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 {
auto samples = data.read();
std::for_each(samples.begin(), samples.end(), [](Sample<Meter>& sample) {
std::cout << sample.data() << std::endl;
}
}
dr.listener(lst);
// Print incoming data up to when the user does a Ctrl-C
std::this_thread::join();
return 0;
}
enum UtilityKind {
ELECTRICITY,
GAS,
WATER
};
struct Meter {
string sn;
UtilityKind utility;
float reading;
float error;
};
#pragma keylist Meter sn](https://image.slidesharecdn.com/2017-170213170016/75/DDS-In-Action-Part-II-60-2048.jpg)
![CopyrightPrismTech,2015
Writing Data in Scala
import dds._
import dds.prelude._
import dds.config.DefaultEntities._
object SmartMeter {
def main(args: Array[String]): Unit = {
val topic = Topic[Meter](“SmartMeter”)
val dw = DataWriter[Meter](topic)
while (!done) {
val meter = readMeter()
dw.write(meter)
Thread.sleep(SAMPLING_PERIOD)
}
}
}
enum UtilityKind {
ELECTRICITY,
GAS,
WATER
};
struct Meter {
string sn;
UtilityKind utility;
float reading;
float error;
};
#pragma keylist Meter sn](https://image.slidesharecdn.com/2017-170213170016/75/DDS-In-Action-Part-II-61-2048.jpg)
![CopyrightPrismTech,2015
Reading Data in Scala
import dds._
import dds.prelude._
import dds.config.DefaultEntities._
object SmartMeterLog {
def main(args: Array[String]): Unit = {
val topic = Topic[Meter](“SmartMeter”)
val dr = DataReader[Meter](topic)
dr listen {
case DataAvailable(_) => dr.read.foreach(println)
}
}
}
enum UtilityKind {
ELECTRICITY,
GAS,
WATER
};
struct Meter {
string sn;
UtilityKind utility;
float reading;
float error;
};
#pragma keylist Meter sn](https://image.slidesharecdn.com/2017-170213170016/75/DDS-In-Action-Part-II-62-2048.jpg)
Uploaded byAngelo Corsaro
DDS In Action Part II
This document provides an in-depth overview of the Data Distribution Service (DDS), emphasizing its functioning, dynamic discovery features, and information exchange among applications through topics, data writers, and data readers. It also covers practical examples of coding in various programming languages, illustrating how to read and write data within a DDS framework. Additionally, the document discusses performance metrics, highlighting latency and throughput comparisons with other technologies.
More Related Content
DDS In Action Part II
- 1. Angelo Corsaro, PhD ChiefTechnology Officer ADLINK Technologies Inc. angelo.corsaro@adlinktech.com DDS in Action Part-II
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- 4. CopyrightPrismTech,2015 DDS is astandard technology for ubiquitous, interoperable, secure, platform independent, and real- time data sharing across network connected devices DDS in 131 characters
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- 9. CopyrightPrismTech,2015 Applications can autonomously and asynchronouslyread and write data enjoying spatial and temporal decoupling Virtualised Data Space DDS Global Data Space ... Data Writer Data Writer Data Writer Data Reader Data Reader Data Reader Data Reader Data Writer TopicA QoS TopicB QoS TopicC QoS TopicD QoS
- 10. CopyrightPrismTech,2015 DDS Dynamic discoveriesis responsible for (1) uncovering DDS applications, (2) matching interests and (3) setting up data path between matching readers and writers Dynamic Discovery DDS Global Data Space ... Data Writer Data Writer Data Writer Data Reader Data Reader Data Reader Data Reader Data Writer TopicA QoS TopicB QoS TopicC QoS TopicD QoS
- 11. CopyrightPrismTech,2015 Built-in dynamic discovery isolates applicationsfrom network topology and connectivity details Dynamic Discovery DDS Global Data Space ... Data Writer Data Writer Data Writer Data Reader Data Reader Data Reader Data Reader Data Writer TopicA QoS TopicB QoS TopicC QoS TopicD QoS
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- 15. Domain • DDS datalives within a domain • A domain is identified with a non negative integer, such as 1, 3, 31 • The number 0 identifies the default domain • A domain represent an impassable communication plane DDS Domain
- 16. Partitions • Partitions arethe mechanism provided by DDS to organise information within a domain • Access to partitions is controlled through QoS Policies • Partitions are defined as strings: • “system:telemetry” • “system:log” • “data:row-2:col-3” • Partitions addressed by name or regular expressions: • ”system:telemetry” • “data:row-2:col-*” Partition s
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- 18. Topic • A Topicdefines a domain-wide information’s class • A Topic is defined by means of a (name, type, qos) tuple, where • name: identifies the topic within the domain • type: is the programming language type associated with the topic. Types are extensible and evolvable • qos: is a collection of policies that express the non-functional properties of this topic, e.g. reliability, persistence, etc. Topic Type Name QoS
- 19. Topic and Instances •As explained in the previous slide a topic defines a class/type of information • Topics can be defined as Singleton or can have multiple Instances • Topic Instances are identified by means of the topic key • A Topic Key is identified by a tuple of attributes -- like in databases • Remarks: • A Singleton topic has a single domain-wide instance • A “regular” Topic can have as many instances as the number of different key values, e.g., if the key is an 8-bit character then the topic can have 256 different instances
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- 21. Active Floor • Assumewe are building an active floor • This active floor is made by a matrix of pressure sensors used to detects position, and indirectly movement • This information is leveraged by the application that uses the active floor for positioning or entertainment Cell: (i,j)
- 22. Active Floor • Thegeneric active cell can be modelled with a topic that has an instance for each value of (i,j). The topic type can be defined as: • Each cell is now distinguishable and associated with a topic instance Cell: (i,j) struct TCell { short row; short column; float pressure; // in kPa }; #pragma keylist TCell row column
- 23. Active Floor • Howcan we know when something is on the cell? • The detection can be based on the difference between the atmospheric pressure, say P0, and the pressure sensed by the cell • We can model this as a Singleton Topic ReferencePressure defined by the type: Cell: (i,j) struct TReferencePressure { float pressure; // in kPa float precision; }; #pragma keylist TReferencePressure
- 24. Active Floor • Eachsensor has associated a topic instance identified by the (row,column) coordinate -- the instance key • Each instance produces a stream of pressure values that in DDS terms are called samples 0 1 2 3 0 1 2 3 4 Pressur e time Pressur e time Pressur e time struct TCell { short row; short column; float pressure; // in kPa }; #pragma keylist Cell row column
- 25. Exercise • What ifwe want to extend our model to deal with floors on different levels? • How would you extend the data model? • How would you use partitions? 1 2 3 Pressur e time Pressur e time Pressur e time
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- 27. Data Writer • ADataWriter (DW) is a strongly typed entity used to produce samples for one or more instances of a Topic, with a given QoS • Conceptually, the DataWriter QoS should be the same as the Topic QoS or more stringent • However, DDS does enforce a specific relationship between the Topic and DataWriter QoS Topic Type Name QoS
- 28. Data Writer The DataWritercontrols the life-cycle of Topic Instances and allows to: • Define a new topic instance • Write samples for a topic instance • Dispose the topic instance Topic Type Name QoS
- 29. The DataWriter controlsthe life-cycle of Topic Instances and allows to: • Define a new topic instance • Write samples for a topic instance • Dispose the topic instance Topic Type Name QoS Data Writer
- 30. Writer Cache Each DDSDataWriter has an associated cache • Writes are always local to the cache • This cache provides two degrees of temporal decoupling between writers and readers. One w.r.t. processing speed the other w.r.t. temporal coupling • The writer cache along with DDS QoS Policies provides built-in support for the Circuit-Breaker pattern DataWriter Cache DataWriter ... Samples Instances Cache
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- 32. Data Reader • ADataReader (DR) is a strongly typed entity used to access and/or consume samples for a Topic, with a given QoS • Conceptually, the DataReader QoS should be the same as the Topic QoS or less stringent • However, DDS does enforce a specific relationship between the Topic and DataReader QoS Topic Type Name QoS
- 33. Reader Cache The ReaderCache stores the last n∊𝜨∞ samples for each relevant instance Where: 𝜨∞ =𝜨 ∪ {∞} DataReader Cache DataReader ... Samples Instances Cache
- 34. Data Reader Depending onits QoS a DataReader may provide access to: • last sample • last n samples • all samples produced since the DataReader was created 0 1 2 3 0 1 2 3 4 Pressure time Pressure time Pressure time
- 35. 0 1 23 0 1 2 3 4 Pressure time Pressure time Pressure time Depending on its QoS a DataReader may provide access to: • last sample • last n samples • all samples produced since the DataReader was created n=3 3
- 36. 0 1 23 0 1 2 3 4 Pressure time Pressure time Pressure time Data Reader Depending on its QoS a DataReader may provide access to: • last sample • last n samples • all samples produced since the DataReader was created n=3
- 37. Data Reader Depending onits QoS a DataReader may provide access to: • last sample • last n samples • all samples produced since the DataReader was created n= 3 0 1 2 3 0 1 2 3 4 Pressure time Pressure time Pressure time
- 38. Data Reader Depending onits QoS a DataReader may provide access to: • last sample • last n samples • all samples produced since the DataReader was created n=3 0 1 2 3 0 1 2 3 4 Pressure time Pressure time Pressure time
- 39. Data Reader Depending onits QoS a DataReader may provide access to: • last sample • last n samples • all samples produced since the DataReader was created 0 1 2 3 0 1 2 3 4 n=3 Pressure time Pressure time Pressure time
- 40. Data Reader Depending onits QoS a DataReader may provide access to: • last sample • last n samples • all samples produced since the DataReader was created 0 1 2 3 0 1 2 3 4 n=3 Pressure time Pressure time Pressure time
- 41. Data Reader Depending onits QoS a DataReader may provide access to: • last sample • last n samples • all samples produced since the DataReader was created 0 1 2 3 0 1 2 3 4 n=3 Pressure time Pressure time Pressure time
- 42. Data Reader • Samplesare stored in the DataReader Cache • Samples can be read or taken from the cache • Samples taken are evicted from the cache • Samples read remain in the cache and are simply market as read • The cache content can be selected based on content or state. More on this later... 0 1 2 3 0 1 2 3 4 Pressure time Pressure time Pressure time
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- 44. Reading Data • Theaction of reading samples for a Reader Cache is non-destructive. • Samples are not removed from the cache DataReader Cache DataReader ... DataReader Cache DataReader ...read
- 45. Taking Data • Theaction of taking samples for a Reader Cache is destructive. • Samples are removed from the cache DataReader Cache DataReader ... DataReader Cache DataReader ...take
- 46. Samples Selector • Samplescan be selected using composable content and status predicates DataReader Cache DataReader ...
- 47. Content Filtering • Filtersallow to control what gets into a DataReader cache • Filters are expressed as SQL where clauses or as Java/C/JavaScript predicates DataReader Cache DataReader ... Filter Application Network
- 48. Content Filters Content Filters canbe used to project on the local cache only the Topic data satisfying a given predicate struct CarDynamics { @key string cid; long x; long y; float dx; long dy; } cid x y dx dy GR 33N GO 167 240 45 0 LO 00V IN 65 26 65 0 AN 637 OS 32 853 0 50 AB 123 CD 325 235 80 0 “dx > 50 OR dy > 50” Type CarDynamics cid x y dx dy LO 00V IN 65 26 65 0 AB 123 CD 325 235 80 0 Reader Cache
- 49. Content-Based Selection • Queriesallow to control what gets out of a DataReader Cache • Queries are expressed as SQL where clauses or as Java/C/JavaScript predicates DataReader Cache DataReader ... Query DataReader Cache DataReader ... Application Network
- 50. Queries Queries can be usedto select out of the local cache the data matching a given predicate Reader Cache struct CarDynamics { @key string cid; long x; long y; float dx; long dy; } cid x y dx dy GR 33N GO 167 240 45 0 LO 00V IN 65 26 65 0 AN 637 OS 32 853 0 50 AB 123 CD 325 235 80 0 “dx > 50 OR dy > 50” Type CarDynamics cid x y dx dy GR 33N GO 167 240 45 0 LO 00V IN 65 26 65 0 AN 637 OS 32 853 0 50 AB 123 CD 325 235 80 0 cid x y dx dy LO 00V IN 65 26 65 0 AB 123 CD 325 235 80 0 query
- 51. Copyright 2013, PrismTech – All Rights Reserved. Sample, Instance, andView 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 Instance State (ALIVE, NOT_ALIVE_DISPOSED, NOT_ALIVE_NO_WRITERS) allows to track the life-cycle transitions of the instance to which a sample belongs
- 52. State-Based Selection • Statebased selection allows to control what gets out of a DataReader Cache • State base selectors predicate on samples meta-information DataReader Cache DataReader ... State Selector DataReader Cache DataReader ... Application Network
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- 54. Copyright 2013, PrismTech – All Rights Reserved. Interaction Models Polling •The applicationproactively polls for data availability as well as special events, such as a deadline being missed, etc. Notice that all DDS API calls, exclusion made for wait operations, are non-blocking Synchronous Notification •The application synchronously waits for some conditions to be verified, e.g., data availability, instance lifecycle change, etc. Asynchronous Notification •The application registers the interest to be asynchronously notified when specific condition are satisfied, e.g. data available, a publication matched, etc.
- 55. Copyright 2013, PrismTech – All Rights Reserved. Synchronous Notifications • DDSprovides a mechanism known as WaitSet to synchronously wait for a condition • Condition can predicate on: • communication statuses • data availability • data availability with specific content • user-triggered conditions
- 56. Copyright 2013, PrismTech – All Rights Reserved. Asynchronous Notifications • DDSprovides a mechanism known as Listeners for asynchronous notification of a given condition • Listener interest can predicate on: • communication statuses • data availability
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- 58. CopyrightPrismTech,2015 Anatomy of aDDS Application
- 59. CopyrightPrismTech,2015 Writing Data inC++ #include <dds.hpp> int main(int, char**) { DomainParticipant dp(0); Topic<Meter> topic(“SmartMeter”); Publisher pub(dp); DataWriter<Meter> dw(pub, topic); while (!done) { auto value = readMeter() dw.write(value); std::this_thread::sleep_for(SAMPLING_PERIOD); } return 0; } enum UtilityKind { ELECTRICITY, GAS, WATER }; struct Meter { string sn; UtilityKind utility; float reading; float error; }; #pragma keylist Meter sn
- 60. CopyrightPrismTech,2015 Reading Data inC++ #include <dds.hpp> int main(int, char**) { DomainParticipant dp(0); Topic<Meter> topic(”SmartMeter”); Subscriber sub(dp); DataReader<Meter> dr(dp, topic); LambdaDataReaderListener<DataReader<Meter>> lst; lst.data_available = [](DataReader<Meter>& dr) { auto samples = data.read(); std::for_each(samples.begin(), samples.end(), [](Sample<Meter>& sample) { std::cout << sample.data() << std::endl; } } dr.listener(lst); // Print incoming data up to when the user does a Ctrl-C std::this_thread::join(); return 0; } enum UtilityKind { ELECTRICITY, GAS, WATER }; struct Meter { string sn; UtilityKind utility; float reading; float error; }; #pragma keylist Meter sn
- 61. CopyrightPrismTech,2015 Writing Data inScala import dds._ import dds.prelude._ import dds.config.DefaultEntities._ object SmartMeter { def main(args: Array[String]): Unit = { val topic = Topic[Meter](“SmartMeter”) val dw = DataWriter[Meter](topic) while (!done) { val meter = readMeter() dw.write(meter) Thread.sleep(SAMPLING_PERIOD) } } } enum UtilityKind { ELECTRICITY, GAS, WATER }; struct Meter { string sn; UtilityKind utility; float reading; float error; }; #pragma keylist Meter sn
- 62. CopyrightPrismTech,2015 Reading Data inScala import dds._ import dds.prelude._ import dds.config.DefaultEntities._ object SmartMeterLog { def main(args: Array[String]): Unit = { val topic = Topic[Meter](“SmartMeter”) val dr = DataReader[Meter](topic) dr listen { case DataAvailable(_) => dr.read.foreach(println) } } } enum UtilityKind { ELECTRICITY, GAS, WATER }; struct Meter { string sn; UtilityKind utility; float reading; float error; }; #pragma keylist Meter sn
- 63. CopyrightPrismTech,2015 Writing Data inPython import dds import time if __name__ == '__main__': topic = dds.Topic("SmartMeter", "Meter") dw = dds.Writer(topic) while True: m = readMeter() dw.write(m) time.sleep(0.1) enum UtilityKind { ELECTRICITY, GAS, WATER }; struct Meter { string sn; UtilityKind utility; float reading; float error; }; #pragma keylist Meter sn
- 64. CopyrightPrismTech,2015 Reading Data inPython import dds import sys def readData(dr): samples = dds.range(dr.read()) for s in samples: sys.stdout.write(str(s.getData())) if __name__ == '__main__': t = dds.Topic("SmartMeter", "Meter") dr = dds.Reader(t) dr.onDataAvailable = readData enum UtilityKind { ELECTRICITY, GAS, WATER }; struct Meter { string sn; UtilityKind utility; float reading; float error; }; #pragma keylist Meter sn
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- 67. Latency Vortex DDS latency canbe as low as ~30 usec This is the lowest among DDS implementations several times better than what can be obtained with MQTT, AMQP, OPC-UA
- 68. Throughput Vortex DDS can easilysaturate a 10Gbps network Vortex throughput is 2-3x better than competing technologies
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- 71. CopyrightPrismTech,2015 DDS provides avery high level abstractions to architect and implement distributed systems DDS has built-in support for several patterns that are essential to keep systems working at scale, such as circuit-breakers and temporal decoupling DDS can address the most challenging environment w.r.t. volumes, latencies and scale Wrapping Up