Getting Started with DDS in C++, Java and Scala

Getting Started with DDS
[In C++, Java and Scala]
OpenSplice DDS

Angelo CORSARO, Ph.D.
Chief Technology Ofﬁcer
OMG DDS Sig Co-Chair
PrismTech
angelo.corsaro@prismtech.com

General Information
This tutorial will get you started with DDS. At the end of this course

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☐
you should have a firm grip of DDS concepts and the capacity of
designing and writing DDS applications
OpenSplice DDS

☐ The tutorial will be highly interactive and provide plenty of examples
and live demonstrations

☐ The tutorial will cover the new C++ and Java API

☐ The tutorial will also introduce you into distributed functional
programming with Scala and DDS

Outline
☐ Background

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☐ DDS Basics

☐ Data Reader/Writer Caches
OpenSplice DDS

☐ DDS Quality of Service

☐ Data & State Selectors

☐ Advanced Topics in DDS

☐ Concluding Remarks

OpenSplice DDS

Background

Data Distribution Service
For Real-Time Systems

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☐ Introduced in 2004 to address
the Data Distribution Real-Timeliness Scale

challenges faced by a wide
class of Defense and
Aerospace Applications
OpenSplice DDS

Key requirement for the
Parallelism Determinism Throughput, Availability Scalability, Persistence, Security
☐
standard were to deliver very Systemic
Signal
Data Real-Time Information
Near Real-Time Fault-
Tolerant Information
Complex Information

high and predictable
Processing Processing Management
Processing Processing

performance while scaling
from embedded to ultra- Parallel Systems Distributed Systems

large-scale deployments


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☐ Recommended by key Real-Timeliness Scale

administration worldwide,
e.g. DoD, MoD, EUROCAE,
etc.
OpenSplice DDS

Widely adopted across
Parallelism Determinism Throughput, Availability Scalability, Persistence, Security
☐
several different domains, Systemic
Signal
Data Real-Time Information
Near Real-Time Fault-
Tolerant Information
Complex Information

e.g., Automated Trading,
Processing Processing Management
Processing Processing

Simulations, SCADA,
Telemetry, etc. Parallel Systems Distributed Systems

DDS Standard Ecosystem
Application Application

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2012 API 2012
DDS RMI DDS RMI
2012 2010 2010 2012
ANSI C ISO C++ Java-5 Scala
OpenSplice DDS

2004 2010 2010 201x
Security

Security
X-Types

X-Types
DDS 2004

Wire Protocol
DDSI-RTPS network DDSI-RTPS
2006 2006

Standards: What For?

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Common “Language”
Data Model +
OpenSplice DDS

QoS Requirements

Interoperability
DDSI Wire Interoperability

OpenSplice DDS

DDS
Application

API Standard
Standards: What For?

Portability

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Defense and Aerospace

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OpenSplice DDS

Integrated Modular Vetronics Training & Simulation Systems Naval Combat Systems

Air Trafﬁc Control & Management Unmanned Air Vehicles Aerospace Applications

Commercial Applications

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OpenSplice DDS

Agricultural Vehicle Systems Large Scale SCADA Systems Smart Cities

Train Control Systems Complex Medical Devices High Frequency Auto-Trading

OpenSplice DDS

D
D
BaS ics


DDS provides a Topic-Based Publish/

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Subscribe abstraction based on: Data
Reader
Data
Writer
☐ Topics: data distribution subject’s
OpenSplice DDS

Data
Reader
Data TopicD
Writer
DataWriters: data producers
TopicA
☐
Data
TopicB Reader
Data
Writer
☐ DataReaders: data consumers TopicC
...

Data Data
Writer Reader

DDS Global Data Space


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☐ DataWriters and DataReaders
are automatically and Data
Reader
dynamically matched by the Data
Writer

DDS Dynamic Discovery
OpenSplice DDS

Data
Reader
Data TopicD
Writer
TopicA
☐ A rich set of QoS allows to Data
Reader
TopicB
control existential, temporal,
Data
Writer
TopicC
...
and spatial properties of data Data Data
Writer Reader

DDS Global Data Space

DDS Topics
“Circle”, “Square”, “Triangle”, ...
☐ A Topic defines a class of streams

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☐ A Topic has associated a unique Name
name, a user defined extensible
type and a set of QoS policies Topic

Typ

S
OpenSplice DDS

DURABILITY,

Qo
☐ QoS Policies capture the Topic

e
DEADLINE,
ShapeType
non-functional invariants PRIORITY,
…
☐ Topics can be discovered or
locally defined struct ShapeType {
@Key
string color;
long x;
long y;
long shapesize;
};

Topic Instances
☐ Each unique key value

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Instances Instances
identifies a unique stream
of data color =”Green”

DDS not only
struct ShapeType {
☐ color =”red” @Key string color;
Topic
OpenSplice DDS

long x; long y;

demultiplexes “streams” color = “Blue”
long shapesize;};

but provides also lifecycle
information
☐ A DDS DataWriter can
write multiple instances

Anatomy of a DDS Application
Domain (e.g. Domain 123)

Domain

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Participant

Topic
Partition (e.g. “Telemetry”, “Shapes”, )
OpenSplice DDS

Publisher

Subscriber
Topic Instances/Samples

DataWrter DataReader

[Scala API]

Domain
Domain

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val dp = DomainParticipant(domainId)
Participant

Publisher
Topic
Subscriber
OpenSplice DDS

DataWriter
DataReader

[Scala API]

Domain
Domain

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Participant

Session
// Create a Topic
Publisher
Topic
Subscriber
OpenSplice DDS

val topic = Topic[ShapeType](dp, “Circle”)
// Create a Publisher / Subscriber
val pub = Publisher(dp)
val sub = Subscriber(dp)

DataWriter
DataReader

[Scala API]

Domain
Domain

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Participant

Session
// Create a Topic
Publisher
Topic
Subscriber
OpenSplice DDS


Reader/Writers for User Deﬁned for Types DataWriter
DataReader
// Create a DataWriter/DataWriter
val writer = DataWriter[ShapeType](pub, topic) Reader/Writer for
val reader = DataReader[ShapeType](sub, topic) application deﬁned
Topic Types

[Scala API]

Domain
Domain

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Participant

Session
// Create a Topic
Publisher
Topic
Subscriber
OpenSplice DDS


DataReader
// Write data
val data = new ShapeType(“RED”, 131, 107, 75)
writer write data
Reader/Writer for
// But you can also write like this... application deﬁned
writer ! data Topic Types
// Read new data and print it on the screen
(reader read) foreach (prinln)

[DDS C++ API 2010]

Domain
Domain

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auto dp = DomainParticipant(domainId);
Participant

Session
// Create a Topic
Publisher
Topic
Subscriber
OpenSplice DDS

auto topic = Topic<ShapeType>(dp, “Circle”)
auto pub = Publisher(dp)
auto sub = Subscriber(dp)

DataReader
auto writer = DataWriter<ShapeType>(pub, topic); Reader/Writer for
auto reader = DataReader<ShapeType>(sub, topic);
application deﬁned
Topic Types

[DDS C++ API 2010]

Domain
Domain

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Participant

Session
// Create a Topic
Publisher
Topic
Subscriber
OpenSplice DDS

auto topic = Topic<ShapeType>(dp, “Circle”)
auto pub = Publisher(dp)
auto sub = Subscriber(dp)

DataReader
// Write data
writer.write(ShapeType(“RED”, 131, 107, 89)); Reader/Writer for
// But you can also write like this...
writer << ShapeType(“RED”, 131, 107, 89);
Topic Types
// Read new data (loaned)
auto data = reader.read();

[DDS Java 5 API]

Domain
Domain

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Participant

Session
// Create a Topic
Publisher
Topic
Subscriber
OpenSplice DDS

val topic = Topic<ShapeType>(dp, “Circle”)

DataReader
auto writer = DataWriter<ShapeType>(pub, topic); Reader/Writer for
auto reader = DataReader<ShapeType>(sub, topic);
Topic Types

[DDS Java 5 API]

Domain
Domain

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Do dp = DomainParticipant(domainId);
Participant

Session
// Create a Topic
Publisher
Topic
Subscriber
OpenSplice DDS

val topic = Topic<ShapeType>(dp, “Circle”)

DataReader
// Write data
writer.write(ShapeType(“RED”, 131, 107, 89)); Reader/Writer for
// But you can also write like this...
writer << ShapeType(“RED”, 131, 107, 89);
Topic Types
// Read new data (loaned)
auto data = reader.read();

Data Reader/Writer Caches
OpenSplice DDS

DDS Model
Topic Instances

DataWriter DataReader DataReader

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

...

...
...

...

...

...

...

...
OpenSplice DDS

DataWriter Cache DataReader Cache DataReader Cache

Writer History Reader History

network

DDS Model
DataWriter DataReader DataReader

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

...

...
...

...

...

...

...

...
OpenSplice DDS

DataWriter Cache DataReader Cache DataReader Cache

QoS Policies
‣ History
‣ Destination Order
‣ Presentation network
‣ Partition
‣ Time Based Filter QoS Policies
‣ Ownership ‣ Reliability ‣ Durability
‣ History ‣ Transport Priority
‣ Latency Budget ‣ Time Based Filter

Dynamic View of a Stream

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OpenSplice DDS

...

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

Assumptions:
Reader History = KeepLast (n)
WriterHistory = KeepLast (m)

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Writer History
OpenSplice DDS

...


Assumptions:

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Samples ‘on
Writer History the wire’
OpenSplice DDS

...


Assumptions:

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Samples ‘on
Writer History the wire’ Reader History
OpenSplice DDS

...


Assumptions:

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Samples ‘on
Writer History the wire’ Reader History
‘Past’ Samples
OpenSplice DDS

...


Eventual View of a Stream
Assumptions (Default Settings):
Reader History = KeepLast (1)
WriterHistory = KeepLast (1)

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Writer History
Reader History ‘Past’ Samples
OpenSplice DDS


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

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‘Past’ Samples
OpenSplice DDS


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

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‘Past’ Samples
OpenSplice DDS


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

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

DataReader DataReader

... read ...

DataReader Cache DataReader Cache

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

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

DataReader DataReader

... ...
take
DataReader Cache DataReader Cache

Read vs. Take

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

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

Eventual Consistency
DDS caches provide eventual consistency semantics

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☐

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

☐ 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

QoS Model
☐ QoS-Policies control local and
end-to-end properties of DDS

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Type Matching

entities
QoS matching

QoS QoS QoS QoS QoS QoS QoS

☐ Local properties controlled by Topic
Name

QoS are related resource usage
Publisher Subscriber

... DataWriter writes Type reads DataReader
...
OpenSplice DDS

...
☐ End-to-end properties DomainParticipant DataWriter writes Type reads DataReader DomainParticipant

controlled by QoS are related Topic
Name

to temporal and spatial aspects QoS QoS QoS

of data distribution
☐ Some QoS-Policies are matched
based on a Request vs. Offered
Model thus QoS-enforcement

QoS Policies
[T: Topic] [DR: DataReader] [DW: DataWriter] [P: Publisher] [S: Subscriber] [DP: Domain Participant]

QoS Policy Applicability RxO Modiﬁable

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USER_DATA DP, DR, DW N Y
TOPIC_DATA T N Y Conﬁguration
GROUP_DATA P, S N Y
DURABILITY T, DR, DW Y N
OpenSplice DDS

DURABILITY T, DW N N
SERVICE Data Availability
HISTORY T, DR, DW N N
PRESENTATION P, S Y N
RELIABILITY T, DR, DW Y N
PARTITION P, S N Y
Data Delivery
DESTINATION T, DR, DW Y N
ORDER
LIFESPAN T, DW N Y

QoS Policies
[T: Topic] [DR: DataReader] [DW: DataWriter] [P: Publisher] [S: Subscriber] [DP: Domain Participant]


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DEADLINE T, DR, DW Y Y
LATENCY T, DR, DW Y Y
BUDGET Temporal/
TRANSPORT T, DW N Y Importance
PRIORITY Characteristics
OpenSplice DDS

TIME BASED DR N Y
FILTER
OWNERSHIP T, DR, DW Y N
OWNERSHIP DW N Y Replication
STRENGTH
LIVELINESS T, DR, DW Y N Fault-Detection

OpenSplice DDS

Partition
Data Delivery
Reliability

Presentation
Data Delivery
Order
Destination

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Reliability QoS Policy
RELIABILITY T, DR, DW Y N

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The Reliability Policy controls the level of guarantee offered by
the DDS in delivering data to subscribers

☐ Reliable. In steady-state, and no data writer crashes, the
OpenSplice DDS

middleware guarantees that all samples in the DataWriter
history will eventually be delivered to all the DataReader

☐ Best Effort. Indicates that it is acceptable to not retry
propagation of any samples

Partition QoS Policy
☐ The Partition QoS Policy can PARTITION P, S N Y

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be used as subjects for
organizing the flows of data
☐ The Partition QoS Policy is Subscriber
used to connect Publishers/ Publisher "tracks.kfo" "tracks.ufo"
OpenSplice DDS

Subscribers to a Partitions’
List which might also
contain wildcards, e.g. Subscriber
Publisher
tracks.*
☐ Topics instances are
published and subscribed Publisher Subscriber

across one or more Partition
Partitions

Data Availability
History

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Data
OpenSplice DDS

Lifespan Durability
Availability

Ownership
Ownership
Strength

Durability QoS Policy
DURABILITY T, DR, DW Y N

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The DURABILITY QoS controls the data availability w.r.t. late joiners,
specifically the DDS provides the following variants:

☐ Volatile. No need to keep data instances for late joining data readers
OpenSplice DDS

☐ Transient Local. Data instance availability for late joining data reader is
tied to the data writer availability

☐ Transient. Data instance availability outlives the data writer

☐ Persistent. Data instance availability outlives system restarts

History QoS Policy
HISTORY T, DR, DW N N

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For DataWriters, the HISTORY QoS policy controls the amount of data that
can be made available to late joining DataReaders under
TRANSIENT_LOCAL Durability
OpenSplice DDS

For DataReader, the HISTORY QoS policy controls how many samples will
be kept on the reader cache

☐ Keep Last. DDS will keep the most recent “depth” samples of each
instance of data identified by its key

☐ Keep All. The DDS keep all the samples of each instance of data
identified by its key -- up to reaching some configurable resource limits

Ownership QoS Policy
OWNERSHIP T, DR, DW Y N
STRENGTH DW N Y

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Availability of data producers can be controlled via two QoS Policies
☐ OWNERSHIP (SHARED vs. EXCLUSIVE)
OpenSplice DDS

☐ OWNERSHIP STRENGTH
☐ Instances of exclusively owned Topics can be modified (are
owned) by the higher strength writer
☐ Writer strength is used to coordinate replicated writers

Temporal Properties

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TimeBasedFilter Deadline

[Inbound]
OpenSplice DDS

Throughput LatencyBudget Latency
[Outbound]

TransportPriority

Latency Budget QoS Policy
LATENCY T, DR, DW Y Y
☐ The LATENCY_BUDGET QoS BUDGET

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policy specifies the
maximum acceptable
delay from the time the
data is written until the data
OpenSplice DDS

is inserted in the receiver's
application-cache Latency = T1+T2+T3

DataWriter T3 DataReader
☐ A non-zero latency-budget T1

allows a DDS Batching

implementation to batch T2

samples and improve CPU/
Network utilization

Deadline QoS Policy
DEADLINE T, DR, DW Y Y

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☐ The DEADLINE QoS policy allows to define the maximum inter-arrival
time between data samples
☐ DataWriter indicates that the application commits to write a new
OpenSplice DDS

value at least once every deadline period
☐ DataReaders are notified by the DDS when the DEADLINE QoS
contract is violated

DataWriter Deadline Deadline Deadline Deadline Deadline DataReader

Deadline Violation

Transport Priority QoS Policy
TRANSPORT T, DW N Y
PRIORITY

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☐ The TRANSPORT_PRIORITY QoS policy is a hint to the infrastructure as
OpenSplice DDS

to how to set the priority of the underlying transport used to send
the data.

Time-Based Filter QoS Policy
TIME BASED DR N Y
FILTER

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mit

mit
☐ The Time Based Filter allows to
control the throughput at which
Latency = T1+T2+T3
data is received by a data reader
OpenSplice DDS

DataWriter T3 DataReader
☐ Samples produced more often
T2 than the minimum inter-arrival
time are not delivered to the data
reader

mit mit
mit = minimum inter-arrival time

produced sample delivered sample discarded sample

Setting QoS Policies

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// Setting Partition QoS-Policy on Publisher
C++
qos::PublisherQos pubQos;
pubQos << policy::Partition("Partition");
Publisher pub(dp, pubQoS);
OpenSplice DDS

// Setting various QoS-Policy on a Topic
qos::TopicQos tqos;
tqos << policy::Reliability::Reliable()
<< policy::Durability::Transient()
<< policy::History::KeepLast(5);

Topic<VehicleDynamics> topic(dp,"Partition", tqos);

Read Styles

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The new API supports two read styles

☐ User-Provided Buffers read
OpenSplice DDS

☐ Loaned Buffers read

User-Provided Buffers Read

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! // --- Forward Iterators: --- //
!! template <typename SamplesFWIterator, typename InfoFWIterator>
!! uint32_t
!! read(SamplesFWIterator sfit,
!! ! ! InfoFWIterator ifit,
!! ! ! size_t max_samples);
OpenSplice DDS

!! // --- Back-Inserting Iterators: --- //
!! template <typename SamplesBIIterator, typename InfoBIIterator>
!! uint32_t
!! read(SamplesBIIterator sbit,
!! ! ! InfoBIIterator ibit);

Example

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uint32_t max_size = 10;
std::vector<ShapeType> data(max_size);
std::vector<DDS::SampleInfo> info(max_size);

uint32_t len =
dr.read(data.begin(), info.begin(), max_size);
OpenSplice DDS

for (uint32_t i = 0; i < len; ++i)
! std::cout << data[i] << std::endl;

Loaned Buffers read
template <typename T,
template <typename Q> class DELEGATE>
class dds::sub::LoanedSamples :

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public dds::core::Value< DELEGATE<T> >
{
public:
typedef T DataType;
typedef Sample<DataType> SampleType;
dds::sub::LoanedSamples<T> read(); public:
OpenSplice DDS

/* Snipped... */
};

public:
const Iterator begin() const;

const Iterator end() const;

public:
// explicitly return loan
void return_loan();

};

Cherry Picking in DDS

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☐ DDS provides some very flexible mechanisms for selecting the data
to be read:
OpenSplice DDS

☐ Data Content
☐ Data Status

☐ These mechanisms are composable

Content-Based Data Selection
OpenSplice DDS

Filters and Queries Application

☐ DDS Filters allow to control what gets
into a DataReader cache

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

☐ Filters are defined by means of
...
ContentFilteredTopics

...
...

...
☐ Queries operate in conjunction with DataReader Cache
read operations
Filter
☐ Filters and Queries are expressed as SQL
where clauses

struct ShapeType {

Filters
@Key
string color;
long x;
long y;
[Scala API] };
long shapesize;

/**

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* NOTE: The Scala API if not provided with DP/Sub/Pub assumes
* default domains and default partition.
**/
// Create a Topic
val topic = Topic[ShapeType](“Circle”)

// Define filter expression and parameters
OpenSplice DDS

val query = Query(“x < %0 AND y < %1”, List(“200”, “300”))

// Define content filtered topic
val cftopic =
ContentFilteredTopic[ShapeType](“Circle”, topic, query)

// Create a DataReader for the content-filtered Topic
val reader =
DataReader[ShapeType](cftopic)

struct ShapeType {

QueryAPI 2010]
@Key
string color;
long x;
long y;

[DDS C++ };
long shapesize;

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// Define the query and the parameters

std::vector<std::string> p;
p.push_back("100");
p.push_back("100");
OpenSplice DDS

dds::core::Query q("x < %0 AND y < %1", p.begin(), p.end());

auto data = reader
.selector()
.filter_content(q)
.read();

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

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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
OpenSplice DDS

// C++
auto data = reader
.selector()
.instance(handle)
.read();

// Scala
val data = reader read(handle)

State-Based Selection
OpenSplice DDS

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

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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
OpenSplice DDS

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

State Selector in Action

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// Read only new samples
auto data = reader
C++ Scala
.selector() // Read only new samples
.filter_state(status::DataState::new_data()) val data = reader read
.read()
// Read any samples from live instances
OpenSplice DDS

// Read any samples from live instances val data = reader read(SampleSelector.AnyData)
auto data = reader
.selector()
.filter_state(status::DataState::any_data())
.read()

Putting all Together

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☐ Selectors can be composed in a flexible and expressive manner
OpenSplice DDS

auto data = reader C++
! .selector()
.instance(handle)
! ! .filter_state(status::DataState::new_data())
! ! .filter_content(q)
.read();

OpenSplice DDS

Communication Statuses

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OpenSplice DDS

Liveliness Changed Status

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Let’s Experiment!
OpenSplice DDS

Advanced Topics
OpenSplice DDS

Advanced Topics

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☐ Depending on time and attendees interest, I’ll be covering a set of
advanced topics such as:
OpenSplice DDS

☐ Distributed State and Events
☐ Advanced Distributed Algorithms with DDS, such as Leader Election,
Mutual Exclusion, etc.

Distributed Events
vs.
OpenSplice DDS

Distributed State

OpenSplice DDS

Foundations

Deﬁning a System
Cyber/Physical.

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World.
☐ A set of interacting or System.
OpenSplice DDS

interdependent parts Input& !  State& Output&
forming an integrated !  Transi"ons&

whole

s"mulus&
(events/commands)&&

Deﬁning a Distributed System
Cyber/Physical.

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World.

Distributed.
☐ A Distributed System is a System.
System whose parts can Input&
!  State&
Output&
OpenSplice DDS

!  Transi"ons&
only interact by
communicating over a
network s"mulus&
(events/commands)&&

State in a Distributed System
☐ The State of a distributed system Cyber/Physical.

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is the collection of the states of its
parts plus the stimulus currently
World.
propagating through the system
System.
OpenSplice DDS

☐ As Distributed Systems don’t Input& !  State& Output&
share memory, one problem to !  Transi"ons&
address is how to access arbitrary
subsets of its state (or of its parts)
☐ The other problem is the s"mulus&
consistency of this state... (events/commands)&&

Stimulus in a Distributed System
Cyber/Physical.

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☐ Internal and Environmental
World.
Stimuli in a distributed
system are used to:
evolve the system state
System.
OpenSplice DDS

☐
Input& !  State& Output&
(commands, i.e. do !  Transi"ons&
something)
☐ notify particular condition
on the state (events, i.e.
something happened) s"mulus&
(events/commands)&&

State vs Stimulus
Temp State%

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☐ The state of a system is always
defined to have a value
time
OpenSplice DDS

☐ A Stimulus only exists at a OverheatAlarm S&mulus%
particular point in time

time

State and Events in DDS
OpenSplice DDS

OpenSplice DDS

State in DDS

Distributed State with DDS

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☐ The “public” state of the elements making the distributed system
can easily be captured via topic definitions

☐ Representing state with topics is more a matter of discipline w.r.t. to
OpenSplice DDS

the QoS being used and the way in which it is accessed

State’s DDS QoS
Topics representing state should have the following QoS Settings

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☐ RELIABILITY = RELIABLE
☐ HISTORY = KEEP_LAST(1)
OpenSplice DDS

☐ DURABILITY = (TRANSIENT |PERSISTENT)
☐ OWNERSHIP = EXCLUSIVE
☐ DESTINATION_ORDER = SOURCE_TIMESTAMP

Soft-State’s DDS QoS
Topics representing soft-state, meaning state that is periodically

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updated, should have the following QoS Settings

☐ RELIABILITY = BEST_EFFORT
OpenSplice DDS

☐ HISTORY = KEEP_LAST(1)

☐ DURABILITY = VOLATILE
☐ OWNERSHIP = EXCLUSIVE


Accessing State in DDS
☐ The DataReader.read operation should be used to access topics

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representing state
☐ This ensures that the last value for the state will be kept in DDS and will
be readable again and again
OpenSplice DDS

☐ The DataReader data should be accessed with the following flags:
☐ ANY_SAMPLE_STATE
☐ ALIVE_INSTANCE_STATE
☐ ANY_VIEW_STATE

Example [1/3]

A Robot Position in 2D is an example of state

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☐

☐ Let’s assume that the Robot only update position when it moves

☐ Topic Type:
OpenSplice DDS

struct RobotPosition {
@key
long rid;
long x;
long y;
};

Example [2/3]

☐ The Topic and DataReader would be constructed as follows

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// Create Topic Qos
val tQos =
OpenSplice DDS

TopicQos() <= KeepLastHistory(1)
<= Reliable()
<= TransientDurability()
<= ExclusiveOwnership()
<= SourceTimestamp();
// Create Topic
val rpt = Topic[RobotPosition](“RobotPosition”,topicQos)

// Create DataReader
val rpdr = DataReader[RobotPosition](rpt, DataReaderQos(tqos))

Example [3/3]

☐ Data can be read as follows

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// Read data
val data = rpdr.read(ReadState.AllData)
OpenSplice DDS

// Or specific to Escalier
val data = rpdr.history

OpenSplice DDS

Events in DDS

Distributed Events with DDS
☐ Events raised by a distributed system can be easily captured via

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topic definitions

☐ Representing events with topics is more a matter of discipline w.r.t.
to the QoS being used and the way in which it is accessed
OpenSplice DDS

☐ Event topics are often keyless

Events’ DDS QoS
Events should have the following QoS Settings

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☐ RELIABILITY = RELIABLE

☐ HISTORY = KEEP_ALL
OpenSplice DDS

☐ DURABILITY = VOLATILE

☐ OWNERSHIP = SHARED


Accessing Events in DDS

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☐ The DataReader.take operation should be used to access topics
representing events
☐ This ensures that the DDS cache is always freed by available events
OpenSplice DDS

☐ The DataReader data should be accessed with the following flags:
☐ NEW_SAMPLE_STATE
☐ ALIVE_INSTANCE_STATE
☐ ANY_VIEW_STATE

Example [1/3]

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☐ A CollisionEvent could be raised by a Robot when it is colliding (or
about to collide) with something

☐ Topic Type:
OpenSplice DDS

struct CollisionEvent {
long detectingRobotId;
long collidingRobotId;
long xe;
long ye;
};

Example [2/3]

☐ The Topic and DataReader would be constructed as follows

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// Create Topic Qos
val tQos =
TopicQos() <= KeepAll()
OpenSplice DDS

<= Reliable()
<= VolatileDurability()
<= SharedOwnership()
<= SourceTimestamp();
// Create Topic
val cet = Topic[CollisionEvent](“CollisionEvent”,topicQos)

// Create DataReader
val cedr = DataReader[CollisionEvent](cet, DataReaderQos(tqos))

OpenSplice DDS

☐
Example

// Take data
[3/3]

val data = cedr.take()
Data can be read as follows

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Distributed Mutex
OpenSplice DDS

Lamport’s Distributed Mutex
☐ A relatively simple Distributed Mutex Algorithm was proposed by Leslie

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Lamport as an example application of Lamport’s Logical Clocks

☐ The basic protocol (with Agrawala optimization) works as follows
(sketched):
OpenSplice DDS

☐ When a process needs to enter a critical section sends a MUTEX request by
tagging it with its current logical clock
☐ The process obtains the Mutex only when he has received ACKs from all the
other process in the group
☐ When process receives a Mutex requests he sends an ACK only if he has not
an outstanding Mutex request timestamped with a smaller logical clock

Mutex Abstraction
☐ A base class defines the

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Mutex Protocol abstract class Mutex {
def acquire()
☐ The Mutex companion def release()
uses dependency injection }
OpenSplice DDS

to decide which concrete
mutex implementation to
use

Foundation Abstractions

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☐ The mutual exclusion algorithm requires essentially:
☐ FIFO communication channels between group members
☐ Logical Clocks
OpenSplice DDS

☐ MutexRequest and MutexAck Messages

These needs, have now to be translated in terms of topic types,
topics, readers/writers and QoS Settings

Topic Types

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☐ For implementing the Mutual Exclusion Algorithm it is sufficient to
define the following topic types:
OpenSplice DDS

struct TLogicalClock {
long ts;
long mid;
};
#pragma keylist LogicalClock mid

struct TAck {
long amid; // acknowledged member-id
LogicalClock ts;
};
#pragma keylist TAck ts.mid

Topics
We need essentially two topics:

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☐ One topic for representing the Mutex Requests, and
☐ Another topic for representing Acks
OpenSplice DDS

This leads us to:
☐ Topic(name = MutexRequest, type = TLogicalClock,
QoS = {Reliability.Reliable, History.KeepAll})
☐ Topic(name = MutexAck, type = TAck,
QoS = {Reliability.Reliable, History.KeepAll})

Distinguishing Groups
☐ To distinguish between members belonging to different groups we

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introduce a group-id gid that is used to uniquely identify a group

☐ At a DDS-level, the gid is used to name the partition in which all the
group related traffic will take place
OpenSplice DDS

Partition associated to
the group with gid=2
“2”
“1”
“3” DDS Domain

Show me the Code!

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☐ All the algorithms presented were implemented using DDS and
Scala
OpenSplice DDS

☐ Specifically we’ve used the OpenSplice Escalier language
mapping for Scala

☐ The resulting library has been baptized “dada” (DDS Advanced
Distributed Algorithms) and is available under LGPL-v3

LCMutex
☐ The LCMutex is one of the possible Mutex protocol, implementing
the Agrawala variation of the classical Lamport’s Algorithm

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class LCMutex(val mid: Int, val gid: Int, val n: Int)(implicit val logger: Logger) extends Mutex {

private var group = Group(gid)
private var ts = LogicalClock(0, mid)
OpenSplice DDS

private var receivedAcks = new AtomicLong(0)

private var pendingRequests = new SynchronizedPriorityQueue[LogicalClock]()
private var myRequest = LogicalClock.Infinite

private val reqDW =
DataWriter[TLogicalClock](LCMutex.groupPublisher(gid), LCMutex.mutexRequestTopic, LCMutex.dwQos)
private val reqDR =
DataReader[TLogicalClock](LCMutex.groupSubscriber(gid), LCMutex.mutexRequestTopic, LCMutex.drQos)

private val ackDW =
DataWriter[TAck](LCMutex.groupPublisher(gid), LCMutex.mutexAckTopic, LCMutex.dwQos)
private val ackDR =
DataReader[TAck](LCMutex.groupSubscriber(gid), LCMutex.mutexAckTopic, LCMutex.drQos)

private val ackSemaphore = new Semaphore(0)

LCMutex.acquire

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def acquire() {
ts = ts.inc()
myRequest = ts Notice that as the LCMutex
reqDW ! myRequest is single-threaded we can’t
ackSemaphore.acquire() issue concurrent acquire.
OpenSplice DDS

}

LCMutex.release

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def release() {
myRequest = LogicalClock.Infinite Notice that as the LCMutex
(pendingRequests dequeueAll) foreach { req => is single-threaded we can’t
ts = ts inc()
issue a new request before
ackDW ! new TAck(req.id, ts)
} we release.
OpenSplice DDS

LCMutex.onACK
ackDR.reactions += {
case DataAvailable(dr) => {

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// Count only the ACK for us
val acks = ((ackDR take) filter (_.amid == mid))
val k = acks.length
// Set the local clock to the max (tsi, tsj) + 1
synchronized {
val maxTs = math.max(ts.ts, (acks map (_.ts.ts)).max) + 1
ts = LogicalClock(maxTs, ts.id)
OpenSplice DDS

}
val ra = receivedAcks.addAndGet(k)
val groupSize = group.size
// If received sufficient many ACKs we can enter our Mutex!
if (ra == groupSize - 1) {
receivedAcks.set(0)
ackSemaphore.release()
}
}
}

LCMutex.onReq
reqDR.reactions += {
case DataAvailable(dr) => {
val requests = (reqDR take) filter (_.mid != mid)

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synchronized {
val maxTs = math.max((requests map (_.ts)).max, ts.ts) + 1
ts = LogicalClock(maxTs, ts.id)
}
requests foreach (r => {
if (r < myRequest) {
ts = ts inc()
OpenSplice DDS

val ack = new TAck(r.mid, ts)
ackDW ! ack
}
else {
(pendingRequests find (_ == r)).getOrElse( {
pendingRequests.enqueue(r)
r
})

}
})
}
}

Dealing with Faults...
OpenSplice DDS

How to deal with Faults?
☐ The algorithm presented here intentionally ignores failures to keep
the presentation simple

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☐ The failure of a single group member would violate progress
☐ It is not hard to extend the algorithm to deal with failures,
OpenSplice DDS

especially under the assumption of eventual synchrony
☐ If you want to learn more attend the following RTWS-2012
presentation this Thursday:

Classical Distributed Algorithms with DDS
Sara Tucci-Piergiovanni, Research Engineer, CEA LIST
Angelo Corsaro, Chief Technology Officer, PrismTech

Concluding Remarks
OpenSplice DDS

Concluding Remarks
☐ The DDS provides a powerful and feature-
rich topic-based publish/subscribe

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abstraction
☐ This technology is widely used in mission
and business critical systems and it being
OpenSplice DDS

swiftly adopted in data-centric/big-data
systems
☐ Differently from what some people think,
DDS is very simple to get-started with
☐ Very good Open Source implementation
are available... Good Hacking!

References ¥ DDS-based Advanced Distributed
Algorithms Toolkit
¥ Open Source
¥ github.com/kydos/dada

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OpenSplice | DDS Escalier
¥ #1 OMG DDS Implementation ¥ Fastest growing JVM Language ¥ Scala API for OpenSplice DDS
¥ Open Source ¥ Open Source ¥ Open Source
¥ www.opensplice.org ¥ www.scala-lang.org ¥ github.com/kydos/escalier
OpenSplice DDS

[C++] [Java] DDS-PSM-Cxx 2010
¥ Simple C++ API for DDS ¥ DDS-PSM-Java for OpenSplice DDS ¥ DDS-PSM-Cxx API Standard
¥ Open Source ¥ Open Source ¥ Open Source
¥ code.google.com/p/simd-cxx ¥ github.com/kydos/simd-java ¥ github.com/kydos/dds-psm-cxx

:: Connect with Us ::

¥opensplice.com ¥forums.opensplice.org
¥@acorsaro
¥opensplice.org ¥opensplicedds@prismtech.com ¥@prismtech
OpenSplice DDS

¥ crc@prismtech.com
¥sales@prismtech.com
¥youtube.com/opensplicetube ¥slideshare.net/angelo.corsaro

OpenSplice DDS

Appendix

Stepping into Scala
OpenSplice DDS

Angelo CORSARO, Ph.D.
Chief Technology Ofﬁcer
OMG DDS Sig Co-Chair
PrismTech
angelo.corsaro@prismtech.com

What is Scala

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☐ Scala (pronounced Skah-lah) stands for “Scalable language”

☐ It is a language that carefully and creatively blends Object
Oriented and Functional constructs into a statically typed
OpenSplice DDS

language with sophisticated type inference

☐ Scala targets the JVM and .NET runtime and is 100% compatible
with Java

Why Should You Care?
Scala is simple to write, extremely compact and easy to

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☐
understand

☐ Scala is strongly typed with a structural type system
OpenSplice DDS

☐ Scala is an extensible language (many construct are build in the
standard library)

☐ Scala makes it easy to design Domain Specific Language

Case Study: Complex Numbers
OpenSplice DDS

Complex Numbers

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☐ To explore some of the nice features of Scala, let’s see how we
might design a Complex number class

☐ What we expect to be able to do is all mathematical operations
OpenSplice DDS

between complex numbers as well as scalar multiplications and
division
☐ [(1+i2)+2*(3-i5)*(i4)]/(1+i3)
☐ ~(1+i2) [conjugate]
☐ !(3+i4) [Modulo]

Constructor
☐ Scala allows to implicitly create constructors and attributes starting from

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a simple argument list associated with the class declaration
OpenSplice DDS

class Complex(val re: Float, val im: Float)

In Java
public class Complex {

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! private final float re;
! private final float im;

! public Complex(float re, float im) {
! ! this.re = re;
OpenSplice DDS

! ! this.im = im;
! }
! public Complex(float f) {
! ! this.re = f;
! ! this.im = 0;
! }

! public float re() { return re;}

! public float im() { return im;}

Methods
☐ Everything in Scala is a method even operators

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☐ Methods name can be symbols such as *, !, +, etc.
OpenSplice DDS

def + (c: Complex) : Complex = Complex(re + c.re, im + c.im)

or, taking advantage of type inference....
def + (c: Complex) = Complex(re + c.re, im + c.im)

As a result...
val result = Complex(1,2) + Complex(2,3)

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OpenSplice DDS

Complex c1 = new Complex(1, 2);
Complex c2 = new Complex (3,4);
Complex c3 = c1.add(c2);
or...
Complex c3 = (new Complex(1, 2).add(new Complex (3,4));

Companion Object
☐ Scala does not have the concept of static methods/attributes
☐ On the other hand it provides built-in support for Singletons,

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which are specified with the “object” keyword as opposed to
the “class” keyword
OpenSplice DDS

object Complex {
The companion object, is def apply(real: Float, img: Float) = new Complex(real, img)

the object associated with def apply(real: Float) = new Complex(real, 0)
a class, which shares the
same name and provides implicit def floatToReComplex (f: Float) = new ReComplex(f)

typically helper methods implicit def intToReComplex(i : Int) = new ReComplex(i)
}

“apply” Magic
☐ When an instance of a class is followed by parentheses with a list of zero

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or more parameters, the compiler invokes the apply method for that
instance
☐ This is true for an object with a defined apply method (such as a
companion object), as well as an instance of a class that defines an
OpenSplice DDS

apply method

val result = Complex(1,2)
is the same as....
val result = Complex.apply(1,2)

Negation and Scalar Multiplication

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☐ In order to design a Complex class that is well integrated in our type
system we should be able to support the following cases:
☐ -(a+ib)
☐ c*(a+ib)
OpenSplice DDS

☐ (a+ib)*c
☐ How can we supporting something like -(a+ib) and c*(a+ib)?

Scala Unary Operators
☐ Scala allows to define unary operators for the following method

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identifiers +, -, !, ~

def unary_-() = Complex(-re, -im)
OpenSplice DDS

!
def unary_!() = Math.sqrt(re*re + im*im)
!
def unary_~() = Complex(re, -im)
as a result we can write:
val result = -Complex(1,2) + ~Complex(2,3)

Scala Implicit Conversions

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☐ The expression: val c3 = 3*Complex(5, 7)

☐ Is equivalent to:
OpenSplice DDS

val c3 = 3.*(Complex(5, 7))
☐ Yet, the method to multiply a Integer to a Complex is not present in the
Scala Int class
☐ What can we do to make the trick?

Scala Implicit Conversions

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☐ Scala does not support Open Classes, thus allowing to add new
methods to existing classes
☐ Yet Scala supports implicit conversions that can be used to achieve the
same result
OpenSplice DDS

☐ Lets see how...

Scala Implicit Conversion
object Complex {

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implicit def floatToReComplex (f: Float) = new ReComplex(f)

implicit def intToReComplex(i : Int) = new ReComplex(i)
}
OpenSplice DDS

class ReComplex(re: Float) {

def * (that: Complex) = Complex(re*that.re,re*that.im)

}

Putting it all together
case class Complex(val re: Float, val im: Float) { // Unary Operators

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def unary_-() = Complex(-re, -im)
// Binary Operators def unary_!() = Math.sqrt(re*re + im*im)
def + (c: Complex) = Complex(re + c.re, im + c.im) def unary_~() = Complex(re, -im)
def - (c: Complex) = Complex(re - c.re, im - c.im)
// Formatting
def * (f: Float) = Complex(f*re, f*im) override def toString() : String = {
OpenSplice DDS

def * (c: Complex) = Complex((re*c.re) - (im*c.im), if (im > 0) re + "+i" + im
((re*c.im) + (im*c.re))) else if (im < 0) re + "-i" + (-im)
else re.toString
def / (c: Complex) = {

val d = c.re*c.re + c.im*c.im
}
Complex(((re*c.re) + (im + c.im))/d,
}
((im*c.re) - (re*c.im))/d )
}

Functions, Closures and Currying
OpenSplice DDS

Functions

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☐ Scala has first-class functions
☐ Functions can be defined and called, but equally functions can
be defined as unnamed literals and passed as values
OpenSplice DDS

def inc(x: Int) = x + 1 val vinc = (x: Int) => x+1
inc(5) vinc(5)

Notice once again the uniform access principle

Playing with Functions
val list = List(1,2,3,4,5,6,7,8,9)

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val g5 = list.filter((x: Int) => x > 5)
g5: List[Int] = List(6, 7, 8, 9)
OpenSplice DDS

Or with placeholder syntax

val list = List(1,2,3,4,5,6,7,8,9)
val g5 = list.filter(_ > 5)
g5: List[Int] = List(6, 7, 8, 9)

Closures

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☐ Scala allows you to define functions which include free variables
meaning variables whose value is not bound to the parameter list
☐ Free variable are resolved at runtime considering the closure of visible
variable
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☐ Example:

def mean(e : Array[Float]) : Float = { def mean(e : Array[Float]) : Float = {
var sum = 0.0F var sum = 0.0F
e.foreach((x: Int) => sum += x) e.foreach(sum += _)
return sum/e.length return sum/e.length
} }

Currying
☐ Scala provides support for curried functions which are applied to

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multiple argument lists, instead of just one
☐ Currying is the mechanism Scala provides for introducing new control
abstraction
OpenSplice DDS

def curriedSum(x: Int)(y: Int) = x + y

curriedSum(1) {
3 +5
}

OpenSplice DDS

Traits

Traits
☐ Scala supports single inheritance from classes but can mix-in multiple

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traits
☐ A trait is the unit of code reuse for Scala. It encapsulate methods and
field definitions
Traits usually expect a class to implement an abstract method, which
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☐
constitutes the “narrow” interface that allows to implement a rich
behaviour
☐ Traits are also very useful for dependency injection

Ordered Complex Numbers

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☐ Our complex numbers are not comparable
☐ Let’s assume that we wanted to make them comparable, and lets
supposed that we define the total order as based on the module of the
complex number
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☐ How can we implement this behavior?

Ordered Trait

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2011,
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☐ The Ordered[T] traits encapsulates the set of methods that allow to
define a total ordering over a type
☐ All the behaviour is defined in terms of an abstract method, namely
“compare”
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☐ Classes that mix-in this trait have to implement the “compare” method

class Complex(val re: Float, val im: Float) extends
Ordering[Complex] {
def compare(x: Complex, y: Complex) = {
if (x == y) 0
else if (!x > !y) 1
else -1
}

Case Classes
&
OpenSplice DDS

Pattern Matching

Case Classes and Pattern Matching

☐ Case Classes and Pattern Matching are twin constructs that are pretty

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useful when dealing with tree-like recursive data structures
☐ These constructs allow to match patterns in an expression and
reconstruct the object graph that makes it up
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☐ Lets see an example...

Case Classes and Pattern
abstract class Expr
case class Var(name: String) extends Expr

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case class Number(num: Float) extends Expr
case class UnOp(operator: String, arg: Expr) extends Expr
case class BinOp(operator: String, left: Expr, right: Expr)
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def simplifyExpr(expr: Expr) : Expr = expr match {
case UnOp("-", UnOp("-", e)) => e
case BinOp("+", e, Number("0")) => e
case BinOp("*", e, Number("1")) => e
case _ => expr
}

Type Parametrization
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Type Parametrization

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☐ Scala provides support for type parametrization and makes it
available for both classes as well as traits
trait Queue[T] {
def head: T
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def tail: Queue[T]
def append(x: T) : Queue[T]
}
☐ Scala allows to annotate the parametrized type to control the
resulting type variance

Type Variance
☐ If S <: T is Queue[S] <: Queue[T]?

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☐ By default Scala makes generic types nonvariant. This behaviour can be
changed using the following annotations:
☐ Queue[+T] indicates that the the sub-typing is covariant in the
parameter T
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☐ Queue[-T] indicates that the the sub-typing is contravariant in the
parameter T

Getting Started with DDS in C++, Java and Scala

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