JSON Structures To Define Siddhi Apps

General Types

These are general structures that are used in most of the siddhi element definitions, they are not stand alone elements (that is why they do not have an 'id').

Key-Value Pair JSON

This is used to define a map of key-value pair and is named as {Key-Value Pair JSON} when the structure is referred.

{
    `key`: `value`,
    ...
}

Attributes

Define's attributes of a Stream, Table etc.. that have the same format. So they all use the same structure which is named attributeList where ever they are used in a element definition.

[
    {
        name*: ‘’,
        type*: ‘STRING|INT|LONG|DOUBLE|FLOAT|BOOL|OBJECT’
    },
    ...
]

NOTE - The JSON attributes that end with a * symbol means that the attribute cannot be left null/empty

Example: To define the attributes (name string, age int) the JSON structure would look like this,

[
    {
        name: 'name',
        type: 'STRING'
    },
    {
        name: 'age',
        type: 'INT'
    }
]

Annotations

Defines the annotations of any Siddhi element, as all annotations have somewhat the same structure. They are defined in as a part of a Siddhi element using the name annotationList, and have the following JSON structure.

["annotation1", "annotation2", ...]

Example: To define the annotations @Async(buffer.size='1024') @PrimaryKey('name','age') the JSON structure would look like this,

["@Async(buffer.size='1024')", "@PrimaryKey('name', 'age')"]

Note - Sources, Sinks & Stores do not come under the general annotation struct as they have a different structure and are shown separately

Store

The store annotation is only used for tables and aggregations. So they are only defined in both table and aggregation JSON structs as the name store in the following format:

{
    type*: ‘’,
    options*: {Key-Value Pair JSON}
}

Example:

@Store(type="rdbms",
    jdbc.url="jdbc:mysql://localhost:3306/production",
    username="wso2",
    password="123" ,
    jdbc.driver.name="com.mysql.jdbc.Driver")

The JSON for the above store definition is,

{
    type: 'rdbms',
    options: {
        'jdbc.url': 'jdbc:mysql://localhost:3306/production',
        'username': 'wso2',
        'password': '123',
        'jdbc.driver.name': 'com.mysql.jdbc.Driver'
    }
}

Stream Handler

Streams in queries can have multiple filters and functions, but only one window.

If the query is of type window-filter-projection, then it's window can be placed anywhere in the input stream element section.
If the query is of type join, then it's last element in the input stream element must end with a Window (i.e. If the input stream element has atleast one function or filter).
If the query is of type pattern & sequence, then it's input stream element cannot have any window at all.

The JSON structure (denoted as streamHandlerList in other JSON structures) for an input stream element Filter/Function/Window in a query is given below,

[
    {
        type*: 'FILTER',
        value*: ''
    },
    << and|or >>
    {
        type*: 'FUNCTION|WINDOW',
        value*: {
            function*: '',
            parameters: ['value1',...],
        }
    },
    ...
]

Example: Consider the following streamHandlerList of a plain window-filter-projection query

from Instream[name == 'Mark']#window.time(10 min)#str:tokenize('<Test>', '<Test Regex>')
select ...

The JSON for the above example would look like this,

[
    {
        type: 'FILTER',
        value: 'name == \'Mark\''
    },
    {
        type: 'WINDOW',
        value: {
            function: 'time',
            parameters: ['10 min']
        }
    },
    {
        type: 'FUNCTION',
        value: {
            function: 'str:tokenize',
            parameters: ['<Test>', '<Test Regex>']
        }
    }
]

Stream Definition

{
    id*: '',
    name*: '',
    attributeList*: {Attributes JSON Array},
    annotationList: {Annotations JSON Array}
}

Example:

@Async(buffer.size="1024")
define stream InStream (name string, age int);

The JSON for the above stream definition is,

{
    id: 'InStream',
    name: 'InStream',
    isInnerStream: false,
    attributeList: [
        {
            name: 'name',
            type: 'STRING'
        },
        {
            name: 'age',
            type: 'INT'
        }
    ],
    annotationList: ["@Async(buffer.size='1024')"]
}

Note that if a stream is an inner stream, then it's attributes cannot be defined and it cannot have any annotations. Have to handle this in another way.

Table Definition

{
    id*: ‘’,
    name*: ‘’,
    attributeList*: {Attributes JSON Array},
    store: {Store JSON},
    annotationList: {Annotations JSON Array}
}

Example:

define table InTable (name string, age int);

The JSON for the above table definition is,

{
    id: 'InTable',
    name: 'InTable',
    attributeList: [
        {
            name: 'name',
            type: 'STRING'
        },
        {
            name: 'age',
            type: 'INT'
        }
    ],
    store: {},
    annotationList: []
}

Window Definition

{
    id*: ‘’,
    name*: ‘’,
    attributeList*: {Attributes JSON Array},
    function*: ‘time|length|timeBatch|lengthBatch...’,
    parameters*: ['value1',...],
    outputEventType: ‘current_events|expired_events|all_events’,
    annotationList: {Annotations JSON Array}
}

Example:

define window SensorWindow (name string, value float) timeBatch(1 second) output expired events;

The JSON for the above window definition is,

{
    id: 'SensorWindow',
    name: 'SensorWindow',
    attributeList: [
        {
            name: 'name',
            type: 'string'
        },
        {
            name: 'value',
            type: 'float'
        }
    ],
    function: 'timeBatch',
    parameters: ['1 second'],
    outputEventType: 'expired',
    annotationsList: []
}

Trigger Definition

{
    id*: ‘’,
    name*: ‘’,
    at*: ‘’
    annotationList: {Annotations JSON Array}
}

Example:

define trigger FiveMinTrigger at every 5 min;

The JSON for the above trigger definition is,

{
    id: 'FiveMinTrigger',
    name: 'FiveMinTrigger',
    at: 'every 5 min',
    annotationList: []
}

Aggregation Definition

{
    id*: ‘’,
    name*: ‘’,
    from*: ‘’,
    select*: {Query Select JSON},
    groupBy: ['value1',...],
    aggregateByAttribute: '',
    aggregateByTimePeriod*: {
        type*: 'RANGE',
        value*: {
            min*: '',
            max*: ''
        }
        << or >>
        type*: 'INTERVAL',
        value*: ['sec', 'min', ...] // Atleast one value must be available
    }, 
    store: {Store JSON},
    annotationList: {Annotations JSON Array}
}

Example:

define aggregation TradeAggregation
    from TradeStream
        select symbol, avg(price) as avgPrice, sum(price) as total
        group by symbol
        aggregate by timestamp every sec...year;

The JSON for the above aggregation definition is,

{
    id: 'TradeAggregation',
    name: 'TradeAggregation',
    from: 'TradeStream',
    select: {
        type: 'user_defined',
        value: [
            {
                expression: 'symbol',
                as: ''
            },
            {
                expression: 'avg(price)',
                as: 'avgPrice'
            },
            {
                expression: 'sum(price)',
                as: 'total'
            }
        ]
    },
    groupBy: ['symbol'],
    aggregateByAttribute: 'timestamp',
    aggregateByTimePeriod: {
        type: 'RANGE',
        value: {
            min: 'sec',
            max: 'year'
        }
    },
    store: {},
    annotationList: []
}

Function Definition

{
    id*: '',
    name*: '',
    scriptType*: 'JAVASCRIPT|R|SCALA',
    returnType*: 'STRING|INT|LONG|DOUBLE|FLOAT|BOOL|OBJECT',
    body*: ''
}

Example:

define function concatFn[javascript] return string {
    var str1 = data[0];
    var str2 = data[1];
    var str3 = data[2];
    var responce = str1 + str2 + str3;
    return responce;
};

The JSON for the above function definition is,

{
    id: '',
    name: 'concatFn',
    scriptType: 'JAVASCRIPT',
    returnType: 'STRING',
    body: 'var str1 = data[0];\nvar str2 = data[1];\nvar str3 = data[2];\nvar responce = str1 + str2 + str3;\nreturn responce;'
}

Source And Sink Definition

{
    id*: ‘’,
    annotationType*: 'SINK|SOURCE',
    connectedElementName*: '',
    type*: ‘’,
    options: ['option1', 'option2=value2',...],
    map: {
        type*: ‘’,
        options: {Key-Value Pair JSON},
        payloadOrAttribute: {
            annotationType: 'PAYLOAD|ATTRIBUTES',
            type*: ‘MAP’,
            value*: {Key-Value Pair JSON}
        }
        << or >>
        payloadOrAttribute: {
            annotationType: 'PAYLOAD|ATTRIBUTES',
            type*: ‘LIST’,
            value*: ['value1',...]
        }
    }
}

IMPORTANT

Map attributes for sources are either key-value pair JSON or list (no single), but the map attributes for sinks are either key-value pair JSON or single (no list).
If the annotationType is SOURCE then the map attributes are denoted using @attributes(...), but if the definitionType is SINK then the map attributes are defined as @payload(...) in Siddhi.

Example 1 (Source):

@Source(
    type = 'http',
    receiver.url='http://localhost:8006/productionStream',
    basic.auth.enabled='false',
    @map(type='json')
)

The JSON for the above source definition is,

{
    id: '<UUID>',
    annotationType: 'SOURCE',
    connectedElementName: '<StreamName||TriggerName>',
    type: 'http',
    options: ["receiver.url = 'http://localhost:8006/productionStream'", "basic.auth.enabled = 'false'"],
    map: {
        type: 'json',
        options: [],
        attributes: []
    },
}

Example 2 (Sink):

@sink(
    type='http',
    publisher.url='http://localhost:8005/endpoint', 
    method='POST', 
    headers='Accept-Date:20/02/2017', 
    basic.auth.username='admin', 
    basic.auth.password='admin', 
    basic.auth.enabled='true',
    @map(type='json')
)

The JSON for the above sink definition is,

{
    id: '<UUID>',
    annotationType: 'SINK',
    connectedElementName: '<StreamName||TriggerName>',
    type: 'http',
    options: ["publisher.url = 'http://localhost:8005/endpoint'", "method = 'POST'", "headers = 'Accept-Date:20/02/2017'", "basic.auth.username = 'admin'", "basic.auth.password = 'admin'", "basic.auth.enabled = 'true'"],
    map: {
        type: 'json',
        options: [],
        attributes: []
    }
}

Query Definition

All queries have the following JSON body structure

{
    id*: '',
    queryInput*: {Query Input JSON},
    select*: {Query Select JSON},
    groupBy: ['value1',...],
    orderBy: [
        {
            value*: '',
            order: 'asc|desc'
        },
        ...
    ],
    limit: <long>, 
    having: '',
    outputRateLimit: ''
    queryOutput*: {Query Output JSON},
    annotationList: {Annotation JSON Array}
}

Query Input

The query input can be of the following types:

Window-Filter-Projection
Join
Pattern & Sequence

JSON Structure for `Window-Filter-Projection` query input type:

Note that for this type there are a few conditions for each type even though they have the same JSON structure:

If the type is window, then the query must have a window and can have an optional filter.
If the type is filter, then the query must have a filter and no window.
If the type is projection, then the query cannot have a filter or window.

{
    type*: 'window|filter|projection',
    from*: '',
    streamHandlerList: {Stream Handler JSON}
}

Example:

from InputStream[age >= 18]#window.time(1 hour)[age < 30]
select ...

The JSON for the above Window-Filter-Projection input is,

{
    type: 'window',
    from: 'InputStream',
    streamHandlerList: [
        {
            type: 'FILTER',
            value: 'age >= 18'
        },
        {
            type: 'WINDOW',
            value: {
                function: 'time',
                parameters: ['1 hour']
            }
        },
        {
            type: 'FILTER',
            value: 'age < 30'
        }
    ]
}

Please note that even triggers can be used in window-filter-projection queries as they are treated as streams underneath in the siddhi runtime

JSON Structure for `Join` query input type:

A join query can be one of 4 types:

Join Stream
Join Table
Join Aggregation
Join Window
Join Trigger

The way to identify a join query type is by using the joinWith attribute. However all 4 of these join types will be defined using the same JSON structure.

{
    type*: 'join',
    joinWith*: 'stream|table|window|aggregation|trigger',
    left*: {Join Element JSON},
    joinType*: 'join|left_outer|right_outer|full_outer',
    right*: {Join Element JSON},
    on: '',
    within: '', // If joinWith == aggregation
    per: '' // If joinWith == aggregation
}

The Join Element JSON has the following structure:

{
    type*: 'stream|table|window|aggregation|trigger',
    from*: '',
    streamHandlerList: {Stream Handler JSON} // If there is a filter, there must be a window for joins (the only exception is when type = window).
    as: '',
    isUnidirectional: true|false // Only one 'isUnidirectional' value can be true at a time (either left definition|right definition|none)
}

There are a few conditions that must be met for a join query input to be a valid one:

At least one, left or right JSON value must be of stream or trigger type, or else it is not a valid join query input.
If a Join element JSON is of type window, then that element's window attribute must be null. This is because a window definition cannot have another window within it.
If there is a Join Element JSON of type aggregation, then the within and per attributes in the JSON structure cannot be null. If there is no aggregation definition, then those attributes have to be null.
If a Join Element JSON has a filter, then it must have a window as well or else it is invalid (except if that element is of type window definition).
Only one Join Element JSON can be marked as isUnderectional: true. It is either the left definition, right definition or neither of them.

Example:

from StockStream as S join TradeAggregation as T
    on S.symbol == T.symbol 
    within "2014-02-15 00:00:00 +05:30", "2014-03-16 00:00:00 +05:30" 
    per "days" 
select ...

The JSON for the above Join Aggregation input is,

{
    type: 'join',
    joinWith: 'aggregation',
    left: {
        type: 'stream',
        from: 'StockStream',
        streamHandlerList: [],
        as: 'S',
        isUnidirectional: false
    },
    joinType: 'join',
    right: {
        type: 'aggregation',
        from: 'TradeAggregation',
        streamHandlerList: [],
        as: 'T',
        isUnidirectional: false
    },
    on: 'S.symbol == T.symbol',
    within: '\"2014-02-15 00:00:00 +05:30\", \"2014-03-16 00:00:00 +05:30\"',
    per: '\"days\"'
}

JSON Structure for `Pattern` & `Sequence` query input types:

The JSON structure for both patterns & sequences are identical:

{
    type*: 'pattern|sequence',
    conditionList*: [
        {
            conditionId*: '',
            streamName*: '',
            streamHandlerList: {Stream Handler JSON}
        },
        ...
    ],
    logic*: ''
}

Example

from every event1=InStream[age < 100]<21:234> within 10 min ->
    every event2=InStream[age > 30] and event3=InStream[age < 50]->
    every not InStream[age >= 18] for 5 sec ->
    every not InStream[age < 18] and event6=InStream[age > 30]
select ...

The above pattern input is defined by the following JSON structure:

{
    type: 'pattern',
    conditionList: [
        {
            conditionId: 'event1',
            streamName: 'InStream',
            streamHandlerList: [
                {
                    type: 'FILTER',
                    value: 'age < 100'
                }
            ]
        },
        {
            conditionId: 'event2',
            streamName: 'InStream',
            streamHandlerList: [
                {
                    type: 'FILTER',
                    value: 'age > 30'
                }
            ]
        },
        {
            conditionId: 'event3',
            streamName: 'InStream',
            streamHandlerList: [
                {
                    type: 'FILTER',
                    value: 'age < 50'
                }
            ]
        },
        {
            conditionId: 'event4',
            streamName: 'InStream',
            streamHandlerList: [
                {
                    type: 'FILTER',
                    value: 'age >= 18'
                }
            ]
        },
        {
            conditionId: 'event5',
            streamName: 'InStream',
            streamHandlerList: [
                {
                    type: 'FILTER',
                    value: 'age < 18'
                }
            ]
        },
        {
            conditionId: 'event6',
            streamName: 'InStream',
            streamHandlerList: [
                {
                    type: 'FILTER',
                    value: 'age > 30'
                }
            ]
        }
    ],
    logic: 'every event1<21:234> within 10 min -> every event2 and event3 -> every not event4 for 5 sec -> every not event5 and event6'
}

Note - If their is a not statement before a conditionId in the logic attribute, then that conditionId will not be displayed in the source view. For an example:

{
    type: 'sequence',
    conditionList: [
        {
            conditionId: 'e1',
            streamName*: 'InStream',
            streamHandlerList: [
                {
                    type: 'FILTER',
                    value: 'age >= 18'
                }
            ]
        },
        {
            conditionId: 'e2',
            streamName*: 'InStream',
            streamHandlerList: [
                {
                    type: 'FILTER',
                    value: 'age < 30'
                }
            ]
        }
    ],
    logic: 'every e1+ within 10 min, not e2 for 10 min'
}

The siddhi code for the above JSON would look like this:

from 
    every e1=InStream[age >= 18], 
    not InStream[age < 30] for 10 min
select ...

Notice that the second sequence not InStream[age < 30] for 10 min does not have the phrase "e2=" reference like e1 does because it has a not statement before it.

Query Select

{
    type*: 'user_defined',
    value*: [
        {
            expression*: '',
            as: ''
        },
        ...
    ]
    << or >>
    type*: 'all',
    value*: '*'
}

Example:

select Stream1.id as UID, avg(price) as avgPrice, ((TempStream.temp - 32) * 5)/9 as Celsius, isInStock ...

The JSON for the above select function is,

{
    type: 'user_defined',
    value: [
        {
            expression: 'Stream1.id',
            as: 'UID'
        },
        {
            expression: 'avg(price)',
            as: 'avgPrice'
        },
        {
            expression: '((TempStream.temp - 32) * 5)/9',
            as: 'Celsius'
        },              
        {
            expression: 'isInStock',
            as: ''
        }
    ]
}

Query Output

All query outputs have the following generic structure:

{
    type*: 'insert|delete|update|update-or-insert-into',
    output*: {INSERT JSON|DELETE JSON|UPDATE JSON|UPDATE-OR-INSERT JSON},
    target*: ''
}

The output Attribute Can Be Of 4 JSON Structures:

Insert
Delete
Update
Update or insert into

JSON structure for `insert` query output type:

{
    eventType: 'current_events|expired_events|all_events'
}

Example:

insert all events into LogStream;

The JSON for the above insert function is,

{
    type: 'insert',
    output: {
        eventType: 'all'
    },
    target: 'LogStream'
}

JSON structure for the `delete` query output type:

{
    eventType: 'current_events|expired_events|all_events',
    on*: ''
}

Example:

delete RoomTypeTable
    for all events 
    on RoomTypeTable.roomNo == roomNumber;

The JSON for the above insert function is,

{
    type: 'delete',
    output: {
        eventType: 'all',
        on: 'RoomTypeTable.roomNo == roomNumber'
    },
    target: 'RoomTypeTable',
}

JSON structure for the `update` query output type:

{
    eventType: 'current_events|expired_events|all_events',
    set: [
        {
            attribute*: '',
            value*: ''
        },
        ...
    ],
    on*: ''
}

Example:

update RoomTypeTable
    set RoomTypeTable.people = RoomTypeTable.people + arrival - exit
    on RoomTypeTable.roomNo == roomNumber;

The JSON for the above update function is,

{
    type: 'update',
    output: {
        eventType: '',
        set: [
            {
                attribute: 'RoomTypeTable.people',
                value: 'RoomTypeTable.people + arrival - exit'
            }
        ],
        on: 'RoomTypeTable.roomNo == roomNumber'
    },
    target: 'RoomTypeTable',
}

JSON structure for the `update or insert into` query output type:

{
    eventType: 'current_events|expired_events|all_events',
    set: [
        {
            attribute*: '',
            value*: ''
        },
        ...
    ],
    on*: ''
}

Example:

update or insert into RoomAssigneeTable
    set RoomAssigneeTable.assignee = assignee
    on RoomAssigneeTable.roomNo == roomNo;

The JSON for the above update or insert into function is,

{
    type: 'update_or_insert_into',
    output: {
        eventType: '',
        set: [
            {
                attribute: 'RoomAssigneeTable.assignee',
                value: 'assignee'
            }
        ],
        on: 'RoomAssigneeTable.roomNo == roomNo'
    },
    target: 'RoomAssigneeTable',
}

Partition Definition

{
    id*: ‘’,
    queryLists: [
        {
            '<queryType>': [{Query JSON},...]
        },
        ...
    ],
    innerStreamList: [{Stream Definition JSON},...],
    partitionWith*: [
        {
            streamName*: '',
            expression*: ''
        },
        ..
    ],
    annotationList: {Annotation JSON Array}
}

Example:

@info(name='TestPartition')
partition with ( roomNo >= 1030 as 'serverRoom' or
                 roomNo < 1030 and roomNo >= 330 as 'officeRoom' or
                 roomNo < 330 as 'lobby' of TempStream)
begin
    @info(name='TestQuery1') 
    from TempStream
    select *
    insert into #OutputStream;

    @info(name='TestQuery2') 
    from TempStream#window.time(10 min)
    select roomNo, deviceID, avg(temp) as avgTemp
    insert into AreaTempStream;
end;

The JSON for the above partition is,

{
    id: ‘TestPartition’,
    queryLists: [
        {
            '<queryType>': [{Query JSON},...]
        },
        ...
    ],
    streamList: [{#OutputStream JSON}],
    partitionWith: [
        {
            expression: 'roomNo >= 1030 as \'serverRoom\' or roomNo < 1030 and roomNo >= 330 as \'officeRoom\' or roomNo < 330 as \'lobby\''
            streamName: 'TempStream',
        }
    ],
    annotationList: ['@info(name=\'TestPartition\')']
}

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README.md

README.md

JSON Structures To Define Siddhi Apps

Contents

General Types

Key-Value Pair JSON

Attributes

Annotations

Store

Stream Handler

Stream Definition

Table Definition

Window Definition

Trigger Definition

Aggregation Definition

Function Definition

Source And Sink Definition

Query Definition

Query Input

JSON Structure for `Window-Filter-Projection` query input type:

JSON Structure for `Join` query input type:

JSON Structure for `Pattern` & `Sequence` query input types:

Query Select

Query Output

JSON structure for `insert` query output type:

JSON structure for the `delete` query output type:

JSON structure for the `update` query output type:

JSON structure for the `update or insert into` query output type:

Partition Definition

Files

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README.md

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JSON Structures To Define Siddhi Apps

Contents

General Types

Key-Value Pair JSON

Attributes

Annotations

Store

Stream Handler

Stream Definition

Table Definition

Window Definition

Trigger Definition

Aggregation Definition

Function Definition

Source And Sink Definition

Query Definition

Query Input

JSON Structure for Window-Filter-Projection query input type:

JSON Structure for Join query input type:

JSON Structure for Pattern & Sequence query input types:

Query Select

Query Output

JSON structure for insert query output type:

JSON structure for the delete query output type:

JSON structure for the update query output type:

JSON structure for the update or insert into query output type:

Partition Definition

JSON Structure for `Window-Filter-Projection` query input type:

JSON Structure for `Join` query input type:

JSON Structure for `Pattern` & `Sequence` query input types:

JSON structure for `insert` query output type:

JSON structure for the `delete` query output type:

JSON structure for the `update` query output type:

JSON structure for the `update or insert into` query output type: