rampant creative group - Page 345

SQL query to get lowest 2 values of a counted query selection (using db2)?

- by jNoob

Hi, Imagine I already have a query that returns the following: Col1 | Col2 ------------ A | 2 B | 3 C | 3 D | 4 E | 8 ... Say I used something like this: select Col1, count ( * ) as Col2 \ from ... where ... order by Col2 \ group by Col1 \ So now, all I want to select are (Col1, Col2) such that it returns the selections (a, b) and (c, d) where (b >= all (Col2)) and (d >= ((all (Col2)) - a)). So for the above example, it would return {(A, 2), (B, 3), (C, 3)}. How do I go about doing this? Any help would be highly appreciated. Thanks.

Read the article

MySQL pivot tables - covert rows to columns

- by user2723490

This is the structure of my table: Then I run a query SELECT `date`,`index_name`,`results` FROM `mst_ind` WHERE `index_name` IN ('MSCI EAFE Mid NR USD', 'Alerian MLP PR USD') AND `time_period`='M1' and get a table like How can I convert "index_name" rows to columns like: date | MSCI EAFE Mid NR USD | Alerian MLP PR USD etc In other words I need each column to represent an index and rows to represent date-result. I understand that MySQL doesn't have pivot table functions. What is the easiest way of doing this? I've tried this code, but it generates an error: SELECT `date`, MAX(IF(index_name = 'Alerian MLP PR USD' AND `time_period`='M1', results, NULL)) AS res1, MAX(IF(index_name = 'MSCI EAFE Mid NR USD' AND `time_period`='M1', results, NULL)) AS res2 FROM `mst_ind` GROUP BY `date I need to make the conversion on the query level - not PHP. Please suggest a nice and elegant solution. Thanks!

Read the article

Select products with users

- by Ploppe

I have not worked with SQL for quite a long time, and I need some help for a basic query. I have the three following tables: users (id, name) products (id, name) owners (userid, productid, date) One product can be sold by user A to user B and then back to A. Now, I want the list of all products currently owned by every single user with the date of transaction. Currently, my query is this one, but I'm stuck with old data (first association of one product to one user, and not the newest one): SELECT users.name, products.name, date FROM products JOIN owners ON products.id = owners.id JOIN users ON owners.id = user.id GROUP BY product.id Do you have some hints? Thanks

Read the article

Select multiple unique lines in MySQL

- by MartinW

Hi, I've got a table with the following columns: ID, sysid, x, y, z, timereceived ID is a unique number for each row. sysid is an ID number for a specific device (about 100 different of these) x, y and z is data received from the device. (totally random numbers) timereceived is a timestamp for when the data was received. I need a SQL query to show me the last inserted row for device a, device b, device c and so on. I've been playing around with a lot of different Select statements, but never got anything that works. I manage to get unique rows by using group by, but the rest of the information is random (or at least it feels very random). Anyone able to help me? There could be hundreds of thousands records in this table.

Read the article

Fill data gaps without UNION

- by Dave Jarvis

Problem There are data gaps that need to be filled, possibly using PARTITION BY. Query Statement The select statement reads as follows: SELECT count( r.incident_id ) AS incident_tally, r.severity_cd, r.incident_typ_cd FROM report_vw r GROUP BY r.severity_cd, r.incident_typ_cd ORDER BY r.severity_cd, r.incident_typ_cd Code Tables The severity codes and incident type codes are from: severity_vw incident_type_vw Actual Result Data 36 0 ENVIRONMENT 1 1 DISASTER 27 1 ENVIRONMENT 4 2 SAFETY 1 3 SAFETY Required Result Data 36 0 ENVIRONMENT 0 0 DISASTER 0 0 SAFETY 27 1 ENVIRONMENT 0 1 DISASTER 0 1 SAFETY 0 2 ENVIRONMENT 0 2 DISASTER 4 2 SAFETY 0 3 ENVIRONMENT 0 3 DISASTER 1 3 SAFETY Any ideas how to use PARTITION BY (or JOINs) to fill in the zero counts?

Read the article

Approach to Selecting top item matching a criteria

- by jkelley

I have a SQL problem that I've come up against routinely, and normally just solved w/ a nested query. I'm hoping someone can suggest a more elegant solution. It often happens that I need to select a result set for a user, conditioned upon it being the most recent, or the most sizeable or whatever. For example: Their complete list of pages created, but I only want the most recent name they applied to a page. It so happens that the database contains many entries for each page, and only the most recent one is desired. I've been using a nested select like: SELECT pg.customName, pg.id FROM ( select id, max(createdAt) as mostRecent from pages where userId = @UserId GROUP BY id ) as MostRecentPages JOIN pages pg ON pg.id = MostRecentPages.id AND pg.createdAt = MostRecentPages.mostRecent Is there a better syntax to perform this selection?

Read the article

return not breaking loop (c#)

- by David Wick

I'm trying to determine if a user is a member of a group or not in AD. However, the following doesn't seem to be working for some reason... public bool MemberOf(string sObjectName, string sGroup, bool bIsGroup) { DirectoryEntry dEntry = CreateDirectoryEntry(); DirectorySearcher dSearcher = new DirectorySearcher(dEntry); if (bIsGroup) dSearcher.Filter = "(distinguishedName=" + sObjectName + ")"; else dSearcher.Filter = "(&(sAMAccountName=" + sObjectName + ")(objectClass=user))"; SearchResult sResult = dSearcher.FindOne(); if (sResult != null) { foreach (object oGroup in sResult.Properties["MemberOf"]) { if (oGroup.ToString() == sGroup) return true; else this.MemberOf(oGroup.ToString(), sGroup, true); } } return false; } Another variation: http://users.business.uconn.edu/dwick/work/wtf/6-14-2010%201-15-15%20PM.png Doesn't work either. This seems like a really dumb question... but shouldn't it break the loop upon "return true;"

Read the article

How to play an mp3 using fancybox

- by user2980783

I am adding fancybox to my page and to display different types of formats. I was able to implement video, text, and images seamlessly but when it doesn't load the audio. Once I click on the audio file on the gallery it opens on a blank page. I am trying to play the track as iframe if possible. Can anyone help with this. Thank you. <a class="hover-wrap" data-fancybox-type="iframe" data-fancybox-group="music" title="Breathin'" href="_include/download/Breathin'.mp3"> <span class="overlay-img"></span> <span class="overlay-img-thumb font-icon-plus"></span> <p class="description"> <span class="title">Song</span> </p> </a>

Read the article

How to make a SUM of Dictionary Value nested into a list with LINQ ?

- by user551108

Hi All, I have a product object declared as : Product { int ProductID; string ProductName; int ProductTypeID; string ProductTypeName; int UnitsSold Dictionary <string, int> UnitsSoldByYear; } I want to make a sum on UnitsSold and UnitsSoldByYear properties with a Linq query but I didn't know how to make this kind of sum on a dictionary ! Here is my begining linq query code : var ProductTypeSum = from i in ProductsList group i by new { i.ProductTypeID, i.ProductTypeName} into pt select new { ProductTypeID= pt.Key.ProductTypeID, ProductTypeName= pt.Key.ProductTypeName, UnitsSoldSum= pt.Sum(i => i.UnitsSold), // How to make a Dictionary sum here } Thank you for your help !

Read the article

many-to-many relationship in CI (not using ORM)

- by Ross

I'm implementing a categories system in my CI app and trying to work out the best way of working with many to many relationships. I'm not using an ORM at this stage, but could use say Doctrine if necessary. Each entry may have multiple categories. I have three tables (simplified) Entries: entryID, entryName Categories: categoryID, categoryname Entry_Category: entryID, categoryID my CI code returns a record set like this: entryID, entryName, categoryID, categoryName but, as expected with Many-to-Many relationships, each "entry" is repeated for each "category". What would the best way to "group" the categories so that when I output the results, I am left with something like: Entry Name Appears in Category: Foo, Bar rather than: Entry Name Appears in Category: Foo Entry Name Appears in Category: Bar I believe the option is to track if the post ID matches a previous entry, and if so, store the respective category, and output it as one, rather than several, but am unsure of how to do this in CI. thanks for any pointers (I appreciate this is may be a vague/complex question without a better knowledge of the system).

Read the article

concatenation output problem (toString Array) - java

- by dowln

Hello, I am trying to display the output as "1(10) 2(23) 3(29)" but instead getting output as "1 2 3 (10)(23)(29)". I would be grateful if someone could have a look the code and possible help me. I don't want to use arraylist. the code this // int[] Groups = {10, 23, 29}; in the constructor public String toString() { String tempStringB = ""; String tempStringA = " "; String tempStringC = " "; for (int x = 1; x<=3; x+=1) { tempStringB = tempStringB + x + " "; } for(int i = 0; i < Group.length;i++) { tempStringA = tempStringA + "(" + Groups[i] + ")"; } tempStringC = tempStringB + tempStringA; return tempStringC; }

Read the article

making mysql query using splite string?

- by Marco

lets say i have a group of number like (3,2,5) the normal way i use to split them and searching mysql to get value is to split them using explode in PHP EXAMPLE $string = '3,4,5'; $array = explode(',',$string); foreach($array as $value){ $query = 'SELECT ID FROM TABLE WHERE ID = "'.$value.'"'; } it work like this but it make the script extremely slow i need now if there is away to split this string into the query it self and return the result without looping with PHP ?

Read the article

mySQL : using BETWEEN in table ?

- by Meko

I have a table that includes somestudent group name ,lesson time,day names like Schedule. I am using C# whit MYSql and I want to find which lesson is when user press button from table. I can find it like entering exact value like in table 08:30 or 10:25 , it finds. But I want to make that getting system time and checking that is it between 08:30 and 10:25 or 10:25 and 12:30 . Then I can sythat it is first lesson or it is second lesson . I have also table includes Table_Time column has 5 record like 08:20 , 10:25 , 12:20 so on. Could I use like : select Lesson_Time from mydb.clock where Lesson_Time between (current time)-30 AND (current time)+30 Or can I use between operator between two columns ? Like creating Lesson_Time_Start and Lesson_Time_End and compairing current time like Lesson_Start_Time

Read the article

Put empty spaces in an SQL select

- by David Undy

I'm having difficulty creating a month-count select query in SQL. Basically, I have a list of entries, all of which have a date associated with them. What I want the end result to be, is a list containing 12 rows (one for each month), and each row would contain the month number (1 for January, 2 for February, etc), and a count of how many entries had that month set as it's date. Something like this: Month - Count 1 - 12 2 - 0 3 - 7 4 - 0 5 - 9 6 - 0 I can get an result containing months that have a count of higher than 0, but if the month contains no entries, the row isn't created. I get this result just by doing SELECT Month(goalDate) as monthNumber, count(*) as monthCount FROM goalsList WHERE Year(goalDate) = 2012 GROUP BY Month(goalDate) ORDER BY monthNumber Thanks in advance for the help!

Read the article

mysql query for change in values in a logging table

- by kiasectomondo

I have a table like this: Index , PersonID , ItemCount , UnixTimeStamp 1 , 1 , 1 , 1296000000 2 , 1 , 2 , 1296000100 3 , 2 , 4 , 1296003230 4 , 2 , 6 , 1296093949 5 , 1 , 0 , 1296093295 Time and index always go up. Its basically a logging table to log the itemcount each time it changes. I get the most recent ItemCount for each Person like this: SELECT * FROM table a INNER JOIN ( SELECT MAX(index) as i FROM table GROUP BY PersonID) b ON a.index = b.i; What I want to do is get get the most recent record for each PersonID that is at least 24 hours older than the most recent record for each Person ID. Then I want to take the difference in ItemCount between these two to get a change in itemcount for each person over the last 24 hours: personID ChangeInItemCountOverAtLeast24Hours 1 3 2 -11 3 6 Im sort of stuck with what to do next. How can I join another itemcount based on latest adjusted timestamp of individual rows?

Read the article

MySQL query to find the most popular value in a column joined by another value in a second table

- by Budove

I have two tables: users: user_id, user_zip settings: user_id, pref_ex_loc I need to find the single most popular 'pref_ex_loc' from the settings table based on a particular user_zip, which will be specified as the variable $userzip. Here is the query that I have now and obviously it doesn't work. $popularexloc = "SELECT pref_ex_loc, user_id COUNT(pref_ex_loc) AS countloc FROM settings FULL OUTER JOIN users ON settings.user_id = users.user_id WHERE users.user_zip='$userzip' GROUP BY settings.pref_ex_loc ORDER BY countloc LIMIT 1"; $popexloc = mysql_query($popularexloc) or die('SQL Error :: '.mysql_error()); $exlocrow = mysql_fetch_array($popexloc); $mostpopexloc=$exlocrow[0]; echo '<option value="'.$mostpopexloc.'">'.$mostpopexloc.'</option>'; What am I doing wrong here? I'm not getting any kind of error from this either.

Read the article

complex data requirement.

- by Abulalia

Here is my query: select Table1.a, Table1.b, Table1.c, Table1.d, Table2.e, Table3.f, Table4.g, Table5.h from Table1 left join Table6 on Table1.b=Table6.b left join Table3 on Table6.j=Table3.j left join Table7 on Table1.b=Table7.b left join Table5 on Table7.h=Table5.h inner join Table4 on Table1.k=Table4.k inner join Table2 on Table1.m=Table2.m where Table2.e <= x and Table2.n = y and Table3.f in (‘r’, ‘s’) and Table1.d = z group by Table1.a, Table1.b, Table1.c, Table1.d, Table2.e, Table3.f, Table4.g, Table5.h order by Table1.a, Table1.b, Table1.c I am looking for records (a,b,c,d,e,f,g,h) for every a when the very first record b (there are multiple records b for each a) is either 'r' or 's'. Can someone help?

Read the article

match word '90%' using regular expression

- by amadhu

Hi All, I want word '90%' to be matched with my String "I have 90% shares of this company". how can I write regular expression for same? I tried something like this: Pattern p = Pattern.compile("\\b90\\%\\b", Pattern.CASE_INSENSITIVE | Pattern.MULTILINE); Matcher m = p.matcher("I have 90% shares of this company"); while (m.find()){ System.out.println(m.group()); } but no luck. Can any one thow some lights on this? Many thanks, Archi

Read the article

Windows Azure Learning Plan - Security

- by BuckWoody

This is one in a series of posts on a Windows Azure Learning Plan. You can find the main post here. This one deals with Security for Windows Azure. General Security Information Overview and general information about Windows Azure Security - what it is, how it works, and where you can learn more. General Security Whitepaper – answers most questions http://blogs.msdn.com/b/usisvde/archive/2010/08/10/security-white-paper-on-windows-azure-answers-many-faq.aspx Windows Azure Security Notes from the Patterns and Practices site http://blogs.msdn.com/b/jmeier/archive/2010/08/03/now-available-azure-security-notes-pdf.aspx Overview of Azure Security http://www.windowsecurity.com/articles/Microsoft-Azure-Security-Cloud.html Azure Security Resources http://reddevnews.com/articles/2010/08/19/microsoft-releases-windows-azure-security-resources.aspx Cloud Computing Security Considerations http://www.microsoft.com/downloads/en/details.aspx?FamilyID=68fedf9c-1c27-4642-aa5b-0a34472303ea&utm_source=feedburner&utm_medium=feed&utm_campaign=Feed%3A+MicrosoftDownloadCenter+%28Microsoft+Download+Center Security in Cloud Computing – a Microsoft Perspective http://www.microsoft.com/downloads/en/details.aspx?FamilyID=7c8507e8-50ca-4693-aa5a-34b7c24f4579&utm_source=feedburner&utm_medium=feed&utm_campaign=Feed%3A+MicrosoftDownloadCenter+%28Microsoft+Download+Center Physical Security for Microsoft’s Online Computing Information on the Infrastructure and Locations for Azure Physical Security. The Global Foundation Services Group at Microsoft handles physical security http://www.globalfoundationservices.com/security/index.html Microsoft’s Security Response Center http://www.microsoft.com/security/msrc/ Software Security for Microsoft’s Online Computing Steps we take as a company to develop secure software Windows Azure is developed using the Trustworthy Computing Initiative http://www.microsoft.com/about/twc/en/us/default.aspx and http://msdn.microsoft.com/en-us/library/ms995349.aspx Identity and Access in the Cloud http://blogs.msdn.com/b/technology_titbits_by_rajesh_makhija/archive/2010/10/29/identity-and-access-in-the-cloud.aspx Security Steps you should take While Microsoft takes great pains to secure the infrastructure, platform and code for Windows Azure, you have a responsibility to write secure code. These pointers can help you do that. Securing your cloud architecture, step-by-step http://technet.microsoft.com/en-us/magazine/gg296364.aspx Security Guidelines for Windows Azure http://redmondmag.com/articles/2010/06/15/microsoft-issues-security-guidelines-for-windows-azure.aspx Best Practices for Windows Azure Security http://blogs.msdn.com/b/vbertocci/archive/2010/06/14/security-best-practices-for-developing-windows-azure-applications.aspx Active Directory and Windows Azure http://blogs.msdn.com/b/plankytronixx/archive/2010/10/22/projecting-your-active-directory-identity-to-the-azure-cloud.aspx Understanding Encryption (great overview and tutorial) http://blogs.msdn.com/b/plankytronixx/archive/2010/10/23/crypto-primer-understanding-encryption-public-private-key-signatures-and-certificates.aspx Securing your Connection Strings (SQL Azure) http://blogs.msdn.com/b/sqlazure/archive/2010/09/07/10058942.aspx Getting started with Windows Identity Foundation (WIF) quickly http://blogs.msdn.com/b/alikl/archive/2010/10/26/windows-identity-foundation-wif-fast-track.aspx

Read the article

Oracle Coherence, Split-Brain and Recovery Protocols In Detail

- by Ricardo Ferreira

This article provides a high level conceptual overview of Split-Brain scenarios in distributed systems. It will focus on a specific example of cluster communication failure and recovery in Oracle Coherence. This includes a discussion on the witness protocol (used to remove failed cluster members) and the panic protocol (used to resolve Split-Brain scenarios). Note that the removal of cluster members does not necessarily indicate a Split-Brain condition. Oracle Coherence does not (and cannot) detect a Split-Brain as it occurs, the condition is only detected when cluster members that previously lost contact with each other regain contact. Cluster Topology and Configuration In order to create an good didactic for the article, let's assume a cluster topology and configuration. In this example we have a six member cluster, consisting of one JVM on each physical machine. The member IDs are as follows: Member ID IP Address 1 10.149.155.76 2 10.149.155.77 3 10.149.155.236 4 10.149.155.75 5 10.149.155.79 6 10.149.155.78 Members 1, 2, and 3 are connected to a switch, and members 4, 5, and 6 are connected to a second switch. There is a link between the two switches, which provides network connectivity between all of the machines. Member 1 is the first member to join this cluster, thus making it the senior member. Member 6 is the last member to join this cluster. Here is a log snippet from Member 6 showing the complete member set: 2010-02-26 15:27:57.390/3.062 Oracle Coherence GE 3.5.3/465p2 <Info> (thread=main, member=6): Started DefaultCacheServer... SafeCluster: Name=cluster:0xDDEB Group{Address=224.3.5.3, Port=35465, TTL=4} MasterMemberSet ( ThisMember=Member(Id=6, Timestamp=2010-02-26 15:27:58.635, Address=10.149.155.78:8088, MachineId=1102, Location=process:228, Role=CoherenceServer) OldestMember=Member(Id=1, Timestamp=2010-02-26 15:27:06.931, Address=10.149.155.76:8088, MachineId=1100, Location=site:usdhcp.oraclecorp.com,machine:dhcp-burlington6-4fl-east-10-149,process:511, Role=CoherenceServer) ActualMemberSet=MemberSet(Size=6, BitSetCount=2 Member(Id=1, Timestamp=2010-02-26 15:27:06.931, Address=10.149.155.76:8088, MachineId=1100, Location=site:usdhcp.oraclecorp.com,machine:dhcp-burlington6-4fl-east-10-149,process:511, Role=CoherenceServer) Member(Id=2, Timestamp=2010-02-26 15:27:17.847, Address=10.149.155.77:8088, MachineId=1101, Location=site:usdhcp.oraclecorp.com,machine:dhcp-burlington6-4fl-east-10-149,process:296, Role=CoherenceServer) Member(Id=3, Timestamp=2010-02-26 15:27:24.892, Address=10.149.155.236:8088, MachineId=1260, Location=site:usdhcp.oraclecorp.com,machine:dhcp-burlington6-4fl-east-10-149,process:32459, Role=CoherenceServer) Member(Id=4, Timestamp=2010-02-26 15:27:39.574, Address=10.149.155.75:8088, MachineId=1099, Location=process:800, Role=CoherenceServer) Member(Id=5, Timestamp=2010-02-26 15:27:49.095, Address=10.149.155.79:8088, MachineId=1103, Location=site:usdhcp.oraclecorp.com,machine:dhcp-burlington6-4fl-east-10-149,process:3229, Role=CoherenceServer) Member(Id=6, Timestamp=2010-02-26 15:27:58.635, Address=10.149.155.78:8088, MachineId=1102, Location=process:228, Role=CoherenceServer) ) RecycleMillis=120000 RecycleSet=MemberSet(Size=0, BitSetCount=0 ) ) At approximately 15:30, the connection between the two switches is severed: Thirty seconds later (the default packet timeout in development mode) the logs indicate communication failures across the cluster. In this example, the communication failure was caused by a network failure. In a production setting, this type of communication failure can have many root causes, including (but not limited to) network failures, excessive GC, high CPU utilization, swapping/virtual memory, and exceeding maximum network bandwidth. In addition, this type of failure is not necessarily indicative of a split brain. Any communication failure will be logged in this fashion. Member 2 logs a communication failure with Member 5: 2010-02-26 15:30:32.638/196.928 Oracle Coherence GE 3.5.3/465p2 <Warning> (thread=PacketPublisher, member=2): Timeout while delivering a packet; requesting the departure confirmation for Member(Id=5, Timestamp=2010-02-26 15:27:49.095, Address=10.149.155.79:8088, MachineId=1103, Location=site:usdhcp.oraclecorp.com,machine:dhcp-burlington6-4fl-east-10-149,process:3229, Role=CoherenceServer) by MemberSet(Size=2, BitSetCount=2 Member(Id=1, Timestamp=2010-02-26 15:27:06.931, Address=10.149.155.76:8088, MachineId=1100, Location=site:usdhcp.oraclecorp.com,machine:dhcp-burlington6-4fl-east-10-149,process:511, Role=CoherenceServer) Member(Id=4, Timestamp=2010-02-26 15:27:39.574, Address=10.149.155.75:8088, MachineId=1099, Location=process:800, Role=CoherenceServer) ) The Coherence clustering protocol (TCMP) is a reliable transport mechanism built on UDP. In order for the protocol to be reliable, it requires an acknowledgement (ACK) for each packet delivered. If a packet fails to be acknowledged within the configured timeout period, the Coherence cluster member will log a packet timeout (as seen in the log message above). When this occurs, the cluster member will consult with other members to determine who is at fault for the communication failure. If the witness members agree that the suspect member is at fault, the suspect is removed from the cluster. If the witnesses unanimously disagree, the accuser is removed. This process is known as the witness protocol. Since Member 2 cannot communicate with Member 5, it selects two witnesses (Members 1 and 4) to determine if the communication issue is with Member 5 or with itself (Member 2). However, Member 4 is on the switch that is no longer accessible by Members 1, 2 and 3; thus a packet timeout for member 4 is recorded as well: 2010-02-26 15:30:35.648/199.938 Oracle Coherence GE 3.5.3/465p2 <Warning> (thread=PacketPublisher, member=2): Timeout while delivering a packet; requesting the departure confirmation for Member(Id=4, Timestamp=2010-02-26 15:27:39.574, Address=10.149.155.75:8088, MachineId=1099, Location=process:800, Role=CoherenceServer) by MemberSet(Size=2, BitSetCount=2 Member(Id=1, Timestamp=2010-02-26 15:27:06.931, Address=10.149.155.76:8088, MachineId=1100, Location=site:usdhcp.oraclecorp.com,machine:dhcp-burlington6-4fl-east-10-149,process:511, Role=CoherenceServer) Member(Id=6, Timestamp=2010-02-26 15:27:58.635, Address=10.149.155.78:8088, MachineId=1102, Location=process:228, Role=CoherenceServer) ) Member 1 has the ability to confirm the departure of member 4, however Member 6 cannot as it is also inaccessible. At the same time, Member 3 sends a request to remove Member 6, which is followed by a report from Member 3 indicating that Member 6 has departed the cluster: 2010-02-26 15:30:35.706/199.996 Oracle Coherence GE 3.5.3/465p2 <D5> (thread=Cluster, member=2): MemberLeft request for Member 6 received from Member(Id=3, Timestamp=2010-02-26 15:27:24.892, Address=10.149.155.236:8088, MachineId=1260, Location=site:usdhcp.oraclecorp.com,machine:dhcp-burlington6-4fl-east-10-149,process:32459, Role=CoherenceServer) 2010-02-26 15:30:35.709/199.999 Oracle Coherence GE 3.5.3/465p2 <D5> (thread=Cluster, member=2): MemberLeft notification for Member 6 received from Member(Id=3, Timestamp=2010-02-26 15:27:24.892, Address=10.149.155.236:8088, MachineId=1260, Location=site:usdhcp.oraclecorp.com,machine:dhcp-burlington6-4fl-east-10-149,process:32459, Role=CoherenceServer) The log for Member 3 determines how Member 6 departed the cluster: 2010-02-26 15:30:35.161/191.694 Oracle Coherence GE 3.5.3/465p2 <Warning> (thread=PacketPublisher, member=3): Timeout while delivering a packet; requesting the departure confirmation for Member(Id=6, Timestamp=2010-02-26 15:27:58.635, Address=10.149.155.78:8088, MachineId=1102, Location=process:228, Role=CoherenceServer) by MemberSet(Size=2, BitSetCount=2 Member(Id=1, Timestamp=2010-02-26 15:27:06.931, Address=10.149.155.76:8088, MachineId=1100, Location=site:usdhcp.oraclecorp.com,machine:dhcp-burlington6-4fl-east-10-149,process:511, Role=CoherenceServer) Member(Id=2, Timestamp=2010-02-26 15:27:17.847, Address=10.149.155.77:8088, MachineId=1101, Location=site:usdhcp.oraclecorp.com,machine:dhcp-burlington6-4fl-east-10-149,process:296, Role=CoherenceServer) ) 2010-02-26 15:30:35.165/191.698 Oracle Coherence GE 3.5.3/465p2 <Info> (thread=Cluster, member=3): Member departure confirmed by MemberSet(Size=2, BitSetCount=2 Member(Id=1, Timestamp=2010-02-26 15:27:06.931, Address=10.149.155.76:8088, MachineId=1100, Location=site:usdhcp.oraclecorp.com,machine:dhcp-burlington6-4fl-east-10-149,process:511, Role=CoherenceServer) Member(Id=2, Timestamp=2010-02-26 15:27:17.847, Address=10.149.155.77:8088, MachineId=1101, Location=site:usdhcp.oraclecorp.com,machine:dhcp-burlington6-4fl-east-10-149,process:296, Role=CoherenceServer) ); removing Member(Id=6, Timestamp=2010-02-26 15:27:58.635, Address=10.149.155.78:8088, MachineId=1102, Location=process:228, Role=CoherenceServer) In this case, Member 3 happened to select two witnesses that it still had connectivity with (Members 1 and 2) thus resulting in a simple decision to remove Member 6. Given the departure of Member 6, Member 2 is left with a single witness to confirm the departure of Member 4: 2010-02-26 15:30:35.713/200.003 Oracle Coherence GE 3.5.3/465p2 <Info> (thread=Cluster, member=2): Member departure confirmed by MemberSet(Size=1, BitSetCount=2 Member(Id=1, Timestamp=2010-02-26 15:27:06.931, Address=10.149.155.76:8088, MachineId=1100, Location=site:usdhcp.oraclecorp.com,machine:dhcp-burlington6-4fl-east-10-149,process:511, Role=CoherenceServer) ); removing Member(Id=4, Timestamp=2010-02-26 15:27:39.574, Address=10.149.155.75:8088, MachineId=1099, Location=process:800, Role=CoherenceServer) In the meantime, Member 4 logs a missing heartbeat from the senior member. This message is also logged on Members 5 and 6. 2010-02-26 15:30:07.906/150.453 Oracle Coherence GE 3.5.3/465p2 <Info> (thread=PacketListenerN, member=4): Scheduled senior member heartbeat is overdue; rejoining multicast group. Next, Member 4 logs a TcpRing failure with Member 2, thus resulting in the termination of Member 2: 2010-02-26 15:30:21.421/163.968 Oracle Coherence GE 3.5.3/465p2 <D4> (thread=Cluster, member=4): TcpRing: Number of socket exceptions exceeded maximum; last was "java.net.SocketTimeoutException: connect timed out"; removing the member: 2 For quick process termination detection, Oracle Coherence utilizes a feature called TcpRing which is a sparse collection of TCP/IP-based connections between different members in the cluster. Each member in the cluster is connected to at least one other member, which (if at all possible) is running on a different physical box. This connection is not used for any data transfer, only heartbeat communications are sent once a second per each link. If a certain number of exceptions are thrown while trying to re-establish a connection, the member throwing the exceptions is removed from the cluster. Member 5 logs a packet timeout with Member 3 and cites witnesses Members 4 and 6: 2010-02-26 15:30:29.791/165.037 Oracle Coherence GE 3.5.3/465p2 <Warning> (thread=PacketPublisher, member=5): Timeout while delivering a packet; requesting the departure confirmation for Member(Id=3, Timestamp=2010-02-26 15:27:24.892, Address=10.149.155.236:8088, MachineId=1260, Location=site:usdhcp.oraclecorp.com,machine:dhcp-burlington6-4fl-east-10-149,process:32459, Role=CoherenceServer) by MemberSet(Size=2, BitSetCount=2 Member(Id=4, Timestamp=2010-02-26 15:27:39.574, Address=10.149.155.75:8088, MachineId=1099, Location=process:800, Role=CoherenceServer) Member(Id=6, Timestamp=2010-02-26 15:27:58.635, Address=10.149.155.78:8088, MachineId=1102, Location=process:228, Role=CoherenceServer) ) 2010-02-26 15:30:29.798/165.044 Oracle Coherence GE 3.5.3/465p2 <Info> (thread=Cluster, member=5): Member departure confirmed by MemberSet(Size=2, BitSetCount=2 Member(Id=4, Timestamp=2010-02-26 15:27:39.574, Address=10.149.155.75:8088, MachineId=1099, Location=process:800, Role=CoherenceServer) Member(Id=6, Timestamp=2010-02-26 15:27:58.635, Address=10.149.155.78:8088, MachineId=1102, Location=process:228, Role=CoherenceServer) ); removing Member(Id=3, Timestamp=2010-02-26 15:27:24.892, Address=10.149.155.236:8088, MachineId=1260, Location=site:usdhcp.oraclecorp.com,machine:dhcp-burlington6-4fl-east-10-149,process:32459, Role=CoherenceServer) Eventually we are left with two distinct clusters consisting of Members 1, 2, 3 and Members 4, 5, 6, respectively. In the latter cluster, Member 4 is promoted to senior member. The connection between the two switches is restored at 15:33. Upon the restoration of the connection, the cluster members immediately receive cluster heartbeats from the two senior members. In the case of Members 1, 2, and 3, the following is logged: 2010-02-26 15:33:14.970/369.066 Oracle Coherence GE 3.5.3/465p2 <Warning> (thread=Cluster, member=1): The member formerly known as Member(Id=4, Timestamp=2010-02-26 15:30:35.341, Address=10.149.155.75:8088, MachineId=1099, Location=process:800, Role=CoherenceServer) has been forcefully evicted from the cluster, but continues to emit a cluster heartbeat; henceforth, the member will be shunned and its messages will be ignored. Likewise for Members 4, 5, and 6: 2010-02-26 15:33:14.343/336.890 Oracle Coherence GE 3.5.3/465p2 <Warning> (thread=Cluster, member=4): The member formerly known as Member(Id=1, Timestamp=2010-02-26 15:30:31.64, Address=10.149.155.76:8088, MachineId=1100, Location=site:usdhcp.oraclecorp.com,machine:dhcp-burlington6-4fl-east-10-149,process:511, Role=CoherenceServer) has been forcefully evicted from the cluster, but continues to emit a cluster heartbeat; henceforth, the member will be shunned and its messages will be ignored. This message indicates that a senior heartbeat is being received from members that were previously removed from the cluster, in other words, something that should not be possible. For this reason, the recipients of these messages will initially ignore them. After several iterations of these messages, the existence of multiple clusters is acknowledged, thus triggering the panic protocol to reconcile this situation. When the presence of more than one cluster (i.e. Split-Brain) is detected by a Coherence member, the panic protocol is invoked in order to resolve the conflicting clusters and consolidate into a single cluster. The protocol consists of the removal of smaller clusters until there is one cluster remaining. In the case of equal size clusters, the one with the older Senior Member will survive. Member 1, being the oldest member, initiates the protocol: 2010-02-26 15:33:45.970/400.066 Oracle Coherence GE 3.5.3/465p2 <Warning> (thread=Cluster, member=1): An existence of a cluster island with senior Member(Id=4, Timestamp=2010-02-26 15:27:39.574, Address=10.149.155.75:8088, MachineId=1099, Location=process:800, Role=CoherenceServer) containing 3 nodes have been detected. Since this Member(Id=1, Timestamp=2010-02-26 15:27:06.931, Address=10.149.155.76:8088, MachineId=1100, Location=site:usdhcp.oraclecorp.com,machine:dhcp-burlington6-4fl-east-10-149,process:511, Role=CoherenceServer) is the senior of an older cluster island, the panic protocol is being activated to stop the other island's senior and all junior nodes that belong to it. Member 3 receives the panic: 2010-02-26 15:33:45.803/382.336 Oracle Coherence GE 3.5.3/465p2 <Error> (thread=Cluster, member=3): Received panic from senior Member(Id=1, Timestamp=2010-02-26 15:27:06.931, Address=10.149.155.76:8088, MachineId=1100, Location=site:usdhcp.oraclecorp.com,machine:dhcp-burlington6-4fl-east-10-149,process:511, Role=CoherenceServer) caused by Member(Id=4, Timestamp=2010-02-26 15:27:39.574, Address=10.149.155.75:8088, MachineId=1099, Location=process:800, Role=CoherenceServer) Member 4, the senior member of the younger cluster, receives the kill message from Member 3: 2010-02-26 15:33:44.921/367.468 Oracle Coherence GE 3.5.3/465p2 <Error> (thread=Cluster, member=4): Received a Kill message from a valid Member(Id=3, Timestamp=2010-02-26 15:27:24.892, Address=10.149.155.236:8088, MachineId=1260, Location=site:usdhcp.oraclecorp.com,machine:dhcp-burlington6-4fl-east-10-149,process:32459, Role=CoherenceServer); stopping cluster service. In turn, Member 4 requests the departure of its junior members 5 and 6: 2010-02-26 15:33:44.921/367.468 Oracle Coherence GE 3.5.3/465p2 <Error> (thread=Cluster, member=4): Received a Kill message from a valid Member(Id=3, Timestamp=2010-02-26 15:27:24.892, Address=10.149.155.236:8088, MachineId=1260, Location=site:usdhcp.oraclecorp.com,machine:dhcp-burlington6-4fl-east-10-149,process:32459, Role=CoherenceServer); stopping cluster service. 2010-02-26 15:33:43.343/349.015 Oracle Coherence GE 3.5.3/465p2 <Error> (thread=Cluster, member=6): Received a Kill message from a valid Member(Id=4, Timestamp=2010-02-26 15:27:39.574, Address=10.149.155.75:8088, MachineId=1099, Location=process:800, Role=CoherenceServer); stopping cluster service. Once Members 4, 5, and 6 restart, they rejoin the original cluster with senior member 1. The log below is from Member 4. Note that it receives a different member id when it rejoins the cluster. 2010-02-26 15:33:44.921/367.468 Oracle Coherence GE 3.5.3/465p2 <Error> (thread=Cluster, member=4): Received a Kill message from a valid Member(Id=3, Timestamp=2010-02-26 15:27:24.892, Address=10.149.155.236:8088, MachineId=1260, Location=site:usdhcp.oraclecorp.com,machine:dhcp-burlington6-4fl-east-10-149,process:32459, Role=CoherenceServer); stopping cluster service. 2010-02-26 15:33:46.921/369.468 Oracle Coherence GE 3.5.3/465p2 <D5> (thread=Cluster, member=4): Service Cluster left the cluster 2010-02-26 15:33:47.046/369.593 Oracle Coherence GE 3.5.3/465p2 <D5> (thread=Invocation:InvocationService, member=4): Service InvocationService left the cluster 2010-02-26 15:33:47.046/369.593 Oracle Coherence GE 3.5.3/465p2 <D5> (thread=OptimisticCache, member=4): Service OptimisticCache left the cluster 2010-02-26 15:33:47.046/369.593 Oracle Coherence GE 3.5.3/465p2 <D5> (thread=ReplicatedCache, member=4): Service ReplicatedCache left the cluster 2010-02-26 15:33:47.046/369.593 Oracle Coherence GE 3.5.3/465p2 <D5> (thread=DistributedCache, member=4): Service DistributedCache left the cluster 2010-02-26 15:33:47.046/369.593 Oracle Coherence GE 3.5.3/465p2 <D5> (thread=Invocation:Management, member=4): Service Management left the cluster 2010-02-26 15:33:47.046/369.593 Oracle Coherence GE 3.5.3/465p2 <D5> (thread=Cluster, member=4): Member 6 left service Management with senior member 5 2010-02-26 15:33:47.046/369.593 Oracle Coherence GE 3.5.3/465p2 <D5> (thread=Cluster, member=4): Member 6 left service DistributedCache with senior member 5 2010-02-26 15:33:47.046/369.593 Oracle Coherence GE 3.5.3/465p2 <D5> (thread=Cluster, member=4): Member 6 left service ReplicatedCache with senior member 5 2010-02-26 15:33:47.046/369.593 Oracle Coherence GE 3.5.3/465p2 <D5> (thread=Cluster, member=4): Member 6 left service OptimisticCache with senior member 5 2010-02-26 15:33:47.046/369.593 Oracle Coherence GE 3.5.3/465p2 <D5> (thread=Cluster, member=4): Member 6 left service InvocationService with senior member 5 2010-02-26 15:33:47.046/369.593 Oracle Coherence GE 3.5.3/465p2 <D5> (thread=Cluster, member=4): Member(Id=6, Timestamp=2010-02-26 15:33:47.046, Address=10.149.155.78:8088, MachineId=1102, Location=process:228, Role=CoherenceServer) left Cluster with senior member 4 2010-02-26 15:33:49.218/371.765 Oracle Coherence GE 3.5.3/465p2 <Info> (thread=main, member=n/a): Restarting cluster 2010-02-26 15:33:49.421/371.968 Oracle Coherence GE 3.5.3/465p2 <D5> (thread=Cluster, member=n/a): Service Cluster joined the cluster with senior service member n/a 2010-02-26 15:33:49.625/372.172 Oracle Coherence GE 3.5.3/465p2 <Info> (thread=Cluster, member=n/a): This Member(Id=5, Timestamp=2010-02-26 15:33:50.499, Address=10.149.155.75:8088, MachineId=1099, Location=process:800, Role=CoherenceServer, Edition=Grid Edition, Mode=Development, CpuCount=2, SocketCount=1) joined cluster "cluster:0xDDEB" with senior Member(Id=1, Timestamp=2010-02-26 15:27:06.931, Address=10.149.155.76:8088, MachineId=1100, Location=site:usdhcp.oraclecorp.com,machine:dhcp-burlington6-4fl-east-10-149,process:511, Role=CoherenceServer, Edition=Grid Edition, Mode=Development, CpuCount=2, SocketCount=2) Cool isn't it?

Read the article

Host AngularJS (Html5Mode) in ASP.NET vNext

- by Shaun

Originally posted on: http://geekswithblogs.net/shaunxu/archive/2014/06/10/host-angularjs-html5mode-in-asp.net-vnext.aspxMicrosoft had announced ASP.NET vNext in BUILD and TechED recently and as a developer, I found that we can add features into one ASP.NET vNext application such as MVC, WebAPI, SignalR, etc.. Also it's cross platform which means I can host ASP.NET on Windows, Linux and OS X. If you are following my blog you should knew that I'm currently working on a project which uses ASP.NET WebAPI, SignalR and AngularJS. Currently the AngularJS part is hosted by Express in Node.js while WebAPI and SignalR are hosted in ASP.NET. I was looking for a solution to host all of them in one platform so that my SignalR can utilize WebSocket. Currently AngularJS and SignalR are hosted in the same domain but different port so it has to use ServerSendEvent. It can be upgraded to WebSocket if I host both of them in the same port. Host AngularJS in ASP.NET vNext Static File Middleware ASP.NET vNext utilizes middleware pattern to register feature it uses, which is very similar as Express in Node.js. Since AngularJS is a pure client side framework in theory what I need to do is to use ASP.NET vNext as a static file server. This is very easy as there's a build-in middleware shipped alone with ASP.NET vNext. Assuming I have "index.html" as below. 1: <html data-ng-app="demo"> 2: <head> 3: <script type="text/javascript" src="angular.js" /> 4: <script type="text/javascript" src="angular-ui-router.js" /> 5: <script type="text/javascript" src="app.js" /> 6: </head> 7: <body> 8: <h1>ASP.NET vNext with AngularJS</h1> 9: <div> 10: <a href="javascript:void(0)" data-ui-sref="view1">View 1</a> | 11: <a href="javascript:void(0)" data-ui-sref="view2">View 2</a> 12: </div> 13: <div data-ui-view></div> 14: </body> 15: </html> And the AngularJS JavaScript file as below. Notices that I have two views which only contains one line literal indicates the view name. 1: 'use strict'; 2: 3: var app = angular.module('demo', ['ui.router']); 4: 5: app.config(['$stateProvider', '$locationProvider', function ($stateProvider, $locationProvider) { 6: $stateProvider.state('view1', { 7: url: '/view1', 8: templateUrl: 'view1.html', 9: controller: 'View1Ctrl' }); 10: 11: $stateProvider.state('view2', { 12: url: '/view2', 13: templateUrl: 'view2.html', 14: controller: 'View2Ctrl' }); 15: }]); 16: 17: app.controller('View1Ctrl', function ($scope) { 18: }); 19: 20: app.controller('View2Ctrl', function ($scope) { 21: }); All AngularJS files are located in "app" folder and my ASP.NET vNext files are besides it. The "project.json" contains all dependencies I need to host static file server. 1: { 2: "dependencies": { 3: "Helios" : "0.1-alpha-*", 4: "Microsoft.AspNet.FileSystems": "0.1-alpha-*", 5: "Microsoft.AspNet.Http": "0.1-alpha-*", 6: "Microsoft.AspNet.StaticFiles": "0.1-alpha-*", 7: "Microsoft.AspNet.Hosting": "0.1-alpha-*", 8: "Microsoft.AspNet.Server.WebListener": "0.1-alpha-*" 9: }, 10: "commands": { 11: "web": "Microsoft.AspNet.Hosting server=Microsoft.AspNet.Server.WebListener server.urls=http://localhost:22222" 12: }, 13: "configurations" : { 14: "net45" : { 15: }, 16: "k10" : { 17: "System.Diagnostics.Contracts": "4.0.0.0", 18: "System.Security.Claims" : "0.1-alpha-*" 19: } 20: } 21: } Below is "Startup.cs" which is the entry file of my ASP.NET vNext. What I need to do is to let my application use FileServerMiddleware. 1: using System; 2: using Microsoft.AspNet.Builder; 3: using Microsoft.AspNet.FileSystems; 4: using Microsoft.AspNet.StaticFiles; 5: 6: namespace Shaun.AspNet.Plugins.AngularServer.Demo 7: { 8: public class Startup 9: { 10: public void Configure(IBuilder app) 11: { 12: app.UseFileServer(new FileServerOptions() { 13: EnableDirectoryBrowsing = true, 14: FileSystem = new PhysicalFileSystem(System.IO.Path.Combine(AppDomain.CurrentDomain.BaseDirectory, "app")) 15: }); 16: } 17: } 18: } Next, I need to create "NuGet.Config" file in the PARENT folder so that when I run "kpm restore" command later it can find ASP.NET vNext NuGet package successfully. 1: <?xml version="1.0" encoding="utf-8"?> 2: <configuration> 3: <packageSources> 4: <add key="AspNetVNext" value="https://www.myget.org/F/aspnetvnext/api/v2" /> 5: <add key="NuGet.org" value="https://nuget.org/api/v2/" /> 6: </packageSources> 7: <packageSourceCredentials> 8: <AspNetVNext> 9: <add key="Username" value="aspnetreadonly" /> 10: <add key="ClearTextPassword" value="4d8a2d9c-7b80-4162-9978-47e918c9658c" /> 11: </AspNetVNext> 12: </packageSourceCredentials> 13: </configuration> Now I need to run "kpm restore" to resolve all dependencies of my application. Finally, use "k web" to start the application which will be a static file server on "app" sub folder in the local 22222 port. Support AngularJS Html5Mode AngularJS works well in previous demo. But you will note that there is a "#" in the browser address. This is because by default AngularJS adds "#" next to its entry page so ensure all request will be handled by this entry page. For example, in this case my entry page is "index.html", so when I clicked "View 1" in the page the address will be changed to "/#/view1" which means it still tell the web server I'm still looking for "index.html". This works, but makes the address looks ugly. Hence AngularJS introduces a feature called Html5Mode, which will get rid off the annoying "#" from the address bar. Below is the "app.js" with Html5Mode enabled, just one line of code. 1: 'use strict'; 2: 3: var app = angular.module('demo', ['ui.router']); 4: 5: app.config(['$stateProvider', '$locationProvider', function ($stateProvider, $locationProvider) { 6: $stateProvider.state('view1', { 7: url: '/view1', 8: templateUrl: 'view1.html', 9: controller: 'View1Ctrl' }); 10: 11: $stateProvider.state('view2', { 12: url: '/view2', 13: templateUrl: 'view2.html', 14: controller: 'View2Ctrl' }); 15: 16: // enable html5mode 17: $locationProvider.html5Mode(true); 18: }]); 19: 20: app.controller('View1Ctrl', function ($scope) { 21: }); 22: 23: app.controller('View2Ctrl', function ($scope) { 24: }); Then let's went to the root path of our website and click "View 1" you will see there's no "#" in the address. But the problem is, if we hit F5 the browser will be turn to blank. This is because in this mode the browser told the web server I want static file named "view1" but there's no file on the server. So underlying our web server, which is built by ASP.NET vNext, responded 404. To fix this problem we need to create our own ASP.NET vNext middleware. What it needs to do is firstly try to respond the static file request with the default StaticFileMiddleware. If the response status code was 404 then change the request path value to the entry page and try again. 1: public class AngularServerMiddleware 2: { 3: private readonly AngularServerOptions _options; 4: private readonly RequestDelegate _next; 5: private readonly StaticFileMiddleware _innerMiddleware; 6: 7: public AngularServerMiddleware(RequestDelegate next, AngularServerOptions options) 8: { 9: _next = next; 10: _options = options; 11: 12: _innerMiddleware = new StaticFileMiddleware(next, options.FileServerOptions.StaticFileOptions); 13: } 14: 15: public async Task Invoke(HttpContext context) 16: { 17: // try to resolve the request with default static file middleware 18: await _innerMiddleware.Invoke(context); 19: Console.WriteLine(context.Request.Path + ": " + context.Response.StatusCode); 20: // route to root path if the status code is 404 21: // and need support angular html5mode 22: if (context.Response.StatusCode == 404 && _options.Html5Mode) 23: { 24: context.Request.Path = _options.EntryPath; 25: await _innerMiddleware.Invoke(context); 26: Console.WriteLine(">> " + context.Request.Path + ": " + context.Response.StatusCode); 27: } 28: } 29: } We need an option class where user can specify the host root path and the entry page path. 1: public class AngularServerOptions 2: { 3: public FileServerOptions FileServerOptions { get; set; } 4: 5: public PathString EntryPath { get; set; } 6: 7: public bool Html5Mode 8: { 9: get 10: { 11: return EntryPath.HasValue; 12: } 13: } 14: 15: public AngularServerOptions() 16: { 17: FileServerOptions = new FileServerOptions(); 18: EntryPath = PathString.Empty; 19: } 20: } We also need an extension method so that user can append this feature in "Startup.cs" easily. 1: public static class AngularServerExtension 2: { 3: public static IBuilder UseAngularServer(this IBuilder builder, string rootPath, string entryPath) 4: { 5: var options = new AngularServerOptions() 6: { 7: FileServerOptions = new FileServerOptions() 8: { 9: EnableDirectoryBrowsing = false, 10: FileSystem = new PhysicalFileSystem(System.IO.Path.Combine(AppDomain.CurrentDomain.BaseDirectory, rootPath)) 11: }, 12: EntryPath = new PathString(entryPath) 13: }; 14: 15: builder.UseDefaultFiles(options.FileServerOptions.DefaultFilesOptions); 16: 17: return builder.Use(next => new AngularServerMiddleware(next, options).Invoke); 18: } 19: } Now with these classes ready we will change our "Startup.cs", use this middleware replace the default one, tell the server try to load "index.html" file if it cannot find resource. The code below is just for demo purpose. I just tried to load "index.html" in all cases once the StaticFileMiddleware returned 404. In fact we need to validation to make sure this is an AngularJS route request instead of a normal static file request. 1: using System; 2: using Microsoft.AspNet.Builder; 3: using Microsoft.AspNet.FileSystems; 4: using Microsoft.AspNet.StaticFiles; 5: using Shaun.AspNet.Plugins.AngularServer; 6: 7: namespace Shaun.AspNet.Plugins.AngularServer.Demo 8: { 9: public class Startup 10: { 11: public void Configure(IBuilder app) 12: { 13: app.UseAngularServer("app", "/index.html"); 14: } 15: } 16: } Now let's run "k web" again and try to refresh our browser and we can see the page loaded successfully. In the console window we can find the original request got 404 and we try to find "index.html" and return the correct result. Summary In this post I introduced how to use ASP.NET vNext to host AngularJS application as a static file server. I also demonstrated how to extend ASP.NET vNext, so that it supports AngularJS Html5Mode. You can download the source code here. Hope this helps, Shaun All documents and related graphics, codes are provided "AS IS" without warranty of any kind. Copyright © Shaun Ziyan Xu. This work is licensed under the Creative Commons License.

Read the article

SignalR Auto Disconnect when Page Changed in AngularJS

- by Shaun

Originally posted on: http://geekswithblogs.net/shaunxu/archive/2014/05/30/signalr-auto-disconnect-when-page-changed-in-angularjs.aspxIf we are using SignalR, the connection lifecycle was handled by itself very well. For example when we connect to SignalR service from browser through SignalR JavaScript Client the connection will be established. And if we refresh the page, close the tab or browser, or navigate to another URL then the connection will be closed automatically. This information had been well documented here. In a browser, SignalR client code that maintains a SignalR connection runs in the JavaScript context of a web page. That's why the SignalR connection has to end when you navigate from one page to another, and that's why you have multiple connections with multiple connection IDs if you connect from multiple browser windows or tabs. When the user closes a browser window or tab, or navigates to a new page or refreshes the page, the SignalR connection immediately ends because SignalR client code handles that browser event for you and calls the "Stop" method. But unfortunately this behavior doesn't work if we are using SignalR with AngularJS. AngularJS is a single page application (SPA) framework created by Google. It hijacks browser's address change event, based on the route table user defined, launch proper view and controller. Hence in AngularJS we address was changed but the web page still there. All changes of the page content are triggered by Ajax. So there's no page unload and load events. This is the reason why SignalR cannot handle disconnect correctly when works with AngularJS. If we dig into the source code of SignalR JavaScript Client source code we will find something below. It monitors the browser page "unload" and "beforeunload" event and send the "stop" message to server to terminate connection. But in AngularJS page change events were hijacked, so SignalR will not receive them and will not stop the connection. 1: // wire the stop handler for when the user leaves the page 2: _pageWindow.bind("unload", function () { 3: connection.log("Window unloading, stopping the connection."); 4: 5: connection.stop(asyncAbort); 6: }); 7: 8: if (isFirefox11OrGreater) { 9: // Firefox does not fire cross-domain XHRs in the normal unload handler on tab close. 10: // #2400 11: _pageWindow.bind("beforeunload", function () { 12: // If connection.stop() runs runs in beforeunload and fails, it will also fail 13: // in unload unless connection.stop() runs after a timeout. 14: window.setTimeout(function () { 15: connection.stop(asyncAbort); 16: }, 0); 17: }); 18: } Problem Reproduce In the codes below I created a very simple example to demonstrate this issue. Here is the SignalR server side code. 1: public class GreetingHub : Hub 2: { 3: public override Task OnConnected() 4: { 5: Debug.WriteLine(string.Format("Connected: {0}", Context.ConnectionId)); 6: return base.OnConnected(); 7: } 8: 9: public override Task OnDisconnected() 10: { 11: Debug.WriteLine(string.Format("Disconnected: {0}", Context.ConnectionId)); 12: return base.OnDisconnected(); 13: } 14: 15: public void Hello(string user) 16: { 17: Clients.All.hello(string.Format("Hello, {0}!", user)); 18: } 19: } Below is the configuration code which hosts SignalR hub in an ASP.NET WebAPI project with IIS Express. 1: public class Startup 2: { 3: public void Configuration(IAppBuilder app) 4: { 5: app.Map("/signalr", map => 6: { 7: map.UseCors(CorsOptions.AllowAll); 8: map.RunSignalR(new HubConfiguration() 9: { 10: EnableJavaScriptProxies = false 11: }); 12: }); 13: } 14: } Since we will host AngularJS application in Node.js in another process and port, the SignalR connection will be cross domain. So I need to enable CORS above. In client side I have a Node.js file to host AngularJS application as a web server. You can use any web server you like such as IIS, Apache, etc.. Below is the "index.html" page which contains a navigation bar so that I can change the page/state. As you can see I added jQuery, AngularJS, SignalR JavaScript Client Library as well as my AngularJS entry source file "app.js". 1: <html data-ng-app="demo"> 2: <head> 3: <script type="text/javascript" src="jquery-2.1.0.js"></script> 1: 2: <script type="text/javascript" src="angular.js"> 1: </script> 2: <script type="text/javascript" src="angular-ui-router.js"> 1: </script> 2: <script type="text/javascript" src="jquery.signalR-2.0.3.js"> 1: </script> 2: <script type="text/javascript" src="app.js"></script> 4: </head> 5: <body> 6: <h1>SignalR Auto Disconnect with AngularJS by Shaun</h1> 7: <div> 8: <a href="javascript:void(0)" data-ui-sref="view1">View 1</a> | 9: <a href="javascript:void(0)" data-ui-sref="view2">View 2</a> 10: </div> 11: <div data-ui-view></div> 12: </body> 13: </html> Below is the "app.js". My SignalR logic was in the "View1" page and it will connect to server once the controller was executed. User can specify a user name and send to server, all clients that located in this page will receive the server side greeting message through SignalR. 1: 'use strict'; 2: 3: var app = angular.module('demo', ['ui.router']); 4: 5: app.config(['$stateProvider', '$locationProvider', function ($stateProvider, $locationProvider) { 6: $stateProvider.state('view1', { 7: url: '/view1', 8: templateUrl: 'view1.html', 9: controller: 'View1Ctrl' }); 10: 11: $stateProvider.state('view2', { 12: url: '/view2', 13: templateUrl: 'view2.html', 14: controller: 'View2Ctrl' }); 15: 16: $locationProvider.html5Mode(true); 17: }]); 18: 19: app.value('$', $); 20: app.value('endpoint', 'http://localhost:60448'); 21: app.value('hub', 'GreetingHub'); 22: 23: app.controller('View1Ctrl', function ($scope, $, endpoint, hub) { 24: $scope.user = ''; 25: $scope.response = ''; 26: 27: $scope.greeting = function () { 28: proxy.invoke('Hello', $scope.user) 29: .done(function () {}) 30: .fail(function (error) { 31: console.log(error); 32: }); 33: }; 34: 35: var connection = $.hubConnection(endpoint); 36: var proxy = connection.createHubProxy(hub); 37: proxy.on('hello', function (response) { 38: $scope.$apply(function () { 39: $scope.response = response; 40: }); 41: }); 42: connection.start() 43: .done(function () { 44: console.log('signlar connection established'); 45: }) 46: .fail(function (error) { 47: console.log(error); 48: }); 49: }); 50: 51: app.controller('View2Ctrl', function ($scope, $) { 52: }); When we went to View1 the server side "OnConnect" method will be invoked as below. And in any page we send the message to server, all clients will got the response. If we close one of the client, the server side "OnDisconnect" method will be invoked which is correct. But is we click "View 2" link in the page "OnDisconnect" method will not be invoked even though the content and browser address had been changed. This might cause many SignalR connections remain between the client and server. Below is what happened after I clicked "View 1" and "View 2" links four times. As you can see there are 4 live connections. Solution Since the reason of this issue is because, AngularJS hijacks the page event that SignalR need to stop the connection, we can handle AngularJS route or state change event and stop SignalR connect manually. In the code below I moved the "connection" variant to global scope, added a handler to "$stateChangeStart" and invoked "stop" method of "connection" if its state was not "disconnected". 1: var connection; 2: app.run(['$rootScope', function ($rootScope) { 3: $rootScope.$on('$stateChangeStart', function () { 4: if (connection && connection.state && connection.state !== 4 /* disconnected */) { 5: console.log('signlar connection abort'); 6: connection.stop(); 7: } 8: }); 9: }]); Now if we refresh the page and navigated to View 1, the connection will be opened. At this state if we clicked "View 2" link the content will be changed and the SignalR connection will be closed automatically. Summary In this post I demonstrated an issue when we are using SignalR with AngularJS. The connection cannot be closed automatically when we navigate to other page/state in AngularJS. And the solution I mentioned below is to move the SignalR connection as a global variant and close it manually when AngularJS route/state changed. You can download the full sample code here. Moving the SignalR connection as a global variant might not be a best solution. It's just for easy to demo here. In production code I suggest wrapping all SignalR operations into an AngularJS factory. Since AngularJS factory is a singleton object, we can safely put the connection variant in the factory function scope. Hope this helps, Shaun All documents and related graphics, codes are provided "AS IS" without warranty of any kind. Copyright © Shaun Ziyan Xu. This work is licensed under the Creative Commons License.

Read the article

Improving Partitioned Table Join Performance

- by Paul White

The query optimizer does not always choose an optimal strategy when joining partitioned tables. This post looks at an example, showing how a manual rewrite of the query can almost double performance, while reducing the memory grant to almost nothing. Test Data The two tables in this example use a common partitioning partition scheme. The partition function uses 41 equal-size partitions: CREATE PARTITION FUNCTION PFT (integer) AS RANGE RIGHT FOR VALUES ( 125000, 250000, 375000, 500000, 625000, 750000, 875000, 1000000, 1125000, 1250000, 1375000, 1500000, 1625000, 1750000, 1875000, 2000000, 2125000, 2250000, 2375000, 2500000, 2625000, 2750000, 2875000, 3000000, 3125000, 3250000, 3375000, 3500000, 3625000, 3750000, 3875000, 4000000, 4125000, 4250000, 4375000, 4500000, 4625000, 4750000, 4875000, 5000000 ); GO CREATE PARTITION SCHEME PST AS PARTITION PFT ALL TO ([PRIMARY]); There two tables are: CREATE TABLE dbo.T1 ( TID integer NOT NULL IDENTITY(0,1), Column1 integer NOT NULL, Padding binary(100) NOT NULL DEFAULT 0x, CONSTRAINT PK_T1 PRIMARY KEY CLUSTERED (TID) ON PST (TID) ); CREATE TABLE dbo.T2 ( TID integer NOT NULL, Column1 integer NOT NULL, Padding binary(100) NOT NULL DEFAULT 0x, CONSTRAINT PK_T2 PRIMARY KEY CLUSTERED (TID, Column1) ON PST (TID) ); The next script loads 5 million rows into T1 with a pseudo-random value between 1 and 5 for Column1. The table is partitioned on the IDENTITY column TID: INSERT dbo.T1 WITH (TABLOCKX) (Column1) SELECT (ABS(CHECKSUM(NEWID())) % 5) + 1 FROM dbo.Numbers AS N WHERE n BETWEEN 1 AND 5000000; In case you don’t already have an auxiliary table of numbers lying around, here’s a script to create one with 10 million rows: CREATE TABLE dbo.Numbers (n bigint PRIMARY KEY); WITH L0 AS(SELECT 1 AS c UNION ALL SELECT 1), L1 AS(SELECT 1 AS c FROM L0 AS A CROSS JOIN L0 AS B), L2 AS(SELECT 1 AS c FROM L1 AS A CROSS JOIN L1 AS B), L3 AS(SELECT 1 AS c FROM L2 AS A CROSS JOIN L2 AS B), L4 AS(SELECT 1 AS c FROM L3 AS A CROSS JOIN L3 AS B), L5 AS(SELECT 1 AS c FROM L4 AS A CROSS JOIN L4 AS B), Nums AS(SELECT ROW_NUMBER() OVER (ORDER BY (SELECT NULL)) AS n FROM L5) INSERT dbo.Numbers WITH (TABLOCKX) SELECT TOP (10000000) n FROM Nums ORDER BY n OPTION (MAXDOP 1); Table T1 contains data like this: Next we load data into table T2. The relationship between the two tables is that table 2 contains ‘n’ rows for each row in table 1, where ‘n’ is determined by the value in Column1 of table T1. There is nothing particularly special about the data or distribution, by the way. INSERT dbo.T2 WITH (TABLOCKX) (TID, Column1) SELECT T.TID, N.n FROM dbo.T1 AS T JOIN dbo.Numbers AS N ON N.n >= 1 AND N.n <= T.Column1; Table T2 ends up containing about 15 million rows: The primary key for table T2 is a combination of TID and Column1. The data is partitioned according to the value in column TID alone. Partition Distribution The following query shows the number of rows in each partition of table T1: SELECT PartitionID = CA1.P, NumRows = COUNT_BIG(*) FROM dbo.T1 AS T CROSS APPLY (VALUES ($PARTITION.PFT(TID))) AS CA1 (P) GROUP BY CA1.P ORDER BY CA1.P; There are 40 partitions containing 125,000 rows (40 * 125k = 5m rows). The rightmost partition remains empty. The next query shows the distribution for table 2: SELECT PartitionID = CA1.P, NumRows = COUNT_BIG(*) FROM dbo.T2 AS T CROSS APPLY (VALUES ($PARTITION.PFT(TID))) AS CA1 (P) GROUP BY CA1.P ORDER BY CA1.P; There are roughly 375,000 rows in each partition (the rightmost partition is also empty): Ok, that’s the test data done. Test Query and Execution Plan The task is to count the rows resulting from joining tables 1 and 2 on the TID column: SET STATISTICS IO ON; DECLARE @s datetime2 = SYSUTCDATETIME(); SELECT COUNT_BIG(*) FROM dbo.T1 AS T1 JOIN dbo.T2 AS T2 ON T2.TID = T1.TID; SELECT DATEDIFF(Millisecond, @s, SYSUTCDATETIME()); SET STATISTICS IO OFF; The optimizer chooses a plan using parallel hash join, and partial aggregation: The Plan Explorer plan tree view shows accurate cardinality estimates and an even distribution of rows across threads (click to enlarge the image): With a warm data cache, the STATISTICS IO output shows that no physical I/O was needed, and all 41 partitions were touched: Running the query without actual execution plan or STATISTICS IO information for maximum performance, the query returns in around 2600ms. Execution Plan Analysis The first step toward improving on the execution plan produced by the query optimizer is to understand how it works, at least in outline. The two parallel Clustered Index Scans use multiple threads to read rows from tables T1 and T2. Parallel scan uses a demand-based scheme where threads are given page(s) to scan from the table as needed. This arrangement has certain important advantages, but does result in an unpredictable distribution of rows amongst threads. The point is that multiple threads cooperate to scan the whole table, but it is impossible to predict which rows end up on which threads. For correct results from the parallel hash join, the execution plan has to ensure that rows from T1 and T2 that might join are processed on the same thread. For example, if a row from T1 with join key value ‘1234’ is placed in thread 5’s hash table, the execution plan must guarantee that any rows from T2 that also have join key value ‘1234’ probe thread 5’s hash table for matches. The way this guarantee is enforced in this parallel hash join plan is by repartitioning rows to threads after each parallel scan. The two repartitioning exchanges route rows to threads using a hash function over the hash join keys. The two repartitioning exchanges use the same hash function so rows from T1 and T2 with the same join key must end up on the same hash join thread. Expensive Exchanges This business of repartitioning rows between threads can be very expensive, especially if a large number of rows is involved. The execution plan selected by the optimizer moves 5 million rows through one repartitioning exchange and around 15 million across the other. As a first step toward removing these exchanges, consider the execution plan selected by the optimizer if we join just one partition from each table, disallowing parallelism: SELECT COUNT_BIG(*) FROM dbo.T1 AS T1 JOIN dbo.T2 AS T2 ON T2.TID = T1.TID WHERE $PARTITION.PFT(T1.TID) = 1 AND $PARTITION.PFT(T2.TID) = 1 OPTION (MAXDOP 1); The optimizer has chosen a (one-to-many) merge join instead of a hash join. The single-partition query completes in around 100ms. If everything scaled linearly, we would expect that extending this strategy to all 40 populated partitions would result in an execution time around 4000ms. Using parallelism could reduce that further, perhaps to be competitive with the parallel hash join chosen by the optimizer. This raises a question. If the most efficient way to join one partition from each of the tables is to use a merge join, why does the optimizer not choose a merge join for the full query? Forcing a Merge Join Let’s force the optimizer to use a merge join on the test query using a hint: SELECT COUNT_BIG(*) FROM dbo.T1 AS T1 JOIN dbo.T2 AS T2 ON T2.TID = T1.TID OPTION (MERGE JOIN); This is the execution plan selected by the optimizer: This plan results in the same number of logical reads reported previously, but instead of 2600ms the query takes 5000ms. The natural explanation for this drop in performance is that the merge join plan is only using a single thread, whereas the parallel hash join plan could use multiple threads. Parallel Merge Join We can get a parallel merge join plan using the same query hint as before, and adding trace flag 8649: SELECT COUNT_BIG(*) FROM dbo.T1 AS T1 JOIN dbo.T2 AS T2 ON T2.TID = T1.TID OPTION (MERGE JOIN, QUERYTRACEON 8649); The execution plan is: This looks promising. It uses a similar strategy to distribute work across threads as seen for the parallel hash join. In practice though, performance is disappointing. On a typical run, the parallel merge plan runs for around 8400ms; slower than the single-threaded merge join plan (5000ms) and much worse than the 2600ms for the parallel hash join. We seem to be going backwards! The logical reads for the parallel merge are still exactly the same as before, with no physical IOs. The cardinality estimates and thread distribution are also still very good (click to enlarge): A big clue to the reason for the poor performance is shown in the wait statistics (captured by Plan Explorer Pro): CXPACKET waits require careful interpretation, and are most often benign, but in this case excessive waiting occurs at the repartitioning exchanges. Unlike the parallel hash join, the repartitioning exchanges in this plan are order-preserving ‘merging’ exchanges (because merge join requires ordered inputs): Parallelism works best when threads can just grab any available unit of work and get on with processing it. Preserving order introduces inter-thread dependencies that can easily lead to significant waits occurring. In extreme cases, these dependencies can result in an intra-query deadlock, though the details of that will have to wait for another time to explore in detail. The potential for waits and deadlocks leads the query optimizer to cost parallel merge join relatively highly, especially as the degree of parallelism (DOP) increases. This high costing resulted in the optimizer choosing a serial merge join rather than parallel in this case. The test results certainly confirm its reasoning. Collocated Joins In SQL Server 2008 and later, the optimizer has another available strategy when joining tables that share a common partition scheme. This strategy is a collocated join, also known as as a per-partition join. It can be applied in both serial and parallel execution plans, though it is limited to 2-way joins in the current optimizer. Whether the optimizer chooses a collocated join or not depends on cost estimation. The primary benefits of a collocated join are that it eliminates an exchange and requires less memory, as we will see next. Costing and Plan Selection The query optimizer did consider a collocated join for our original query, but it was rejected on cost grounds. The parallel hash join with repartitioning exchanges appeared to be a cheaper option. There is no query hint to force a collocated join, so we have to mess with the costing framework to produce one for our test query. Pretending that IOs cost 50 times more than usual is enough to convince the optimizer to use collocated join with our test query: -- Pretend IOs are 50x cost temporarily DBCC SETIOWEIGHT(50); -- Co-located hash join SELECT COUNT_BIG(*) FROM dbo.T1 AS T1 JOIN dbo.T2 AS T2 ON T2.TID = T1.TID OPTION (RECOMPILE); -- Reset IO costing DBCC SETIOWEIGHT(1); Collocated Join Plan The estimated execution plan for the collocated join is: The Constant Scan contains one row for each partition of the shared partitioning scheme, from 1 to 41. The hash repartitioning exchanges seen previously are replaced by a single Distribute Streams exchange using Demand partitioning. Demand partitioning means that the next partition id is given to the next parallel thread that asks for one. My test machine has eight logical processors, and all are available for SQL Server to use. As a result, there are eight threads in the single parallel branch in this plan, each processing one partition from each table at a time. Once a thread finishes processing a partition, it grabs a new partition number from the Distribute Streams exchange…and so on until all partitions have been processed. It is important to understand that the parallel scans in this plan are different from the parallel hash join plan. Although the scans have the same parallelism icon, tables T1 and T2 are not being co-operatively scanned by multiple threads in the same way. Each thread reads a single partition of T1 and performs a hash match join with the same partition from table T2. The properties of the two Clustered Index Scans show a Seek Predicate (unusual for a scan!) limiting the rows to a single partition: The crucial point is that the join between T1 and T2 is on TID, and TID is the partitioning column for both tables. A thread that processes partition ‘n’ is guaranteed to see all rows that can possibly join on TID for that partition. In addition, no other thread will see rows from that partition, so this removes the need for repartitioning exchanges. CPU and Memory Efficiency Improvements The collocated join has removed two expensive repartitioning exchanges and added a single exchange processing 41 rows (one for each partition id). Remember, the parallel hash join plan exchanges had to process 5 million and 15 million rows. The amount of processor time spent on exchanges will be much lower in the collocated join plan. In addition, the collocated join plan has a maximum of 8 threads processing single partitions at any one time. The 41 partitions will all be processed eventually, but a new partition is not started until a thread asks for it. Threads can reuse hash table memory for the new partition. The parallel hash join plan also had 8 hash tables, but with all 5,000,000 build rows loaded at the same time. The collocated plan needs memory for only 8 * 125,000 = 1,000,000 rows at any one time. Collocated Hash Join Performance The collated join plan has disappointing performance in this case. The query runs for around 25,300ms despite the same IO statistics as usual. This is much the worst result so far, so what went wrong? It turns out that cardinality estimation for the single partition scans of table T1 is slightly low. The properties of the Clustered Index Scan of T1 (graphic immediately above) show the estimation was for 121,951 rows. This is a small shortfall compared with the 125,000 rows actually encountered, but it was enough to cause the hash join to spill to physical tempdb: A level 1 spill doesn’t sound too bad, until you realize that the spill to tempdb probably occurs for each of the 41 partitions. As a side note, the cardinality estimation error is a little surprising because the system tables accurately show there are 125,000 rows in every partition of T1. Unfortunately, the optimizer uses regular column and index statistics to derive cardinality estimates here rather than system table information (e.g. sys.partitions). Collocated Merge Join We will never know how well the collocated parallel hash join plan might have worked without the cardinality estimation error (and the resulting 41 spills to tempdb) but we do know: Merge join does not require a memory grant; and Merge join was the optimizer’s preferred join option for a single partition join Putting this all together, what we would really like to see is the same collocated join strategy, but using merge join instead of hash join. Unfortunately, the current query optimizer cannot produce a collocated merge join; it only knows how to do collocated hash join. So where does this leave us? CROSS APPLY sys.partitions We can try to write our own collocated join query. We can use sys.partitions to find the partition numbers, and CROSS APPLY to get a count per partition, with a final step to sum the partial counts. The following query implements this idea: SELECT row_count = SUM(Subtotals.cnt) FROM ( -- Partition numbers SELECT p.partition_number FROM sys.partitions AS p WHERE p.[object_id] = OBJECT_ID(N'T1', N'U') AND p.index_id = 1 ) AS P CROSS APPLY ( -- Count per collocated join SELECT cnt = COUNT_BIG(*) FROM dbo.T1 AS T1 JOIN dbo.T2 AS T2 ON T2.TID = T1.TID WHERE $PARTITION.PFT(T1.TID) = p.partition_number AND $PARTITION.PFT(T2.TID) = p.partition_number ) AS SubTotals; The estimated plan is: The cardinality estimates aren’t all that good here, especially the estimate for the scan of the system table underlying the sys.partitions view. Nevertheless, the plan shape is heading toward where we would like to be. Each partition number from the system table results in a per-partition scan of T1 and T2, a one-to-many Merge Join, and a Stream Aggregate to compute the partial counts. The final Stream Aggregate just sums the partial counts. Execution time for this query is around 3,500ms, with the same IO statistics as always. This compares favourably with 5,000ms for the serial plan produced by the optimizer with the OPTION (MERGE JOIN) hint. This is another case of the sum of the parts being less than the whole – summing 41 partial counts from 41 single-partition merge joins is faster than a single merge join and count over all partitions. Even so, this single-threaded collocated merge join is not as quick as the original parallel hash join plan, which executed in 2,600ms. On the positive side, our collocated merge join uses only one logical processor and requires no memory grant. The parallel hash join plan used 16 threads and reserved 569 MB of memory: Using a Temporary Table Our collocated merge join plan should benefit from parallelism. The reason parallelism is not being used is that the query references a system table. We can work around that by writing the partition numbers to a temporary table (or table variable): SET STATISTICS IO ON; DECLARE @s datetime2 = SYSUTCDATETIME(); CREATE TABLE #P ( partition_number integer PRIMARY KEY); INSERT #P (partition_number) SELECT p.partition_number FROM sys.partitions AS p WHERE p.[object_id] = OBJECT_ID(N'T1', N'U') AND p.index_id = 1; SELECT row_count = SUM(Subtotals.cnt) FROM #P AS p CROSS APPLY ( SELECT cnt = COUNT_BIG(*) FROM dbo.T1 AS T1 JOIN dbo.T2 AS T2 ON T2.TID = T1.TID WHERE $PARTITION.PFT(T1.TID) = p.partition_number AND $PARTITION.PFT(T2.TID) = p.partition_number ) AS SubTotals; DROP TABLE #P; SELECT DATEDIFF(Millisecond, @s, SYSUTCDATETIME()); SET STATISTICS IO OFF; Using the temporary table adds a few logical reads, but the overall execution time is still around 3500ms, indistinguishable from the same query without the temporary table. The problem is that the query optimizer still doesn’t choose a parallel plan for this query, though the removal of the system table reference means that it could if it chose to: In fact the optimizer did enter the parallel plan phase of query optimization (running search 1 for a second time): Unfortunately, the parallel plan found seemed to be more expensive than the serial plan. This is a crazy result, caused by the optimizer’s cost model not reducing operator CPU costs on the inner side of a nested loops join. Don’t get me started on that, we’ll be here all night. In this plan, everything expensive happens on the inner side of a nested loops join. Without a CPU cost reduction to compensate for the added cost of exchange operators, candidate parallel plans always look more expensive to the optimizer than the equivalent serial plan. Parallel Collocated Merge Join We can produce the desired parallel plan using trace flag 8649 again: SELECT row_count = SUM(Subtotals.cnt) FROM #P AS p CROSS APPLY ( SELECT cnt = COUNT_BIG(*) FROM dbo.T1 AS T1 JOIN dbo.T2 AS T2 ON T2.TID = T1.TID WHERE $PARTITION.PFT(T1.TID) = p.partition_number AND $PARTITION.PFT(T2.TID) = p.partition_number ) AS SubTotals OPTION (QUERYTRACEON 8649); The actual execution plan is: One difference between this plan and the collocated hash join plan is that a Repartition Streams exchange operator is used instead of Distribute Streams. The effect is similar, though not quite identical. The Repartition uses round-robin partitioning, meaning the next partition id is pushed to the next thread in sequence. The Distribute Streams exchange seen earlier used Demand partitioning, meaning the next partition id is pulled across the exchange by the next thread that is ready for more work. There are subtle performance implications for each partitioning option, but going into that would again take us too far off the main point of this post. Performance The important thing is the performance of this parallel collocated merge join – just 1350ms on a typical run. The list below shows all the alternatives from this post (all timings include creation, population, and deletion of the temporary table where appropriate) from quickest to slowest: Collocated parallel merge join: 1350ms Parallel hash join: 2600ms Collocated serial merge join: 3500ms Serial merge join: 5000ms Parallel merge join: 8400ms Collated parallel hash join: 25,300ms (hash spill per partition) The parallel collocated merge join requires no memory grant (aside from a paltry 1.2MB used for exchange buffers). This plan uses 16 threads at DOP 8; but 8 of those are (rather pointlessly) allocated to the parallel scan of the temporary table. These are minor concerns, but it turns out there is a way to address them if it bothers you. Parallel Collocated Merge Join with Demand Partitioning This final tweak replaces the temporary table with a hard-coded list of partition ids (dynamic SQL could be used to generate this query from sys.partitions): SELECT row_count = SUM(Subtotals.cnt) FROM ( VALUES (1),(2),(3),(4),(5),(6),(7),(8),(9),(10), (11),(12),(13),(14),(15),(16),(17),(18),(19),(20), (21),(22),(23),(24),(25),(26),(27),(28),(29),(30), (31),(32),(33),(34),(35),(36),(37),(38),(39),(40),(41) ) AS P (partition_number) CROSS APPLY ( SELECT cnt = COUNT_BIG(*) FROM dbo.T1 AS T1 JOIN dbo.T2 AS T2 ON T2.TID = T1.TID WHERE $PARTITION.PFT(T1.TID) = p.partition_number AND $PARTITION.PFT(T2.TID) = p.partition_number ) AS SubTotals OPTION (QUERYTRACEON 8649); The actual execution plan is: The parallel collocated hash join plan is reproduced below for comparison: The manual rewrite has another advantage that has not been mentioned so far: the partial counts (per partition) can be computed earlier than the partial counts (per thread) in the optimizer’s collocated join plan. The earlier aggregation is performed by the extra Stream Aggregate under the nested loops join. The performance of the parallel collocated merge join is unchanged at around 1350ms. Final Words It is a shame that the current query optimizer does not consider a collocated merge join (Connect item closed as Won’t Fix). The example used in this post showed an improvement in execution time from 2600ms to 1350ms using a modestly-sized data set and limited parallelism. In addition, the memory requirement for the query was almost completely eliminated – down from 569MB to 1.2MB. The problem with the parallel hash join selected by the optimizer is that it attempts to process the full data set all at once (albeit using eight threads). It requires a large memory grant to hold all 5 million rows from table T1 across the eight hash tables, and does not take advantage of the divide-and-conquer opportunity offered by the common partitioning. The great thing about the collocated join strategies is that each parallel thread works on a single partition from both tables, reading rows, performing the join, and computing a per-partition subtotal, before moving on to a new partition. From a thread’s point of view… If you have trouble visualizing what is happening from just looking at the parallel collocated merge join execution plan, let’s look at it again, but from the point of view of just one thread operating between the two Parallelism (exchange) operators. Our thread picks up a single partition id from the Distribute Streams exchange, and starts a merge join using ordered rows from partition 1 of table T1 and partition 1 of table T2. By definition, this is all happening on a single thread. As rows join, they are added to a (per-partition) count in the Stream Aggregate immediately above the Merge Join. Eventually, either T1 (partition 1) or T2 (partition 1) runs out of rows and the merge join stops. The per-partition count from the aggregate passes on through the Nested Loops join to another Stream Aggregate, which is maintaining a per-thread subtotal. Our same thread now picks up a new partition id from the exchange (say it gets id 9 this time). The count in the per-partition aggregate is reset to zero, and the processing of partition 9 of both tables proceeds just as it did for partition 1, and on the same thread. Each thread picks up a single partition id and processes all the data for that partition, completely independently from other threads working on other partitions. One thread might eventually process partitions (1, 9, 17, 25, 33, 41) while another is concurrently processing partitions (2, 10, 18, 26, 34) and so on for the other six threads at DOP 8. The point is that all 8 threads can execute independently and concurrently, continuing to process new partitions until the wider job (of which the thread has no knowledge!) is done. This divide-and-conquer technique can be much more efficient than simply splitting the entire workload across eight threads all at once. Related Reading Understanding and Using Parallelism in SQL Server Parallel Execution Plans Suck © 2013 Paul White – All Rights Reserved Twitter: @SQL_Kiwi

Read the article

[MINI HOW-TO] Change the Default Color Scheme in Office 2010

- by Mysticgeek

Like in Office 2007 the default color scheme for 2010 is blue. If you are not a fan of it, here we show you how to change it to silver or black. In this example we are using Microsoft Word, but it works the same way in Excel, Outlook, and PowerPoint as well. Once you change the color scheme in one Office application, it will change it for all of the other apps in the suite. Change Color Scheme To change the color scheme click on the File tab to access Backstage View and click on Options. In Word Options the General section should open by default…use the dropdown menu next to Color Scheme to change it to Silver, Blue, or Black then click OK. Here is what Black looks like…who knows why Microsoft decided to leave the blue around the edges. This is the default Blue color scheme… And finally we take a look at the Silver color scheme in Excel… That is all there is to it! It would be nice if they would incorporate other color schemes to Office 2010, as some of you may not be happy with only three choices. If you’re using Office 2007 check out our article on how to change the color scheme in it. Also, The Geek has a cool article on how to set the Color Scheme of Office 2007 with a quick registry hack. Similar Articles Productive Geek Tips Set the Office 2007 Color Scheme With a Quick Registry HackChange The Default Color Scheme In Office 2007Maximize Space by "Auto-Hiding" the Ribbon in Office 2007How To Personalize the Windows Command PromptOrganize & Group Your Tabs in Firefox the Easy Way TouchFreeze Alternative in AutoHotkey The Icy Undertow Desktop Windows Home Server – Backup to LAN The Clear & Clean Desktop Use This Bookmarklet to Easily Get Albums Use AutoHotkey to Assign a Hotkey to a Specific Window Latest Software Reviews Tinyhacker Random Tips Xobni Plus for Outlook All My Movies 5.9 CloudBerry Online Backup 1.5 for Windows Home Server Snagit 10 2010 World Cup Schedule Boot Snooze – Reboot and then Standby or Hibernate Customize Everything Related to Dates, Times, Currency and Measurement in Windows 7 Google Earth replacement Icon (Icons we like) Build Great Charts in Excel with Chart Advisor tinysong gives a shortened URL for you to post on Twitter (or anywhere)

Read the article

Toorcon 15 (2013)

- by danx

The Toorcon gang (senior staff): h1kari (founder), nfiltr8, and Geo Introduction to Toorcon 15 (2013) A Tale of One Software Bypass of MS Windows 8 Secure Boot Breaching SSL, One Byte at a Time Running at 99%: Surviving an Application DoS Security Response in the Age of Mass Customized Attacks x86 Rewriting: Defeating RoP and other Shinanighans Clowntown Express: interesting bugs and running a bug bounty program Active Fingerprinting of Encrypted VPNs Making Attacks Go Backwards Mask Your Checksums—The Gorry Details Adventures with weird machines thirty years after "Reflections on Trusting Trust" Introduction to Toorcon 15 (2013) Toorcon 15 is the 15th annual security conference held in San Diego. I've attended about a third of them and blogged about previous conferences I attended here starting in 2003. As always, I've only summarized the talks I attended and interested me enough to write about them. Be aware that I may have misrepresented the speaker's remarks and that they are not my remarks or opinion, or those of my employer, so don't quote me or them. Those seeking further details may contact the speakers directly or use The Google. For some talks, I have a URL for further information. A Tale of One Software Bypass of MS Windows 8 Secure Boot Andrew Furtak and Oleksandr Bazhaniuk Yuri Bulygin, Oleksandr ("Alex") Bazhaniuk, and (not present) Andrew Furtak Yuri and Alex talked about UEFI and Bootkits and bypassing MS Windows 8 Secure Boot, with vendor recommendations. They previously gave this talk at the BlackHat 2013 conference. MS Windows 8 Secure Boot Overview UEFI (Unified Extensible Firmware Interface) is interface between hardware and OS. UEFI is processor and architecture independent. Malware can replace bootloader (bootx64.efi, bootmgfw.efi). Once replaced can modify kernel. Trivial to replace bootloader. Today many legacy bootkits—UEFI replaces them most of them. MS Windows 8 Secure Boot verifies everything you load, either through signatures or hashes. UEFI firmware relies on secure update (with signed update). You would think Secure Boot would rely on ROM (such as used for phones0, but you can't do that for PCs—PCs use writable memory with signatures DXE core verifies the UEFI boat loader(s) OS Loader (winload.efi, winresume.efi) verifies the OS kernel A chain of trust is established with a root key (Platform Key, PK), which is a cert belonging to the platform vendor. Key Exchange Keys (KEKs) verify an "authorized" database (db), and "forbidden" database (dbx). X.509 certs with SHA-1/SHA-256 hashes. Keys are stored in non-volatile (NV) flash-based NVRAM. Boot Services (BS) allow adding/deleting keys (can't be accessed once OS starts—which uses Run-Time (RT)). Root cert uses RSA-2048 public keys and PKCS#7 format signatures. SecureBoot — enable disable image signature checks SetupMode — update keys, self-signed keys, and secure boot variables CustomMode — allows updating keys Secure Boot policy settings are: always execute, never execute, allow execute on security violation, defer execute on security violation, deny execute on security violation, query user on security violation Attacking MS Windows 8 Secure Boot Secure Boot does NOT protect from physical access. Can disable from console. Each BIOS vendor implements Secure Boot differently. There are several platform and BIOS vendors. It becomes a "zoo" of implementations—which can be taken advantage of. Secure Boot is secure only when all vendors implement it correctly. Allow only UEFI firmware signed updates protect UEFI firmware from direct modification in flash memory protect FW update components program SPI controller securely protect secure boot policy settings in nvram protect runtime api disable compatibility support module which allows unsigned legacy Can corrupt the Platform Key (PK) EFI root certificate variable in SPI flash. If PK is not found, FW enters setup mode wich secure boot turned off. Can also exploit TPM in a similar manner. One is not supposed to be able to directly modify the PK in SPI flash from the OS though. But they found a bug that they can exploit from User Mode (undisclosed) and demoed the exploit. It loaded and ran their own bootkit. The exploit requires a reboot. Multiple vendors are vulnerable. They will disclose this exploit to vendors in the future. Recommendations: allow only signed updates protect UEFI fw in ROM protect EFI variable store in ROM Breaching SSL, One Byte at a Time Yoel Gluck and Angelo Prado Angelo Prado and Yoel Gluck, Salesforce.com CRIME is software that performs a "compression oracle attack." This is possible because the SSL protocol doesn't hide length, and because SSL compresses the header. CRIME requests with every possible character and measures the ciphertext length. Look for the plaintext which compresses the most and looks for the cookie one byte-at-a-time. SSL Compression uses LZ77 to reduce redundancy. Huffman coding replaces common byte sequences with shorter codes. US CERT thinks the SSL compression problem is fixed, but it isn't. They convinced CERT that it wasn't fixed and they issued a CVE. BREACH, breachattrack.com BREACH exploits the SSL response body (Accept-Encoding response, Content-Encoding). It takes advantage of the fact that the response is not compressed. BREACH uses gzip and needs fairly "stable" pages that are static for ~30 seconds. It needs attacker-supplied content (say from a web form or added to a URL parameter). BREACH listens to a session's requests and responses, then inserts extra requests and responses. Eventually, BREACH guesses a session's secret key. Can use compression to guess contents one byte at-a-time. For example, "Supersecret SupersecreX" (a wrong guess) compresses 10 bytes, and "Supersecret Supersecret" (a correct guess) compresses 11 bytes, so it can find each character by guessing every character. To start the guess, BREACH needs at least three known initial characters in the response sequence. Compression length then "leaks" information. Some roadblocks include no winners (all guesses wrong) or too many winners (multiple possibilities that compress the same). The solutions include: lookahead (guess 2 or 3 characters at-a-time instead of 1 character). Expensive rollback to last known conflict check compression ratio can brute-force first 3 "bootstrap" characters, if needed (expensive) block ciphers hide exact plain text length. Solution is to align response in advance to block size Mitigations length: use variable padding secrets: dynamic CSRF tokens per request secret: change over time separate secret to input-less servlets Future work eiter understand DEFLATE/GZIP HTTPS extensions Running at 99%: Surviving an Application DoS Ryan Huber Ryan Huber, Risk I/O Ryan first discussed various ways to do a denial of service (DoS) attack against web services. One usual method is to find a slow web page and do several wgets. Or download large files. Apache is not well suited at handling a large number of connections, but one can put something in front of it Can use Apache alternatives, such as nginx How to identify malicious hosts short, sudden web requests user-agent is obvious (curl, python) same url requested repeatedly no web page referer (not normal) hidden links. hide a link and see if a bot gets it restricted access if not your geo IP (unless the website is global) missing common headers in request regular timing first seen IP at beginning of attack count requests per hosts (usually a very large number) Use of captcha can mitigate attacks, but you'll lose a lot of genuine users. Bouncer, goo.gl/c2vyEc and www.github.com/rawdigits/Bouncer Bouncer is software written by Ryan in netflow. Bouncer has a small, unobtrusive footprint and detects DoS attempts. It closes blacklisted sockets immediately (not nice about it, no proper close connection). Aggregator collects requests and controls your web proxies. Need NTP on the front end web servers for clean data for use by bouncer. Bouncer is also useful for a popularity storm ("Slashdotting") and scraper storms. Future features: gzip collection data, documentation, consumer library, multitask, logging destroyed connections. Takeaways: DoS mitigation is easier with a complete picture Bouncer designed to make it easier to detect and defend DoS—not a complete cure Security Response in the Age of Mass Customized Attacks Peleus Uhley and Karthik Raman Peleus Uhley and Karthik Raman, Adobe ASSET, blogs.adobe.com/asset/ Peleus and Karthik talked about response to mass-customized exploits. Attackers behave much like a business. "Mass customization" refers to concept discussed in the book Future Perfect by Stan Davis of Harvard Business School. Mass customization is differentiating a product for an individual customer, but at a mass production price. For example, the same individual with a debit card receives basically the same customized ATM experience around the world. Or designing your own PC from commodity parts. Exploit kits are another example of mass customization. The kits support multiple browsers and plugins, allows new modules. Exploit kits are cheap and customizable. Organized gangs use exploit kits. A group at Berkeley looked at 77,000 malicious websites (Grier et al., "Manufacturing Compromise: The Emergence of Exploit-as-a-Service", 2012). They found 10,000 distinct binaries among them, but derived from only a dozen or so exploit kits. Characteristics of Mass Malware: potent, resilient, relatively low cost Technical characteristics: multiple OS, multipe payloads, multiple scenarios, multiple languages, obfuscation Response time for 0-day exploits has gone down from ~40 days 5 years ago to about ~10 days now. So the drive with malware is towards mass customized exploits, to avoid detection There's plenty of evicence that exploit development has Project Manager bureaucracy. They infer from the malware edicts to: support all versions of reader support all versions of windows support all versions of flash support all browsers write large complex, difficult to main code (8750 lines of JavaScript for example Exploits have "loose coupling" of multipe versions of software (adobe), OS, and browser. This allows specific attacks against specific versions of multiple pieces of software. Also allows exploits of more obscure software/OS/browsers and obscure versions. Gave examples of exploits that exploited 2, 3, 6, or 14 separate bugs. However, these complete exploits are more likely to be buggy or fragile in themselves and easier to defeat. Future research includes normalizing malware and Javascript. Conclusion: The coming trend is that mass-malware with mass zero-day attacks will result in mass customization of attacks. x86 Rewriting: Defeating RoP and other Shinanighans Richard Wartell Richard Wartell The attack vector we are addressing here is: First some malware causes a buffer overflow. The malware has no program access, but input access and buffer overflow code onto stack Later the stack became non-executable. The workaround malware used was to write a bogus return address to the stack jumping to malware Later came ASLR (Address Space Layout Randomization) to randomize memory layout and make addresses non-deterministic. The workaround malware used was to jump t existing code segments in the program that can be used in bad ways "RoP" is Return-oriented Programming attacks. RoP attacks use your own code and write return address on stack to (existing) expoitable code found in program ("gadgets"). Pinkie Pie was paid $60K last year for a RoP attack. One solution is using anti-RoP compilers that compile source code with NO return instructions. ASLR does not randomize address space, just "gadgets". IPR/ILR ("Instruction Location Randomization") randomizes each instruction with a virtual machine. Richard's goal was to randomize a binary with no source code access. He created "STIR" (Self-Transofrming Instruction Relocation). STIR disassembles binary and operates on "basic blocks" of code. The STIR disassembler is conservative in what to disassemble. Each basic block is moved to a random location in memory. Next, STIR writes new code sections with copies of "basic blocks" of code in randomized locations. The old code is copied and rewritten with jumps to new code. the original code sections in the file is marked non-executible. STIR has better entropy than ASLR in location of code. Makes brute force attacks much harder. STIR runs on MS Windows (PEM) and Linux (ELF). It eliminated 99.96% or more "gadgets" (i.e., moved the address). Overhead usually 5-10% on MS Windows, about 1.5-4% on Linux (but some code actually runs faster!). The unique thing about STIR is it requires no source access and the modified binary fully works! Current work is to rewrite code to enforce security policies. For example, don't create a *.{exe,msi,bat} file. Or don't connect to the network after reading from the disk. Clowntown Express: interesting bugs and running a bug bounty program Collin Greene Collin Greene, Facebook Collin talked about Facebook's bug bounty program. Background at FB: FB has good security frameworks, such as security teams, external audits, and cc'ing on diffs. But there's lots of "deep, dark, forgotten" parts of legacy FB code. Collin gave several examples of bountied bugs. Some bounty submissions were on software purchased from a third-party (but bounty claimers don't know and don't care). We use security questions, as does everyone else, but they are basically insecure (often easily discoverable). Collin didn't expect many bugs from the bounty program, but they ended getting 20+ good bugs in first 24 hours and good submissions continue to come in. Bug bounties bring people in with different perspectives, and are paid only for success. Bug bounty is a better use of a fixed amount of time and money versus just code review or static code analysis. The Bounty program started July 2011 and paid out $1.5 million to date. 14% of the submissions have been high priority problems that needed to be fixed immediately. The best bugs come from a small % of submitters (as with everything else)—the top paid submitters are paid 6 figures a year. Spammers like to backstab competitors. The youngest sumitter was 13. Some submitters have been hired. Bug bounties also allows to see bugs that were missed by tools or reviews, allowing improvement in the process. Bug bounties might not work for traditional software companies where the product has release cycle or is not on Internet. Active Fingerprinting of Encrypted VPNs Anna Shubina Anna Shubina, Dartmouth Institute for Security, Technology, and Society (I missed the start of her talk because another track went overtime. But I have the DVD of the talk, so I'll expand later) IPsec leaves fingerprints. Using netcat, one can easily visually distinguish various crypto chaining modes just from packet timing on a chart (example, DES-CBC versus AES-CBC) One can tell a lot about VPNs just from ping roundtrips (such as what router is used) Delayed packets are not informative about a network, especially if far away from the network More needed to explore about how TCP works in real life with respect to timing Making Attacks Go Backwards Fuzzynop FuzzyNop, Mandiant This talk is not about threat attribution (finding who), product solutions, politics, or sales pitches. But who are making these malware threats? It's not a single person or group—they have diverse skill levels. There's a lot of fat-fingered fumblers out there. Always look for low-hanging fruit first: "hiding" malware in the temp, recycle, or root directories creation of unnamed scheduled tasks obvious names of files and syscalls ("ClearEventLog") uncleared event logs. Clearing event log in itself, and time of clearing, is a red flag and good first clue to look for on a suspect system Reverse engineering is hard. Disassembler use takes practice and skill. A popular tool is IDA Pro, but it takes multiple interactive iterations to get a clean disassembly. Key loggers are used a lot in targeted attacks. They are typically custom code or built in a backdoor. A big tip-off is that non-printable characters need to be printed out (such as "[Ctrl]" "[RightShift]") or time stamp printf strings. Look for these in files. Presence is not proof they are used. Absence is not proof they are not used. Java exploits. Can parse jar file with idxparser.py and decomile Java file. Java typially used to target tech companies. Backdoors are the main persistence mechanism (provided externally) for malware. Also malware typically needs command and control. Application of Artificial Intelligence in Ad-Hoc Static Code Analysis John Ashaman John Ashaman, Security Innovation Initially John tried to analyze open source files with open source static analysis tools, but these showed thousands of false positives. Also tried using grep, but tis fails to find anything even mildly complex. So next John decided to write his own tool. His approach was to first generate a call graph then analyze the graph. However, the problem is that making a call graph is really hard. For example, one problem is "evil" coding techniques, such as passing function pointer. First the tool generated an Abstract Syntax Tree (AST) with the nodes created from method declarations and edges created from method use. Then the tool generated a control flow graph with the goal to find a path through the AST (a maze) from source to sink. The algorithm is to look at adjacent nodes to see if any are "scary" (a vulnerability), using heuristics for search order. The tool, called "Scat" (Static Code Analysis Tool), currently looks for C# vulnerabilities and some simple PHP. Later, he plans to add more PHP, then JSP and Java. For more information see his posts in Security Innovation blog and NRefactory on GitHub. Mask Your Checksums—The Gorry Details Eric (XlogicX) Davisson Eric (XlogicX) Davisson Sometimes in emailing or posting TCP/IP packets to analyze problems, you may want to mask the IP address. But to do this correctly, you need to mask the checksum too, or you'll leak information about the IP. Problem reports found in stackoverflow.com, sans.org, and pastebin.org are usually not masked, but a few companies do care. If only the IP is masked, the IP may be guessed from checksum (that is, it leaks data). Other parts of packet may leak more data about the IP. TCP and IP checksums both refer to the same data, so can get more bits of information out of using both checksums than just using one checksum. Also, one can usually determine the OS from the TTL field and ports in a packet header. If we get hundreds of possible results (16x each masked nibble that is unknown), one can do other things to narrow the results, such as look at packet contents for domain or geo information. With hundreds of results, can import as CSV format into a spreadsheet. Can corelate with geo data and see where each possibility is located. Eric then demoed a real email report with a masked IP packet attached. Was able to find the exact IP address, given the geo and university of the sender. Point is if you're going to mask a packet, do it right. Eric wouldn't usually bother, but do it correctly if at all, to not create a false impression of security. Adventures with weird machines thirty years after "Reflections on Trusting Trust" Sergey Bratus Sergey Bratus, Dartmouth College (and Julian Bangert and Rebecca Shapiro, not present) "Reflections on Trusting Trust" refers to Ken Thompson's classic 1984 paper. "You can't trust code that you did not totally create yourself." There's invisible links in the chain-of-trust, such as "well-installed microcode bugs" or in the compiler, and other planted bugs. Thompson showed how a compiler can introduce and propagate bugs in unmodified source. But suppose if there's no bugs and you trust the author, can you trust the code? Hell No! There's too many factors—it's Babylonian in nature. Why not? Well, Input is not well-defined/recognized (code's assumptions about "checked" input will be violated (bug/vunerabiliy). For example, HTML is recursive, but Regex checking is not recursive. Input well-formed but so complex there's no telling what it does For example, ELF file parsing is complex and has multiple ways of parsing. Input is seen differently by different pieces of program or toolchain Any Input is a program input executes on input handlers (drives state changes & transitions) only a well-defined execution model can be trusted (regex/DFA, PDA, CFG) Input handler either is a "recognizer" for the inputs as a well-defined language (see langsec.org) or it's a "virtual machine" for inputs to drive into pwn-age ELF ABI (UNIX/Linux executible file format) case study. Problems can arise from these steps (without planting bugs): compiler linker loader ld.so/rtld relocator DWARF (debugger info) exceptions The problem is you can't really automatically analyze code (it's the "halting problem" and undecidable). Only solution is to freeze code and sign it. But you can't freeze everything! Can't freeze ASLR or loading—must have tables and metadata. Any sufficiently complex input data is the same as VM byte code Example, ELF relocation entries + dynamic symbols == a Turing Complete Machine (TM). @bxsays created a Turing machine in Linux from relocation data (not code) in an ELF file. For more information, see Rebecca "bx" Shapiro's presentation from last year's Toorcon, "Programming Weird Machines with ELF Metadata" @bxsays did same thing with Mach-O bytecode Or a DWARF exception handling data .eh_frame + glibc == Turning Machine X86 MMU (IDT, GDT, TSS): used address translation to create a Turning Machine. Page handler reads and writes (on page fault) memory. Uses a page table, which can be used as Turning Machine byte code. Example on Github using this TM that will fly a glider across the screen Next Sergey talked about "Parser Differentials". That having one input format, but two parsers, will create confusion and opportunity for exploitation. For example, CSRs are parsed during creation by cert requestor and again by another parser at the CA. Another example is ELF—several parsers in OS tool chain, which are all different. Can have two different Program Headers (PHDRs) because ld.so parses multiple PHDRs. The second PHDR can completely transform the executable. This is described in paper in the first issue of International Journal of PoC. Conclusions trusting computers not only about bugs! Bugs are part of a problem, but no by far all of it complex data formats means bugs no "chain of trust" in Babylon! (that is, with parser differentials) we need to squeeze complexity out of data until data stops being "code equivalent" Further information See and langsec.org. USENIX WOOT 2013 (Workshop on Offensive Technologies) for "weird machines" papers and videos.

Search Results

Search found 11313 results on 453 pages for 'rampant creative group'.

Page 345/453 | < Previous Page | 341 342 343 344 345 346 347 348 349 350 351 352 | Next Page >

- by jNoob

- by user2723490

- by Ploppe

- by MartinW

- by Dave Jarvis

- by jkelley

- by David Wick

- by user2980783

- by user551108

- by Ross

- by dowln

- by Marco

- by Meko

- by David Undy

- by kiasectomondo

- by Budove

- by Abulalia

- by amadhu

- by BuckWoody

- by Ricardo Ferreira

- by Shaun

- by Shaun

- by Paul White

- by Mysticgeek

- by danx

< Previous Page | 341 342 343 344 345 346 347 348 349 350 351 352 | Next Page >