Overview

Last week we released the first RC of GreptimeDB v1.0. This roadmap outlines the objectives for GreptimeDB in 2026. Following the v1.0 GA release, we will continue to optimize performance and resource management, enhance observability ecosystem capabilities, and gradually introduce more advanced features.

Milestones

Version	Timeline	Highlights
v1.0 GA	End of March	Production ready release
v1.1	Q2	Remote Compaction/Indexing production ready, Metric Engine optimization, Vector Index and AI Functions, Import/Export Tool v2, JSON Type v2
v1.2	Q3	Adaptive Resource Management (Phase 1), Auto Rollup, Flow Engine enhancements, Major compaction
v1.3	Q4	Adaptive Resource Management (Phase 2), Pandas DataFrame SQL query, Log context search, Open table format compatibility

Details

High Availability & Reliability

• Remote Compaction/Indexing: testing, validation, production ready
• Hot configuration reload: config changes without restart

Database Operability

• Import/Export Tool v2: enhanced data migration and backup
• Major compaction: force deletion, data reorganization, index rebuild for historical data

Adaptive Resource Management

Beyond ongoing ingestion and query performance improvements, Adaptive Resource Management is our most significant engineering effort this year, delivered in two phases:

• Fine-grained memory tracking and control
• Cost-based adaptive scheduling
• Zero-tuning adaptive spilling
• Adaptive cache management
• Workload-aware compaction scheduling
• Resource quota & admission control

Metric Engine Optimization

• Bulk ingestion for OSS: enterprise capability available in open source
• Compaction optimization
• Metadata management optimization: high cardinality scenarios
• Prometheus remote write 2.0 support
• Automatic multi-value optimization

Flow Engine

• Extended time windows and aggregation functions

Logs

• JSON Type v2: field-level index, dynamic fields
• Log context search: similar to grep -C in *nix systems

New Features

• Vector Index and AI Functions
• Auto Rollup: automatic aggregation and downsampling
• Pandas DataFrame SQL query: query DataFrames with SQL, zero-copy
• Open table format compatibility: Iceberg/DeltaLake integration

Notes

• Version timelines are estimates and may be adjusted based on actual progress
• Community participation in discussions and contributions is welcome

Get Involved

For tasks with tracking issues, feel free to jump in and comment directly. If you have ideas or suggestions, leave a comment on the tracking issue, start a thread on GitHub Discussions, or join the GreptimeDB Community on Slack.