原文地址：http://shiyanjun.cn/archives/78.html
Cloudera公司已经推出了基于Hadoop平台的查询统计分析工具Impala，只要熟悉SQL，就可以熟练地使用Impala来执行查询与分析的功能。不过Impala的SQL和关系数据库的SQL还是有一点微妙地不同的。
下面，我们设计一个表，通过该表中的数据，来将SQL查询与统计的语句，使用Solr查询的方式来与SQL查询对应。这个翻译的过程，是非常有趣的，你可以看到Solr一些很不错的功能。
用来示例的表结构设计，如图所示：
下面，我们通过给出一些SQL查询统计语句，然后对应翻译成Solr查询语句，然后对比结果。
查询对比
SQL查询语句：
1 |
SELECT log_id,start_time,end_time,prov_id,city_id,area_id,idt_id,cnt,net_type |
3 |
WHERE prov_id = 1 AND net_type = 1 AND area_id = 10304 AND time_type = 1 AND time_id >= 20130801 AND time_id <= 20130815 |
4 |
ORDER BY log_id LIMIT 10; |
查询结果，如图所示：
Solr查询URL：
1 |
http://slave1:8888/solr-cloud/i_event/select?q=*:*&fl=log_id,start_time,end_time,prov_id,city_id,area_id,idt_id,cnt,net_type&fq=prov_id:1 AND net_type:1 AND area_id:10304 AND time_type:1 AND time_id:[20130801 TO 20130815]&sort=log_id asc&start=0&rows=10 |
查询结果，如下所示：
<response>
<lst name="responseHeader">
<int name="status">0</int>
<int name="QTime">4</int>
</lst>
<result name="response" numFound="77" start="0">
<doc>
<int name="log_id">6827</int>
<long name="start_time">1375072117</long>
<long name="end_time">1375081683</long>
<int name="prov_id">1</int>
<int name="city_id">103</int>
<int name="area_id">10304</int>
<int name="idt_id">11002</int>
<int name="cnt">0</int>
<int name="net_type">1</int>
</doc>
<doc>
<int name="log_id">6827</int>
<long name="start_time">1375072117</long>
<long name="end_time">1375081683</long>
<int name="prov_id">1</int>
<int name="city_id">103</int>
<int name="area_id">10304</int>
<int name="idt_id">11000</int>
<int name="cnt">0</int>
<int name="net_type">1</int>
</doc>
<doc>
<int name="log_id">6851</int>
<long name="start_time">1375142158</long>
<long name="end_time">1375146391</long>
<int name="prov_id">1</int>
<int name="city_id">103</int>
<int name="area_id">10304</int>
<int name="idt_id">14001</int>
<int name="cnt">5</int>
<int name="net_type">1</int>
</doc>
<doc>
<int name="log_id">6851</int>
<long name="start_time">1375142158</long>
<long name="end_time">1375146391</long>
<int name="prov_id">1</int>
<int name="city_id">103</int>
<int name="area_id">10304</int>
<int name="idt_id">11002</int>
<int name="cnt">23</int>
<int name="net_type">1</int>
</doc>
<doc>
<int name="log_id">6851</int>
<long name="start_time">1375142158</long>
<long name="end_time">1375146391</long>
<int name="prov_id">1</int>
<int name="city_id">103</int>
<int name="area_id">10304</int>
<int name="idt_id">10200</int>
<int name="cnt">55</int>
<int name="net_type">1</int>
</doc>
<doc>
<int name="log_id">6851</int>
<long name="start_time">1375142158</long>
<long name="end_time">1375146391</long>
<int name="prov_id">1</int>
<int name="city_id">103</int>
<int name="area_id">10304</int>
<int name="idt_id">14000</int>
<int name="cnt">4</int>
<int name="net_type">1</int>
</doc>
<doc>
<int name="log_id">6851</int>
<long name="start_time">1375142158</long>
<long name="end_time">1375146391</long>
<int name="prov_id">1</int>
<int name="city_id">103</int>
<int name="area_id">10304</int>
<int name="idt_id">11000</int>
<int name="cnt">1</int>
<int name="net_type">1</int>
</doc>
<doc>
<int name="log_id">6851</int>
<long name="start_time">1375142158</long>
<long name="end_time">1375146391</long>
<int name="prov_id">1</int>
<int name="city_id">103</int>
<int name="area_id">10304</int>
<int name="idt_id">10201</int>
<int name="cnt">31</int>
<int name="net_type">1</int>
</doc>
<doc>
<int name="log_id">6851</int>
<long name="start_time">1375142158</long>
<long name="end_time">1375146391</long>
<int name="prov_id">1</int>
<int name="city_id">103</int>
<int name="area_id">10304</int>
<int name="idt_id">8002</int>
<int name="cnt">8</int>
<int name="net_type">1</int>
</doc>
<doc>
<int name="log_id">6851</int>
<long name="start_time">1375142158</long>
<long name="end_time">1375146391</long>
<int name="prov_id">1</int>
<int name="city_id">103</int>
<int name="area_id">10304</int>
<int name="idt_id">8000</int>
<int name="cnt">30</int>
<int name="net_type">1</int>
</doc>
</result>
</response>
对比上面结果，除了根据idt_id排序方式不同以外（Impala是升序，Solr是降序），其他是相同的。
SQL查询语句：
1 |
SELECT prov_id, SUM(cnt) AS sum_cnt, AVG(cnt) AS avg_cnt, MAX(cnt) AS max_cnt, MIN(cnt) ASmin_cnt, COUNT(cnt) AS count_cnt |
查询结果，如图所示：
Solr查询URL：
查询结果，如下所示：
<response>
<lst name="responseHeader">
<int name="status">0</int>
<int name="QTime">2</int>
</lst>
<result name="response" numFound="4088" start="0"></result>
<lst name="stats">
<lst name="stats_fields">
<lst name="cnt">
<double name="min">0.0</double>
<double name="max">1258.0</double>
<long name="count">4088</long>
<long name="missing">0</long>
<double name="sum">32587.0</double>
<double name="sumOfSquares">9170559.0</double>
<double name="mean">7.971379647749511</double>
<double name="stddev">46.69344567709268</double>
<lst name="facets" />
</lst>
</lst>
</lst>
</response>
对比查询结果，Solr提供了更多的统计项，如标准差（stddev）等，与SQL查询结果是一致的。
SQL查询语句：
1 |
SELECT log_id,start_time,end_time,prov_id,city_id,area_id,idt_id,cnt,net_typ |
3 |
WHERE prov_id = 1 AND net_type = 1 AND city_id IN(106,103) AND idt_idIN(12011,5004,6051,6056,8002) AND time_type = 1 AND time_id >= 20130801 AND time_id <= 20130815 |
4 |
ORDER BY log_id, start_time DESC LIMIT 10; |
查询结果，如图所示：
Solr查询URL：
http://slave1:8888/solr-cloud/i_event/select?q=*:*&fl=log_id,start_time,end_time,prov_id,city_id,area_id,idt_id, cnt,net_type&fq=prov_id:1 AND net_type:1 AND (city_id:106 OR city_id:103) AND (idt_id:12011 OR idt_id:5004 OR idt_id:6051 OR idt_id:6056 OR idt_id:8002) AND time_type:1 AND time_id:[20130801 TO 20130815]&sort=log_id asc ,start_time desc&start=0&rows=10
或者：
http://slave1:8888/solr-cloud/i_event/select?q=*:*&fl=log_id,start_time,end_time,prov_id,city_id,area_id,idt_id, cnt ,net_type&fq=prov_id:1&fq=net_type:1&fq=(city_id:106 OR city_id:103)&fq=(idt_id:12011 OR idt_id:5004 OR idt_id:6051 OR idt_id:6056 OR idt_id:8002)&fq=time_type:1&fq=time_id:[20130801 TO 20130815]&sort=log_id asc,start_time desc&start=0&rows=10
查询结果，如下所示：
<response>
<lst name="responseHeader">
<int name="status">0</int>
<int name="QTime">6</int>
</lst>
<result name="response" numFound="63" start="0">
<doc>
<int name="log_id">6553</int>
<long name="start_time">1374054184</long>
<long name="end_time">1374054254</long>
<int name="prov_id">1</int>
<int name="city_id">103</int>
<int name="area_id">10307</int>
<int name="idt_id">12011</int>
<int name="cnt">0</int>
<int name="net_type">1</int>
</doc>
<doc>
<int name="log_id">6553</int>
<long name="start_time">1374054184</long>
<long name="end_time">1374054254</long>
<int name="prov_id">1</int>
<int name="city_id">103</int>
<int name="area_id">10307</int>
<int name="idt_id">5004</int>
<int name="cnt">2</int>
<int name="net_type">1</int>
</doc>
<doc>
<int name="log_id">6555</int>
<long name="start_time">1374055060</long>
<long name="end_time">1374055158</long>
<int name="prov_id">1</int>
<int name="city_id">103</int>
<int name="area_id">70104</int>
<int name="idt_id">5004</int>
<int name="cnt">3</int>
<int name="net_type">1</int>
</doc>
<doc>
<int name="log_id">6555</int>
<long name="start_time">1374055060</long>
<long name="end_time">1374055158</long>
<int name="prov_id">1</int>
<int name="city_id">103</int>
<int name="area_id">70104</int>
<int name="idt_id">12011</int>
<int name="cnt">0</int>
<int name="net_type">1</int>
</doc>
<doc>
<int name="log_id">6595</int>
<long name="start_time">1374292508</long>
<long name="end_time">1374292639</long>
<int name="prov_id">1</int>
<int name="city_id">103</int>
<int name="area_id">10307</int>
<int name="idt_id">5004</int>
<int name="cnt">4</int>
<int name="net_type">1</int>
</doc>
<doc>
<int name="log_id">6611</int>
<long name="start_time">1374461233</long>
<long name="end_time">1374461245</long>
<int name="prov_id">1</int>
<int name="city_id">103</int>
<int name="area_id">10307</int>
<int name="idt_id">5004</int>
<int name="cnt">1</int>
<int name="net_type">1</int>
</doc>
<doc>
<int name="log_id">6612</int>
<long name="start_time">1374461261</long>
<long name="end_time">1374461269</long>
<int name="prov_id">1</int>
<int name="city_id">103</int>
<int name="area_id">10307</int>
<int name="idt_id">5004</int>
<int name="cnt">1</int>
<int name="net_type">1</int>
</doc>
<doc>
<int name="log_id">6612</int>
<long name="start_time">1374461261</long>
<long name="end_time">1374461269</long>
<int name="prov_id">1</int>
<int name="city_id">103</int>
<int name="area_id">10307</int>
<int name="idt_id">12011</int>
<int name="cnt">0</int>
<int name="net_type">1</int>
</doc>
<doc>
<int name="log_id">6613</int>
<long name="start_time">1374461422</long>
<long name="end_time">1374461489</long>
<int name="prov_id">1</int>
<int name="city_id">103</int>
<int name="area_id">10307</int>
<int name="idt_id">6056</int>
<int name="cnt">1</int>
<int name="net_type">1</int>
</doc>
<doc>
<int name="log_id">6613</int>
<long name="start_time">1374461422</long>
<long name="end_time">1374461489</long>
<int name="prov_id">1</int>
<int name="city_id">103</int>
<int name="area_id">10307</int>
<int name="idt_id">6051</int>
<int name="cnt">1</int>
<int name="net_type">1</int>
</doc>
</result>
</response>
对比查询结果，是一致的。
SQL查询语句：
1 |
SELECT log_id,start_time,end_time,prov_id,city_id,area_id,idt_id,cnt,net_type |
3 |
WHERE net_type = 1 AND idt_id IN(12011,5004,6051,6056,8002) AND time_type = 1 ANDstart_time >= 1373598465 AND end_time < 1374055254 |
4 |
ORDER BY log_id, start_time, idt_id DESC LIMIT 30; |
查询结果，如图所示：
Solr查询URL：
1 |
http://slave1:8888/solr-cloud/i_event/select?q=*:*&fl=log_id,start_time,end_time,prov_id,city_id,area_id,idt_id,cnt,net_type&fq=net_type:1 AND (idt_id:12011 OR idt_id:5004 OR idt_id:6051 OR idt_id:6056 OR idt_id:8002) AND time_type:1 AND start_time:[1373598465 TO 1374055254]&fq =-start_time:1374055254&sort=log_id asc,start_time asc,idt_id desc&start=0&rows=30 |
或
1 |
http://slave1:8888/solr-cloud/i_event/select?q=*:*&fl=log_id,start_time,end_time,prov_id,city_id,area_id,idt_id,cnt,net_type&fq=net_type:1 AND (idt_id:12011 OR idt_id:5004 OR idt_id:6051 OR idt_id:6056 OR idt_id:8002) AND time_type:1 AND start_time:[1373598465 TO 1374055254] AND -start_time:1374055254&sort=log_id asc,start_time asc,idt_id desc&start=0&rows=30 |
或
1 |
http://slave1:8888/solr-cloud/i_event/select?q=*:*&fl=log_id,start_time,end_time,prov_id,city_id,area_id,idt_id,cnt,net_type&fq=net_type:1&fq=idt_id:12011 OR idt_id:5004 OR idt_id:6051 OR idt_id:6056 OR idt_id:8002&fq =time_type:1&fq=start_time:[1373598465 TO 1374055254]&fq =-start_time:1374055254&sort=log_id asc,start_time asc,idt_id desc&start=0&rows=30 |
查询结果，如下所示：
<response>
<lst name="responseHeader">
<int name="status">0</int>
<int name="QTime">5</int>
</lst>
<result name="response" numFound="4" start="0">
<doc>
<int name="log_id">6553</int>
<long name="start_time">1374054184</long>
<long name="end_time">1374054254</long>
<int name="prov_id">1</int>
<int name="city_id">103</int>
<int name="area_id">10307</int>
<int name="idt_id">12011</int>
<int name="cnt">0</int>
<int name="net_type">1</int>
</doc>
<doc>
<int name="log_id">6553</int>
<long name="start_time">1374054184</long>
<long name="end_time">1374054254</long>
<int name="prov_id">1</int>
<int name="city_id">103</int>
<int name="area_id">10307</int>
<int name="idt_id">5004</int>
<int name="cnt">2</int>
<int name="net_type">1</int>
</doc>
<doc>
<int name="log_id">6555</int>
<long name="start_time">1374055060</long>
<long name="end_time">1374055158</long>
<int name="prov_id">1</int>
<int name="city_id">103</int>
<int name="area_id">70104</int>
<int name="idt_id">12011</int>
<int name="cnt">0</int>
<int name="net_type">1</int>
</doc>
<doc>
<int name="log_id">6555</int>
<long name="start_time">1374055060</long>
<long name="end_time">1374055158</long>
<int name="prov_id">1</int>
<int name="city_id">103</int>
<int name="area_id">70104</int>
<int name="idt_id">5004</int>
<int name="cnt">3</int>
<int name="net_type">1</int>
</doc>
</result>
</response>
SQL查询语句：
1 |
SELECT city_id, area_id, COUNT(cnt) AS count_cnt |
3 |
WHERE prov_id = 1 AND net_type = 1 |
4 |
GROUP BY city_id, area_id; |
查询结果，如图所示：
Solr查询URL：
查询结果，如下所示：
<response>
<lst name="responseHeader">
<int name="status">0</int>
<int name="QTime">72</int>
</lst>
<result name="response" numFound="1171" start="0"></result>
<lst name="facet_counts">
<lst name="facet_queries" />
<lst name="facet_fields" />
<lst name="facet_dates" />
<lst name="facet_ranges" />
<lst name="facet_pivot">
<arr name="city_id,area_id">
<lst>
<str name="field">city_id</str>
<int name="value">103</int>
<int name="count">678</int>
<arr name="pivot">
<lst>
<str name="field">area_id</str>
<int name="value">10307</int>
<int name="count">298</int>
</lst>
<lst>
<str name="field">area_id</str>
<int name="value">10315</int>
<int name="count">120</int>
</lst>
<lst>
<str name="field">area_id</str>
<int name="value">10317</int>
<int name="count">86</int>
</lst>
<lst>
<str name="field">area_id</str>
<int name="value">10304</int>
<int name="count">67</int>
</lst>
<lst>
<str name="field">area_id</str>
<int name="value">10310</int>
<int name="count">49</int>
</lst>
<lst>
<str name="field">area_id</str>
<int name="value">70104</int>
<int name="count">48</int>
</lst>
<lst>
<str name="field">area_id</str>
<int name="value">10308</int>
<int name="count">6</int>
</lst>
<lst>
<str name="field">area_id</str>
<int name="value">0</int>
<int name="count">2</int>
</lst>
<lst>
<str name="field">area_id</str>
<int name="value">10311</int>
<int name="count">2</int>
</lst>
</arr>
</lst>
<lst>
<str name="field">city_id</str>
<int name="value">0</int>
<int name="count">463</int>
<arr name="pivot">
<lst>
<str name="field">area_id</str>
<int name="value">0</int>
<int name="count">395</int>
</lst>
<lst>
<str name="field">area_id</str>
<int name="value">10307</int>
<int name="count">68</int>
</lst>
</arr>
</lst>
<lst>
<str name="field">city_id</str>
<int name="value">106</int>
<int name="count">10</int>
<arr name="pivot">
<lst>
<str name="field">area_id</str>
<int name="value">10304</int>
<int name="count">10</int>
</lst>
</arr>
</lst>
<lst>
<str name="field">city_id</str>
<int name="value">110</int>
<int name="count">8</int>
<arr name="pivot">
<lst>
<str name="field">area_id</str>
<int name="value">0</int>
<int name="count">8</int>
</lst>
</arr>
</lst>
<lst>
<str name="field">city_id</str>
<int name="value">118</int>
<int name="count">8</int>
<arr name="pivot">
<lst>
<str name="field">area_id</str>
<int name="value">10316</int>
<int name="count">8</int>
</lst>
</arr>
</lst>
<lst>
<str name="field">city_id</str>
<int name="value">105</int>
<int name="count">4</int>
<arr name="pivot">
<lst>
<str name="field">area_id</str>
<int name="value">0</int>
<int name="count">4</int>
</lst>
</arr>
</lst>
</arr>
</lst>
</lst>
</response>
对比上面结果，Solr查询结果，需要从上面的各组中进行合并，得到最终的统计结果，结果和SQL结果是一致的。
- 多个字段分组统计（支持count、sum、max、min等函数）
一次对多个字段进行独立分组统计，Solr可以很好的支持。这相当于执行两个带有GROUP BY子句的SQL，这两个GROUP BY分别只对一个字段进行汇总统计。
SQL查询语句：
1 |
SELECT city_id, area_id, COUNT(cnt) AS count_cnt |
3 |
WHERE prov_id = 1 AND net_type = 1 |
6 |
SELECT city_id, area_id, COUNT(cnt) AS count_cnt |
8 |
WHERE prov_id = 1 AND net_type = 1 |
查询结果，不再显示。
Solr查询URL：
查询结果，如下所示：
<response>
<lst name="responseHeader">
<int name="status">0</int>
<int name="QTime">6</int>
</lst>
<result name="response" numFound="1171" start="0"></result>
<lst name="stats">
<lst name="stats_fields">
<lst name="cnt">
<double name="min">0.0</double>
<double name="max">167.0</double>
<long name="count">1171</long>
<long name="missing">0</long>
<double name="sum">3701.0</double>
<double name="sumOfSquares">249641.0</double>
<double name="mean">3.1605465414175917</double>
<double name="stddev">14.260812879164407</double>
<lst name="facets">
<lst name="city_id">
<lst name="0">
<double name="min">0.0</double>
<double name="max">167.0</double>
<long name="count">463</long>
<long name="missing">0</long>
<double name="sum">2783.0</double>
<double name="sumOfSquares">238819.0</double>
<double name="mean">6.010799136069115</double>
<double name="stddev">21.92524420257807</double>
<lst name="facets" />
</lst>
<lst name="110">
<double name="min">0.0</double>
<double name="max">1.0</double>
<long name="count">8</long>
<long name="missing">0</long>
<double name="sum">3.0</double>
<double name="sumOfSquares">3.0</double>
<double name="mean">0.375</double>
<double name="stddev">0.5175491695067657</double>
<lst name="facets" />
</lst>
<lst name="106">
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</lst>
<lst name="area_id">
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<double name="min">0.0</double>
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<lst name="10310">
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<lst name="0">
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<lst name="10311">
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<lst name="10316">
<double name="min">0.0</double>
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<lst name="10317">
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<double name="sumOfSquares">262.0</double>
<double name="mean">1.1627906976744187</double>
<double name="stddev">1.3093371930442208</double>
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</lst>
</lst>
</lst>
</response>
- 多个字段联合分组统计（支持count、sum、max、min等函数）
SQL查询语句：
1 |
SELECT city_id, area_id, SUM(cnt) AS sum_cnt, AVG(cnt) AS avg_cnt, MAX(cnt) AS max_cnt,MIN(cnt) AS min_cnt, COUNT(cnt) AS count_cnt |
3 |
WHERE prov_id = 1 AND net_type = 1 |
4 |
GROUP BY city_id, area_id; |
查询结果，如图所示：
Solr目前不能简单的支持这种查询，如果想要满足这种查询统计，需要在schema的设计上，将一个字段设置为多值，然后通过多个值进行分组统计。如果应用中查询统计分析的模式比较固定，预先知道哪些字段会用于联合分组统计，完全可以在设计的时候，考虑设置多值字段来满足这种需求。
参考链接