Start Cluster Serving#

Launching Service of Serving#

Before do inference (predict), you have to start serving service. This section shows how to start/stop the service.


You can use following command to start Cluster Serving.


Normally, when calling cluster-serving-start, your config.yaml should be in current directory. You can also use cluster-serving-start -c config_path to pass config path config_path to Cluster Serving manually.


You can use Flink UI in localhost:8081 by default, to cancel your Cluster Serving job.

Or you can use ${FLINK_HOME}/bin/flink list to get serving job ID and call ${FLINK_HOME|/bin/flink cancel $ID.

Shut Down#

You can use following command to shutdown Cluster Serving. This operation will stop all Cluster Serving jobs and Redis server. Note that your data in Redis will be removed when you shutdown.


If you are using Docker, you could also run docker rm to shutdown Cluster Serving.

Start Multiple Serving#

To run multiple Cluster Serving job, e.g. the second job name is serving2, then use following configuration

# model path must be provided
# modelPath: /path/to/model

# name, default is serving_stream, you need to specify if running multiple servings
# jobName: serving2

then call cluster-serving-start in this directory would start another Cluster Serving job with this new configuration.

Then, in Python API, pass name=serving2 argument during creating object, e.g.


Then the Python API would interact with job serving2.

HTTP Server#

If you want to use sync API for inference, you should start a provided HTTP server first. User can submit HTTP requests to the HTTP server through RESTful APIs. The HTTP server will parse the input requests and pub them to Redis input queues, then retrieve the output results and render them as json results in HTTP responses.


User can download a bigdl-${VERSION}-http.jar from the Nexus Repository with GAVP:


User can also build from the source code:

mvn clean package -P spark_2.4+ -Dmaven.test.skip=true

Start the HTTP Server#

User can start the HTTP server with following command.

java -jar bigdl-bigdl_${BIGDL_VERSION}-spark_${SPARK_VERSION}-${BIGDL_VERSION}-http.jar

And check the status of the HTTP server with:


If you get a response like “welcome to BigDL web serving frontend”, that means the HTTP server is started successfully.

Start options#

User can pass options to the HTTP server when start it:

java -jar bigdl-bigdl_${BIGDL_VERSION}-spark_${SPARK_VERSION}-${BIGDL_VERSION}-http.jar --redisHost=""

All the supported parameter are listed here:

  • interface: the binded server interface, default is “”

  • port: the binded server port, default is 10020

  • redisHost: the host IP of redis server, default is “localhost”

  • redisPort: the host port of redis server, default is 6379

  • redisInputQueue: the input queue of redis server, default is “serving_stream”

  • redisOutputQueue: the output queue of redis server, default is “result:”

  • parallelism: the parallelism of requests processing, default is 1000

  • timeWindow: the timeWindow wait to pub inputs to redis, default is 0

  • countWindow: the timeWindow wait to ub inputs to redis, default is 56

  • tokenBucketEnabled: the switch to enable/disable RateLimiter, default is false

  • tokensPerSecond: the rate of permits per second, default is 100

  • tokenAcquireTimeout: acquires a permit from this RateLimiter if it can be obtained without exceeding the specified timeout(ms), default is 100

User can adjust these options to tune the performance of the HTTP server.