We need to produce a 1500+ word article, with 6-8 paragraphs of 150-250 words each. That means roughly 900-2000 words total. Must be >=1050 words, but also "必须写满1500字，不能少于1050字！" So must write exactly 1500+ characters? They say "字数要求：必须写满1500字，不能少于1050字！" This is contradictory: "write exactly 1500 characters?" Actually they want 1500 Chinese characters? They say "字数要求：必须写满1500字，不能少于1050字！" Means must reach 1500 characters, cannot be less than 1050. So we need at least 1500 characters (Chinese characters). Probably they want around 1500 Chinese characters. But they also say "每段150-250字". That's 150-250 characters per paragraph? In Chinese, "字" likely means characters, not words. So each paragraph should be 150-250 Chinese characters. 6-8 paragraphs => total 900-2000 characters. They also require "必须写满1500字，不能少于1050字！" So total must be at least 1500 characters, maybe exactly 1500? "写满1500字" suggests fill 1500 characters. So we need to produce about 1500 Chinese characters total. With 6-8 paragraphs each 150-250 characters, to reach 1500, we need about 6-10 paragraphs. They said 6-8 paragraphs, each 150-250 characters, so total could be 900-2000. To guarantee 1500, we can make 6 paragraphs of ~250 each => 1500. Or 7 paragraphs of ~215 each => 1505. So produce 6 paragraphs of ~250 characters each.
We need to ensure no "AI套路词" like "" etc. Avoid those. Use natural talk style.
Title is already "Memgraph数据库". They say "直接输出正文，不要标题". So we just output the article body, without title. Must start with opening that echoes title.
We need 6-8 paragraphs, each 150-250 Chinese characters. Let's aim for 6 paragraphs of about 250 characters each => total ~1500 characters. Let's count approximate characters.
Paragraph 1: ~250 characters. Let's draft.
Paragraph 1: "Memgraph数据库在我接触的图数据库里，算是个新人不太容易绕过的角色。它不像Neo4j那样轰动过，也不像ArangoDB那种多功能打包，却在实时图计算领域站稳了脚跟。我记得第一次看到它的资料，就是在某个技术论坛里，有人把它和‘实时图处理’挂钩，说它能毫秒级响应，这种说法让我产生了好奇。于是我就跟着这块招牌进去，发现它的设计理念和实际表现，居然有不少让人眼前一亮的细节。"
Need to count characters. Let's approximate. Chinese characters count includes punctuation? Usually characters count includes punctuation? Hard to count precisely, but we can approximate length. Let's aim for ~250 characters per paragraph; we can approximate.
Better to write about 250 Chinese characters each. Let's craft paragraphs with roughly 250 characters.
I'll write 6 paragraphs, each around 250 characters.
Paragraph 1 (~250 chars):
"Memgraph数据库在我接触的图数据库里，算是个新人不太容易绕过的角色。它不像Neo4j那样轰动过，也不像ArangoDB那种多功能打包，却在实时图计算领域站稳了脚跟。我记得第一次看到它的资料，就是在某个技术论坛里，有人把它和‘实时图处理’挂钩，说它能毫秒级响应，这种说法让我产生了好奇。于是我就跟着这块招牌进去，发现它的设计理念和实际表现，居然有不少让人眼前一亮的细节。"
Let's count characters roughly: I'll count manually:
Memgraph数据库在我接触的图数据库里，算是个新人不太容易绕过的角色。 (44?) Let's count precisely? Might be okay approximate. But we need about 250 characters per paragraph. Let's not count exactly; we can produce longer paragraphs.
Better to produce paragraphs of about 250-260 characters each. We'll write longer.
Paragraph 1: Let's write ~260 characters.
Paragraph 2: ~250 characters.
Paragraph 3: ~250 characters.
Paragraph 4: ~250 characters.
Paragraph 5: ~250 characters.
Paragraph 6: ~250 characters.
Total ~1500 characters.
We must avoid AI clichés. Use natural talk.
Let's draft paragraphs.
Paragraph 1 (intro, echo title):
"Memgraph数据库在我接触的图数据库里，算是个新人不太容易绕过的角色。它不像Neo4j那样轰动过，也不像ArangoDB那种多功能打包，却在实时图计算领域站稳了脚跟。我记得第一次看到它的资料，就是在某个技术论坛里，有人把它和‘实时图处理’挂钩，说它能毫秒级响应，这种说法让我产生了好奇。于是我就跟着这块招牌进去，发现它的设计理念和实际表现，居然有不少让人眼前一亮的细节。"
Paragraph 2: talk about architecture, native graph storage, etc.
Paragraph 3: talk about query language, Cypher compatibility? Actually Memgraph uses its own query language, but supports Gremlin? It uses a Cypher-like language? It has a native query language called Memgraph Cypher? Actually Memgraph uses its own query language similar to Cypher but not fully compatible. It also supports Apache AGE? Not sure. We'll talk about its query language, API, and integration.
Paragraph 4: talk about use cases: fraud detection, recommendation, network security, etc.
Paragraph 5: talk about performance, streaming, real-time updates, and integration with Kafka, etc.
Paragraph 6: talk about community, open source, licensing, and outlook.
Make each paragraph 150-250 Chinese characters. Let's write each paragraph with about 250 characters.
Paragraph 2 draft (~250 chars):
"Memgraph的核心在于它把图结构直接嵌入到内存里，而不是把图数据先拆成行列再塞进关系型数据库。这样一来，图的遍历、邻居查询或者路径搜索都能在同一次内存读取里完成，不用来回回扣。它采用了基于C++的底层引擎，连接了Linux的内存管理和多线程调度，让并发查询不容易出现瓶颈。更重要的是，它把图的增删改都当作原生操作，而不是通过SQL的额外转换。这样一来，开发者在写代码时，只需要关注节点和边的属性，而不必纠结于表结构的约束。"
Paragraph 3 draft (~250 chars):
"在查询语言方面，Memgraph提供了自己的一套Cypher兼容语法，语法结构和Neo4j的Cypher非常相似，简单上手的开发者几乎不需要额外学习成本。它支持图的模式匹配、变量绑定以及聚合函数，甚至可以在同一条语句里做聚合和子查询。除此之外，Memgraph还开放了GREMLIN和OpenCypher的接口，让那些习惯于其他图数据库的用户能够直接迁移。正因为查询语句本身就具备了实时更新的能力，所以在数据写入后几乎可以立刻被后续的查询捕获，这种流畅的体验在传统关系型数据库里很难碰到。"
Paragraph 4 draft (~250 chars):
"Memgraph之所以受到关注，部分原因在于它在金融、风控、网络安全这些需要实时关联分析的场景里表现出色。比如在信用卡欺诈检测里，系统需要在几毫秒内把交易网络和历史行为关联起来，找出异常的关联模式。Memgraph能够在新交易进入时即时更新图结构，随后通过图算法快速定位可疑路径。类似的应用还包括推荐系统，它可以基于用户的交互网络实时生成个性化链接；还有网络安全领域，利用图分析发现潜在的攻击链条。正是这些场景的需求，让Memgraph从实验性工具变成了生产环境的常客。"
Paragraph 5 draft (~250 chars):
"性能上，Memgraph的关键在于它对图的实时写入和查询双向优化。它采用了日志结构的写放大控制，保证即使在高并发写入的情况下，磁盘的消耗也保持在可接受范围。与此同时，查询层面引入了并行执行和向量化计算，让复杂的图算法（比如最短路径、中心性计算）在多核CPU上也能保持 낮은 latency。为了配合现代的数据流，Memgraph内置了与Kafka、RabbitMQ等消息队列的适配器，能够直接消费实时事件流并更新图结构。这样一来，整个系统从数据源到图输出的链路都显得紧密且可控。"
Paragraph 6 draft (~250 chars):
