ydb-platform/ydb
Jun 2025 – Mar 2026
10 Months active
Languages Used
C++Protocol BuffersSQLprotobufProtojsonPythonJSON
Technical Skills
C++C++ DevelopmentCompiler OptimizationConfiguration ManagementDatabase InternalsDatabase Optimization
Denis Khalikov
PROFILE
Denis Khalikov
Over ten months, contributed to the ydb-platform/ydb repository by building and optimizing advanced OLAP and SQL query processing features. Focused on backend development in C++ and SQL, the work included implementing rule-based optimizers, aggregation logic, and predicate pushdown to improve analytical query performance and scalability. Enhanced the physical planning stack, expanded support for complex aggregations, and introduced parallel execution paths for large-scale analytics. Emphasized type safety, memory management, and robust unit testing to ensure reliability and maintainability. The technical approach combined code refactoring, benchmarking, and extensible architecture, resulting in faster, safer, and more flexible distributed query execution.
Overall Statistics
Feature vs Bugs
84%Features
Repository Contributions
125Total
Bugs
9
Commits
125
Features
46
Lines of code
153,348
Activity Months10
Your Network
580 peopleShared Repositories
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Work History
March 2026
7 Commits • 3 Features
Mar 1, 2026March 2026 performance summary for ydb-platform/ydb. Focused on strengthening type safety and expanding parallel data processing to boost reliability and throughput in multi-source environments, while enabling safer, faster OLAP queries and decimal-based analytics. Delivered RBO-oriented benchmarking support to accelerate optimizer iteration with minimal validation overhead.
7 Commits • 3 Features
Mar 1, 2026March 2026 performance summary for ydb-platform/ydb. Focused on strengthening type safety and expanding parallel data processing to boost reliability and throughput in multi-source environments, while enabling safer, faster OLAP queries and decimal-based analytics. Delivered RBO-oriented benchmarking support to accelerate optimizer iteration with minimal validation overhead.
March 2026
February 2026
21 Commits • 17 Features
Feb 1, 2026February 2026 — Delivered substantial RBO (Row-By-Operator) and OLAP enhancements for ydb-platform/ydb, with architecture improvements, expanded workload coverage, and testing. Focused on reliability, performance, and memory safety to enable larger analytics workloads and faster planning cycles.
February 2026
21 Commits • 17 Features
Feb 1, 2026February 2026 — Delivered substantial RBO (Row-By-Operator) and OLAP enhancements for ydb-platform/ydb, with architecture improvements, expanded workload coverage, and testing. Focused on reliability, performance, and memory safety to enable larger analytics workloads and faster planning cycles.
January 2026
22 Commits • 6 Features
Jan 1, 2026Month: 2026-01. Focused on delivering a robust upgrade to the physical planning and optimization stack in ydb-platform/ydb, with end-to-end improvements to the RBO-based execution path and associated builders, plus optimizer and channel enhancements. The work reduces plan generation time, improves execution efficiency for complex aggregations, and enables more scalable distributed planning. Key areas and deliverables include: - RBO-driven physical plan generation and stage graph enhancements: PhysicalAggregationBuilder and expanded aggregation support, wide lambdas, output index for connections, multi-consumer stages, Kqp and YQL peepholes, separation of physical stage graph construction, wide sorts, physical join builder, and elimination of pre-aggregation map, plus WideMap/WideFilter support in plan generation. - Optimizer enhancements: pushdown for left join on key predicates and a flag to create a stage for aggregation, enabling more aggressive, data-reducing execution plans. - Physical builders for RBO (operation, source, and query): added physical op builder, physical source builder, and physical query builder to enable consistent, extensible plan construction. - Channel and type handling improvements: wide channels and block propagation to improve throughput, and type preservation for first-level stage children to maintain type information through planning and execution. - Lambda packaging adjustments: disable packaging of wide lambdas to struct to simplify packaging paths and improve plan stability. Business impact: Reduced planning latency for complex queries, increased parallelism and plan quality for large/aggregated workloads, and improved maintainability and extensibility of the planning/optimizer stack.
22 Commits • 6 Features
Jan 1, 2026Month: 2026-01. Focused on delivering a robust upgrade to the physical planning and optimization stack in ydb-platform/ydb, with end-to-end improvements to the RBO-based execution path and associated builders, plus optimizer and channel enhancements. The work reduces plan generation time, improves execution efficiency for complex aggregations, and enables more scalable distributed planning. Key areas and deliverables include: - RBO-driven physical plan generation and stage graph enhancements: PhysicalAggregationBuilder and expanded aggregation support, wide lambdas, output index for connections, multi-consumer stages, Kqp and YQL peepholes, separation of physical stage graph construction, wide sorts, physical join builder, and elimination of pre-aggregation map, plus WideMap/WideFilter support in plan generation. - Optimizer enhancements: pushdown for left join on key predicates and a flag to create a stage for aggregation, enabling more aggressive, data-reducing execution plans. - Physical builders for RBO (operation, source, and query): added physical op builder, physical source builder, and physical query builder to enable consistent, extensible plan construction. - Channel and type handling improvements: wide channels and block propagation to improve throughput, and type preservation for first-level stage children to maintain type information through planning and execution. - Lambda packaging adjustments: disable packaging of wide lambdas to struct to simplify packaging paths and improve plan stability. Business impact: Reduced planning latency for complex queries, increased parallelism and plan quality for large/aggregated workloads, and improved maintainability and extensibility of the planning/optimizer stack.
January 2026
December 2025
22 Commits • 7 Features
Dec 1, 2025December 2025 monthly summary focusing on key accomplishments and business value for the ydb-platform/ydb repository. Delivered foundational RBO core initialization and enhanced aggregation, expanded YQL/TPCH capabilities and test coverage, and introduced pushdown optimizations and code extensibility patterns. Enabled more robust OLAP workloads via physical rule pushdowns and peephole enhancements, while stabilizing behavior around empty-key aggregations.
December 2025
22 Commits • 7 Features
Dec 1, 2025December 2025 monthly summary focusing on key accomplishments and business value for the ydb-platform/ydb repository. Delivered foundational RBO core initialization and enhanced aggregation, expanded YQL/TPCH capabilities and test coverage, and introduced pushdown optimizations and code extensibility patterns. Enabled more robust OLAP workloads via physical rule pushdowns and peephole enhancements, while stabilizing behavior around empty-key aggregations.
November 2025
14 Commits • 3 Features
Nov 1, 2025November 2025: Delivered major KQP RBO and OLAP enhancements for ydb-platform/ydb, enabling richer analytics, more flexible and correct SQL, and higher production reliability. Key features include advanced aggregations (count, count(*), distinct, distinct_all, avg), aggregation on expressions and across identical columns; SQL improvements such as group-by on expressions and IN with NULL; OLAP capabilities including OLAP table support and improved json_value compute projections; and a reliability upgrade via a YQL optimizer fallback when NewRBO compilation fails. These changes provide measurable business value: deeper analytics with fewer manual workarounds, safer query planning, and more resilient production deployments.
14 Commits • 3 Features
Nov 1, 2025November 2025: Delivered major KQP RBO and OLAP enhancements for ydb-platform/ydb, enabling richer analytics, more flexible and correct SQL, and higher production reliability. Key features include advanced aggregations (count, count(*), distinct, distinct_all, avg), aggregation on expressions and across identical columns; SQL improvements such as group-by on expressions and IN with NULL; OLAP capabilities including OLAP table support and improved json_value compute projections; and a reliability upgrade via a YQL optimizer fallback when NewRBO compilation fails. These changes provide measurable business value: deeper analytics with fewer manual workarounds, safer query planning, and more resilient production deployments.
November 2025
October 2025
8 Commits • 2 Features
Oct 1, 2025For 2025-10, delivered substantial KQP RBO enhancements and safety fixes in ydb-platform/ydb, expanding aggregation, grouping, and UNION ALL support in the KQP Rule-Based Optimizer, along with a critical OLAP projection pushdown safety fix. These changes improve analytical query performance, broaden supported patterns, reduce risk of incorrect projections, and reinforce engineering quality through tests and formatting.
October 2025
8 Commits • 2 Features
Oct 1, 2025For 2025-10, delivered substantial KQP RBO enhancements and safety fixes in ydb-platform/ydb, expanding aggregation, grouping, and UNION ALL support in the KQP Rule-Based Optimizer, along with a critical OLAP projection pushdown safety fix. These changes improve analytical query performance, broaden supported patterns, reduce risk of incorrect projections, and reinforce engineering quality through tests and formatting.
September 2025
2 Commits • 1 Features
Sep 1, 2025September 2025 monthly summary for ydb-platform/ydb focusing on performance optimization in the KQP (KQP Query Processing) path for OLAP workloads. Implemented targeted enhancements to predicate pushdown and plan simplification to improve runtime performance on large analytics datasets. Changes are designed to reduce plan complexity, lower latency for OLAP queries, and improve maintainability through clear, commit-traceable changes.
2 Commits • 1 Features
Sep 1, 2025September 2025 monthly summary for ydb-platform/ydb focusing on performance optimization in the KQP (KQP Query Processing) path for OLAP workloads. Implemented targeted enhancements to predicate pushdown and plan simplification to improve runtime performance on large analytics datasets. Changes are designed to reduce plan complexity, lower latency for OLAP queries, and improve maintainability through clear, commit-traceable changes.
September 2025
August 2025
9 Commits • 3 Features
Aug 1, 2025August 2025 monthly summary for repository ydb-platform/ydb: Delivered high-impact OLAP and KQP optimizer improvements, parallel execution enhancements, and targeted query optimizations that reduce latency on analytical workloads. Key features include OLAP Pushdown Enhancements and KQP Optimizer Improvements, TPCDS Q6 Analytic Query Optimization, and Parallel Union All/Extend Execution. Implemented safety toggles and type annotations to improve maintainability and reduce risk of regressions. Overall impact: faster OLAP query processing, improved decorrelation and execution efficiency, and more scalable parallel query execution for complex analytics.
August 2025
9 Commits • 3 Features
Aug 1, 2025August 2025 monthly summary for repository ydb-platform/ydb: Delivered high-impact OLAP and KQP optimizer improvements, parallel execution enhancements, and targeted query optimizations that reduce latency on analytical workloads. Key features include OLAP Pushdown Enhancements and KQP Optimizer Improvements, TPCDS Q6 Analytic Query Optimization, and Parallel Union All/Extend Execution. Implemented safety toggles and type annotations to improve maintainability and reduce risk of regressions. Overall impact: faster OLAP query processing, improved decorrelation and execution efficiency, and more scalable parallel query execution for complex analytics.
July 2025
13 Commits • 1 Features
Jul 1, 2025Month: 2025-07 — Delivered substantial enhancements to KQP-based query optimization and OLAP pushdown in ydb-platform/ydb, along with a robustness fix for constant folding. These efforts improved analytic performance, reduced data scanned in large BI workloads, and increased stability of optimization paths used in production analytics.
13 Commits • 1 Features
Jul 1, 2025Month: 2025-07 — Delivered substantial enhancements to KQP-based query optimization and OLAP pushdown in ydb-platform/ydb, along with a robustness fix for constant folding. These efforts improved analytic performance, reduced data scanned in large BI workloads, and increased stability of optimization paths used in production analytics.
July 2025
June 2025
7 Commits • 3 Features
Jun 1, 2025Monthly summary for 2025-06 (ydb-platform/ydb): This period focused on delivering performance-oriented query optimization features, expanding support for time-based predicates, and hardening the KQP constant folding path. The work improved latency and data locality, while increasing safety and test coverage across the KQP and storage layers.
June 2025
7 Commits • 3 Features
Jun 1, 2025Monthly summary for 2025-06 (ydb-platform/ydb): This period focused on delivering performance-oriented query optimization features, expanding support for time-based predicates, and hardening the KQP constant folding path. The work improved latency and data locality, while increasing safety and test coverage across the KQP and storage layers.
Activity
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Quality Metrics
Correctness86.0%
Maintainability82.2%
Architecture83.2%
Performance81.0%
AI Usage26.8%
Skills & Technologies
Programming Languages
C++JSONProtoProtocol BuffersPythonSQLYQLjsonprotobuf
Technical Skills
Aggregation LogicBackend DevelopmentC++C++ DevelopmentC++ ProgrammingC++ developmentC++ programmingCode FormattingCode OptimizationCode RefactoringCode optimizationCompiler DesignCompiler DevelopmentCompiler OptimizationConfiguration Management
Repositories Contributed To
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