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  • Cannot get a connection, pool error Timeout waiting for idle object :sakai

    - by siddhant
    iam using sakai 2.9.1 after a few operations the server stops responding and prints log:- 2014-02-20 12:48:47,085 WARN http-bio-8080-exec-18 org.sakaiproject.db.impl.BasicSqlService - Sql.dbRead: sql: select SAKAI_SITE.SITE_ID,SAKAI_SITE.TITLE,SAKAI_SITE.TYPE,SAKAI_SITE.SHO RT_DESC,SAKAI_SITE.DESCRIPTION,SAKAI_SITE.ICON_URL,SAKAI_SITE.INFO_URL,SAKAI_SITE.SKIN,SAKAI_SITE.PUBLISHED,SAKAI_SITE.JOINABLE,SAKAI_SITE.PUBVIEW,SAKAI_SITE.JOIN_ROLE,SAKAI_SITE.IS_SPE CIAL,SAKAI_SITE.IS_USER,SAKAI_SITE.CREATEDBY,SAKAI_SITE.MODIFIEDBY,SAKAI_SITE.CREATEDON,SAKAI_SITE.MODIFIEDON,SAKAI_SITE.CUSTOM_PAGE_ORDERED,SAKAI_SITE.IS_SOFTLY_DELETED,SAKAI_SITE.SOFT LY_DELETED_DATE from SAKAI_SITE where ( SITE_ID = ? ) !admin org.apache.commons.dbcp.SQLNestedException: Cannot get a connection, pool error Timeout waiting for idle object at org.apache.commons.dbcp.PoolingDataSource.getConnection(PoolingDataSource.java:104) at org.apache.commons.dbcp.BasicDataSource.getConnection(BasicDataSource.java:880) at org.sakaiproject.db.impl.BasicSqlService.borrowConnection(BasicSqlService.java:260) at org.sakaiproject.db.impl.BasicSqlService.dbRead(BasicSqlService.java:540) at org.sakaiproject.util.BaseDbFlatStorage.getResource(BaseDbFlatStorage.java:341) at org.sakaiproject.util.BaseDbFlatStorage.getResource(BaseDbFlatStorage.java:321) at org.sakaiproject.site.impl.DbSiteService$DbStorage.get(DbSiteService.java:236) at org.sakaiproject.site.impl.BaseSiteService.getDefinedSite(BaseSiteService.java:616) at org.sakaiproject.site.impl.BaseSiteService.getSite(BaseSiteService.java:702) at org.sakaiproject.site.impl.BaseSiteService.getSiteVisit(BaseSiteService.java:780) at org.sakaiproject.site.cover.SiteService.getSiteVisit(SiteService.java:140) at org.sakaiproject.presence.tool.PresenceTool.doGet(PresenceTool.java:141) at javax.servlet.http.HttpServlet.service(HttpServlet.java:621) at javax.servlet.http.HttpServlet.service(HttpServlet.java:722) at org.apache.catalina.core.ApplicationFilterChain.internalDoFilter(ApplicationFilterChain.java:305) at org.apache.catalina.core.ApplicationFilterChain.doFilter(ApplicationFilterChain.java:210) at org.sakaiproject.util.RequestFilter.doFilter(RequestFilter.java:634) at org.apache.catalina.core.ApplicationFilterChain.internalDoFilter(ApplicationFilterChain.java:243) at org.apache.catalina.core.ApplicationFilterChain.doFilter(ApplicationFilterChain.java:210) at org.apache.catalina.core.ApplicationDispatcher.invoke(ApplicationDispatcher.java:684) at org.apache.catalina.core.ApplicationDispatcher.processRequest(ApplicationDispatcher.java:471) at org.apache.catalina.core.ApplicationDispatcher.doForward(ApplicationDispatcher.java:369) at org.apache.catalina.core.ApplicationDispatcher.forward(ApplicationDispatcher.java:329) at org.sakaiproject.tool.impl.ActiveToolComponent$MyActiveTool.forward(ActiveToolComponent.java:511) at org.sakaiproject.portal.charon.SkinnableCharonPortal.forwardTool(SkinnableCharonPortal.java:1470) at org.sakaiproject.portal.charon.handlers.PresenceHandler.doPresence(PresenceHandler.java:140) at org.sakaiproject.portal.charon.handlers.PresenceHandler.doGet(PresenceHandler.java:70) at org.sakaiproject.portal.charon.SkinnableCharonPortal.doGet(SkinnableCharonPortal.java:881) at javax.servlet.http.HttpServlet.service(HttpServlet.java:621) at javax.servlet.http.HttpServlet.service(HttpServlet.java:722) at org.apache.catalina.core.ApplicationFilterChain.internalDoFilter(ApplicationFilterChain.java:305) at org.apache.catalina.core.ApplicationFilterChain.doFilter(ApplicationFilterChain.java:210) at org.sakaiproject.util.RequestFilter.doFilter(RequestFilter.java:695) at org.apache.catalina.core.ApplicationFilterChain.internalDoFilter(ApplicationFilterChain.java:243) at org.apache.catalina.core.ApplicationFilterChain.doFilter(ApplicationFilterChain.java:210) at org.apache.catalina.core.StandardWrapperValve.invoke(StandardWrapperValve.java:224) at org.apache.catalina.core.StandardContextValve.invoke(StandardContextValve.java:169) at org.apache.catalina.authenticator.AuthenticatorBase.invoke(AuthenticatorBase.java:472) at org.apache.catalina.core.StandardHostValve.invoke(StandardHostValve.java:168) at org.apache.catalina.valves.ErrorReportValve.invoke(ErrorReportValve.java:98) at org.apache.catalina.valves.AccessLogValve.invoke(AccessLogValve.java:927) at org.apache.catalina.core.StandardEngineValve.invoke(StandardEngineValve.java:118) at org.apache.catalina.connector.CoyoteAdapter.service(CoyoteAdapter.java:407) at org.apache.coyote.http11.AbstractHttp11Processor.process(AbstractHttp11Processor.java:987) at org.apache.coyote.AbstractProtocol$AbstractConnectionHandler.process(AbstractProtocol.java:579) at org.apache.tomcat.util.net.JIoEndpoint$SocketProcessor.run(JIoEndpoint.java:307) at java.util.concurrent.ThreadPoolExecutor$Worker.runTask(ThreadPoolExecutor.java:886) at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:908) at java.lang.Thread.run(Thread.java:619) Caused by: java.util.NoSuchElementException: Timeout waiting for idle object at org.apache.commons.pool.impl.GenericObjectPool.borrowObject(GenericObjectPool.java:1167) at org.apache.commons.dbcp.PoolingDataSource.getConnection(PoolingDataSource.java:96)

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  • The algorithm used to generate recommendations in Google News?

    - by Siddhant
    Hi everyone. I'm study recommendation engines, and I went through the paper that defines how Google News generates recommendations to users for news items which might be of their interest, based on collaborative filtering. One interesting technique that they mention is Minhashing. I went through what it does, but I'm pretty sure that what I have is a fuzzy idea and there is a strong chance that I'm wrong. The following is what I could make out of it :- Collect a set of all news items. Define a hash function for a user. This hash function returns the index of the first item from the news items which this user viewed, in the list of all news items. Collect, say "n" number of such values, and represent a user with this list of values. Based on the similarity count between these lists, we can calculate the similarity between users as the number of common items. This reduces the number of comparisons a lot. Based on these similarity measures, group users into different clusters. This is just what I think it might be. In Step 2, instead of defining a constant hash function, it might be possible that we vary the hash function in a way that it returns the index of a different element. So one hash function could return the index of the first element from the user's list, another hash function could return the index of the second element from the user's list, and so on. So the nature of the hash function satisfying the minwise independent permutations condition, this does sound like a possible approach. Could anyone please confirm if what I think is correct? Or the minhashing portion of Google News Recommendations, functions in some other way? I'm new to internal implementations of recommendations. Any help is appreciated a lot. Thanks!

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  • How can I find the common ancestor of two nodes in a binary tree?

    - by Siddhant
    The Binary Tree here is not a Binary Search Tree. Its just a Binary Tree. The structure could be taken as - struct node { int data; struct node *left; struct node *right; }; The maximum solution I could work out with a friend was something of this sort - Consider this binary tree (from http://lcm.csa.iisc.ernet.in/dsa/node87.html) : The inorder traversal yields - 8, 4, 9, 2, 5, 1, 6, 3, 7 And the postorder traversal yields - 8, 9, 4, 5, 2, 6, 7, 3, 1 So for instance, if we want to find the common ancestor of nodes 8 and 5, then we make a list of all the nodes which are between 8 and 5 in the inorder tree traversal, which in this case happens to be [4, 9, 2]. Then we check which node in this list appears last in the postorder traversal, which is 2. Hence the common ancestor for 8 and 5 is 2. The complexity for this algorithm, I believe is O(n) (O(n) for inorder/postorder traversals, the rest of the steps again being O(n) since they are nothing more than simple iterations in arrays). But there is a strong chance that this is wrong. :-) But this is a very crude approach, and I'm not sure if it breaks down for some case. Is there any other (possibly more optimal) solution to this problem?

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