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• NFS Mounts Issues

  - by user554005

  Having some issue with a NFS Setup on the clients it just times out refuses to connect [root@host9 ~]# mount 192.168.0.17:/home/export /mnt/export mount: mount to NFS server '192.168.0.17' failed: timed out (retrying). mount: mount to NFS server '192.168.0.17' failed: timed out (retrying). mount: mount to NFS server '192.168.0.17' failed: timed out (retrying). mount: mount to NFS server '192.168.0.17' failed: timed out (retrying). Here are the settings I'm using: [root@host17 /home/export]# cat /etc/hosts.allow # # hosts.allow This file contains access rules which are used to # allow or deny connections to network services that # either use the tcp_wrappers library or that have been # started through a tcp_wrappers-enabled xinetd. # # See 'man 5 hosts_options' and 'man 5 hosts_access' # for information on rule syntax. # See 'man tcpd' for information on tcp_wrappers # portmap: 192.168.0.0/255.255.255.0 lockd: 192.168.0.0/255.255.255.0 rquotad: 192.168.0.0/255.255.255.0 mountd: 192.168.0.0/255.255.255.0 statd: 192.168.0.0/255.255.255.0 [root@host17 /home/export]# cat /etc/hosts.deny # # hosts.deny This file contains access rules which are used to # deny connections to network services that either use # the tcp_wrappers library or that have been # started through a tcp_wrappers-enabled xinetd. # # The rules in this file can also be set up in # /etc/hosts.allow with a 'deny' option instead. # # See 'man 5 hosts_options' and 'man 5 hosts_access' # for information on rule syntax. # See 'man tcpd' for information on tcp_wrappers # portmap:ALL lockd:ALL mountd:ALL rquotad:ALL statd:ALL [root@host17 /home/export]# cat /etc/exports /home/export 192.168.0.0/255.255.255.0(rw) [root@host17 /home/export]# iptables -L Chain INPUT (policy ACCEPT) target prot opt source destination RH-Firewall-1-INPUT all -- anywhere anywhere Chain FORWARD (policy ACCEPT) target prot opt source destination RH-Firewall-1-INPUT all -- anywhere anywhere Chain OUTPUT (policy ACCEPT) target prot opt source destination Chain RH-Firewall-1-INPUT (2 references) target prot opt source destination ACCEPT all -- anywhere anywhere ACCEPT icmp -- anywhere anywhere icmp any ACCEPT esp -- anywhere anywhere ACCEPT ah -- anywhere anywhere ACCEPT udp -- anywhere 224.0.0.251 udp dpt:mdns ACCEPT udp -- anywhere anywhere udp dpt:ipp ACCEPT tcp -- anywhere anywhere tcp dpt:ipp ACCEPT all -- anywhere anywhere state RELATED,ESTABLISHED ACCEPT tcp -- anywhere anywhere state NEW tcp dpt:ssh ACCEPT tcp -- anywhere anywhere state NEW tcp dpt:http ACCEPT tcp -- anywhere anywhere state NEW tcp dpt:https ACCEPT tcp -- anywhere anywhere state NEW tcp dpt:6379 ACCEPT udp -- 192.168.0.0/24 anywhere state NEW udp dpt:sunrpc ACCEPT tcp -- 192.168.0.0/24 anywhere state NEW tcp dpt:sunrpc ACCEPT tcp -- 192.168.0.0/24 anywhere state NEW tcp dpt:nfs ACCEPT tcp -- 192.168.0.0/24 anywhere state NEW tcp dpt:32803 ACCEPT udp -- 192.168.0.0/24 anywhere state NEW udp dpt:filenet-rpc ACCEPT tcp -- 192.168.0.0/24 anywhere state NEW tcp dpt:892 ACCEPT udp -- 192.168.0.0/24 anywhere state NEW udp dpt:892 ACCEPT tcp -- 192.168.0.0/24 anywhere state NEW tcp dpt:rquotad ACCEPT udp -- 192.168.0.0/24 anywhere state NEW udp dpt:rquotad ACCEPT tcp -- 192.168.0.0/24 anywhere state NEW tcp dpt:pftp ACCEPT udp -- 192.168.0.0/24 anywhere state NEW udp dpt:pftp REJECT all -- anywhere anywhere reject-with icmp-host-prohibited on the clients here is some rpcinfos [root@host9 ~]# rpcinfo -p 192.168.0.17 program vers proto port 100000 4 tcp 111 portmapper 100000 3 tcp 111 portmapper 100000 2 tcp 111 portmapper 100000 4 udp 111 portmapper 100000 3 udp 111 portmapper 100000 2 udp 111 portmapper 100011 1 udp 875 rquotad 100011 2 udp 875 rquotad 100011 1 tcp 875 rquotad 100011 2 tcp 875 rquotad 100005 1 udp 45857 mountd 100005 1 tcp 55772 mountd 100005 2 udp 34021 mountd 100005 2 tcp 59542 mountd 100005 3 udp 60930 mountd 100005 3 tcp 53086 mountd 100003 2 udp 2049 nfs 100003 3 udp 2049 nfs 100003 4 udp 2049 nfs 100227 2 udp 2049 nfs_acl 100227 3 udp 2049 nfs_acl 100003 2 tcp 2049 nfs 100003 3 tcp 2049 nfs 100003 4 tcp 2049 nfs 100227 2 tcp 2049 nfs_acl 100227 3 tcp 2049 nfs_acl 100021 1 udp 59832 nlockmgr 100021 3 udp 59832 nlockmgr 100021 4 udp 59832 nlockmgr 100021 1 tcp 36140 nlockmgr 100021 3 tcp 36140 nlockmgr 100021 4 tcp 36140 nlockmgr 100024 1 udp 46494 status 100024 1 tcp 49672 status [root@host9 ~]# [root@host9 ~]# rpcinfo -u 192.168.0.17 nfs rpcinfo: RPC: Timed out program 100003 version 0 is not available [root@host9 ~]# rpcinfo -u 192.168.0.17 portmap program 100000 version 2 ready and waiting program 100000 version 3 ready and waiting program 100000 version 4 ready and waiting [root@host9 ~]# rpcinfo -u 192.168.0.17 mount rpcinfo: RPC: Timed out program 100005 version 0 is not available [root@host9 ~]# I'm running CentOS 5.8 on all systems

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• TCP stops sending weirdly.

  - by Utoah

  In case to find out the cause of TCP retransmits on my Linux (RHEL, kernel 2.6.18) servers connecting to the same switch. I had a client-server pair send "Hello" to each other every 200us and captured the packets with tcpdump on the client machine. The command I used to mimic client and server are: while [ 0 ]; do echo "Hello"; usleep 200; done | nc server 18510 while [ 0 ]; do echo "Hello"; usleep 200; done | nc -l 18510 When the server machine was busy serving some other requests, the client suffered from abrupt retransmits occasionally. But the output of tcpdump seemed irrational. 16:04:58.898970 IP server.18510 > client.34533: P 4531:4537(6) ack 3204 win 123 <nop,nop,timestamp 1923778643 3452833828> 16:04:58.901797 IP client.34533 > server.18510: P 3204:3210(6) ack 4537 win 33 <nop,nop,timestamp 3452833831 1923778643> 16:04:58.901855 IP server.18510 > client.34533: P 4537:4549(12) ack 3210 win 123 <nop,nop,timestamp 1923778646 3452833831> 16:04:58.903871 IP client.34533 > server.18510: P 3210:3216(6) ack 4549 win 33 <nop,nop,timestamp 3452833833 1923778646> 16:04:58.903950 IP server.18510 > client.34533: P 4549:4555(6) ack 3216 win 123 <nop,nop,timestamp 1923778648 3452833833> 16:04:58.905796 IP client.34533 > server.18510: P 3216:3222(6) ack 4555 win 33 <nop,nop,timestamp 3452833835 1923778648> 16:04:58.905860 IP server.18510 > client.34533: P 4555:4561(6) ack 3222 win 123 <nop,nop,timestamp 1923778650 3452833835> 16:04:58.908903 IP client.34533 > server.18510: P 3222:3228(6) ack 4561 win 33 <nop,nop,timestamp 3452833838 1923778650> 16:04:58.908966 IP server.18510 > client.34533: P 4561:4567(6) ack 3228 win 123 <nop,nop,timestamp 1923778653 3452833838> 16:04:58.911855 IP client.34533 > server.18510: P 3228:3234(6) ack 4567 win 33 <nop,nop,timestamp 3452833841 1923778653> 16:04:59.112573 IP client.34533 > server.18510: P 3228:3234(6) ack 4567 win 33 <nop,nop,timestamp 3452834042 1923778653> 16:04:59.112648 IP server.18510 > client.34533: P 4567:5161(594) ack 3234 win 123 <nop,nop,timestamp 1923778857 3452834042> 16:04:59.112659 IP client.34533 > server.18510: P 3234:3672(438) ack 5161 win 35 <nop,nop,timestamp 3452834042 1923778857> 16:04:59.114427 IP server.18510 > client.34533: P 5161:5167(6) ack 3672 win 126 <nop,nop,timestamp 1923778858 3452834042> 16:04:59.114439 IP client.34533 > server.18510: P 3672:3678(6) ack 5167 win 35 <nop,nop,timestamp 3452834044 1923778858> 16:04:59.116435 IP server.18510 > client.34533: P 5167:5173(6) ack 3678 win 126 <nop,nop,timestamp 1923778860 3452834044> 16:04:59.116444 IP client.34533 > server.18510: P 3678:3684(6) ack 5173 win 35 <nop,nop,timestamp 3452834046 1923778860> Packet 3228:3234(6) from client was retransmitted due to ack timeout. What I could not understand was that the client machine did not send out any packets after the first 3228:3234(6) packets was sent. The server machine had advertised a window (scaled) large enough. The data transfer up to the retransmit was fine which meant no slow start should be in action. What can cause the client machine to stop sending until the packet timed out? BTW, I am unable to run tcpdump on the server machine.

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• Is there any real benefit to using ASP.Net Authentication with ASP.Net MVC?

  - by alchemical

  I've been researching this intensely for the past few days. We're developing an ASP.Net MVC site that needs to support 100,000+ users. We'd like to keep it fast, scalable, and simple. We have our own SQL database tables for user and user_role, etc. We are not using server controls. Given that there are no server controls, and a custom membershipProvider would need to be created, where is there any benefit left to use ASP.Net Auth/Membership? The other alternative would seem to be to create custom code to drop a UniqueID CustomerID in a cookie and authenticate with that. Or, if we're paranoid about sniffers, we could encrypt the cookie as well. Is there any real benefit in this scenario (MVC and customer data is in our own tables) to using the ASP.Net auth/membership framework, or is the fully custom solution a viable route?

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• Asp.Net MVC - Plugins Directory, Community etc?

  - by Jörg Battermann

  Good evening everyone, I am currently starting to dive into asp.net mvc and I really like what I see so far.. BUT I am somewhat confused about 'drop-in' functionality (similiar to what rails and it's plugins and nowadays gems are), an active community to contact etc. For rails there's github with one massiv index of plugins/gems/code-examples regarding mostly rails (despite their goal being generic source-code hosting..), for blogs, mailing lists etc it's also pretty easy to find the places the other developers flock around, but... for asp.net mvc I am somewhat lost where to go/look. It all seems scattered across codeplex and private sites, google code hosting etc etc.. but is there one (or few places) where to turn to regarding asp.net mvc development, sample code etc? Cheers and thanks, -Jörg

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• advise how to implement a code generator for asp.NET mvc 2

  - by loviji

  Hello, I would like your advice about how best to solve my problem. In a Web server is running. NET Framework 4.0. Whatever the methods and technologies you would advise me. applications built on the basis Asp.NET MVC 2. I have a database table in MS SQL Server. For each database, I must implement the interface for viewing, editing, and deleting. So code generator must generate model, controller and views.. Generation should happen after clicking on the button. as model I use .NET Entity Framework. Now, I need to generate controllers and views. So if i have a table with name tableN1. and below its colums: [ID] [bigint] IDENTITY(1,1) NOT NULL, [name] [nvarchar 20] NOT NULL, [fullName] [nvarchar 50] NOT NULL, [age] [int] NOT NULL [active] [bit] NULL for this table, i want to generate views and controller. thanks.

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• My first .net web app - should I go straight to MVC framework (c.f. ASP.net)

  - by Greg

  Hi, I'm done some WinForms work in C# but now moving to have to develop a web application front end in .NET (C#). I have experience developing web apps in Ruby on Rails (& a little with Java with JSP pages & struts mvc). Should I jump straight to MVC framework? (as opposed to going ASP.net) That is from the point of view of future direction for Microsoft & as well ease in ramping up from myself. Or if you like, given my experience to date, what would the pros/cons for me re MVC versus ASP.net? thanks

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• Cisco 881 losing NAT NVI translation config after reload

  - by MasterRoot24

  This is a weird one, so I'll try to explain in as much detail as I can so I'm giving the whole picture. As I've mentioned in my other questions, I'm in the process of setting up a new Cisco 881 as my WAN router and NAT firewall. I'm facing an issue where NAT NVI rules that I have configured are not enabled after a reload of the router, regardless of the fact that they are present in the startup-config. In order to clarify this a little, here's the relevant section of my current running-config: Router1#show running-config | include nat source ip nat source list 1 interface FastEthernet4 overload ip nat source list 2 interface FastEthernet4 overload ip nat source static tcp 192.168.1.x 1723 interface FastEthernet4 1723 ip nat source static tcp 192.168.1.x 80 interface FastEthernet4 80 ip nat source static tcp 192.168.1.x 443 interface FastEthernet4 443 ip nat source static tcp 192.168.1.x 25 interface FastEthernet4 25 ip nat source static tcp 192.168.1.x 587 interface FastEthernet4 587 ip nat source static tcp 192.168.1.x 143 interface FastEthernet4 143 ip nat source static tcp 192.168.1.x 993 interface FastEthernet4 993 ...and here's the mappings 'in action': Router1#show ip nat nvi translations | include --- tcp <WAN IP>:25 192.168.1.x:25 --- --- tcp <WAN IP>:80 192.168.1.x:80 --- --- tcp <WAN IP>:143 192.168.1.x:143 --- --- tcp <WAN IP>:443 192.168.1.x:443 --- --- tcp <WAN IP>:587 192.168.1.x:587 --- --- tcp <WAN IP>:993 192.168.1.x:993 --- --- tcp <WAN IP>:1723 192.168.1.x:1723 --- --- ...and here's proof that the mappings are saved to startup-config: Router1#show startup-config | include nat source ip nat source list 1 interface FastEthernet4 overload ip nat source list 2 interface FastEthernet4 overload ip nat source static tcp 192.168.1.x 1723 interface FastEthernet4 1723 ip nat source static tcp 192.168.1.x 80 interface FastEthernet4 80 ip nat source static tcp 192.168.1.x 443 interface FastEthernet4 443 ip nat source static tcp 192.168.1.x 25 interface FastEthernet4 25 ip nat source static tcp 192.168.1.x 587 interface FastEthernet4 587 ip nat source static tcp 192.168.1.x 143 interface FastEthernet4 143 ip nat source static tcp 192.168.1.x 993 interface FastEthernet4 993 However, look what happens after a reload of the router: Router1#reload Proceed with reload? [confirm]Connection to router closed by remote host. Connection to router closed. $ ssh joe@router Password: Authorized Access only Router1>en Password: Router1#show ip nat nvi translations | include --- Router1# Router1#show ip nat translations | include --- tcp 188.222.181.173:25 192.168.1.2:25 --- --- tcp 188.222.181.173:80 192.168.1.2:80 --- --- tcp 188.222.181.173:143 192.168.1.2:143 --- --- tcp 188.222.181.173:443 192.168.1.2:443 --- --- tcp 188.222.181.173:587 192.168.1.2:587 --- --- tcp 188.222.181.173:993 192.168.1.2:993 --- --- tcp 188.222.181.173:1723 192.168.1.2:1723 --- --- Router1# Here's proof that the running config should have the mappings setup as NVI: Router1#show running-config | include nat source ip nat source list 1 interface FastEthernet4 overload ip nat source list 2 interface FastEthernet4 overload ip nat source static tcp 192.168.1.2 1723 interface FastEthernet4 1723 ip nat source static tcp 192.168.1.2 80 interface FastEthernet4 80 ip nat source static tcp 192.168.1.2 443 interface FastEthernet4 443 ip nat source static tcp 192.168.1.2 25 interface FastEthernet4 25 ip nat source static tcp 192.168.1.2 587 interface FastEthernet4 587 ip nat source static tcp 192.168.1.2 143 interface FastEthernet4 143 ip nat source static tcp 192.168.1.2 993 interface FastEthernet4 993 At this point, the mappings are not working (inbound connections from WAN on the HTTP/IMAP fail). I presume that this is because my interfaces are using ip nat enable for use with NVI mappings, instead of ip nat inside/outside. So, I re-apply the mappings: Router1#configure ter Router1#configure terminal Enter configuration commands, one per line. End with CNTL/Z. Router1(config)#ip nat source static tcp 192.168.1.2 1723 interface FastEthernet4 1723 Router1(config)#ip nat source static tcp 192.168.1.2 80 interface FastEthernet4 80 Router1(config)#ip nat source static tcp 192.168.1.2 443 interface FastEthernet4 443 Router1(config)#ip nat source static tcp 192.168.1.2 25 interface FastEthernet4 25 Router1(config)#ip nat source static tcp 192.168.1.2 587 interface FastEthernet4 587 Router1(config)#ip nat source static tcp 192.168.1.2 143 interface FastEthernet4 143 Router1(config)#ip nat source static tcp 192.168.1.2 993 interface FastEthernet4 993 Router1(config)#end ... then they show up correctly: Router1#show ip nat nvi translations | include --- tcp 188.222.181.173:25 192.168.1.2:25 --- --- tcp 188.222.181.173:80 192.168.1.2:80 --- --- tcp 188.222.181.173:143 192.168.1.2:143 --- --- tcp 188.222.181.173:443 192.168.1.2:443 --- --- tcp 188.222.181.173:587 192.168.1.2:587 --- --- tcp 188.222.181.173:993 192.168.1.2:993 --- --- tcp 188.222.181.173:1723 192.168.1.2:1723 --- --- Router1# Router1#show ip nat translations | include --- Router1# ... furthermore, now from both WAN and LAN, the services mapped above now work until the next reload. All of the above is required every time I have to reload the router (which is all too often at the moment :-( ). Here's my full current config: ! ! Last configuration change at 20:20:15 UTC Tue Dec 11 2012 by xxx version 15.2 no service pad service timestamps debug datetime msec service timestamps log datetime msec service password-encryption ! hostname xxx ! boot-start-marker boot-end-marker ! ! enable secret 4 xxxx ! aaa new-model ! ! aaa authentication login local_auth local ! ! ! ! ! aaa session-id common ! memory-size iomem 10 ! crypto pki trustpoint TP-self-signed-xxx enrollment selfsigned subject-name cn=IOS-Self-Signed-Certificate-xxx revocation-check none rsakeypair TP-self-signed-xxx ! ! crypto pki certificate chain TP-self-signed-xxx certificate self-signed 01 xxx quit ip gratuitous-arps ip auth-proxy max-login-attempts 5 ip admission max-login-attempts 5 ! ! ! ! ! ip domain list dmz.xxx.local ip domain list xxx.local ip domain name dmz.xxx.local ip name-server 192.168.1.x ip cef login block-for 3 attempts 3 within 3 no ipv6 cef ! ! multilink bundle-name authenticated license udi pid CISCO881-SEC-K9 sn xxx ! ! username admin privilege 15 secret 4 xxx username joe secret 4 xxx ! ! ! ! ! ip ssh time-out 60 ! ! ! ! ! ! ! ! ! interface FastEthernet0 no ip address ! interface FastEthernet1 no ip address ! interface FastEthernet2 no ip address ! interface FastEthernet3 switchport access vlan 2 no ip address ! interface FastEthernet4 ip address dhcp ip access-group 101 in ip nat enable duplex auto speed auto ! interface Vlan1 ip address 192.168.1.x 255.255.255.0 no ip redirects no ip unreachables no ip proxy-arp ip nat enable ! interface Vlan2 ip address 192.168.0.x 255.255.255.0 ! ip forward-protocol nd ip http server ip http access-class 1 ip http authentication local ip http secure-server ! ! ip nat source list 1 interface FastEthernet4 overload ip nat source list 2 interface FastEthernet4 overload ip nat source static tcp 192.168.1.x 1723 interface FastEthernet4 1723 ! ! access-list 1 permit 192.168.0.0 0.0.0.255 access-list 2 permit 192.168.1.0 0.0.0.255 access-list 101 permit udp 193.x.x.0 0.0.0.255 any eq 5060 access-list 101 deny udp any any eq 5060 access-list 101 permit ip any any ! ! ! ! control-plane ! ! banner motd Authorized Access only ! line con 0 exec-timeout 15 0 login authentication local_auth line aux 0 exec-timeout 15 0 login authentication local_auth line vty 0 4 access-class 2 in login authentication local_auth length 0 transport input all ! ! end I'd appreciate it greatly if anyone can help me find out why these mappings are not setup correctly using the saved config after a reload.

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• Simple way of converting server side objects into client side using JSON serialization for asp.net websites

  - by anil.kasalanati

   Introduction:- With the growth of Web2.0 and the need for faster user experience the spotlight has shifted onto javascript based applications built using REST pattern or asp.net AJAX Pagerequest manager. And when we are working with javascript wouldn’t it be much better if we could create objects in an OOAD way and easily push it to the client side.  Following are the reasons why you would push the server side objects onto client side -          Easy availability of the complex object. -          Use C# compiler and rick intellisense to create and maintain the objects but use them in the javascript. You could run code analysis etc. -          Reduce the number of calls we make to the server side by loading data on the pageload.   I would like to explain about the 3rd point because that proved to be highly beneficial to me when I was fixing the performance issues of a major website. There could be a scenario where in you be making multiple AJAX based webrequestmanager calls in order to get the same response in a single page. This happens in the case of widget based framework when all the widgets are independent but they need some common information available in the framework to load the data. So instead of making n multiple calls we could load the data needed during pageload. The above picture shows the scenario where in all the widgets need the common information and then call GetData webservice on the server side. Ofcourse the result can be cached on the client side but a better solution would be to avoid the call completely.  In order to do that we need to JSONSerialize the content and send it in the DOM.                                                                                                                                                                                                                                                                                                                                                                                            Example:- I have developed a simple application to demonstrate the idea and I would explaining that in detail here. The class called SimpleClass would be sent as serialized JSON to the client side .   And this inherits from the base class which has the implementation for the GetJSONString method. You can create a single base class and all the object which need to be pushed to the client side can inherit from that class. The important thing to note is that the class should be annotated with DataContract attribute and the methods should have the Data Member attribute. This is needed by the .Net DataContractSerializer and this follows the opt-in mode so if you want to send an attribute to the client side then you need to annotate the DataMember attribute. So if I didn’t want to send the Result I would simple remove the DataMember attribute. This is default WCF/.Net 3.5 stuff but it provides the flexibility of have a fullfledged object on the server side but sending a smaller object to the client side. Sometimes you may hide some values due to security constraints. And thing you will notice is that I have marked the class as Serializable so that it can be stored in the Session and used in webfarm deployment scenarios. Following is the implementation of the base class –  This implements the default DataContractJsonSerializer and for more information or customization refer to following blogs – http://softcero.blogspot.com/2010/03/optimizing-net-json-serializing-and-ii.html http://weblogs.asp.net/gunnarpeipman/archive/2010/12/28/asp-net-serializing-and-deserializing-json-objects.aspx The next part is pretty simple, I just need to inject this object into the aspx page.   And in the aspx markup I have the following line – <script type="text/javascript"> var data =(<%=SimpleClassJSON  %>);   alert(data.ResultText); </script>   This will output the content as JSON into the variable data and this can be any element in the DOM. And you can verify the element by checking data in the Firebug console.    Design Consideration – If you have a lot of javascripts then you need to think about using Script # and you can write javascript in C#. Refer to Nikhil’s blog – http://projects.nikhilk.net/ScriptSharp Ensure that you are taking security into consideration while exposing server side objects on to client side. I have seen application exposing passwords, secret key so it is not a good practice.   The application can be tested using the following url – http://techconsulting.vpscustomer.com/Samples/JsonTest.aspx The source code is available at http://techconsulting.vpscustomer.com/Source/HistoryTest.zip

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• Help me solve my problem with NPR Media Player

  - by Calcipher

  First of, let me apologize for this getting a bit technical. Several weeks ago, I found that while using NPR's media player (e.g. click on 'Listen to the Show' - this is what I've been using as a test) the stream would suddenly halt after a minute or three. I could not get the stream to restart without reloading the page. Now, I assumed this was an issue with NPR's player and Linux (or just a bug in their stuff in general) so I began to dig, the following is what I have tried to date (please note, the tldr; option is to skip to the latest thing as I think I know what is causing the problem). Note: All testing has been done, for consistency purposes, on a clean install of Chromium with no pluggins running. My machine is Ubuntu 10.10x64. First thing I always try, I disabled all firewall stuff on the system (UFW, default deny all, allow ssh). No change, firewall back up for all additional tests unless otherwise noted. In any case, UFW is stateful, so connections it started on a non-specified on different ports will continue to work. I deleted my ~/.macromeda and ~/.adobe folders, restarted (just to be sure) and tried. Program still froze. I decided the problem might be with my install of flash, so I purged the version I had (and the home folders again). I installed the x64 version of flash from a PPA. This had no effect. I decided that the problem might be with the version of flash, so I purged the x64 version and installed the standard x32 version that comes with Ubuntu. No luck. Back to the x64 version for consistency, I decided to set up a 64-bit mini 'clone' of my system in VirtualBox. I was able to run the media player with no problem. I rsynced (in archive mode) my home directory from my real machine to the virtual machine (with bridged networking, so it was fully visible on the network). I also used a few tricks to install ALL of the same software (and repositories) from the real machine to the virtual machine. I was still able to listen to the player. I decided that the problem was with my install (after all, it had gone through two major version upgrades). As I have /home/ on a separate partition it was easy to reinstall and use the same trick from #6 to have my system up and running again within about an hour. I continue to have issues with the NPR Media Player. By this point the weekend had come. At work, I use a wired connection while at home I use a wireless connection. For some reason I forgot that I was having problems and used the NPR Media Player over the weekend. Low and behold it worked just fine at home on wireless (note: for various reasons, I could not test this on wired at home). Following from #6, I decided that the problem was either something with the network at work or still something with my account. As the latter was easier to test, I created a new account on my system and used that at work. The Media Player worked. At a loss, I decided to watch the traffic with tshark (the text based brother of wireshark) - X's to protect the innocent, I am the XXX.24.200.XXX: sudo tshark -i eth0 -p -t a -R "ip.addr == XXX.24.200.XXX && ip.addr == XXX.166.98.XXX" As you would expect, there were tons and tons of packets, but each and every time the player froze, this is what I got 08:42:20.679200 XXX.166.98.XXX - XXX.24.200.XXX TCP macromedia-fcs 56371 [PSH, ACK] Seq=817686 Ack=6 Win=65535 Len=1448 TSV=495713325 TSER=396467 08:42:20.718602 XXX.24.200.XXX - XXX.166.98.XXX TCP [TCP ZeroWindow] 56371 macromedia-fcs [ACK] Seq=6 Ack=819134 Win=0 Len=0 TSV=396475 TSER=495713325 08:42:21.050183 XXX.166.98.XXX - XXX.24.200.XXX TCP [TCP ZeroWindowProbe] macromedia-fcs 56371 [ACK] Seq=819134 Ack=6 Win=65535 Len=1 TSV=495713362 TSER=396475 08:42:21.050221 XXX.24.200.XXX - XXX.166.98.XXX TCP [TCP ZeroWindowProbeAck] [TCP ZeroWindow] 56371 macromedia-fcs [ACK] Seq=6 Ack=819134 Win=0 Len=0 TSV=396508 TSER=495713362 08:42:21.680548 XXX.166.98.XXX - XXX.24.200.XXX TCP [TCP ZeroWindowProbe] macromedia-fcs 56371 [ACK] Seq=819134 Ack=6 Win=65535 Len=1 TSV=495713425 TSER=396508 08:42:21.680605 XXX.24.200.XXX - XXX.166.98.XXX TCP [TCP ZeroWindowProbeAck] [TCP ZeroWindow] 56371 macromedia-fcs [ACK] Seq=6 Ack=819134 Win=0 Len=0 TSV=396571 TSER=495713425 08:42:22.910354 XXX.166.98.XXX - XXX.24.200.XXX TCP [TCP ZeroWindowProbe] macromedia-fcs 56371 [ACK] Seq=819134 Ack=6 Win=65535 Len=1 TSV=495713548 TSER=396571 08:42:22.910400 XXX.24.200.XXX - XXX.166.98.XXX TCP [TCP ZeroWindowProbeAck] [TCP ZeroWindow] 56371 macromedia-fcs [ACK] Seq=6 Ack=819134 Win=0 Len=0 TSV=396694 TSER=495713548 08:42:25.340458 XXX.166.98.XXX - XXX.24.200.XXX TCP [TCP ZeroWindowProbe] macromedia-fcs 56371 [ACK] Seq=819134 Ack=6 Win=65535 Len=1 TSV=495713791 TSER=396694 08:42:25.340517 XXX.24.200.XXX - XXX.166.98.XXX TCP [TCP ZeroWindowProbeAck] [TCP ZeroWindow] 56371 macromedia-fcs [ACK] Seq=6 Ack=819134 Win=0 Len=0 TSV=396937 TSER=495713791 08:42:30.170698 XXX.166.98.XXX - XXX.24.200.XXX TCP [TCP ZeroWindowProbe] macromedia-fcs 56371 [ACK] Seq=819134 Ack=6 Win=65535 Len=1 TSV=495714274 TSER=396937 08:42:30.170746 XXX.24.200.XXX - XXX.166.98.XXX TCP [TCP ZeroWindowProbeAck] [TCP ZeroWindow] 56371 macromedia-fcs [ACK] Seq=6 Ack=819134 Win=0 Len=0 TSV=397420 TSER=495714274 08:42:39.801738 XXX.166.98.XXX - XXX.24.200.XXX TCP [TCP ZeroWindowProbe] macromedia-fcs 56371 [ACK] Seq=819134 Ack=6 Win=65535 Len=1 TSV=495715237 TSER=397420 08:42:39.801784 XXX.24.200.XXX - XXX.166.98.XXX TCP [TCP ZeroWindowProbeAck] [TCP ZeroWindow] 56371 macromedia-fcs [ACK] Seq=6 Ack=819134 Win=0 Len=0 TSV=398383 TSER=495715237 08:42:59.032648 XXX.166.98.XXX - XXX.24.200.XXX TCP [TCP ZeroWindowProbe] macromedia-fcs 56371 [ACK] Seq=819134 Ack=6 Win=65535 Len=1 TSV=495717160 TSER=398383 08:42:59.032696 XXX.24.200.XXX - XXX.166.98.XXX TCP [TCP ZeroWindowProbeAck] [TCP ZeroWindow] 56371 macromedia-fcs [ACK] Seq=6 Ack=819134 Win=0 Len=0 TSV=400306 TSER=495717160 08:43:00.267721 XXX.24.200.XXX - XXX.166.98.XXX TCP 56371 macromedia-fcs [FIN, ACK] Seq=6 Ack=819134 Win=0 Len=0 TSV=400430 TSER=495717160 08:43:00.267827 XXX.24.200.XXX - XXX.166.98.XXX TCP 56371 macromedia-fcs [RST, ACK] Seq=7 Ack=819134 Win=65535 Len=0 TSV=400430 TSER=495717160 So, as you can see, my machine is sending out a ZeroWindow packet (which I think means some buffer or another filled up) which causes the Media Player to halt (unfortunately, terminally - no controls on it really do anything anymore). Any ideas, at all, what would cause this? Why only on eth0 under my main account?

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• Custom ASPNetMembership FailureInformation always null, OnValidatingPassword issue

  - by bigb

  As stated here http://msdn.microsoft.com/en-us/library/system.web.security.membershipprovider.onvalidatingpassword.aspx "When the ValidatingPassword event has completed, the properties of the ValidatePasswordEventArgs object supplied as the e parameter can be examined to determine whether the current action should be canceled and if a particular Exception, stored in the FailureInformation property, should be thrown." Here is some details/code which really shows why FailureInformation shouldn't be always null http://forums.asp.net/t/991002.aspx if any password security conditions not matched. According with my Membership settings i should get an exception that password does not match password security conditions, but it is not happened. Then i did try to debug System.Web.ApplicationServices.dll(in .NET 4.0 System.Web.Security located here) Framework Code to see whats really happens there, but i cant step into this assembly, may be because of this [TypeForwardedFrom("System.Web, Version=2.0.0.0, Culture=Neutral, PublicKeyToken=b03f5f7f11d50a3a")] public abstract class MembershipProvider : ProviderBase Easily i may step into any another .NET 4.0 assembly, but in this one not. I did check, symbols for System.Web.ApplicationServices.dll loaded. Now i have only one idea how ti fix it - to override method OnValidatingPassword(ValidatePasswordEventArgs e). Thats my story. May be some one may help: 1) Any ideas why OnValidatingPassword not working? 2) Any ideas how to step into it?

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• Reuse security code between WCF and MVC.NET

  - by mrjoltcola

  First the background: I jumped into MVC.NET from the Java MVC world, so my implementation below is possibly cheating, I don't know. I avoided fooling with a custom membership provider and I just implemented the base code needed to authenticate and load roles in my LogOn action. Typically I just need to check roles programatically, and have no use for all of the other membership features, so I didn't originally think I needed a full Membership provider. I have a successful WCF project with a custom authentication and authorization layer that I did at least write per the proper API. I implemented it with custom IPrincipal, UserNamePasswordValidator and IAuthorizationPolicy classes to load from an Oracle database. In my WCF services, I use declarative security: [PrincipalPermission(SecurityAction.Demand, Role="ADMIN")]. The question (on the ASP.NET/MCV.NET side): All my reading indicates I should implement a custom Membership/Roles provider, and use [Authorize(Roles="ADMIN")] on my controller actions. At this point, I don't have a true Membership provider, but I'm using the same User class that implements the IPrincipal interface that works with the WCF security. I plan to share common code between the WCF and ASP.NET modules. So my LogOn action is not using the FormsService (and I assume this is bad). I had commented it out, and just used my "UserService" to access the Oracle db. Note my "TODO" comment below. public ActionResult LogOn(LogOnModel model, string returnUrl) { log.Info("Login attempt by " + model.UserName); if (ModelState.IsValid) { User user = userService.findByUserName(model.UserName); // Commented original MemberShipService code, this is probably bad // if (MembershipService.ValidateUser(model.UserName, model.Password)) if (user != null && user.Authenticate(model.Password) == true) { log.Info("Login success by " + model.UserName); FormsService.SignIn(model.UserName, model.RememberMe); // TODO: Override with Custom identity / roles? user.AddRoles(userService.listRolesByUser(user)); // pull in roles from db if (!String.IsNullOrEmpty(returnUrl)) return Redirect(returnUrl); else return RedirectToAction("Index", "Home"); } else { log.Info("Login failure by " + model.UserName); ModelState.AddModelError("", "The user name or password provided is incorrect."); } } // If we got this far, something failed, redisplay form return View(model); } So can I make the above work? Can I stick the IPrincipal (User) into the CurrentContext or HttpContext? Can I integrate the custom IPrincipal I've already created without writing a full Membership/Roles Provider? I currently stick the User object into the session and access it from all MVC.NET controllers with "CurrentUser" property which grabs it from the session on demand. But this doesn't work with the [Authorize] attribute; I assume that is because it knows nothing about my custom Principal in the session, and is instead using whatever FormsService.SignIn() produces. I also found that session timeouts screw up the login redirect, the user doesn't get forwarded, instead we get a null exception accessing User from the session, and I assume it is related to my "skipping steps" to get a quick implementation. Thanks.

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• Parallelism in .NET – Part 5, Partitioning of Work

  - by Reed

  When parallelizing any routine, we start by decomposing the problem.  Once the problem is understood, we need to break our work into separate tasks, so each task can be run on a different processing element.  This process is called partitioning. Partitioning our tasks is a challenging feat.  There are opposing forces at work here: too many partitions adds overhead, too few partitions leaves processors idle.  Trying to work the perfect balance between the two extremes is the goal for which we should aim.  Luckily, the Task Parallel Library automatically handles much of this process.  However, there are situations where the default partitioning may not be appropriate, and knowledge of our routines may allow us to guide the framework to making better decisions. First off, I’d like to say that this is a more advanced topic.  It is perfectly acceptable to use the parallel constructs in the framework without considering the partitioning taking place.  The default behavior in the Task Parallel Library is very well-behaved, even for unusual work loads, and should rarely be adjusted.  I have found few situations where the default partitioning behavior in the TPL is not as good or better than my own hand-written partitioning routines, and recommend using the defaults unless there is a strong, measured, and profiled reason to avoid using them.  However, understanding partitioning, and how the TPL partitions your data, helps in understanding the proper usage of the TPL. I indirectly mentioned partitioning while discussing aggregation.  Typically, our systems will have a limited number of Processing Elements (PE), which is the terminology used for hardware capable of processing a stream of instructions.  For example, in a standard Intel i7 system, there are four processor cores, each of which has two potential hardware threads due to Hyperthreading.  This gives us a total of 8 PEs – theoretically, we can have up to eight operations occurring concurrently within our system. In order to fully exploit this power, we need to partition our work into Tasks.  A task is a simple set of instructions that can be run on a PE.  Ideally, we want to have at least one task per PE in the system, since fewer tasks means that some of our processing power will be sitting idle.  A naive implementation would be to just take our data, and partition it with one element in our collection being treated as one task.  When we loop through our collection in parallel, using this approach, we’d just process one item at a time, then reuse that thread to process the next, etc.  There’s a flaw in this approach, however.  It will tend to be slower than necessary, often slower than processing the data serially. The problem is that there is overhead associated with each task.  When we take a simple foreach loop body and implement it using the TPL, we add overhead.  First, we change the body from a simple statement to a delegate, which must be invoked.  In order to invoke the delegate on a separate thread, the delegate gets added to the ThreadPool’s current work queue, and the ThreadPool must pull this off the queue, assign it to a free thread, then execute it.  If our collection had one million elements, the overhead of trying to spawn one million tasks would destroy our performance. The answer, here, is to partition our collection into groups, and have each group of elements treated as a single task.  By adding a partitioning step, we can break our total work into small enough tasks to keep our processors busy, but large enough tasks to avoid overburdening the ThreadPool.  There are two clear, opposing goals here: Always try to keep each processor working, but also try to keep the individual partitions as large as possible. When using Parallel.For, the partitioning is always handled automatically.  At first, partitioning here seems simple.  A naive implementation would merely split the total element count up by the number of PEs in the system, and assign a chunk of data to each processor.  Many hand-written partitioning schemes work in this exactly manner.  This perfectly balanced, static partitioning scheme works very well if the amount of work is constant for each element.  However, this is rarely the case.  Often, the length of time required to process an element grows as we progress through the collection, especially if we’re doing numerical computations.  In this case, the first PEs will finish early, and sit idle waiting on the last chunks to finish.  Sometimes, work can decrease as we progress, since previous computations may be used to speed up later computations.  In this situation, the first chunks will be working far longer than the last chunks.  In order to balance the workload, many implementations create many small chunks, and reuse threads.  This adds overhead, but does provide better load balancing, which in turn improves performance. The Task Parallel Library handles this more elaborately.  Chunks are determined at runtime, and start small.  They grow slowly over time, getting larger and larger.  This tends to lead to a near optimum load balancing, even in odd cases such as increasing or decreasing workloads.  Parallel.ForEach is a bit more complicated, however. When working with a generic IEnumerable<T>, the number of items required for processing is not known in advance, and must be discovered at runtime.  In addition, since we don’t have direct access to each element, the scheduler must enumerate the collection to process it.  Since IEnumerable<T> is not thread safe, it must lock on elements as it enumerates, create temporary collections for each chunk to process, and schedule this out.  By default, it uses a partitioning method similar to the one described above.  We can see this directly by looking at the Visual Partitioning sample shipped by the Task Parallel Library team, and available as part of the Samples for Parallel Programming.  When we run the sample, with four cores and the default, Load Balancing partitioning scheme, we see this: The colored bands represent each processing core.  You can see that, when we started (at the top), we begin with very small bands of color.  As the routine progresses through the Parallel.ForEach, the chunks get larger and larger (seen by larger and larger stripes). Most of the time, this is fantastic behavior, and most likely will out perform any custom written partitioning.  However, if your routine is not scaling well, it may be due to a failure in the default partitioning to handle your specific case.  With prior knowledge about your work, it may be possible to partition data more meaningfully than the default Partitioner. There is the option to use an overload of Parallel.ForEach which takes a Partitioner<T> instance.  The Partitioner<T> class is an abstract class which allows for both static and dynamic partitioning.  By overriding Partitioner<T>.SupportsDynamicPartitions, you can specify whether a dynamic approach is available.  If not, your custom Partitioner<T> subclass would override GetPartitions(int), which returns a list of IEnumerator<T> instances.  These are then used by the Parallel class to split work up amongst processors.  When dynamic partitioning is available, GetDynamicPartitions() is used, which returns an IEnumerable<T> for each partition.  If you do decide to implement your own Partitioner<T>, keep in mind the goals and tradeoffs of different partitioning strategies, and design appropriately. The Samples for Parallel Programming project includes a ChunkPartitioner class in the ParallelExtensionsExtras project.  This provides example code for implementing your own, custom allocation strategies, including a static allocator of a given chunk size.  Although implementing your own Partitioner<T> is possible, as I mentioned above, this is rarely required or useful in practice.  The default behavior of the TPL is very good, often better than any hand written partitioning strategy.

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• Parallelism in .NET – Part 3, Imperative Data Parallelism: Early Termination

  - by Reed

  Although simple data parallelism allows us to easily parallelize many of our iteration statements, there are cases that it does not handle well.  In my previous discussion, I focused on data parallelism with no shared state, and where every element is being processed exactly the same. Unfortunately, there are many common cases where this does not happen.  If we are dealing with a loop that requires early termination, extra care is required when parallelizing. Often, while processing in a loop, once a certain condition is met, it is no longer necessary to continue processing.  This may be a matter of finding a specific element within the collection, or reaching some error case.  The important distinction here is that, it is often impossible to know until runtime, what set of elements needs to be processed. In my initial discussion of data parallelism, I mentioned that this technique is a candidate when you can decompose the problem based on the data involved, and you wish to apply a single operation concurrently on all of the elements of a collection.  This covers many of the potential cases, but sometimes, after processing some of the elements, we need to stop processing. As an example, lets go back to our previous Parallel.ForEach example with contacting a customer.  However, this time, we’ll change the requirements slightly.  In this case, we’ll add an extra condition – if the store is unable to email the customer, we will exit gracefully.  The thinking here, of course, is that if the store is currently unable to email, the next time this operation runs, it will handle the same situation, so we can just skip our processing entirely.  The original, serial case, with this extra condition, might look something like the following: foreach(var customer in customers) { // Run some process that takes some time... DateTime lastContact = theStore.GetLastContact(customer); TimeSpan timeSinceContact = DateTime.Now - lastContact; // If it's been more than two weeks, send an email, and update... if (timeSinceContact.Days > 14) { // Exit gracefully if we fail to email, since this // entire process can be repeated later without issue. if (theStore.EmailCustomer(customer) == false) break; customer.LastEmailContact = DateTime.Now; } } .csharpcode, .csharpcode pre { font-size: small; color: black; font-family: consolas, "Courier New", courier, monospace; background-color: #ffffff; /*white-space: pre;*/ } .csharpcode pre { margin: 0em; } .csharpcode .rem { color: #008000; } .csharpcode .kwrd { color: #0000ff; } .csharpcode .str { color: #006080; } .csharpcode .op { color: #0000c0; } .csharpcode .preproc { color: #cc6633; } .csharpcode .asp { background-color: #ffff00; } .csharpcode .html { color: #800000; } .csharpcode .attr { color: #ff0000; } .csharpcode .alt { background-color: #f4f4f4; width: 100%; margin: 0em; } .csharpcode .lnum { color: #606060; } Here, we’re processing our loop, but at any point, if we fail to send our email successfully, we just abandon this process, and assume that it will get handled correctly the next time our routine is run.  If we try to parallelize this using Parallel.ForEach, as we did previously, we’ll run into an error almost immediately: the break statement we’re using is only valid when enclosed within an iteration statement, such as foreach.  When we switch to Parallel.ForEach, we’re no longer within an iteration statement – we’re a delegate running in a method. This needs to be handled slightly differently when parallelized.  Instead of using the break statement, we need to utilize a new class in the Task Parallel Library: ParallelLoopState.  The ParallelLoopState class is intended to allow concurrently running loop bodies a way to interact with each other, and provides us with a way to break out of a loop.  In order to use this, we will use a different overload of Parallel.ForEach which takes an IEnumerable<T> and an Action<T, ParallelLoopState> instead of an Action<T>.  Using this, we can parallelize the above operation by doing: Parallel.ForEach(customers, (customer, parallelLoopState) => { // Run some process that takes some time... DateTime lastContact = theStore.GetLastContact(customer); TimeSpan timeSinceContact = DateTime.Now - lastContact; // If it's been more than two weeks, send an email, and update... if (timeSinceContact.Days > 14) { // Exit gracefully if we fail to email, since this // entire process can be repeated later without issue. if (theStore.EmailCustomer(customer) == false) parallelLoopState.Break(); else customer.LastEmailContact = DateTime.Now; } }); There are a couple of important points here.  First, we didn’t actually instantiate the ParallelLoopState instance.  It was provided directly to us via the Parallel class.  All we needed to do was change our lambda expression to reflect that we want to use the loop state, and the Parallel class creates an instance for our use.  We also needed to change our logic slightly when we call Break().  Since Break() doesn’t stop the program flow within our block, we needed to add an else case to only set the property in customer when we succeeded.  This same technique can be used to break out of a Parallel.For loop. That being said, there is a huge difference between using ParallelLoopState to cause early termination and to use break in a standard iteration statement.  When dealing with a loop serially, break will immediately terminate the processing within the closest enclosing loop statement.  Calling ParallelLoopState.Break(), however, has a very different behavior. The issue is that, now, we’re no longer processing one element at a time.  If we break in one of our threads, there are other threads that will likely still be executing.  This leads to an important observation about termination of parallel code: Early termination in parallel routines is not immediate.  Code will continue to run after you request a termination. This may seem problematic at first, but it is something you just need to keep in mind while designing your routine.  ParallelLoopState.Break() should be thought of as a request.  We are telling the runtime that no elements that were in the collection past the element we’re currently processing need to be processed, and leaving it up to the runtime to decide how to handle this as gracefully as possible.  Although this may seem problematic at first, it is a good thing.  If the runtime tried to immediately stop processing, many of our elements would be partially processed.  It would be like putting a return statement in a random location throughout our loop body – which could have horrific consequences to our code’s maintainability. In order to understand and effectively write parallel routines, we, as developers, need a subtle, but profound shift in our thinking.  We can no longer think in terms of sequential processes, but rather need to think in terms of requests to the system that may be handled differently than we’d first expect.  This is more natural to developers who have dealt with asynchronous models previously, but is an important distinction when moving to concurrent programming models. As an example, I’ll discuss the Break() method.  ParallelLoopState.Break() functions in a way that may be unexpected at first.  When you call Break() from a loop body, the runtime will continue to process all elements of the collection that were found prior to the element that was being processed when the Break() method was called.  This is done to keep the behavior of the Break() method as close to the behavior of the break statement as possible. We can see the behavior in this simple code: var collection = Enumerable.Range(0, 20); var pResult = Parallel.ForEach(collection, (element, state) => { if (element > 10) { Console.WriteLine("Breaking on {0}", element); state.Break(); } Console.WriteLine(element); }); If we run this, we get a result that may seem unexpected at first: 0 2 1 5 6 3 4 10 Breaking on 11 11 Breaking on 12 12 9 Breaking on 13 13 7 8 Breaking on 15 15 What is occurring here is that we loop until we find the first element where the element is greater than 10.  In this case, this was found, the first time, when one of our threads reached element 11.  It requested that the loop stop by calling Break() at this point.  However, the loop continued processing until all of the elements less than 11 were completed, then terminated.  This means that it will guarantee that elements 9, 7, and 8 are completed before it stops processing.  You can see our other threads that were running each tried to break as well, but since Break() was called on the element with a value of 11, it decides which elements (0-10) must be processed. If this behavior is not desirable, there is another option.  Instead of calling ParallelLoopState.Break(), you can call ParallelLoopState.Stop().  The Stop() method requests that the runtime terminate as soon as possible , without guaranteeing that any other elements are processed.  Stop() will not stop the processing within an element, so elements already being processed will continue to be processed.  It will prevent new elements, even ones found earlier in the collection, from being processed.  Also, when Stop() is called, the ParallelLoopState’s IsStopped property will return true.  This lets longer running processes poll for this value, and return after performing any necessary cleanup. The basic rule of thumb for choosing between Break() and Stop() is the following. Use ParallelLoopState.Stop() when possible, since it terminates more quickly.  This is particularly useful in situations where you are searching for an element or a condition in the collection.  Once you’ve found it, you do not need to do any other processing, so Stop() is more appropriate. Use ParallelLoopState.Break() if you need to more closely match the behavior of the C# break statement. Both methods behave differently than our C# break statement.  Unfortunately, when parallelizing a routine, more thought and care needs to be put into every aspect of your routine than you may otherwise expect.  This is due to my second observation: Parallelizing a routine will almost always change its behavior. This sounds crazy at first, but it’s a concept that’s so simple its easy to forget.  We’re purposely telling the system to process more than one thing at the same time, which means that the sequence in which things get processed is no longer deterministic.  It is easy to change the behavior of your routine in very subtle ways by introducing parallelism.  Often, the changes are not avoidable, even if they don’t have any adverse side effects.  This leads to my final observation for this post: Parallelization is something that should be handled with care and forethought, added by design, and not just introduced casually.

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• Parallelism in .NET – Part 7, Some Differences between PLINQ and LINQ to Objects

  - by Reed

  In my previous post on Declarative Data Parallelism, I mentioned that PLINQ extends LINQ to Objects to support parallel operations.  Although nearly all of the same operations are supported, there are some differences between PLINQ and LINQ to Objects.  By introducing Parallelism to our declarative model, we add some extra complexity.  This, in turn, adds some extra requirements that must be addressed. In order to illustrate the main differences, and why they exist, let’s begin by discussing some differences in how the two technologies operate, and look at the underlying types involved in LINQ to Objects and PLINQ . LINQ to Objects is mainly built upon a single class: Enumerable.  The Enumerable class is a static class that defines a large set of extension methods, nearly all of which work upon an IEnumerable<T>.  Many of these methods return a new IEnumerable<T>, allowing the methods to be chained together into a fluent style interface.  This is what allows us to write statements that chain together, and lead to the nice declarative programming model of LINQ: double min = collection .Where(item => item.SomeProperty > 6 && item.SomeProperty < 24) .Min(item => item.PerformComputation()); .csharpcode, .csharpcode pre { font-size: small; color: black; font-family: consolas, "Courier New", courier, monospace; background-color: #ffffff; /*white-space: pre;*/ } .csharpcode pre { margin: 0em; } .csharpcode .rem { color: #008000; } .csharpcode .kwrd { color: #0000ff; } .csharpcode .str { color: #006080; } .csharpcode .op { color: #0000c0; } .csharpcode .preproc { color: #cc6633; } .csharpcode .asp { background-color: #ffff00; } .csharpcode .html { color: #800000; } .csharpcode .attr { color: #ff0000; } .csharpcode .alt { background-color: #f4f4f4; width: 100%; margin: 0em; } .csharpcode .lnum { color: #606060; } Other LINQ variants work in a similar fashion.  For example, most data-oriented LINQ providers are built upon an implementation of IQueryable<T>, which allows the database provider to turn a LINQ statement into an underlying SQL query, to be performed directly on the remote database. PLINQ is similar, but instead of being built upon the Enumerable class, most of PLINQ is built upon a new static class: ParallelEnumerable.  When using PLINQ, you typically begin with any collection which implements IEnumerable<T>, and convert it to a new type using an extension method defined on ParallelEnumerable: AsParallel().  This method takes any IEnumerable<T>, and converts it into a ParallelQuery<T>, the core class for PLINQ.  There is a similar ParallelQuery class for working with non-generic IEnumerable implementations. This brings us to our first subtle, but important difference between PLINQ and LINQ – PLINQ always works upon specific types, which must be explicitly created. Typically, the type you’ll use with PLINQ is ParallelQuery<T>, but it can sometimes be a ParallelQuery or an OrderedParallelQuery<T>.  Instead of dealing with an interface, implemented by an unknown class, we’re dealing with a specific class type.  This works seamlessly from a usage standpoint – ParallelQuery<T> implements IEnumerable<T>, so you can always “switch back” to an IEnumerable<T>.  The difference only arises at the beginning of our parallelization.  When we’re using LINQ, and we want to process a normal collection via PLINQ, we need to explicitly convert the collection into a ParallelQuery<T> by calling AsParallel().  There is an important consideration here – AsParallel() does not need to be called on your specific collection, but rather any IEnumerable<T>.  This allows you to place it anywhere in the chain of methods involved in a LINQ statement, not just at the beginning.  This can be useful if you have an operation which will not parallelize well or is not thread safe.  For example, the following is perfectly valid, and similar to our previous examples: double min = collection .AsParallel() .Select(item => item.SomeOperation()) .Where(item => item.SomeProperty > 6 && item.SomeProperty < 24) .Min(item => item.PerformComputation()); However, if SomeOperation() is not thread safe, we could just as easily do: double min = collection .Select(item => item.SomeOperation()) .AsParallel() .Where(item => item.SomeProperty > 6 && item.SomeProperty < 24) .Min(item => item.PerformComputation()); In this case, we’re using standard LINQ to Objects for the Select(…) method, then converting the results of that map routine to a ParallelQuery<T>, and processing our filter (the Where method) and our aggregation (the Min method) in parallel. PLINQ also provides us with a way to convert a ParallelQuery<T> back into a standard IEnumerable<T>, forcing sequential processing via standard LINQ to Objects.  If SomeOperation() was thread-safe, but PerformComputation() was not thread-safe, we would need to handle this by using the AsEnumerable() method: double min = collection .AsParallel() .Select(item => item.SomeOperation()) .Where(item => item.SomeProperty > 6 && item.SomeProperty < 24) .AsEnumerable() .Min(item => item.PerformComputation()); Here, we’re converting our collection into a ParallelQuery<T>, doing our map operation (the Select(…) method) and our filtering in parallel, then converting the collection back into a standard IEnumerable<T>, which causes our aggregation via Min() to be performed sequentially. This could also be written as two statements, as well, which would allow us to use the language integrated syntax for the first portion: var tempCollection = from item in collection.AsParallel() let e = item.SomeOperation() where (e.SomeProperty > 6 && e.SomeProperty < 24) select e; double min = tempCollection.AsEnumerable().Min(item => item.PerformComputation()); This allows us to use the standard LINQ style language integrated query syntax, but control whether it’s performed in parallel or serial by adding AsParallel() and AsEnumerable() appropriately. The second important difference between PLINQ and LINQ deals with order preservation.  PLINQ, by default, does not preserve the order of of source collection. This is by design.  In order to process a collection in parallel, the system needs to naturally deal with multiple elements at the same time.  Maintaining the original ordering of the sequence adds overhead, which is, in many cases, unnecessary.  Therefore, by default, the system is allowed to completely change the order of your sequence during processing.  If you are doing a standard query operation, this is usually not an issue.  However, there are times when keeping a specific ordering in place is important.  If this is required, you can explicitly request the ordering be preserved throughout all operations done on a ParallelQuery<T> by using the AsOrdered() extension method.  This will cause our sequence ordering to be preserved. For example, suppose we wanted to take a collection, perform an expensive operation which converts it to a new type, and display the first 100 elements.  In LINQ to Objects, our code might look something like: // Using IEnumerable<SourceClass> collection IEnumerable<ResultClass> results = collection .Select(e => e.CreateResult()) .Take(100); If we just converted this to a parallel query naively, like so: IEnumerable<ResultClass> results = collection .AsParallel() .Select(e => e.CreateResult()) .Take(100); We could very easily get a very different, and non-reproducable, set of results, since the ordering of elements in the input collection is not preserved.  To get the same results as our original query, we need to use: IEnumerable<ResultClass> results = collection .AsParallel() .AsOrdered() .Select(e => e.CreateResult()) .Take(100); This requests that PLINQ process our sequence in a way that verifies that our resulting collection is ordered as if it were processed serially.  This will cause our query to run slower, since there is overhead involved in maintaining the ordering.  However, in this case, it is required, since the ordering is required for correctness. PLINQ is incredibly useful.  It allows us to easily take nearly any LINQ to Objects query and run it in parallel, using the same methods and syntax we’ve used previously.  There are some important differences in operation that must be considered, however – it is not a free pass to parallelize everything.  When using PLINQ in order to parallelize your routines declaratively, the same guideline I mentioned before still applies: Parallelization is something that should be handled with care and forethought, added by design, and not just introduced casually.

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• Parallelism in .NET – Part 9, Configuration in PLINQ and TPL

  - by Reed

  Parallel LINQ and the Task Parallel Library contain many options for configuration.  Although the default configuration options are often ideal, there are times when customizing the behavior is desirable.  Both frameworks provide full configuration support. When working with Data Parallelism, there is one primary configuration option we often need to control – the number of threads we want the system to use when parallelizing our routine.  By default, PLINQ and the TPL both use the ThreadPool to schedule tasks.  Given the major improvements in the ThreadPool in CLR 4, this default behavior is often ideal.  However, there are times that the default behavior is not appropriate.  For example, if you are working on multiple threads simultaneously, and want to schedule parallel operations from within both threads, you might want to consider restricting each parallel operation to using a subset of the processing cores of the system.  Not doing this might over-parallelize your routine, which leads to inefficiencies from having too many context switches. In the Task Parallel Library, configuration is handled via the ParallelOptions class.  All of the methods of the Parallel class have an overload which accepts a ParallelOptions argument. We configure the Parallel class by setting the ParallelOptions.MaxDegreeOfParallelism property.  For example, let’s revisit one of the simple data parallel examples from Part 2: Parallel.For(0, pixelData.GetUpperBound(0), row => { for (int col=0; col < pixelData.GetUpperBound(1); ++col) { pixelData[row, col] = AdjustContrast(pixelData[row, col], minPixel, maxPixel); } }); .csharpcode, .csharpcode pre { font-size: small; color: black; font-family: consolas, "Courier New", courier, monospace; background-color: #ffffff; /*white-space: pre;*/ } .csharpcode pre { margin: 0em; } .csharpcode .rem { color: #008000; } .csharpcode .kwrd { color: #0000ff; } .csharpcode .str { color: #006080; } .csharpcode .op { color: #0000c0; } .csharpcode .preproc { color: #cc6633; } .csharpcode .asp { background-color: #ffff00; } .csharpcode .html { color: #800000; } .csharpcode .attr { color: #ff0000; } .csharpcode .alt { background-color: #f4f4f4; width: 100%; margin: 0em; } .csharpcode .lnum { color: #606060; } Here, we’re looping through an image, and calling a method on each pixel in the image.  If this was being done on a separate thread, and we knew another thread within our system was going to be doing a similar operation, we likely would want to restrict this to using half of the cores on the system.  This could be accomplished easily by doing: var options = new ParallelOptions(); options.MaxDegreeOfParallelism = Math.Max(Environment.ProcessorCount / 2, 1); Parallel.For(0, pixelData.GetUpperBound(0), options, row => { for (int col=0; col < pixelData.GetUpperBound(1); ++col) { pixelData[row, col] = AdjustContrast(pixelData[row, col], minPixel, maxPixel); } }); Now, we’re restricting this routine to using no more than half the cores in our system.  Note that I included a check to prevent a single core system from supplying zero; without this check, we’d potentially cause an exception.  I also did not hard code a specific value for the MaxDegreeOfParallelism property.  One of our goals when parallelizing a routine is allowing it to scale on better hardware.  Specifying a hard-coded value would contradict that goal. Parallel LINQ also supports configuration, and in fact, has quite a few more options for configuring the system.  The main configuration option we most often need is the same as our TPL option: we need to supply the maximum number of processing threads.  In PLINQ, this is done via a new extension method on ParallelQuery<T>: ParallelEnumerable.WithDegreeOfParallelism. Let’s revisit our declarative data parallelism sample from Part 6: double min = collection.AsParallel().Min(item => item.PerformComputation()); Here, we’re performing a computation on each element in the collection, and saving the minimum value of this operation.  If we wanted to restrict this to a limited number of threads, we would add our new extension method: int maxThreads = Math.Max(Environment.ProcessorCount / 2, 1); double min = collection .AsParallel() .WithDegreeOfParallelism(maxThreads) .Min(item => item.PerformComputation()); This automatically restricts the PLINQ query to half of the threads on the system. PLINQ provides some additional configuration options.  By default, PLINQ will occasionally revert to processing a query in parallel.  This occurs because many queries, if parallelized, typically actually cause an overall slowdown compared to a serial processing equivalent.  By analyzing the “shape” of the query, PLINQ often decides to run a query serially instead of in parallel.  This can occur for (taken from MSDN): Queries that contain a Select, indexed Where, indexed SelectMany, or ElementAt clause after an ordering or filtering operator that has removed or rearranged original indices. Queries that contain a Take, TakeWhile, Skip, SkipWhile operator and where indices in the source sequence are not in the original order. Queries that contain Zip or SequenceEquals, unless one of the data sources has an originally ordered index and the other data source is indexable (i.e. an array or IList(T)). Queries that contain Concat, unless it is applied to indexable data sources. Queries that contain Reverse, unless applied to an indexable data source. If the specific query follows these rules, PLINQ will run the query on a single thread.  However, none of these rules look at the specific work being done in the delegates, only at the “shape” of the query.  There are cases where running in parallel may still be beneficial, even if the shape is one where it typically parallelizes poorly.  In these cases, you can override the default behavior by using the WithExecutionMode extension method.  This would be done like so: var reversed = collection .AsParallel() .WithExecutionMode(ParallelExecutionMode.ForceParallelism) .Select(i => i.PerformComputation()) .Reverse(); Here, the default behavior would be to not parallelize the query unless collection implemented IList<T>.  We can force this to run in parallel by adding the WithExecutionMode extension method in the method chain. Finally, PLINQ has the ability to configure how results are returned.  When a query is filtering or selecting an input collection, the results will need to be streamed back into a single IEnumerable<T> result.  For example, the method above returns a new, reversed collection.  In this case, the processing of the collection will be done in parallel, but the results need to be streamed back to the caller serially, so they can be enumerated on a single thread. This streaming introduces overhead.  IEnumerable<T> isn’t designed with thread safety in mind, so the system needs to handle merging the parallel processes back into a single stream, which introduces synchronization issues.  There are two extremes of how this could be accomplished, but both extremes have disadvantages. The system could watch each thread, and whenever a thread produces a result, take that result and send it back to the caller.  This would mean that the calling thread would have access to the data as soon as data is available, which is the benefit of this approach.  However, it also means that every item is introducing synchronization overhead, since each item needs to be merged individually. On the other extreme, the system could wait until all of the results from all of the threads were ready, then push all of the results back to the calling thread in one shot.  The advantage here is that the least amount of synchronization is added to the system, which means the query will, on a whole, run the fastest.  However, the calling thread will have to wait for all elements to be processed, so this could introduce a long delay between when a parallel query begins and when results are returned. The default behavior in PLINQ is actually between these two extremes.  By default, PLINQ maintains an internal buffer, and chooses an optimal buffer size to maintain.  Query results are accumulated into the buffer, then returned in the IEnumerable<T> result in chunks.  This provides reasonably fast access to the results, as well as good overall throughput, in most scenarios. However, if we know the nature of our algorithm, we may decide we would prefer one of the other extremes.  This can be done by using the WithMergeOptions extension method.  For example, if we know that our PerformComputation() routine is very slow, but also variable in runtime, we may want to retrieve results as they are available, with no bufferring.  This can be done by changing our above routine to: var reversed = collection .AsParallel() .WithExecutionMode(ParallelExecutionMode.ForceParallelism) .WithMergeOptions(ParallelMergeOptions.NotBuffered) .Select(i => i.PerformComputation()) .Reverse(); On the other hand, if are already on a background thread, and we want to allow the system to maximize its speed, we might want to allow the system to fully buffer the results: var reversed = collection .AsParallel() .WithExecutionMode(ParallelExecutionMode.ForceParallelism) .WithMergeOptions(ParallelMergeOptions.FullyBuffered) .Select(i => i.PerformComputation()) .Reverse(); Notice, also, that you can specify multiple configuration options in a parallel query.  By chaining these extension methods together, we generate a query that will always run in parallel, and will always complete before making the results available in our IEnumerable<T>.

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• Parallelism in .NET – Part 12, More on Task Decomposition

  - by Reed

  Many tasks can be decomposed using a Data Decomposition approach, but often, this is not appropriate.  Frequently, decomposing the problem into distinctive tasks that must be performed is a more natural abstraction. However, as I mentioned in Part 1, Task Decomposition tends to be a bit more difficult than data decomposition, and can require a bit more effort.  Before we being parallelizing our algorithm based on the tasks being performed, we need to decompose our problem, and take special care of certain considerations such as ordering and grouping of tasks. Up to this point in this series, I’ve focused on parallelization techniques which are most appropriate when a problem space can be decomposed by data.  Using PLINQ and the Parallel class, I’ve shown how problem spaces where there is a collection of data, and each element needs to be processed, can potentially be parallelized. However, there are many other routines where this is not appropriate.  Often, instead of working on a collection of data, there is a single piece of data which must be processed using an algorithm or series of algorithms.  Here, there is no collection of data, but there may still be opportunities for parallelism. As I mentioned before, in cases like this, the approach is to look at your overall routine, and decompose your problem space based on tasks.  The idea here is to look for discrete “tasks,” individual pieces of work which can be conceptually thought of as a single operation. Let’s revisit the example I used in Part 1, an application startup path.  Say we want our program, at startup, to do a bunch of individual actions, or “tasks”.  The following is our list of duties we must perform right at startup: Display a splash screen Request a license from our license manager Check for an update to the software from our web server If an update is available, download it Setup our menu structure based on our current license Open and display our main, welcome Window Hide the splash screen The first step in Task Decomposition is breaking up the problem space into discrete tasks. This, naturally, can be abstracted as seven discrete tasks.  In the serial version of our program, if we were to diagram this, the general process would appear as: These tasks, obviously, provide some opportunities for parallelism.  Before we can parallelize this routine, we need to analyze these tasks, and find any dependencies between tasks.  In this case, our dependencies include: The splash screen must be displayed first, and as quickly as possible. We can’t download an update before we see whether one exists. Our menu structure depends on our license, so we must check for the license before setting up the menus. Since our welcome screen will notify the user of an update, we can’t show it until we’ve downloaded the update. Since our welcome screen includes menus that are customized based off the licensing, we can’t display it until we’ve received a license. We can’t hide the splash until our welcome screen is displayed. By listing our dependencies, we start to see the natural ordering that must occur for the tasks to be processed correctly. The second step in Task Decomposition is determining the dependencies between tasks, and ordering tasks based on their dependencies. Looking at these tasks, and looking at all the dependencies, we quickly see that even a simple decomposition such as this one can get quite complicated.  In order to simplify the problem of defining the dependencies, it’s often a useful practice to group our tasks into larger, discrete tasks.  The goal when grouping tasks is that you want to make each task “group” have as few dependencies as possible to other tasks or groups, and then work out the dependencies within that group.  Typically, this works best when any external dependency is based on the “last” task within the group when it’s ordered, although that is not a firm requirement.  This process is often called Grouping Tasks.  In our case, we can easily group together tasks, effectively turning this into four discrete task groups: 1. Show our splash screen – This needs to be left as its own task.  First, multiple things depend on this task, mainly because we want this to start before any other action, and start as quickly as possible. 2. Check for Update and Download the Update if it Exists - These two tasks logically group together.  We know we only download an update if the update exists, so that naturally follows.  This task has one dependency as an input, and other tasks only rely on the final task within this group. 3. Request a License, and then Setup the Menus – Here, we can group these two tasks together.  Although we mentioned that our welcome screen depends on the license returned, it also depends on setting up the menu, which is the final task here.  Setting up our menus cannot happen until after our license is requested.  By grouping these together, we further reduce our problem space. 4. Display welcome and hide splash - Finally, we can display our welcome window and hide our splash screen.  This task group depends on all three previous task groups – it cannot happen until all three of the previous groups have completed. By grouping the tasks together, we reduce our problem space, and can naturally see a pattern for how this process can be parallelized.  The diagram below shows one approach: The orange boxes show each task group, with each task represented within.  We can, now, effectively take these tasks, and run a large portion of this process in parallel, including the portions which may be the most time consuming.  We’ve now created two parallel paths which our process execution can follow, hopefully speeding up the application startup time dramatically. The main point to remember here is that, when decomposing your problem space by tasks, you need to: Define each discrete action as an individual Task Discover dependencies between your tasks Group tasks based on their dependencies Order the tasks and groups of tasks

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• Parallelism in .NET – Part 4, Imperative Data Parallelism: Aggregation

  - by Reed

  In the article on simple data parallelism, I described how to perform an operation on an entire collection of elements in parallel.  Often, this is not adequate, as the parallel operation is going to be performing some form of aggregation. Simple examples of this might include taking the sum of the results of processing a function on each element in the collection, or finding the minimum of the collection given some criteria.  This can be done using the techniques described in simple data parallelism, however, special care needs to be taken into account to synchronize the shared data appropriately.  The Task Parallel Library has tools to assist in this synchronization. The main issue with aggregation when parallelizing a routine is that you need to handle synchronization of data.  Since multiple threads will need to write to a shared portion of data.  Suppose, for example, that we wanted to parallelize a simple loop that looked for the minimum value within a dataset: double min = double.MaxValue; foreach(var item in collection) { double value = item.PerformComputation(); min = System.Math.Min(min, value); } .csharpcode, .csharpcode pre { font-size: small; color: black; font-family: consolas, "Courier New", courier, monospace; background-color: #ffffff; /*white-space: pre;*/ } .csharpcode pre { margin: 0em; } .csharpcode .rem { color: #008000; } .csharpcode .kwrd { color: #0000ff; } .csharpcode .str { color: #006080; } .csharpcode .op { color: #0000c0; } .csharpcode .preproc { color: #cc6633; } .csharpcode .asp { background-color: #ffff00; } .csharpcode .html { color: #800000; } .csharpcode .attr { color: #ff0000; } .csharpcode .alt { background-color: #f4f4f4; width: 100%; margin: 0em; } .csharpcode .lnum { color: #606060; } This seems like a good candidate for parallelization, but there is a problem here.  If we just wrap this into a call to Parallel.ForEach, we’ll introduce a critical race condition, and get the wrong answer.  Let’s look at what happens here: // Buggy code! Do not use! double min = double.MaxValue; Parallel.ForEach(collection, item => { double value = item.PerformComputation(); min = System.Math.Min(min, value); }); This code has a fatal flaw: min will be checked, then set, by multiple threads simultaneously.  Two threads may perform the check at the same time, and set the wrong value for min.  Say we get a value of 1 in thread 1, and a value of 2 in thread 2, and these two elements are the first two to run.  If both hit the min check line at the same time, both will determine that min should change, to 1 and 2 respectively.  If element 1 happens to set the variable first, then element 2 sets the min variable, we’ll detect a min value of 2 instead of 1.  This can lead to wrong answers. Unfortunately, fixing this, with the Parallel.ForEach call we’re using, would require adding locking.  We would need to rewrite this like: // Safe, but slow double min = double.MaxValue; // Make a "lock" object object syncObject = new object(); Parallel.ForEach(collection, item => { double value = item.PerformComputation(); lock(syncObject) min = System.Math.Min(min, value); }); This will potentially add a huge amount of overhead to our calculation.  Since we can potentially block while waiting on the lock for every single iteration, we will most likely slow this down to where it is actually quite a bit slower than our serial implementation.  The problem is the lock statement – any time you use lock(object), you’re almost assuring reduced performance in a parallel situation.  This leads to two observations I’ll make: When parallelizing a routine, try to avoid locks. That being said: Always add any and all required synchronization to avoid race conditions. These two observations tend to be opposing forces – we often need to synchronize our algorithms, but we also want to avoid the synchronization when possible.  Looking at our routine, there is no way to directly avoid this lock, since each element is potentially being run on a separate thread, and this lock is necessary in order for our routine to function correctly every time. However, this isn’t the only way to design this routine to implement this algorithm.  Realize that, although our collection may have thousands or even millions of elements, we have a limited number of Processing Elements (PE).  Processing Element is the standard term for a hardware element which can process and execute instructions.  This typically is a core in your processor, but many modern systems have multiple hardware execution threads per core.  The Task Parallel Library will not execute the work for each item in the collection as a separate work item. Instead, when Parallel.ForEach executes, it will partition the collection into larger “chunks” which get processed on different threads via the ThreadPool.  This helps reduce the threading overhead, and help the overall speed.  In general, the Parallel class will only use one thread per PE in the system. Given the fact that there are typically fewer threads than work items, we can rethink our algorithm design.  We can parallelize our algorithm more effectively by approaching it differently.  Because the basic aggregation we are doing here (Min) is communitive, we do not need to perform this in a given order.  We knew this to be true already – otherwise, we wouldn’t have been able to parallelize this routine in the first place.  With this in mind, we can treat each thread’s work independently, allowing each thread to serially process many elements with no locking, then, after all the threads are complete, “merge” together the results. This can be accomplished via a different set of overloads in the Parallel class: Parallel.ForEach<TSource,TLocal>.  The idea behind these overloads is to allow each thread to begin by initializing some local state (TLocal).  The thread will then process an entire set of items in the source collection, providing that state to the delegate which processes an individual item.  Finally, at the end, a separate delegate is run which allows you to handle merging that local state into your final results. To rewriting our routine using Parallel.ForEach<TSource,TLocal>, we need to provide three delegates instead of one.  The most basic version of this function is declared as: public static ParallelLoopResult ForEach<TSource, TLocal>( IEnumerable<TSource> source, Func<TLocal> localInit, Func<TSource, ParallelLoopState, TLocal, TLocal> body, Action<TLocal> localFinally ) The first delegate (the localInit argument) is defined as Func<TLocal>.  This delegate initializes our local state.  It should return some object we can use to track the results of a single thread’s operations. The second delegate (the body argument) is where our main processing occurs, although now, instead of being an Action<T>, we actually provide a Func<TSource, ParallelLoopState, TLocal, TLocal> delegate.  This delegate will receive three arguments: our original element from the collection (TSource), a ParallelLoopState which we can use for early termination, and the instance of our local state we created (TLocal).  It should do whatever processing you wish to occur per element, then return the value of the local state after processing is completed. The third delegate (the localFinally argument) is defined as Action<TLocal>.  This delegate is passed our local state after it’s been processed by all of the elements this thread will handle.  This is where you can merge your final results together.  This may require synchronization, but now, instead of synchronizing once per element (potentially millions of times), you’ll only have to synchronize once per thread, which is an ideal situation. Now that I’ve explained how this works, lets look at the code: // Safe, and fast! double min = double.MaxValue; // Make a "lock" object object syncObject = new object(); Parallel.ForEach( collection, // First, we provide a local state initialization delegate. () => double.MaxValue, // Next, we supply the body, which takes the original item, loop state, // and local state, and returns a new local state (item, loopState, localState) => { double value = item.PerformComputation(); return System.Math.Min(localState, value); }, // Finally, we provide an Action<TLocal>, to "merge" results together localState => { // This requires locking, but it's only once per used thread lock(syncObj) min = System.Math.Min(min, localState); } ); Although this is a bit more complicated than the previous version, it is now both thread-safe, and has minimal locking.  This same approach can be used by Parallel.For, although now, it’s Parallel.For<TLocal>.  When working with Parallel.For<TLocal>, you use the same triplet of delegates, with the same purpose and results. Also, many times, you can completely avoid locking by using a method of the Interlocked class to perform the final aggregation in an atomic operation.  The MSDN example demonstrating this same technique using Parallel.For uses the Interlocked class instead of a lock, since they are doing a sum operation on a long variable, which is possible via Interlocked.Add. By taking advantage of local state, we can use the Parallel class methods to parallelize algorithms such as aggregation, which, at first, may seem like poor candidates for parallelization.  Doing so requires careful consideration, and often requires a slight redesign of the algorithm, but the performance gains can be significant if handled in a way to avoid excessive synchronization.

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• Should a c# dev switch to VB.net when the team language base is mixed?

  - by jjr2527

  I recently joined a new development team where the language preferences are mixed on the .net platform. Dev 1: Knows VB.net, does not know c# Dev 2: Knows VB.net, does not know c# Dev 3: Knows c# and VB.net, prefers c# Dev 4: Knows c# and VB6(VB.net should be pretty easy to pick up), prefers c# It seems to me that the thought leaders in the .net space are c# devs almost universally. I also thought that some 3rd party tools didn't support VB.net but when I started looking into it I didn't find any good examples. I would prefer to get the whole team on c# but if there isn't any good reason to force the issue aside from preference then I don't think that is the right choice. Are there any reasons I should lead folks away from VB.net?

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• NHunspellTextBoxExtender - A Spellchecking IExtenderProvider for TextBoxes using Hunspell for .NET

  Extends controls inheriting TextBoxBase to provide off-line spell checking functionality

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• .NET Weak Event Handlers – Part II

  - by João Angelo

  On the first part of this article I showed two possible ways to create weak event handlers. One using reflection and the other using a delegate. For this performance analysis we will further differentiate between creating a delegate by providing the type of the listener at compile time (Explicit Delegate) vs creating the delegate with the type of the listener being only obtained at runtime (Implicit Delegate). As expected, the performance between reflection/delegate differ significantly. With the reflection based approach, creating a weak event handler is just storing a MethodInfo reference while with the delegate based approach there is the need to create the delegate which will be invoked later. So, at creating the weak event handler reflection clearly wins, but what about when the handler is invoked. No surprises there, performing a call through reflection every time a handler is invoked is costly. In conclusion, if you want good performance when creating handlers that only sporadically get triggered use reflection, otherwise use the delegate based approach. The explicit delegate approach always wins against the implicit delegate, but I find the syntax for the latter much more intuitive. // Implicit delegate - The listener type is inferred at runtime from the handler parameter public static EventHandler WrapInDelegateCall(EventHandler handler); public static EventHandler<TArgs> WrapInDelegateCall<TArgs>(EventHandler<TArgs> handler) where TArgs : EventArgs; // Explicite delegate - TListener is the type that defines the handler public static EventHandler WrapInDelegateCall<TListener>(EventHandler handler); public static EventHandler<TArgs> WrapInDelegateCall<TArgs, TListener>(EventHandler<TArgs> handler) where TArgs : EventArgs;

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• Parallelism in .NET – Part 19, TaskContinuationOptions

  - by Reed

  My introduction to Task continuations demonstrates continuations on the Task class.  In addition, I’ve shown how continuations allow handling of multiple tasks in a clean, concise manner.  Continuations can also be used to handle exceptional situations using a clean, simple syntax. In addition to standard Task continuations , the Task class provides some options for filtering continuations automatically.  This is handled via the TaskContinationOptions enumeration, which provides hints to the TaskScheduler that it should only continue based on the operation of the antecedent task. This is especially useful when dealing with exceptions.  For example, we can extend the sample from our earlier continuation discussion to include support for handling exceptions thrown by the Factorize method: // Get a copy of the UI-thread task scheduler up front to use later var uiScheduler = TaskScheduler.FromCurrentSynchronizationContext(); // Start our task var factorize = Task.Factory.StartNew( () => { int primeFactor1 = 0; int primeFactor2 = 0; bool result = Factorize(10298312, ref primeFactor1, ref primeFactor2); return new { Result = result, Factor1 = primeFactor1, Factor2 = primeFactor2 }; }); // When we succeed, report the results to the UI factorize.ContinueWith(task => textBox1.Text = string.Format("{0}/{1} [Succeeded {2}]", task.Result.Factor1, task.Result.Factor2, task.Result.Result), CancellationToken.None, TaskContinuationOptions.NotOnFaulted, uiScheduler); // When we have an exception, report it factorize.ContinueWith(task => textBox1.Text = string.Format("Error: {0}", task.Exception.Message), CancellationToken.None, TaskContinuationOptions.OnlyOnFaulted, uiScheduler); .csharpcode, .csharpcode pre { font-size: small; color: black; font-family: consolas, "Courier New", courier, monospace; background-color: #ffffff; /*white-space: pre;*/ } .csharpcode pre { margin: 0em; } .csharpcode .rem { color: #008000; } .csharpcode .kwrd { color: #0000ff; } .csharpcode .str { color: #006080; } .csharpcode .op { color: #0000c0; } .csharpcode .preproc { color: #cc6633; } .csharpcode .asp { background-color: #ffff00; } .csharpcode .html { color: #800000; } .csharpcode .attr { color: #ff0000; } .csharpcode .alt { background-color: #f4f4f4; width: 100%; margin: 0em; } .csharpcode .lnum { color: #606060; } The above code works by using a combination of features.  First, we schedule our task, the same way as in the previous example.  However, in this case, we use a different overload of Task.ContinueWith which allows us to specify both a specific TaskScheduler (in order to have your continuation run on the UI’s synchronization context) as well as a TaskContinuationOption.  In the first continuation, we tell the continuation that we only want it to run when there was not an exception by specifying TaskContinuationOptions.NotOnFaulted.  When our factorize task completes successfully, this continuation will automatically run on the UI thread, and provide the appropriate feedback. However, if the factorize task has an exception – for example, if the Factorize method throws an exception due to an improper input value, the second continuation will run.  This occurs due to the specification of TaskContinuationOptions.OnlyOnFaulted in the options.  In this case, we’ll report the error received to the user. We can use TaskContinuationOptions to filter our continuations by whether or not an exception occurred and whether or not a task was cancelled.  This allows us to handle many situations, and is especially useful when trying to maintain a valid application state without ever blocking the user interface.  The same concepts can be extended even further, and allow you to chain together many tasks based on the success of the previous ones.  Continuations can even be used to create a state machine with full error handling, all without blocking the user interface thread.

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• Red Gate .NET Tool Support for Visual Studio 2010

  - by Bart Read

  A *small* matter left unaddressed in my last post....(read more)

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• Building a better .NET Application Configuration Class - revisited

  - by Rick Strahl

  Managing configuration settings is an important part of successful applications. It should be easy to ensure that you can easily access and modify configuration values within your applications. If it's not - well things don't get parameterized as much as they should. In this post I discuss a custom Application Configuration class that makes it super easy to create reusable configuration objects in your applications using a code-first approach and the ability to persist configuration information into various types of configuration stores.

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• .NET Properties - Use Private Set or ReadOnly Property?

  - by tgxiii

  In what situation should I use a Private Set on a property versus making it a ReadOnly property? Take into consideration the two very simplistic examples below. First example: Public Class Person Private _name As String Public Property Name As String Get Return _name End Get Private Set(ByVal value As String) _name = value End Set End Property Public Sub WorkOnName() Dim txtInfo As TextInfo = _ Threading.Thread.CurrentThread.CurrentCulture.TextInfo Me.Name = txtInfo.ToTitleCase(Me.Name) End Sub End Class // ---------- public class Person { private string _name; public string Name { get { return _name; } private set { _name = value; } } public void WorkOnName() { TextInfo txtInfo = System.Threading.Thread.CurrentThread.CurrentCulture.TextInfo; this.Name = txtInfo.ToTitleCase(this.Name); } } Second example: Public Class AnotherPerson Private _name As String Public ReadOnly Property Name As String Get Return _name End Get End Property Public Sub WorkOnName() Dim txtInfo As TextInfo = _ Threading.Thread.CurrentThread.CurrentCulture.TextInfo _name = txtInfo.ToTitleCase(_name) End Sub End Class // --------------- public class AnotherPerson { private string _name; public string Name { get { return _name; } } public void WorkOnName() { TextInfo txtInfo = System.Threading.Thread.CurrentThread.CurrentCulture.TextInfo; _name = txtInfo.ToTitleCase(_name); } } They both yield the same results. Is this a situation where there's no right and wrong, and it's just a matter of preference?

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• ASP.NET ViewState Tips and Tricks #1

  - by João Angelo

  In User Controls or Custom Controls DO NOT use ViewState to store non public properties. Persisting non public properties in ViewState results in loss of functionality if the Page hosting the controls has ViewState disabled since it can no longer reset values of non public properties on page load. Example: public class ExampleControl : WebControl { private const string PublicViewStateKey = "Example_Public"; private const string NonPublicViewStateKey = "Example_NonPublic"; // DO public int Public { get { object o = this.ViewState[PublicViewStateKey]; if (o == null) return default(int); return (int)o; } set { this.ViewState[PublicViewStateKey] = value; } } // DO NOT private int NonPublic { get { object o = this.ViewState[NonPublicViewStateKey]; if (o == null) return default(int); return (int)o; } set { this.ViewState[NonPublicViewStateKey] = value; } } } // Page with ViewState disabled public partial class ExamplePage : Page { protected override void OnLoad(EventArgs e) { base.OnLoad(e); this.Example.Public = 10; // Restore Public value this.Example.NonPublic = 20; // Compile Error! } }

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