, and data processing correctness. Focus areas included: (1) Kqp Core Refactor: Task Graph and Executer Consolidation to a dedicated class, centralizing task-graph management and metadata and reducing duplication across execution paths; (2) major bugs fixed: HashShuffle propagation (prevent mixing of scalar and block connections), AddQuery response delivery (always send response to avoid deadlocks), and enforcement of ArrayBufferMinFillPercentage from configuration; (3) overall impact: more robust KQP optimizer data handling, elimination of potential deadlocks, and consistent configurability. Technologies/skills demonstrated: C++ refactoring, architecture consolidation, concurrency/dataflow correctness, and configuration-driven behavior.
August 2025
7 Commits • 2 Features
Aug 1, 2025Month: 2025-08. The ydb-platform/ydb team delivered a major modernization of the KQP system, focused on scheduling reliability, resource utilization, and execution flexibility, with targeted improvements across the compute path and query execution graph handling. Key deliverables: - HDRF-based KQP compute scheduler integration with dynamic throttling resume under under-utilized fair-share, improving utilization of idle compute capacity. - Proactive pool and database registration and explicit resource initialization before each query to ensure consistent startup behavior and reduced latency spikes. - Efficient cleanup of finished queries from the compute scheduler to reduce stale state and improve scheduling throughput. - Support for pre-generated task graphs in KQP Data/Scan executors and a refactor to allow executors to consume pre-generated graphs for faster, more predictable execution. - KQP Scan Query LLVM optimization toggle via config flag to enable/disable LLVM-based enhancements for scan queries. Commits illustrating the work include: 96c86f315793d87ff6696fb29aae52298463c622 (Use new HDRF compute scheduler), 1c921e3763d163027e5c819efbc6d97c358cf279 (Resume throttled tasks when the pool has under-utilized fair-share), 52aa81f626ea3c53903508d5feec8100f3d6fa59 (Add pool to compute scheduler as soon as possible), 9a4d6cd6ac60a0a12d28b3f0bdfa9268ddf188a8 (Remove finished queries from compute scheduler), 9eda0afd788007cc83320ad35433d75f51d8fc13 (Deliberately add database and pool before each query), 27e81c9014555418a807d7358c090d6af4503912 (Refactor executor to accept pre-generated graph with tasks), and 5a841fa7d7c59e2888064497aa63225ef213ec7a (Add config flag for scan query LLVM usage).
7 Commits • 2 Features
Aug 1, 2025Month: 2025-08. The ydb-platform/ydb team delivered a major modernization of the KQP system, focused on scheduling reliability, resource utilization, and execution flexibility, with targeted improvements across the compute path and query execution graph handling. Key deliverables: - HDRF-based KQP compute scheduler integration with dynamic throttling resume under under-utilized fair-share, improving utilization of idle compute capacity. - Proactive pool and database registration and explicit resource initialization before each query to ensure consistent startup behavior and reduced latency spikes. - Efficient cleanup of finished queries from the compute scheduler to reduce stale state and improve scheduling throughput. - Support for pre-generated task graphs in KQP Data/Scan executors and a refactor to allow executors to consume pre-generated graphs for faster, more predictable execution. - KQP Scan Query LLVM optimization toggle via config flag to enable/disable LLVM-based enhancements for scan queries. Commits illustrating the work include: 96c86f315793d87ff6696fb29aae52298463c622 (Use new HDRF compute scheduler), 1c921e3763d163027e5c819efbc6d97c358cf279 (Resume throttled tasks when the pool has under-utilized fair-share), 52aa81f626ea3c53903508d5feec8100f3d6fa59 (Add pool to compute scheduler as soon as possible), 9a4d6cd6ac60a0a12d28b3f0bdfa9268ddf188a8 (Remove finished queries from compute scheduler), 9eda0afd788007cc83320ad35433d75f51d8fc13 (Deliberately add database and pool before each query), 27e81c9014555418a807d7358c090d6af4503912 (Refactor executor to accept pre-generated graph with tasks), and 5a841fa7d7c59e2888064497aa63225ef213ec7a (Add config flag for scan query LLVM usage).
August 2025
July 2025
7 Commits • 3 Features
Jul 1, 2025Monthly summary for 2025-07 across the ydb-platform/ydb repository. Focused on delivering observability, reliability, and performance improvements with config-driven capabilities and cross-platform stability. Key features delivered include advanced KQP Scheduler instrumentation and refactor, HashV2 hashing algorithm support enabled via configuration, MKQL Arrow default allocator integration, and a Windows build stability fix. These efforts improved query observability and scheduling reliability, provided configurable hashing for data distribution and integrity, and enhanced memory management for MKQL workloads while ensuring Windows builds remain stable. Top achievements for 2025-07: - KQP Scheduler Instrumentation and Refactor (feature): Instrumentation, introspection, and structural refactor of the KQP scheduler to improve observability and scheduling reliability for existing queries, including CPU accounting adjustments. Commits: 2e3f1cb8db4d7b511010f35b78ef83ff4a6c1b43; a5cd972162d55fccb611128a64aa8ece364167a9; af0e69c6ff56bb31f256bf34b7c292fcfbb8f3f2; 53e0418cb3697140dceb9c0d0b1d7aa570988785. - Windows build stability: conditional signal backtrace (bug): Fix Windows build by conditionally compiling and linking the signal backtrace library only on non-Windows platforms to prevent build failures. Commit: 60134814641d0e49c02a238226c1350774a2e638. - HashV2 hashing algorithm support (feature): Introduce HashV2 as a new hashing algorithm and enable configuration-based selection across KQP components. Commit: 22022022f9375b09b9c84e50fc60a2f2efa1d1f5. - MKQL Arrow default allocator integration (feature): Enable a default system Arrow allocator in MKQL to improve memory management when enabled by configuration. Commit: 7d89457a693f442a5e61af6d5ea761417d024525. Overall impact and accomplishments: - Improved observability, scheduling reliability, and CPU accounting accuracy for long-running queries. Enhanced cross-platform stability and reduced build failures on Windows. Enabled configurable memory and hashing behaviors to optimize workloads. These changes reduce latency, improve throughput, and simplify future maintenance and feature work. Technologies/skills demonstrated: - Observability instrumentation, scheduler introspection, and refactoring; CPU accounting adjustments - Cross-platform build hygiene and conditional compilation - Config-driven feature toggles for hashing (HashV2) and memory allocators (MKQL Arrow) - Memory management strategies and allocator integration for MKQL pipelines.
July 2025
7 Commits • 3 Features
Jul 1, 2025Monthly summary for 2025-07 across the ydb-platform/ydb repository. Focused on delivering observability, reliability, and performance improvements with config-driven capabilities and cross-platform stability. Key features delivered include advanced KQP Scheduler instrumentation and refactor, HashV2 hashing algorithm support enabled via configuration, MKQL Arrow default allocator integration, and a Windows build stability fix. These efforts improved query observability and scheduling reliability, provided configurable hashing for data distribution and integrity, and enhanced memory management for MKQL workloads while ensuring Windows builds remain stable. Top achievements for 2025-07: - KQP Scheduler Instrumentation and Refactor (feature): Instrumentation, introspection, and structural refactor of the KQP scheduler to improve observability and scheduling reliability for existing queries, including CPU accounting adjustments. Commits: 2e3f1cb8db4d7b511010f35b78ef83ff4a6c1b43; a5cd972162d55fccb611128a64aa8ece364167a9; af0e69c6ff56bb31f256bf34b7c292fcfbb8f3f2; 53e0418cb3697140dceb9c0d0b1d7aa570988785. - Windows build stability: conditional signal backtrace (bug): Fix Windows build by conditionally compiling and linking the signal backtrace library only on non-Windows platforms to prevent build failures. Commit: 60134814641d0e49c02a238226c1350774a2e638. - HashV2 hashing algorithm support (feature): Introduce HashV2 as a new hashing algorithm and enable configuration-based selection across KQP components. Commit: 22022022f9375b09b9c84e50fc60a2f2efa1d1f5. - MKQL Arrow default allocator integration (feature): Enable a default system Arrow allocator in MKQL to improve memory management when enabled by configuration. Commit: 7d89457a693f442a5e61af6d5ea761417d024525. Overall impact and accomplishments: - Improved observability, scheduling reliability, and CPU accounting accuracy for long-running queries. Enhanced cross-platform stability and reduced build failures on Windows. Enabled configurable memory and hashing behaviors to optimize workloads. These changes reduce latency, improve throughput, and simplify future maintenance and feature work. Technologies/skills demonstrated: - Observability instrumentation, scheduler introspection, and refactoring; CPU accounting adjustments - Cross-platform build hygiene and conditional compilation - Config-driven feature toggles for hashing (HashV2) and memory allocators (MKQL Arrow) - Memory management strategies and allocator integration for MKQL pipelines.
June 2025
6 Commits • 4 Features
Jun 1, 2025June 2025 monthly performance summary for ydb-platform/ydb: Focused delivery on scheduling robustness and memory management improvements, with deployment observability enhancements. Implemented HDRF scheduler foundation and a more robust dynamic allocation algorithm to boost task execution, throttling, and delay responsiveness. Added startup-configurable memory buffer page size and corrected BufferPageAllocSize data tracking to ensure accurate memory accounting. Refactored KQP scheduler compute and schedulable actors to simplify execution flow, adjusted throttling and resuming behavior, and introduced a delay histogram to improve scheduling fairness. Enabled verbose memory limit exceptions by default in deployment configuration for better visibility and quicker debugging. Key changes are traceable to commits across #19618, #19893, #19724, #20036, #20195, and #20040.
6 Commits • 4 Features
Jun 1, 2025June 2025 monthly performance summary for ydb-platform/ydb: Focused delivery on scheduling robustness and memory management improvements, with deployment observability enhancements. Implemented HDRF scheduler foundation and a more robust dynamic allocation algorithm to boost task execution, throttling, and delay responsiveness. Added startup-configurable memory buffer page size and corrected BufferPageAllocSize data tracking to ensure accurate memory accounting. Refactored KQP scheduler compute and schedulable actors to simplify execution flow, adjusted throttling and resuming behavior, and introduced a delay histogram to improve scheduling fairness. Enabled verbose memory limit exceptions by default in deployment configuration for better visibility and quicker debugging. Key changes are traceable to commits across #19618, #19893, #19724, #20036, #20195, and #20040.
June 2025
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Quality Metrics
Correctness85.6%
Maintainability84.0%
Architecture83.4%
Performance77.4%
AI Usage22.2%
Skills & Technologies
Programming Languages
C++ProtoPythonprotobuf
Technical Skills
Actor ModelAlgorithm DevelopmentBackend DevelopmentBug FixingBuild SystemsC++C++ DevelopmentC++ developmentCode OrganizationCode RefactoringCompute SchedulingConcurrencyConfiguration ManagementCore DevelopmentCross-Platform Development
Repositories Contributed To
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