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- There are huge volumes of data in ClickHouse databases, viewing them as graph data with graph analytics capability brings another level of abstraction and boosts productivity with graph tools, in ways beyond relational analytics alone.
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- Research shows relational analytics with columnar stores and vectorized execution engines like ClickHouse provide superior analytical performance and scalability to graph-native technologies, which usually leverage explicit adjacency representations and are more suitable for local-area graph traversals.
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- View-based graph analytics offer the benefits of zero-ETL without the hassle of data migration and duplicate cost, yet better performance and scalability than most of the native graph analytics options.
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- Neo4j Bolt protocol support gives instant access to the tools available, including graph visualization and the MCP server.
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- Neo4j Bolt protocol support gives access to the tools available based on the Bolt protocol.
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## 🚀 What's New in v0.6.1 (January 12, 2026)
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### Development Release: WITH Clause Fixes & GraphRAG Enhancements
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**v0.6.1 brings critical WITH clause fixes, GraphRAG multi-type VLP support, and LDBC SNB benchmark progress (15/41 queries passing).**
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## 🚀 What's New in v0.6.2-dev
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### Highlights ✨
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-**WITH Clause Fixes** - Fixed CartesianProduct recursion and chained WITH CTE remapping (+6 LDBC queries)
-**Configurable Type Inference** - `max_inferred_types` parameter for complex GraphRAG schemas (default: 5)
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-**OpenCypher Per-MATCH WHERE** - Each MATCH clause can have its own WHERE (9/9 tests passing)
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-**Multiple UNWIND Clauses** - Cartesian product support for multiple consecutive UNWIND clauses (e.g., `UNWIND [1,2] AS x UNWIND [10,20] AS y` → 4 rows)
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-**Pattern Comprehensions** - Extract values from graph patterns into lists with `[(pattern) | expression]` syntax
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### Bug Fixes 🐛
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-**Type inference** - Bottom-up processing for multi-hop pattern label resolution
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-**Denormalization metadata** - Copy `is_denormalized`, `from_node_properties`, `to_node_properties` from schema
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-**VLP ID columns** - Use relationship schema columns (`from_id`/`to_id`)
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-**Cycle prevention** - Skip for single-hop patterns (can't have cycles)
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-**Neo4j Browser Support** - Connect Neo4j Browser directly to ClickGraph via Bolt protocol for live graph visualization. See [`demos/neo4j-browser/`](demos/neo4j-browser/README.md) for a ready-to-run demo.
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-**Graph-Notebook Support** - Run Jupyter graph notebooks against your ClickHouse data using the `graph-notebook` library. See [`demos/graph-notebook/`](demos/graph-notebook/README.md) for setup instructions.
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-**Numerous rounds of refactoring** - help to improve the code quality, including the following.
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-**Improved WITH Clause Correctness** - Chained `WITH` queries (multi-step aggregation, filtering, and renaming) now produce correct results across a wider range of patterns.
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-**More Reliable Query Results** - Fixed result ordering, column projection in UNION queries, and variable resolution after `WITH` — queries return what Cypher semantics require.
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-**LDBC SNB Progress** - 14/37 benchmark queries passing (38%), up from 10/37 (27%).
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-**Test Coverage** - 1,032 unit tests passing; 3,026 integration tests at parity with prior release.
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