ARTICLE
Introduction to the Refinitiv Real-Time C/C++ Docker Image
The Refinitiv Real-Time SDK is a suite of modern and open source APIs that is designed to simplify development through a strong focus on ease of use and standardized access to a broad set of Refinitiv and proprietary content and services.
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 Refinitiv 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 Refinitiv Real-Time SDK on the Docker platform. It has three sections including:
- Refinitiv APIs docker images on the Docker Hub
- Deploying the Refinitiv Real-Time SDK C/C++ docker image
- Running and modifying examples in the docker container
Refinitiv APIs docker images
The source of the Refinitiv Real-Time SDK is available in GitHub. The Refinitiv Real-Time SDK C/C++ and Java libraries can be built by using CMake and Gradle, respectively. Steps to build the Refinitiv Real-Time SDK libraries are mentioned in GitHub (C/C++, and Java). Moreover, you can create your own Refinitiv Real-Time SDK docker image by following steps in the Deploy and Run Refinitiv Real-Time SDK in Docker article.
Fortunately, the Refinitiv Real-Time SDK Docker images are now available in Docker Hub, as shown below.
- refinitivapis/realtimesdk_c: This docker image contains the latest version of Refinitiv Real-Time SDK C/C++ libraries and examples. The image is rebuilt when the new version of Refinitiv Real-Time SDK is available in GitHub. It uses Oracle Linux 7 as a based image and the GCC version is 4.8.5.
- refinitivapis/realtimesdk_java: This docker image contains the latest version of Refinitiv Real-Time SDK Java libraries and examples. The image is rebuilt when the new version of Refinitiv Real-Time SDK is available in GitHub. It uses Oracle Linux 7 as a based image and the Oracle OpenJDK version is 11.
- refinitivapis/websocket_api: This docker image contains WebSocket API Examples used to connect to Refinitiv Real-Time Distribution System or Refinitiv 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 refinitivapis/realtimesdk_c docker image.
In the next section, I will demonstrate how to deploy the refinitivapis/realtimesdk_c image on Docker engines and Azure Containers.
Deploying the Refinitiv 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 Refinitiv 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.
# docker version
Client: Docker Engine - Community
Version: 19.03.1-ol
API version: 1.40
Go version: go1.12.5
Git commit: ead9442
Built: Wed Sep 11 06:40:28 2019
OS/Arch: linux/amd64
Experimental: false
Server: Docker Engine - Community
Engine:
Version: 19.03.1-ol
API version: 1.40 (minimum version 1.12)
Go version: go1.12.5
Git commit: ead9442
Built: Wed Sep 11 06:38:43 2019
OS/Arch: linux/amd64
Experimental: false
Default Registry: docker.io
containerd:
Version: v1.2.0-rc.0-108-gc444666
GitCommit: c4446665cb9c30056f4998ed953e6d4ff22c7c39
runc:
Version: 1.0.0-rc5+dev
GitCommit: 4bb1fe4ace1a32d3676bb98f5d3b6a4e32bf6c58
docker-init:
Version: 0.18.0
GitCommit: fec3683
When the docker engine is started, the "docker pull refinitivapis/realtimesdk_c" command can be used to download the latest Refinitiv Real-Time SDK C/C++ docker image from the Docker Hub.
# docker pull refinitivapis/realtimesdk_c
Using default tag: latest
Trying to pull repository docker.io/refinitivapis/realtimesdk_c ...
latest: Pulling from docker.io/refinitivapis/realtimesdk_c
01eb3c96478b: Already exists
ef4497be746b: Pull complete
Digest: sha256:4fd77224a4a4a72acb6cf0168fc59584b83bbf2b22f57a60bb7c5a5887f24da9
Status: Downloaded newer image for refinitivapis/realtimesdk_c:latest
refinitivapis/realtimesdk_c:latest
After downloading, the "docker images" command can be used to verify that the Refinitiv Real-Time SDK C/C++ docker image is available on the machine.
# docker images
REPOSITORY TAG IMAGE ID CREATED SIZE
refinitivapis/realtimesdk_c latest 91e77e948e9f 3 hours ago 2.74GB
The output of the "docker images" command must contain the refinitivapis/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 Refinitiv Real-Time SDK C/C++ docker image.
# docker run -it --name realtimec refinitivapis/realtimesdk_c
sh-4.2# ls
CHANGELOG.md CMakeLists.txt Ema Eta README.md RTSDK-C-Edition_Test_Plan.xlsx etc
sh-4.2# pwd
/opt/refinitiv/Real-Time-SDK/Cpp-C
After running this command, you can access shell in the container. Then, you run the Refinitiv Real-Time SDK examples in the container. The Refinitiv Real-Time SDK C/C++ is in /opt/refinitiv/Real-Time-SDK/Cpp-C directory.
Deploying the Refinitiv Real-Time SDK C/C++ docker image on Azure
Instead of running docker containers on the local machine, you can run the Refinitiv 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 refinitivapis/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 created resource (refinitivsdkc).
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.
Running examples in the container
This section demonstrates how to run EMA and ETA examples in the container to consume real-time data from Refinitiv Real-Time - Optimized. The Refinitiv Real-Time SDK C/C++ package is in the /opt/refinitiv/Real-Time-SDK/Cpp-C directory and the build directory is /opt/refinitiv/Real-Time-SDK/rtsdk/.
To connect to Refinitiv Real-Time - Optimized, the following information is required.
- UserID/MachineID
- Password
- AppKey (Client ID)
For more information, please refer to the Getting Start with Refinitiv Data Platform article.
Running an EMA example
In the Refinitiv Real-Time SDK package, 113_MP_SessionMgmt is an OMM Consumer application example that demonstrates the basic usage of the EMA library in accessing and parsing OMM MarketPrice data from Refinitiv Real-Time - Optimized. The source code and executable files of this example are available in the following directories:
- Source code files: /opt/refinitiv/Real-Time-SDK/Cpp-C/Ema/Examples/Training/Consumer/100_Series/113_MP_SessionMgmt
- Executable file: /opt/refinitiv/Real-Time-SDK/Cpp-C/Ema/Executables/OL7_64_GCC485/Optimized/
Follow these steps To run the Con113 example:
1. Change the current directory to /opt/refinitiv/Real-Time-SDK/Cpp-C/Ema/Executables/OL7_64_GCC485/Optimized/
sh-4.2# cd /opt/refinitiv/Real-Time-SDK/Cpp-C/Ema/Executables/OL7_64_GCC485/Optimized/
2. Copy the EmaConfig.xml from the EMA directory to the current directory
sh-4.2# cp /opt/refinitiv/Real-Time-SDK/Cpp-C/Ema/EmaConfig.xml .
3. Set the LD_LIBRARY_PATH environment variable with the directory path of libcurl.so
sh-4.2# export LD_LIBRARY_PATH=/opt/refinitiv/Real-Time-SDK/rtsdk/install/lib64
4. Run the Cons113 example with the following parameters
sh-4.2# ./Cons113 -username <UserID or MachineID> -password <Password> -clientId <AppKey> -itemName JPY=
loggerMsg
TimeStamp: 03:34:17.202
ClientName: ChannelCallbackClient
Severity: Success
Text: Received ChannelUp event on channel Channel_4
Instance Name Consumer_4_1
Connected component version: ads3.2.1.L1.linux.tis.rrg 64-bit
loggerMsgEnd
RefreshMsg
streamId="5"
domain="MarketPrice Domain"
Solicited
RefreshComplete
ClearCache
state="Open / Ok / None / '*All is well'"
itemGroup="00 AC"
permissionData="03 01 01 52 6C"
qos="RealTime/JustInTimeConflated"
seqNum="26574"
name="JPY="
nameType="1"
serviceId="257"
serviceName="ELEKTRON_DD"
Payload dataType="FieldList"
FieldList FieldListNum="99" DictionaryId="1"
FieldEntry fid="1" name="PROD_PERM" dataType="UInt" value="526"
FieldEntry fid="2" name="RDNDISPLAY" dataType="UInt" value="153"
FieldEntry fid="3" name="DSPLY_NAME" dataType="Rmtes" value="ASANPAC IFIBK MOW"
FieldEntry fid="5" name="TIMACT" dataType="Time" value="03:38:00:000 :000:000"
FieldEntry fid="11" name="NETCHNG_1" dataType="Real" value="0.18"
FieldEntry fid="12" name="HIGH_1" dataType="Real" value="105.19"
FieldEntry fid="13" name="LOW_1" dataType="Real" value="104.91"
FieldEntry fid="15" name="CURRENCY" dataType="Enum" value="392"
FieldEntry fid="17" name="ACTIV_DATE" dataType="Date" value="23 SEP 2020"
FieldEntry fid="19" name="OPEN_PRC" dataType="Real" value="104.92"
FieldEntry fid="21" name="HST_CLOSE" dataType="Real" value="104.91"
FieldEntry fid="22" name="BID" dataType="Real" value="105.09"
The example will connect to Refinitiv 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 Refinitiv Real-Time - Optimized. The source code and executable files of this example are available in the following directories:
- Source code files: /opt/refinitiv/Real-Time-SDK/Cpp-C/Eta/Applications/Examples/VAConsume
- Executable file: /opt/refinitiv/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/refinitiv/Real-Time-SDK/Cpp-C/Eta/Executables/OL7_64_GCC485/Optimized
sh-4.2# cd /opt/refinitiv/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
sh-4.2# export LD_LIBRARY_PATH=/opt/refinitiv/Real-Time-SDK/rtsdk/install/lib64
3. Run the VAConsumer example with the following parameters
sh-4.2# ./VAConsumer -encryptedSocket ap-southeast-1-aws-3-med.optimized-pricing-api.refinitiv.net:14002 ELEKTRON_DD mp:JPY= -uname <UserID or MachineID> -passwd <Password> -clientId <AppKey> -sessionMgnt
Connections:
apac-1-t2.streaming-pricing-api.refinitiv.com:14002 ELEKTRON_DD
MarketPriceItems: IBM.N
MarketByOrderItems:
MarketByPriceItems:
YieldCurveItems:
MarketPriceItems (Private Stream):
MarketByOrderItems (Private Stream):
MarketByPriceItems (Private Stream):
YieldCurveItems (Private Stream):
...
Received Source Directory Response: State: Open/Ok/None - text: ""
Received serviceName: PERM_SVR_SNKDRV
Received serviceName: ELEKTRON_DD
Service State: Up
Received serviceName: ERT_FD3_LF1
Received Dictionary Response: RWFFld
Field Dictionary complete.
Received Dictionary Response: RWFEnum
Enumerated Types Dictionary complete.
(Channel 9):
JPY=
DOMAIN: RSSL_DMT_MARKET_PRICE
State: Open/Ok/None - text: "*All is well"
PROD_PERM 526
RDNDISPLAY 153
DSPLY_NAME BARCLAYS LON
TIMACT 08:16:00:000:000:000
NETCHNG_1 -0.25
HIGH_1 107.30
LOW_1 106.94
CURRENCY JPY(392)
ACTIV_DATE 15 JUL 2020
OPEN_PRC 107.22
The example will connect to Refinitiv 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
sh-4.2# cd /opt/refinitiv/Real-Time-SDK/Cpp-C/Ema/Examples/Training/Consumer/100_Series/113_MP_SessionMgmt
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="
int main( int argc, char* argv[] )
{
try {
AppClient client;
OmmConsumerConfig config;
UInt8 userNameSet = 0;
UInt8 passwordSet = 0;
UInt8 clientIdSet = 0;
EmaString itemName = "JPY=";
3. Save changes and then exit the text editor
4. Change the directory to /opt/refinitiv/Real-Time-SDK/rtsdk/
sh-4.2# cd /opt/refinitiv/Real-Time-SDK/rtsdk/
5. Run the make command to rebuild the Cons113 example
sh-4.2# make Cons113
6. Then, follow steps in the Running an EMA example section to re-run the Cons113 example.
Summary
This article introduces Refinitiv Real-Time SDK and WebSocket API Docker images in Docker Hub. The refinitivapis/realtimesdk_c docker image contains the latest version of Refinitiv Real-Time C/C++ libraries and examples. The refinitivapis/realtimesdk_java docker image contains the latest version of Refinitiv Real-Time SDK Java libraries and examples. The refinitivapis/websocket_api docker image contains WebSocket API Examples used to connect to Refinitiv Real-Time Distribution System or Refinitiv Data Platform to consume real-time data through the WebSocket connections. Then, it demonstrates how to deploy the refinitivapis/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 Refinitiv Real-Time – Optimized.
References
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