apache/geaflow

What & why GeaFlow has PageRank and ClosenessCentrality, but not Betweenness Centrality — a measure of how often a vertex sits on shortest paths between other vertices, i.e. how much of a "bridge" it is. It's a core metric for social-network and key-node analysis. It's also genuinely hard: the classic Brandes algorithm has two phases (shortest-path counting + dependency accumulation), which makes it a satisfying high-difficulty challenge. GeaFlow 有 PageRank、ClosenessCentrality,但缺介数中心性(Betweenness)——衡量一个 顶点作为"最短路径桥梁"的重要性,是社交网络/关键节点分析的核心指标。它实现难度大 (经典 Brandes 算法需要两阶段:最短路径计数 + 依赖累积),非常适合作为高难度挑战题。 The task Implement Betweenness Centrality on GeaFlow's vertex-centric / message-passing model (an unweighted-graph version is a fine first cut). 基于 GeaFlow 的顶点中心/消息传递模型实现介数中心性(可先做无

What & why There are 7 store backends today, but none is a zero-external-dependency embedded option: SQLite (embedded SQL) or LevelDB (embedded KV). For single-node / edge / testing scenarios, this is lighter than RocksDB and adds persistence over Memory — a clear value gap. 现有 store 后端 7 种,但缺少零外部依赖的嵌入式选项:SQLite(嵌入式 SQL)或 LevelDB(嵌入式 KV)。对单机/边缘/测试场景,比 RocksDB 更轻、比 Memory 多了持久化,价值明确。 The task Create geaflow-store-sqlite (or -leveldb), implementing the storage SPI with basic KV + graph storage and state archive/recovery. 新建 geaflow-store-sqlite(或 -leveldb),实现存储 SPI,支持 KV 与图存储基本能力 + 状态归档/恢复。 Where to look / 怎么做 Simple baseline: geaflow-store-memory. Persistence/versioning: geaflow-store-rocksdb. Relational: geaflow-store-jdbc. Implement XxxStoreBuilder (IStoreBuilder) + the matching IGraphStore / IKVStore / IStatefulStore. SPI regis

What & why The project already has a Redis store (a state backend) but no Redis connector (treating Redis as a data source/sink). They serve different roles: the store backs the engine's internal state, while a connector backs DSL table reads/writes. Redis as a dimension table or result cache is very common. 项目已有 Redis store(状态后端),但没有 Redis connector(把 Redis 当数据源/汇)。 两者职责不同:store 服务于引擎内部状态,connector 服务于 DSL 建表读写。Redis 作维表/结果缓存很常见。 The task Create geaflow-dsl-connector-redis: the sink supports writing string/hash, the source supports scanning by key pattern. 新建 geaflow-dsl-connector-redis,sink 支持写 string/hash,source 支持按 key 模式扫描读取。 Where to look / 怎么做 References: the minimal geaflow-dsl-connector-console (sink only) and the connection-managing geaflow-dsl-connector-elasticsearch. Reuse the Redis client already in the project — check which client

What & why ClickHouse is a popular OLAP database, often the downstream analytics layer for graph-computing results. You can technically connect today via the JDBC connector, but it lacks ClickHouse-tuned batch writes (buffering + columnar writes), so performance suffers. ClickHouse 是流行的 OLAP 数据库,常作为图计算结果的下游分析层。现在用 JDBC connector 勉强能连,但缺针对 ClickHouse 的高效批量写(攒批 + 列式写入),性能差。 The task Create geaflow-dsl-connector-clickhouse, focusing the sink on efficient batched writes (buffer + flush threshold); the source should support parallel partitioned reads. 新建 geaflow-dsl-connector-clickhouse,重点优化 sink 批量写(buffer + flush 阈值), source 支持按分区并行读。 Where to look / 怎么做 Use geaflow-dsl-connector-jdbc as a baseline and spot the differences between "generic JDBC" and "ClickHouse-specific". Sink batching: write() goes into a buffer; commit in bulk on threshold or f

What & why The most basic graph metric is a vertex's degree — in-degree, out-degree, total. It's a building block for many heavier algorithms, but there's no built-in one-shot version yet. This is the best possible first taste of the vertex-centric / message-passing model: simpler than PageRank, but you still walk the full algorithm interface end to end. 图分析最基础的指标就是顶点的度(入度/出度/总度),它是很多复杂算法的前置特征, 但目前没有内置算法。这是入门"顶点中心 / 消息传递"模型的最佳第一题——比 PageRank 简单, 却完整走一遍算法接口。 The task Implement Degree: for each vertex, output (id, in_degree, out_degree). No message passing needed — you can count directly with loadEdges(IN/OUT), which makes it a clean way to learn the API. 实现 Degree:每个顶点输出 (id, in_degree, out_degree)。不用消息传递,直接用 loadEdges(IN/OUT) 统计即可,特别适合熟悉 API。 Where to look The simple