#Webcopier timeouts how to#

There are two options for avoiding the IOException: A new connection object will be created for subsequent requests. When the latter happens, the client-side engine marks that the connection object currently in use to be destroyed. There is no way for either of those components to know if the Socket is open or closed until it tries a request, and is either successful in reading from or writing to the Socket, or catches an IOException when it tries to do so.

This is the nature of Java™, not a limitation or bug in the web services engine or HTTP channel framework.

But if the server or intermediate server for http client, closes the Socket prior to x seconds, then the engine will not know that the Socket is closed until it actually tries to read or write to the Socket and encounters the IOException. If the last time the outbound connection object was used is less than x seconds, the engine will reuse this same object for performance reasons (and to provide persistent connections as part of HTTP/1.1). What this means is that the client-side engine allows the HTTP outbound connection object-the object responsible for sending SOAP over HTTP requests and reading resulting responses-to remain idle for x seconds. What is relevant is the value of the web services client property named .nnectionIdleTimeout, a JVM system property. WSE JVM or HttpOutboundServiceContextImpl should recognize the current state of TCP connection. This closes the socket, but the web services engine does not recognize this as being closed and tries to reuse it, resulting in exceptions like: In this situation, the keep alive on the http client is very low, 3 or so seconds.

HttpOutboundC 1 WSWS3228E: Error: Exception: java.io.IOException: Connection close: Read failed.

If someone has any experience with anything similar, or any thoughts about would this even result in an improvement I would very much appreciate it.Client Timers, connectionIdleTimeout and syncTimeout:

#Webcopier timeouts how to#

I thought about somehow taking the distances and the number of occurances of the class in the top 5 in order to get more accurate results, but for the heck of me I don't know how to do that. So, right now my program would classify this as 32, which is incorrect. So my logic is this: if, for example, the query class is marked as '45' and the results are like this: (32, distance), (45, distance+), (45, distance++), (21, distance+++), (24, distance++++). Since I'm already getting the top 5 most similar vectors, and since there are multiple different vectors for each class, there is a big chance (I presume) that there will be multiple vectors of the same class in the top 5. This works pretty good but I had an idea that could potentially somewhat improve this without slowing it down. I'm currently doing a similarity based classification, meaning that I take the query vector, do the similarity search and make a prediction that is the class of the embedding vector that was the most similar.

In the index, there are multiple embedding vectors for each class. I have a large index of image embedding vectors. WholesomeGhost Asks: Is there a way to improve similarity based classification?