Query Guide

Introduction

Macrometa Stream QL is designed to process streams of events. It can be used to implement streaming data integration, streaming analytics, rule based and adaptive decision making use cases. It is an evolution of Complex Event Processing (CEP) and Stream Processing systems, hence it can also be used to process stateful computations, detecting of complex event patterns, and sending notifications in real-time.

Stream QL uses SQL like syntax, and annotations to consume events from diverse event sources with various data formats, process then using stateful and stateless operators and send outputs to multiple endpoints according to their accepted event formats. It also supports exposing rule based and adaptive decision making as service endpoints such that external programs and systems can synchronously get decision support form Stream.

The following sections explains how to write processing logic using Stream QL.

Stream Application

The processing logic for your program can be written using the Streaming SQL and put together as a single declarative spec called as the Stream Application or the StreamApp.

StreamApps are named by adding @app:name('<name>') annotation on the top of the StreamApp spec. When the annotation is not added stream processor assigns a random UUID as the name of the StreamApp.

Purpose

StreamApp provides an isolated execution environment for your processing logic that allows you to deploy and execute processing logic independent of other StreamApps in the system. Therefore it's always recommended to have a processing logic related to single use case in a single StreamApp. This will help you to group processing logic and easily manage addition and removal of various use cases.

The following diagram depicts some of the key Stream QL elements of Stream Application and how event flows through the elements.

tip

"Have different business use cases in separate Stream Applications." This is recommended as it allows users to selectively deploy the applications based their on business needs. It is also recommended to move the repeated steam processing logic that exist in multiple Stream Applications such as message retrieval and preprocessing, to a common Stream Application, whereby reducing code duplication and improving maintainability. In this case, to pass the events from one Stream App to another, configure them using a common stream or collection using stream Sink and stream Source.

Below table provides brief description of a few key elements in the Stream QL Language.

Elements	Description
Stream	A logical series of events ordered in time with a uniquely identifiable name, and a defined set of typed attributes defining its schema.
Event	An event is a single event object associated with a stream. All events of a stream contains a timestamp and an identical set of typed attributes based on the schema of the stream they belong to.
Table	A structured representation of data stored with a defined schema. Stored data is backed by C8DB. The tables (aka collections) can be local or geo-replicated. Similarly the tables can be document or graph collections. The tables can be accessed and manipulated at runtime.
Named Window	A structured representation of data stored with a defined schema and eviction policy. Window data is stored In-Memory and automatically cleared by the named window constrain. Other stream processor elements can only query the values in windows at runtime but they cannot modify them.
Named Aggregation	A structured representation of data that's incrementally aggregated and stored with a defined schema and aggregation granularity such as seconds, minutes, hours, etc. Aggregation data is stored in database. Other stream processor elements can only query the values in windows at runtime but they cannot modify them.
Query	A logical construct that processes events in streaming manner by by consuming data from one or more streams, tables, windows and aggregations, and publishes output events into a stream, table or a window.
Source	A construct that consumes data from external sources (such as database, stream, TCP, Kafka, HTTP, etc) with various event formats such as XML, JSON, binary, etc, convert then to stream events, and passes into streams for processing.
Sink	A construct that consumes events arriving at a stream, maps them to a predefined data format (such as XML, JSON, binary, etc), and publishes them to external endpoints (such as E-mail, TCP, Kafka, HTTP, etc).
Stream/Query Callback	A mechanism to programmatically consume output events from streams or queries.
Partition	A logical container that isolates the processing of queries based on the partition keys derived from the events.
Inner Stream	A positionable stream that connects portioned queries with each other within the partition.

Syntax

StreamApp is a collection of Stream QL elements composed together as a script. Here each stream query element must be separated by a semicolon ;.

High level syntax of StreamApp is as follows.

<stream app>  :
        <app annotation> *
        ( <stream definition> | <table definition> | ... ) +
        ( <query> | <partition> ) +
        ;

For example, this stream application with name Temperature-Analytics creates a stream named TempStream and a query named 5minAvgQuery.

@app:name('Temperature-Analytics')
@App:qlVersion("2")

CREATE STREAM TempStream (deviceID long, roomNo int, temp double);

@name('5minAvgQuery')
insert into OutputStream
select roomNo, avg(temp) as avgTemp
from TempStream#window.time(5 min)
group by roomNo;

Stream

A stream is a logical series of events ordered in time. Its schema is defined via the stream definition. A stream definition contains the stream name and a set of attributes with specific types and uniquely identifiable names within the stream. All events associated to the stream will have the same schema (i.e., have the same attributes in the same order).

Stream processor groups common types of events together with a schema. This helps in various ways such as, processing all events together in queries and performing data format transformations together when they are consumed and published via sources and sinks.

Syntax

The syntax for defining a new stream is as follows.

CREATE STREAM <stream_name> (<attribute_name> <attribute_type>,
                             <attribute_name> <attribute_type>, ... );

The following parameters are used to configure a stream definition.

Parameter	Description
stream name	The name of the stream created. (It is recommended to define a stream name in PascalCase.)
attribute name	Uniquely identifiable name of the stream attribute. (It is recommended to define attribute names in camelCase.)
attribute type	The type of each attribute defined in the schema. This can be STRING, INT, LONG, DOUBLE, FLOAT, BOOL or OBJECT.

To use and refer stream and attribute names that do not follow [a-zA-Z_][a-zA-Z_0-9]* format enclose them in `. E.g. `$test(0)`.

To make the stream process events with multi-threading and asynchronously, we add Async to the WITH() property. For example: WITH(async='true')

This example creates a stream called TempStream with the following attributes:

• deviceID of type long
• roomNo of type int
• temp of type double

CREATE STREAM TempStream (deviceID long, roomNo int, temp double);

Source

Sources receive events via multiple transports and in various data formats, and direct them into streams for processing.

A source configuration allows to define a mapping in order to convert each incoming event from its native data format to a stream event. When customizations to such mappings are not provided, stream processor assumes that the arriving event adheres to the predefined format based on the stream definition and the configured message mapping type.

Purpose

Source provides a way to consume events from internal & external services and convert them to be processed by the associated stream.

Syntax

To configure a stream that consumes events via a source, add the source configuration to a stream definition by adding the source.type annotation with the required parameter values.

The source syntax is as follows:

CREATE SOURCE <source_name> WITH (type = 'source_type', <static_key>='<value>', map.type='json') (<attribute1>='<attribute mapping>', <attribute2>='<attribute mapping>')

This syntax includes the following annotations.

Source

The type parameter of CREATE SOURCE annotation defines the source type that receives events. The other parameters of source.type annotation depends upon the selected source type, and here some of its parameters can be optional.

The following is the list of source types supported by Stream:

Source type	Description
database	Allow stream app to consume events from collections (doc, graphs) running in the same or different geo fabric.
stream	Allow stream app to consume events from streams (local, geo-replicated) running in the same or different geo fabric.
Kafka	Subscribe to Kafka topic to consume events.

Source Mapper

Each source.type configuration can have a mapping denoted by the map.type annotation that defines how to convert the incoming event format to Stream events.

The type parameter of the map.type defines the map type to be used in converting the incoming events. The other parameters of map.type annotation depends on the mapper selected, and some of its parameters can be optional.

For detailed information about the parameters see the documentation of the relevant mapper.

Map Attributes

attributes is an optional annotation used with map.type to define custom mapping. When attributes is not provided, each mapper assumes that the incoming events adheres to its own default message format and attempt to convert the events from that format. By adding the attributes annotation, users can selectively extract data from the incoming message and assign them to the attributes.

There are two ways to configure attributes. In both cases, add the attributes in parentheses after the query:

• Define attribute names as keys, and mapping configurations as values:

  ... ( <attribute1>='<mapping>', <attributeN>='<mapping>')

• Define the mapping configurations in the same order as the attributes defined in stream definition:

  ... ( '<mapping for attribute1>', '<mapping for attributeN>')

Supported Source Mapping Types

The following is the list of source mapping types supported by Stream:

Source mapping type	Description
PassThrough	Omits data conversion on Stream events.
JSON	Converts JSON messages to Stream events.
XML	Converts XML messages to Stream events.
TEXT	Converts plain text messages to Stream events.
Key Value	Converts key-value HashMaps to Stream events.
CSV	Converts CSV like delimiter separated events to Stream events.

tip

When the map.type annotation is not provided map.type='passThrough' is used as default, that passes the consumed stream events directly to the streams without any data conversion.

Example 1

Receive JSON messages via stream, and direct them to InputStream stream for processing. The stream expects the JSON messages to be on the default data format that's supported by the JSON mapper as follows.

{
  "name":"Paul",
  "age":20,
  "country":"UK"
}

The configuration of the stream source and JSON source mapper to achieve the above is as follows.

CREATE SOURCE InputStream WITH (source.type='stream', streams.list='foo', map.type='json') (name string, age int, country string);

Example 2

Receive JSON messages via database, and direct them to StockStream stream for processing. Here the incoming JSON, as given below, do not adhere to the default data format that's supported by the JSON mapper.

{
  "portfolio":{
    "stock":{
      "volume":100,
      "company":{
        "symbol":"FB"
      },
      "price":55.6
    }
  }
}

The configuration of the database source and the custom JSON source mapping to achieve the above is as follows.

CREATE SOURCE StockStream WITH (source.type='database', collection='foo', map.type='json', enclosing.element="$.portfolio",) (symbol = "stock.company.symbol", price = "stock.price", volume = "stock.volume");

The same can also be configured by omitting the attribute names as below.

CREATE SOURCE StockStream WITH (source.type='database', collection='foo', map.type='json', enclosing.element="$.portfolio",) ("stock.company.symbol", "stock.price", "stock.volume");

Sink

Sinks consumes events from streams and publish them via multiple transports to external endpoints in various data formats.

A sink configuration allows users to define a mapping to convert the Stream events in to the required output data format (such as JSON, TEXT, XML, etc.) and publish the events to the configured endpoints. When customizations to such mappings are not provided, Stream converts events to the predefined event format based on the stream definition and the configured message mapper type before publishing the events.

Purpose

Sink provides a way to publish stream events of a stream to external systems by converting events to their supported format.

Syntax

To configure a stream to publish events via a sink, add the sink configuration to a stream definition by adding the sink.type annotation with the required parameter values.

The sink syntax is as follows:

CREATE SINK <stream name> WITH (sink.type='<sink type>', <static.key>='<value>', <dynamic.key>='{{<value>}}', map.type='<map type>', <static.key>='<value>', <dynamic.key>='{{<value>}}', map.payload'<payload mapping>')) (<attribute1> <type>, <attributeN> <type>);

note

"Dynamic Properties" The sink and sink mapper properties that are categorized as dynamic have the ability to absorb attribute values dynamically from the Stream events of their associated streams. This can be configured by enclosing the relevant attribute names in double curly braces as{{...}}, and using it within the property values.

Some valid dynamic properties values are:

• '{{attribute1}}'
• 'This is {{attribute1}}'
• {{attribute1}} > {{attributeN}}

Here the attribute names in the double curly braces will be replaced with the values from the events before they are published.

This syntax includes the following annotations.

Sink

The type parameter of the sink.type annotation defines the sink type that publishes the events. The other parameters of the sink.type annotation depends upon the selected sink type, and here some of its parameters can be optional and/or dynamic.

The following is a list of sink types supported by stream processor:

Source type	Description
database	Allow StreamApp to publish events to collections (doc, graphs) in the same or different geofabric.
HTTP	Publish events to an HTTP endpoint.
Kafka	Publish events to Kafka topic.
TCP	Publish events to a TCP service.
Email	Send emails via SMTP protocols.
Web Socket	Publish events to a Web Socket

Distributed Sink

Distributed Sinks publish events from a defined stream to multiple endpoints using load balancing or partitioning strategies.

Any sink can be used as a distributed sink. A distributed sink configuration allows users to define a common mapping to convert and send the Stream events for all its destination endpoints.

Purpose

Distributed sink provides a way to publish Stream events to multiple endpoints in the configured event format.

Syntax

To configure distributed sink add the sink configuration to a stream definition by adding the sink.type property and add the configuration parameters that are common of all the destination endpoints inside it, along with the common parameters also add the distribution.strategy property specifying the distribution strategy (i.e. roundRobin or partitioned) and destination properties providing each endpoint specific configurations.

The distributed sink syntax is as follows:

RoundRobin Distributed Sink

Publishes events to defined destinations in a round robin manner.

CREATE SINK <stream name> WITH (sink.type='<sink type>', <common.static.key>='<value>', <common.dynamic.key>='{{<value>}}', map.type='<map type>', <static.key>='<value>', <dynamic.key>='{{<value>}}', map.payload='<payload mapping>' distribution.strategy='roundRobin', destination.<key>='<value>', destination.<key>='<value>') (<attribute1> <type>, <attributeN> <type>);

Partitioned Distributed Sink

Publishes events to defined destinations by partitioning them based on the partitioning key.

CREATE SINK <stream name> WITH (sink.type='<sink type>', <common.static.key>='<value>', <common.dynamic.key>='{{<value>}}', map.type='<map type>', <static.key>='<value>', <dynamic.key>='{{<value>}}', map.payload='<payload mapping>', distribution.strategy='partitioned', partitionKey='<partition key>', destination.<key>='<value>', destination.<key>='<value>') (<attribute1> <type>, <attributeN> <type>);

Sink Mapper

Each sink.type configuration can have a mapping denoted by the map.type annotation that defines how to convert Stream events to outgoing messages with the defined format.

The type parameter of the map.type defines the map type to be used in converting the outgoing events. The other parameters of map.type annotation depends on the mapper selected, and some of its parameters can be optional and/or dynamic.

For detailed information about the parameters see the documentation of the relevant mapper.

Map Payload

map.payload is an optional annotation used with map.type to define custom mapping. When the map.payload annotation is not provided, each mapper maps the outgoing events to its own default event format. The map.payload annotation allow users to configure mappers to produce the output payload of their choice, and by using dynamic properties within the payload they can selectively extract and add data from the published Stream events.

There are two ways you to configure map.payload annotation.

1. Some mappers such as XML, JSON, and Test only accept one output payload:

   map.payload='This is a test message from {{user}}.'
2. Some mappers such key-value accept series of mapping values:

   map.payload= key1='mapping_1', 'key2'='user : {{user}}'
   Here, the keys of payload mapping can be defined using the dot notation as a.b.c, or using any constant string value as '$abc'.

Supported Sink Mapping Types

The following is a list of sink mapping types supported by Stream:

Sink mapping type	Description
PassThrough	Omits data conversion on Stream events.
JSON	Converts JSON messages to Stream events.
XML	Converts XML messages to Stream events.
TEXT	Converts plain text messages to Stream events.
Key Value	Converts key-value HashMaps to Stream events.
CSV	Converts CSV like delimiter separated events to Stream events.

tip

When the map.type annotation is not provided map.type='passThrough' is used as default, that passes the outgoing Stream events directly to the sinks without any data conversion.

Example 1

Publishes OutputStream events by converting them to JSON messages with the default format, and by sending to an HTTP endpoint http://localhost:8005/endpoint1, using POST method, Accept header, and basic authentication having admin is both username and password.

The configuration of the HTTP sink and JSON sink mapper to achieve the above is as follows.

CREATE SINK OutputStream WITH (sink.type='http', publisher.url='http://localhost:8005/endpoint', method='POST', headers='Accept-Date:20/02/2017', basic.auth.enabled='true', basic.auth.username='admin', basic.auth.password='admin', map.type='json') (name string, age int, country string);

This will publish a JSON message on the following format:

{
  "event":{
    "name":"Paul",
    "age":20,
    "country":"UK"
  }
}

Example 2

Publishes StockStream events by converting them to user defined JSON messages, and by sending to an HTTP endpoint http://localhost:8005/stocks.

The configuration of the HTTP sink and custom JSON sink mapping to achieve the above is as follows.

CREATE SINK StockStream WITH (sink.type='http', publisher.url='http://localhost:8005/stocks', map.type='json', validate.json='true', enclosing.element='$.Portfolio', map.payload="""{"StockData":{ "Symbol":"{{symbol}}", "Price":{{price}} }}""") (symbol string, price float, volume long);

This will publish a single event as the JSON message on the following format:

{
  "Portfolio":{
    "StockData":{
      "Symbol":"GOOG",
      "Price":55.6
    }
  }
}

This can also publish multiple events together as a JSON message on the following format:

{
  "Portfolio":[
    {
      "StockData":{
        "Symbol":"GOOG",
        "Price":55.6
      }
    },
    {
      "StockData":{
        "Symbol":"FB",
        "Price":57.0
      }
    }
  ]  
}

Example 3

Publishes events from the OutputStream stream to multiple the HTTP endpoints using a partitioning strategy. Here the events are sent to either http://localhost:8005/endpoint1 or http://localhost:8006/endpoint2 based on the partitioning key country. It uses default JSON mapping, POST method, and used admin as both the username and the password when publishing to both the endpoints.

The configuration of the distributed HTTP sink and JSON sink mapper to achieve the above is as follows.

CREATE SINK OutputStream WITH (sink.type='http', method='POST', basic.auth.enabled='true', basic.auth.username='admin', basic.auth.password='admin', map.type='json', distribution.strategy='partitioned', partitionKey='country', destination.publisher.url='http://localhost:8005/endpoint1', destination.publisher.url='http://localhost:8006/endpoint2') (name string, age int, country string);

This will partition the outgoing events and publish all events with the same country attribute value to the same endpoint. The JSON message published will be on the following format:

{
  "event":{
    "name":"Paul",
    "age":20,
    "country":"UK"
  }
}

Error Handling

Errors in Stream can be handled at the Streams and in Sinks.

Error Handling at Stream

When errors are thrown by Stream elements subscribed to the stream, the error gets propagated up to the stream that delivered the event to those Stream elements. By default the error is logged and dropped at the stream, but this behavior can be altered by by adding OnError property to the corresponding stream definition.

OnError property can help users to capture the error and the associated event, and handle them gracefully by sending them to a fault stream.

The OnError property and the required action to be specified as below.

CREATE SOURCE <stream name> WITH (OnError.action='<action>') (<attribute name> <attribute type>, <attribute name> <attribute type>, ... );

The action parameter of the OnError property defines the action to be executed during failure scenarios.

The following actions can be specified to OnError property to handle erroneous scenarios.

• STREAM: Creates a fault stream and redirects the event and the error to it. The created fault stream will have all the attributes defined in the base stream to capture the error causing event, and in addition it also contains _error attribute of type object to containing the error information. The fault stream can be referred by adding ! in front of the base stream name as !<stream name>.

Example

Handle errors in TempStream by redirecting the errors to a fault stream.

The configuration of TempStream stream and OnError property is as follows.

CREATE STREAM TempStream WITH(OnError.action="STREAM") (deviceID long, roomNo int, temp double;

Stream infers and automatically defines the fault stream of TempStream as given below.

CREATE STREAM !TempStream (deviceID long, roomNo int, temp double, _error object);

The StreamApp extending the above the use-case by adding failure generation and error handling with the use of queries is as follows.

Note: Details on writing processing logics via queries will be explained in later sections.

-- Define fault stream to handle error occurred at TempStream subscribers

CREATE STREAM TempStream WITH(OnError.action="STREAM") (deviceID long, roomNo int, temp double;

-- Error generation through a custom function `createError()`
@name('error-generation')
insert into IgnoreStream1
from TempStream#custom:createError();

-- Handling error by simply logging the event and error.
@name('handle-error')
insert into IgnoreStream2
select deviceID, roomNo, temp, _error
from !TempStream#log("Error Occurred!");

Error Handling at Sink

There can be cases where external systems becoming unavailable or coursing errors when the events are published to them. By default sinks log and drop the events causing event losses, and this can be handled gracefully by configuring on.error parameter of the sink.type annotation.

The on.error parameter of the sink.type annotation can be specified as below.

CREATE SINK <stream name> WITH (sink.type='<sink type>', on.error.action='<on error action>', <key>='<value>', ...) (<attribute name> <attribute type>, <attribute name> <attribute type>, ... );

The following actions can be specified to on.error parameter of sink.type annotation to handle erroneous scenarios.

• WAIT : Publishing threads wait in back-off and re-trying mode, and only send the events when the connection is re-established. During this time the threads will not consume any new messages causing the systems to introduce back pressure on the systems that publishes to it.

• STREAM: Pushes the failed events with the corresponding error to the associated fault stream the sink belongs to.

Example 1

Introduce back pressure on the threads who bring events via TempStream when the system cannot connect to Kafka.

The configuration of TempStream stream and sink.type Kafka annotation with on.error property is as follows.

CREATE SINK TempStream WITH (sink.type='kafka', on.error.action='WAIT', topic='{{roomNo}}', bootstrap.servers='localhost:9092', map.type='xml') (deviceID long, roomNo int, temp double);

Example 2

Send events to the fault stream of TempStream when the system cannot connect to Kafka.

The configuration of TempStream stream with associated fault stream, sink.type Kafka annotation with on.error property and a queries to handle the error is as follows.

Note: Details on writing processing logics via queries will be explained in later sections.

CREATE SINK TempStream WITH (sink.type='kafka', on.error.action='STREAM', topic='{{roomNo}}', bootstrap.servers='localhost:9092', map.type='xml') (deviceID long, roomNo int, temp double);

-- Handling error by simply logging the event and error.
@name('handle-error')
insert into IgnoreStream;
select deviceID, roomNo, temp, _error
from !TempStream#log("Error Occurred!")

Query

Query defines the processing logic in Stream. It consumes events from one or more streams, named-windows, tables, and/or named-aggregations, process the events in a streaming manner, and generate output events into a stream, named-window, or table.

Purpose

A query provides a way to process the events in the order they arrive and produce output using both stateful and stateless complex event processing and stream processing operations.

Syntax

The high level query syntax for defining processing logics is as follows:

@name('<query name>')
<output action>
<projection>
from <input>

The following parameters are used to configure a stream definition.

Parameter	Description
query name	The name of the query. Since naming the query (i.e the @name('<query name>') annotation) is optional, when the name is not provided Stream assign a system generated name for the query.
input	Defines the means of event consumption via streams, named-windows, tables, and/or named-aggregations, and defines the processing logic using filters, windows, stream-functions, joins, patterns and sequences.
projection	Generates output event attributes using select, functions, aggregation-functions, and group by operations, and filters the generated the output using having, limit & offset, order by, and output rate limiting operations before sending them out. Here the projection is optional and when it is omitted all the input events will be sent to the output as it is.
output action	Defines output action (such as insert into, update, delete, etc) that needs to be performed by the generated events on a stream, named-window, or table

Example

A query consumes events from the TempStream stream and output only the roomNo and temp attributes to the RoomTempStream stream, from which another query consumes the events and sends all its attributes to AnotherRoomTempStream stream.

CREATE STREAM TempStream (deviceID long, roomNo int, temp double);

insert into RoomTempStream
select roomNo, temp
from TempStream;

insert into AnotherRoomTempStream
from RoomTempStream;

"Inferred Stream"

Here, the RoomTempStream and AnotherRoomTempStream streams are an inferred streams, which means their stream definitions are inferred from the queries and they can be used same as any other defined stream without any restrictions.

Value

Values are typed data, that can be manipulated, transferred and stored. Values can be referred by the attributes defined in definitions such as streams, and tables.

Stream supports values of type STRING, INT (Integer), LONG, DOUBLE, FLOAT, BOOL (Boolean) and OBJECT.

The syntax of each type and their example use as a constant value is as follows,

Attribute Type	Format	Example
int	`&ltdigit&gt+`	`123`, `-75`, `+95`
long	`&ltdigit&gt+L`	`123000L`, `-750l`, `+154L`
float	`(&ltdigit&gt+)?('.'&ltdigit&gt*)?(E(-|+)?&ltdigit&gt+)?F`	`123.0f`, `-75.0e-10F`,`+95.789f`
double	`(&ltdigit&gt+)?('.'&ltdigit&gt*)?(E(-|+)?&ltdigit&gt+)?D?`	`123.0`,`123.0D`,`-75.0e-10D`,`+95.789d`
bool	`(true|false)`	`true`, `false`, `TRUE`, `FALSE`
string	`'(<char>* !('|"|"""|&ltnew line&gt))'` or `"(<char>* !("|"""|&ltnew line&gt))"` or `"""(<char>* !("""))"""`	`'Any text.'`, `"Text with 'single' quotes."`, """ Text with 'single' quotes, "double" quotes, and new lines. """

Time

Time is a special type of LONG value that denotes time using digits and their unit in the format (<digit>+ <unit>)+. At execution, the time gets converted into milliseconds and returns a LONG value.

Unit	Syntax
Year	`year` | `years`
Month	`month` | `months`
Week	`week` | `weeks`
Day	`day` | `days`
Hour	`hour` | `hours`
Minutes	`minute` | `minutes` | `min`
Seconds	`second` | `seconds` | `sec`
Milliseconds	`millisecond` | `milliseconds`

Example

1 hour and 25 minutes can by written as 1 hour and 25 minutes which is equal to the LONG value 5100000.

Select

The select clause in stream query defines the output event attributes of the query. Following are some basic query projection operations supported by select.

Function

Functions are pre-configured operations that can consumes zero, or more parameters and always produce a single value as result. It can be used anywhere an attribute can be used.

Purpose

Functions encapsulate pre-configured reusable execution logic allowing users to execute the logic anywhere just by calling the function. This also make writing StreamApps simple and easy to understand.

Syntax

The syntax of function is as follows,

<function name>( <parameter>* )

Here <function name> uniquely identifies the function. The <parameter> defined input parameters the function can accept. The input parameters can be attributes, constant values, results of other functions, results of mathematical or logical expressions, or time values. The number and type of parameters a function accepts depend on the function itself.

note

Functions, mathematical expressions, and logical expressions can be used in a nested manner.

Example 1

Function name add accepting two input parameters, is called with an attribute named input and a constant value 75.

add(input, 75)

Example 2

Function name alertAfter accepting two input parameters, is called with a time value of 1 hour and 25 minutes and a mathematical addition operation of startTime + 56.

add(1 hour and 25 minutes, startTime + 56)

Inbuilt functions

Following are some inbuilt Stream functions, for more functions refer Functions.

Inbuilt function	Description
eventTimestamp	Returns event's timestamp.
currentTimeMillis	Returns current time of StreamApp runtime.
default	Returns a default value if the parameter is null.
ifThenElse	Returns parameters based on a conditional parameter.
UUID	Generates a UUID.
cast	Casts parameter type.
convert	Converts parameter type.
coalesce	Returns first not null input parameter.
maximum	Returns the maximum value of all parameters.
minimum	Returns the minimum value of all parameters.
instanceOfBoolean	Checks if the parameter is an instance of Boolean.
instanceOfDouble	Checks if the parameter is an instance of Double.
instanceOfFloat	Checks if the parameter is an instance of Float.
instanceOfInteger	Checks if the parameter is an instance of Integer.
instanceOfLong	Checks if the parameter is an instance of Long.
instanceOfString	Checks if the parameter is an instance of String.
createSet	Creates HashSet with given input parameters.
sizeOfSet	Returns number of items in the HashSet, that's passed as a parameter.

Example

Query that converts the roomNo to string using convert function, finds the maximum temperature reading with maximum function, and adds a unique messageID using the UUID function.

insert into RoomTempStream
select convert(roomNo, 'string') as roomNo,
       maximum(tempReading1, tempReading2) as temp,
       UUID() as messageID
from TempStream;

Filter

Filters provide a way of filtering input stream events based on a specified condition. It accepts any type of condition including a combination of functions and/or attributes that produces a Boolean result. Filters allow events to passthrough if the condition results in true, and drops if it results in a false.

Purpose

Filter helps to select the events that are relevant for the processing and omit the ones that are not.

Syntax

Filter conditions should be defined in square brackets ([]) next to the input stream as shown below.

insert into <output stream>
select <attribute name>, <attribute name>, ...
from <input stream>[<filter condition>] ;

Example

Query to filter TempStream stream events, having roomNo within the range of 100-210 and temperature greater than 40 degrees, and insert them into HighTempStream stream.

insert into HighTempStream
select roomNo, temp
from TempStream[(roomNo >= 100 and roomNo < 210) and temp > 40];

Window

Window provides a way to capture a subset of events from an input stream and retain them for a period of time based on a specified criterion. The criterion defines when and how the events should be evicted from the windows. Such as events getting evicted from the window based on the time duration, or number of events and they events are evicted in a sliding (one by one) or tumbling (batch) manner.

Within a query, each input stream can at most have only one window associated with it.

Purpose

Windows help to retain events based on a criterion, such that the values of those events can be aggregated, or checked if an event of interest is within the window or not.

Syntax

Window should be defined by using the #window prefix next to the input stream as shown below.

insert <ouput event type>? into <output stream>
select <attribute name>, <attribute name>, ...
from <input stream>#window.<window name>(<parameter>, <parameter>, ... );

note

Filter conditions can be applied both before and/or after the window.

Inbuilt windows

Following are some inbuilt Stream windows, for more windows refer execution extensions.

Inbuilt function	Description
time	Retains events based on time in a sliding manner.
timeBatch	Retains events based on time in a tumbling/batch manner.
length	Retains events based on number of events in a sliding manner.
lengthBatch	Retains events based on number of events in a tumbling/batch manner.
timeLength	Retains events based on time and number of events in a sliding manner.
session	Retains events for each session based on session key.
batch	Retains events of last arrived event chunk.
sort	Retains top-k or bottom-k events based on a parameter value.
cron	Retains events based on cron time in a tumbling/batch manner.
externalTime	Retains events based on event time value passed as a parameter in a sliding manner.
externalTimeBatch	Retains events based on event time value passed as a parameter in a a tumbling/batch manner.
delay	Retains events and delays the output by the given time period in a sliding manner.

Example 1

Query to find out the maximum temperature out of the last 10 events, using the window of length 10 and max() aggregation function, from the TempStream stream and insert the results into the MaxTempStream stream.

select max(temp) as maxTemp
from TempStream#window.length(10)
insert into MaxTempStream;

Here, the length window operates in a sliding manner where the following 3 event subsets are calculated and outputted when a list of 12 events are received in sequential order.

Subset	Event Range
1	1 - 10
2	2 - 11
3	3 - 12

Example 2

Query to find out the maximum temperature out of the every 10 events, using the window of lengthBatch 10 and max() aggregation function, from the TempStream stream and insert the results into the MaxTempStream stream.

insert into MaxTempStream
select max(temp) as maxTemp
from TempStream#window.lengthBatch(10);

Here, the window operates in a batch/tumbling manner where the following 3 event subsets are calculated and outputted when a list of 30 events are received in a sequential order.

Subset	Event Range
1	1 - 10
2	11 - 20
3	21 - 30

Example 3

Query to find out the maximum temperature out of the events arrived during last 10 minutes, using the window of time 10 minutes and max() aggregation function, from the TempStream stream and insert the results into the MaxTempStream stream.

insert into MaxTempStream
select max(temp) as maxTemp
from TempStream#window.time(10 min);

Here, the time window operates in a sliding manner with millisecond accuracy, where it will process events in the following 3 time durations and output aggregated events when a list of events are received in a sequential order.

Subset	Time Range (in ms)
1	1:00:00.001 - 1:10:00.000
2	1:00:01.001 - 1:10:01.000
3	1:00:01.033 - 1:10:01.034

Example 4

Query to find out the maximum temperature out of the events arriving every 10 minutes, using the window of timeBatch 10 and max() aggregation function, from the TempStream stream and insert the results into the MaxTempStream stream.

insert into MaxTempStream
select max(temp) as maxTemp
from TempStream#window.timeBatch(10 min);

Here, the window operates in a batch/tumbling manner where the window will process evetns in the following 3 time durations and output aggregated events when a list of events are received in a sequential order.

Subset	Time Range (in ms)
1	1:00:00.001 - 1:10:00.000
2	1:10:00.001 - 1:20:00.000
3	1:20:00.001 - 1:30:00.000

Event Type

Query output depends on the current and expired event types it produces based on its internal processing state. By default all queries produce current events upon event arrival to the query. The queries containing windows additionally produce expired events when events expire from the windows.

Purpose

Event type helps to specify when a query should output events to the stream, such as output upon current events, expired events or upon both current and expired events.

Syntax

Event type should be defined in between insert and into keywords for insert queries as follows.

insert <event type> into <output stream>
select <attribute name>, <attribute name>, ...
from <input stream>#window.<window name>(<parameter>, <parameter>, ... )

Event type should be defined next to the for keyword for delete queries as follows.

select <attribute name>, <attribute name>, ...
from <input stream>#window.<window name>(<parameter>, <parameter>, ... )
delete <table> (for <event type>)?
    on <condition>

Event type should be defined next to the for keyword for update queries as follows.

select <attribute name>, <attribute name>, ...
from <input stream>#window.<window name>(<parameter>, <parameter>, ... )
update <table> (for <event type>)?
    set <table>.<attribute name> = (<attribute name>|<expression>)?, <table>.<attribute name> = (<attribute name>|<expression>)?, ...
    on <condition>

Event type should be defined next to the for keyword for update or insert queries as follows.

select <attribute name>, <attribute name>, ...
from <input stream>#window.<window name>(<parameter>, <parameter>, ... )
update or insert into <table> (for <event type>)?
    set <table>.<attribute name> = <expression>, <table>.<attribute name> = <expression>, ...
    on <condition>

note

Controlling query output based on the event types neither alters query execution nor its accuracy.

The event types can be defined using the following keywords to manipulate query output.

Event types	Description
current events	Outputs events only when incoming events arrive to be processed by the query. This is default behavior when no specific event type is specified.
expired events	Outputs events only when events expires from the window.
all events	Outputs events when incoming events arrive to be processed by the query as well as when events expire from the window.

Example

Query to output only the expired events from a 1 minute time window to the DelayedTempStream stream. This can be used for delaying the events by a minute.

insert expired events into DelayedTempStream
select *
from TempStream#window.time(1 min)

note

This is just to illustrate how expired events work, it is recommended to use delay window for usecases where we need to delay events by a given time period.

Aggregate Function

Aggregate functions are pre-configured aggregation operations that can consumes zero, or more parameters from multiple events and always produce a single value as result. They can be only used in the query projection (as part of the select clause). When a query comprises a window, the aggregation will be contained to the events in the window, and when it does not have a window, the aggregation is performed from the first event the query has received.

Purpose

Aggregate functions encapsulate pre-configured reusable aggregate logic allowing users to aggregate values of multiple events together. When used with batch/tumbling windows this can also help to reduce the number of output events produced.

Syntax

Aggregate function can be used in query projection (as part of the select clause) alone or as a part of another expression. In all cases, the output produced by the query should be properly mapped to the output stream attribute using the as keyword.

The syntax of aggregate function is as follows,

insert into <output stream>
select <aggregate function>(<parameter>, <parameter>, ... ) as <attribute name>, <attribute2 name>, ...
from <input stream>#window.<window name>(<parameter>, <parameter>, ... );

Here <aggregate function> uniquely identifies the aggregate function. The <parameter> defined input parameters the aggregate function can accept. The input parameters can be attributes, constant values, results of other functions or aggregate functions, results of mathematical or logical expressions, or time values. The number and type of parameters an aggregate function accepts depend on the function itself.

Inbuilt aggregate functions

Following are some inbuilt aggregation functions.

Inbuilt aggregate function	Description
sum	Calculates the sum from a set of values.
count	Calculates the count from a set of values.
distinctcount	Calculates the distinct count based on a parameter from a set of values.
avg	Calculates the average from a set of values.
max	Finds the maximum value from a set of values.
min	Finds the minimum value from a set of values.
maxForever	Finds the maximum value from all events throughout its lifetime irrespective of the windows.
minForever	Finds the minimum value from all events throughout its lifetime irrespective of the windows.
stddev	Calculates the standard deviation from a set of values.
and	Calculates boolean and from a set of values.
or	Calculates boolean or from a set of values.
unionSet	Calculates union as a Set from a set of values.

Example

Query to calculate average, maximum, and minimum values on temp attribute of the TempStream stream in a sliding manner, from the events arrived over the last 10 minutes and to produce outputs avgTemp, maxTemp and minTemp respectively to the AvgTempStream output stream.

insert into AvgTempStream
select avg(temp) as avgTemp, max(temp) as maxTemp, min(temp) as minTemp
from TempStream#window.time(10 min);

Group By

Group By provides a way of grouping events based on one or more specified attributes to perform aggregate operations.

Purpose

Group By allows users to aggregate values of multiple events based on the given group-by fields.

Syntax

The syntax for the Group By with aggregate function is as follows.

insert into <output stream>
select <aggregate function>( <parameter>, <parameter>, ...) as <attribute1 name>, <attribute2 name>, ...
from <input stream>#window.<window name>(...)
group by <attribute1 name>, <attribute2 name>, ...;

Here the group by attributes should be defined next to the group by keyword separating each attribute by a comma.

Example

Query to calculate the average temp per roomNo and deviceID combination, from the events arrived from TempStream stream, during the last 10 minutes time-window in a sliding manner.

insert into AvgTempStream
select roomNo, deviceID, avg(temp) as avgTemp
from TempStream#window.time(10 min)
group by roomNo, deviceID;

Having

Having provide a way of filtering events based on a specified condition of the query output stream attributes. It accepts any type of condition including a combination of functions and/or attributes that produces a Boolean result. Having, allow events to passthrough if the condition results in true, and drops if it results in a false.

Purpose

Having helps to select the events that are relevant for the output based on the attributes those are produced by the select clause and omit the ones that are not.

Syntax

The syntax for the Having clause is as follows.

insert into <output stream>
select <aggregate function>( <parameter>, <parameter>, ...) as <attribute1 name>, <attribute2 name>, ...
from <input stream>#window.<window name>( ... )
group by <attribute1 name>, <attribute2 name> ...
having <condition>;

Here the having <condition> should be defined next to the having keyword and having can be used with or without group by clause.

Example

Query to calculate the average temp per roomNo for the last 10 minutes, and alerts if the avgTemp exceeds 30 degrees.

insert into AlertStream
select roomNo, avg(temp) as avgTemp
from TempStream#window.time(10 min)
group by roomNo
having avgTemp > 30;

Order By

Order By, orders the query results in ascending and or descending order based on one or more specified attributes. When an attribute is used for order by, by default Stream orders the events in ascending order of that attribute's value, and by adding desc keyword, the events can be ordered in descending order. When more than one attribute is defined the attributes defined towards the left will have more precedence in ordering than the ones defined in right.

Purpose

Order By helps to sort the events in the outputs chunks produced by the query. Order By will be more helpful for batch windows, and queries where they output many of event together then for sliding window use cases where the output will be one or few events at a time.

Syntax

The syntax for the Order By clause is as follows:

insert into <output stream>
select <aggregate function>( <parameter>, <parameter>, ...) as <attribute1 name>, <attribute2 name>, ...
from <input stream>#window.<window name>( ... )
group by <attribute1 name>, <attribute2 name> ...
having <condition>
order by <attribute1 name> (asc|desc)?, <attribute2 name> (asc|desc)?, ...;

Here the order by attributes should be defined next to the order by keyword separating each by a comma, and optionally specifying the event ordering using asc (default) or desc keywords.

Example

Query to calculate the average temp per roomNo and deviceID combination on every 10 minutes batches, and order the generated output events in ascending order by avgTemp and then by descending order of roomNo (if the more than one event have the same avgTemp value).

insert into AvgTempStream
select roomNo, deviceID, avg(temp) as avgTemp
from TempStream#window.timeBatch(10 min)
group by roomNo, deviceID
order by avgTemp, roomNo desc;

Limit & Offset

These provide a way to select the number of events (via limit) from the desired index (by specifying an offset) from the output event chunks produced by the query.

Purpose

Limit & Offset helps to output only the selected set of events from large event batches. This will be more useful with Order By clause where one can order the output for topK, bottomK, or even to paginate through the dataset by obtaining a set of events from the middle.

Syntax

The syntax for the Limit & Offset clauses is as follows:

insert into <output stream>
select <aggregate function>( <parameter>, <parameter>, ...) as <attribute1 name>, <attribute2 name>, ...
from <input stream>#window.<window name>( ... )
group by <attribute1 name>, <attribute2 name> ...
having <condition>
order by <attribute1 name> (asc | desc)?, <attribute2 name> (<ascend/descend>)?, ...
limit <positive integer>?
offset <positive integer>?;

Here both limit and offset are optional, when limit is omitted the query will output all the events, and when offset is omitted 0 is taken as the default offset value.

Example 1

Query to calculate the average temp per roomNo and deviceID combination for every 10 minutes batches, from the events arriving at the TempStream stream, and emit only two events having the highest avgTemp value.

insert into HighestAvgTempStream
select roomNo, deviceID, avg(temp) as avgTemp
from TempStream#window.timeBatch(10 min)
group by roomNo, deviceID
order by avgTemp desc
limit 2;

Example 2 Query to calculate the average temp per roomNo and deviceID combination for every 10 minutes batches, for events that arriving at the TempStream stream, and emits only the third, forth and fifth events when sorted in descending order based on their avgTemp value.

insert into HighestAvgTempStream
select roomNo, deviceID, avg(temp) as avgTemp
from TempStream#window.timeBatch(10 min)
group by roomNo, deviceID
order by avgTemp desc
limit 3
offset 2;

Join (Stream)

Joins allow you to get a combined result from two streams in real-time based on a specified condition.

Purpose

Streams are stateless. Therefore, in order to join two streams, they need to be connected to a window so that there is a pool of events that can be used for joining. Joins also accept conditions to join the appropriate events from each stream.

During the joining process each incoming event of each stream is matched against all the events in the other stream's window based on the given condition, and the output events are generated for all the matching event pairs.

note

Join can also be performed with stored data, aggregation or externally named windows.

Syntax

The syntax for a join is as follows:

insert into <output stream>
select <attribute name>, <attribute name>, ...
from <input stream>#window.<window name>(<parameter>, ... ) {unidirectional} {as <reference>}
         join <input stream>#window.<window name>(<parameter>,  ... ) {unidirectional} {as <reference>}
    on <join condition>

Here, the <join condition> allows you to match the attributes from both the streams.

Unidirectional join operation

By default, events arriving at either stream can trigger the joining process. However, if you want to control the join execution, you can add the unidirectional keyword next to a stream in the join definition as depicted in the syntax in order to enable that stream to trigger the join operation. Here, events arriving at other stream only update the window of that stream, and this stream does not trigger the join operation.

note

The unidirectional keyword cannot be applied to both the input streams because the default behaviour already allows both streams to trigger the join operation.

Example

Assuming that the temperature of regulators are updated every minute. Following is a Stream App that controls the temperature regulators if they are not already on for all the rooms with a room temperature greater than 30 degrees.

CREATE STREAM TempStream (deviceID long, roomNo int, temp double);
CREATE STREAM RegulatorStream (deviceID long, roomNo int, isOn bool);

insert into RegulatorActionStream
select T.roomNo, R.deviceID, 'start' as action
from TempStream[temp > 30.0]#window.time(1 min) as T
  join RegulatorStream[isOn == false]#window.length(1) as R
  on T.roomNo == R.roomNo;

Supported join types

Following are the supported operations of a join clause.

• Inner join (join)

  This is the default behaviour of a join operation. join is used as the keyword to join both the streams. The output is generated only if there is a matching event in both the streams.

• Left outer join

  The left outer join operation allows you to join two streams to be merged based on a condition. left outer join is used as the keyword to join both the streams.

  Here, it returns all the events of left stream even if there are no matching events in the right stream by having null values for the attributes of the right stream.

  Example

  The following query generates output events for all events from the StockStream stream regardless of whether a matching symbol exists in the TwitterStream stream or not.

  select S.symbol as symbol, T.tweet, S.price from StockStream#window.time(1 min) as S left outer join TwitterStream#window.length(1) as T on S.symbol== T.symbol insert into outputStream ;    

• Right outer join

  This is similar to a left outer join. Right outer join is used as the keyword to join both the streams. It returns all the events of the right stream even if there are no matching events in the left stream.

• Full outer join

  The full outer join combines the results of left outer join and right outer join. full outer join is used as the keyword to join both the streams. Here, output event are generated for each incoming event even if there are no matching events in the other stream.

  Example

  The following query generates output events for all the incoming events of each stream regardless of whether there is a match for the symbol attribute in the other stream or not.

  insert into outputStream select S.symbol as symbol, T.tweet, S.price from StockStream#window.time(1 min) as S full outer join TwitterStream#window.length(1) as T on S.symbol== T.symbol;    

Patterns

This is a state machine implementation that allows you to detect patterns in the events that arrive over time. This can correlate events within a single stream or between multiple streams.

Purpose

Patterns allow you to identify trends in events over a time period.

Syntax

The following is the syntax for a pattern query:

select <event reference>.<attribute name>, <event reference>.<attribute name>, ...
from (every)? <event reference>=<input stream>[<filter condition>] ->
    (every)? <event reference>=<input stream [<filter condition>] ->
    ...
    (within <time gap>)?     
insert into <output stream>

Items	Description
->	This is used to indicate an event that should be following another event. The subsequent event does not necessarily have to occur immediately after the preceding event. The condition to be met by the preceding event should be added before the sign, and the condition to be met by the subsequent event should be added after the sign.
<event reference>	This allows you to add a reference to the the matching event so that it can be accessed later for further processing.
(within <time gap>)?	The within clause is optional. It defines the time duration within which all the matching events should occur.
every	every is an optional keyword. This defines whether the event matching should be triggered for every event arrival in the specified stream with the matching condition. When this keyword is not used, the matching is carried out only once.

Stream also supports pattern matching with counting events and matching events in a logical order such as (and, or, and not). These are described in detail further below in this guide.

Example

This query sends an alert if the temperature of a room increases by 5 degrees within 10 min.

insert into AlertStream
select e1.roomNo, e1.temp as initialTemp, e2.temp as finalTemp
from every( e1=TempStream ) -> e2=TempStream[ e1.roomNo == roomNo and (e1.temp + 5) <= temp ]
    within 10 min;

Here, the matching process begins for each event in the TempStream stream (because every is used with e1=TempStream), and if another event arrives within 10 minutes with a value for the temp attribute that is greater than or equal to e1.temp + 5 of the event e1, an output is generated via the AlertStream.

Counting Pattern

Counting patterns allow you to match multiple events that may have been received for the same matching condition. The number of events matched per condition can be limited via condition postfixes.

Syntax

Each matching condition can contain a collection of events with the minimum and maximum number of events to be matched as shown in the syntax below.

insert into <output stream>
select <event reference>([event index])?.<attribute name>, ...
from (every)? <event reference>=<input stream>[<filter condition>] (<<min count>:<max count>>)? ->  
    ...
    (within <time gap>)?     

Postfix	Description	Example
<n1:n2>	This matches n1 to n2 events (including n1 and not more than n2).	1:4 matches 1 to 4 events.
<n:>	This matches n or more events (including n).	<2:> matches 2 or more events.
<:n>	This matches up to n events (excluding n).	<:5> matches up to 5 events.
<n>	This matches exactly n events.	<5> matches exactly 5 events.

Specific occurrences of the event in a collection can be retrieved by using an event index with its reference. Square brackets can be used to indicate the event index where 1 can be used as the index of the first event and last can be used as the index for the last available event in the event collection. If you provide an index greater then the last event index, the system returns null. The following are some valid examples.

• e1[3] refers to the 3rd event.
• e1[last] refers to the last event.
• e1[last - 1] refers to the event before the last event.

Example

The following Stream App calculates the temperature difference between two regulator events.

CREATE STREAM TempStream (deviceID long, roomNo int, temp double);
CREATE STREAM RegulatorStream (deviceID long, roomNo int, tempSet double, isOn bool);

insert into TempDiffStream
select e1.roomNo, e2[0].temp - e2[last].temp as tempDiff
from every( e1=RegulatorStream) -> e2=TempStream[e1.roomNo==roomNo]<1:> -> e3=RegulatorStream[e1.roomNo==roomNo];

Logical Patterns

Logical patterns match events that arrive in temporal order and correlate them with logical relationships such as and, or and not.

Syntax

insert into <output stream>
select <event reference>([event index])?.<attribute name>, ...
from (every)? (not)? <event reference>=<input stream>[<filter condition>]
          ((and|or) <event reference>=<input stream>[<filter condition>])? (within <time gap>)? ->  
    ...

Keywords such as and, or, or not can be used to illustrate the logical relationship.

Key Word	Description
and	This allows both conditions of and to be matched by two events in any order.
or	The state succeeds if either condition of or is satisfied. Here the event reference of the other condition is null.
not <condition1> and <condition2>	When not is included with and, it identifies the events that match <condition2> arriving before any event that match <condition1>.
not <condition> for <time period>	When not is included with for, it allows you to identify a situation where no event that matches <condition1> arrives during the specified <time period>. e.g.,from not TemperatureStream[temp > 60] for 5 sec.

Here the not pattern can be followed by either an and clause or the effective period of not can be concluded after a given <time period>. Further in Stream more than two streams cannot be matched with logical conditions using and, or, or not clauses at this point.

Detecting Non-occurring Events

Stream allows you to detect non-occurring events via multiple combinations of the key words specified above as shown in the table below.

In the patterns listed, P* can be either a regular event pattern, an absent event pattern or a logical pattern.

Pattern	Detected Scenario
not A for <time period>	The non-occurrence of event A within <time period> after system start up. e.g., Generating an alert if a taxi has not reached its destination within 30 minutes, to indicate that the passenger might be in danger.
not A for <time period> and B	After system start up, event A does not occur within time period, but event B occurs at some point in time. e.g., Generating an alert if a taxi has not reached its destination within 30 minutes, and the passenger marked that he/she is in danger at some point in time.
not A for <time period 1> and not B for <time period 2>	After system start up, event A doess not occur within time period 1, and event B also does not occur within <time period 2>. e.g., Generating an alert if the driver of a taxi has not reached the destination within 30 minutes, and the passenger has not marked himself/herself to be in danger within that same time period.
not A for <time period> or B	After system start up, either event A does not occur within <time period>, or event B occurs at some point in time. e.g., Generating an alert if the taxi has not reached its destination within 30 minutes, or if the passenger has marked that he/she is in danger at some point in time.
not A for <time period 1> or not B for <time period 2>	After system start up, either event A does not occur within <time period 1>, or event B occurs within <time period 2>. e.g., Generating an alert to indicate that the driver is not on an expected route if the taxi has not reached destination A within 20 minutes, or reached destination B within 30 minutes.
A → not B for <time period>	Event B does not occur within <time period> after the occurrence of event A. e.g., Generating an alert if the taxi has reached its destination, but this was not followed by a payment record.
P* → not A for <time period> and B	After the occurrence of P*, event A does not occur within <time period>, and event B occurs at some point in time.
P* → not A for <time period 1> and not B for <time period 2>	After the occurrence of P*, event A does not occur within <time period 1>, and event B does not occur within <time period 2>.
P* → not A for <time period> or B	After the occurrence of P*, either event A does not occur within <time period>, or event B occurs at some point in time.
P* → not A for <time period 1> or not B for <time period 2>	After the occurrence of P*, either event A does not occur within <time period 1>, or event B does not occur within <time period 2>.
not A for <time period> → B	Event A does occur within <time period> after the system start up, but event B occurs after that <time period> has elapsed.
not A for <time period> and B → P*	Event A does not occur within <time period>, and event B occurs at some point in time. Then P* occurs after the <time period> has elapsed, and after B has occurred.
not A for <time period 1> and not B for <time period 2> → P*	After system start up, event A does not occur within <time period 1>, and event B does not occur within <time period 2>. However, P* occurs after both A and B.
not A for <time period> or B → P*	After system start up, event A does not occur within <time period> or event B occurs at some point in time. The P* occurs after <time period> has elapsed, or after B has occurred.
not A for <time period 1> or not B for <time period 2> → P*	After system start up, either event A does not occur within <time period 1>, or event B does not occur within <time period 2>. Then P* occurs after both <time period 1> and <time period 2> have elapsed.
not A and B	Event A does not occur before event B.
A and not B	Event B does not occur before event A.

Example

Following Stream App, sends the stop control action to the regulator when the key is removed from the hotel room.

CREATE STREAM RegulatorStateChangeStream (deviceID long, roomNo int, tempSet double, action string);
CREATE STREAM RoomKeyStream (deviceID long, roomNo int, action string);

insert into RegulatorActionStream
select e1.roomNo, ifThenElse( e2 is null, 'none', 'stop' ) as action
from every( e1=RegulatorStateChangeStream[ action == 'on' ] ) ->
      e2=RoomKeyStream[ e1.roomNo == roomNo and action == 'removed' ] or e3=RegulatorStateChangeStream[ e1.roomNo == roomNo and action == 'off']
having action != 'none';

This Stream Application generates an alert if we have switch off the regulator before the temperature reaches 12 degrees.

CREATE STREAM RegulatorStateChangeStream (deviceID long, roomNo int, tempSet double, action string);
CREATE STREAM TempStream (deviceID long, roomNo int, temp double);

insert into AlertStream
select e1.roomNo as roomNo
from e1=RegulatorStateChangeStream[action == 'start'] -> not TempStream[e1.roomNo == roomNo and temp < 12] and e2=RegulatorStateChangeStream[action == 'off'];

This Stream Application generates an alert if the temperature does not reduce to 12 degrees within 5 minutes of switching on the regulator.

CREATE STREAM RegulatorStateChangeStream (deviceID long, roomNo int, tempSet double, action string);
CREATE STREAM TempStream (deviceID long, roomNo int, temp double);

insert into AlertStream
select e1.roomNo as roomNo
from e1=RegulatorStateChangeStream[action == 'start'] -> not TempStream[e1.roomNo == roomNo and temp < 12] for '5 min';

Sequence

Sequence is a state machine implementation that allows you to detect the sequence of event occurrences over time. Here all matching events need to arrive consecutively to match the sequence condition, and there cannot be any non-matching events arriving within a matching sequence of events. This can correlate events within a single stream or between multiple streams.

Purpose

This allows you to detect a specified event sequence over a specified time period.

Syntax

The syntax for a sequence query is as follows:

insert into <output stream>
select <event reference>.<attribute name>, <event reference>.<attribute name>, ...
from (every)? <event reference>=<input stream>[<filter condition>],
    <event reference>=<input stream [<filter condition>],
    ...
    (within <time gap>)?     

Items	Description
,	This represents the immediate next event i.e., when an event that matches the first condition arrives, the event that arrives immediately after it should match the second condition.
<event reference>	This allows you to add a reference to the the matching event so that it can be accessed later for further processing.
(within <time gap>)?	The within clause is optional. It defines the time duration within which all the matching events should occur.
every	every is an optional keyword. This defines whether the matching event should be triggered for every event that arrives at the specified stream with the matching condition. When this keyword is not used, the matching is carried out only once.

Example

This query generates an alert if the increase in the temperature between two consecutive temperature events exceeds one degree.

insert into AlertStream
select e1.temp as initialTemp, e2.temp as finalTemp
from every e1=TempStream, e2=TempStream[e1.temp + 1 < temp];

Counting Sequence

Counting sequences allow you to match multiple events for the same matching condition. The number of events matched per condition can be limited via condition postfixes such as Counting Patterns, or by using the *, +, and ? operators.

The matching events can also be retrieved using event indexes, similar to how it is done in Counting Patterns.

Syntax

Each matching condition in a sequence can contain a collection of events as shown below.

insert into <output stream>
select <event reference>.<attribute name>, <event reference>.<attribute name>, ...
from (every)? <event reference>=<input stream>[<filter condition>](+|*|?)?,
    <event reference>=<input stream [<filter condition>](+|*|?)?,
    ...
    (within <time gap>)?     

Postfix symbol	Required/Optional	Description
+	Optional	This matches one or more events to the given condition.
*	Optional	This matches zero or more events to the given condition.
?	Optional	This matches zero or one events to the given condition.

Example

This Stream application identifies temperature peeks.

CREATE STREAM TempStream(deviceID long, roomNo int, temp double);

insert into PeekTempStream
select e1.temp as initialTemp, e2[last].temp as peakTemp
from every e1=TempStream, e2=TempStream[e1.temp <= temp]+, e3=TempStream[e2[last].temp > temp];

Logical Sequence

Logical sequences identify logical relationships using and, or and not on consecutively arriving events.

Syntax The syntax for a logical sequence is as follows:

insert into <output stream>
select <event reference>([event index])?.<attribute name>, ...
from (every)? (not)? <event reference>=<input stream>[<filter condition>]
          ((and|or) <event reference>=<input stream>[<filter condition>])? (within <time gap>)?,
    ...

Keywords such as and, or, or not can be used to illustrate the logical relationship, similar to how it is done in Logical Patterns.

Example

This Stream application notifies the state when a regulator event is immediately followed by both temperature and humidity events.

CREATE STREAM TempStream(deviceID long, temp double);
CREATE STREAM HumidStream(deviceID long, humid double);
CREATE STREAM RegulatorStream(deviceID long, isOn bool);

insert into StateNotificationStream
select e2.temp, e3.humid
from every e1=RegulatorStream, e2=TempStream and e3=HumidStream;

Output rate limiting

Output rate limiting allows queries to output events periodically based on a specified condition.

Purpose

This allows you to limit the output to avoid overloading the subsequent executions, and to remove unnecessary information.

Syntax

The syntax of an output rate limiting configuration is as follows:

insert into <output stream>
select <attribute name>, <attribute name>, ...
from <input stream> ...
output <rate limiting configuration>

Stream supports three types of output rate limiting configurations as explained in the following table:

Rate limiting configuration	Syntax	Description
Based on time	<output event> every <time interval>	This outputs <output event> every <time interval> time interval.
Based on number of events	<output event> every <event interval> events	This outputs <output event> for every <event interval> number of events.
Snapshot based output	snapshot every <time interval>	This outputs all events in the window (or the last event if no window is defined in the query) for every given <time interval> time interval.

Here the <output event> specifies the event(s) that should be returned as the output of the query. The possible values are as follows:

• first : Only the first event processed by the query during the specified time interval/sliding window is emitted.
• last : Only the last event processed by the query during the specified time interval/sliding window is emitted.
• all : All the events processed by the query during the specified time interval/sliding window are emitted. When no <output event> is defined, all is used by default.

Examples

• Returning events based on the number of events

  Here, events are emitted every time the specified number of events arrive. You can also specify whether to emit only the first event/last event, or all the events out of the events that arrived.

  In this example, the last temperature per sensor is emitted for every 10 events.

  insert into LowRateTempStream select temp, deviceID from TempStreamselect group by deviceID output last every 10 events;    

• Returning events based on time

  Here events are emitted for every predefined time interval. You can also specify whether to emit only the first event, last event, or all events out of the events that arrived during the specified time interval.

  In this example, emits all temperature events every 10 seconds

  insert into LowRateTempStream from TempStreamoutput output every 10 sec;    

• Returning a periodic snapshot of events

  This method works best with windows. When an input stream is connected to a window, snapshot rate limiting emits all the current events that have arrived and do not have corresponding expired events for every predefined time interval. If the input stream is not connected to a window, only the last current event for each predefined time interval is emitted.

  This query emits a snapshot of the events in a time window of 5 seconds every 1 second.

  insert into SnapshotTempStream from TempStream#window.time(5 sec) output snapshot every 1 sec;    

Partition

Partitions divide streams and queries into isolated groups in order to process them in parallel and in isolation. A partition can contain one or more queries and there can be multiple instances where the same queries and streams are replicated for each partition.

Each partition is tagged with a partition key. Those partitions only process the events that match the corresponding partition key.

Purpose

Partitions allow you to process the events groups in isolation so that event processing can be performed using the same set of queries for each group.

Partition key generation

A partition key can be generated in the following two methods:

• Partition by value

  This is created by generating unique values using input stream attributes.

  Syntax

  partition with ( <expression> of <stream name>, <expression> of <stream name>, ... ) begin <query> <query> ... end; 

  Example

  This query calculates the maximum temperature recorded within the last 10 events per deviceID.

  partition with ( deviceID of TempStream ) begin insert into DeviceTempStream select roomNo, deviceID, max(temp) as maxTemp from TempStream#window.length(10); end;

• Partition by range

  This is created by mapping each partition key to a range condition of the input streams numerical attribute.

  Syntax

  partition with ( <condition> as <partition key> or <condition> as <partition key> or ... of <stream name>, ... ) begin <query> <query> ... end;

  Example

  This query calculates the average temperature for the last 10 minutes per office area.

  partition with ( roomNo >= 1030 as 'serverRoom' or
                   roomNo < 1030 and roomNo >= 330 as 'officeRoom' or
                   roomNo < 330 as 'lobby' of TempStream)
  begin
      insert into AreaTempStream
      select roomNo, deviceID, avg(temp) as avgTemp
      from TempStream#window.time(10 min)
  end;

Inner Stream

Queries inside a partition block can use inner streams to communicate with each other while preserving partition isolation. Inner streams are denoted by a "#" placed before the stream name, and these streams cannot be accessed outside a partition block.

Purpose

Inner streams allow you to connect queries within the partition block so that the output of a query can be used as an input only by another query within the same partition. Therefore, you do not need to repartition the streams if they are communicating within the partition.

Example

This partition calculates the average temperature of every 10 events for each sensor, and sends an output to the DeviceTempIncreasingStream stream if the consecutive average temperature values increase by more than 5 degrees.

partition with ( deviceID of TempStream )
begin
    insert into #AvgTempStream
    select roomNo, deviceID, avg(temp) as avgTemp
    from TempStream#window.lengthBatch(10)

    insert into DeviceTempIncreasingStream
    select e1.deviceID, e1.avgTemp as initialAvgTemp, e2.avgTemp as finalAvgTemp
    from every (e1=#AvgTempStream),e2=#AvgTempStream[e1.avgTemp + 5 < avgTemp]
end;

Purge Partition

Based on the partition key used for the partition, multiple instances of streams and queries will be generated. When an extremely large number of unique partition keys are used there is a possibility of very high instances of streams and queries getting generated and eventually system going out of memory. In order to overcome this, users can define a purge interval to clean partitions that will not be used anymore.

Purpose

@purge allows you to clean the partition instances that will not be used anymore.

Syntax

The syntax of partition purge configuration is as follows:

@purge(enable='true', interval='<purge interval>', idle.period='<idle period of partition instance>')
partition with ( <partition key> of <input stream> )
begin
    insert into <output stream>
    select <attribute name>, <attribute name>, ...
    from <input stream> ...
end;

When using purge with an Aggregation, use a WITH() property instead. For example:

Partition purge configuration	Description
Purge interval	The periodic time interval to purge the purgeable partition instances.
Idle period of partition instance	The period, a particular partition instance (for a given partition key) needs to be idle before it becomes purgeable.

Examples

Mark partition instances eligible for purging, if there are no events from a particular deviceID for 15 seconds, and periodically purge those partition instances every 1 second.

@purge(enable='true', interval='1 sec', idle.period='15 sec')
partition with ( deviceID of TempStream )
begin
    insert into #AvgTempStream
    select roomNo, deviceID, avg(temp) as avgTemp
    from TempStream#window.lengthBatch(10)

    insert into DeviceTempIncreasingStream
    select e1.deviceID, e1.avgTemp as initialAvgTemp, e2.avgTemp as finalAvgTemp
    from every (e1=#AvgTempStream),e2=#AvgTempStream[e1.avgTemp + 5 < avgTemp]
end;

Table (Collection)

A table is a stored version of an stream or a table of events. Its schema is defined via the table definition that is similar to a stream definition. These events are stored in database.

Purpose

Tables allow stream processor to work with stored events. By defining a schema for tables stream processor enables them to be processed by queries using their defined attributes with the streaming data. You can also interactively query the state of the stored events in the table.

Syntax

The syntax for a new table definition is as follows:

CREATE TABLE <table name> (<attribute name> <attribute type>, <attribute name> <attribute type>, ... );

The following parameters are configured in a table definition:

Parameter	Description
table name	The name of the table defined. (PascalCase is used for table name as a convention.)
attribute name	The schema of the table is defined by its attributes with uniquely identifiable attribute names (camelCase is used for attribute names as a convention.)
attribute type	The type of each attribute defined in the schema. This can be STRING, INT, LONG, DOUBLE, FLOAT, BOOL or OBJECT.

Example

The following defines a table named RoomTypeTable with roomNo and type attributes of data types int and string respectively.

CREATE TABLE RoomTypeTable ( roomNo int, type string );

Primary Keys

Tables can be configured with primary keys to avoid the duplication of data.

Primary keys are configured by including the PrimaryKey property to the table definition. Each event table configuration can have only one PrimaryKey property. The number of attributes supported differ based on the table implementations. When more than one attribute is used for the primary key, the uniqueness of the events stored in the table is determined based on the combination of values for those attributes.

Examples

This query creates an event table with the symbol attribute as the primary key. Therefore each entry in this table must have a unique value for symbol attribute.

CREATE TABLE StockTable WITH (PrimaryKey='symbol', Index='key1', Index='key2') (symbol string, price float, volume long);

Indexes

Indexes allow tables to be searched/modified much faster.

Indexes are configured by including the @Index( 'key1', 'key2' ) annotation to the table definition. Each event table configuration can have 0-1 @Index annotations. Support for the @Index annotation and the number of attributes supported differ based on the table type i.e., doc collection or edge collection. When more then one attribute is used for index, each one of them is used to index the table for fast access of the data.

Indexes can be configured together with primary keys.

Examples

This query creates an indexed event table named RoomTypeTable with the roomNo attribute as the index key.

@Index('roomNo')
CREATE TABLE RoomTypeTable (roomNo int, type string);

Store

Store is a table that refers to data/events stored in data stores outside of stream. Store is defined via the @store annotation, and the store schema is defined via a table definition associated with it.

Purpose

Store allows stream processor to search, retrieve and manipulate data stored in database through stream queries.

Syntax

The syntax for a defining store and it's associated table definition is as follows:

CREATE TABLE TableName WITH (store.type='store_type', static.option.key1='static_option_value1', static.option.keyN='static_option_valueN') (attribute1 Type1, attributeN TypeN);

Example

The following defines a database having a table RoomTypeTable with columns roomNo of INTEGER and type of VARCHAR(255) mapped to Stream data types int and string respectively.

CREATE TABLE RoomTypeTable WITH (Store.type="database", collection="RoomTypeTable") ( roomNo int, type string );

Operators on Table

The following operators can be performed on tables.

Insert

This allows events to be inserted into tables. This is similar to inserting events into streams.

warning

If the table is defined with primary keys, and if you insert duplicate data, primary key constrain violations can occur. In such cases use the update or insert into operation.

Syntax

insert into <table>
select <attribute name>, <attribute name>, ...
from <input stream>

Similar to streams, you need to use the current events, expired events or the all events keyword between insert and into keywords in order to insert only the specific event types.

For more information, see Event Type

Example

This query inserts all the events from the TempStream stream to the TempTable table.

insert into TempTable
select *
from TempStream;

Join (Table)

This allows a stream to retrieve information from a table in a streaming manner.

note

Joins can also be performed with two streams, aggregation or against externally named windows.

Syntax

insert into <output stream>
select (<input stream>|<table>).<attribute name>, (<input stream>|<table>).<attribute name>, ...
from <input stream> join <table>
    on <condition>

note

A table can only be joint with a stream. Two tables cannot be joint because there must be at least one active entity to trigger the join operation.

Example

This Stream App performs a join to retrieve the room type from RoomTypeTable table based on the room number, so that it can filter the events related to server-rooms.

CREATE TABLE RoomTypeTable (roomNo int, type string);
CREATE STREAM TempStream (deviceID long, roomNo int, temp double);

insert into ServerRoomTempStream
select deviceID, RoomTypeTable.type as roomType, type, temp
    having roomType == 'server-room'
from TempStream join RoomTypeTable
    on RoomTypeTable.roomNo == TempStream.roomNo;

Supported join types

Table join supports following join operations.

• Inner join (join)

  This is the default behaviour of a join operation. join is used as the keyword to join the stream with the table. The output is generated only if there is a matching event in both the stream and the table.

• Left outer join

  The left outer join operation allows you to join a stream on left side with a table on the right side based on a condition. Here, it returns all the events of left stream even if there are no matching events in the right table by having null values for the attributes of the right table.

• Right outer join

  This is similar to a left outer join. right outer join is used as the keyword to join a stream on right side with a table on the left side based on a condition. It returns all the events of the right stream even if there are no matching events in the left table.

Delete

To delete selected events that are stored in a table.

Syntax

select <attribute name>, <attribute name>, ...
from <input stream>
delete <table> (for <event type>)?
    on <condition>

The condition element specifies the basis on which events are selected to be deleted. When specifying the condition, table attributes should be referred to with the table name.

To execute delete for specific event types, use the current events, expired events or the all events keyword with for as shown in the syntax. For more information, see Event Type

note

Table attributes must be always referred to with the table name as follows: <table name>.<attibute name>

Example

In this example, the script deletes a record in the RoomTypeTable table if it has a value for the roomNo attribute that matches the value for the roomNumber attribute of an event in the DeleteStream stream.

CREATE TABLE RoomTypeTable (roomNo int, type string);

CREATE STREAM DeleteStream (roomNumber int);

from DeleteStream
delete RoomTypeTable
    on RoomTypeTable.roomNo == roomNumber;

Update

This operator updates selected event attributes stored in a table based on a condition.

Syntax

select <attribute name>, <attribute name>, ...
from <input stream>
update <table> (for <event type>)?
    set <table>.<attribute name> = (<attribute name>|<expression>)?, <table>.<attribute name> = (<attribute name>|<expression>)?, ...
    on <condition>

The condition element specifies the basis on which events are selected to be updated. When specifying the condition, table attributes must be referred to with the table name.

You can use the set keyword to update selected attributes from the table. Here, for each assignment, the attribute specified in the left must be the table attribute, and the one specified in the right can be a stream/table attribute a mathematical operation, or other. When the set clause is not provided, all the attributes in the table are updated.

To execute an update for specific event types use the current events, expired events or the all events keyword with for as shown in the syntax. For more information, see Event Type.

note

Table attributes must be always referred to with the table name as shown below: <table name>.<attibute name>.

Example

This stream application updates the room occupancy in the RoomOccupancyTable table for each room number based on new arrivals and exits from the UpdateStream stream.

CREATE TABLE RoomOccupancyTable (roomNo int, people int);
CREATE STREAM UpdateStream (roomNumber int, arrival int, exit int);

select *
from UpdateStream
update RoomOccupancyTable
    set RoomOccupancyTable.people = RoomOccupancyTable.people + arrival - exit
    on RoomOccupancyTable.roomNo == roomNumber;

Update or Insert

This allows you update if the event attributes already exist in the table based on a condition, or else insert the entry as a new attribute.

Syntax

select <attribute name>, <attribute name>, ...
from <input stream>
update or insert into <table> (for <event type>)?
    set <table>.<attribute name> = <expression>, <table>.<attribute name> = <expression>, ...
    on <condition>

The condition element specifies the basis on which events are selected for update. When specifying the condition, table attributes should be referred to with the table name. If a record that matches the condition does not already exist in the table, the arriving event is inserted into the table.

The set clause is only used when an update is performed during the insert/update operation. When set clause is used, the attribute to the left is always a table attribute, and the attribute to the right can be a stream/table attribute, mathematical operation or other. The attribute to the left (i.e., the attribute in the event table) is updated with the value of the attribute to the right if the given condition is met. When the set clause is not provided, all the attributes in the table are updated.

note

When the attribute to the right is a table attribute, the operations supported differ based on the database type.

To execute update upon specific event types use the current events, expired events or the all events keyword with for as shown in the syntax. To understand more see Event Type.

note

Table attributes should be always referred to with the table name as <table name>.<attibute name>.

Example

The following query update for events in the UpdateTable event table that have room numbers that match the same in the UpdateStream stream. When such events are found in the event table, they are updated. When a room number available in the stream is not found in the event table, it is inserted from the stream.

CREATE TABLE RoomAssigneeTable (roomNo int, type string, assignee string);
CREATE STREAM RoomAssigneeStream (roomNumber int, type string, assignee string);

select roomNumber as roomNo, type, assignee
from RoomAssigneeStream
update or insert into RoomAssigneeTable
    set RoomAssigneeTable.assignee = assignee
    on RoomAssigneeTable.roomNo == roomNo;

In

This allows the stream to check whether the expected value exists in the table as a part of a conditional operation.

Syntax

insert into <output stream>
select <attribute name>, <attribute name>, ...
from <input stream>[<condition> in <table>]

The condition element specifies the basis on which events are selected to be compared. When constructing the condition, the table attribute must be always referred to with the table name as shown below:

<table>.<attibute name>

Example

This Stream application filters only room numbers that are listed in the ServerRoomTable table.

CREATE TABLE ServerRoomTable (roomNo int);
CREATE STREAM TempStream (deviceID long, roomNo int, temp double);

insert into ServerRoomTempStream
from TempStream[ServerRoomTable.roomNo == roomNo in ServerRoomTable];

Named Aggregation

Named aggregation allows you to obtain aggregates in an incremental manner for a specified set of time periods.

This not only allows you to calculate aggregations with varied time granularity, but also allows you to access them in an interactive manner for reports, dashboards, and for further processing. Its schema is defined via the aggregation definition.

Purpose

Named aggregation allows you to retrieve the aggregate values for different time durations. That is, it allows you to obtain aggregates such as sum, count, avg, min, max, count and distinctCount of stream attributes for durations such as sec, min, hour, etc.

This is of considerable importance in many Analytics scenarios because aggregate values are often needed for several time periods. Furthermore, this ensures that the aggregations are not lost due to unexpected system failures because aggregates can be stored in different persistence stores.

Syntax

CREATE AGGREGATION <aggregator name> WITH (store.type='<store type>', purge.enable='<true or false>', purge.interval='<purging interval>', purge.retention.period='<retention period>')
from <input stream>
select <attribute name>, <aggregate function>(<attribute name>) as <attribute name>, ...
    group by <attribute name>
    aggregate by <timestamp attribute> every <time periods> ;

The above syntax includes the following:

Item	Description
store	This annotation is used to refer to the data store where the calculated aggregate results are stored. This annotation is optional. When no annotation is provided, the data is stored in the in-memory store.
purge	This annotation is used to configure purging in aggregation granularities. If this annotation is not provided, the default purging mentioned above is applied. If you want to disable automatic data purging, you can use this annotation as follows:purge.enable='false'/You should disable data purging if the aggregation query in included in the Stream application for read-only purposes.
purge.retention.Period	This annotation is used to specify the length of time the data needs to be retained when carrying out data purging. If this annotation is not provided, the default retention period is applied.
<aggregator name>	This specifies a unique name for the aggregation so that it can be referred when accessing aggregate results.
<input stream>	The stream that feeds the aggregation. Note! this stream should be already defined.
group by <attribute name>	The group by clause is optional. If it is included in a Stream application, aggregate values are calculated per each group by attribute. If it is not used, all the events are aggregated together.
by <timestamp attribute>	This clause is optional. This defines the attribute that should be used as the timestamp. If this clause is not used, the event time is used by default. The timestamp could be given as either a string or a long value. If it is a long value, the unix timestamp in milliseconds is expected (e.g. 1496289950000). If it is a string value, the supported formats are <yyyy>-<MM>-<dd> <HH>:<mm>:<ss> (if time is in GMT) and <yyyy>-<MM>-<dd> <HH>:<mm>:<ss> <Z> (if time is not in GMT), here the ISO 8601 UTC offset must be provided for <Z> .(e.g., +05:30, -11:00).
<time periods>	Time periods can be specified as a range where the minimum and the maximum value are separated by three dots, or as comma-separated values. e.g., A range can be specified as sec...year where aggregation is done per second, minute, hour, day, month and year. Comma-separated values can be specified as min, hour. Skipping time durations (e.g., min, day where the hour duration is skipped) when specifying comma-separated values is supported only from v4.1.1 onwards

Aggregation's granularity data holders are automatically purged every 15 minutes. When carrying out data purging, the retention period you have specified for each granularity in the named aggregation query is taken into account. The retention period defined for a granularity needs to be greater than or equal to its minimum retention period as specified in the table below. If no valid retention period is defined for a granularity, the default retention period (as specified in the table below) is applied.

Granularity	Default retention	Minimum retention
second	120 seconds	120 seconds
minute	24 hours	120 minutes
hour	30 days	25 hours
day	1 year	32 days
month	All	13 month
year	All	none

note

Aggregation is carried out at calendar start times for each granularity with the GMT timezone

note

the aggregation input stream should only feed events to one aggregation definition).

Example

This Stream Application defines an aggregation named TradeAggregation to calculate the average and sum for the price attribute of events arriving at the TradeStream stream. These aggregates are calculated per every time granularity in the second-year range.

CREATE STREAM TradeStream (symbol string, price double, volume long, timestamp long);

CREATE AGGREGATION TradeAggregation WITH (purge.enable='true', purge.interval='10 sec', purge.retentionPeriod.sec='120 sec', purge.retentionPeriod.min='24 hours', purge.retentionPeriod.hours='30 days', purge.retentionPeriod.days='1 year', purge.retentionPeriod.months='all', purge.retentionPeriod.years='all')
  from TradeStream
  select symbol, avg(price) as avgPrice, sum(price) as total
    group by symbol
    aggregate by timestamp every sec ... year;

Distributed Aggregation

Distributed Aggregation allows you to partially process aggregations in different shards. This allows Stream app in one shard to be responsible only for processing a part of the aggregation.

Syntax

@store(type="<store type>", ...)
CREATE AGGREGATION <aggregator name> WITH (PartitionById.enable='false')
select <attribute name>, <aggregate function>(<attribute name>) as <attribute name>, ...
    group by <attribute name>
    aggregate by <timestamp attribute> every <time periods> ;
from <input stream>

Following table includes the annotation to be used to enable distributed aggregation,

Item	Description
@artitionById	If the property is given, then the distributed aggregation is enabled. Further this can be disabled by using enable element, PartitionById.enable='false'.

Further, following system properties are also available,

System Property	Description	Possible Values	Optional	Default Value
shardId	The id of the shard one of the distributed aggregation is running in. This should be unique to a single shard	Any string	No	<Empty_String>
partitionById	This allows user to enable/disable distributed aggregation for all aggregations running in one stream processing manager.	true/false	Yesio	false

note

ShardIds should not be changed after the first configuration in order to keep data consistency.

Join (Aggregation)

This allows a stream to retrieve calculated aggregate values from the aggregation.

note

A join can also be performed with two streams, with a table and a stream, or with a stream against externally named windows.

Syntax

A join with aggregation is similer to the join with table, but with additional within and per clauses.

select <attribute name>, <attribute name>, ...
from <input stream> join <aggrigation>
  on <join condition>
  within <time range>
  per <time granularity>
insert into <output stream>;

Apart from constructs of table join this includes the following. Please note that the 'on' condition is optional :

Item	Description
within <time range>	This allows you to specify the time interval for which the aggregate values need to be retrieved. This can be specified by providing the start and end time separated by a comma as string or long values, or by using the wildcard string specifying the data range. For details refer examples.
per <time granularity>	This specifies the time granularity by which the aggregate values must be grouped and returned. e.g., If you specify days, the retrieved aggregate values are grouped for each day within the selected time interval.

within and per clauses also accept attribute values from the stream. The timestamp of the aggregations can be accessed through the AGG_TIMESTAMP attribute.

Example

Following aggregation definition will be used for the examples.

CREATE STREAM TradeStream (symbol string, price double, volume long, timestamp long);

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

This query retrieves daily aggregations within the time range "2014-02-15 00:00:00 +05:30", "2014-03-16 00:00:00 +05:30" (Please note that +05:30 can be omitted if timezone is GMT)

CREATE STREAM StockStream (symbol string, value int);

insert into AggregateStockStream
select S.symbol, T.total, T.avgPrice
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";

This query retrieves hourly aggregations within the day 2014-02-15.

CREATE STREAM StockStream (symbol string, value int);

insert into AggregateStockStream
select S.symbol, T.total, T.avgPrice
from StockStream as S join TradeAggregation as T
  on S.symbol == T.symbol
  within "2014-02-15 **:**:** +05:30"
  per "hours";

This query retrieves all aggregations per perValue stream attribute within the time period between timestamps 1496200000000 and 1596434876000.

CREATE STREAM StockStream (symbol string, value int, perValue string);

insert into AggregateStockStream
select S.symbol, T.total, T.avgPrice
from StockStream as S join TradeAggregation as T
  on S.symbol == T.symbol
  within 1496200000000L, 1596434876000L
  per S.perValue;

Supported join types

Aggregation join supports following join operations.

• Inner join (join)

  This is the default behaviour of a join operation. join is used as the keyword to join the stream with the aggregation. The output is generated only if there is a matching event in the stream and the aggregation.

• Left outer join

  The left outer join operation allows you to join a stream on left side with a aggregation on the right side based on a condition. Here, it returns all the events of left stream even if there are no matching events in the right aggregation by having null values for the attributes of the right aggregation.

• Right outer join

  This is similar to a left outer join. right outer join is used as the keyword to join a stream on right side with a aggregation on the left side based on a condition. It returns all the events of the right stream even if there are no matching events in the left aggregation.

Named Window

A named window is a window that can be shared across multiple queries. Events can be inserted to a named window from one or more queries and it can produce output events based on the named window type.

Syntax

The syntax for a named window is as follows:

CREATE WINDOW <window name> (<attribute name> <attribute type>, <attribute name> <attribute type>, ... ) <window type>(<parameter>, <parameter>, …) <event type>;

The following parameters are configured in a table definition:

Parameter	Description
window name	The name of the window defined. (PascalCase is used for window names as a convention.)
attribute name	The schema of the window is defined by its attributes with uniquely identifiable attribute names (camelCase is used for attribute names as a convention.)
attribute type	The type of each attribute defined in the schema. This can be STRING, INT, LONG, DOUBLE, FLOAT, BOOL or OBJECT.
<window type>(<parameter>, ...)	The window type associated with the window and its parameters.
output <event type>	This is optional. Keywords such as current events, expired events and all events (the default) can be used to specify when the window output should be exposed. For more information, see Event Type.

Examples

• Returning all output when events arrive and when events expire from the window.

  In this query, the event type is not specified. Therefore, it returns both current and expired events as the output.

  CREATE WINDOW SensorWindow (name string, value float, roomNo int, deviceID string) timeBatch(1 second);

• Returning an output only when events expire from the window.

  In this query, the event type of the window is expired events. Therefore, it only returns the events that have expired from the window as the output.

  CREATE WINDOW SensorWindow (name string, value float, roomNo int, deviceID string) timeBatch(1 second) output expired events;

Operators on Named Windows

The following operators can be performed on named windows.

Insert

This allows events to be inserted into windows. This is similar to inserting events into streams.

Syntax

insert into <window>
select <attribute name>, <attribute name>, ...
from <input stream>

To insert only events of a specific event type, add the current events, expired events or the all events keyword between insert and into keywords (similar to how it is done for streams).

For more information, see Event Type.

Example

This query inserts all events from the TempStream stream to the OneMinTempWindow window.

CREATE STREAM TempStream(tempId string, temp double);
CREATE WINDOW OneMinTempWindow(tempId string, temp double) time(1 min);

insert into OneMinTempWindow
select *
from TempStream;

Join (Window)

To allow a stream to retrieve information from a window based on a condition.

note

A join can also be performed with two streams, aggregation or with tables tables.

Syntax

insert into <output stream>
select (<input stream>|<window>).<attribute name>, (<input stream>|<window>).<attribute name>, ...
from <input stream> join <window>
    on <condition>

Example

This Stream Application performs a join count the number of temperature events having more then 40 degrees within the last 2 minutes.

CREATE WINDOW TwoMinTempWindow (roomNo int, temp double) time(2 min);
CREATE STREAM CheckStream (requestId string);

insert into HighTempCountStream
select requestId, count(T.temp) as count
from CheckStream as C join TwoMinTempWindow as T
    on T.temp > 40;

Supported join types

Window join supports following operations of a join clause.

• Inner join (join)

  This is the default behaviour of a join operation. join is used as the keyword to join two windows or a stream with a window. The output is generated only if there is a matching event in both stream/window.

• Left outer join

  The left outer join operation allows you to join two windows or a stream with a window to be merged based on a condition. Here, it returns all the events of left stream/window even if there are no matching events in the right stream/window by having null values for the attributes of the right stream/window.

• Right outer join

  This is similar to a left outer join. Right outer join is used as the keyword to join two windows or a stream with a window. It returns all the events of the right stream/window even if there are no matching events in the left stream/window.

• Full outer join

  The full outer join combines the results of left outer join and right outer join. full outer join is used as the keyword to join two windows or a stream with a window. Here, output event are generated for each incoming event even if there are no matching events in the other stream/window.

From

A window can be an input to a query, similar to streams.

Note !!! When window is used as an input to a query, another window cannot be applied on top of this.

Syntax

insert into <output stream>
select <attribute name>, <attribute name>, ...
from <window>

Example

This Stream Application calculates the maximum temperature within the last 5 minutes.

CREATE WINDOW FiveMinTempWindow (roomNo int, temp double) time(5 min);

insert into MaxSensorReadingStream
select max(temp) as maxValue, roomNo
from FiveMinTempWindow;

Trigger

Triggers allow events to be periodically generated. Trigger definition can be used to define a trigger. A trigger also works like a stream with a predefined schema.

Purpose

For some use cases the system should be able to periodically generate events based on a specified time interval to perform some periodic executions.

A trigger can be performed for a 'start' operation, for a given <time interval>, or for a given '<cron expression>'.

Syntax

The syntax for a trigger definition is as follows.

CREATE TRIGGER <trigger name> at ('start'| every <time interval>| '<cron expression>');

Similar to streams, triggers can be used as inputs. They adhere to the following stream definition and produce the triggered_time attribute of the long type.

CREATE STREAM <trigger name> (triggered_time long);

The following types of triggeres are currently supported:

Trigger type	Description
'start'	An event is triggered when Stream is started.
every <time interval>	An event is triggered periodically at the given time interval.
'<cron expression>'	An event is triggered periodically based on the given cron expression. For configuration details, see quartz-scheduler.

Examples

• Triggering events regularly at specific time intervals

  The following query triggers events every 5 minutes.

  CREATE TRIGGER FiveMinTriggerStream at every 5 min;

• Triggering events at a specific time on specified days

  The following query triggers an event at 10.15 AM on every weekdays.

  CREATE TRIGGER FiveMinTriggerStream at '0 15 10 ? * MON-FRI';

Script

Scripts allow you to write functions in other programming languages and execute them within Stream queries. Functions defined via scripts can be accessed in queries similar to any other inbuilt function. Function definitions can be used to define these scripts.

Function parameters are passed into the function logic as Object[] and with the name data .

Purpose

Scripts allow you to define a function operation that is not provided in Stream core or its extension. It is not required to write an extension to define the function logic.

Syntax

The syntax for a Script definition is as follows.

define function <function name>[<language name>] return <return type> {
    <operation of the function>
};

The following parameters are configured when defining a script.

Parameter	Description
function name	The name of the function (camelCase is used for the function name) as a convention.
language name	The name of the programming language used to define the script, such as javascript, r and scala.
return type	The attribute type of the function’s return. This can be int, long, float, double, string, bool or object. Here the function implementer should be responsible for returning the output attribute on the defined return type for proper functionality.
operation of the function	Here, the execution logic of the function is added. This logic should be written in the language specified under the language name, and it should return the output in the data type specified via the return type parameter.

Examples

This query performs concatenation using JavaScript, and returns the output as a string.

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;
};

CREATE STREAM TempStream(deviceID long, roomNo int, temp double);

insert into DeviceTempStream
select concatFn(roomNo,'-',deviceID) as id, temp
from TempStream;

Store

Stream store queries are a set of on-demand queries that can be used to perform operations on Stream tables, windows, and aggregators.

Purpose

Store queries allow you to execute the following operations on Stream tables, windows, and aggregators without the intervention of streams.

Queries supported for tables:

• INSERT
• SELECT
• DELETE
• UPDATE
• UPDATE OR INSERT

Queries supported for windows and aggregators:

• SELECT

This is be done by submitting the store query to the Stream application runtime using its query() method.

In order to execute store queries, the Stream application of the Stream application runtime you are using, should have a store defined, which contains the table that needs to be queried.

Example

If you need to query the table named RoomTypeTable the it should have been defined in the Stream application.

In order to execute a store query on RoomTypeTable, you need to submit the store query using query() method.

(Table/Window) Select

The SELECT store query retrieves records from the specified table or window, based on the given condition.

Syntax

from <table/window>
<on condition>?
select <attribute name>, <attribute name>, ...
<group by>?
<having>?
<order by>?
<limit>?

Example

This query retrieves room numbers and types of the rooms starting from room no 10.

select roomNo, type
from roomTypeTable
on roomNo >= 10;

(Aggregation) Select

The SELECT store query retrieves records from the specified aggregation, based on the given condition, time range, and granularity.

Syntax

from <aggregation>
<on condition>?
within <time range>
per <time granularity>
select <attribute name>, <attribute name>, ...
<group by>?
<having>?
<order by>?
<limit>?

Example

Following aggregation definition will be used for the examples.

CREATE STREAM TradeStream (symbol string, price double, volume long, timestamp long);

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

This query retrieves daily aggregations within the time range "2014-02-15 00:00:00 +05:30", "2014-03-16 00:00:00 +05:30" (Please note that +05:30 can be omitted if timezone is GMT)

select symbol, total, avgPrice 
from TradeAggregation
  within "2014-02-15 00:00:00 +05:30", "2014-03-16 00:00:00 +05:30"
  per "days";

This query retrieves hourly aggregations of "FB" symbol within the day 2014-02-15.

select symbol, total, avgPrice
from TradeAggregation
  on symbol == "FB"
  within "2014-02-15 **:**:** +05:30"
  per "hours";

Insert

This allows you to insert a new record to the table with the attribute values you define in the select section.

Syntax

insert into <table>
select <attribute name>, <attribute name>, ...;

Example

This store query inserts a new record to the table RoomOccupancyTable, with the specified attribute values.

insert into RoomOccupancyTable
select 10 as roomNo, 2 as people

Delete

The DELETE store query deletes selected records from a specified table.

Syntax

<select>?  
delete <table>  
on <conditional expresssion>

The condition element specifies the basis on which records are selected to be deleted.

note

Table attributes must always be referred to with the table name as shown below: <table name>.<attibute name>.

Example

In this example, query deletes a record in the table named RoomTypeTable if it has value for the roomNo attribute that matches the value for the roomNumber attribute of the selection which has 10 as the actual value.

select 10 as roomNumber
delete RoomTypeTable
on RoomTypeTable.roomNo == roomNumber;

delete RoomTypeTable
on RoomTypeTable.roomNo == 10;

Update

The UPDATE store query updates selected attributes stored in a specific table, based on a given condition.

Syntax

select <attribute name>, <attribute name>, ...?
update <table>
    set <table>.<attribute name> = (<attribute name>|<expression>)?, <table>.<attribute name> = (<attribute name>|<expression>)?, ...
    on <condition>

The condition element specifies the basis on which records are selected to be updated. When specifying the condition, table attributes must be referred to with the table name.

You can use the set keyword to update selected attributes from the table. Here, for each assignment, the attribute specified in the left must be the table attribute, and the one specified in the right can be a stream/table attribute a mathematical operation, or other. When the set clause is not provided, all the attributes in the table are updated.

note

Table attributes must always be referred to with the table name as shown below: <table name>.<attibute name>.

Example

The following query updates the room occupancy by increasing the value of people by 1, in the RoomOccupancyTable table for each room number greater than 10.

select 10 as roomNumber, 1 as arrival
update RoomTypeTable
    set RoomTypeTable.people = RoomTypeTable.people + arrival
    on RoomTypeTable.roomNo == roomNumber;

update RoomTypeTable
    set RoomTypeTable.people = RoomTypeTable.people + 1
    on RoomTypeTable.roomNo == 10;

Update or Insert

This allows you to update selected attributes if a record that meets the given conditions already exists in the specified table. If a matching record does not exist, the entry is inserted as a new record.

Syntax

select <attribute name>, <attribute name>, ...
update or insert into <table>
    set <table>.<attribute name> = <expression>, <table>.<attribute name> = <expression>, ...
    on <condition>

The condition element specifies the basis on which records are selected for update. When specifying the condition, table attributes should be referred to with the table name. If a record that matches the condition does not already exist in the table, the arriving event is inserted into the table.

The set clause is only used when an update is performed during the insert/update operation. When set clause is used, the attribute to the left is always a table attribute, and the attribute to the right can be a stream/table attribute, mathematical operation or other. The attribute to the left (i.e., the attribute in the event table) is updated with the value of the attribute to the right if the given condition is met. When the set clause is not provided, all the attributes in the table are updated.

note

Table attributes must always be referred to with the table name as shown below: <table name>.<attibute name>.

Example

The following query tries to update the records in the RoomAssigneeTable table that have room numbers that match the same in the selection. If such records are not found, it inserts a new record based on the values provided in the selection.

select 10 as roomNo, "single" as type, "abc" as assignee
update or insert into RoomAssigneeTable
    set RoomAssigneeTable.assignee = assignee
    on RoomAssigneeTable.roomNo == roomNo;

Event Playback

When @app:playback annotation is added to the app, the timestamp of the event (specified via an attribute) is treated as the current time. This results in events being processed faster.

The following elements are configured with this annotation.

Annotation	Description
idle.time	If no events are received during a time interval specified (in milliseconds) via this element, the Stream system time is incremented by a number of seconds specified via the increment element.
increment	The number of seconds by which the Stream system time must be incremented if no events are received during the time interval specified via the idle.time element.

e.g., In the following example, the Stream system time is incremented by two seconds if no events arrive for a time interval of 100 milliseconds.

@app:playback(idle.time = '100 millisecond', increment = '2 sec')