It currently supports C, C++, and Java programming languages and can be run on several operating systems including Windows, Oracle Linux, Red Hat Enterprise Linux, and CentOS

Note: Although the Real-Time SDK can also run on Docker, it isn't officially qualified on the Docker platform. If you find any problems while running it on the Docker platform, the issues must be replicated on bare metal machines before contacting the helpdesk support.

This article demonstrates how to use the Real-Time SDK on the Docker platform. It has three sections including:

• Real-Time APIs docker images on the Docker Hub
• Deploying the Real-Time SDK C/C++ docker image
• Running and modifying examples in the docker container 

Real-Time APIs docker images

The source of the Real-Time SDK is available in GitHub. The Real-Time SDK C/C++ and Java libraries can be built by using CMake and Gradle, respectively. Steps to build the Real-Time SDK libraries are mentioned in GitHub (C/C++, and Java). Moreover, you can create your own Real-Time SDK docker image by following steps in the Deploy and Run Real-Time SDK in Docker article.

Fortunately, the Real-Time SDK Docker images are now available in Docker Hub, as shown below.

• lsegrealtime/realtimesdk_c: This docker image contains the latest version of Real-Time SDK C/C++ libraries and examples. The image is rebuilt when the new version of Real-Time SDK is available in GitHub. It uses Oracle Linux 7 as a based image and the GCC version is 4.8.5.
• lsegrealtime/realtimesdk_java: This docker image contains the latest version of Real-Time SDK Java libraries and examples. The image is rebuilt when the new version of Real-Time SDK is available in GitHub. It uses Oracle Linux 7 as a based image and the Oracle OpenJDK version is 11.
• lsegrealtime/websocket_api: This docker image contains WebSocket API Examples used to connect to Real-Time Distribution System or Data Platform to consume real-time data through the WebSocket connections. There are examples of various programming languages such as CSharp, Java, Python, Go, Ruby, Node, Perl, and R. The examples are available in GitHub. The image uses Oracle Linux 8 as a base image and it installs Python 3.9 used to run Python WebSocket API examples.

This article only focuses on the lsegrealtime/realtimesdk_c docker image.

In the next section, I will demonstrate how to deploy the lsegrealtime/realtimesdk_c image on Docker engines and Azure Containers.

Deploying the Real-Time SDK C/C++ docker image

The docker images can be run as docker containers on local machines or cloud platforms. To follow steps in this section, the docker engine must be installed on a local machine or an Azure account is ready to be used.

Deploying the Real-Time SDK C/C++ docker image on a local machine

First, the docker engine must be installed and run properly on the machine. The "docker version" command can be used to verify the docker engine.

When the docker engine is started, the "docker pull lsegrealtime/realtimesdk_c" command can be used to download the latest Real-Time SDK C/C++ docker image from the Docker Hub.

After downloading, the "docker images" command can be used to verify that the Real-Time SDK C/C++ docker image is available on the machine.

The output of the "docker images" command must contain the lsegrealtime/realtimesdk_c repository.

A Docker image can be run inside a container. The "docker run" command creates a new running container instance of the docker image. The following command can be used to run a container instance of the Real-Time SDK C/C++ docker image.

After running this command, you can access shell in the container. Then, you run the Real-Time SDK examples in the container. The Real-Time SDK C/C++ is in /opt/lseg/Real-Time-SDK/Cpp-C directory.

Deploying the Real-Time SDK C/C++ docker image on Azure

Instead of running docker containers on the local machine, you can run the Real-Time SDK C/C++ docker container on Azure Container Instances. Azure Container Instances are used to create and manage Docker containers in Azure without having to set up virtual machines or manage additional infrastructure.

First, log in to Azure and then create a new Container Instances resource.

On the Create container instance page, fill in all required information. Select Docker Hub or other registry for the Image source and enter lsegrealtime/realtimesdk_c for the Image. Then, click on the Next: Networking button.

On the Networking tab, keep the default setting. Then click on the Next: Advanced button.

On the Advanced tab, enter [ "/bin/bash", "-c", "echo hello; sleep 60m" ] for the Command override. This command will keep the container running for 60 minutes before exiting. You can use another command or change the sleep time to keep the container running. Then, click on Review + create button.

Next, review the information and then click on the Create button to create a container instance.

Wait for the deployment process to be completed.

After the deployment is complete, click on the "Go to resource".

On the overview page, verify that the status of the container is Running.

Then, select Containers under Settings, click on the Connect tab, choose the shell to be used, and then click on the Connect button.

Now, you can access shell in the container.

2. Copy the EmaConfig.xml from the EMA directory to the current directory

3. Set the LD_LIBRARY_PATH environment variable with the directory path of libcurl.so

4. Run the Cons113 example with the following parameters

Connecting to RTO with the V1 authentication

Connecting to RTO with the V2 authentication

The example will connect to Real-Time - Optimized and subscribe to JPY= from the ELEKTRON_DD service.

Running an ETA example

VAConsumer is an ETA example that uses ValueAdd component to consume data from an OMM Provider, ADS device, OMM Provider application or Real-Time - Optimized. The source code and executable files of this example are available in the following directories:

• Source code files: /opt/lseg/Real-Time-SDK/Cpp-C/Eta/Applications/Examples/VAConsumer
• Executable file: /opt/lseg/Real-Time-SDK/Cpp-C/Eta/Executables/OL7_64_GCC485/Optimized
  

Follow these steps to run the VAConsumer example:

1. Change the current directory to /opt/lseg/Real-Time-SDK/Cpp-C/Eta/Executables/OL7_64_GCC485/Optimized

2. Set the LD_LIBRARY_PATH environment variable with the directory path of libcurl.so

3. Run the VAConsumer example with the following parameters

Connecting to RTO with the V1 authentication

Connecting to RTO with the V2 authentication

The example will connect to Real-Time - Optimized (apac-1-t2.streaming-pricing-api.refinitiv.com:14002) and subscribe to JPY= from the ELEKTRON_DD service.

Modifying an example in the container

This section demonstrates how to modify the 113MarketPriceSessionManagement example by changing the default RIC from IBM.N to JPY= and then re-build the Cons113 example.

1. Change the directory to the 113MarketPriceSessionManagement example

2. Use a text editor to modify the Consumer.cpp file. In the main function, change the value in the itemName variable from "IBM.N" to "JPY="

3. Save changes and then exit the text editor

4. Change the directory to /opt/lseg/Real-Time-SDK/rtsdk/

5. Run the make command to rebuild the Cons113 example

6. Then, follow steps in the Running an EMA example section to re-run the Cons113 example.

Summary

This article introduces Real-Time SDK and WebSocket API Docker images in Docker Hub. The lsegrealtime/realtimesdk_c docker image contains the latest version of Real-Time C/C++ libraries and examples. The lsegrealtime/realtimesdk_java docker image contains the latest version of Real-Time SDK Java libraries and examples. The lsegrealtime/websocket_api docker image contains WebSocket API Examples used to connect to Real-Time Distribution System or Data Platform to consume real-time data through the WebSocket connections. Then, it demonstrates how to deploy the lsegrealtime/realtimesdk_c docker image on the Docker engines and Azure Containers. Finally, it shows how to run and modify examples to retrieve real-time data from Real-Time – Optimized.

References

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