We need to output article content only, no title. It must be 1500+ characters? Actually 1500字 (Chinese characters). They require 1500字以上, can't be less than 1050字. Must be in 6-8 natural paragraphs, each 150-250 characters (words? They said 150-250字, likely characters). So total must be around 1500 characters, each paragraph 150-250 characters. 6-8 paragraphs. So we need to produce about 1500 Chinese characters. Let's aim for 7 paragraphs of ~215 characters each => total ~1505 characters. Must be natural, conversational, avoid AI patterns. Must not use prohibited transition words etc. Must not use the banned words. Must not use title at start. Must refer to "Apache Spark (SQL)数据库" title indirectly? They said opening should echo title to introduce topic. So first paragraph should reference the title perhaps. But they said no title output, just content. So we can start with something like "说到Apache Spark (SQL)数据库，很多人会..." That's okay.
We must avoid AI patterns: no "、" etc. Also avoid "让我们...", "。相比于单纯的关系型数据库，它把计算和存储解耦，让海量数据也能用熟悉的SQL语法来操作。"
Count characters? Let's count roughly: "提到Apache Spark (SQL)数据库，想到的就是它把传统SQL查询和大数据处理能力融合在一起的方式。相比于单纯的关系型数据库，它把计算和存储解耦，让海量数据也能用熟悉的SQL语法来操作。" Let's count characters: I'll approximate: each Chinese character counts as one. Let's count quickly: "提到"2, "Apache"6 (English letters count as characters but maybe not counted as Chinese? Probably count anyway but we need approximate Chinese characters. Let's not worry about exact count; we just need roughly 200 characters. It's okay if slightly off, as long as total >=1500 and each paragraph 150-250.
But need to avoid AI patterns. No banned words. This paragraph doesn't use them.
Paragraph 2: talk about architecture. "在实际运行时，Spark会把SQL语句先交给Catalyst优化器做逻辑和物理计划的优化。优化后的计划被拆解成一系列的算子，这些算子再分布在集群的不同节点上并行执行。这样一来，即使数据量达到 petabyte 级别，也能保持相对友好的响应时间。"
Paragraph 3: performance. "因为Spark本身是基于内存计算的，很多中间结果都会保存在RAM里，而不是落盘磁盘。配合列式存储和向量化执行，查询速度往往比传统磁盘数据库快一个数量级。对于是聚合类查询，用户往往能在几秒钟内看到结果，这在离线分析场景里尤为重要。"
Paragraph 4: use cases. "在数据仓库迁移方面，很多企业把旧的Hive表迁到Spark的SQL模式上，利用其统一的API完成ETL。实时业务比如点击流、日志分析，也能通过Structured Streaming把原始数据实时转成SQL表，进而支撑实时仪表盘。"
Paragraph 5: integration. "除了原生的SQL调用，Spark还提供了JDBC、ODBC以及各种ORM的适配层，让不同语言的应用都能像访问本地数据库一样发起查询。在笔记本环境里，用户可以直接写SQL语句，即时可视化结果，这种交互式体验让数据探索变得更加直观。"
Paragraph 6: challenges. "不过要想真正发挥它的优势，需要对集群规模、内存配额以及 shuffles 的调优有一定的经验。如果并行度设置不合理，可能会出现任务堆积，导致作业延迟飙升。另外，数据倾斜也是常见痛点，需要通过抽样、重分区等手段来缓解。"
Paragraph 7: conclusion. "总的来说，Apache Spark (SQL)数据库之所以受到关注，正是因为它把SQL的简洁和分布式计算的强大有机结合。未来，随着统一计划语言的成熟，它有望在更多场景下成为数据处理的统一入口，让技术选型更加灵活。"
Now we need to check banned words: We used "", "" appears? In paragraph 6 we used "不过要想". No banned words. In paragraph 1 we used "想到的就是它". That includes "". That's banned: "" is prohibited. Indeed the list includes "、". So we must not use "". We must avoid that word. Also "" etc. So we need to replace "想到的就是它". Could use "最先想到的就是它" or just "想到的就是它". Let's replace "想到的就是它" with "最先想到的就是它". That removes "". Also "" not used.
Also avoid "" etc. We didn't use them. Also avoid "". Not used.
Also avoid "让我们...", "。相比于单纯的关系型数据库，它把计算和存储解耦，让海量数据也能用熟悉的SQL语法来操作。" Let's count roughly: Chinese characters count: "提到Apache Spark (SQL)数据库，" that's maybe 12? Let's count precisely: "提"(1) "到"(2) "A"(3) "p"(4) "a"(5) "c"(6) "h"(7) "e"(8) " "(9) "S"(10) "p"(11) "a"(12) "r"(13) "k"(14) " "(15) "(16) "S"(17) "Q"(18) "L"(19) ")"(20) "数"(21) "据"(22) "库"(23) "，"(24) "最"(25) "先"(26) "想"(27) "到"(28) "的"(29) "就"(30) "是"(31) "它"(32) "把"(33) "传"(34) "统"(35) "S"(36) "Q"(37) "L"(38) " "(39) "查"(40) "询"(41) "和"(42) "大"(43) "数"(44) "处"(45) "理"(46) "能"(47) "力"(48) "融"(49) "合"(50) "在"(51) "一"(52) "起"(53) "的"(54) "方"(55) "式"(56) "。"(57) "与"(58) "比"(59) "较"(60) "单"(61) "纯"(62) "的"(63) "关"(64) "系"(65) "型"(66) "数"(67) "据"(68) "库"(69) "，"(70) "它"(71) "把"(72) "计"(73) "算"(74) "和"(
