mongodb|SPL---强大的集算器 nosql|database|sql|数据库

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SQL必须基于RDB工作，而很多场景下并没有RDB，比如遇到csv\restful json\MongoDB等数据源，或进行这些数据源之间的混合计算，比如csv和xls之间。
在这些场景下，很多人会选择用JAVA或C#等高级语言硬写算法，这要从头编写冗长的底层函数，执行效率也很难保证，很容易堆积出人人痛恨的“代码屎山”。
也有人会把数据写入数据库，再用SQL进行计算，但入库的过程非常繁琐，实时性也很差，有时还要求助于ETL工具，架构重上加重，风险增了又增，遇到混合计算更是加倍的麻烦。现在好了，集算器SPL可以解决这些问题。
SPL是开源的计算技术，完全覆盖了SQL的计算能力，支持种类繁多的数据源，没有RDB也可以用SQL进行结构化数据计算了。

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对于交互式的计算分析，SPL有专业的IDE，不仅具有完整的调试功能，还可以用表格直观观察每一步的中间结算结果。
SPL的本意是Structure Process Language，是一种专门用于结构化数据处理的语言，在前面的例子中也已经展示了部分SPL本身的语法（那些扩展函数）。SQL只是SPL顺带提供的一种功能， SPL本身还拥有比SQL更强大便捷的计算能力。有些计算逻辑比较复杂，用SQL甚至存储过程都很难写，而用SPL则可以用简单的代码完成计算。
比如这个任务，计算某支股票最长的连续上涨天数，SQL要用多层嵌套的子查询和窗口函数，代码冗长难懂：

select max(continuousDays)-1 from (select count(*) continuousDays from (select sum(changeSign) over(order by tradeDate) unRiseDays from (select tradeDate, case when price>lag(price) over(order by tradeDate) then 0 else 1 end changeSign from AAPL) ) group by unRiseDays)

而SPL只需两行：

	A	B
1	=T("d:/AAPL.xlsx")	读Excel文件，首行为列名
2	=a=0,A1.max(a=if(price>price[-1],a+1,0))	求最长连续上涨天数

对于简单的运算，使用基本的SQL是很方便的，但运算需求变复杂时，SQL就不适用了，即使提供更多的功能（比如窗口函数）也不能简化计算。这种情况下，我们推荐用户直接使用代码简洁的SPL，而不必再写多层嵌套的复杂SQL了。基于这个原因，SPL中的SQL也只支持到SQL92标准，没有提供包括窗口函数在内的更多语法。
完善的SQL计算能力 SPL提供了相当于SQL92标准的语法，可以进行足够丰富多样的数据计算，包括过滤、计算列、选择部分列、改名等等，可以直接把文本、xls等文件当成数据表来执行SQL。下面以csv文件作为数据源为例说明：

1. 过滤基本的比较运算：$select * from d:/Orders.csv where Amount>=100like：$select * from d:/Orders.csv where Client like '%bro%'空值判断：$select * from d:/Orders.csv where Client is null与、或、非这样的逻辑运算符可以把比较运算组合起来，实现组合过滤：$select * from d:/Orders.csv where not Amount>=100 and Client like 'bro' or OrderDate is nullin：$select * from d:/Orders.csv where Client in ('TAS','KBRO','PNS')多层括号：$select * from d:/Orders.csv where (OrderDate=date('2020-12-31') and Amount>100)2.计算列SPL有丰富的数学函数、字符串函数、日期函数：$select round(Amount,2), price*quantity from d:/Orders.csv$select left(Client,4) from d:/Orders.csv$select year(OrderDate) from d:/Orders.csvcase when：$select case year(OrderDate) when 2021 then 'this year' when 2020 then 'last year' else 'previous years' end from d:/Orders.csvcoalesce反显空值：$select coalesce(Client,'unknown') from d:/Orders.csv3.SELECT$select OrderId, Amount, OrderDate from d:/Orders.csv4.ORDER BY$select * from d:/Orders.csv order by Client, Amount desc5.DISTINCT$select distinct Client ,Sellerid from d:/Orders.csv6.GROUP BY … HAVING$select year(OrderDate),Client ,sum(Amount),count(1) from d:/Orders.csv group by year(OrderDate),Client having sum(Amount)<=100聚合函数包括sum、count、avg、max、min，不分组也可以直接汇总：$select avg(Amount) from d:/Orders.csv7.JOIN左关联：$select o.OrderId,o.Client,e.Name e.Dept,e.EId from d:/Orders.txt o left join d:/Employees.txt e on o.SellerId=e.Eid右关联：$select o.OrderId,o.Client,e.Name e.Dept,e.EId from d:/Employees.txt e right join d:/Orders.txt o on o.SellerId=e.Eid全关联：$select o.OrderId,o.Client,e.Name e.Dept,e.EId from d:/Employees.txt e full join d:/Orders.txt o on o.SellerId=e.EId内关联：$select o.OrderId,o.Client,e.Name e.Dept from d:/Orders.csv o inner join d:/Employees.csv e on o.SellerId=e.Eid内关联还可以写成WHERE形式：$select o.OrderId,o.Client,e.Name e.Dept from d:/Orders.csv o ,d:/Employees.csv e whereo.SellerId=e.Eid8.子查询$select t.Client, t.s, ct.Name, ct.address from (select Client ,sum(amount) s from d:/Orders.csv group by Client) t left join ClientTable ct on t.Client=ct.Clientwith：$with t as (select Client ,sum(amount) s from d:/Orders.csv group by Client) select t.Client, t.s, ct.Name, ct.address from t left join ClientTable ct on t.Client=ct.ClientIn中的子查询：$select * from d:/Orders.txt owhere o.sellerid in (select eid from d:/Employees.txt)9.AS使用as关键字，可对字段、计算列、物理表、子查询改名：$select price*quantity as subtotal from d:/detail.csv10. 集合运算union、union all、intersect、minus都有，举一例：Select * from Orders1.csv Union all Select * from Orders2.csv11. into输出结果查询结果可用into关键字写入文件：$select dept,count(1) c,sum(salary) s into deptResult.xlsx from employee.txt group by dept having s>100000

丰富的数据源支持 SPL支持各种非数据库的数据源，包括各种非标准格式的文本，前面例子中已展示过csv。TAB分隔的txt也一样可以支持，SPL会根据扩展名自动处理：

$select * from d:/Orders.txt where Amount>=100 and Client like 'bro' or OrderDate is null

如果分隔符不是逗号和tab，就要用SPL扩展函数处理了，比如分隔符是冒号：

$select * from {file("d:/Orders.txt").import@t (; ":")} where Amount>=100 and Client like 'bro' or OrderDate is null

没有标题行的文件，可以用序号表示列名：

$select * from {file("d:/Orders.txt").import()} where _4>=100 and _2 like 'bro' or _5 is null

某些特殊格式的字符串也要用扩展函数解析，比如日期格式不是标准的yyyy-MM-dd：

$select year(OrderDate),sum(Amount) from {file("d:/Orders.txt").import@t (orderid,client,sellerid,amount,orderdate:date:"dd-MM-yyyy")} group by year(OrderDate)

Excel文件上也可以执行SQL，对于格式规范的Excel，只需直接引用文件名：

$select * from d:/Orders.xlsx where Amount>=100 and Client like 'bro' or OrderDate is null

可以读取指定sheet：

$select * from {file("D:/Orders.xlsx").xlsimport@t (; "sheet3")} where Amount>=100 and Client like 'bro' or OrderDate is null

从远程网站下载来的csv/xls文件：

$select * from {httpfile("http://127.0.0.1:6868/Orders.csv).import@tc() } where Amount>=100 and Client like 'bro' or OrderDate is null

HTTP协议的特性很多，比如字符集、端口号、post参数、header参数、登录认证等等，SPL扩展函数都可以支持。扩展函数还可以抓取网页中的表格数据，也支持从FTP服务器下载文件，这里不再赘述。
Json文件要先读为字符串再解析：

$select * from {json(file("d:\\data.json").read())} where Amount>=100 and Client like 'bro' or OrderDate is null

二维Json比较少，多层才是常态，SPL扩展函数可以把多层数据转为二维记录，再用SQL计算，这里就不展开了。
Restful json

$select * from {json(httpfile("http://127.0.0.1:6868/api/getData").read())} where Amount>=100 and Client like 'bro' or OrderDate is null

类似csv/xls，SPL也可以读取HTTP网站上的json/xml文件。
XML

$select * from {xml(file("d:/data.xml").read(),"xml/row")} where Amount>=100 and Client like 'bro' or OrderDate is null

Web Service

$select * from {ws_call(ws_client("http://。/entityWS.asmx?wsdl"),"entityWS ":" entityWSSoap":"getData")} where Amount>=100 and Client like'bro' or OrderDate is null

NoSQL也不在话下。
MongoDB

$select * from {mongo_shell@x (mongo_open("mongodb://127.0.0.1:27017/mongo"),"main.find()")} where Amount>=100 and Client like 'bro' or OrderDate is null

MongoDB经常是多层数据，包括前面的restful、web Service，用SPL扩展函数都可以转为二维。
Salesforce

$select * from {sf_query(sf_open(),"/services/data/v51.0/query","Select Id,CaseNumber,Subject From Case where Status='New'")} where Amount>=100 and Client like 'bro' or OrderDate is null

Hadoop HDFS上的csv/xls/json/xml：

	A
1	=hdfs_open(; "hdfs://192.168.0.8:9000")
2	=hdfs_file(A1,"/user/Orders.csv":"GBK")
3	=A2.import@t()
4	=hdfs_close(A1)
5	$select Client,sum(Amount)from {A3} group by Client

HBase也支持：

	A
1	=hbase_open("hdfs://192.168.0.8","192.168.0.8")
2	=hbase_scan(A1,"Orders")
3	=hbase_close(A1)
4	$select Client,sum(Amount)from {A2} group by Client

Hbase还有filter、cmp等取数方式，SPL都可以支持。
Hive有公共的JDBC接口，但性能较差，SPL提供了高性能接口：

	A
1	=hive_client("hdfs://192.168.0.8:9000","thrift://192.168.0.8:9083","hive","asus")
2	=hive_query(A1,"select*fromtable")
3	=hive_close()
4	$select Client,sum(Amount)from {A2} group by Client

Spark

	A
1	=spark_client("hdfs://192.168.0.8:9000","thrift://192.168.0.8:9083","aa")
2	=spark_query(A1,"select* from tablename")
3	=spark_close(A1)
4	$selectClient,sum(Amount) from {A2} group by Client

阿里云

	A
1	=ali_open("http://test.ots.aliyuncs.com","LTAIXZNG5zzSPHTQ","sa","test")
2	=ali_query@x(A1,"test",["id1","id2"],[1,"10001"]:[10,"70001"],["id1","id2","f1","f2"],f1>=2000.0)
3	$select Client,sum(Amount)from {A2} group by Client

Cassandra

	A
1	=stax_open("127.0.0.1":9042,"mycasdb","cassandra":"cassandra")
2	=stax_query(A1,"select* from user where id=?",1)
3	=stax_close(A1)
4	$select Client,sum(Amount)from {A2} group by Client

ElasticSearch

	A
1	=es_open("localhost:9200","user":"un1234")
2	=es_get(A1,"/person/_mget","{\"ids\":[\"1\",\"2\",\"5\"]}")
3	=es_close(A1)
4	$selectClient,sum(Amount) from {A2} group by Client

Redis

	A
1	=redis_open()
2	=redis_hscan(A1,"runoobkey", "v*", 3)
3	=redis_close (A1)
4	$select key,value from{A2} where value>=2000andvalue<3000

SAP BW

	A
1	=sap_open("userName","passWord","192.168.0.188","00","000",”E")
2	=sap_cursor(A1,"Z_TEST1","IT_ROOM").fetch()
3	=sap_close(A1)
4	$select * from{A2} where Vendor like '%software%'

InfluxDB

	A
1	=influx_open("http://127.0.0.1:8086","mydb", "autogen", "admin", "admin")
2	=influx_query(A1,"SELECT * FROM Orders")
3	=influx_close(A1)
4	$selectClient,sum(Amount) from {A2} group by Client

Kafka

	A
1	=kafka_open("D://kafka.properties"; "topic-test")
2	=kafka_poll(A1)
3	=kafka_close (A1)
4	$selectClient,sum(Amount) from {A2} group by Client

MDX多维数据库

	A
1	=olap_open("http://192.168.0.178:8088/msmdpump.dll","CubeTest","Administrator","admin")
2	=olap_query(A1,"with member [Measures].[AnnualInterestRate]as'[Measures].[SalesAmount]/[Measures].[StandardCost]-1'select{[Measures].[SalesAmount],[Measures].[StandardCost],[Measures].[AnnualInterestRate]} on columns, {[Order Date].[Calendar Year].[CalendarYear]} on rows from [DataSourceMulti]")
3	=olap_close(A1)
4	$select * from {A2} whereSalesAmount>10000

SPL除了支持种类繁多的数据源，也可以进行数据源之间的混合计算。比如csv和RDB之间：

$selecto.OrderId,o.Client,e.Name e.Dept from d:/Orders.csv o inner joind:/Employees.xls e on o.SellerId=e.Eid