streamsx.topology.topology¶

Streaming application definition.

Overview¶

IBM Streams is an advanced analytic platform that allows user-developed applications to quickly ingest, analyze and correlate information as it arrives from thousands of real-time sources. Streams can handle very high data throughput rates, millions of events or messages per second.

With this API Python developers can build streaming applications that can be executed using IBM Streams, including the processing being distributed across multiple computing resources (hosts or machines) for scalability.

Topology¶

A Topology declares a graph of streams and operations against tuples (data items) on those streams.

After being declared, a Topology is submitted to be compiled into a Streams application bundle (sab file) and then executed. The sab file is a self contained bundle that can be executed in a distributed Streams instance either using the Streaming Analytics service on IBM Cloud or an on-premise IBM Streams installation.

The compilation step invokes the Streams compiler to produce a bundle. This effectively, from a Python point of view, produces a runnable version of the Python topology that includes application specific Python C extensions to optimize performance.

The bundle also includes any required Python packages or modules that were used in the declaration of the application. For example the Python module containing the callable used in a map() invocation. These modules are copied into the bundle from their local location. This allows the bundle to be self-contained, and thus not the Streams instance have all the required Python packages pre-installed. The addition of packages to the bundle can be controlled with Topology.include_packages and Topology.exclude_packages.

The Streams runtime distributes the application’s operations across the resources available in the instance.

Note

Topology represents a declaration of a streaming application that will be executed by a Streams instance as a job, either using the Streaming Analytics service on IBM Cloud or an on-premises distributed instance. Topology does not represent a running application, so an instance of Stream class does not contain the tuples, it is only a declaration of a stream.

Stream¶

A Stream can be an infinite sequence of tuples, such as a stream for a traffic flow sensor. Alternatively, a stream can be finite, such as a stream that is created from the contents of a file. When a streams processing application contains infinite streams, the application runs continuously without ending.

A stream has a schema that defines the type of each tuple on the stream. The schema for a Python Topology is either:

Python - A tuple may be any Python object. This is the default.
String - Each tuple is a Unicode string.
Binary - Each tuple is a blob.
Json - Each tuple is a Python dict that can be expressed as a JSON object.
Structured - A stream that has a structured schema of a ordered list of attributes, with each attribute having a fixed type (e.g. float64 or int32) and a name. The schema of a structured stream is defined using StreamSchema.

Stream processing¶

Callables¶

A stream is processed to produce zero or more transformed streams, such as filtering a stream to drop unwanted tuples, producing a stream that only contains the required tuples.

Streaming processing is per tuple based, as each tuple is submitted to a stream consuming operators have their processing logic invoked for that tuple.

A functional operator is declared by methods on Stream such as map() which maps the tuples on its input stream to tuples on its output stream. Stream uses a functional model where each stream processing operator is defined in terms a Python callable that is invoked passing input tuples and whose return defines what output tuples are submitted for downstream processing.

The Python callable used for functional processing in this API may be:

A Python lambda function.
A Python function.
An instance of a Python callable class.

For example a stream words containing only string objects can be processed by a filter() using a lambda function:

# Filter the stream so it only contains words starting with py
pywords = words.filter(lambda word : word.startswith('py'))

Stateful operations¶

Use of a class instance allows the operation to be stateful by maintaining state in instance attributes across invocations.

Note

For support with consistent region or checkpointing instances should ensure that the object’s state can be pickled. See https://docs.python.org/3.5/library/pickle.html#handling-stateful-objects

Initialization and shutdown¶

Execution of a class instance effectively run in a context manager so that an instance’s __enter__ method is called when the processing element containing the instance is initialized and its __exit__ method called when the processing element is stopped. To take advantage of this the class must define both __enter__ and __exit__ methods.

Note

Since an instance of a class is passed to methods such as map() __init__ is only called when the topology is declared, not at runtime. Initialization at runtime, such as opening connections, occurs through the __enter__ method.

Example of using __enter__ to create custom metrics:

import streamsx.ec as ec

class Sentiment(object):
    def __init__(self):
        pass

    def __enter__(self):
        self.positive_metric = ec.CustomMetric(self, "positiveSentiment")
        self.negative_metric = ec.CustomMetric(self, "negativeSentiment")

    def __exit__(self, exc_type, exc_value, traceback):
        pass

    def __call__(self):
        pass

When an instance defines a valid __exit__ method then it will be called with an exception when:

the instance raises an exception during processing of a tuple

a data conversion exception is raised converting a value to an structutured schema tuple or attribute

If __exit__ returns a true value then the exception is suppressed and processing continues, otherwise the enclosing processing element will be terminated.

Tuple semantics¶

Python objects on a stream may be passed by reference between callables (e.g. the value returned by a map callable may be passed by reference to a following filter callable). This can only occur when the functions are executing in the same PE (process). If an object is not passed by reference a deep-copy is passed. Streams that cross PE (process) boundaries are always passed by deep-copy.

Thus if a stream is consumed by two map and one filter callables in the same PE they may receive the same object reference that was sent by the upstream callable. If one (or more) callable modifies the passed in reference those changes may be seen by the upstream callable or the other callables. The order of execution of the downstream callables is not defined. One can prevent such potential non-deterministic behavior by one or more of these techniques:

Passing immutable objects
Not retaining a reference to an object that will be submitted on a stream
Not modifying input tuples in a callable
Using copy/deepcopy when returning a value that will be submitted to a stream.

Applications cannot rely on pass-by reference, it is a performance optimization that can be made in some situations when stream connections are within a PE.

Application log and trace¶

IBM Streams provides application trace and log services which are accesible through standard Python loggers from the logging module.

See Application log and trace.

SPL operators¶

In addition an application declared by Topology can include stream processing defined by SPL primitive or composite operators. This allows reuse of adapters and analytics provided by IBM Streams, open source and third-party SPL toolkits.

See streamsx.spl.op

Module contents¶

Classes

`PendingStream`	Pending stream connection.
`Routing`	Defines how tuples are routed to channels in a parallel region.
`Sink`	Termination of a Stream.
`Stream`	The Stream class is the primary abstraction within a streaming application.
`SubscribeConnection`	Connection mode between a subscriber and matching publishers.
`Topology`	The Topology class is used to define data sources, and is passed as a parameter when submitting an application.
`View`	The View class provides access to a continuously updated sampling of data items on a Stream after submission.
`Window`	Declaration of a window of tuples on a Stream.

class streamsx.topology.topology.PendingStream(topology)¶

Pending stream connection.

A pending stream is an initially disconnected stream. The stream attribute can be used as an input stream when the required stream is not yet available. Once the required stream is available the connection is made using complete().

The schema of the pending stream is defined by the stream passed into complete.

A simple example is creating a source stream after the filter that will use it:

# Create the pending or placeholder stream
pending_source = PendingStream(topology)

# Create a filter against the placeholder stream
f = pending_source.stream.filter(lambda : t : t.startswith("H"))

source = topology.source(['Hello', 'World'])

# Now complete the connection
pending_source.complete(source)

Streams allows feedback loops in its flow graphs, where downstream processing can produce a stream that is fed back into the input port of an upstream operator. Typically, feedback loops are used to modify the state of upstream transformations, rather than repeat processing of tuples.

A feedback loop can be created by using a PendingStream. The upstream transformation or operator that will end the feedback loop uses stream as one of its inputs. A processing pipeline is then created and once the downstream starting point of the feedback loop is available, it is passed to complete() to create the loop.

complete(stream)¶

Complete the pending stream.

Any connections made to stream are connected to stream once this method returns.

Parameters:	stream (Stream) – Stream that completes the connection.

is_complete()¶: Has this connection been completed.

class streamsx.topology.topology.Routing¶

Defines how tuples are routed to channels in a parallel region.

A parallel region is started by parallel() and ended with end_parallel() or for_each().

BROADCAST = 0¶: Tuples are routed to every channel in the parallel region.

HASH_PARTITIONED = 3¶

Tuples are routed based upon a hash value so that tuples with the same hash and thus same value are always routed to the same channel. When a hash function is specified it is passed the tuple and the return value is the hash. When no hash function is specified then hash(tuple) is used.

Each tuple is only sent to a single channel.

Warning

A consistent hash function is required to guarantee that a tuple with the same value is always routed to the same channel. hash() is not consistent in that for types str, bytes and datetime objects are “salted” with an unpredictable random value (Python 3.5). Thus if the processing element is restarted channel routing for a hash based upon a str, bytes or datetime will change. In addition code executing in the channels can see a different hash value to other channels and the execution that routed the tuple due to being in different processing elements.

ROUND_ROBIN = 1¶

Tuples are routed to maintain an even distribution of tuples to the channels.

Each tuple is only sent to a single channel.

class streamsx.topology.topology.Sink(op)¶

Termination of a Stream.

A Stream is terminated by processing that typically sends the tuples to an external system.

Note

A Stream may have multiple terminations.

See also

for_each(), publish(), print()

New in version 1.7.

category¶

Category for this processing logic.

An arbitrary application label allowing grouping of application elements by category.

Assign categories based on common function. For example, database is a common category that you can use to group all database sinks in an application.

A category is not required and defaults to None meaning no assigned category.

Streams console supports visualization based upon categories.

Raises:	`TypeError` – No directly associated processing logic.

Note

A category has no affect on the execution of the application.

New in version 1.9.

colocate(others)¶

Colocate this processing logic with others.

Colocating processing logic requires execution in the same Streams processing element (operating system process).

When a job is submitted Streams may colocate (fuse) processing logic into the same processing element based upon flow analysis and current resource usage. This call instructs that this logic and others must be executed in the same processing element.

Parameters:	others – Processing logic such as a `Stream` or `Sink`. A single value can be passed or an iterable, such as a list of streams.
Returns:	This logic.
Return type:	self

resource_tags¶

Resource tags for this processing logic.

Tags are a mechanism for differentiating and identifying resources that have different physical characteristics or logical uses. For example a resource (host) that has external connectivity for public data sources may be tagged ingest.

Processing logic can be associated with one or more tags to require running on suitably tagged resources. For example adding tags ingest and db requires that the processing element containing the callable that created the stream runs on a host tagged with both ingest and db.

A Stream that was not created directly with a Python callable cannot have tags associated with it. For example a stream that is a union() of multiple streams cannot be tagged. In this case this method returns an empty frozenset which cannot be modified.

See https://www.ibm.com/support/knowledgecenter/en/SSCRJU_4.2.1/com.ibm.streams.admin.doc/doc/tags.html for more details of tags within IBM Streams.

Returns:	Set of resource tags, initially empty.
Return type:	set

Warning

If no resources exist with the required tags then job submission will fail.

New in version 1.7.

New in version 1.9: Support for Sink and Invoke.

class streamsx.topology.topology.Stream(topology, oport)¶

The Stream class is the primary abstraction within a streaming application. It represents a potentially infinite series of tuples which can be operated upon to produce another stream, as in the case of map(), or terminate a stream, as in the case of for_each().

aliased_as(name)¶

Create an alias of this stream.

Returns an alias of this stream with name name. When invocation of an SPL operator requires an Expression against an input port this can be used to ensure expression matches the input port alias regardless of the name of the actual stream.

Example use where the filter expression for a Filter SPL operator uses IN to access input tuple attribute seq:

s = ...
s = s.aliased_as('IN')

params =  {'filter': op.Expression.expression('IN.seq % 4ul == 0ul')}
f = op.Map('spl.relational::Filter', stream, params = params)

Parameters:	name (str) – Name for returned stream.
Returns:	Alias of this stream with `name` equal to name.
Return type:	Stream

New in version 1.9.

as_json(force_object=True, name=None)¶

Declares a stream converting each tuple on this stream into a JSON value.

The stream is typed as a JSON stream.

Each tuple must be supported by JSONEncoder.

If force_object is True then each tuple that not a dict will be converted to a JSON object with a single key payload containing the tuple. Thus each object on the stream will be a JSON object.

If force_object is False then each tuple is converted to a JSON value directly using json package.

If this stream is already typed as a JSON stream then it will be returned (with no additional processing against it and force_object and name are ignored).

Parameters:	force_object (bool) – Force conversion of non dicts to JSON objects. name (str) – Name of the resulting stream. When None defaults to a generated name.

New in version 1.6.1.

Returns:	Stream containing the JSON representations of tuples on this stream.
Return type:	Stream

as_string(name=None)¶

Declares a stream converting each tuple on this stream into a string using str(tuple).

The stream is typed as a string stream.

If this stream is already typed as a string stream then it will be returned (with no additional processing against it and name is ignored).

Parameters:	name (str) – Name of the resulting stream. When None defaults to a generated name.

New in version 1.6.

New in version 1.6.1: name parameter added.

Returns:	Stream containing the string representations of tuples on this stream.
Return type:	Stream

autonomous()¶

Starts an autonomous region for downstream processing. By default IBM Streams processing is executed in an autonomous region where any checkpointing of operator state is autonomous (independent) of other operators.

This method may be used to end a consistent region by starting an autonomous region. This may be called even if this stream is in an autonomous region.

Autonomous is not applicable when a topology is submitted to a STANDALONE contexts and will be ignored.

New in version 1.6.

Returns:	Stream whose subsequent downstream processing is in an autonomous region.
Return type:	Stream

batch(size)¶

Declares a tumbling window to support batch processing against this stream.

The number of tuples in the batch is defined by size.

If size is an int then it is the count of tuples in the batch. For example, with size=10 each batch will nominally contain ten tuples. Thus processing against the returned Window, such as aggregate() will be executed every ten tuples against the last ten tuples on the stream. For example the first three aggregations would be against the first ten tuples on the stream, then the next ten tuples and then the third ten tuples, etc.

If size is an datetime.timedelta then it is the duration of the batch using wallclock time. With a timedelta representing five minutes then the window contains any tuples that arrived in the last five minutes. Thus processing against the returned Window, such as aggregate() will be executed every five minutes tuples against the batch of tuples arriving in the last five minutes on the stream. For example the first three aggregations would be against any tuples on the stream in the first five minutes, then the next five minutes and then minutes ten to fifteen. A batch can contain no tuples if no tuples arrived on the stream in the defined duration.

Each tuple on the stream appears only in a single batch.

The number of tuples seen by processing against the returned window may be less than size (count or time based) when:

the stream is finite, the final batch may contain less tuples than the defined size,

the stream is in a consistent region, drain processing will complete the current batch without waiting for it to batch to reach its nominal size.

Examples:

# Create batches against stream s of 100 tuples each
w = s.batch(size=100)

# Create batches against stream s every five minutes
w = s.last(size=datetime.timedelta(minutes=5))

Parameters:	size – The size of each batch, either an int to define the number of tuples or datetime.timedelta to define the duration of the batch.
Returns:	Window allowing batch processing on this stream.
Return type:	Window

New in version 1.11.

category¶

Category for this processing logic.

An arbitrary application label allowing grouping of application elements by category.

Assign categories based on common function. For example, database is a common category that you can use to group all database sinks in an application.

A category is not required and defaults to None meaning no assigned category.

Streams console supports visualization based upon categories.

Raises:	`TypeError` – No directly associated processing logic.

Note

A category has no affect on the execution of the application.

New in version 1.9.

colocate(others)¶

Colocate this processing logic with others.

Colocating processing logic requires execution in the same Streams processing element (operating system process).

When a job is submitted Streams may colocate (fuse) processing logic into the same processing element based upon flow analysis and current resource usage. This call instructs that this logic and others must be executed in the same processing element.

Parameters:	others – Processing logic such as a `Stream` or `Sink`. A single value can be passed or an iterable, such as a list of streams.
Returns:	This logic.
Return type:	self

end_low_latency()¶

Returns a Stream that is no longer guaranteed to run in the same process as the calling stream.

Returns:	Stream

end_parallel()¶

Ends a parallel region by merging the channels into a single stream.

Returns:	Stream for which subsequent transformations are no longer parallelized.
Return type:	Stream

filter(func, name=None)¶

Filters tuples from this stream using the supplied callable func.

For each tuple on the stream func(tuple) is called, if the return evaluates to True the tuple will be present on the returned stream, otherwise the tuple is filtered out.

Parameters:	func – Filter callable that takes a single parameter for the tuple. name (str) – Name of the stream, defaults to a generated name.

If invoking func for a tuple on the stream raises an exception then its processing element will terminate. By default the processing element will automatically restart though tuples may be lost.

If func is a callable object then it may suppress exceptions by return a true value from its __exit__ method. When an exception is suppressed no tuple is submitted to the filtered stream corresponding to the input tuple that caused the exception.

Returns:	A Stream containing tuples that have not been filtered out.
Return type:	Stream

flat_map(func=None, name=None)¶

Maps and flatterns each tuple from this stream into 0 or more tuples.

For each tuple on this stream func(tuple) is called. If the result is not None then the the result is iterated over with each value from the iterator that is not None will be submitted to the return stream.

If the result is None or an empty iterable then no tuples are submitted to the returned stream.

Parameters:	func – A callable that takes a single parameter for the tuple. If not supplied then a function equivalent to `lambda tuple_ : tuple_` is used. This is suitable when each tuple on this stream is an iterable to be flattened. name (str) – Name of the flattened stream, defaults to a generated name.

If invoking func for a tuple on the stream raises an exception then its processing element will terminate. By default the processing element will automatically restart though tuples may be lost.

If func is a callable object then it may suppress exceptions by return a true value from its __exit__ method. When an exception is suppressed no tuples are submitted to the flattened and mapped stream corresponding to the input tuple that caused the exception.

Returns:	A Stream containing flattened and mapped tuples.
Return type:	Stream
Raises:	`TypeError` – if func does not return an iterator nor None

Changed in version 1.11: func is optional.

for_each(func, name=None)¶

Sends information as a stream to an external system.

For each tuple t on the stream func(t) is called.

Parameters:	func – A callable that takes a single parameter for the tuple and returns None. name (str) – Name of the stream, defaults to a generated name.

If invoking func for a tuple on the stream raises an exception then its processing element will terminate. By default the processing element will automatically restart though tuples may be lost.

If func is a callable object then it may suppress exceptions by return a true value from its __exit__ method. When an exception is suppressed no further processing occurs for the input tuple that caused the exception.

Returns:	Stream termination.
Return type:	streamsx.topology.topology.Sink

Changed in version 1.7: Now returns a Sink instance.

isolate()¶

Guarantees that the upstream operation will run in a separate processing element from the downstream operation

Returns:	Stream whose subsequent immediate processing will occur in a separate processing element.
Return type:	Stream

last(size=1)¶

Declares a slding window containing most recent tuples on this stream.

The number of tuples maintained in the window is defined by size.

If size is an int then it is the count of tuples in the window. For example, with size=10 the window always contains the last (most recent) ten tuples.

If size is an datetime.timedelta then it is the duration of the window. With a timedelta representing five minutes then the window contains any tuples that arrived in the last five minutes.

Parameters:	size – The size of the window, either an int to define the number of tuples or datetime.timedelta to define the duration of the window.

Examples:

# Create a window against stream s of the last 100 tuples
w = s.last(size=100)

# Create a window against stream s of tuples
# arrived on the stream in the last five minutes
w = s.last(size=datetime.timedelta(minutes=5))

Returns:	Window of the last (most recent) tuples on this stream.
Return type:	Window

low_latency()¶

The function is guaranteed to run in the same process as the upstream Stream function. All streams that are created from the returned stream are also guaranteed to run in the same process until end_low_latency() is called.

Returns:	Stream

map(func=None, name=None, schema=None)¶

Maps each tuple from this stream into 0 or 1 stream tuples.

For each tuple on this stream result = func(tuple) is called. If result is not None then the result will be submitted as a tuple on the returned stream. If result is None then no tuple submission will occur.

By default the submitted tuple is result without modification resulting in a stream of picklable Python objects. Setting the schema parameter changes the type of the stream and modifies each result before submission.

Python - The default: result is submitted.
String - A stream of strings: str(result) is submitted.
Json - A stream of JSON objects: result must be convertable to a JSON object using json package.
StreamSchema - A structured stream. result must be a dict or (Python) tuple. When a dict is returned the outgoing stream tuple attributes are set by name, when a tuple is returned stream tuple attributes are set by position.

Parameters:	func – A callable that takes a single parameter for the tuple. If not supplied then a function equivalent to `lambda tuple_ : tuple_` is used. name (str) – Name of the mapped stream, defaults to a generated name. schema (StreamSchema) – Schema of the resulting stream.

If invoking func for a tuple on the stream raises an exception then its processing element will terminate. By default the processing element will automatically restart though tuples may be lost.

If func is a callable object then it may suppress exceptions by return a true value from its __exit__ method. When an exception is suppressed no tuple is submitted to the mapped stream corresponding to the input tuple that caused the exception.

Returns:	A stream containing tuples mapped by func.
Return type:	Stream

New in version 1.7: schema argument added to allow conversion to a structured stream.

New in version 1.8: Support for submitting dict objects as stream tuples to a structured stream (in addition to existing support for tuple objects).

Changed in version 1.11: func is optional.

multi_transform(func, name=None)¶: Equivalent to calling flat_map().

Deprecated since version 1.7: Replaced by flat_map().

name¶

Unique name of the stream.

When declaring a stream a name parameter can be provided. If the supplied name is unique within its topology then it will be used as-is, otherwise a variant will be provided that is unique within the topology.

If a name parameter was not provided when declaring a stream then the stream is assigned a unique generated name.

Returns:	Name of the stream.
Return type:	str

See also

subscribe()

Buffered = 1¶

Buffered connection between a subscriber and and matching publishers.

With a buffered connection tuples from publishers are placed in a single queue owned by the subscriber. This allows a slower subscriber to handle brief spikes in tuples from publishers.

A subscriber can fully isolate itself from matching publishers by adding a CongestionPolicy that drops tuples when the queue is full. In this case when the subscriber is not able to keep up with the tuple rate from all matching subscribers it will have a minimal effect on matching publishers.

Direct = 0¶

Direct connection between a subscriber and and matching publishers.

When connected directly a slow subscriber will cause back-pressure against the publishers, forcing them to slow tuple processing to the slowest publisher.

class streamsx.topology.topology.Topology(name=None, namespace=None, files=None)¶

The Topology class is used to define data sources, and is passed as a parameter when submitting an application. Topology keeps track of all sources, sinks, and transformations within your application.

Submission of a Topology results in a Streams application that has the name namespace::name.

Parameters:	name (str) – Name of the topology. Defaults to a name dervied from the calling evironment if it can be determined, otherwise a random name. namespace (str) – Namespace of the topology. Defaults to a name dervied from the calling evironment if it can be determined, otherwise a random name.

include_packages¶: set[str] – Python package names to be included in the built application. Any package in this list is copied into the bundle and made available at runtime to the Python callables used in the application. By default a Topology will automatically discover which packages and modules are required to be copied, this field may be used to add additional packages that were not automatically discovered.

exclude_packages¶: set[str] – Python top-level package names to be excluded from the built application. Excluding a top-level packages excludes all sub-modules at any level in the package, e.g. sound excludes sound.effects.echo. Only the top-level package can be defined, e.g. sound rather than sound.filters. Behavior when adding a module within a package is undefined. When compiling the application using Anaconda this set is pre-loaded with Python packages from the Anaconda pre-loaded set.

Package names in include_packages take precedence over package names in exclude_packages.

All declared streams in a Topology are available through their name using topology[name]. The stream’s name is defined by Stream.name() and will differ from the name parameter passed when creating the stream if the application uses duplicate names.

Changed in version 1.11: Declared streams available through topology[name].

add_file_dependency(path, location)¶

Add a file or directory dependency into an Streams application bundle.

Ensures that the file or directory at path on the local system will be available at runtime.

The file will be copied and made available relative to the application directory. Location determines where the file is relative to the application directory. Two values for location are supported etc and opt. The runtime path relative to application directory is returned.

The copy is made during the submit call thus the contents of the file or directory must remain availble until submit returns.

For example calling add_file_dependency('/tmp/conf.properties', 'etc') will result in contents of the local file conf.properties being available at runtime at the path application directory/etc/conf.properties. This call returns etc/conf.properties.

Python callables can determine the application directory at runtime with get_application_directory(). For example the path above at runtime is os.path.join(streamsx.ec.get_application_directory(), 'etc', 'conf.properties')

Parameters:	path (str) – Path of the file on the local system. location (str) – Location of the file in the bundle relative to the application directory.
Returns:	Path relative to application directory that can be joined at runtime with `get_application_directory`.
Return type:	str

New in version 1.7.

add_pip_package(requirement)¶

Add a Python package dependency for this topology.

If the package defined by the requirement specifier is not pre-installed on the build system then the package is installed using pip and becomes part of the Streams application bundle (sab file). The package is expected to be available from pypi.org.

If the package is already installed on the build system then it is not added into the sab file. The assumption is that the runtime hosts for a Streams instance have the same Python packages installed as the build machines. This is always true for the Streaming Analytics service on IBM Cloud.

The project name extracted from the requirement specifier is added to exclude_packages to avoid the package being added by the dependency resolver. Thus the package should be added before it is used in any stream transformation.

When an application is run with trace level info the available Python packages on the running system are listed to application trace. This includes any packages added by this method.

Example:

topo = Topology()
# Add dependency on pint package
# and astral at version 0.8.1
topo.add_pip_package('pint')
topo.add_pip_package('astral==0.8.1')

Parameters:	requirement (str) – Package requirements specifier.

Warning

Only supported when using the remote build service with the Streaming Analytics service.

New in version 1.9.

checkpoint_period¶

Enable checkpointing for the topology, and define the checkpoint period.

When checkpointing is enabled, the state of all stateful operators is saved periodically. If the operator restarts, its state is restored from the most recent checkpoint.

The checkpoint period is the frequency at which checkpoints will be taken. It can either be a timedelta value or a floating point value in seconds. It must be at 0.001 seconds or greater.

A stateful operator is an operator whose callable is an instance of a Python callable class.

Examples:

# Create a topology that will checkpoint every thirty seconds
topo = Topology()
topo.checkpoint_period = 30.0

# Create a topology that will checkpoint every two minutes
topo = Topology()
topo.checkpoint_period = datetime.timedelta(minutes=2)

New in version 1.11.

create_submission_parameter(name, default=None, type_=None)¶

Create a submission parameter.

A submission parameter is a handle for a value that is not defined until topology submission time. Submission parameters enable the creation of reusable topology bundles.

A submission parameter has a name. The name must be unique within the topology.

The returned parameter is a callable. Prior to submitting the topology, while constructing the topology, invoking it returns None.

After the topology is submitted, invoking the parameter within the executing topology returns the actual submission time value (or the default value if it was not set at submission time).

Submission parameters may be used within functional logic. e.g.:

threshold = topology.create_submission_parameter('threshold', 100);

# s is some stream of integers
s = ...
s = s.filter(lambda v : v > threshold())

Note

The parameter (value returned from this method) is only supported within a lambda expression or a callable that is not a function.

The default type of a submission parameter’s value is a str (unicode on Python 2.7). When a default is specified the type of the value matches the type of the default.

If default is not set, then the type can be set with type_.

The types supported are str, int, float and bool.

Topology submission behavior when a submission parameter lacking a default value is created and a value is not provided at submission time is defined by the underlying topology execution runtime.

Submission fails for contexts DISTRIBUTED, STANDALONE, and STREAMING_ANALYTICS_SERVICE.

Parameters:	name (str) – Name for submission parameter. default – Default parameter when submission parameter is not set. type – Type of parameter value when default is not set. Supported values are str, int, float and bool.

New in version 1.9.

name¶

Name of the topology.

Returns:	Name of the topology.
Return type:	str

namespace¶

Namespace of the topology.

Returns:	Namespace of the topology.
Return type:	str

source(func, name=None)¶

Declare a source stream that introduces tuples into the application.

Typically used to create a stream of tuple from an external source, such as a sensor or reading from an external system.

Tuples are obtained from an iterator obtained from the passed iterable or callable that returns an iterable.

Each tuple that is not None from the iterator is present on the returned stream.

Each tuple is a Python object and must be picklable to allow execution of the application to be distributed across available resources in the Streams instance.

Parameters:	func (callable) – An iterable or a zero-argument callable that returns an iterable of tuples. name (str) – Name of the stream, defaults to a generated name.

Exceptions raised by func or its iterator will cause its processing element will terminate.

If func is a callable object then it may suppress exceptions by return a true value from its __exit__ method.

Suppressing an exception raised by func.__iter__ causes the source to be empty, no tuples are submitted to the stream.

Suppressing an exception raised by __next__ on the iterator results in no tuples being submitted for that call to __next__. Processing continues with calls to __next__ to fetch subsequent tuples.

Returns:	A stream whose tuples are the result of the iterable obtained from func.
Return type:	Stream

subscribe(topic, schema=<CommonSchema.Python: <streamsx.topology.schema.StreamSchema object>>, name=None, connect=None, buffer_capacity=None, buffer_full_policy=None)¶

Subscribe to a topic published by other Streams applications. A Streams application may publish a stream to allow other Streams applications to subscribe to it. A subscriber matches a publisher if the topic and schema match.

By default a stream is subscribed as Python objects which connects to streams published to topic by Python Streams applications.

JSON streams are subscribed to using schema Json. Each tuple on the returned stream will be a Python dictionary object created by json.loads(tuple). A Streams application publishing JSON streams may have been implemented in any programming language supported by Streams.

String streams are subscribed to using schema String. Each tuple on the returned stream will be a Python string object. A Streams application publishing JSON streams may have been implemented in any programming language supported by Streams.

Subscribers can ensure they do not slow down matching publishers by using a buffered connection with a buffer full policy that drops tuples.

Parameters:	topic (str) – Topic to subscribe to. schema (StreamSchema) – schema to subscribe to. name (str) – Name of the subscribed stream, defaults to a generated name. connect (SubscribeConnection) – How subscriber will be connected to matching publishers. Defaults to `Direct` connection. buffer_capacity (int) – Buffer capacity in tuples when connect is set to `Buffered`. Defaults to 1000 when connect is Buffered. Ignored when connect is None or Direct. buffer_full_policy (CongestionPolicy) – Policy when a pulished tuple arrives and the subscriber’s buffer is full. Defaults to Wait when connect is Buffered. Ignored when connect is None or Direct.
Returns:	A stream whose tuples have been published to the topic by other Streams applications.
Return type:	Stream

Changed in version 1.9: connect, buffer_capacity and buffer_full_policy parameters added.

class streamsx.topology.topology.View(name)¶

The View class provides access to a continuously updated sampling of data items on a Stream after submission. A view object is produced by the view method, and will access data items from the stream on which it is invoked.

For example, a View object could be created and used as follows:

>>> topology = Topology()
>>> rands = topology.source(lambda: random.random())
>>> view = rands.view()
>>> submit(ContextTypes.DISTRIBUTED, topology)
>>> queue = view.start_data_fetch()
>>> for val in iter(queue.get, None):
... print(val)
...
0.6527
0.1963
0.0512

initialize_rest()¶: Used to initialize the View object on first use.

start_data_fetch()¶

Starts a background thread which begins accessing data from the remote Stream. The data items are placed asynchronously in a queue, which is returned from this method.

Returns:	A Queue object which is populated with the data items of the stream.

stop_data_fetch()¶: Terminates the background thread fetching stream data items.

class streamsx.topology.topology.Window(stream, window_type)¶

Declaration of a window of tuples on a Stream.

A Window can be passed as the input of an SPL operator invocation to indicate the operator’s input port is windowed.

Example invoking the SPL Aggregate operator with a sliding window of the last two minutes, triggering every five tuples:

win = s.last(datetime.timedelta(minutes=2)).trigger(5)

agg = op.Map('spl.relational::Aggregate', win,
            schema = 'tuple<uint64 sum, uint64 max>')
agg.sum = agg.output('Sum(val)')
agg.max = agg.output('Max(val)')

aggregate(function, name=None)¶

Aggregates the contents of the window when the window is triggered.

Upon a window trigger, the supplied function is passed a list containing the contents of the window: function(items). The order of the window items in the list are the order in which they were each received by the window. If the function’s return value is not None then the result will be submitted as a tuple on the returned stream. If the return value is None then no tuple submission will occur. For example, a window that calculates a moving average of the last 10 tuples could be written as follows:

win = s.last(10).trigger(1)
moving_averages = win.aggregate(lambda tuples: sum(tuples)/len(tuples))

Note

If a tumbling (batch()) window’s stream is finite then a final aggregation is performed if the window is not empty. Thus function may be passed fewer tuples for a window sized using a count. For example a stream with 105 tuples and a batch size of 25 tuples will perform four aggregations with 25 tuples each and a final aggregation of 5 tuples.

Parameters:	function – The function which aggregates the contents of the window name (str) – The name of the returned stream. Defaults to a generated name.
Returns:	A Stream of the returned values of the supplied function.
Return type:	Stream

Warning

In Python 3.5 or later if the stream being aggregated has a structured schema that contains a blob type then any blob value will not be maintained in the window. Instead its memoryview object will have been released. If the blob value is required then perform a map() transformation (without setting schema) copying any required blob value in the tuple using memoryview.tobytes().

New in version 1.8.

Changed in version 1.11: Support for aggregation of streams with structured schemas.

trigger(when=1)¶

Declare a window with this window’s size and a trigger policy.

When the window is triggered is defined by when.

If when is an int then the window is triggered every when tuples. For example, with when=5 the window will be triggered every five tuples.

If when is an datetime.timedelta then it is the period of the trigger. With a timedelta representing one minute then the window is triggered every minute.

By default, when trigger has not been called on a Window it triggers for every tuple inserted into the window (equivalent to when=1).

Parameters:	when – The size of the window, either an int to define the number of tuples or datetime.timedelta to define the duration of the window.
Returns:	Window that will be triggered.
Return type:	Window

Warning

A trigger is only supported for a sliding window such as one created by last().