Stream 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.

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

Grammar

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

Hight level syntax of StreamApp is as follows.

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

Example

Stream Application with name Temperature-Analytics defined with a stream named TempStream and a query named 5minAvgQuery.

@app:name('Temperature-Analytics')

define stream TempStream (deviceID long, roomNo int, temp double);

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

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).

Purpose

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.

define stream <stream name> (<attribute name> <attribute type>,
                             <attribute name> <attribute type>, ... );

The following parameters are used to configure a stream definition.

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 in multi-threading and asynchronous way use the @Async annotation as shown in Multi-threading and Asynchronous Processing configuration section.

Example

define stream TempStream (deviceID long, roomNo int, temp double);

The above creates a stream with name TempStream having the following attributes.

• deviceID of type long
• roomNo of type int
• temp of type 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 annotation with the required parameter values.

The source syntax is as follows:

@source(type='<source type>', <static.key>='<value>', <static.key>='<value>',
    @map(type='<map type>', <static.key>='<value>', <static.key>='<value>',
        @attributes( <attribute1>='<attribute mapping>', <attributeN>='<attribute mapping>')
    )
)
define stream <stream name> (<attribute1> <type>, <attributeN> <type>);

This syntax includes the following annotations.

Source

The type parameter of @source annotation defines the source type that receives events. The other parameters of @source 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 Mapper¶

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

The type parameter of the @map defines the map type to be used in converting the incoming events. The other parameters of @map 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 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.

1. Define attribute names as keys, and mapping configurations as values:
   

   @attributes( <attribute1>='<mapping>', <attributeN>='<mapping>')
   

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

   @attributes( '<mapping for attribute1>', '<mapping for attributeN>')
   

Supported Source Mapping Types

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

Example 1

Receive JSON messages via c8streams, 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 c8streams source and JSON source mapper to achieve the above is as follows.

@source(type='c8streams', streams.list='foo', @map(type='json'))
define stream InputStream (name string, age int, country string);

Example 2

Receive JSON messages via c8db, and direct them to StockStream stream for processing. Here the incoming JSON, as given bellow, 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 c8db source and the custom JSON source mapping to achieve the above is as follows.

@source(type='c8db', collection='foo',
  @map(type='json', enclosing.element="$.portfolio",
    @attributes(symbol = "stock.company.symbol", price = "stock.price",
                volume = "stock.volume")))

define stream StockStream (symbol string, price float, volume long);

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

@source(type='c8db', collection='foo',
  @map(type='json', enclosing.element="$.portfolio",
    @attributes("stock.company.symbol", "stock.price", "stock.volume")))

define stream StockStream (symbol string, price float, volume long);

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 annotation with the required parameter values.

The sink syntax is as follows:

@sink(type='<sink type>', <static.key>='<value>', <dynamic.key>='{{<value>}}',
    @map(type='<map type>', <static.key>='<value>', <dynamic.key>='{{<value>}}',
        @payload('<payload mapping>')
    )
)

define stream <stream name> (<attribute1> <type>, <attributeN> <type>);

This syntax includes the following annotations.

Sink

The type parameter of the @sink annotation defines the sink type that publishes the events. The other parameters of the @sink 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:

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 annotation and add the configuration parameters that are common of all the destination endpoints inside it, along with the common parameters also add the @distribution annotation specifying the distribution strategy (i.e. roundRobin or partitioned) and @destination annotations 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.

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

Partitioned Distributed Sink

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

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

Sink Mapper¶

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

The type parameter of the @map defines the map type to be used in converting the outgoing events. The other parameters of @map 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

@payload is an optional annotation used with @map to define custom mapping. When the @payload annotation is not provided, each mapper maps the outgoing events to its own default event format. The @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 @payload annotation.

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

   @payload( 'This is a test message from {{user}}.')
   

2. Some mappers such key-value accept series of mapping values:
   

   @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:

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.

@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'))

define stream OutputStream (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.

@sink(type='http', publisher.url='http://localhost:8005/stocks',
      @map(type='json', validate.json='true', enclosing.element='$.Portfolio',
           @payload("""{"StockData":{ "Symbol":"{{symbol}}", "Price":{{price}} }}""")))

define stream StockStream (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.

@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')))

define stream OutputStream (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 annotation to the corresponding stream definition.

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

The @OnError annotation and the required action to be specified as bellow.

@OnError(action='on error action')
define stream <stream name> (<attribute name> <attribute type>,
                             <attribute name> <attribute type>, ... );

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

The following actions can be specified to @OnError annotation 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 annotation is as follows.

@OnError(action='STREAM')
define stream TempStream (deviceID long, roomNo int, temp double);

Stream will infer and automatically defines the fault stream of TempStream as given bellow.

define 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
@OnError(action='STREAM')
define stream TempStream (deviceID long, roomNo int, temp double);

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

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

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 annotation.

The on.error parameter of the @sink annotation can be specified as bellow.

@sink(type='<sink type>', on.error='<on error action>', <key>='<value>', ...)

define stream <stream name> (<attribute name> <attribute type>,
                             <attribute name> <attribute type>, ... );

The following actions can be specified to on.error parameter of @sink 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 Kafka annotation with on.error property is as follows.

@sink(type='kafka', on.error='WAIT', topic='{{roomNo}}',
      bootstrap.servers='localhost:9092',
      @map(type='xml'))

define stream TempStream (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 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.

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

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

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>')
from <input>
<projection>
<output action>

The following parameters are used to configure a stream definition.

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.

define stream TempStream (deviceID long, roomNo int, temp double);

from TempStream
select roomNo, temp
insert into RoomTempStream;

from RoomTempStream
insert into AnotherRoomTempStream;

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,

"""
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.

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.

from TempStream
select roomNo, temp
insert into RoomTempStream;

from TempStream
select * 
insert into NewTempStream;

from TempStream
insert into NewTempStream;

from TempStream 
select roomNo, temp as temperature, currentTimeMillis() as timeinsert into RoomTempStream;

from TempStream
select roomNo, temp, 'C' as scale
insert into RoomTempStream;

select roomNo, temp * 9/5 + 32 as temp, 'F' as scale,
       roomNo > 10 and roomNo < 15 as isServerRoom
from TempStream
insert into RoomTempStream;

Function¶

Function 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.

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 .

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.

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

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.

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

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.

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

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.

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

Inbuilt windows

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

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.

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.

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

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.

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.

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

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.

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.

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

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.

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.

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

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>

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

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.

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

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,

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

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.

| 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.

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

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.

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

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.

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

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.

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

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.

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

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:

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)?, ...
insert into <output stream>;

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).

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

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:

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>?
insert into <output stream>;

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.

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

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.

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
insert into HighestAvgTempStream;

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.

Syntax

The syntax for a join is as follows:

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>
insert into <output stream>

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.

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.

define stream TempStream(deviceID long, roomNo int, temp double);
define stream RegulatorStream(deviceID long, roomNo int, isOn bool);

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
insert into RegulatorActionStream;

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.

  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
  insert into outputStream ;    

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>

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.

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
insert into AlertStream;

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.

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

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 3^rd 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.

define stream TempStream (deviceID long, roomNo int, temp double);
define stream RegulatorStream (deviceID long, roomNo int, tempSet double, isOn bool);

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]
insert into TempDiffStream;

Logical Patterns¶

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

Syntax

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>)? ->  
    ...
insert into <output stream>

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

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.

Example

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

define stream RegulatorStateChangeStream(deviceID long, roomNo int, tempSet double, action string);
define stream RoomKeyStream(deviceID long, roomNo int, action string);

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'
insert into RegulatorActionStream;

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

define stream RegulatorStateChangeStream(deviceID long, roomNo int, tempSet double, action string);
define stream TempStream (deviceID long, roomNo int, temp double);

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

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

define stream RegulatorStateChangeStream(deviceID long, roomNo int, tempSet double, action string);
define stream TempStream (deviceID long, roomNo int, temp double);

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

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:

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>)?     
insert into <output stream>

Example

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

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

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.

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>)?     
insert into <output stream>

Example

This Stream application identifies temperature peeks.

define stream TempStream(deviceID long, roomNo int, temp double);

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]
insert into PeekTempStream;

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:

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>)?,
    ...
insert into <output stream>

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.

define stream TempStream(deviceID long, temp double);
define stream HumidStream(deviceID long, humid double);
define stream RegulatorStream(deviceID long, isOn bool);

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

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:

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

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

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.

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

• 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

  from TempStreamoutput
  output every 10 sec
  insert into LowRateTempStream;    

• 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.

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

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
      from TempStream#window.length(10)
      select roomNo, deviceID, max(temp) as maxTemp
      insert into DeviceTempStream;
  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
      from TempStream#window.time(10 min)
      select roomNo, deviceID, avg(temp) as avgTemp
      insert into AreaTempStream
  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
    from TempStream#window.lengthBatch(10)
    select roomNo, deviceID, avg(temp) as avgTemp
    insert into #AvgTempStream

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

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

    select e1.deviceID, e1.avgTemp as initialAvgTemp, e2.avgTemp as finalAvgTemp
    from every (e1=#AvgTempStream),e2=#AvgTempStream[e1.avgTemp + 5 < avgTemp]
    insert into DeviceTempIncreasingStream
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 c8db.

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:

define table <table name> (<attribute name> <attribute type>, <attribute name> <attribute type>, ... );

The following parameters are configured in a table definition:

Example

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

define 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( 'key1', 'key2' ) annotation to the table definition. Each event table configuration can have only one @PrimaryKey annotation. 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.

@PrimaryKey('symbol')
define table StockTable (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')
define 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 c8db through stream queries.

Syntax

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

@store(type='store_type', static.option.key1='static_option_value1', static.option.keyN='static_option_valueN')
define table TableName (attribute1 Type1, attributeN TypeN);

Example

The following defines a c8db having a table RoomTypeTable with columns roomNo of INTEGER and type of VARCHAR(255) mapped to Stream data types int and string respectively.

@Store(type="c8db", collection="RoomTypeTable")
define table 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.

Syntax

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

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.

select *
from TempStream
insert into TempTable;

Join (Table)¶

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

Syntax

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

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.

define table RoomTypeTable (roomNo int, type string);
define stream TempStream (deviceID long, roomNo int, temp double);

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

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

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.

define table RoomTypeTable (roomNo int, type string);

define 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.

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.

define table RoomOccupancyTable (roomNo int, people int);
define 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.

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.

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.

define table RoomAssigneeTable (roomNo int, type string, assignee string);
define 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

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

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.

define table ServerRoomTable (roomNo int);
define stream TempStream (deviceID long, roomNo int, temp double);

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

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

@store(type="<store type>", ...)
@purge(enable="<true or false>",interval=<purging interval>,@retentionPeriod(<granularity> = <retention period>, ...) )

define aggregation <aggregator name>
from <input stream>
select <attribute name>, <aggregate function>(<attribute name>) as <attribute name>, ...
    group by <attribute name>
    aggregate by <timestamp attribute> every <time periods> ;

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.

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.

define stream TradeStream (symbol string, price double, volume long, timestamp long);

@purge(enable='true', interval='10 sec',@retentionPeriod(sec='120 sec',min='24 hours',hours='30 days',days='1 year',months='all',years='all'))
define aggregation TradeAggregation
  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>", ...)
@PartitionById
define aggregation <aggregator name>
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,

Further, following system properties are also available,

Join (Aggregation)¶

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

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 :

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.

define stream TradeStream (symbol string, price double, volume long, timestamp long);

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

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)

define stream StockStream (symbol string, value int);

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

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

define stream StockStream (symbol string, value int);

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

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

define stream StockStream (symbol string, value int, perValue string);

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

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:

define 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:

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.

  define 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.

  define 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

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

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.

define stream TempStream(tempId string, temp double);
define window OneMinTempWindow(tempId string, temp double) time(1 min);

select *
from TempStream
insert into OneMinTempWindow;

Join (Window)¶

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

Syntax

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

Example

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

define window TwoMinTempWindow (roomNo int, temp double) time(2 min);
define stream CheckStream (requestId string);

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

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

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

Example

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

define window FiveMinTempWindow (roomNo int, temp double) time(5 min);

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

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.

define 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.

define stream <trigger name> (triggered_time long);

The following types of triggeres are currently supported:

Examples

• Triggering events regularly at specific time intervals

  The following query triggers events every 5 minutes.

  define 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.

  define 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.

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;
};

define stream TempStream(deviceID long, roomNo int, temp double);

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

Store Query¶

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:

• SELECT
• INSERT
• 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.

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

(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.

define stream TradeStream (symbol string, price double, volume long, timestamp long);

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

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)

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

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

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

Insert¶

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

Syntax

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

Example

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

select 10 as roomNo, 2 as people
insert into RoomOccupancyTable

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.

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.

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 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.

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.

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')