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• PHP problems when transfering code from Windows to OS X

  - by Makka95

  I have recently bought a new MacBook Pro. Before I had my MacBook Pro I was working on a website on my desktop computer. And now I want to transfer this code to my new MacBook Pro. The problem is that when I transfered the code (I put it on Dropbox and simply downloaded it on my MacBook Pro) I started to see lots of error messages in my PHP code. The error message I”m receiving is: Warning: Cannot modify header information - headers already sent by (output started at /some/file.php:1) in /some/file.php on line 23 I have done some research on this and it seems that this error is most frequently caused by a new line, simple whitespace or any output before the <?php sign. I have looked through all the places where I have cookies that are being sent in the HTTP request and also where I'm using the header() function. I haven’t detected any output or whitespace that possibly could interfere and cause this problem. Noteworthy is that the error always says that the output is started at line 1. Which got me thinking if there is some kind of coding differences in the way that the Mac OS X and Windows operating systems handle new lines or white spaces? Or could the Dropbox transfer messed something up? The code on one of the sites(login.php) which produces the error: <?php include "mysql_database.php"; login(); $id = $_SESSION['Loggedin']; setcookie("login", $id, (time()+60*60*24*30)); header('Location: ' . $_SERVER['HTTP_REFERER']); ?> login function: function login() { $connection = connecttodatabase(); $pass = ""; $user = ""; $query = ""; if (isset($_POST['user']) && $_POST['user'] != null) { $user = $_POST['user']; if (isset($_POST['pass']) && $_POST['pass'] != null) { $pass = md5($_POST['pass']); $query = "SELECT ID FROM Anvandare WHERE Nickname='$user' AND Password ='$pass'"; } } if ($query != "") { $id = $connection->query($query); $id = mysqli_fetch_assoc($id); $id = $id['ID']; $_SESSION['Loggedin'] = $id; } closeconnection($connection); } Complete error: Warning: Cannot modify header information - headers already sent by (output started at /Users/name/GitHub/website/login.php:1) in /Users/namn/GitHub/website/login.php on line 9

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• PHP class function.

  - by Jordan Pagaduan

  What is wrong with this code? <?php class users { var $user_id, $f_name, $l_name, $db_host, $db_user, $db_name, $db_table; function user_input() { $this->$db_host = 'localhost'; $this->$db_user = 'root'; $this->$db_name = 'input_oop'; $this->$db_table = 'users'; } function userInput($f_name, $l_name) { $dbc = mysql_connect($this->db_host , $this->db_user, "") or die ("Cannot connect to database : " .mysql_error()); mysql_select_db($this->db_name) or die (mysql_error()); $query = "insert into $this->db_table values (NULL, \"$f_name\", \"$l_name\")"; $result = mysql_query($query); if(!$result) die (mysql_error()); $this->userID = mysql_insert_id(); mysql_close($dbc); $this->first_name = $f_name; $this->last_name = $l_name; } function userUpdate($new_f_name, $new_l_name) { $dbc = mysql_connect($this->db_host, $this->db_user, "") or die (mysql_error()); mysql_select_db($this->db_name) or die (mysql_error()); $query = "UPDATE $this->db_table set = \"$new_f_name\" , \"$new_l_name\" WHERE user_id = \"$this->user_id\""; $result = mysql_query($query); $this->f_name = $new_f_name; $this->l_name = $new_l_name; $this->user_id = $user_id; mysql_close($dbc); } function userDelete() { $dbc = mysql_connect($this->db_host, $this->db_user, "") or die (mysql_error()); mysql_select_db($this->db_name) or die (mysql_error()); $query = "DELETE FROM $this->db_table WHERE $user_id = \"$this->user_id\""; mysql_close($dbc); } } ?> The error is: Warning: mysql_connect() [function.mysql-connect]: Access denied for user 'ODBC'@'localhost' (using password: NO) in C:\xampp\htdocs\jordan_pagaduan\class.php on line 21 Cannot connect to database : Access denied for user 'ODBC'@'localhost' (using password: NO) The code cannot define this "$this->db_host" as "localhost".

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• Are python list comprehensions always a good programming practice?

  - by dln385

  To make the question clear, I'll use a specific example. I have a list of college courses, and each course has a few fields (all of which are strings). The user gives me a string of search terms, and I return a list of courses that match all of the search terms. This can be done in a single list comprehension or a few nested for loops. Here's the implementation. First, the Course class: class Course: def __init__(self, date, title, instructor, ID, description, instructorDescription, *args): self.date = date self.title = title self.instructor = instructor self.ID = ID self.description = description self.instructorDescription = instructorDescription self.misc = args Every field is a string, except misc, which is a list of strings. Here's the search as a single list comprehension. courses is the list of courses, and query is the string of search terms, for example "history project". def searchCourses(courses, query): terms = query.lower().strip().split() return tuple(course for course in courses if all( term in course.date.lower() or term in course.title.lower() or term in course.instructor.lower() or term in course.ID.lower() or term in course.description.lower() or term in course.instructorDescription.lower() or any(term in item.lower() for item in course.misc) for term in terms)) You'll notice that a complex list comprehension is difficult to read. I implemented the same logic as nested for loops, and created this alternative: def searchCourses2(courses, query): terms = query.lower().strip().split() results = [] for course in courses: for term in terms: if (term in course.date.lower() or term in course.title.lower() or term in course.instructor.lower() or term in course.ID.lower() or term in course.description.lower() or term in course.instructorDescription.lower()): break for item in course.misc: if term in item.lower(): break else: continue break else: continue results.append(course) return tuple(results) That logic can be hard to follow too. I have verified that both methods return the correct results. Both methods are nearly equivalent in speed, except in some cases. I ran some tests with timeit, and found that the former is three times faster when the user searches for multiple uncommon terms, while the latter is three times faster when the user searches for multiple common terms. Still, this is not a big enough difference to make me worry. So my question is this: which is better? Are list comprehensions always the way to go, or should complicated statements be handled with nested for loops? Or is there a better solution altogether?

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• mysql search using for loop from php.

  - by deb

  hi, i am a beginner. but I'm practicing a lot for few days with php mysql, and I am trying to use for loop to search an exploded string, one by one from mysql server. Till now I have no results. I'm giving my codes, <?php // Example 1 $var = @$_GET['s'] ; $limit=500; echo " "; echo "$var"; echo " "; $trimmed_array = explode(" ", $var); echo "$trimmed_array[0]"; // piece1 echo " "; $count= count($trimmed_array); echo $count; for($j=0;$j<$count;$j++) { e cho "$trimmed_array[$j]";; echo " "; } echo " "; for($i=0; $i<$count ; $i++){ $query = "select * from book where name like \"%$trimmed_array[$i]%\" order by name"; $numresults=mysql_query($query); $numrows =mysql_num_rows($numresults); if ($numrows == 0) { echo "<h4>Results</h4>"; echo "<p>Sorry, your search: &quot;" . $trimmed_array[i] . "&quot; returned zero results</p>"; } if (empty($s)) { $s=0; } $query .= " limit $s,$limit"; $result = mysql_query($query) or die("Couldn't execute query"); echo "<p>You searched for: &quot;" . $var . "&quot;</p>"; echo "Results<br /><br />"; $count=1; while ($row= mysql_fetch_array($result)) { $name = $row["name"]; $publisher=$row["publisher"]; $total=$row["total"]; $issued=$row["issued"]; $available=$row["available"]; $category=$row["category"]; echo "<table border='1'><tr><td>$count)</td><td>$name&nbsp;</td><td>$publisher&nbsp;</td><td>$total&nbsp;</td><td>$issued&nbsp;</td><td>$available&nbsp;</td><td>$category&nbsp;</td></tr></table>" ; $count++ ; } } ?>

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• Custom SNMP Cacti Data Source fails to update

  - by Andrew Wilkinson

  I'm trying to create a custom SNMP datasource for Cacti but despite everything I can check being correct, it is not creating the rrd file, or updating it even when I create it. Other, standard SNMP sources are working correctly so it's not SNMP or permissions that are the problem. I've created a new Data Query, which when I click on "Verbose Query" on the device screen returns the following: + Running data query [10]. + Found type = '3' [SNMP Query]. + Found data query XML file at '/volume1/web/cacti/resource/snmp_queries/syno_volume_stats.xml' + XML file parsed ok. + missing in XML file, 'Index Count Changed' emulated by counting oid_index entries + Executing SNMP walk for list of indexes @ '.1.3.6.1.2.1.25.2.3.1.3' Index Count: 8 + Index found at OID: '.1.3.6.1.2.1.25.2.3.1.3.1' value: 'Physical memory' + Index found at OID: '.1.3.6.1.2.1.25.2.3.1.3.3' value: 'Virtual memory' + Index found at OID: '.1.3.6.1.2.1.25.2.3.1.3.6' value: 'Memory buffers' + Index found at OID: '.1.3.6.1.2.1.25.2.3.1.3.7' value: 'Cached memory' + Index found at OID: '.1.3.6.1.2.1.25.2.3.1.3.10' value: 'Swap space' + Index found at OID: '.1.3.6.1.2.1.25.2.3.1.3.31' value: '/' + Index found at OID: '.1.3.6.1.2.1.25.2.3.1.3.32' value: '/volume1' + Index found at OID: '.1.3.6.1.2.1.25.2.3.1.3.33' value: '/opt' + index_parse at OID: '.1.3.6.1.2.1.25.2.3.1.3.1' results: '1' + index_parse at OID: '.1.3.6.1.2.1.25.2.3.1.3.3' results: '3' + index_parse at OID: '.1.3.6.1.2.1.25.2.3.1.3.6' results: '6' + index_parse at OID: '.1.3.6.1.2.1.25.2.3.1.3.7' results: '7' + index_parse at OID: '.1.3.6.1.2.1.25.2.3.1.3.10' results: '10' + index_parse at OID: '.1.3.6.1.2.1.25.2.3.1.3.31' results: '31' + index_parse at OID: '.1.3.6.1.2.1.25.2.3.1.3.32' results: '32' + index_parse at OID: '.1.3.6.1.2.1.25.2.3.1.3.33' results: '33' + Located input field 'index' [walk] + Executing SNMP walk for data @ '.1.3.6.1.2.1.25.2.3.1.3' + Found item [index='Physical memory'] index: 1 [from value] + Found item [index='Virtual memory'] index: 3 [from value] + Found item [index='Memory buffers'] index: 6 [from value] + Found item [index='Cached memory'] index: 7 [from value] + Found item [index='Swap space'] index: 10 [from value] + Found item [index='/'] index: 31 [from value] + Found item [index='/volume1'] index: 32 [from value] + Found item [index='/opt'] index: 33 [from value] + Located input field 'volsizeunit' [walk] + Executing SNMP walk for data @ '.1.3.6.1.2.1.25.2.3.1.4' + Found item [volsizeunit='1024 Bytes'] index: 1 [from value] + Found item [volsizeunit='1024 Bytes'] index: 3 [from value] + Found item [volsizeunit='1024 Bytes'] index: 6 [from value] + Found item [volsizeunit='1024 Bytes'] index: 7 [from value] + Found item [volsizeunit='1024 Bytes'] index: 10 [from value] + Found item [volsizeunit='4096 Bytes'] index: 31 [from value] + Found item [volsizeunit='4096 Bytes'] index: 32 [from value] + Found item [volsizeunit='4096 Bytes'] index: 33 [from value] + Located input field 'volsize' [walk] + Executing SNMP walk for data @ '.1.3.6.1.2.1.25.2.3.1.5' + Found item [volsize='1034712'] index: 1 [from value] + Found item [volsize='3131792'] index: 3 [from value] + Found item [volsize='1034712'] index: 6 [from value] + Found item [volsize='775904'] index: 7 [from value] + Found item [volsize='2097080'] index: 10 [from value] + Found item [volsize='612766'] index: 31 [from value] + Found item [volsize='1439812394'] index: 32 [from value] + Found item [volsize='1439812394'] index: 33 [from value] + Located input field 'volused' [walk] + Executing SNMP walk for data @ '.1.3.6.1.2.1.25.2.3.1.6' + Found item [volused='1022520'] index: 1 [from value] + Found item [volused='1024096'] index: 3 [from value] + Found item [volused='32408'] index: 6 [from value] + Found item [volused='775904'] index: 7 [from value] + Found item [volused='1576'] index: 10 [from value] + Found item [volused='148070'] index: 31 [from value] + Found item [volused='682377865'] index: 32 [from value] + Found item [volused='682377865'] index: 33 [from value] AS you can see it appears to be returning the correct data. I've also set up data templates and graph templates to display the data. The create graphs for a device screen shows the correct data, and when selecting one row can clicking create a new data source and graph are created. Unfortunately the data source is never updated. Increasing the poller log level shows that it appears to not even be querying the data source, despite it being used? What should my next steps to debug this issue be?

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• Setting up a DNS name server for a mass virtual host with Bind9

  - by Dez

  I am trying to set up a chrooted DNS name server in a local LAN like this everyone connected in the LAN can have access to the mass virtual hosts defined for a development ambience without having to edit manually their local /etc/hosts one by one. The mass virtual host is named example.user.dev (VirtualDocumentRoot /home/user/example ) and example.test (DocumentRoot /var/www/example). I set up everything and the /var/log/syslog doesn't show any error, but when checking the DNS with: host -v example.test Doesn't find the host. Also using the dig command I don't receive answer. dig -x example.test ; << DiG 9.5.1-P3 << -x imprimere ;; global options: printcmd ;; Got answer: ;; -HEADER<<- opcode: QUERY, status: NXDOMAIN, id: 47844 ;; flags: qr rd ra; QUERY: 1, ANSWER: 0, AUTHORITY: 1, ADDITIONAL: 0 ;; QUESTION SECTION: ;imprimere.in-addr.arpa. IN PTR ;; AUTHORITY SECTION: in-addr.arpa. 600 IN SOA a.root-servers.net. dns-ops.arin.net. 2010042604 1800 900 691200 10800 ;; Query time: 108 msec ;; SERVER: 80.58.0.33#53(80.58.0.33) ;; WHEN: Mon Apr 26 11:15:53 2010 ;; MSG SIZE rcvd: 107 My configuration is the following: /etc/bind/named.conf.local zone "example.test" { type master; allow-query { any; }; file "/etc/bind/zones/master_example.test"; notify yes; }; zone "1.168.192.in-addr.arpa" { type master; allow-query { any; }; file "/etc/bind/zones/master_1.168.192.in-addr.arpa"; notify yes; }; /etc/bind/named.conf.options Note: We have an static IP address so I forward the querys to DNS server to said IP address. options{ directory "/var/cache/bind"; forwarders { 80.34.100.160; }; auth-nxdomain no; listen-on-v6 { any; }; }; /etc/bind/zones/master_example.test $ORIGIN example.test. $TTL 86400 @ IN SOA example.test. root.example.test. ( 201004227 ; serial 28800 ; refresh 14400 ; retry 3600000 ; expire 86400 ) ; min ; TXT "example.test, DNS service" @ IN NS example.test. localhost A 127.0.0.1 example.test. A 192.168.1.52 example A 192.168.1.52 www CNAME example.test. /etc/hosts 127.0.0.1 localhost example 192.168.1.52 localhost example example.test /etc/resolv.conf Note: For Bind I just added the 3 last lines. nameserver 80.58.0.33 nameserver 80.58.61.250 nameserver 80.58.61.254 search example.test search example nameserver 192.168.1.52

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• dns server bind is not work

  - by milad

  I just installed bind on RHEL 6 and point a domain to that server. but actually when i ping domain it returns error 1214: Here is my named.conf: // // named.conf // // Provided by Red Hat bind package to configure the ISC BIND named(8) DNS // server as a caching only nameserver (as a localhost DNS resolver only). // // See /usr/share/doc/bind*/sample/ for example named configuration files. // options { listen-on port 53 { any; }; listen-on-v6 port 53 { ::1; }; directory "/var/named"; dump-file "/var/named/data/cache_dump.db"; statistics-file "/var/named/data/named_stats.txt"; memstatistics-file "/var/named/data/named_mem_stats.txt"; allow-query { any; }; recursion yes; dnssec-enable yes; dnssec-validation yes; dnssec-lookaside auto; /* Path to ISC DLV key */ bindkeys-file "/etc/named.iscdlv.key"; managed-keys-directory "/var/named/dynamic"; }; logging { channel default_debug { file "data/named.run"; severity dynamic; }; }; zone "." IN { type hint; file "named.ca"; }; include "/etc/named.rfc1912.zones"; include "/etc/named.root.key"; zone "mydomain.com"{ type master; file "/var/named/data/named.mydomain.com"; allow-update { none; }; };` AND The content of "/var/named/data/named.mydomain.com": $TTL 38400 mydomain.com. IN SOA ns1.mydomain.com. milad.yahoo.com. ( 2012101201 ; serial number YYMMDDNN 28800 ; Refresh 7200 ; Retry 864000 ; Expire 38400 ; Min TTL ) mydomain.com. IN A 1.2.3.4 www IN A 1.2.3.4 ns1.mydomain.com. IN A 1.2.3.4 ns2.mydomain.com. IN A 1.2.3.4 mydomain.com. IN NS ns1.mydomain.com. mydomain.com. IN NS ns2.mydomain.com. AND i'm sure the named service is running: [root@server ~]# service named status version: 9.8.2rc1-RedHat-9.8.2-0.10.rc1.el6_3.3 CPUs found: 8 worker threads: 8 number of zones: 20 debug level: 0 xfers running: 0 xfers deferred: 0 soa queries in progress: 0 query logging is OFF recursive clients: 0/0/1000 tcp clients: 0/100 server is up and running named (pid 26299) is running... Thanks for your answers. i know that the ping is not the job of bind, i use it just to check whether domain is pointed to host or not.(ping is open in my server as i got reply in pinging ip) i use network-tools.com to ping domain. here the output of dig utility: dig mydomain.com ; <<>> DiG 9.8.2rc1-RedHat-9.8.2-0.10.rc1.el6_3.3 <<>> mydomain.com ;; global options: +cmd ;; Got answer: ;; ->>HEADER<<- opcode: QUERY, status: SERVFAIL, id: 6806 ;; flags: qr rd ra; QUERY: 1, ANSWER: 0, AUTHORITY: 0, ADDITIONAL: 0 ;; QUESTION SECTION: ;mydomain.com. IN A ;; Query time: 321 msec ;; SERVER: 5.6.7.8#53(5.6.7.8)##note that 5.6.7.8 is my idc dns ip ;; WHEN: Sun Oct 14 23:53:47 2012

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• mySQL Optimization Suggestions

  - by Brian Schroeter

  I'm trying to optimize our mySQL configuration for our large Magento website. The reason I believe that mySQL needs to be configured further is because New Relic has shown that our SELECT queries are taking a long time (20,000+ ms) in some categories. I ran MySQLTuner 1.3.0 and got the following results... (Disclaimer: I restarted mySQL earlier after tweaking some settings, and so the results here may not be 100% accurate): >> MySQLTuner 1.3.0 - Major Hayden <[email protected]> >> Bug reports, feature requests, and downloads at http://mysqltuner.com/ >> Run with '--help' for additional options and output filtering [OK] Currently running supported MySQL version 5.5.37-35.0 [OK] Operating on 64-bit architecture -------- Storage Engine Statistics ------------------------------------------- [--] Status: +ARCHIVE +BLACKHOLE +CSV -FEDERATED +InnoDB +MRG_MYISAM [--] Data in MyISAM tables: 7G (Tables: 332) [--] Data in InnoDB tables: 213G (Tables: 8714) [--] Data in PERFORMANCE_SCHEMA tables: 0B (Tables: 17) [--] Data in MEMORY tables: 0B (Tables: 353) [!!] Total fragmented tables: 5492 -------- Security Recommendations ------------------------------------------- [!!] User '@host5.server1.autopartsnetwork.com' has no password set. [!!] User '@localhost' has no password set. [!!] User 'root@%' has no password set. -------- Performance Metrics ------------------------------------------------- [--] Up for: 5h 3m 4s (5M q [317.443 qps], 42K conn, TX: 18B, RX: 2B) [--] Reads / Writes: 95% / 5% [--] Total buffers: 35.5G global + 184.5M per thread (1024 max threads) [!!] Maximum possible memory usage: 220.0G (174% of installed RAM) [OK] Slow queries: 0% (6K/5M) [OK] Highest usage of available connections: 5% (61/1024) [OK] Key buffer size / total MyISAM indexes: 512.0M/3.1G [OK] Key buffer hit rate: 100.0% (102M cached / 45K reads) [OK] Query cache efficiency: 66.9% (3M cached / 5M selects) [!!] Query cache prunes per day: 3486361 [OK] Sorts requiring temporary tables: 0% (0 temp sorts / 812K sorts) [!!] Joins performed without indexes: 1328 [OK] Temporary tables created on disk: 11% (126K on disk / 1M total) [OK] Thread cache hit rate: 99% (61 created / 42K connections) [!!] Table cache hit rate: 19% (9K open / 49K opened) [OK] Open file limit used: 2% (712/25K) [OK] Table locks acquired immediately: 100% (5M immediate / 5M locks) [!!] InnoDB buffer pool / data size: 32.0G/213.4G [OK] InnoDB log waits: 0 -------- Recommendations ----------------------------------------------------- General recommendations: Run OPTIMIZE TABLE to defragment tables for better performance MySQL started within last 24 hours - recommendations may be inaccurate Reduce your overall MySQL memory footprint for system stability Enable the slow query log to troubleshoot bad queries Increasing the query_cache size over 128M may reduce performance Adjust your join queries to always utilize indexes Increase table_cache gradually to avoid file descriptor limits Read this before increasing table_cache over 64: http://bit.ly/1mi7c4C Variables to adjust: *** MySQL's maximum memory usage is dangerously high *** *** Add RAM before increasing MySQL buffer variables *** query_cache_size (> 512M) [see warning above] join_buffer_size (> 128.0M, or always use indexes with joins) table_cache (> 12288) innodb_buffer_pool_size (>= 213G) My my.cnf configuration is as follows... [client] port = 3306 [mysqld_safe] nice = 0 [mysqld] tmpdir = /var/lib/mysql/tmp user = mysql port = 3306 skip-external-locking character-set-server = utf8 collation-server = utf8_general_ci event_scheduler = 0 key_buffer = 512M max_allowed_packet = 64M thread_stack = 512K thread_cache_size = 512 sort_buffer_size = 24M read_buffer_size = 8M read_rnd_buffer_size = 24M join_buffer_size = 128M # for some nightly processes client sessions set the join buffer to 8 GB auto-increment-increment = 1 auto-increment-offset = 1 myisam-recover = BACKUP max_connections = 1024 # max connect errors artificially high to support behaviors of NetScaler monitors max_connect_errors = 999999 concurrent_insert = 2 connect_timeout = 5 wait_timeout = 180 net_read_timeout = 120 net_write_timeout = 120 back_log = 128 # this table_open_cache might be too low because of MySQL bugs #16244691 and #65384) table_open_cache = 12288 tmp_table_size = 512M max_heap_table_size = 512M bulk_insert_buffer_size = 512M open-files-limit = 8192 open-files = 1024 query_cache_type = 1 # large query limit supports SOAP and REST API integrations query_cache_limit = 4M # larger than 512 MB query cache size is problematic; this is typically ~60% full query_cache_size = 512M # set to true on read slaves read_only = false slow_query_log_file = /var/log/mysql/slow.log slow_query_log = 0 long_query_time = 0.2 expire_logs_days = 10 max_binlog_size = 1024M binlog_cache_size = 32K sync_binlog = 0 # SSD RAID10 technically has a write capacity of 10000 IOPS innodb_io_capacity = 400 innodb_file_per_table innodb_table_locks = true innodb_lock_wait_timeout = 30 # These servers have 80 CPU threads; match 1:1 innodb_thread_concurrency = 48 innodb_commit_concurrency = 2 innodb_support_xa = true innodb_buffer_pool_size = 32G innodb_file_per_table innodb_flush_log_at_trx_commit = 1 innodb_log_buffer_size = 2G skip-federated [mysqldump] quick quote-names single-transaction max_allowed_packet = 64M I have a monster of a server here to power our site because our catalog is very large (300,000 simple SKUs), and I'm just wondering if I'm missing anything that I can configure further. :-) Thanks!

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• NDepend tool – Why every developer working with Visual Studio.NET must try it!

  - by hajan

  In the past two months, I have had a chance to test the capabilities and features of the amazing NDepend tool designed to help you make your .NET code better, more beautiful and achieve high code quality. In other words, this tool will definitely help you harmonize your code. I mean, you’ve probably heard about Chaos Theory. Experienced developers and architects are already advocates of the programming chaos that happens when working with complex project architecture, the matrix of relationships between objects which simply even if you are the one who have written all that code, you know how hard is to visualize everything what does the code do. When the application get more and more complex, you will start missing a lot of details in your code… NDepend will help you visualize all the details on a clever way that will help you make smart moves to make your code better. The NDepend tool supports many features, such as: Code Query Language – which will help you write custom rules and query your own code! Imagine, you want to find all your methods which have more than 100 lines of code :)! That’s something simple! However, I will dig much deeper in one of my next blogs which I’m going to dedicate to the NDepend’s CQL (Code Query Language) Architecture Visualization – You are an architect and want to visualize your application’s architecture? I’m thinking how many architects will be really surprised from their architectures since NDepend shows your whole architecture showing each piece of it. NDepend will show you how your code is structured. It shows the architecture in graphs, but if you have very complex architecture, you can see it in Dependency Matrix which is more suited to display large architecture Code Metrics – Using NDepend’s panel, you can see the code base according to Code Metrics. You can do some additional filtering, like selecting the top code elements ordered by their current code metric value. You can use the CQL language for this purpose too. Smart Search – NDepend has great searching ability, which is again based on the CQL (Code Query Language). However, you have some options to search using dropdown lists and text boxes and it will generate the appropriate CQL code on fly. Moreover, you can modify the CQL code if you want it to fit some more advanced searching tasks. Compare Builds and Code Difference – NDepend will also help you compare previous versions of your code with the current one at one of the most clever ways I’ve seen till now. Create Custom Rules – using CQL you can create custom rules and let NDepend warn you on each build if you break a rule Reporting – NDepend can automatically generate reports with detailed stats, graph representation, dependency matrixes and some additional advanced reporting features that will simply explain you everything related to your application’s code, architecture and what you’ve done. And that’s not all. As I’ve seen, there are many other features that NDepend supports. I will dig more in the upcoming days and will blog more about it. The team who built the NDepend have also created good documentation, which you can find on the NDepend website. On their website, you can also find some good videos that will help you get started quite fast. It’s easy to install and what is very important it is fully integrated with Visual Studio. To get you started, you can watch the following Getting Started Online Demo and Tutorial with explanations and screenshots. If you are interested to know more about how to use the features of this tool, either visit their website or wait for my next blogs where I will show some real examples of using the tool and how it helps make your code better. And the last thing for this blog, I would like to copy one sentence from the NDepend’s home page which says: ‘Hence the software design becomes concrete, code reviews are effective, large refactoring are easy and evolution is mastered.’ Website: www.ndepend.com Getting Started: http://www.ndepend.com/GettingStarted.aspx Features: http://www.ndepend.com/Features.aspx Download: http://www.ndepend.com/NDependDownload.aspx Hope you like it! Please do let me know your feedback by providing comments to my blog post. Kind Regards, Hajan

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• SQL Server IO handling mechanism can be severely affected by high CPU usage

  - by sqlworkshops

  Are you using SSD or SAN / NAS based storage solution and sporadically observe SQL Server experiencing high IO wait times or from time to time your DAS / HDD becomes very slow according to SQL Server statistics? Read on… I need your help to up vote my connect item – https://connect.microsoft.com/SQLServer/feedback/details/744650/sql-server-io-handling-mechanism-can-be-severely-affected-by-high-cpu-usage. Instead of taking few seconds, queries could take minutes/hours to complete when CPU is busy.In SQL Server when a query / request needs to read data that is not in data cache or when the request has to write to disk, like transaction log records, the request / task will queue up the IO operation and wait for it to complete (task in suspended state, this wait time is the resource wait time). When the IO operation is complete, the task will be queued to run on the CPU. If the CPU is busy executing other tasks, this task will wait (task in runnable state) until other tasks in the queue either complete or get suspended due to waits or exhaust their quantum of 4ms (this is the signal wait time, which along with resource wait time will increase the overall wait time). When the CPU becomes free, the task will finally be run on the CPU (task in running state).The signal wait time can be up to 4ms per runnable task, this is by design. So if a CPU has 5 runnable tasks in the queue, then this query after the resource becomes available might wait up to a maximum of 5 X 4ms = 20ms in the runnable state (normally less as other tasks might not use the full quantum).In case the CPU usage is high, let’s say many CPU intensive queries are running on the instance, there is a possibility that the IO operations that are completed at the Hardware and Operating System level are not yet processed by SQL Server, keeping the task in the resource wait state for longer than necessary. In case of an SSD, the IO operation might even complete in less than a millisecond, but it might take SQL Server 100s of milliseconds, for instance, to process the completed IO operation. For example, let’s say you have a user inserting 500 rows in individual transactions. When the transaction log is on an SSD or battery backed up controller that has write cache enabled, all of these inserts will complete in 100 to 200ms. With a CPU intensive parallel query executing across all CPU cores, the same inserts might take minutes to complete. WRITELOG wait time will be very high in this case (both under sys.dm_io_virtual_file_stats and sys.dm_os_wait_stats). In addition you will notice a large number of WAITELOG waits since log records are written by LOG WRITER and hence very high signal_wait_time_ms leading to more query delays. However, Performance Monitor Counter, PhysicalDisk, Avg. Disk sec/Write will report very low latency times.Such delayed IO handling also occurs to read operations with artificially very high PAGEIOLATCH_SH wait time (with number of PAGEIOLATCH_SH waits remaining the same). This problem will manifest more and more as customers start using SSD based storage for SQL Server, since they drive the CPU usage to the limits with faster IOs. We have a few workarounds for specific scenarios, but we think Microsoft should resolve this issue at the product level. We have a connect item open – https://connect.microsoft.com/SQLServer/feedback/details/744650/sql-server-io-handling-mechanism-can-be-severely-affected-by-high-cpu-usage - (with example scripts) to reproduce this behavior, please up vote the item so the issue will be addressed by the SQL Server product team soon.Thanks for your help and best regards,Ramesh MeyyappanHome: www.sqlworkshops.comLinkedIn: http://at.linkedin.com/in/rmeyyappan

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• Advanced Record-Level Business Intelligence with Inner Queries

  - by gt0084e1

  While business intelligence is generally applied at an aggregate level to large data sets, it's often useful to provide a more streamlined insight into an individual records or to be able to sort and rank them. For instance, a salesperson looking at a specific customer could benefit from basic stats on that account. A marketer trying to define an ideal customer could pull the top entries and look for insights or patterns. Inner queries let you do sophisticated analysis without the overhead of traditional BI or OLAP technologies like Analysis Services. Example - Order History Constancy Let's assume that management has realized that the best thing for our business is to have customers ordering every month. We'll need to identify and rank customers based on how consistently they buy and when their last purchase was so sales & marketing can respond accordingly. Our current application may not be able to provide this and adding an OLAP server like SSAS may be overkill for our needs. Luckily, SQL Server provides the ability to do relatively sophisticated analytics via inner queries. Here's the kind of output we'd like to see. Creating the Queries Before you create a view, you need to create the SQL query that does the calculations. Here we are calculating the total number of orders as well as the number of months since the last order. These fields might be very useful to sort by but may not be available in the app. This approach provides a very streamlined and high performance method of delivering actionable information without radically changing the application. It's also works very well with self-service reporting tools like Izenda. SELECT CustomerID,CompanyName, ( SELECT COUNT(OrderID) FROM Orders WHERE Orders.CustomerID = Customers.CustomerID ) As Orders, DATEDIFF(mm, ( SELECT Max(OrderDate) FROM Orders WHERE Orders.CustomerID = Customers.CustomerID) ,getdate() ) AS MonthsSinceLastOrder FROM Customers Creating Views To turn this or any query into a view, just put CREATE VIEW AS before it. If you want to change it use the statement ALTER VIEW AS. Creating Computed Columns If you'd prefer not to create a view, inner queries can also be applied by using computed columns. Place you SQL in the (Formula) field of the Computed Column Specification or check out this article here. Advanced Scoring and Ranking One of the best uses for this approach is to score leads based on multiple fields. For instance, you may be in a business where customers that don't order every month require more persistent follow up. You could devise a simple formula that shows the continuity of an account. If they ordered every month since their first order, they would be at 100 indicating that they have been ordering 100% of the time. Here's the query that would calculate that. It uses a few SQL tricks to make this happen. We are extracting the count of unique months and then dividing by the months since initial order. This query will give you the following information which can be used to help sales and marketing now where to focus. You could sort by this percentage to know where to start calling or to find patterns describing your best customers. Number of orders First Order Date Last Order Date Percentage of months order was placed since last order. SELECT CustomerID, (SELECT COUNT(OrderID) FROM Orders WHERE Orders.CustomerID = Customers.CustomerID) As Orders, (SELECT Max(OrderDate) FROM Orders WHERE Orders.CustomerID = Customers.CustomerID) AS LastOrder, (SELECT Min(OrderDate) FROM Orders WHERE Orders.CustomerID = Customers.CustomerID) AS FirstOrder, DATEDIFF(mm,(SELECT Min(OrderDate) FROM Orders WHERE Orders.CustomerID = Customers.CustomerID),getdate()) AS MonthsSinceFirstOrder, 100*(SELECT COUNT(DISTINCT 100*DATEPART(yy,OrderDate) + DATEPART(mm,OrderDate)) FROM Orders WHERE Orders.CustomerID = Customers.CustomerID) / DATEDIFF(mm,(SELECT Min(OrderDate) FROM Orders WHERE Orders.CustomerID = Customers.CustomerID),getdate()) As OrderPercent FROM Customers

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• SQL Server Optimizer Malfunction?

  - by Tony Davis

  There was a sharp intake of breath from the audience when Adam Machanic declared the SQL Server optimizer to be essentially "stuck in 1997". It was during his fascinating "Query Tuning Mastery: Manhandling Parallelism" session at the recent PASS SQL Summit. Paraphrasing somewhat, Adam (blog | @AdamMachanic) offered a convincing argument that the optimizer often delivers flawed plans based on assumptions that are no longer valid with today’s hardware. In 1997, when Microsoft engineers re-designed the database engine for SQL Server 7.0, SQL Server got its initial implementation of a cost-based optimizer. Up to SQL Server 2000, the developer often had to deploy a steady stream of hints in SQL statements to combat the occasionally wilful plan choices made by the optimizer. However, with each successive release, the optimizer has evolved and improved in its decision-making. It is still prone to the occasional stumble when we tackle difficult problems, join large numbers of tables, perform complex aggregations, and so on, but for most of us, most of the time, the optimizer purrs along efficiently in the background. Adam, however, challenged further any assumption that the current optimizer is competent at providing the most efficient plans for our more complex analytical queries, and in particular of offering up correctly parallelized plans. He painted a picture of a present where complex analytical queries have become ever more prevalent; where disk IO is ever faster so that reads from disk come into buffer cache faster than ever; where the improving RAM-to-data ratio means that we have a better chance of finding our data in cache. Most importantly, we have more CPUs at our disposal than ever before. To get these queries to perform, we not only need to have the right indexes, but also to be able to split the data up into subsets and spread its processing evenly across all these available CPUs. Improvements such as support for ColumnStore indexes are taking things in the right direction, but, unfortunately, deficiencies in the current Optimizer mean that SQL Server is yet to be able to exploit properly all those extra CPUs. Adam’s contention was that the current optimizer uses essentially the same costing model for many of its core operations as it did back in the days of SQL Server 7, based on assumptions that are no longer valid. One example he gave was a "slow disk" bias that may have been valid back in 1997 but certainly is not on modern disk systems. Essentially, the optimizer assesses the relative cost of serial versus parallel plans based on the assumption that there is no IO cost benefit from parallelization, only CPU. It assumes that a single request will saturate the IO channel, and so a query would not run any faster if we parallelized IO because the disk system simply wouldn’t be able to handle the extra pressure. As such, the optimizer often decides that a serial plan is lower cost, often in cases where a parallel plan would improve performance dramatically. It was challenging and thought provoking stuff, as were his techniques for driving parallelism through query logic based on subsets of rows that define the "grain" of the query. I highly recommend you catch the session if you missed it. I’m interested to hear though, when and how often people feel the force of the optimizer’s shortcomings. Barring mistakes, such as stale statistics, how often do you feel the Optimizer fails to find the plan you think it should, and what are the most common causes? Is it fighting to induce it toward parallelism? Combating unexpected plans, arising from table partitioning? Something altogether more prosaic? Cheers, Tony.

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• Consumer Oriented Search In Oracle Endeca Information Discovery - Part 2

  - by Bob Zurek

  As discussed in my last blog posting on this topic, Information Discovery, a core capability of the Oracle Endeca Information Discovery solution enables businesses to search, discover and navigate through a wide variety of big data including structured, unstructured and semi-structured data. With search as a core advanced capabilities of our product it is important to understand some of the key differences and capabilities in the underlying data store of Oracle Endeca Information Discovery and that is our Endeca Server. In the last post on this subject, we talked about Exploratory Search capabilities along with support for cascading relevance. Additional search capabilities in the Endeca Server, which differentiate from simple keyword based "search boxes" in other Information Discovery products also include: The Endeca Server Supports Set Search.  The Endeca Server is organized around set retrieval, which means that it looks at groups of results (all the documents that match a search), as well as the relationship of each individual result to the set. Other approaches only compute the relevance of a document by comparing the document to the search query – not by comparing the document to all the others. For example, a search for “U.S.” in another approach might match to the title of a document and get a high ranking. But what if it were a collection of government documents in which “U.S.” appeared in many titles, making that clue less meaningful? A set analysis would reveal this and be used to adjust relevance accordingly. The Endeca Server Supports Second-Order Relvance. Unlike simple search interfaces in traditional BI tools, which provide limited relevance ranking, such as a list of results based on key word matching, Endeca enables users to determine the most salient terms to divide up the result. Determining this second-order relevance is the key to providing effective guidance. Support for Queries and Filters. Search is the most common query type, but hardly complete, and users need to express a wide range of queries. Oracle Endeca Information Discovery also includes navigation, interactive visualizations, analytics, range filters, geospatial filters, and other query types that are more commonly associated with BI tools. Unlike other approaches, these queries operate across structured, semi-structured and unstructured content stored in the Endeca Server. Furthermore, this set is easily extensible because the core engine allows for pluggable features to be added. Like a search engine, queries are answered with a results list, ranked to put the most likely matches first. Unlike “black box” relevance solutions, which generalize one strategy for everyone, we believe that optimal relevance strategies vary across domains. Therefore, it provides line-of-business owners with a set of relevance modules that let them tune the best results based on their content. The Endeca Server query result sets are summarized, which gives users guidance on how to refine and explore further. Summaries include Guided Navigation® (a form of faceted search), maps, charts, graphs, tag clouds, concept clusters, and clarification dialogs. Users don’t explicitly ask for these summaries; Oracle Endeca Information Discovery analytic applications provide the right ones, based on configurable controls and rules. For example, the analytic application might guide a procurement agent filtering for in-stock parts by visualizing the results on a map and calculating their average fulfillment time. Furthermore, the user can interact with summaries and filters without resorting to writing complex SQL queries. The user can simply just click to add filters. Within Oracle Endeca Information Discovery, all parts of the summaries are clickable and searchable. We are living in a search driven society where business users really seem to enjoy entering information into a search box. We do this everyday as consumers and therefore, we have gotten used to looking for that box. However, the key to getting the right results is to guide that user in a way that provides additional Discovery, beyond what they may have anticipated. This is why these important and advanced features of search inside the Endeca Server have been so important. They have helped to guide our great customers to success. 

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• Rethinking Oracle Optimizer Statistics for P6 Part 2

  - by Brian Diehl

  In the previous post (Part 1), I tried to draw some key insights about the relationship between P6 and Oracle Optimizer Statistics.  The first is that average cardinality has the greatest impact on query optimization and that the particular queries generated by P6 are more likely to use this average during calculations. The second is that these are statistics that are unlikely to change greatly over the life of the application. Ultimately, our goal is to get the best query optimization possible.  Or is it? Stability No application administrator wants to get the call at 9am that their application users cannot get there work done because everything is running slow. This is a possibility with a regularly scheduled nightly collection of statistics. It may not just be slow performance, but a complete loss of service because one or more queries are optimized poorly. Ideally, this should not be the case. The database optimizer should make better decisions with more up-to-date data. Better statistics may give incremental performance benefit. However, this benefit must be balanced against the potential cost of system down time.  It is stability that we ultimately desire and not absolute optimal performance. We do want the benefit from more accurate statistics and better query plans, but not at the risk of an unusable system. As a result, I've developed the following methodology around managing database statistics for the P6 database.  1. No Automatic Re-Gathering - The daily, weekly, or other interval of statistic gathering is unlikely to be beneficial. Quite the opposite. It is more likely to cause problems. 2. Smart Re-Gathering - The time to collect statistics is when things have changed significantly. For a new installation of P6, this is happening more often because the data is growing from a few rows to thousands and more. But for a mature system, the data is not changing significantly from week-to-week. There are times to collect statistics: New releases of the application Changes in the underlying hardware or software versions (ex. new Oracle RDBMS version) When additional user groups are added. The new groups may use the software in significantly different ways. After significant changes in the data. This may be monthly, quarterly or yearly.  3. Always Test - If you take away one thing from this post, it would be to always have a plan to test after changing statistics. In reality, statistics can be collected as often as you desire provided there are tests in place to verify that performance is the same or better. These might be automated tests or simply a manual script of application functions. 4. Have a Way Out - Never change the statistics without a way to return to the previous set. Think of the statistics as one part of the overall application code that also includes the source code--both application and RDBMS. It would be foolish to change to the new code without a way to get back to the previous version. In the final post, I will talk about the actual script I created for P6 PMDB and possible future direction for managing query performance. 

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• sp_send_dbmail attach files stored as varbinary in database

  - by Mindstorm Interactive

  I have a two part question relating to sending query results as attachments using sp_send_dbmail. Problem 1: Only basic .txt files will open. Any other format like .pdf or .jpg are corrupted. Problem 2: When attempting to send multiple attachments, I receive one file with all file names glued together. I'm running SQL Server 2005 and I have a table storing uploaded documents: CREATE TABLE [dbo].[EmailAttachment]( [EmailAttachmentID] [int] IDENTITY(1,1) NOT NULL, [MassEmailID] [int] NULL, -- foreign key [FileData] [varbinary](max) NOT NULL, [FileName] [varchar](100) NOT NULL, [MimeType] [varchar](100) NOT NULL I also have a MassEmail table with standard email stuff. Here is the SQL Send Mail script. For brevity, I've excluded declare statements. while ( (select count(MassEmailID) from MassEmail where status = 20 )>0) begin select @MassEmailID = Min(MassEmailID) from MassEmail where status = 20 select @Subject = [Subject] from MassEmail where MassEmailID = @MassEmailID select @Body = Body from MassEmail where MassEmailID = @MassEmailID set @query = 'set nocount on; select cast(FileData as varchar(max)) from Mydatabase.dbo.EmailAttachment where MassEmailID = '+ CAST(@MassEmailID as varchar(100)) select @filename = '' select @filename = COALESCE(@filename+ ',', '') +FileName from EmailAttachment where MassEmailID = @MassEmailID exec msdb.dbo.sp_send_dbmail @profile_name = 'MASS_EMAIL', @recipients = '[email protected]', @subject = @Subject, @body =@Body, @body_format ='HTML', @query = @query, @query_attachment_filename = @filename, @attach_query_result_as_file = 1, @query_result_separator = '; ', @query_no_truncate = 1, @query_result_header = 0; update MassEmailset status= 30,SendDate = GetDate() where MassEmailID = @MassEmailID end I am able to successfully read files from the database so I know the binary data is not corrupted. .txt files only read when I cast FilaData to varchar. But clearly original headers are lost. It's also worth noting that attachment file sizes are different than the original files. That is most likely due to improper encoding as well. So I'm hoping there's a way to create file headers using the stored mimetype, or some way to include file headers in the binary data? I'm also not confident in the values of the last few parameters, and I know coalesce is not quite right, because it prepends the first file name with a comma. But good documentation is nearly impossible to find. Please help!

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• How to get foreignSecurityPrincipal from group. using DirectorySearcher

  - by kain64b

  What I tested with 0 results: string queryForeignSecurityPrincipal = "(&(objectClass=foreignSecurityPrincipal)(memberof:1.2.840.113556.1.4.1941:={0})(uSNChanged>={1})(uSNChanged<={2}))"; sidsForeign = GetUsersSidsByQuery(groupName, string.Format(queryForeignSecurityPrincipal, groupPrincipal.DistinguishedName, 0, 0)); public IList<SecurityIdentifier> GetUsersSidsByQuery(string groupName, string query) { List<SecurityIdentifier> results = new List<SecurityIdentifier>(); try{ using (var context = new PrincipalContext(ContextType.Domain, DomainName, User, Password)) { using (var groupPrincipal = GroupPrincipal.FindByIdentity(context, IdentityType.SamAccountName, groupName)) { DirectoryEntry directoryEntry = (DirectoryEntry)groupPrincipal.GetUnderlyingObject(); do { directoryEntry = directoryEntry.Parent; } while (directoryEntry.SchemaClassName != "domainDNS"); DirectorySearcher searcher = new DirectorySearcher(directoryEntry){ SearchScope=System.DirectoryServices.SearchScope.Subtree, Filter=query, PageSize=10000, SizeLimit = 15000 }; searcher.PropertiesToLoad.Add("objectSid"); searcher.PropertiesToLoad.Add("distinguishedname"); using (SearchResultCollection result = searcher.FindAll()) { foreach (var obj in result) { if (obj != null) { var valueProp = ((SearchResult)obj).Properties["objectSid"]; foreach (var atributeValue in valueProp) { SecurityIdentifier value = (new SecurityIdentifier((byte[])atributeValue, 0)); results.Add(value); } } } } } } } catch (Exception e) { WriteSystemError(e); } return results; } I tested it on usual users with query: "(&(objectClass=user)(memberof:1.2.840.113556.1.4.1941:={0})(uSNChanged>={1})(uSNChanged<={2}))" and it is work, I test with objectClass=* ... nothing help... But If I call groupPrincipal.GetMembers,I get all foreing user account from group. BUT groupPrincipal.GetMembers HAS MEMORY LEAK. Any Idea how to fix my query????

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• DataSet does not support System.Nullable<>

  - by a_m0d

  I'm trying to set the DataSource for a Crystal Reports report, but I've run into a few problems. I've been following a guide written by Mohammad Mahdi Ramezanpour, and have managed to get all the way to the last part now (setting the DataSource). However, I have a problem that Mohammad does not seem to have - when I pass the results of my query to the report, I end up with the following exception: DataSet does not support System.Nullable< This is the query I am using: public IQueryable<Part> GetPartsToDisplayOnStockReport() { return from part in db.Parts where part.showOnStockReport == true select part; } and the way I pass it to the Report: public ActionResult ViewStockReport() { StockReport stockReport = new StockReport(); var parts = ordersRepository.GetPartsToDisplayOnStockReport().ToList(); stockReport.SetDataSource(parts); Stream stream = stockReport.ExportToStream(CrystalDecisions.Shared.ExportFormatType.PortableDocFormat); return File(stream, "application/pdf"); } I have also tried changing my query to this code, in the hope that it would fix my problem: return (from part in db.Parts where part.showOnStockReport == true select part) ?? db.Parts.DefaultIfEmpty(); but it still complained about the same problem. How can I pass the results of this query to my report, to use it as a data source? Also, if each of my Parts object contains other objects / collections of other objects, will I be able to reference them in the report with a datasource like this?

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• IQueryable<> from stored procedure (entity framework)

  - by mmcteam

  I want to get IQueryable<> result when executing stored procedure. Here is peace of code that works fine: IQueryable<SomeEntitiy> someEntities; var globbalyFilteredSomeEntities = from se in m_Entities.SomeEntitiy where se.GlobalFilter == 1234 select se; I can use this to apply global filter, and later use result in such way result = globbalyFilteredSomeEntities .OrderByDescending(se => se.CreationDate) .Skip(500) .Take(10); What I want to do - use some stored procedures in global filter. I tried: Add stored procedure to m_Entities, but it returns IEnumerable<> and executes sp immediately: var globbalyFilteredSomeEntities = from se in m_Entities.SomeEntitiyStoredProcedure(1234); Materialize query using EFExtensions library, but it is IEnumerable<>. If I use AsQueryable() and OrderBy(), Skip(), Take() and after that ToList() to execute that query - I get exception that DataReader is open and I need to close it first(can't paste error - it is in russian). var globbalyFilteredSomeEntities = m_Entities.CreateStoreCommand("exec SomeEntitiyStoredProcedure(1234)") .Materialize<SomeEntitiy>(); //.AsQueryable() //.OrderByDescending(se => se.CreationDate) //.Skip(500) //.Take(10) //.ToList(); Also just skipping .AsQueryable() is not helpful - same exception. When I put ToList() query executes, but it is too expensive to execute query without Skip(), Take().

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• c# WebRequest to connect to wikipedia API

  - by NickJ

  Hey, This may be a pathetically simple problem but I cannot seem to format the post webrequest/response to get data from the wikipedia api. I have posted my code below if anyone can help me see my problem. string pgTitle = txtPageTitle.Text; Uri address = new Uri("http://en.wikipedia.org/w/api.php"); HttpWebRequest request = WebRequest.Create(address) as HttpWebRequest; request.Method = "POST"; request.ContentType = "application/x-www-form-urlencoded"; string action = "query"; string query = pgTitle; StringBuilder data = new StringBuilder(); data.Append("action=" + HttpUtility.UrlEncode(action)); data.Append("&query=" + HttpUtility.UrlEncode(query)); byte[] byteData = UTF8Encoding.UTF8.GetBytes(data.ToString()); request.ContentLength = byteData.Length; using (Stream postStream = request.GetRequestStream()) { postStream.Write(byteData, 0, byteData.Length); } using (HttpWebResponse response = request.GetResponse() as HttpWebResponse) { // Get the response stream StreamReader reader = new StreamReader(response.GetResponseStream()); divWikiData.InnerText = reader.ReadToEnd(); }

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• LINQ to Entities exceptions (ElementAtOrDefault and CompareObjectEqual)

  - by OffApps Cory

  I am working on a shipping platform which will eventually automate shipping through several major carriers. I have a ShipmentsView Usercontrol which displayes a list of Shipments (returned by EntityFramework), and when a user clicks on a shipment item, it spawns a ShipmentEditView and passes the ShipmentID (RecordKey) to that view. I initially wrestled with trying to get the context from the parent (ShipmentsView) and finally gave up resolving to get to it later. I wanted to do this to keep a single instance of the context. anyhow, I now create a new instance of the context in my ShipmentEditViewModel, and query against it for the Shipment record. I know I could just pass the record, but I wanted to use the Ocean Framework that Karl Shifflett wrote and don't want to muck about writing new transition methods. So anyhow, I query and when stepping through, I can see that it returns a record, as soon as execution reached the point where it assigned the query result to the e.Result property, it throws up the following exception depending on the query I used. LINQToEntities Dim RecordID As Decimal = CDec(e.Argument) Dim myResult = From ship In _Context.Shipment _ Where ship.ShipID = e.Argument _ Select ship Select Case myResult.Count Case 0 e.Result = New Shipment Case 1 e.Result = myResult(0) Case Else e.Result = Nothing End Select "LINQ to Entities does not recognize the method 'System.Object.CompareObjectEqual(System.Object, System.Object, Boolean)' method, and this method cannot be translated into a store expression. LINQToEntities via Method calls Dim RecordID As Decimal = CDec(e.Argument) Dim myResult = _Context.Shipment.Where(Function(s) s.ShipID = RecordID) Select Case myResult.Count Case 0 e.Result = New Shipment Case 1 e.Result = myResult(0) Case Else e.Result = Nothing End Select LINQ to Entities does not recognize the method 'SnazzyShippingDAL.Shipment ElementAtOrDefault[Shipment] (System.Linq.IQueryable`1[SnazzyShippingDAL.Shipment], Int32)' method, and this method cannot be translated into a store expression. I have been trying to get this thing to display a record for like three days. i am seriously thinking about going back and re=-engineering it without the MVVM pattern (which I realize I am only starting to learn and understand) if only to make the &$^%ed thing work. Any help will be muchly appreciated. Cory

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• Ext.data.Store, Javascript Arrays and Ext.grid.ColumnModel

  - by Michael Wales

  I am using Ext.data.Store to call a PHP script which returns a JSON response with some metadata about fields that will be used in a query (unique name, table, field, and user-friendly title). I then loop through each of the Ext.data.Record objects, placing the data I need into an array (this_column), push that array onto the end of another array (columns), and eventually pass this to an Ext.grid.ColumnModel object. The problem I am having is - no matter which query I am testing against (I have a number of them, varying in size and complexity), the columns array always works as expected up to columns[15]. At columns[16], all indexes from that point and previous are filled with the value of columns[15]. This behavior continues until the loop reaches the end of the Ext.data.Store object, when the entire arrays consists of the same value. Here's some code: columns = []; this_column = []; var MetaData = Ext.data.Record.create([ {name: 'id'}, {name: 'table'}, {name: 'field'}, {name: 'title'} ]); // Query the server for metadata for the query we're about to run metaDataStore = new Ext.data.Store({ autoLoad: true, reader: new Ext.data.JsonReader({ totalProperty: 'results', root: 'fields', id: 'id' }, MetaData), proxy: new Ext.data.HttpProxy({ url: 'index.php/' + type + '/' + slug }), listeners: { 'load': function () { metaDataStore.each(function(r) { this_column['id'] = r.data['id']; this_column['header'] = r.data['title']; this_column['sortable'] = true; this_column['dataIndex'] = r.data['table'] + '.' + r.data['field']; // This display valid information, through the entire process console.info(this_column['id'] + ' : ' + this_column['header'] + ' : ' + this_column['sortable'] + ' : ' + this_column['dataIndex']); columns.push(this_column); }); // This goes nuts at columns[15] console.info(columns); gridColModel = new Ext.grid.ColumnModel({ columns: columns });

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• LINQ to Entities pulling back entire table

  - by Focus

  In my application I'm pulling back a user's "feed". This contains all of that user's activities, events, friend requests from other users, etc. When I pull back the feed I'm calling various functions to filter the request along the way. var userFeed = GetFeed(db); // Query to pull back all data userFeed = FilterByUser(userFeed, user, db); // Filter for the user userFeed = SortFeed(userFeed, page, sortBy, typeName); // Sort it The data that is returned is exactly what I need, however when I look at a SQL Profile Trace I can see that the query that is getting this data does not filter it at the database level and instead is selecting ALL data in the table(s). This query does not execute until I iterate through the results on my view. All of these functions return an IEnumerable object. I was under the impression that LINQ would take all of my filters and form one query to pull back the data I want instead of pulling back all the data and then filtering it on the server. What am I doing wrong or what don't I understand about the way LINQ evaluates queries?

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• Problem with Remember Me Service in Spring Security

  - by Gearóid

  Hi, I'm trying to implement a "remember me" functionality in my website using Spring. The cookie and entry in the persistent_logins table are getting created correctly. Additionally, I can see that the correct user is being restored as the username is displayed at the top of the page. However, once I try to access any information for this user when they return after they were "remembered", I get a NullPointerException. It looks as though the user isn't being set in the session again. My applicationContext-security.xml contains the following: <remember-me data-source-ref="dataSource" user-service-ref="userService"/> ... <authentication-provider user-service-ref="userService" /> <jdbc-user-service id="userService" data-source-ref="dataSource" role-prefix="ROLE_" users-by-username-query="select email as username, password, 1 as ENABLED from user where email=?" authorities-by-username-query="select user.id as id, upper(role.name) as authority from user, role, users_roles where users_roles.user_fk=id and users_roles.role_fk=role.name and user.email=?"/> I thought it may have had something to do with users-by-username query but surely login wouldn't work correctly if this query was incorrect? Any help on this would be greatly appreciated. Thanks, gearoid.

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• ASP.Net Entity Framework, objectcontext error

  - by Chris Klepeis

  I'm building a 4 layered ASP.Net web application. The layers are: Data Layer Entity Layer Business Layer UI Layer The entity layer has my data model classes and is built from my entity data model (edmx file) in the datalayer using T4 templates (POCO). The entity layer is referenced in all other layers. My data layer has a class called SourceKeyRepository which has a function like so: public IEnumerable<SourceKey> Get(SourceKey sk) { using (dmc = new DataModelContainer()) { var query = from SourceKey in dmc.SourceKeys select SourceKey; if (sk.sourceKey1 != null) { query = from SourceKey in query where SourceKey.sourceKey1 == sk.sourceKey1 select SourceKey; } return query; } } Lazy loading is disabled since I do not want my queries to run in other layers of this application. I'm receiving the following error when attempting to access the information in the UI layer: The ObjectContext instance has been disposed and can no longer be used for operations that require a connection. I'm sure this is because my DataModelContainer "dmc" was disposed. How can I return this IEnumerable object from my data layer so that it does not rely on the ObjectContext, but solely on the DataModel? Is there a way to limit lazy loading to only occur in the data layer?

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• Why is OracleDataAdapter.Fill() Very Slow?

  - by John Gietzen

  I am using a pretty complex query to retrieve some data out of one of our billing databases. I'm running in to an issue where the query seems to complete fairly quickly when executed with SQL Developer, but does not seem to ever finish when using the OracleDataAdapter.Fill() method. I'm only trying to read about 1000 rows, and the query completes in SQL Developer in about 20 seconds. What could be causing such drastic differences in performance? I have tons of other queries that run quickly using the same function. Here is the code I'm using to execute the query: using Oracle.DataAccess.Client; ... public DataTable ExecuteExternalQuery(string connectionString, string providerName, string queryText) { DbConnection connection = null; DbCommand selectCommand = null; DbDataAdapter adapter = null; switch (providerName) { case "System.Data.OracleClient": case "Oracle.DataAccess.Client": connection = new OracleConnection(connectionString); selectCommand = connection.CreateCommand(); adapter = new OracleDataAdapter((OracleCommand)selectCommand); break; ... } DataTable table = null; try { connection.Open(); selectCommand.CommandText = queryText; selectCommand.CommandTimeout = 300000; selectCommand.CommandType = CommandType.Text; table = new DataTable("result"); table.Locale = CultureInfo.CurrentCulture; adapter.Fill(table); } finally { adapter.Dispose(); if (connection.State != ConnectionState.Closed) { connection.Close(); } } return table; } And here is the general outline of the SQL I'm using: with trouble_calls as ( select work_order_number, account_number, date_entered from work_orders where date_entered >= sysdate - (15 + 31) -- Use the index to limit the number of rows scanned and wo_status not in ('Cancelled') and wo_type = 'Trouble Call' ) select account_number, work_order_number, date_entered from trouble_calls wo where wo.icoms_date >= sysdate - 15 and ( select count(*) from trouble_calls repeat where wo.account_number = repeat.account_number and wo.work_order_number <> repeat.work_order_number and wo.date_entered - repeat.date_entered between 0 and 30 ) >= 1

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