Why my job is safe

I spent the last couple of days with coding a new registration module for a private website that I administrate. The requirements were the very basic ones:

  • Make an HTML form
  • Validate the inputs
  • Send a confirmation email with a link
  • On link activation: Retrieve the data and write it into a table in the database

As those requirements are common in so many registration forms all over the web, I thought: Before coding something again, just have a look at Google and see if  you can find something useful to embed. And I found stuff, lots of stuff but surprisingly every example I found did the following:

  • HTML form
  • Write data into the table with an “active” column set to false
  • Send a confirmation email with an link with an unique identifier for the inserted row (most times a md5 hash value)
  • On link activation: Retrieve the unique identifier
  • Update the row in the database with active flag set to true

And, at least some examples, took also care about the house keeping and cleaned the inactive rows from time to time.

Well, this does the job of course but the way how it does it is simply dirty – very dirty!

From the functional aspect there are following issues:

  • Every SQL statement for retrieving the active data has to make sure to include ” AND active = TRUE” in the WHERE clause
  • If an user is unlucky, the cleanup process removes his data before he can click the activation link if the process doesn’t implement a time range

From the performance side however:

All I want to do is:

  • Validate the data from the form
  • Save it temporarily until the link is clicked or the data gets expired
  • Write it into the database

With those solutions found on the internet however I would have done following:

  • Validate the data
  • Write it into the database (with an column extension for an active flag)
  • Retrieve the data again and update it to set it active
  • Create a cleanup job to purge inactive data
  • Extend all my SQLs against that table with a “AND active = TRUE” WHERE clause

So I have more steps, more unnecessary hops to the database, probably maintain another index over time for the cleanup job. Sometimes I wonder why programmers don’t think first before the code. Of course many roads lead to Rome but that doesn’t mean that you should just take a random one. But as long as there are programmers out there who don’t think or don’t think hard enough there will be always performance problems and people like me needed to solve them. So I guess I shouldn’t complain but my dream of the perfect IT world is still alive…

Well, the webpage in my case is made in PHP which has one nice feature called: Sessions
That does the job perfectly fine and most important: Clean! It is enabled by default, allows you to store data within a session and includes also an expire functionality which is also enabled by default. It supports two ways of storing the session id: Either within a cookie in the browser (enabled by default) or you can also pass the session id over POST and GET requests. As I use a confirmation email to activate the registration I just had to make sure to deactivate the cookie storage feature first as I pass the session id over a link/GET request. I wouldn’t have to deactivate the cookie storage from a functional point of view as the cookie would be just never read again and would expire some time but from security and the “do it right” aspect this has to be deactivated. So all I did was following:

// Disable cookie storage
// Start the session

// Save the data within the session
$_SESSION["Title"] = $Title;
$_SESSION["FirstName"] = $FistName;
$_SESSION["LastName"] = $LastName;

//Send the email with the activation link
if (mail($Email, "Please activate your registration", "http://www.mywebsite.com/activate.php?sid=".htmlspecialchars (session_id()))
 // If sending the mail was successful, store all session data and close the session
 // If sending the mail failed, report an error, delete the session data and destroy the session!
 // Error reporting....

And all the activation.php file does, is:

// Disable cookie storage
// Set the session id from GET request
// Start session
// Get variables
$Title = $_SESSION["Title"];
$FistName = $_SESSION["FirstName"];
$LastName = $_SESSION["LastName"];

// Connect to database
$hDB = @mysql_connect("localhost", "user","password");
mysql_select_db("myDB", $hDB);

// Insert data into database
if (mysql_query("INSERT INTO table (Title, FirstName, LastName) VALUES ('".mysql_real_escape_string($Title)."','".mysql_real_escape_string($FirstName)."','".mysql_real_escape_string($LastName)."'")
 // Destroy session

The big picture

A couple of weeks ago a customer had some troubles with the overall performance. He complained that everything is slow and (of course) nothing changed since the last few weeks. They also immediately blamed the database on their side to be the issue. The usual stuff also, I’m sure that sounds just too familiar to you.

So as they “identified” the database already as being the issue my team requested some AWR reports and as we got them I noticed a strange but all to common behavior. My team mates got the AWR reports, went to the SQL Statistics, sections “SQL ordered by Elapsed Time” and “SQL ordered by CPU Time” and identified immediately a materialized view rebuild as cause for the problem. That looked like this:

SQL ordered by Elapsed Time

  • Resources reported for PL/SQL code includes the resources used by all SQL statements called by the code.
  • % Total DB Time is the Elapsed Time of the SQL statement divided into the Total Database Time multiplied by 100
Elapsed Time (s) CPU Time (s) Executions Elap per Exec (s) % Total DB Time SQL Id SQL Module SQL Text
1,343 679 1 1342.85 33.00 f20ccnxhvbk65 DECLARE job BINARY_INTEGER := …
770 323 1 770.24 18.93 1usnr4gmcq60d /* MV_REFRESH (DEL) */ delete …
571 355 1 571.23 14.04 gz04689vd55db /* MV_REFRESH (INS) */INSERT /…
311 311 1 310.91 7.64 0vhmfumrjchnh SQL*Plus BEGIN dm_incr_symbols_post_pro…
288 9 0 7.09 bb3f2gjndvjss oracle@crptd1 (TNS V1-V3) SELECT /*+ OPAQUE_TRANSFORM */…
258 258 1 257.76 6.34 75vtwb7j4jzdm SQL*Plus INSERT INTO SYMB_EXTRACTT SELE…
149 121 1 148.80 3.66 90wtn50vy6af6 DECLARE job BINARY_INTEGER := …
114 103 1 113.75 2.80 9993mp6h7kqkp INSERT /*+ BYPASS_RECURSIVE_CH…
48 38 2 24.00 1.18 3nkcg1h5ysqss DECLARE job BINARY_INTEGER := …
48 38 2 23.98 1.18 fvb5prrr7b0c3 MERGE INTO FT_E_UPS1 UPS1 USIN…

Back to SQL Statistics
Back to Top

SQL ordered by CPU Time

  • Resources reported for PL/SQL code includes the resources used by all SQL statements called by the code.
  • % Total DB Time is the Elapsed Time of the SQL statement divided into the Total Database Time multiplied by 100
CPU Time (s) Elapsed Time (s) Executions CPU per Exec (s) % Total DB Time SQL Id SQL Module SQL Text
679 1,343 1 679.06 33.00 f20ccnxhvbk65 DECLARE job BINARY_INTEGER := …
355 571 1 355.50 14.04 gz04689vd55db /* MV_REFRESH (INS) */INSERT /…
323 770 1 323.18 18.93 1usnr4gmcq60d /* MV_REFRESH (DEL) */ delete …
311 311 1 310.89 7.64 0vhmfumrjchnh SQL*Plus BEGIN dm_incr_symbols_post_pro…
258 258 1 257.74 6.34 75vtwb7j4jzdm SQL*Plus INSERT INTO SYMB_EXTRACTT SELE…
121 149 1 121.05 3.66 90wtn50vy6af6 DECLARE job BINARY_INTEGER := …
103 114 1 102.58 2.80 9993mp6h7kqkp INSERT /*+ BYPASS_RECURSIVE_CH…
38 48 2 19.20 1.18 3nkcg1h5ysqss DECLARE job BINARY_INTEGER := …
38 48 2 19.20 1.18 fvb5prrr7b0c3 MERGE INTO FT_E_UPS1 UPS1 USIN…
29 29 12 2.38 0.70 68930z34bm3db SQL*Plus select ‘file[‘ || substr(trim(…
9 288 0 7.09 bb3f2gjndvjss oracle@dftg1 (TNS V1-V3) SELECT /*+ OPAQUE_TRANSFORM */…

So here we have a PL/SQL job which does the materialized view refresh (first line) and the statements for the refresh itself as second and third line. For completeness here the first statement:

 job BINARY_INTEGER := :job;
 next_date DATE := :mydate;
 broken BOOLEAN := FALSE;
 :mydate := next_date;
 IF broken THEN
 :b := 1;
 :b := 0;

So all what they did was to go there, look the first SQL with high elapsed and cpu time and nearly reported back to them that this is the problem and they have to solve this. I call this the “lucky shot method”. Sometimes when you are lucky than the reason for the issue on the DB is a bad SQL or a bunch of bad SQLs which max out the DB on CPU power, or I/O or whatever. In such a case you just go to those sections, identify the SQLs, fix them and everything is good again – you were lucky. This works sometimes and you are a hero because it took you just 5 minutes for fixing the issue but sometimes it doesn’t and you blame it on some weird constellation of OS, network and something else so that nobody recognizes that you are just a fool and didn’t look at the big picture. Thinking of that I noticed that this is just far too common in IT. Tech admins, DBA, developers – in every section you have people like this. Looking 5 minutes into the issue and telling you then that this and that is the issue. You go ahead and fix them but still no change. So next round trip, and next, and next, and next. All could have been prevented if the person had just once a look into the big picture…

So what is the big picture, what do I mean by that?

I’m a fan of knowing what happens and why. Always analyze all the information you got, even request some more if you think that some vital information is missing and make your conclusions out of that. Stop the try and error method, the lucky shot method. The AWR report I got from the customer is an all too good example. There were 2 simple lines which made me curious:

Snap Id Snap Time Sessions Cursors/Session
Begin Snap: 17934 05-May-10 09:00:59 129 144.1
End Snap: 17935 05-May-10 10:00:12 130 143.0
Elapsed: 59.21 (mins)
DB Time: 67.81 (mins)

I’m talking about the last two. The first tells me that the snapshot which got compared are in a time range of 59.21 minutes. The DB time, so the time when the DB was actually working was 67.81 minutes. So something looks strange here. If I would be in a single core environment I wouldn’t be over my 59.21. If I would be in a dual-core environment then the database was just working 57% of the time (59.21 x 2 cores = 118.42 – DB time multiplies by the amount of cores where work was performed parallel). Next I had a look into the locks going on. If you have high locking then the DB time is also idle but the throughput is low. But it turned that this was also no issue. The next information confirmed then that the DB wasn’t the problem at all:

Operating System Statistics

Statistic Total

The average idle time statistic is much higher than the busy time. So I requested the amount of cores on that system and it turned out that there are 14 available. A busy database would have a DB time of max. 828.94 minutes. No locking going on. I followed up with them and it turned out as I thought: The database wasn’t the problem at all. In fact it was pretty much idle over the whole time. The issue was on the application side. A java application which memory got filled up so that it had to do full garbage collection all the time to continue processing.

Instead of doing the lucky shot we prevented us and them from a lot of headache by looking into the big picture.

C is back at number 1 position

TIOBE just released the “Tiobe programming community index” for April 2010. And guess what – C is back on top with 18.058% rating closely followed by Java (of course) with 18.051%. PL/SQL fell down to position 14 (before 12) and Objective-C made a huge jump up to position 11 (before 42) – I guess there are more people outside who want to make money with the iPhone Apps! 🙂 For everyone who doesn’t know: Objective-C is the programming language to write application on the Apple architecture. For more info about Objective-C see on Wikipedia.