datalens-tech/datalens-backend
Nov 2024 – Apr 2026
9 Months active
Languages Used
PythonYAMLTOML
Technical Skills
API DevelopmentAPI IntegrationAPI SecurityAWS S3 integrationAbstract Base ClassesAsynchronous Programming
Nick Proskurin
PROFILE
Nick Proskurin
Over nine months, Mikhail contributed to the datalens-tech/datalens-backend repository, delivering features and fixes that improved backend reliability, data integrity, and developer workflows. He enhanced data source validation by introducing title-aware checks, refactored query execution paths for maintainability, and optimized formula parsing for performance. Using Python, SQLAlchemy, and AWS SDKs, Mikhail upgraded dependencies to ensure compatibility and security, stabilized CI pipelines, and addressed edge cases in date arithmetic for Oracle databases. His work included robust error handling, modular code organization, and targeted test improvements, resulting in a more stable, maintainable backend that supports faster feature delivery and higher data quality.
Overall Statistics
Feature vs Bugs
61%Features
Repository Contributions
32Total
Bugs
9
Commits
32
Features
14
Lines of code
5,919
Activity Months9
Your Network
126 peopleSame Organization
@yandex-team.ru113
Work History
April 2026
1 Commits
Apr 1, 2026April 2026 (2026-04) highlights a focused improvement in query robustness for datalens-backend. Delivered a targeted bug fix that introduces validation for unknown fields in the BEFORE FILTER BY clause (and related IGNORE DIMENSIONS path), including a new error class and error handling to prevent execution when undefined fields are referenced. This work aligns with BI-5886/BI-0 and reduces production incidents by surfacing invalid queries earlier, improving data quality and user trust. The changes lay groundwork for stronger input validation, clearer error signaling, and easier triage for data teams.
1 Commits
Apr 1, 2026April 2026 (2026-04) highlights a focused improvement in query robustness for datalens-backend. Delivered a targeted bug fix that introduces validation for unknown fields in the BEFORE FILTER BY clause (and related IGNORE DIMENSIONS path), including a new error class and error handling to prevent execution when undefined fields are referenced. This work aligns with BI-5886/BI-0 and reduces production incidents by surfacing invalid queries earlier, improving data quality and user trust. The changes lay groundwork for stronger input validation, clearer error signaling, and easier triage for data teams.
April 2026
February 2026
1 Commits
Feb 1, 2026February 2026 monthly performance summary for datalens-backend. Key features delivered: None this month; Major bugs fixed: PostgreSQL Execution Adapter flaky test reliability. Summary: The team stabilized the PostgreSQL Execution Adapter tests by refining the query logic to ensure only the current connection's active queries are considered, significantly reducing flaky failures. This improvement enhances CI determinism and reduces debugging time, enabling faster iteration on backend changes. Impact: more deterministic test outcomes, improved release readiness, and increased developer velocity. Technologies/skills demonstrated: SQL query refinement, test stability engineering, robust bug-fix discipline, and traceable commits.
February 2026
1 Commits
Feb 1, 2026February 2026 monthly performance summary for datalens-backend. Key features delivered: None this month; Major bugs fixed: PostgreSQL Execution Adapter flaky test reliability. Summary: The team stabilized the PostgreSQL Execution Adapter tests by refining the query logic to ensure only the current connection's active queries are considered, significantly reducing flaky failures. This improvement enhances CI determinism and reduces debugging time, enabling faster iteration on backend changes. Impact: more deterministic test outcomes, improved release readiness, and increased developer velocity. Technologies/skills demonstrated: SQL query refinement, test stability engineering, robust bug-fix discipline, and traceable commits.
January 2026
1 Commits
Jan 1, 2026January 2026: Date arithmetic edge-case fix for month-based intervals in datalens-backend. Introduced a robust date-addition implementation to properly handle edge cases (e.g., January 31st) and address Oracle date handling issues. This change improves reliability of date calculations for scheduling and monthly reporting, reducing downstream errors. Commit 2b76c5098ba4de9ec8c5d16bec34f7b5185a810f (fix(formula): BI-0 fix Oracle's 'date not valid for month specified' error).
1 Commits
Jan 1, 2026January 2026: Date arithmetic edge-case fix for month-based intervals in datalens-backend. Introduced a robust date-addition implementation to properly handle edge cases (e.g., January 31st) and address Oracle date handling issues. This change improves reliability of date calculations for scheduling and monthly reporting, reducing downstream errors. Commit 2b76c5098ba4de9ec8c5d16bec34f7b5185a810f (fix(formula): BI-0 fix Oracle's 'date not valid for month specified' error).
January 2026
July 2025
1 Commits • 1 Features
Jul 1, 2025July 2025: Focused on data source validation and backend data integrity. Delivered a title-aware data source validation enhancement in datalens-backend, introduced add_dataset_source, and refactored make_dataset to use it. Reduced duplicates by title and improved code organization and reusability, laying groundwork for broader data-source validation rules.
July 2025
1 Commits • 1 Features
Jul 1, 2025July 2025: Focused on data source validation and backend data integrity. Delivered a title-aware data source validation enhancement in datalens-backend, introduced add_dataset_source, and refactored make_dataset to use it. Reduced duplicates by title and improved code organization and reusability, laying groundwork for broader data-source validation rules.
March 2025
1 Commits • 1 Features
Mar 1, 2025Month: 2025-03 — Datalens Backend (datalens-tech/datalens-backend). Key feature delivered: AWS SDK Libraries Upgrade. Updated aiobotocore, boto3, and botocore across multiple project configurations to ensure compatibility and leverage newer features or fixes in the AWS SDK. Implemented via commit 1d2e885fabd593852eaf2d95aec9f5a1185cb7e6 (deps: BI-6043 bump (aio)botocore and boto3 versions).
1 Commits • 1 Features
Mar 1, 2025Month: 2025-03 — Datalens Backend (datalens-tech/datalens-backend). Key feature delivered: AWS SDK Libraries Upgrade. Updated aiobotocore, boto3, and botocore across multiple project configurations to ensure compatibility and leverage newer features or fixes in the AWS SDK. Implemented via commit 1d2e885fabd593852eaf2d95aec9f5a1185cb7e6 (deps: BI-6043 bump (aio)botocore and boto3 versions).
March 2025
February 2025
3 Commits • 2 Features
Feb 1, 2025February 2025 monthly summary for datalens-backend: Delivered two strategic backend improvements focused on stability, performance, and maintainability. Upgraded the Snowflake connector to version 3.13.1 across configuration files, unlocking stability enhancements, performance gains, and access to new features in the Snowflake ecosystem. Completed a Query Layer refactor by removing abstract base classes (QueryExecutorBase and RawQueryCompilerBase) and inline/renaming functionality into concrete implementations, simplifying the execution path and reducing technical debt. These efforts establish a stronger foundation for faster feature delivery, easier maintenance, and more predictable deployments.
February 2025
3 Commits • 2 Features
Feb 1, 2025February 2025 monthly summary for datalens-backend: Delivered two strategic backend improvements focused on stability, performance, and maintainability. Upgraded the Snowflake connector to version 3.13.1 across configuration files, unlocking stability enhancements, performance gains, and access to new features in the Snowflake ecosystem. Completed a Query Layer refactor by removing abstract base classes (QueryExecutorBase and RawQueryCompilerBase) and inline/renaming functionality into concrete implementations, simplifying the execution path and reducing technical debt. These efforts establish a stronger foundation for faster feature delivery, easier maintenance, and more predictable deployments.
January 2025
8 Commits • 4 Features
Jan 1, 2025January 2025 (2025-01) backend-focused delivery for datalens-backend with emphasis on performance, reliability, and CI accuracy. Highlights include: (1) Formula field handling improvements and avatar deletion tests—regex optimization for BIField.rename_in_formula, caching optimizations for MultiQueryMutatorFactory, plus tests verifying avatar deletion behavior. (2) Flexible task scheduling with external instance_id to enable external control of task instance identification. (3) CI and package-change detection improvements, with Path-based change detection and refined parsing to accurately detect affected packages (newline delimiter and trimming). (4) Test infrastructure stabilization and constants centralization to improve test reliability and consistency (common test creds, stabilized mypy/conftest setup). (5) Refined dataset permission checks to exclude ignored sources, reducing unnecessary denials. Overall impact: more reliable data operations, faster and more deterministic CI feedback, easier integration with external task controllers, and a more maintainable test suite. Technologies/skills demonstrated: Python, Pathlib usage, advanced regex optimization, caching patterns for factory classes, test infrastructure modernization (constants, mypy, conftest), and disciplined change-detection in CI pipelines. Business value: improved feature delivery cadence, fewer false positives in permissions, and higher confidence in releases due to stable tests and clearer ownership of task identifiers.
8 Commits • 4 Features
Jan 1, 2025January 2025 (2025-01) backend-focused delivery for datalens-backend with emphasis on performance, reliability, and CI accuracy. Highlights include: (1) Formula field handling improvements and avatar deletion tests—regex optimization for BIField.rename_in_formula, caching optimizations for MultiQueryMutatorFactory, plus tests verifying avatar deletion behavior. (2) Flexible task scheduling with external instance_id to enable external control of task instance identification. (3) CI and package-change detection improvements, with Path-based change detection and refined parsing to accurately detect affected packages (newline delimiter and trimming). (4) Test infrastructure stabilization and constants centralization to improve test reliability and consistency (common test creds, stabilized mypy/conftest setup). (5) Refined dataset permission checks to exclude ignored sources, reducing unnecessary denials. Overall impact: more reliable data operations, faster and more deterministic CI feedback, easier integration with external task controllers, and a more maintainable test suite. Technologies/skills demonstrated: Python, Pathlib usage, advanced regex optimization, caching patterns for factory classes, test infrastructure modernization (constants, mypy, conftest), and disciplined change-detection in CI pipelines. Business value: improved feature delivery cadence, fewer false positives in permissions, and higher confidence in releases due to stable tests and clearer ownership of task identifiers.
January 2025
December 2024
2 Commits
Dec 1, 2024December 2024: Backend stability, security data flow, and formula engine reliability improvements for datalens-backend. Focused on enabling correct Row-Level Security (RLS) processing and optimizing conditional evaluation in the formula engine; both with targeted tests and clear commit-level traceability.
December 2024
2 Commits
Dec 1, 2024December 2024: Backend stability, security data flow, and formula engine reliability improvements for datalens-backend. Focused on enabling correct Row-Level Security (RLS) processing and optimizing conditional evaluation in the formula engine; both with targeted tests and clear commit-level traceability.
November 2024
14 Commits • 6 Features
Nov 1, 2024November 2024 monthly summary for datalens-backend focusing on delivering high-value features, enhancing performance, improving reliability, and strengthening build/serialization hygiene. The work emphasizes business value through reduced operation costs, faster data transfer, and more robust connection handling.
14 Commits • 6 Features
Nov 1, 2024November 2024 monthly summary for datalens-backend focusing on delivering high-value features, enhancing performance, improving reliability, and strengthening build/serialization hygiene. The work emphasizes business value through reduced operation costs, faster data transfer, and more robust connection handling.
November 2024
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Quality Metrics
Correctness90.0%
Maintainability88.6%
Architecture84.6%
Performance84.0%
AI Usage20.6%
Skills & Technologies
Programming Languages
PythonTOMLYAML
Technical Skills
API DevelopmentAPI IntegrationAPI SecurityAWS S3 integrationAWS SDKAbstract Base ClassesAsynchronous ProgrammingBackend DevelopmentBuild ToolsCI/CDCode CleanupCode ModularityCode OptimizationConstants ManagementData Connectors
Repositories Contributed To
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