We need to write a 1500+ word article about "Apache Impala数据库". Must be natural, conversational style, no AI flavor. Must have 6-8 paragraphs, each 150-250 words. Total must be at least 1500 characters? Actually 1500 words. Must be >=1050 words, but they require 1500 words minimum. So we need about 1500 words. If we have 6-8 paragraphs each 150-250 words, that's roughly 900-2000 words. To meet 1500 words, we need about 6-7 paragraphs of ~250 words each. Let's aim for 7 paragraphs of ~220 words each => about 1540 words. Must not include title, only the body. Must not use the banned words. Must avoid official language, avoid certain transition words etc. Must be natural, like chatting with a friend.
We must avoid using "" "" "" etc. Also avoid "让我们" "。" Then continue.
We need 230 words. Let's write and then count roughly.
I'll draft paragraph 1 ~230 words.
"说到大数据查询，很多人会想到Hive，但如果你想要更快的响应，Impala往往是更好的选择。它不是一个独立的存储系统，而是直接在Hadoop集群里运行的查询引擎，能够把SQL语句直接扔给HDFS或HBase去执行。正因为不需要把数据搬来搬去，Impala能够在几秒钟内返回结果，而不是分钟甚至小时。对于那些需要即时分析的业务场景，比如实时风控、点击流统计或者交互式报表，这种延迟的差距往往决定了是否能及时做出决策。而且，它支持的SQL特性相当丰富，几乎不需要额外学习新的查询语言，只要会写普通的SQL，就能直接上手。正因为如此，很多企业在搭建数据仓库时会把Impala放在最核心的位置，让业务人员能够自己动手探索数据，而不是等待数据工程师写好复杂的MapReduce作业。"
Now count words: Let's count roughly.
"说到(1)大数据2查询，3很多人4会5想到6Hive，7但8如果9你10想要11更快12的13响应，14Impala15往往16是17更好的18选择。19它20不是21一个22独立23的24存储25系统，26而是27直接28在29Hadoop30集群31里32运行33的34查询35引擎，36能够37把38SQL39语句40直接41扔给42HDFS43或44HBase45去46执行。47正因为48不需要49把50数据51搬来搬去，52Impala53能够54在55几秒钟56内57返回58结果，59而不是60分钟61甚至62小时。63对于64那些65需要66即时67分析的68业务场景，比如69实时70风控、71点击流72统计或者73交互式74报表，75这种76延迟的77差距78往往79决定了80是否81能82及时83做出84决策。85而且，86它支持的87SQL88特性89相当90丰富，91几乎92不需要93额外94学习95新的96查询97语言，98只要99会100写101普通的102SQL，103就104能105直接106上手。107正因为如此，108很多109企业110在111搭建112数据仓库时113会把114Impala115放在116最核心的117位置，118让119业务120人员121能够122自己123动手124探索125数据，而不是126等待127数据128工程师129写好130复杂的131MapReduce132作业。"
Word count approx 132? Actually we counted up to 132 words? Let's recount: we counted up to 132? Actually we counted numbers up to 132? The last number is 132? Let's see: we ended at 132? Actually we ended at "作业。" which we counted as 132? Let's check: we counted each token separated by space? We gave numbers up to 132? At the end we had "作业。" as 132? Let's see: we had numbers up to 132? The last number we gave was 132? Actually we stopped at 132? Let's recount more systematically: The count we gave ended at 132? The last number we wrote was 132? Let's see: we wrote "作业。" after 130? Let's scroll: we had "...复杂的MapReduce作业。" and we gave number 132? Actually we gave number 132 before "作业。"? Let's check: we counted "MapReduce" as 130? Actually we counted "MapReduce" as 130? Let's recount quickly: we had numbers up to 132 maybe. Anyway approximate 130-150 words. That's less than needed 150-250. Need 150-250 words per paragraph. So we need to expand.
We need each paragraph 150-250 words. So we need to add more content to reach ~200 words.
Let's add more sentences.
Add: "比如在电商促销期间，营销团队可以直接在BI工具里点开一个SQL，快速看到各个渠道的转化率，而不需要等到 nightly batch job 完成后才能看到报表。" That's about 30 words.
Add more: "而且，Impala的查询计划会在执行前先分析数据的分区结构，只扫描需要的文件，这种 pruning 能力让即使是多 terabyte 的表也能在可接受的时间内完成扫描。" That's about 30 words.
Add more: "因为它直接使用列式存储，只读取需要的列，能够大幅降低 I/O 开销，尤其是在只关心少数字段的情况下，性能提升更加明显。" That's about 30 words.
Now total maybe ~200.
Let's rewrite paragraph 1 with about 210-220 words.
Paragraph 1 final version:
"说到大数据查询，很多人会想到Hive，但如果你想要更快的响应，Impala往往是更好的选择。它不是一个独立的存储系统，而是直接在Hadoop集群里运行的查询引擎，能够把SQL语句直接扔给HDFS或HBase去执行。正因为不需要把数据搬来搬去，Impala能够在几秒钟内返回结果，而不是分钟甚至小时。对于那些需要即时分析的业务场景，比如实时风控、点击流统计或者交互式报表，这种延迟的差距往往决定了是否能及时做
