siddhi-base

• 事件驱动：消耗、处理、发送的数据都视作一次事件

• 流处理、复杂事件处理平台

• siddhi application是类似于sql的脚本。.siddhi脚本中分号表示一句结束。

• 包括消费者(sources)、生产者(sinks)、流、查询、表和方法及其他必要的约定

• 可以接受\发送给许多不同类型的事件输入\输出，如tcp,http,kafka,file等。

• 可以接受并转化为不同的数据格式：json,text,xml,key-value

• 处理事件并进行转化，分析

• 过程：接受事件进行消费，将事件传递给相应的查询处理，根据逻辑形成新事件，将新事件发送给流

• Stream and Query

  • define stream InputTemperatureStream (sensorId string, temperature double);-- 定义一个流，参数是String的传感器ID和double的温度
    @info(name = 'Pass-through')-- query的可选注解
    from InputTemperatureStream
    select *
    insert into TemperatureAndSensorStream; -- 从 InputTemperatureStream 中消费数据，通过select * 获取所有属性生产新事件，输出到 TemperatureAndSensorStream
    @info(name = 'Simple-selection')
    from TemperatureAndSensorStream -- 从 TemperatureAndSensorStream中消费事件
    select temperature
    insert into TemperatureOnlyStream;-- 只将 temperature 输出到 TemperatureOnlyStream
    -- 值为['aq-14', 35.4]的事件被输入到InputTemperatureStream
    -- InputTemperatureStream : ['aq-14', 35.4]
    -- TemperatureAndSensorStream : ['aq-14', 35.4]
    -- TemperatureOnlyStream : [35.4]
    

  • 一共使用了 @info 1个注解 define stream、from、select、insert into这几个关键字

• Source and Sink

  • @source(type='http', receiver.url='http://0.0.0.0:8006/temp',@map(type='json'))
    -- 消费者是http,接受的地址是 ,接受json数据
    define stream TemperatureStream (sensorId string, temperature double);
    -- 定义流TemperatureStream
    @sink(type='log') -- 生产者 输出流是log
    @sink(type='kafka',topic='temperature',bootstrap.servers='localhost:9092',@map(type='json',@payload("""{"temp":"{{temperature}}"}""")))
    -- 生产者 输出流是kafka 输出为json topic为temperature bootstrap.servers  temperature的值映射到json名为temp
    define stream TemperatureOnlyStream (temperature double); -- 定义输出流
    @info(name = 'Simple-selection') 
    from TemperatureStream 
    select temperature 
    insert into TemperatureOnlyStream;
    -- query 从TemperatureStream中 查询出 temperature属性 输入到 TemperatureOnlyStream
    
    
    -- input json类型的消息发送到http://0.0.0.0:8006/temp时，一个事件就会产生
    {
       "event":{
          "sensorId":"aq-14",
          "temperature":35.4
       }
    }
    -- 数据处理后 事件就会到达stream TemperatureOnlyStream 通过log 和 kafka的方式发送到 TemperatureOnlyStream  stream
    -- log 通过passThrough 方式生产输出到控制台
    Event{timestamp=1574515771712, data=[35.4], isExpired=false}
    -- kafka 把stream映射到json 发送到 temperature topic
    {"temp":"35.4"}
    

  • 一共使用了@source、@sink、@map、@info 【kafka】 [@payload]这几个注解

• Table and Store

  • define stream TemperatureStream (sensorId string, temperature double); -- define stream
    define table TemperatureLogTable (sensorId string, roomNo string, temperature double); -- define stream
    @store(type="rdbms",jdbc.url="jdbc:mysql://localhost:3306/sid",username="root", password="root",jdbc.driver.name="com.mysql.jdbc.Driver")
    -- 存储到 关系型数据库 连接为 用户名 密码 驱动名指定的数据库
    define table SensorIdInfoTable (sensorId string, roomNo string); -- 定义表 存储在内存中
    @info(name = 'Join-query')
    from TemperatureStream as t join SensorIdInfoTable as s
         on t.sensorId == s.sensorId
    -- 定义 query  stream join table on 关键字相等
    select t.sensorId as sensorId, s.roomNo as roomNo, t.temperature as temperature
    insert into TemperatureLogTable;
    -- 将笛卡尔积查询结果存入表中
    
    -- 当 SensorIdInfoTable包含一条记录['aq-14', '789'],且当一个事件 ['aq-14', 35.4] 到达 TemperatureStream时，一个事件就会被产生，转换为['aq-14', '789', 35.4]，并添加到TemperatureLogTable中。
    
    -- 查询表中的数据
    -- on-demand query 
    from TemperatureDetailsTable
    select *
    -- calling query() method of SiddhiAppRuntime
    

  • 一共使用了@store 1个注解 table 关键字

• Siddhi Application

  • @app:name('Temperature-Processor') -- siddhi app name
    @app:description('App for processing temperature data.') -- app description 
    @source(type='inMemory', topic='SensorDetail') 
    define stream TemperatureStream (sensorId string, temperature double);
    -- 消费者 从内存中读取其他app的事件 定义stream
    @sink(type='inMemory', topic='Temperature')
    define stream TemperatureOnlyStream (temperature double);
    -- 生产者 向内存中其他app公开发布事件 定义stream
    @info(name = 'Simple-selection')
    from TemperatureStream
    select temperature
    insert into TemperatureOnlyStream;
    -- 定义query 自动从消费者中查询出温度 将事件传递给生产者 由生产者公开事件
    --  ['aq-14', 35.4] 从topic SensorDetail中被接收到 就会传递给 TemperatureStream
    --  [35.4]到达 TemperatureOnlyStream  通过 topic Temperature 传输给内存中其他订阅了的app
    

  • 一共使用了 @app:name @app:description 2个关键字

• Basic Types：int long float double string object

  • convert() instanceof()

  • object又分为 list map 等

  • define stream PatientRegistrationInputStream (
                     seqNo long, name string, age int,
                     height float, weight double, photo object,
                     isEmployee bool, wardNo object);
    
    define stream PatientRegistrationStream (
                     seqNo long, name string, age int,
                     height double, weight double, photo object,
                     isPhotoString bool, isEmployee bool,
                     wardNo int);
    -- 定义两个stream 数据类型不同 进行转换
    @info(name = 'Type-processor')
    from PatientRegistrationInputStream
    select seqNo, name, age,
           convert(height, 'double') as height,
    
           weight, photo,
           instanceOfString(photo) as isPhotoString,
    
           isEmployee,
           cast(wardNo, 'int') as wardNo
           
    -- convert(value,to_type) 返回转换类型后的值 
    -- instanceofString(value) 返回是否属于string类型
    -- cast(value,to_type) 返回转换类型后的值 
    -- convert是显式转换，cast是强转
    
    insert into PatientRegistrationStream;
    
    -- [1200098, 'Peter Johnson', 34, 194.3f, 69.6, #Fjoiu59%3hkjnknk$#nFT, true, 34]是PatientRegistrationInputStream event
    -- [1200098, 'Peter Johnson', 34, 194.3, 69.6, #Fjoiu59%3hkjnknk$#nFT, false, true, 34] PatientRegistrationStream  event
    

  • 一共使用了 convert() instanceofXXX() cast() convert() 4个方法

• Map

  • define stream CoupleDealInfoStream (item1 string, price1 double,item2 string, price2 double);
    @info(name = 'Create-map')
    from CoupleDealInfoStream
    select map:create(item1, price1, item2, price2)
    as itemPriceMap
    -- query map:create(key1,value1,...,...) 创建map 键值对为i1:p1 i2:p2
    insert into NewMapStream;
    
    
    @info(name = 'Check-map')
    from NewMapStream
    select map:isMap(itemPriceMap) as isMap,
    
           map:containsKey(itemPriceMap, 'Cookie')
                as isCookiePresent,
    
           map:containsValue(itemPriceMap, 24.0)
                as isThereItemWithPrice24,
    
           map:isEmpty(itemPriceMap) as isEmpty,
    
           map:keys(itemPriceMap) as keys,
    
           map:size(itemPriceMap) as size
    insert into MapAnalysisStream;
    -- map:isMap(map) 判断是否是map
    -- map:containsKey(map,key) 判断有无key
    -- map:containsValue(map,value) 判断有没有这个值
    -- map:isEmpty(map) 判断map是否为空
    -- map:keys(map) 返回keyset
    -- map:size(map) 返回map的大小
    
    @info(name = 'Clone-and-update')
    from NewMapStream
    select map:replace(
                       map:put(map:clone(itemPriceMap),
                               "Gift",
                               1.0),
                       "Cake",
                       12.0) as itemPriceMap
    
    insert into ItemInsertedMapStream;
    -- map:replace(map,key,value) map中key的value替换为新的value 
    -- map:put(map,key,value) 在map中添加键值对,返回这个map
    -- map:clone(map) 返回一个克隆的map
    
    
    -- CoupleDealInfoStream 收到事件 ['Chocolate', 18.0, 'Ice Cream', 24.0]
    -- NewMapStream [{Ice Cream=24.0, Chocolate =18.0}]
    -- MapAnalysisStream [true, false, true, false, [Ice Cream, Chocolate], 2]
    -- ItemInsertedMapStream [{Ice Cream=12.0, Gift=1.0, Chocolate =18.0}]
    

• List

  • define stream ProductComboStream (product1 string, product2 string, product3 string);
    
    @info(name = 'Create-list')
    from ProductComboStream
    select list:create(product1, product2, product3)
                as productList
    
    insert into NewListStream;
    -- list:create(item1,item2,...) 
    @info(name = 'Check-list')
    from NewListStream
    select list:isList(productList) as isList,
    
           list:contains(productList, 'Cake')
                as isCakePresent,
    
           list:isEmpty(productList) as isEmpty,
    
           list:get(productList, 1) as valueAt1,
    
           list:size(productList) as size
    
    insert into ListAnalysisStream;
    -- list:isList(list) 判断是否是list
    -- list:contains(list,item) 判断是否含有元素
    -- list:isEmpty(list) 判断是否为空
    -- list:get(list,index) 获取下标元素
    -- list:size(list) 返回list大小
    
    @info(name = 'Clone-and-update')
    from NewListStream
    select list:remove(
                list:add(list:clone(productList), "Toffee"),
                "Cake") as productList
    
    insert into UpdatedListStream;
    -- list:remove(list,item) 删除元素
    -- list:add(list,item) 添加元素，返回list
    -- list:clone(list)，返回clone的list
    
    -- ProductComboStream 添加事件 ['Ice Cream', 'Chocolate', 'Cake']
    -- NewListStream [[Ice Cream, Chocolate, Cake]] 
    -- ListAnalysisStream [true, true, false, Chocolate, 3]
    -- UpdatedListStream [[Ice Cream, Chocolate, Toffee]]
    

• Null

  • define stream ProductInputStream (item string, price double);
    define Table ProductInfoTable (item string, discount double);
    
    @info(name = 'Check-for-null')
    from ProductInputStream [not(item is null)]
    -- not(test) 返回表达式的结果true/false 
    -- [] 相当于if 判断条件
    -- object is null 返回是否为null
    select item,
           price is null as isPriceNull
    
    insert into ProductValidationStream;
    
    @info(name = 'Outer-join-with-table')
    from ProductInputStream as s
        left outer join ProductInfoTable as t
        on s.item == t.item
    select s.item, s.price, t.discount,
           math:power(t.discount, 2) is null
                as isFunctionReturnsNull,
    
           t is null as isTNull,
           s is null as isSNull,
    
           t.discount is null as isTDiscountNull,
           s.item is null as isSItemNull
    
    insert into DiscountValidationStream;
    -- math:power(number,exp) 幂运算
    -- ProductInputStream ['Cake', 12.0]
    -- ProductValidationStream [Cake, false]
    -- DiscountValidationStream [Cake, 12.0, null, true, true, false, true, false]
    

siddhi-event&data

event cleansing

• value based filtering

  • define stream TemperatureStream (
     sensorId string, temperature double);
    
    @info(name = 'EqualsFilter')
    from TemperatureStream[ sensorId == 'A1234']
    select *
    insert into SenorA1234TemperatureStream;
    -- [] 判断if
    
    @info(name = 'RangeFilter') 
    from TemperatureStream[ temperature > -2 and temperature < 40]
    select *
    insert into NormalTemperatureStream;
    -- > < and 
    
    @info(name = 'NullFilter') 
    from TemperatureStream[ sensorId is null ]
    
    select *
    insert into InValidTemperatureStream;
    -- is null
    

  • [] 判断if > < and is null

• if-then-else

  • define stream TemperatureStream 
            (sensorId string, temperature double);
    
    @info(name = 'SimpleIfElseQuery')
    from TemperatureStream
    select sensorId,
     ifThenElse(temperature > -2, 'Valid', 'InValid') as isValid 
    -- ifThenElse(test,trueValue,falseValue) 三目表达式
    insert into ValidTemperatureStream;
    
    
    @info(name = 'ComplexIfElseQuery') 
    from TemperatureStream
    select sensorId, 
     ifThenElse(temperature > -2, 
            ifThenElse(temperature > 40, 'High', 'Normal'), 
            'InValid') 
        as tempStatus
    -- 嵌套三目表达式
    insert into ProcessedTemperatureStream;
    

  • ifThenElse(test,trueValue,falseValue) 三目表达式

• regex matching

  • define stream SweetProductionStream (name string, amount int);
    
    @info(name='ProcessSweetProductionStream')
    from SweetProductionStream
    select name, 
       regex:matches('chocolate(.*)', name) as isAChocolateProduct, 
       regex:group('.*\s(.*)', name, 1) as sweetType
    -- regex:matches(regex,string) 判断string是否匹配模式,返回true/false
    -- regex:group(regex,string,index) string匹配regex后，划分为group，返回下标为index的元素
    -- '.*\s(.*)'任意字符 中间是空格 任意字符
    insert into ChocolateProductStream; 
    
    -- SweetProductionStream ['chocolate cake', 34]
    -- ChocolateProductStream ['chocolate cake', true, 'cake']
    

• default

  • define stream PatientRegistrationInputStream (
                     seqNo long, name string, age int,
                     height float, weight double, photo object,
                     isEmployee bool, wardNo object);
    
    @info(name = 'SimpleIfElseQuery')
    from PatientRegistrationInputStream
    select 
     default(name, 'invalid') as name, 
     default(seqNo, 0l) as seqNo, 
     default(weight, 0d) as weight,
     default(age, 0) as age, 
     default(height, 0f) as height   
    -- default(param,defaultValue) 如果该值不存在，就为默认值
    insert into PreprocessedPatientRegistrationInputStream;
    -- 各类型的默认值 ['invalid', 0 0.0, 0, 0.0]
    

• type based filtering

  • define stream SweetProductionStream (name string, amount int);
    
    @info(name='ProcessSweetProductionStream')
    from SweetProductionStream
    select 
       instanceOfInteger(amount) as isAIntInstance,
    
        name, 
        amount
    insert into ProcessedSweetProductionStream;
    -- 将判断类型的结果放入event作为过滤
    

• remove duplicate event

  • define stream TemperatureStream
            (sensorId string, seqNo string, temperature double);
            
    @info(name = 'Deduplicate-sensorId')
    from TemperatureStream#unique:deduplicate(sensorId, 1 min)
    
    -- stream_name#unique:deduplicate(id,time_gap) 根据id去重time gap之间到达的event，超出gap的不算重复
    
    select *
    insert into UniqueSensorStream;
    @info(name = 'Deduplicate-sensorId-and-seqNo')
    from TemperatureStream#unique:deduplicate(
                            str:concat(sensorId,'-',seqNo), 1 min)
    -- str:concat(str1,concat_symbol,str2) 
    select *
    insert into UniqueSensorSeqNoStream;  
    

data transformation

• math & logical operation

  • define stream TemperatureStream
            (sensorId string, temperature double);
    @info(name = 'celciusTemperature')
    from TemperatureStream
    select sensorId, 
                    (temperature * 9 / 5) + 32 as temperature
    
    insert into FahrenheitTemperatureStream;
    @info(name = 'Overall-analysis')
    from FahrenheitTemperatureStream
    select sensorId, 
                    math:floor(temperature) as approximateTemp 
    -- math:floor(number) 向下取整
    insert all events into OverallTemperatureStream;
    @info(name = 'RangeFilter') 
    from OverallTemperatureStream
                  [ approximateTemp > -2 and approximateTemp < 40]
    
    select *
    insert into NormalTemperatureStream;
    

• transform json

  • define stream InputStream(jsonString string);
    
    from InputStream 
    select json:toObject(jsonString) as jsonObj 
    insert into PersonalDetails;
    -- json:toObject(string) json->object
    
    from PersonalDetails
    select jsonObj, 
        json:getString(jsonObj,'$.name') as name,
        json:isExists(jsonObj, '$.salary') as isSalaryAvailable,
        json:toString(jsonObj) as jsonString 
    -- json:getString(object,"$.item") 返回item名对应的值
    -- json:isExists(object,"$.item") 判断item名对应的值是否存在
    -- json:toString(object) 返回object对应的字符串
    insert into OutputStream;
    
    from OutputStream[isSalaryAvailable == false]
    select 
        json:setElement(jsonObj, '$', 0f, 'salary') as jsonObj
    -- json:setElement(object,'$',value,'item') 将item的值赋为value，添加到object
    insert into PreprocessedStream;
    
    {
        "name" : "siddhi.user",
        "address" : {
            "country": "Sri Lanka",
        },
        "contact": "+9xxxxxxxx"
    }
    
    OutputStream
    [ {"address":{"country":"Sri Lanka"},"contact":"+9xxxxxxxx","name":"siddhi.user"}, siddhi.user, false,
    "{\"name\" : \"siddhi.user\", \"address\" : { \"country\": \"Sri Lanka\", }, \"contact\": \"+9xxxxxxxx\"}"]
    
    PreprocessedStream
    {
        "name" : "siddhi.user",
        "salary": 0.0
        "address" : {
            "country": "Sri Lanka",
        },
        "contact": "+9xxxxxxxx"
    }
    

data summarization

• sliding time

  • define stream TemperatureStream
            (sensorId string, temperature double);
    
    @info(name = 'Overall-analysis')
    from TemperatureStream#window.time(1 min)
    # window.time(time_gap) 滑动窗口的时间间隔是1分钟以内
    
    select avg(temperature) as avgTemperature,
           max(temperature) as maxTemperature,
           count() as numberOfEvents
    insert all events into OverallTemperatureStream;
    -- avg(number) 
    -- max(number)
    -- count() 
    -- insert all events into stream 在时间间隔内添加所有事件到stream中，并且过期后删除
    
    @info(name = 'SensorId-analysis')
    from TemperatureStream#window.time(30 sec)
    select sensorId,
           avg(temperature) as avgTemperature,
           min(temperature) as maxTemperature
    group by sensorId
    having avgTemperature > 20.0
    -- group by id  having 条件
    insert into SensorIdTemperatureStream;
    -- 筛选后加入到滑动窗口中
    

  • stream#method 可以看做是这个method限制了stream的行为，比如去重、滑动窗口聚合等。

• batch (tumbling) time

  • define stream TemperatureStream
            (sensorId string, temperature double);
    
    @info(name = 'Overall-analysis')
    from TemperatureStream#window.timeBatch(1 min)
    select avg(temperature) as avgTemperature,
           max(temperature) as maxTemperature,
           count() as numberOfEvents
           
    -- stream#window.timeBatch(time_gap) 每time_gap时间视作一个batch，从第一个到达的event开始
    insert into OverallTemperatureStream;
    
    @info(name = 'SensorId-analysis')
    from TemperatureStream#window.timeBatch(30 sec, 0)
    -- stream#window.timeBatch(time_gap,start_timepoint) 每time_gap时间视作一个batch，从开始时刻开始
    select sensorId,
           avg(temperature) as avgTemperature,
           min(temperature) as maxTemperature
    group by sensorId
    having avgTemperature > 20.0
    insert into SensorIdTemperatureStream;
    
    

  • window:time()和window:timeBatch()的区别在于，前者是从时间间隔开始就开始写入stream，后者是时间间隔结束后写入stream。

• sliding event count

  • define stream TemperatureStream
            (sensorId string, temperature double);
    
    @info(name = 'Overall-analysis')
    from TemperatureStream#window.length(4)
    -- stream#window.length(len) 滑动窗口受长度限制，聚合最后len个事件
    select avg(temperature) as avgTemperature,
           max(temperature) as maxTemperature,
           count() as numberOfEvents
    insert into OverallTemperatureStream;
    
    @info(name = 'SensorId-analysis')
    from TemperatureStream#window.length(5)
    select sensorId,
           avg(temperature) as avgTemperature,
           min(temperature) as maxTemperature
    group by sensorId
    having avgTemperature >= 20.0
    insert into SensorIdTemperatureStream;
    

• batch(tumbling) event count

  • define stream TemperatureStream
            (sensorId string, temperature double);
    
    @info(name = 'Overall-analysis')
    from TemperatureStream#window.lengthBatch(4)
    -- stream#window.lengthBatch(len) 滑动窗口受长度限制，聚合最后len个事件为batch
    select avg(temperature) as avgTemperature,
           max(temperature) as maxTemperature,
           count() as numberOfEvents
    insert into OverallTemperatureStream;
    
    @info(name = 'SensorId-analysis')
    from TemperatureStream#window.lengthBatch(5)
    select sensorId,
           avg(temperature) as avgTemperature,
           min(temperature) as maxTemperature
    group by sensorId
    having avgTemperature >= 20.0
    insert into SensorIdTemperatureStream;
    

• session

  • define stream PurchaseStream
            (userId string, item string, price double);
    
    @info(name = 'Session-analysis')
    from PurchaseStream#window.session(1 min, userId)
    # stream#window.session(time_gap,session_id) 通过id聚合事件，在time_gap时间内
    select userId,
           count() as totalItems,
           sum(price) as totalPrice
    group by userId
    insert into UserIdPurchaseStream;
    
    @info(name = 'Session-analysis-with-late-event-arrivals')
    from PurchaseStream#window.session(1 min, userId, 20 sec)
    -- 在基于 userId 的 sessionwindow 上聚合事件，会话间隔为 1 分钟，允许延迟 20 秒以捕获延迟到达。
    select userId,
           count() as totalItems,
           sum(price) as totalPrice
    group by userId
    insert into OutOfOrderUserIdPurchaseStream;
    

• named window

  • define stream TemperatureStream
            (sensorId string, temperature double);
    
    define window OneMinTimeWindow
            (sensorId string, temperature double) time(1 min) ;
    -- define window name() time_type(time_gap)
    @info(name = 'Insert-to-window')
    from TemperatureStream
    insert into OneMinTimeWindow;
    
    @info(name = 'Min-max-analysis')
    from OneMinTimeWindow
    select min(temperature) as minTemperature,
           max(temperature) as maxTemperature
    insert into MinMaxTemperatureOver1MinStream;
    
    @info(name = 'Per-sensor-analysis')
    from OneMinTimeWindow
    select sensorId,
           avg(temperature) as avgTemperature
    group by sensorId
    insert into AvgTemperaturePerSensorStream;
    

data pipelining

• stream join

  • define stream TemperatureStream
            (roomNo string, temperature double);
    
    define stream HumidityStream
            (roomNo string, humidity double);
    
    @info(name = 'Equi-join')
    from TemperatureStream#window.unique:time(roomNo, 1 min) as t
        join HumidityStream#window.unique:time(roomNo, 1 min) as h
        on t.roomNo == h.roomNo
        
    -- stream#window.unique:time(id,time_gap) 只留下时间间隔内唯一的事件
    -- stream join
    select t.roomNo, t.temperature, h.humidity
    insert into TemperatureHumidityStream;
    
    @info(name = 'Join-on-temperature')
    from TemperatureStream as t
        left outer join HumidityStream#window.time(1 min) as h
        on t.roomNo == h.roomNo
    -- stream t 左外连接 stream h 1分钟以内的事件
    select t.roomNo, t.temperature, h.humidity
    insert into EnrichedTemperatureStream;
    

• partition events by value

  • define stream LoginStream
            ( userID string, loginSuccessful bool);
    
    @purge(enable='true', interval='10 sec',
           idle.period='1 hour')
    partition with ( userID of LoginStream )
    -- @purge 每10秒检查一下 1小时之内没有收到过事件的分区实例 并清除
    -- partition 按照userID分区
    begin
        @info(name='Aggregation-query')
        from LoginStream#window.length(3)
        select userID, loginSuccessful, count() as attempts
        group by loginSuccessful
        insert into #LoginAttempts;
    -- #LoginAttempts 是partition的内部stream，只能在内部访问 
    
        @info(name='Alert-query')
        from #LoginAttempts[loginSuccessful==false and attempts==3]
        select userID, "3 consecutive login failures!" as message
        insert into UserSuspensionStream;
    end;
    
    

• scatter and gather(String)

  • define stream PurchaseStream
                    (userId string, items string, store string);
    
    @info(name = 'Scatter-query')
    from PurchaseStream#str:tokenize(items, ',', true)
    select userId, token as item, store
    insert into TokenizedItemStream;
    -- str:tokenize(items,seperator,boolean) items用分隔符连接
    
    @info(name = 'Transform-query')
    from TokenizedItemStream
    select userId, str:concat(store, "-", item) as itemKey
    insert into TransformedItemStream;
    -- str:concat()
    @info(name = 'Gather-query')
    from TransformedItemStream#window.batch()
    select userId, str:groupConcat(itemKey, ",") as itemKeys
    insert into GroupedPurchaseItemStream;
    -- str:groupConcat(items,seperator)  items用分隔符连接
    

• scatter and gather(json)

  • define stream PurchaseStream
                    (order string, store string);
    
    @info(name = 'Scatter-query')
    from PurchaseStream#json:tokenize(order, '$.order.items')
    -- json:tokenize(json,'$.name') 访问name下的值，分隔开
    select json:getString(order, '$.order.id') as orderId, -- 拿出order.id
           jsonElement as item,
           store
    insert into TokenizedItemStream;
    
    @info(name = 'Transform-query')
    from TokenizedItemStream
    select orderId,
           ifThenElse(json:getString(item, 'name') == "cake",
                      json:toString(
                        json:setElement(item, 'price',
                          json:getDouble(item, 'price') - 5
                        )
                      ),
                      item) as item,
           store
    insert into DiscountedItemStream;
    
    @info(name = 'Gather-query')
    from DiscountedItemStream#window.batch()
    select orderId, json:group(item) as items, store
    insert into GroupedItemStream;
    -- json:group(item) 返回一个item json数组
    
    @info(name = 'Format-query')
    from GroupedItemStream
    select str:fillTemplate("""
        {"discountedOrder":
            {"id":"{{1}}", "store":"{{3}}", "items":{{2}} }
        }""", orderId, items, store) as discountedOrder
    insert into DiscountedOrderStream;
    -- str:fillTemplate({{n}}) 类似于format()的占位符 返回一个拼好的string
    

siddhi-others

patterns & trends

• simple pattern 简单模式 一个或多个事件随时间到达

  • define stream TemperatureStream(roomNo int, temp double);
    
    @sink(type = 'log')
    define Stream HighTempAlertStream(roomNo int,
        initialTemp double, finalTemp double);
    
    @info(name='temperature-increase-identifier')
    from every( e1 = TemperatureStream ) ->
        e2 = TemperatureStream[ e1.roomNo == roomNo
            and (e1.temp + 5) <= temp ]
        within 10 min
    select e1.roomNo, e1.temp as initialTemp, e2.temp as finalTemp
    insert into HighTempAlertStream;
    

• count pattern 计数模式 匹配相同条件收到的多个事件

  • define stream TemperatureStream (sensorID long, roomNo int,
        temp double);
    
    define stream RegulatorStream (deviceID long, roomNo int,
        tempSet double, isOn bool);
    
    @sink(type = 'log')
    define stream TemperatureDiffStream(roomNo int,
        tempDiff double);
    
    from every( e1 = RegulatorStream)
        -> e2 = TemperatureStream[e1.roomNo == roomNo] < 1: >
        -> e3 = RegulatorStream[e1.roomNo == roomNo]
    select e1.roomNo, e2[0].temp - e2[last].temp as tempDiff
    insert into TemperatureDiffStream;
    
    

• logical pattern 逻辑模式 匹配时间到达的顺序和逻辑关系

  • define stream RegulatorStateChangeStream(deviceID long,
        roomNo int, tempSet double, action string);
    
    define stream RoomKeyStream(deviceID long, roomNo int,
        action string);
    
    @sink(type='log')
    define stream RegulatorActionStream(roomNo int, action string);
    
    from every e1=RegulatorStateChangeStream[ action == 'on' ]
         -> e2=RoomKeyStream
                [ e1.roomNo == roomNo and action == 'removed' ]
            or e3=RegulatorStateChangeStream
                [ e1.roomNo == roomNo and action == 'off']
    select e1.roomNo,
        ifThenElse( e2 is null, 'none', 'stop' ) as action
    having action != 'none'
    insert into RegulatorActionStream;
    
    

• non-concurrence pattern 非发生模式 检测事件的发生和过期

  • define stream RegulatorStateChangeStream(deviceID long,
        roomNo int, tempSet double, action string);
    
    define stream TemperatureStream (roomNo int, temp double);
    
    @sink(type='log')
    define stream RoomTemperatureAlertStream(roomNo int);
    
    from e1=RegulatorStateChangeStream[action == 'on']
         -> not TemperatureStream[e1.roomNo == roomNo and
            temp <= e1.tempSet] for 30 sec
    select e1.roomNo as roomNo
    insert into RoomTemperatureAlertStream;
    
    

• simple sequence 简单序列 匹配按时间到达的连续事件

  • define stream StockRateStream (symbol string, price float,
        volume int);
    
    @sink(type='log')
    define stream PeakStockRateStream (symbol string,
        rateAtPeak float);
    
    partition with (symbol of StockRateStream)
    begin
    
        from every e1=StockRateStream,
            e2=StockRateStream[e1.price < price],
            e3=StockRateStream[e2.price > price]
            within 10 min
        select e1.symbol, e2.price as rateAtPeak
        insert into PeakStockRateStream ;
    end;
    
    

• sequence with count 按计数序列排序 可以计数的序列

  • define stream TemperatureStream(roomNo int, temp double);
    
    @sink(type='log') 
    define stream PeekTemperatureStream(roomNo int,
        initialTemp double, peekTemp double, firstDropTemp double);
    
    partition with (roomNo of TemperatureStream)
    begin
        @info(name = 'temperature-trend-analyzer')
        from every e1=TemperatureStream,
             e2=TemperatureStream[ifThenElse(e2[last].temp is null,
                    e1.temp <= temp, e2[last].temp <= temp)]+,
             e3=TemperatureStream[e2[last].temp > temp]
        select e1.roomNo, e1.temp as initialTemp,
            e2[last].temp as peekTemp, e3.temp as firstDropTemp
    
        insert into PeekTemperatureStream ;
    end;
    
    

• logical sequence 逻辑序列 匹配重复事件序列 并用逻辑排序

  • define stream TempSensorStream(deviceID long,
        isActive bool);
    
    define stream HumidSensorStream(deviceID long,
        isActive bool);
    
    define stream RegulatorStream(deviceID long, isOn bool);
    
    @sink(type='log')
    define stream StateNotificationStream (deviceID long,
        tempSensorActive bool, humidSensorActive bool);
    
    from every e1=RegulatorStream[isOn == true],
        e2=TempSensorStream and e3=HumidSensorStream
    select e1.deviceID, e2.isActive as tempSensorActive,
        e3.isActive as humidSensorActive
    insert into StateNotificationStream;
    

service integration

• HTTP Service Integration

  • @sink(type='http-call',
        publisher.url='http://localhost:8005/validate-loan',
        method='POST', sink.id='loan-validation',
        @map(type='json'))
    define stream LoanValidationStream (clientId long,
                        name string, amount double);
    -- 生产者 http-call post方式在这个URL上广播json数据
    @source(type='http-call-response', sink.id='loan-validation',
        http.status.code='2\d+',
        @map(type='json', @attributes(customerName='trp:name',
            clientId='trp:clientId', loanAmount='trp:amount',
            interestRate='validation-response.rate',
            totalYears='validation-response.years-approved')))
    define stream SuccessLoanRequestStream(clientId long,
               customerName string, loanAmount double,
               interestRate double, totalYears int);
    -- 消费者 http-call-response 只满足状态码是2开头的情况
    @source(type='http-call-response', sink.id='loan-validation',
        http.status.code='400',
        @map(type='json', @attributes(customerName='trp:name',
            clientId='trp:clientId',
            failureReason='validation-response.reason')))
    define stream FailureLoanRequestStream(clientId long,
                    customerName string, failureReason string);
    -- 消费者 http-call-response 只满足状态码是400的情况
    define stream LoanRequestStream (clientId long, name string,
                    amount double);
    
    @sink(type='log') 
    define stream LoanResponseStream(clientId long,
                    customerName string, message string);
    -- 生产者 log
    @info(name = 'attribute-projection')
    from LoanRequestStream
    select clientId, name, amount
    insert into LoanValidationStream;
    
    @info(name = 'successful-message-generator')
    from SuccessLoanRequestStream
    select clientId, customerName,
        "Loan Request is accepted for processing" as message
    insert into LoanResponseStream;
    
    @info(name = 'failure-message-generator')
    from FailureLoanRequestStream
    select clientId, customerName,
            str:concat("Loan Request is rejected due to ",
                failureReason) as message
    insert into LoanResponseStream;
    

  • 分别定义了http响应情况是正常和400的情况的消费者，通过json验证，结果以log的形式广播

• gRPC service integration

  • define stream TicketBookingStream (name string, phoneNo string,
            movie string, ticketClass string, qty int,
            bookingTime long);
    
    @sink(type='grpc-call',
        publisher.url =
        'grpc://localhost:5003/org.wso2.grpc.EventService/process',
        sink.id= 'ticket-price', @map(type='json'))
    define stream TicketPriceFinderStream (name string,
            phoneNo string, movie string, ticketClass string,
            qty int, bookingTime long);
    -- 生产者
    @source(type='grpc-call-response',
        receiver.url =
        'grpc://localhost:9763/org.wso2.grpc.EventService/process',
        sink.id= 'ticket-price',
        @map(type='json', @attributes(customerName='trp:name',
            phoneNo='trp:phoneNo', movie='trp:movie',
            qty='trp:qty', bookingTime='trp:bookingTime',
            ticketPrice='price')))
    -- 消费者
    define stream TicketPriceResponseStream (customerName string,
            phoneNo string, movie string, qty int,
            ticketPrice double, bookingTime long);
    
    @sink(type='log')
    define stream TotalTicketPaymentStream (customerName string,
            phoneNo string, movie string, totalAmount double,
            bookingTime long);
    -- log 生产者
    @info(name = 'filter-basic-ticket-bookings')
    from TicketBookingStream[ticketClass == "BASIC"]
    select name as customerName, phoneNo, movie,
        qty * 20.0 as totalAmount, bookingTime
    insert into TotalTicketPaymentStream;
    
    @info(name = 'filter-non-basic-tickets')
    from TicketBookingStream[ticketClass != "BASIC"]
    select *
    insert into TicketPriceFinderStream;
    
    @info(name = 'total-price-calculator')
    from TicketPriceResponseStream
    select customerName, phoneNo, movie,
        (qty * ticketPrice) as totalAmount, bookingTime
    insert into TotalTicketPaymentStream;
    
    

rate limiting

• rate limit based on time

  • define stream APIRequestStream (apiName string, version string,
        tier string, user string, userEmail string);
    define stream UserNotificationStream (user string,
        apiName string, version string, tier string,
        userEmail string, throttledCount long);
    @info(name='api-throttler')
    from APIRequestStream#window.timeBatch(1 min, 0, true) 
    select apiName, version, user, tier, userEmail,
        count() as totalRequestCount
    group by apiName, version, user
    having totalRequestCount == 3 or totalRequestCount == 0
    insert all events into ThrottledStream;
    
    @info(name='throttle-flag-generator') 
    from ThrottledStream 
    select apiName, version, user, tier, userEmail,
        ifThenElse(totalRequestCount == 0, false, true)
            as isThrottled
    insert into ThrottleOutputStream;
    
    @info(name='notification-generator') 
    from ThrottleOutputStream[isThrottled]#window.time(1 hour) 
    select user, apiName, version, tier, userEmail,
        count() as throttledCount
    group by user, apiName, version, tier
    having throttledCount > 2
    output first every 15 min 
    insert into UserNotificationStream;
    

error handling

• logging

  • define stream GlucoseReadingStream (locationRoom string,
        locationBed string, timeStamp string, sensorID long,
        patientFirstName string, patientLastName string,
        sensorValue double);
    
    @sink(type = 'http', on.error='log',
        publisher.url = "http://localhost:8080/logger",
        method = "POST",
        @map(type = 'json'))
    -- log 报错
    define stream AbnormalGlucoseReadingStream
        (timeStampInLong long, locationRoom string,
        locationBed string, sensorID long,
        patientFullName string, sensorReadingValue double);
    
    @info(name='abnormal-reading-identifier')
    from GlucoseReadingStream[sensorValue > 220]
    select math:parseLong(timeStamp) as timeStampInLong,
        locationRoom, locationBed, sensorID,
        str:concat(patientFirstName, " ", patientLastName)
            as patientFullName,
            sensorValue as sensorReadingValue
    insert into AbnormalGlucoseReadingStream;
    

• wait&retry

  • define stream GlucoseReadingStream (locationRoom string,
        locationBed string, timeStamp string, sensorID long,
        patientFirstName string, patientLastName string,
        sensorValue double);
    
    @sink(type = 'http', on.error='wait',
        publisher.url = "http://localhost:8080/logger",
        method = "POST",
        @map(type = 'json'))
    -- 遇到错误等待 退出并重试 
    -- on.error 配置处理错误的解决方法
    define stream AbnormalGlucoseReadingStream
        (timeStampInLong long, locationRoom string,
        locationBed string, sensorID long,
        patientFullName string, sensorReadingValue double);
    
    @info(name='abnormal-reading-identifier')
    from GlucoseReadingStream[sensorValue > 220]
    select math:parseLong(timeStamp) as timeStampInLong,
        locationRoom, locationBed, sensorID,
        str:concat(patientFirstName, " ", patientLastName)
            as patientFullName,
            sensorValue as sensorReadingValue
    insert into AbnormalGlucoseReadingStream;
    

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