advanced opensplice programming - part i

Angelo CORSARO, Ph.D.Chief Technology Officer OMG DDS Sig Co-Chair

PrismTechangelo.corsaro@prismtech.com

Advanced OpenSplice DDS Programming- Part I -

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Before we get into technical details let’s try to answer two questions:

1. What does OpenSplice DDS do?

2. What kind of applications are using it?

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OpenSplice DDS enables seamless, loosely-coupled, timely, scalable and dependable data sharing between distributed applications and network connected devices.

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Some Use Cases

Integrated Modular Vetronics Training & Simulation Systems Naval Combat Systems

Air Traffic Control & Management Unmanned Air Vehicles Aerospace Applications

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Some Use Cases

Agricultural Vehicle Systems

Train Control Systems Complex Medical Devices

Smart CitiesLarge Scale SCADA Systems

Big Data (In-Memory) Analytics

A Fresh Look at OpenSplice

From Local to Distributed Data

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Data in Programming Languages

☐ Statically Typed Programming Languages provide mechanisms to declare a variable for given type as well as defining new types

☐ Scoping rules are then used to define what values and types are accessible at any given point in a program text

int x; class Rational { ... }

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“Local” Data☐ Consider the Following code fragment below in an imperative

programming language:

12345678910111213

{ int x; x = 7; { int y; y = 18; double z; z = 3.141593 x = x -‐ 4; y = x + y; z = 2 * z * y; }}

State { } { int x } { x = 7 } { x = 7, { } } { x = 7, { int y } } { x = 7, { y = 18 } } { x = 7, { y = 18, double z } } { x = 7, { y = 18, z = 3.1416 } } { x = 3, { y = 2 } } { x = 3, { y = 21 } } { x = 3, { y = 21, z = 131.9472 } } { x = 3, { }} { }

12345678910111213

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“Local” Data☐ One final consideration to do is that variables in a programming

language are in essence alias to a memory location

int x;int y;

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Distributed Data☐ Extending the elegant model provided by programming

languages to distributed systems has been and still is a quite popular research topic

☐ Historically the solutions to the distributed data problem where divided into those based on Distributed Shared Memory and those based on Message Passing

☐ OpenSplice DDS provide a new interesting way of addressing this problem. Let’s see...

OpenSplice Foundations

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Distributed Data in OpenSplice☐ OpenSplice DDS provides the abstraction

of an “Active” Global Data Space

☐ The Global Data Space is fully distributed and does not suffer Single Point of Failure issues

☐ Applications can autonomously join and leave this data space as well as declare new “Data Types”

☐ In OpenSplice, “data types” are called Topics

OpenSplice DDS Global Data Space

TopicA

TopicB

TopicC

TopicD

TopicE

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Global Data Space☐ The Global Data Space is actually called “Domain” in

OpenSplice DDS

☐ Domains are identified by integer. An application can join a domain by creating a domain participant as follows:

OpenSplice DDS Global Data Space

TopicA

TopicB

TopicC

TopicD

TopicE// Create a Topicint domain_id = 18;auto dp = DomainParticipant(domain_id);

☐ A Domain defines a top-level scope thus the content of a domain is not accessible from another domain

☐ Finally an application can join multiple domains by creating one domain participant per-domain

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Topics☐ A Topic is defined by means of a

(name, type, qos) tuple, where ☐ name: identifies the topic within

OpenSplice’s Global Data Space

☐ type: is the programming language type associated with the topic. This type can be defined in IDL, XML, and Java. Types are extensible and evolvable

☐ qos: is a collection of policies that control non-functional properties of this topic, such as its durability, reliability, etc.

DURABILITY,DEADLINE,PRIORITY,

“Circle”, Square”, “Triangle”

TopicTypeName

ShapeType

struct ShapeType { string color; long x; long y; long size;};

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Defining a Topic

☐ In code, if we wanted to define the Square topic we would do (with the new DDS-PSM-Cxx v1.0 API):

// Create a Topicauto dp = DomainParticipant(18);auto topic = Topic<ShapeType>(dp, “Square”, dp.default_topic_qos());

// Or equivalently and more concisely:auto topic = Topic<ShapeType>(dp, “Square”);

☐ The effect of this code is to define a topic called “Square” with type ShapeType and default QoS within the OpenSplice Global Data Space

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Looking with the Spyglassdomain 18

auto topic = Topic<ShapeType>(dp, “Square”);

Squarestruct&ShapeType&{&&string&&&color;&&&&&&&&long&&&x;&&&&&long&&&y;&&long&&&size;};

Policies

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Topics Scope☐ Topic definitions are (somewhat) equivalent to type definitions in a programming

language. In the sense that they define a new, generalized, type to be used within the global data space

☐ The scope of a Topic definition is the entire Domain, thus one could say that there is only one level of scoping. To make the parallel with Java, one could say that it is as if Topics where defined in a namespace named after the domain-id

domain0 {TopicA, TopicB, ...}

domain1 {Topic1, Topic2, ...}

☐ As a consequence topic definitions in one domain are not visible from a different domain

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If Topic are Types what are Variables?

☐ As described earlier a Topic definition can be thought as a Type definition in a programming language

☐ In a statically typed programming language (e.g. Java), given a type Foo I can define a variable of type Foo as:

Foo x;

x = new Foo(...); ☐ Then I can initialize it as:

☐ How are instance of a Topic defined?

☐ Then I can mutate it, by: x.setBar(...);

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DataWriter

☐ OpenSplice DDS uses DataWriters to declare and write instances of a Topic

☐ A DataWriter for our Square topic can be defined as follows:

// Create a Publisherauto pub = Publisher(dp);

// Create a DataWriterauto dw = DataWriter<ShapeType>(pub, topic);

// Write datadw.write(ShapeType(“RED”,11,77,99));

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Looking with the Spyglass

auto dw = DataWriter<ShapeType>(pub, topic);

Policies

color sizex y

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Policies

color sizex y

"RED" 9911 77

dw.write(ShapeType(“RED”,11,77,99));

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An Experiment...☐ What does the following code do?

// Create DataWritersauto dw1 = DataWriter<ShapeType>(pub, topic);auto dw2 = DataWriter<ShapeType>(pub, topic);

// Write Datadw1.write(ShapeType(“RED”,10,20,30));dw2.write(ShapeType(“BLUE”,30,20, 10));

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Policies

color sizex y

auto dw1 = DataWriter<ShapeType>(pub, topic);auto dw2 = DataWriter<ShapeType>(pub, topic);

Data Writers for the same topic are aliased to the same variable!

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dw1.write(ShapeType(“RED”,10,20,30));

Policies

color sizex y

RED 3010 20

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dw2.write(ShapeType(“BLUE”,30,20, 10));

Policies

color sizex y

RED 1030 20

Policies

color sizex y

BLUE 1030 20

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Topic Instances☐ Topic type can identify an arbitrary number of attributes as defining the

topic key

☐ In this case, each unique key value defines a topic instance

☐ Example:

struct ShapeType { @Key long color; long x; long y; long size;};

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Let’s Experiment...☐ What does the following code do?

// Create DataWritersauto dw = DataWriter<ShapeType>(pub, topic);

// Write Datadw.write(ShapeType(“RED”,10,20,30));dw.write(ShapeType(“BLUE”,30,20,10));

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Policies

color sizex y

RED 3010 20

dw.write(ShapeType(“RED”,10,20,30));

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Policies

color sizex y

RED 3010 20

BLUE 1030 20

dw.write(ShapeType(“BLUE”,30,20,10));

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LongVariable Example

struct LongVariable { @Key string name; long value; };

// Create a Publisherauto lvt = Topic<LongVariable>(dp, “TLongVariable”);

// Create DataWritersauto dw = DataWriter<LongVariable>(pub, topic);

// Write Datadw.write(LongVariable(“x”,10));dw.write(LongVariable(“y”,20));

☐ The “distributed” version of the following code:int x;int y;x = 10;y = 20;

☐ Becomes: Topic Type

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TLongValue

Policies

struct LongVariable { @Key string name; long value; };

valuename

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DataReader

☐ OpenSplice DDS uses DataReaders to read instances of a Topic

☐ A DataReader for our Square topic can be defined as follows:

// Create a Subscriberauto sub = Subscriber(dp);

// Create a DataWriterauto dr = DataReader<ShapeType>(sub, topic);

// Read dataauto data = dr.read();

☐ The read operation will read by default all instance values. Special selector operation are available to pick a specific instance. More later

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DataReader/DataWriter Matching

☐ DataWriters and DataReaders are automatically and dynamically matched by the DDS Dynamic Discovery

☐ A rich set of QoS Policies allows to control existential, temporal, and spatial properties of data and impacts DataReader/DataWriter matching DDS Global Data Space

TopicA

TopicBTopicC

TopicD

Data Writer

Data Reader

Publishers and Subscribers

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Publishers / Subscribers

Publishers and Subscribers in DDS play two roles:

☐ Coordinate actions across DataReaders/DataWriters☐ e.g. Atomic writes across data-writers

☐ Control access to DDS partitions, a mechanism provided by DDS to further

Domain (e.g. Domain 123)

Domain Participant

Publisher

DataWrter

Subscriber

DataReader

Partition (e.g. “Telemetry”, “Shapes”, )

Topic Instances/Samples

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DDS Partitions☐ DDS Publishers and

Subscribers can specify a list of regular expressions representing the partitions to join

☐ Actual partition can be any string, but some structure can be given in order mimic hierarchy

M:0:0 M:0:1 M:0:2

M:1:0 M:1:1 M:1:2

M:2:0 M:2:1 M:2:2

Domain

Partitions

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M:0:0 M:0:1 M:0:2

M:1:0 M:1:1 M:1:2

M:2:0 M:2:1 M:2:2

Domain

Partitions

DDS Partitions Matching

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M:0:0 M:0:1 M:0:2

M:1:0 M:1:1 M:1:2

M:2:0 M:2:1 M:2:2

Domain

Partitions

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M:0:0 M:0:1 M:0:2

M:1:0 M:1:1 M:1:2

M:2:0 M:2:1 M:2:2

Domain

Partitions

M:0:0, M:1:1, M:2:2

Anatomy of a DDS Application

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Domain

Reader/Writers for User Defined for Types

Session

// Create a DataWriter/DataWriterauto writer = DataWriter<ShapeType>(pub, topic);auto reader = DataReader<ShapeType>(sub, topic);

Reader/Writer for application defined

Topic Types

Domain Participant

Publisher

DataWriter

Topic Subscriber

DataReader

[DDS C++ API 2010]

auto dp = DomainParticipant(domainId);

// Create a Topicauto topic = Topic<ShapeType>(dp, “Circle”);// Create a Publisher / Subscriberauto pub = Publisher(dp);auto sub = Subscriber(dp);

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Domain

Reader/Writers for User Defined for Types

Session

// Write datawriter.write(ShapeType(“RED”, 131, 107, 89));// But you can also write like this...writer << ShapeType(“RED”, 131, 107, 89);

// Read new data (loaned)auto data = reader.read();

Reader/Writer for application defined

Topic Types

auto dp = DomainParticipant(domainId); Domain

Participant

Publisher

DataWriter

Topic Subscriber

DataReader

// Create a Topicauto topic = Topic<ShapeType>(dp, “Circle”);// Create a Publisher / Subscriberauto pub = Publisher(dp);auto sub = Subscriber(dp);

[DDS C++ API 2010]

Data Reader/Writer Caches

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Reader/Writer Caches

DataWriter Cache

DataWriter

DataReader Cache

DataReader

DataReader Cache

DataReader

Network

Application Application Application

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DataWriter Cache

DataWriter

DataReader Cache

DataReader

DataReader Cache

DataReader

Network

write( ) Application Application

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DataWriter Cache

DataWriter

DataReader Cache

DataReader

DataReader Cache

DataReader

Network

Topic Instance

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DataWriter Cache

DataWriter

DataReader Cache

DataReader

DataReader Cache

DataReader

Network

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DataWriter Cache

DataWriter

DataReader Cache

DataReader

DataReader Cache

DataReader

Network

Topic Instance Topic Instance

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DataWriter Cache

DataWriter

DataReader Cache

DataReader

DataReader Cache

DataReader

Network

Application Applicationwrite( )

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DataWriter Cache

DataWriter

DataReader Cache

DataReader

DataReader Cache

DataReader

Network

Writer History

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DataWriter Cache

DataWriter

DataReader Cache

DataReader

DataReader Cache

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Network

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DataWriter Cache

DataWriter

DataReader Cache

DataReader

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DataReader

Network

Reader History Reader History

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DataWriter Cache

DataWriter

DataReader Cache

DataReader

DataReader Cache

DataReader

Network

Application Applicationwrite( )

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DataWriter Cache

DataWriter

DataReader Cache

DataReader

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Network

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DataWriter Cache

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DataReader

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Network

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DataWriter Cache

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DataWriter Cache

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Network

‣History‣Destination Order‣ Presentation‣ Partition‣ Time Based Filter‣Ownership

QoS Policies

‣Reliability‣History‣ Latency Budget

QoS Policies‣Durability‣ Transport Priority‣ Time Based Filter

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Writer Cache Properties☐ An entry for each topic instance

☐ Each cache entry might contain one or more samples, the number of samples is controlled by the History QoS Policy

☐ Depending on the setting of the Durability QoS Policy, the content of the cache may be made available to late joiners. The depth of the cache controls how many samples per instance will be delivered to late joiners

DataWriter Cache

DataWriter

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Reader Cache Properties☐ An entry for each topic instance

☐ Each cache entry might contain one or more samples, the number of samples is controlled by the History QoS Policy☐ History.KeepLast(n) keeps the last n samples

per instance☐ History.KeepAll maintains all the samples,

modulo resource constraints

☐ For a given instance:☐ Samples from the same writer are inserted in

write-order☐ Samples from multiple-writers are ordered

using time-stamps (source or reception depending on configurable a QoS Policy)

DataReader Cache

DataReader

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Eventual Consistency☐ DDS caches provide eventual consistency semantics

☐ This means that a read will see the effect of a preceding write eventually

☐ Furthermore, given a data-writer that is currently matching N readers, we can think of DDS as providing eventual consistency with W=0 and R=1☐ W: the number of Acks expected in order to return from a write☐ R: the number of sources from which a read access data

Understanding History

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DataWriter Cache

DataWriter

DataReader Cache

DataReader

DataReader Cache

DataReader

Network

Assumptions: DataReader configured with history depth of 2 and DataWriter with default history depth of 1

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DataWriter Cache

DataWriter

DataReader Cache

DataReader

DataReader Cache

DataReader

Network

Application Application

write( )1

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DataWriter Cache

DataWriter

DataReader Cache

DataReader

DataReader Cache

DataReader

Network

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DataWriter Cache

DataWriter

DataReader Cache

DataReader

DataReader Cache

DataReader

Network

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DataWriter Cache

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DataReader Cache

DataReader

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DataWriter Cache

DataWriter

DataReader Cache

DataReader

DataReader Cache

DataReader

Network

write( )2

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DataWriter Cache

DataWriter

DataReader Cache

DataReader

DataReader Cache

DataReader

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Application

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DataWriter Cache

DataWriter

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DataWriter Cache

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2 1 12 2

write( )3

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3 1 12 2

Application

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DataWriter Cache

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3 1 12 2

Application

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DataWriter Cache

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DataReader

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3 2 23 3

Application

Caches or Streams?

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Dynamic View of a Stream

Stream: Set of samples written over time for a given topic instance.

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Eventual View of a Stream

‘Past’ Samples

Reader HistoryWriter History

Assumptions (Default Settings): Reader History = KeepLast (1) WriterHistory = KeepLast (1)

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‘Past’ Samples

Assumptions: Reader History = KeepLast (n) with n > 1WriterHistory = KeepLast (1)

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‘Past’ Samples

Assumptions: Reader History = KeepLast (n) with n > 1WriterHistory = KeepLast (m) with n > m > 1

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Assumptions: Reader History = KeepAllWriterHistory = KeepLast (m) with n > m > 1

Interacting with the DataReader Cache

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DataReader Cache

DataReader

Reading Data Samples☐ Samples can be read from the Data Reader History Cache

☐ The action of reading a sample is non-destructive. Samples are not removed from the cache

DataReader Cache

DataReader

... read

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Taking Data Samples☐ Samples can be taken from the Data Reader History Cache

☐ The action of taking a sample is destructive. Samples are removed from the cache

DataReader Cache

DataReader

DataReader Cache

DataReader

... take

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Read vs. Take

☐ The read operation should always be access the latest know value for topics that represent distributed state

☐ The take operation should be used to get the last notification from a topic that represent an event

Data Selectors

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Cherry Picking in DDS

☐ DDS provides some very flexible mechanisms for selecting the data to be read:☐ Data Content☐ Data Status

☐ These mechanisms are composable

Content-Based Data Selection

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Filters and Queries☐ DDS Filters allow to control what gets

into a DataReader cache

☐ DDS Queries allow to control what gets out of a DataReader cache

☐ Filters are defined by means of ContentFilteredTopics

☐ Queries operate in conjunction with read operations

☐ Filters and Queries are expressed as SQL where clauses

DataReader Cache

DataReader

...... ... ...

Filter

Application

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Filters/** * NOTE: The Scala API if not provided with DP/Sub/Pub assumes * default domains and default partition. **/// Create a Topicauto topic = Topic<ShapeType>(dp, “Circle”);

// Define filter expression and parametersauto filter = Filter(“x < 100 AND y < 200”);

// Define content filtered topicauto cftopic = ContentFilteredTopic<ShapeType>(“CFCircle”, topic, filter)

// Create a DataReader for the content-filtered Topicauto dr = DataReader<ShapeType>(sub,cftopic)

[C++ API]

struct ShapeType { @Key string color; long x; long y; long shapesize;};

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// Define filter expression and parametersauto dr = DataReader<ShapeType>(sub, topic) val query = Query(dr, “x < 100 AND y < 200”);

dr.select() .content(query) .read();

struct ShapeType { @Key string color; long x; long y; long shapesize;};[C++ API]

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Instances☐ DDS provides a very efficient way of reading data belonging to a

specific Topic Instance

☐ Obviously, one could use queries to match the key’s value, but this is not as efficient as the special purpose instance selector

auto handle = dr.lookup_instance(ShapeType(“RED”, 0, 0, 0));

auto data = dr.select() .instance(handle) .read();

State-Based Selection

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Sample, Instance, and View State☐ The samples included in the DataReader cache have associated

some meta-information which, among other things, describes the status of the sample and its associated stream/instance

☐ The Sample State (READ, NOT_READ) allows to distinguish between new samples and samples that have already been read

☐ The View State (NEW, NOT_NEW) allows to distinguish a new instance from an existing one

☐ The Intance State (ALIVE, NOT_ALIVE_DISPOSED, NOT_ALIVE_NO_WRITERS) allows to track the life-cycle transitions of the instance to which a sample belongs

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State Selector in Action

// Read only new samplesauto data = dr.read()

// Read any samples from live instancesauto data = dr.select() .state(DataState::any_data()) .read();

[C++ API]

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auto data = dr.select() .content(query) .state(data_state) .instance(handle) .read();

Putting all Together

☐ Selectors can be composed in a flexible and expressive manner

[C++ API]

Summing Up

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Concluding Remarks☐ OpenSplice DDS provides a very powerful abstraction for building

distributed applications grounded on the concept of a fully distributed global data space

☐ Topics are used to define the types that populate the global data space while DataWriters and DataReaders are used to produce and consume values

☐ DataReaders allow very flexible data access through cache access operations

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References

¥Fastest growing JVM Language¥Open Source¥www.scala-lang.org

¥ #1 OMG DDS Implementation¥ Open Source¥ www.opensplice.org

OpenSplice | DDS¥Scala API for OpenSplice DDS¥Open Source¥github.com/kydos/escalier

Escalier

¥Simple C++ API for DDS¥Open Source¥github.com/kydos/simd-cxx

¥DDS-PSM-Java for OpenSplice DDS¥Open Source¥github.com/kydos/simd-java

¥ DDS-based Advanced Distributed Algorithms Toolkit

¥Open Source¥github.com/kydos/dada

S ¥@prismtech

¥@acorsaro

¥youtube.com/opensplicetube ¥slideshare.net/angelo.corsaro

¥ crc@prismtech.com¥sales@prismtech.com

¥opensplice.com ¥forums.opensplice.org

¥opensplice.org ¥opensplicedds@prismtech.com

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