Ohh!!! I misunderstand this. I thought this would scale my Aggregate and AEs both.

I want to scale aggregate as well as individual AEs. Is there any way of doing this in UIMA AS/DUCC?


On 04/28/2015 07:14 PM, Jaroslaw Cwiklik wrote:
In async aggregate you scale individual AEs not the aggregate as a whole.
The below configuration should do that. Are there any warnings from
dd2spring at startup with your configuration?

<analysisEngine async="true" >

                                 <delegates>
                                         <analysisEngine
key="ChunkerDescriptor">
                                                 <scaleout
numberOfInstances="5" />
                                         </analysisEngine>
                                         <analysisEngine key="NEDescriptor">
                                                 <scaleout
numberOfInstances="5" />
                                         </analysisEngine>
                                         <analysisEngine
key="StemmerDescriptor">
                                                 <scaleout
numberOfInstances="5" />
                                         </analysisEngine>
                                         <analysisEngine
key="ConsumerDescriptor">
                                                 <scaleout
numberOfInstances="5" />
                                         </analysisEngine>
                                 </delegates>
                         </analysisEngine>

Jerry

On Tue, Apr 28, 2015 at 5:20 AM, reshu.agarwal <reshu.agar...@orkash.com>
wrote:

Hi,

I was trying to scale my processing pipeline to be run in DUCC environment
with uima as process_dd. If I was trying to scale using the below given
configuration, the threads started were not as expected:


<analysisEngineDeploymentDescription
         xmlns="http://uima.apache.org/resourceSpecifier";>

         <name>Uima v3 Deployment Descripter</name>
         <description>Deploys Uima v3 Aggregate AE using the Advanced Fixed
Flow
                 Controller</description>

         <deployment protocol="jms" provider="activemq">
                 <casPool numberOfCASes="5" />
                 <service>
                         <inputQueue endpoint="UIMA_Queue_test"
brokerURL="tcp://localhost:61617?jms.useCompression=true" prefetch="0" />
                         <topDescriptor>
                                 <import
location="../Uima_v3_test/desc/orkash/ae/aggregate/FlowController_Uima.xml"
/>
                         </topDescriptor>
                         <analysisEngine async="true"
key="FlowControllerAgg" internalReplyQueueScaleout="10"
inputQueueScaleout="10">
                                 <scaleout numberOfInstances="5"/>
                                 <delegates>
                                         <analysisEngine
key="ChunkerDescriptor">
                                                 <scaleout
numberOfInstances="5" />
                                         </analysisEngine>
                                         <analysisEngine key="NEDescriptor">
                                                 <scaleout
numberOfInstances="5" />
                                         </analysisEngine>
                                         <analysisEngine
key="StemmerDescriptor">
                                                 <scaleout
numberOfInstances="5" />
                                         </analysisEngine>
                                         <analysisEngine
key="ConsumerDescriptor">
                                                 <scaleout
numberOfInstances="5" />
                                         </analysisEngine>
                                 </delegates>
                         </analysisEngine>
                 </service>
         </deployment>

</analysisEngineDeploymentDescription>


There should be 5 threads of FlowControllerAgg where each thread will have
5 more threads of each ChunkerDescriptor,NEDescriptor,StemmerDescriptor and
ConsumerDescriptor.

But I didn't think it is actually happening in case of DUCC.

Thanks in advance.

Reshu.




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