I am having a hard time figuring out how to get a simple SCDF pipeline functional.
I am using a local setup:
{
"versionInfo": {
"implementation": {
"name": "spring-cloud-dataflow-server-local",
"version": "1.6.0.BUILD-SNAPSHOT"
},
"core": {
"name": "Spring Cloud Data Flow Core",
"version": "1.6.0.BUILD-SNAPSHOT"
},
"dashboard": {
"name": "Spring Cloud Dataflow UI",
"version": "1.6.0.M1"
},
"shell": {
"name": "Spring Cloud Data Flow Shell",
"version": "1.6.0.BUILD-SNAPSHOT",
"url": "https://repo.spring.io/libs-snapshot/org/springframework/cloud/spring-cloud-dataflow-shell/1.6.0.BUILD-SNAPSHOT/spring-cloud-dataflow-shell-1.6.0.BUILD-SNAPSHOT.jar"
}
},
"featureInfo": {
"analyticsEnabled": true,
"streamsEnabled": true,
"tasksEnabled": true,
"skipperEnabled": false
},
"securityInfo": {
"isAuthenticationEnabled": false,
"isAuthorizationEnabled": false,
"isFormLogin": false,
"isAuthenticated": false,
"username": null,
"roles": []
},
"runtimeEnvironment": {
"appDeployer": {
"platformSpecificInfo": {},
"deployerImplementationVersion": "1.3.7.RELEASE",
"deployerName": "LocalAppDeployer",
"deployerSpiVersion": "1.3.2.RELEASE",
"javaVersion": "1.8.0_45",
"platformApiVersion": "Mac OS X 10.13.4",
"platformClientVersion": "10.13.4",
"platformHostVersion": "10.13.4",
"platformType": "Local",
"springBootVersion": "1.5.14.RELEASE",
"springVersion": "4.3.18.RELEASE"
},
"taskLauncher": {
"platformSpecificInfo": {},
"deployerImplementationVersion": "1.3.7.RELEASE",
"deployerName": "LocalTaskLauncher",
"deployerSpiVersion": "1.3.2.RELEASE",
"javaVersion": "1.8.0_45",
"platformApiVersion": "Mac OS X 10.13.4",
"platformClientVersion": "10.13.4",
"platformHostVersion": "10.13.4",
"platformType": "Local",
"springBootVersion": "1.5.14.RELEASE",
"springVersion": "4.3.18.RELEASE"
}
}
}
The pipeline is pretty simple:
http --port=9191 | transform --expression=payload.toUpperCase() | log
When I trigger the http endpoint with cURL like this:
curl -v -H"Referer: http://localhost:8080" -H"Content-Type: text/plain" -XPOST localhost:9191/ -d 'test'
I see the following error message in the logfile of the transform processor:
2018-07-11 09:56:59.758 ERROR 66396 --- [container-0-C-1] o.s.kafka.listener.LoggingErrorHandler : Error while processing: ConsumerRecord(topic = edded.http, partition = 0, offset = 0, CreateTime = 1531295816669, serialized key size = -1, serialized value size = 17, headers = RecordHeaders(headers = [RecordHeader(key = referer, value = [34, 104, 116, 116, 112, 58, 47, 47, 108, 111, 99, 97, 108, 104, 111, 115, 116, 58, 56, 48, 56, 48, 34]), RecordHeader(key = content-length, value = [49, 55]), RecordHeader(key = http_requestMethod, value = [34, 80, 79, 83, 84, 34]), RecordHeader(key = host, value = [34, 108, 111, 99, 97, 108, 104, 111, 115, 116, 58, 57, 49, 57, 49, 34]), RecordHeader(key = http_requestUrl, value = [34, 104, 116, 116, 112, 58, 47, 47, 108, 111, 99, 97, 108, 104, 111, 115, 116, 58, 57, 49, 57, 49, 47, 34]), RecordHeader(key = contentType, value = [123, 34, 116, 121, 112, 101, 34, 58, 34, 116, 101, 120, 116, 34, 44, 34, 115, 117, 98, 116, 121, 112, 101, 34, 58, 34, 112, 108, 97, 105, 110, 34, 44, 34, 112, 97, 114, 97, 109, 101, 116, 101, 114, 115, 34, 58, 123, 34, 99, 104, 97, 114, 115, 101, 116, 34, 58, 34, 85, 84, 70, 45, 56, 34, 125, 44, 34, 113, 117, 97, 108, 105, 116, 121, 86, 97, 108, 117, 101, 34, 58, 49, 46, 48, 44, 34, 99, 104, 97, 114, 115, 101, 116, 34, 58, 34, 85, 84, 70, 45, 56, 34, 44, 34, 119, 105, 108, 100, 99, 97, 114, 100, 84, 121, 112, 101, 34, 58, 102, 97, 108, 115, 101, 44, 34, 119, 105, 108, 100, 99, 97, 114, 100, 83, 117, 98, 116, 121, 112, 101, 34, 58, 102, 97, 108, 115, 101, 44, 34, 99, 111, 110, 99, 114, 101, 116, 101, 34, 58, 116, 114, 117, 101, 125]), RecordHeader(key = user-agent, value = [34, 77, 111, 122, 105, 108, 108, 97, 47, 53, 46, 48, 32, 40, 99, 111, 109, 112, 97, 116, 105, 98, 108, 101, 59, 32, 77, 83, 73, 69, 32, 57, 46, 48, 59, 32, 87, 105, 110, 100, 111, 119, 115, 32, 78, 84, 32, 54, 46, 49, 59, 32, 84, 114, 105, 100, 101, 110, 116, 47, 53, 46, 48, 41, 34]), RecordHeader(key = accept, value = [123, 34, 116, 121, 112, 101, 34, 58, 34, 42, 34, 44, 34, 115, 117, 98, 116, 121, 112, 101, 34, 58, 34, 42, 34, 44, 34, 112, 97, 114, 97, 109, 101, 116, 101, 114, 115, 34, 58, 123, 125, 44, 34, 113, 117, 97, 108, 105, 116, 121, 86, 97, 108, 117, 101, 34, 58, 49, 46, 48, 44, 34, 99, 104, 97, 114, 115, 101, 116, 34, 58, 110, 117, 108, 108, 44, 34, 119, 105, 108, 100, 99, 97, 114, 100, 84, 121, 112, 101, 34, 58, 116, 114, 117, 101, 44, 34, 119, 105, 108, 100, 99, 97, 114, 100, 83, 117, 98, 116, 121, 112, 101, 34, 58, 116, 114, 117, 101, 44, 34, 99, 111, 110, 99, 114, 101, 116, 101, 34, 58, 102, 97, 108, 115, 101, 125]), RecordHeader(key = spring_json_header_types, value = [123, 34, 114, 101, 102, 101, 114, 101, 114, 34, 58, 34, 106, 97, 118, 97, 46, 108, 97, 110, 103, 46, 83, 116, 114, 105, 110, 103, 34, 44, 34, 99, 111, 110, 116, 101, 110, 116, 45, 108, 101, 110, 103, 116, 104, 34, 58, 34, 106, 97, 118, 97, 46, 108, 97, 110, 103, 46, 76, 111, 110, 103, 34, 44, 34, 104, 116, 116, 112, 95, 114, 101, 113, 117, 101, 115, 116, 77, 101, 116, 104, 111, 100, 34, 58, 34, 106, 97, 118, 97, 46, 108, 97, 110, 103, 46, 83, 116, 114, 105, 110, 103, 34, 44, 34, 104, 111, 115, 116, 34, 58, 34, 106, 97, 118, 97, 46, 108, 97, 110, 103, 46, 83, 116, 114, 105, 110, 103, 34, 44, 34, 104, 116, 116, 112, 95, 114, 101, 113, 117, 101, 115, 116, 85, 114, 108, 34, 58, 34, 106, 97, 118, 97, 46, 108, 97, 110, 103, 46, 83, 116, 114, 105, 110, 103, 34, 44, 34, 99, 111, 110, 116, 101, 110, 116, 84, 121, 112, 101, 34, 58, 34, 111, 114, 103, 46, 115, 112, 114, 105, 110, 103, 102, 114, 97, 109, 101, 119, 111, 114, 107, 46, 104, 116, 116, 112, 46, 77, 101, 100, 105, 97, 84, 121, 112, 101, 34, 44, 34, 117, 115, 101, 114, 45, 97, 103, 101, 110, 116, 34, 58, 34, 106, 97, 118, 97, 46, 108, 97, 110, 103, 46, 83, 116, 114, 105, 110, 103, 34, 44, 34, 97, 99, 99, 101, 112, 116, 34, 58, 34, 111, 114, 103, 46, 115, 112, 114, 105, 110, 103, 102, 114, 97, 109, 101, 119, 111, 114, 107, 46, 104, 116, 116, 112, 46, 77, 101, 100, 105, 97, 84, 121, 112, 101, 34, 125])], isReadOnly = false), key = null, value = [B#4bc28689)
org.springframework.messaging.MessageHandlingException: nested exception is org.springframework.expression.spel.SpelEvaluationException: EL1004E: Method call: Method toUpperCase() cannot be found on type byte[]
at org.springframework.integration.handler.MethodInvokingMessageProcessor.processMessage(MethodInvokingMessageProcessor.java:107) ~[spring-integration-core-5.0.6.RELEASE.jar!/:5.0.6.RELEASE]
at org.springframework.integration.handler.ServiceActivatingHandler.handleRequestMessage(ServiceActivatingHandler.java:93) ~[spring-integration-core-5.0.6.RELEASE.jar!/:5.0.6.RELEASE]
at org.springframework.integration.handler.AbstractReplyProducingMessageHandler.handleMessageInternal(AbstractReplyProducingMessageHandler.java:109) ~[spring-integration-core-5.0.6.RELEASE.jar!/:5.0.6.RELEASE]
at org.springframework.integration.handler.AbstractMessageHandler.handleMessage(AbstractMessageHandler.java:158) ~[spring-integration-core-5.0.6.RELEASE.jar!/:5.0.6.RELEASE]
at org.springframework.integration.dispatcher.AbstractDispatcher.tryOptimizedDispatch(AbstractDispatcher.java:116) ~[spring-integration-core-5.0.6.RELEASE.jar!/:5.0.6.RELEASE]
at org.springframework.integration.dispatcher.UnicastingDispatcher.doDispatch(UnicastingDispatcher.java:132) ~[spring-integration-core-5.0.6.RELEASE.jar!/:5.0.6.RELEASE]
at org.springframework.integration.dispatcher.UnicastingDispatcher.dispatch(UnicastingDispatcher.java:105) ~[spring-integration-core-5.0.6.RELEASE.jar!/:5.0.6.RELEASE]
at org.springframework.integration.channel.AbstractSubscribableChannel.doSend(AbstractSubscribableChannel.java:73) ~[spring-integration-core-5.0.6.RELEASE.jar!/:5.0.6.RELEASE]
at org.springframework.integration.channel.AbstractMessageChannel.send(AbstractMessageChannel.java:445) ~[spring-integration-core-5.0.6.RELEASE.jar!/:5.0.6.RELEASE]
at org.springframework.integration.channel.AbstractMessageChannel.send(AbstractMessageChannel.java:394) ~[spring-integration-core-5.0.6.RELEASE.jar!/:5.0.6.RELEASE]
at org.springframework.messaging.core.GenericMessagingTemplate.doSend(GenericMessagingTemplate.java:181) ~[spring-messaging-5.0.7.RELEASE.jar!/:5.0.7.RELEASE]
at org.springframework.messaging.core.GenericMessagingTemplate.doSend(GenericMessagingTemplate.java:160) ~[spring-messaging-5.0.7.RELEASE.jar!/:5.0.7.RELEASE]
at org.springframework.messaging.core.GenericMessagingTemplate.doSend(GenericMessagingTemplate.java:47) ~[spring-messaging-5.0.7.RELEASE.jar!/:5.0.7.RELEASE]
at org.springframework.messaging.core.AbstractMessageSendingTemplate.send(AbstractMessageSendingTemplate.java:108) ~[spring-messaging-5.0.7.RELEASE.jar!/:5.0.7.RELEASE]
at org.springframework.integration.endpoint.MessageProducerSupport.sendMessage(MessageProducerSupport.java:203) ~[spring-integration-core-5.0.6.RELEASE.jar!/:5.0.6.RELEASE]
at org.springframework.integration.kafka.inbound.KafkaMessageDrivenChannelAdapter.access$300(KafkaMessageDrivenChannelAdapter.java:70) ~[spring-integration-kafka-3.0.3.RELEASE.jar!/:3.0.3.RELEASE]
at org.springframework.integration.kafka.inbound.KafkaMessageDrivenChannelAdapter$IntegrationRecordMessageListener.onMessage(KafkaMessageDrivenChannelAdapter.java:387) ~[spring-integration-kafka-3.0.3.RELEASE.jar!/:3.0.3.RELEASE]
at org.springframework.integration.kafka.inbound.KafkaMessageDrivenChannelAdapter$IntegrationRecordMessageListener.onMessage(KafkaMessageDrivenChannelAdapter.java:364) ~[spring-integration-kafka-3.0.3.RELEASE.jar!/:3.0.3.RELEASE]
at org.springframework.kafka.listener.adapter.RetryingMessageListenerAdapter.lambda$onMessage$0(RetryingMessageListenerAdapter.java:120) ~[spring-kafka-2.1.7.RELEASE.jar!/:2.1.7.RELEASE]
at org.springframework.kafka.listener.adapter.RetryingMessageListenerAdapter$$Lambda$659/1406308390.doWithRetry(Unknown Source) ~[na:na]
at org.springframework.retry.support.RetryTemplate.doExecute(RetryTemplate.java:287) ~[spring-retry-1.2.2.RELEASE.jar!/:na]
at org.springframework.retry.support.RetryTemplate.execute(RetryTemplate.java:211) ~[spring-retry-1.2.2.RELEASE.jar!/:na]
at org.springframework.kafka.listener.adapter.RetryingMessageListenerAdapter.onMessage(RetryingMessageListenerAdapter.java:114) ~[spring-kafka-2.1.7.RELEASE.jar!/:2.1.7.RELEASE]
at org.springframework.kafka.listener.adapter.RetryingMessageListenerAdapter.onMessage(RetryingMessageListenerAdapter.java:40) ~[spring-kafka-2.1.7.RELEASE.jar!/:2.1.7.RELEASE]
at org.springframework.kafka.listener.KafkaMessageListenerContainer$ListenerConsumer.doInvokeRecordListener(KafkaMessageListenerContainer.java:1071) [spring-kafka-2.1.7.RELEASE.jar!/:2.1.7.RELEASE]
at org.springframework.kafka.listener.KafkaMessageListenerContainer$ListenerConsumer.doInvokeWithRecords(KafkaMessageListenerContainer.java:1051) [spring-kafka-2.1.7.RELEASE.jar!/:2.1.7.RELEASE]
at org.springframework.kafka.listener.KafkaMessageListenerContainer$ListenerConsumer.invokeRecordListener(KafkaMessageListenerContainer.java:998) [spring-kafka-2.1.7.RELEASE.jar!/:2.1.7.RELEASE]
at org.springframework.kafka.listener.KafkaMessageListenerContainer$ListenerConsumer.invokeListener(KafkaMessageListenerContainer.java:866) [spring-kafka-2.1.7.RELEASE.jar!/:2.1.7.RELEASE]
at org.springframework.kafka.listener.KafkaMessageListenerContainer$ListenerConsumer.run(KafkaMessageListenerContainer.java:724) [spring-kafka-2.1.7.RELEASE.jar!/:2.1.7.RELEASE]
at java.util.concurrent.Executors$RunnableAdapter.call(Executors.java:511) [na:1.8.0_45]
at java.util.concurrent.FutureTask.run(FutureTask.java:266) [na:1.8.0_45]
at java.lang.Thread.run(Thread.java:745) [na:1.8.0_45]
Since i've provided the Content-Type header in HTTP request, and after reading this blog post, I assumed that during message conversion the payload of the message (I understand the default wire format for Kafka is byte[]) would then be converted to a String representation. However, the type of the Message.payload that TransformProcessorConfiguration.transform receives is still byte[].
Does this behavior have something to do with the fact that the Content-Type header appears as a NonTrustedHeaderType in the MessagingMessageConverter.toMessage() call? Stepping through with the debugger shows the following for the contentType header:
headerValue = {"type":"text","subtype":"plain","parameters":{"charset":"UTF-8"},"qualityValue":1.0,"charset":"UTF-8","wildcardType":false,"wildcardSubtype":false,"concrete":true}
untrustedType = "org.springframework.http.MediaType"
This is the list of rawHeaders that the MessagingMessageConverter resolves:
"referer"->"http://localhost:8080"
"content-length"->"17"
"http_requestMethod"->"POST"
"kafka_timestampType"->"CREATE_TIME"
"kafka_receivedMessageKey"->"null"
"kafka_receivedTopic"->"edded.http"
"accept"->"NonTrustedHeaderType
"kafka_offset"->"1"
"scst_nativeHeadersPresent"->"true"
"kafka_consumer"->
"host"->"localhost:9191"
"http_requestUrl"->"http://localhost:9191/"
"kafka_receivedPartitionId"->"0"
"contentType"->"NonTrustedHeaderType
"kafka_receivedTimestamp"->"1531296520235"
"user-agent"->"Mozilla/5.0
Another potentially related issue that I found is described here. However, I have no clue how to control mappers trustedPackages via binder properties, if that is at all related to my problem.
I also tried setting app.*.spring.cloud.stream.bindings.input.producer.headerMode=raw in the deployment properties, did not have any effect.
Thanks!
Actually the blog you pointed should not result in the assumption that there will be conversion based on the content-type header. Conversion is done only based on the type required by the handler and if such type is generic (i.e., Object) or byte[], no conversion will be performed. What is the signature of the TransformProcessorConfiguration.transform(..) method? Also, if you are attempting to do any kind of SPEL evaluation on the payload, you must assume that it is always a byte[] since conversion will happen only when a handler method is about to be invoked, so if you are using some expression in condition on the payload and assume String, don't.
I am using MATLAB to look up the value in two vectors OCT_EXP, OCT_LOG, from two input values u,v and output is val as condition
if (( u == 0 )||( v == 0 ))
val = 0;
else
val = OCT_EXP( OCT_LOG(u) + OCT_LOG(v) + 1);
I tried to use three ways: normal way (no_vectorized way), vectorized way, and mex way. I expected that mex way will be the best way, then vectorized way. However, when I measure time consumption, the first way (no vectorized way) is best, the vectorized way is worst way. What is happen in my code? Thank all
I want to consider the speed up of the function because it will be called many time: 300.000 times
The first way:
function val = gfmult_no_vec( u, v )
OCT_EXP = [ 1, 2, 4, 8, 16, 32, 64, 128, 29, 58, 116, 232, 205, 135, 19, 38,...
76, 152, 45, 90, 180, 117, 234, 201, 143, 3, 6, 12, 24, 48, 96, 192, 157,...
39, 78, 156, 37, 74, 148, 53, 106, 212, 181, 119, 238, 193, 159, 35,...
70, 140, 5, 10, 20, 40, 80, 160, 93, 186, 105, 210, 185, 111, 222,...
161, 95, 190, 97, 194, 153, 47, 94, 188, 101, 202, 137, 15, 30, 60,...
120, 240, 253, 231, 211, 187, 107, 214, 177, 127, 254, 225, 223, 163,...
91, 182, 113, 226, 217, 175, 67, 134, 17, 34, 68, 136, 13, 26, 52,...
104, 208, 189, 103, 206, 129, 31, 62, 124, 248, 237, 199, 147, 59,...
118, 236, 197, 151, 51, 102, 204, 133, 23, 46, 92, 184, 109, 218,...
169, 79, 158, 33, 66, 132, 21, 42, 84, 168, 77, 154, 41, 82, 164, 85,...
170, 73, 146, 57, 114, 228, 213, 183, 115, 230, 209, 191, 99, 198,...
145, 63, 126, 252, 229, 215, 179, 123, 246, 241, 255, 227, 219, 171,...
75, 150, 49, 98, 196, 149, 55, 110, 220, 165, 87, 174, 65, 130, 25,...
50, 100, 200, 141, 7, 14, 28, 56, 112, 224, 221, 167, 83, 166, 81,...
162, 89, 178, 121, 242, 249, 239, 195, 155, 43, 86, 172, 69, 138, 9,...
18, 36, 72, 144, 61, 122, 244, 245, 247, 243, 251, 235, 203, 139, 11,...
22, 44, 88, 176, 125, 250, 233, 207, 131, 27, 54, 108, 216, 173, 71,...
142, 1, 2, 4, 8, 16, 32, 64, 128, 29, 58, 116, 232, 205, 135, 19, 38,...
76, 152, 45, 90, 180, 117, 234, 201, 143, 3, 6, 12, 24, 48, 96, 192,...
157, 39, 78, 156, 37, 74, 148, 53, 106, 212, 181, 119, 238, 193, 159,...
35, 70, 140, 5, 10, 20, 40, 80, 160, 93, 186, 105, 210, 185, 111,...
222, 161, 95, 190, 97, 194, 153, 47, 94, 188, 101, 202, 137, 15, 30,...
60, 120, 240, 253, 231, 211, 187, 107, 214, 177, 127, 254, 225, 223,...
163, 91, 182, 113, 226, 217, 175, 67, 134, 17, 34, 68, 136, 13, 26,...
52, 104, 208, 189, 103, 206, 129, 31, 62, 124, 248, 237, 199, 147,...
59, 118, 236, 197, 151, 51, 102, 204, 133, 23, 46, 92, 184, 109, 218,...
169, 79, 158, 33, 66, 132, 21, 42, 84, 168, 77, 154, 41, 82, 164, 85,...
170, 73, 146, 57, 114, 228, 213, 183, 115, 230, 209, 191, 99, 198,...
145, 63, 126, 252, 229, 215, 179, 123, 246, 241, 255, 227, 219, 171,...
75, 150, 49, 98, 196, 149, 55, 110, 220, 165, 87, 174, 65, 130, 25,...
50, 100, 200, 141, 7, 14, 28, 56, 112, 224, 221, 167, 83, 166, 81,...
162, 89, 178, 121, 242, 249, 239, 195, 155, 43, 86, 172, 69, 138, 9,...
18, 36, 72, 144, 61, 122, 244, 245, 247, 243, 251, 235, 203, 139, 11,...
22, 44, 88, 176, 125, 250, 233, 207, 131, 27, 54, 108, 216, 173, 71,...
142 ];
OCT_LOG = [ 0, 1, 25, 2, 50, 26, 198, 3, 223, 51, 238, 27, 104, 199, 75, 4,...
100, 224, 14, 52, 141, 239, 129, 28, 193, 105, 248, 200, 8, 76, 113, 5,...
138, 101, 47, 225, 36, 15, 33, 53, 147, 142, 218, 240, 18, 130, 69,...
29, 181, 194, 125, 106, 39, 249, 185, 201, 154, 9, 120, 77, 228, 114,... end
166, 6, 191, 139, 98, 102, 221, 48, 253, 226, 152, 37, 179, 16, 145,...
34, 136, 54, 208, 148, 206, 143, 150, 219, 189, 241, 210, 19, 92,...
131, 56, 70, 64, 30, 66, 182, 163, 195, 72, 126, 110, 107, 58, 40,...
84, 250, 133, 186, 61, 202, 94, 155, 159, 10, 21, 121, 43, 78, 212,...
229, 172, 115, 243, 167, 87, 7, 112, 192, 247, 140, 128, 99, 13, 103,...
74, 222, 237, 49, 197, 254, 24, 227, 165, 153, 119, 38, 184, 180,...
124, 17, 68, 146, 217, 35, 32, 137, 46, 55, 63, 209, 91, 149, 188,...
207, 205, 144, 135, 151, 178, 220, 252, 190, 97, 242, 86, 211, 171,...
20, 42, 93, 158, 132, 60, 57, 83, 71, 109, 65, 162, 31, 45, 67, 216,...
183, 123, 164, 118, 196, 23, 73, 236, 127, 12, 111, 246, 108, 161,...
59, 82, 41, 157, 85, 170, 251, 96, 134, 177, 187, 204, 62, 90, 203,...
89, 95, 176, 156, 169, 160, 81, 11, 245, 22, 235, 122, 117, 44, 215,...
79, 174, 213, 233, 230, 231, 173, 232, 116, 214, 244, 234, 168, 80,...
88, 175 ];
if (( u == 0 )||( v == 0 ))
val = 0;
else
val = OCT_EXP( OCT_LOG(u) + OCT_LOG(v) + 1);
The second way: Vectorized way
function val = gfmult_vec( u, v )
OCT_EXP = [ 1, 2, 4, 8, 16, 32, 64, 128, 29, 58, 116, 232, 205, 135, 19, 38,...
76, 152, 45, 90, 180, 117, 234, 201, 143, 3, 6, 12, 24, 48, 96, 192, 157,...
39, 78, 156, 37, 74, 148, 53, 106, 212, 181, 119, 238, 193, 159, 35,...
70, 140, 5, 10, 20, 40, 80, 160, 93, 186, 105, 210, 185, 111, 222,...
161, 95, 190, 97, 194, 153, 47, 94, 188, 101, 202, 137, 15, 30, 60,...
120, 240, 253, 231, 211, 187, 107, 214, 177, 127, 254, 225, 223, 163,...
91, 182, 113, 226, 217, 175, 67, 134, 17, 34, 68, 136, 13, 26, 52,...
104, 208, 189, 103, 206, 129, 31, 62, 124, 248, 237, 199, 147, 59,...
118, 236, 197, 151, 51, 102, 204, 133, 23, 46, 92, 184, 109, 218,...
169, 79, 158, 33, 66, 132, 21, 42, 84, 168, 77, 154, 41, 82, 164, 85,...
170, 73, 146, 57, 114, 228, 213, 183, 115, 230, 209, 191, 99, 198,...
145, 63, 126, 252, 229, 215, 179, 123, 246, 241, 255, 227, 219, 171,...
75, 150, 49, 98, 196, 149, 55, 110, 220, 165, 87, 174, 65, 130, 25,...
50, 100, 200, 141, 7, 14, 28, 56, 112, 224, 221, 167, 83, 166, 81,...
162, 89, 178, 121, 242, 249, 239, 195, 155, 43, 86, 172, 69, 138, 9,...
18, 36, 72, 144, 61, 122, 244, 245, 247, 243, 251, 235, 203, 139, 11,...
22, 44, 88, 176, 125, 250, 233, 207, 131, 27, 54, 108, 216, 173, 71,...
142, 1, 2, 4, 8, 16, 32, 64, 128, 29, 58, 116, 232, 205, 135, 19, 38,...
76, 152, 45, 90, 180, 117, 234, 201, 143, 3, 6, 12, 24, 48, 96, 192,...
157, 39, 78, 156, 37, 74, 148, 53, 106, 212, 181, 119, 238, 193, 159,...
35, 70, 140, 5, 10, 20, 40, 80, 160, 93, 186, 105, 210, 185, 111,...
222, 161, 95, 190, 97, 194, 153, 47, 94, 188, 101, 202, 137, 15, 30,...
60, 120, 240, 253, 231, 211, 187, 107, 214, 177, 127, 254, 225, 223,...
163, 91, 182, 113, 226, 217, 175, 67, 134, 17, 34, 68, 136, 13, 26,...
52, 104, 208, 189, 103, 206, 129, 31, 62, 124, 248, 237, 199, 147,...
59, 118, 236, 197, 151, 51, 102, 204, 133, 23, 46, 92, 184, 109, 218,...
169, 79, 158, 33, 66, 132, 21, 42, 84, 168, 77, 154, 41, 82, 164, 85,...
170, 73, 146, 57, 114, 228, 213, 183, 115, 230, 209, 191, 99, 198,...
145, 63, 126, 252, 229, 215, 179, 123, 246, 241, 255, 227, 219, 171,...
75, 150, 49, 98, 196, 149, 55, 110, 220, 165, 87, 174, 65, 130, 25,...
50, 100, 200, 141, 7, 14, 28, 56, 112, 224, 221, 167, 83, 166, 81,...
162, 89, 178, 121, 242, 249, 239, 195, 155, 43, 86, 172, 69, 138, 9,...
18, 36, 72, 144, 61, 122, 244, 245, 247, 243, 251, 235, 203, 139, 11,...
22, 44, 88, 176, 125, 250, 233, 207, 131, 27, 54, 108, 216, 173, 71,...
142 ];
OCT_LOG = [ 0, 1, 25, 2, 50, 26, 198, 3, 223, 51, 238, 27, 104, 199, 75, 4,...
100, 224, 14, 52, 141, 239, 129, 28, 193, 105, 248, 200, 8, 76, 113, 5,...
138, 101, 47, 225, 36, 15, 33, 53, 147, 142, 218, 240, 18, 130, 69,...
29, 181, 194, 125, 106, 39, 249, 185, 201, 154, 9, 120, 77, 228, 114,...
166, 6, 191, 139, 98, 102, 221, 48, 253, 226, 152, 37, 179, 16, 145,...
34, 136, 54, 208, 148, 206, 143, 150, 219, 189, 241, 210, 19, 92,...
131, 56, 70, 64, 30, 66, 182, 163, 195, 72, 126, 110, 107, 58, 40,...
84, 250, 133, 186, 61, 202, 94, 155, 159, 10, 21, 121, 43, 78, 212,...
229, 172, 115, 243, 167, 87, 7, 112, 192, 247, 140, 128, 99, 13, 103,...
74, 222, 237, 49, 197, 254, 24, 227, 165, 153, 119, 38, 184, 180,...
124, 17, 68, 146, 217, 35, 32, 137, 46, 55, 63, 209, 91, 149, 188,...
207, 205, 144, 135, 151, 178, 220, 252, 190, 97, 242, 86, 211, 171,...
20, 42, 93, 158, 132, 60, 57, 83, 71, 109, 65, 162, 31, 45, 67, 216,...
183, 123, 164, 118, 196, 23, 73, 236, 127, 12, 111, 246, 108, 161,...
59, 82, 41, 157, 85, 170, 251, 96, 134, 177, 187, 204, 62, 90, 203,...
89, 95, 176, 156, 169, 160, 81, 11, 245, 22, 235, 122, 117, 44, 215,...
79, 174, 213, 233, 230, 231, 173, 232, 116, 214, 244, 234, 168, 80,...
88, 175 ];
uv0 = (~(( u == 0 )|( v == 0 )));
val = zeros(size(u));
val(uv0) = OCT_EXP( OCT_LOG(u(uv0)) + OCT_LOG(v(uv0)) + 1);
end
The last way: mex code
#include "mex.h"
double look_up(double u, double v)
{
double OCT_EXP [510] = { 1, 2, 4, 8, 16, 32, 64, 128, 29, 58, 116, 232, 205, 135, 19, 38,
76, 152, 45, 90, 180, 117, 234, 201, 143, 3, 6, 12, 24, 48, 96, 192, 157,
39, 78, 156, 37, 74, 148, 53, 106, 212, 181, 119, 238, 193, 159, 35,
70, 140, 5, 10, 20, 40, 80, 160, 93, 186, 105, 210, 185, 111, 222,
161, 95, 190, 97, 194, 153, 47, 94, 188, 101, 202, 137, 15, 30, 60,
120, 240, 253, 231, 211, 187, 107, 214, 177, 127, 254, 225, 223, 163,
91, 182, 113, 226, 217, 175, 67, 134, 17, 34, 68, 136, 13, 26, 52,
104, 208, 189, 103, 206, 129, 31, 62, 124, 248, 237, 199, 147, 59,
118, 236, 197, 151, 51, 102, 204, 133, 23, 46, 92, 184, 109, 218,
169, 79, 158, 33, 66, 132, 21, 42, 84, 168, 77, 154, 41, 82, 164, 85,
170, 73, 146, 57, 114, 228, 213, 183, 115, 230, 209, 191, 99, 198,
145, 63, 126, 252, 229, 215, 179, 123, 246, 241, 255, 227, 219, 171,
75, 150, 49, 98, 196, 149, 55, 110, 220, 165, 87, 174, 65, 130, 25,
50, 100, 200, 141, 7, 14, 28, 56, 112, 224, 221, 167, 83, 166, 81,
162, 89, 178, 121, 242, 249, 239, 195, 155, 43, 86, 172, 69, 138, 9,
18, 36, 72, 144, 61, 122, 244, 245, 247, 243, 251, 235, 203, 139, 11,
22, 44, 88, 176, 125, 250, 233, 207, 131, 27, 54, 108, 216, 173, 71,
142, 1, 2, 4, 8, 16, 32, 64, 128, 29, 58, 116, 232, 205, 135, 19, 38,
76, 152, 45, 90, 180, 117, 234, 201, 143, 3, 6, 12, 24, 48, 96, 192,
157, 39, 78, 156, 37, 74, 148, 53, 106, 212, 181, 119, 238, 193, 159,
35, 70, 140, 5, 10, 20, 40, 80, 160, 93, 186, 105, 210, 185, 111,
222, 161, 95, 190, 97, 194, 153, 47, 94, 188, 101, 202, 137, 15, 30,
60, 120, 240, 253, 231, 211, 187, 107, 214, 177, 127, 254, 225, 223,
163, 91, 182, 113, 226, 217, 175, 67, 134, 17, 34, 68, 136, 13, 26,
52, 104, 208, 189, 103, 206, 129, 31, 62, 124, 248, 237, 199, 147,
59, 118, 236, 197, 151, 51, 102, 204, 133, 23, 46, 92, 184, 109, 218,
169, 79, 158, 33, 66, 132, 21, 42, 84, 168, 77, 154, 41, 82, 164, 85,
170, 73, 146, 57, 114, 228, 213, 183, 115, 230, 209, 191, 99, 198,
145, 63, 126, 252, 229, 215, 179, 123, 246, 241, 255, 227, 219, 171,
75, 150, 49, 98, 196, 149, 55, 110, 220, 165, 87, 174, 65, 130, 25,
50, 100, 200, 141, 7, 14, 28, 56, 112, 224, 221, 167, 83, 166, 81,
162, 89, 178, 121, 242, 249, 239, 195, 155, 43, 86, 172, 69, 138, 9,
18, 36, 72, 144, 61, 122, 244, 245, 247, 243, 251, 235, 203, 139, 11,
22, 44, 88, 176, 125, 250, 233, 207, 131, 27, 54, 108, 216, 173, 71,
142 };
double OCT_LOG[255] = { 0, 1, 25, 2, 50, 26, 198, 3, 223, 51, 238, 27, 104, 199, 75, 4,
100, 224, 14, 52, 141, 239, 129, 28, 193, 105, 248, 200, 8, 76, 113, 5,
138, 101, 47, 225, 36, 15, 33, 53, 147, 142, 218, 240, 18, 130, 69,
29, 181, 194, 125, 106, 39, 249, 185, 201, 154, 9, 120, 77, 228, 114,
166, 6, 191, 139, 98, 102, 221, 48, 253, 226, 152, 37, 179, 16, 145,
34, 136, 54, 208, 148, 206, 143, 150, 219, 189, 241, 210, 19, 92,
131, 56, 70, 64, 30, 66, 182, 163, 195, 72, 126, 110, 107, 58, 40,
84, 250, 133, 186, 61, 202, 94, 155, 159, 10, 21, 121, 43, 78, 212,
229, 172, 115, 243, 167, 87, 7, 112, 192, 247, 140, 128, 99, 13, 103,
74, 222, 237, 49, 197, 254, 24, 227, 165, 153, 119, 38, 184, 180,
124, 17, 68, 146, 217, 35, 32, 137, 46, 55, 63, 209, 91, 149, 188,
207, 205, 144, 135, 151, 178, 220, 252, 190, 97, 242, 86, 211, 171,
20, 42, 93, 158, 132, 60, 57, 83, 71, 109, 65, 162, 31, 45, 67, 216,
183, 123, 164, 118, 196, 23, 73, 236, 127, 12, 111, 246, 108, 161,
59, 82, 41, 157, 85, 170, 251, 96, 134, 177, 187, 204, 62, 90, 203,
89, 95, 176, 156, 169, 160, 81, 11, 245, 22, 235, 122, 117, 44, 215,
79, 174, 213, 233, 230, 231, 173, 232, 116, 214, 244, 234, 168, 80,
88, 175 };
if (( u == 0 )||( v == 0 ))
return 0;
else
{
int index=OCT_LOG[int(u-1)] + OCT_LOG[int(v-1)] + 1;
return OCT_EXP [index-1];
}
}
void mexFunction(int nlhs, mxArray *plhs[], int nrhs, const mxArray *prhs[])
{
double *u, *v, *uv;
int mrows, ncols;
plhs[0] = mxCreateDoubleMatrix(1,1, mxREAL);
/* Assign pointers to each input and output. */
u = mxGetPr(prhs[0]);
v = mxGetPr(prhs[1]);
uv = mxGetPr(plhs[0]);
*uv = look_up(*u, *v);
}
The measurement code
function main
function test1()
for i=1:200
for j=1:200
gfmult_no_vec(i,j);
end
end
end
function test2()
for i=1:200
for j=1:200
gfmult_vec(i,j);
end
end
end
function test3()
for i=1:200
for j=1:200
gfmult_mex(i,j);
end
end
end
f1=#()test1();
t1=timeit(f1)
f2=#()test2();
t2=timeit(f2)
f3=#()test3();
t3=timeit(f3)
end
Report time:
t1 = 0.1934,
t2 = 1.1739,
t3 = 0.3584
This solution takes 0.0006 second in my computer (and it is vectorized):
u = randi(5,200,1)-1; % some arbitrary data including zeros
v = randi(5,200,1)-1; % some arbitrary data including zeros
[U,V] = ndgrid(u(u~=0),v(v~=0)); % make all possible combinations of u and v
val = zeros(length(u),length(v)); % initialize the output size, in case the last value in u or v is zero.
f = #(u,v) OCT_EXP(OCT_LOG(u)+OCT_LOG(v)+1);
val((u~=0),(v~=0)) = f(U,V);
Now val(u,v) = OCT_EXP(OCT_LOG(u)+OCT_LOG(v)+1) if u and v are both not zeros, otherwise val(u,v) = 0.
If you want gfmult to have a scalar input, then your first method seems to be the fastest way. However, I would define OCT_EXP and OCT_LOG outside the function and pass them to it, instead of assigning this values over and over:
function val = gfmult(OCT_EXP,OCT_LOG,u,v)
if (u==0)||(v==0)
val = 0;
else
val = OCT_EXP(OCT_LOG(u)+OCT_LOG(v)+1);
end
end
in my computer it reduces running time from 0.21444 (with your version) to 0.158 second for 100K iteration, which is not such a big improvement (0.05644 second), but if you have millions of those, it may be significant.