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• Frequent Kernel Panic on CentOS 6.5

  - by Manuel Sopena Ballesteros

  I have a webserver with the configuration below: VMWare ESXi environemt CPanel installed CentOS release 6.5 (Final) 4 CPUs 2G RAM 2x VM disks 100G each LVM system My issue is I am getting kernel panic quite frequently. These is a list of some processes blocked I could see from the console: mysqld queueprocd httpd suphp vmtoolsd loop0 auditd this is my sar logs Linux 2.6.32-431.3.1.el6.x86_64 (test01) 08/22/2014 _x86_64_ (4 CPU) 12:00:01 AM CPU %user %nice %system %iowait %steal %idle 12:10:01 AM all 26.86 0.01 0.98 0.57 0.00 71.57 12:20:01 AM all 1.78 0.02 1.03 0.08 0.00 97.09 12:30:01 AM all 26.34 0.02 0.85 0.05 0.00 72.74 12:40:01 AM all 27.12 0.01 1.11 1.22 0.00 70.54 12:50:01 AM all 1.59 0.02 0.94 0.13 0.00 97.32 01:00:01 AM all 26.10 0.01 0.77 0.04 0.00 73.07 01:10:01 AM all 27.51 0.01 1.16 0.14 0.00 71.18 01:20:01 AM all 1.80 0.07 1.06 0.08 0.00 96.99 01:30:01 AM all 26.19 0.01 0.78 0.05 0.00 72.96 01:40:01 AM all 26.62 0.02 0.87 0.05 0.00 72.45 01:50:02 AM all 1.35 0.01 0.87 0.02 0.00 97.75 02:00:01 AM all 26.11 0.02 0.69 0.02 0.00 73.17 02:10:01 AM all 26.73 0.02 0.89 0.14 0.00 72.21 02:20:01 AM all 1.45 0.01 0.92 0.04 0.00 97.58 02:30:01 AM all 26.59 0.01 1.06 0.03 0.00 72.31 02:40:01 AM all 26.27 0.01 0.72 0.05 0.00 72.95 02:50:01 AM all 0.86 0.01 0.50 0.09 0.00 98.53 03:00:01 AM all 25.61 0.02 0.39 0.03 0.00 73.96 03:10:01 AM all 26.30 0.08 0.66 0.14 0.00 72.82 03:20:01 AM all 0.81 0.01 0.51 0.04 0.00 98.63 03:30:02 AM all 26.15 0.02 0.53 0.07 0.00 73.24 03:40:01 AM all 26.06 0.01 0.47 0.04 0.00 73.42 03:50:01 AM all 0.96 0.02 0.51 0.03 0.00 98.48 Average: all 17.69 0.02 0.79 0.14 0.00 81.36 06:58:14 AM LINUX RESTART 07:00:01 AM CPU %user %nice %system %iowait %steal %idle 07:10:01 AM all 1.04 0.02 0.57 0.95 0.00 97.42 07:20:02 AM all 0.66 0.01 0.39 0.06 0.00 98.87 07:30:01 AM all 25.71 0.01 0.45 0.16 0.00 73.67 07:40:01 AM all 25.88 0.01 0.35 0.08 0.00 73.68 As you can see the server became unresponsive at 03.50 AM and I had to reset the VM at 06.58 AM to fix it. sar -d 03:00:01 PM dev8-16 0.16 0.01 3.37 20.78 0.00 12.40 9.29 0.15 03:00:01 PM dev8-0 4.08 5.72 77.50 20.38 0.06 15.15 3.13 1.28 03:00:01 PM dev253-0 10.37 5.74 80.87 8.35 0.13 12.52 1.24 1.29 03:00:01 PM dev253-1 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 03:10:01 PM dev8-16 0.27 0.17 3.17 12.22 0.00 11.49 7.95 0.22 03:10:01 PM dev8-0 6.37 18.98 136.19 24.34 0.05 7.25 2.18 1.39 03:10:01 PM dev253-0 17.91 19.15 137.94 8.77 0.13 7.11 0.78 1.41 03:10:01 PM dev253-1 0.18 0.00 1.41 8.00 0.00 9.09 0.52 0.01 03:10:01 PM DEV tps rd_sec/s wr_sec/s avgrq-sz avgqu-sz await svctm %util 03:20:01 PM dev8-16 0.17 0.23 2.04 13.39 0.00 6.07 5.29 0.09 03:20:01 PM dev8-0 3.83 18.57 78.45 25.35 0.05 13.25 2.73 1.05 03:20:01 PM dev253-0 10.30 18.80 80.49 9.64 0.14 13.89 1.03 1.06 03:20:01 PM dev253-1 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 03:30:01 PM dev8-16 0.26 0.16 4.59 18.56 0.00 6.44 5.54 0.14 03:30:01 PM dev8-0 5.97 24.07 117.83 23.77 0.05 8.53 2.13 1.27 03:30:01 PM dev253-0 15.90 24.23 122.42 9.22 0.12 7.71 0.81 1.29 03:30:01 PM dev253-1 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 03:40:01 PM dev8-16 0.20 0.00 2.32 11.44 0.00 8.35 5.90 0.12 03:40:01 PM dev8-0 4.39 19.58 77.94 22.24 0.06 12.87 2.12 0.93 03:40:01 PM dev253-0 10.25 19.58 80.25 9.74 0.12 11.63 0.91 0.94 03:40:01 PM dev253-1 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 03:50:01 PM dev8-16 0.23 0.50 2.32 12.44 0.00 6.27 5.13 0.12 03:50:01 PM dev8-0 5.09 9.00 95.04 20.45 0.04 7.36 2.10 1.07 03:50:01 PM dev253-0 12.47 9.50 96.82 8.53 0.08 6.76 0.87 1.08 03:50:01 PM dev253-1 0.07 0.00 0.54 8.00 0.00 14.10 0.40 0.00 04:00:01 PM dev8-16 0.21 0.00 2.04 9.89 0.00 7.00 5.87 0.12 04:00:01 PM dev8-0 4.68 1.64 94.70 20.57 0.05 10.71 2.41 1.13 04:00:01 PM dev253-0 12.27 1.64 96.74 8.02 0.12 9.95 0.93 1.14 sar -q 01:00:01 AM 6 205 2.02 1.32 0.81 01:10:01 AM 3 187 0.08 0.72 0.86 01:20:01 AM 2 187 0.04 0.18 0.49 01:30:01 AM 4 205 2.04 1.34 0.82 01:40:01 AM 2 185 0.02 0.68 0.83 01:50:02 AM 1 185 0.08 0.15 0.45 02:00:01 AM 5 202 2.02 1.30 0.78 02:10:01 AM 4 185 0.11 0.72 0.84 02:20:01 AM 1 183 0.17 0.15 0.45 02:30:01 AM 5 206 2.03 1.32 0.79 02:40:01 AM 2 184 0.08 0.70 0.83 02:50:01 AM 1 183 0.00 0.10 0.43 03:00:01 AM 7 205 2.03 1.32 0.78 03:10:01 AM 2 194 0.34 0.73 0.83 03:20:01 AM 1 184 0.00 0.13 0.44 03:30:02 AM 4 201 2.04 1.32 0.78 03:40:01 AM 2 193 0.06 0.67 0.81 03:50:01 AM 1 183 0.06 0.12 0.43 Average: 3 192 0.68 0.70 0.69 06:58:14 AM LINUX RESTART 07:00:01 AM runq-sz plist-sz ldavg-1 ldavg-5 ldavg-15 07:10:01 AM 2 181 0.00 0.09 0.11 07:20:02 AM 1 179 0.00 0.00 0.04 07:30:01 AM 4 197 2.12 1.33 0.58 sar -r Linux 2.6.32-431.3.1.el6.x86_64 (test01) 08/22/2014 _x86_64_ (4 CPU) 12:00:01 AM kbmemfree kbmemused %memused kbbuffers kbcached kbcommit %commit 12:10:01 AM 227484 1694468 88.16 117444 917004 635308 10.50 12:20:01 AM 219692 1702260 88.57 119556 920540 630940 10.43 12:30:01 AM 196248 1725704 89.79 121376 923592 695048 11.49 12:40:01 AM 127524 1794428 93.36 125004 1016196 633048 10.46 12:50:01 AM 127156 1794796 93.38 128212 1014536 624992 10.33 01:00:01 AM 110764 1811188 94.24 129964 1001608 700016 11.57 01:10:01 AM 160560 1761392 91.65 132260 973472 628640 10.39 01:20:01 AM 133076 1788876 93.08 134144 982608 655524 10.83 01:30:01 AM 121512 1800440 93.68 135548 985676 700500 11.58 01:40:01 AM 140640 1781312 92.68 137220 988576 628280 10.38 01:50:02 AM 139160 1782792 92.76 138688 990672 625224 10.33 02:00:01 AM 106112 1815840 94.48 139940 993976 700360 11.57 02:10:01 AM 155400 1766552 91.91 142112 971864 625656 10.34 02:20:01 AM 154056 1767896 91.98 143732 975556 621352 10.27 02:30:01 AM 110856 1811096 94.23 145032 978288 709360 11.72 02:40:01 AM 140200 1781752 92.71 146568 980656 624872 10.33 02:50:01 AM 137600 1784352 92.84 148940 984484 621948 10.28 03:00:01 AM 105032 1816920 94.54 150208 985736 706060 11.67 03:10:01 AM 168996 1752956 91.21 154708 941500 656312 10.85 03:20:01 AM 169408 1752544 91.19 156096 944100 621780 10.28 03:30:02 AM 132360 1789592 93.11 157724 951612 701296 11.59 03:40:01 AM 159012 1762940 91.73 158940 942560 656292 10.85 03:50:01 AM 163192 1758760 91.51 160312 944576 624544 10.32 Average: 148089 1773863 92.29 140162 969973 653363 10.80 06:58:14 AM LINUX RESTART 07:00:01 AM kbmemfree kbmemused %memused kbbuffers kbcached kbcommit %commit 07:10:01 AM 1016628 905324 47.10 85568 447556 600932 9.93 07:20:02 AM 1009996 911956 47.45 87616 451200 596156 9.85 07:30:01 AM 961128 960824 49.99 89164 464332 658912 10.89 07:40:01 AM 973376 948576 49.35 90880 473084 600176 9.92 dmesg does not show any relevant information. I don't see any bottleneck in sar, any idea what can I check next? thank you very much

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• quick look at: dm_db_index_physical_stats

  - by fatherjack

  A quick look at the key data from this dmv that can help a DBA keep databases performing well and systems online as the users need them. When the dynamic management views relating to index statistics became available in SQL Server 2005 there was much hype about how they can help a DBA keep their servers running in better health than ever before. This particular view gives an insight into the physical health of the indexes present in a database. Whether they are use or unused, complete or missing some columns is irrelevant, this is simply the physical stats of all indexes; disabled indexes are ignored however. In it’s simplest form this dmv can be executed as:   The results from executing this contain a record for every index in every database but some of the columns will be NULL. The first parameter is there so that you can specify which database you want to gather index details on, rather than scan every database. Simply specifying DB_ID() in place of the first NULL achieves this. In order to avoid the NULLS, or more accurately, in order to choose when to have the NULLS you need to specify a value for the last parameter. It takes one of 4 values – DEFAULT, ‘SAMPLED’, ‘LIMITED’ or ‘DETAILED’. If you execute the dmv with each of these values you can see some interesting details in the times taken to complete each step. DECLARE @Start DATETIME DECLARE @First DATETIME DECLARE @Second DATETIME DECLARE @Third DATETIME DECLARE @Finish DATETIME SET @Start = GETDATE() SELECT * FROM [sys].[dm_db_index_physical_stats](DB_ID(), NULL, NULL, NULL, DEFAULT) AS ddips SET @First = GETDATE() SELECT * FROM [sys].[dm_db_index_physical_stats](DB_ID(), NULL, NULL, NULL, 'SAMPLED') AS ddips SET @Second = GETDATE() SELECT * FROM [sys].[dm_db_index_physical_stats](DB_ID(), NULL, NULL, NULL, 'LIMITED') AS ddips SET @Third = GETDATE() SELECT * FROM [sys].[dm_db_index_physical_stats](DB_ID(), NULL, NULL, NULL, 'DETAILED') AS ddips SET @Finish = GETDATE() SELECT DATEDIFF(ms, @Start, @First) AS [DEFAULT] , DATEDIFF(ms, @First, @Second) AS [SAMPLED] , DATEDIFF(ms, @Second, @Third) AS [LIMITED] , DATEDIFF(ms, @Third, @Finish) AS [DETAILED] Running this code will give you 4 result sets; DEFAULT will have 12 columns full of data and then NULLS in the remainder. SAMPLED will have 21 columns full of data. LIMITED will have 12 columns of data and the NULLS in the remainder. DETAILED will have 21 columns full of data. So, from this we can deduce that the DEFAULT value (the same one that is also applied when you query the view using a NULL parameter) is the same as using LIMITED. Viewing the final result set has some details that are worth noting: Running queries against this view takes significantly longer when using the SAMPLED and DETAILED values in the last parameter. The duration of the query is directly related to the size of the database you are working in so be careful running this on big databases unless you have tried it on a test server first. Let’s look at the data we get back with the DEFAULT value first of all and then progress to the extra information later. We know that the first parameter that we supply has to be a database id and for the purposes of this blog we will be providing that value with the DB_ID function. We could just as easily put a fixed value in there or a function such as DB_ID (‘AnyDatabaseName’). The first columns we get back are database_id and object_id. These are pretty explanatory and we can wrap those in some code to make things a little easier to read: SELECT DB_NAME([ddips].[database_id]) AS [DatabaseName] , OBJECT_NAME([ddips].[object_id]) AS [TableName] … FROM [sys].[dm_db_index_physical_stats](DB_ID(), NULL, NULL, NULL, NULL) AS ddips  gives us   SELECT DB_NAME([ddips].[database_id]) AS [DatabaseName] , OBJECT_NAME([ddips].[object_id]) AS [TableName], [i].[name] AS [IndexName] , ….. FROM [sys].[dm_db_index_physical_stats](DB_ID(), NULL, NULL, NULL, NULL) AS ddips INNER JOIN [sys].[indexes] AS i ON [ddips].[index_id] = [i].[index_id] AND [ddips].[object_id] = [i].[object_id]     These handily tie in with the next parameters in the query on the dmv. If you specify an object_id and an index_id in these then you get results limited to either the table or the specific index. Once again we can place a  function in here to make it easier to work with a specific table. eg. SELECT * FROM [sys].[dm_db_index_physical_stats] (DB_ID(), OBJECT_ID(‘AdventureWorks2008.Person.Address’) , 1, NULL, NULL) AS ddips   Note: Despite me showing that functions can be placed directly in the parameters for this dmv, best practice recommends that functions are not used directly in the function as it is possible that they will fail to return a valid object ID. To be certain of not passing invalid values to this function, and therefore setting an automated process off on the wrong path, declare variables for the OBJECT_IDs and once they have been validated, use them in the function: DECLARE @db_id SMALLINT; DECLARE @object_id INT; SET @db_id = DB_ID(N’AdventureWorks_2008′); SET @object_id = OBJECT_ID(N’AdventureWorks_2008.Person.Address’); IF @db_id IS NULL BEGINPRINT N’Invalid database’; ENDELSE IF @object_id IS NULL BEGINPRINT N’Invalid object’; ENDELSE BEGINSELECT * FROM sys.dm_db_index_physical_stats (@db_id, @object_id, NULL, NULL , ‘LIMITED’); END; GO In cases where the results of querying this dmv don’t have any effect on other processes (i.e. simply viewing the results in the SSMS results area)  then it will be noticed when the results are not consistent with the expected results and in the case of this blog this is the method I have used. So, now we can relate the values in these columns to something that we recognise in the database lets see what those other values in the dmv are all about. The next columns are: We’ll skip partition_number, index_type_desc, alloc_unit_type_desc, index_depth and index_level  as this is a quick look at the dmv and they are pretty self explanatory. The final columns revealed by querying this view in the DEFAULT mode are avg_fragmentation_in_percent. This is the amount that the index is logically fragmented. It will show NULL when the dmv is queried in SAMPLED mode. fragment_count. The number of pieces that the index is broken into. It will show NULL when the dmv is queried in SAMPLED mode. avg_fragment_size_in_pages. The average size, in pages, of a single fragment in the leaf level of the IN_ROW_DATA allocation unit. It will show NULL when the dmv is queried in SAMPLED mode. page_count. Total number of index or data pages in use. OK, so what does this give us? Well, there is an obvious correlation between fragment_count, page_count and avg_fragment_size-in_pages. We see that an index that takes up 27 pages and is in 3 fragments has an average fragment size of 9 pages (27/3=9). This means that for this index there are 3 separate places on the hard disk that SQL Server needs to locate and access to gather the data when it is requested by a DML query. If this index was bigger than 72KB then having it’s data in 3 pieces might not be too big an issue as each piece would have a significant piece of data to read and the speed of access would not be too poor. If the number of fragments increases then obviously the amount of data in each piece decreases and that means the amount of work for the disks to do in order to retrieve the data to satisfy the query increases and this would start to decrease performance. This information can be useful to keep in mind when considering the value in the avg_fragmentation_in_percent column. This is arrived at by an internal algorithm that gives a value to the logical fragmentation of the index taking into account the multiple files, type of allocation unit and the previously mentioned characteristics if index size (page_count) and fragment_count. Seeing an index with a high avg_fragmentation_in_percent value will be a call to action for a DBA that is investigating performance issues. It is possible that tables will have indexes that suffer from rapid increases in fragmentation as part of normal daily business and that regular defragmentation work will be needed to keep it in good order. In other cases indexes will rarely become fragmented and therefore not need rebuilding from one end of the year to another. Keeping this in mind DBAs need to use an ‘intelligent’ process that assesses key characteristics of an index and decides on the best, if any, defragmentation method to apply should be used. There is a simple example of this in the sample code found in the Books OnLine content for this dmv, in example D. There are also a couple of very popular solutions created by SQL Server MVPs Michelle Ufford and Ola Hallengren which I would wholly recommend that you review for much further detail on how to care for your SQL Server indexes. Right, let’s get back on track then. Querying the dmv with the fifth parameter value as ‘DETAILED’ takes longer because it goes through the index and refreshes all data from every level of the index. As this blog is only a quick look a we are going to skate right past ghost_record_count and version_ghost_record_count and discuss avg_page_space_used_in_percent, record_count, min_record_size_in_bytes, max_record_size_in_bytes and avg_record_size_in_bytes. We can see from the details below that there is a correlation between the columns marked. Column 1 (Page_Count) is the number of 8KB pages used by the index, column 2 is how full each page is (how much of the 8KB has actual data written on it), column 3 is how many records are recorded in the index and column 4 is the average size of each record. This approximates to: ((Col1*8) * 1024*(Col2/100))/Col3 = Col4*. avg_page_space_used_in_percent is an important column to review as this indicates how much of the disk that has been given over to the storage of the index actually has data on it. This value is affected by the value given for the FILL_FACTOR parameter when creating an index. avg_record_size_in_bytes is important as you can use it to get an idea of how many records are in each page and therefore in each fragment, thus reinforcing how important it is to keep fragmentation under control. min_record_size_in_bytes and max_record_size_in_bytes are exactly as their names set them out to be. A detail of the smallest and largest records in the index. Purely offered as a guide to the DBA to better understand the storage practices taking place. So, keeping an eye on avg_fragmentation_in_percent will ensure that your indexes are helping data access processes take place as efficiently as possible. Where fragmentation recurs frequently then potentially the DBA should consider; the fill_factor of the index in order to leave space at the leaf level so that new records can be inserted without causing fragmentation so rapidly. the columns used in the index should be analysed to avoid new records needing to be inserted in the middle of the index but rather always be added to the end. * – it’s approximate as there are many factors associated with things like the type of data and other database settings that affect this slightly.  Another great resource for working with SQL Server DMVs is Performance Tuning with SQL Server Dynamic Management Views by Louis Davidson and Tim Ford – a free ebook or paperback from Simple Talk. Disclaimer – Jonathan is a Friend of Red Gate and as such, whenever they are discussed, will have a generally positive disposition towards Red Gate tools. Other tools are often available and you should always try others before you come back and buy the Red Gate ones. All code in this blog is provided “as is” and no guarantee, warranty or accuracy is applicable or inferred, run the code on a test server and be sure to understand it before you run it on a server that means a lot to you or your manager.

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• What is a simple deformer in which vertices deform linearly with control points?

  - by sebf

  In my project I want to deform a complex mesh, using a simpler 'proxy' mesh. In effect, each vertex of the proxy/collision mesh will be a control point/bone, which should deform the vertices of the main mesh attached to it depending on weight, but where the weight is not dependant on the absolute distance from the control point but rather distance relative to the other affecting control points. The point of this is to preserve complex three dimensional features of the main mesh while using physics implementations which expect something far simpler, low resolution, single surface, etc. Therefore, the vertices must deform linearly with their respective weighted control points (i.e. no falloff fields or all the mesh features will end up collapsed) - as if each vertex was linked to a point on the plane created by the attached control points and deformed with it. I have tried implementing the weight computation algorithm in this paper (page 4) but it is not working as expected and I am wondering if it is really the best way to do what I want. What is the simplest way to 'skin'* an arbitrary mesh, to another arbitrary mesh? *By skin I mean I need an algorithm to determine the best control points for a vertex, and their weights.

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• How can I find the shortest path between two subgraphs of a larger graph?

  - by Pops

  I'm working with a weighted, undirected multigraph (loops not permitted; most node connections have multiplicity 1; a few node connections have multiplicity 2). I need to find the shortest path between two subgraphs of this graph that do not overlap with each other. There are no other restrictions on which nodes should be used as start/end points. Edges can be selectively removed from the graph at certain times (as explained in my previous question) so it's possible that for two given subgraphs, there might not be any way to connect them. I'm pretty sure I've heard of an algorithm for this before, but I can't remember what it's called, and my Google searches for strings like "shortest path between subgraphs" haven't helped. Can someone suggest a more efficient way to do this than comparing shortest paths between all nodes in one subgraph with all nodes in the other subgraph? Or at least tell me the name of the algorithm so I can look it up myself? For example, if I have the graph below, the nodes circled in red might be one subgraph and the nodes circled in blue might be another. The edges would all have positive integer weights, although they're not shown in the image. I'd want to find whatever path has the shortest total cost as long as it starts at a red node and ends at a blue node. I believe this means the specific node positions and edge weights cannot be ignored. (This is just an example graph I grabbed off Wikimedia and drew on, not my actual problem.)

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• So we've got a code review tool, now what can we use for software documents?

  - by Tini

  We're using Subversion as a full CM for code and also for related project documents. We have JIRA and Fisheye. When we wanted to add a peer review tool, we looked at and tested several candidates. Our weighted requirements included both code and document review, but ultimately, the integration with JIRA slanted the scores in Crucible's favor. Atlassian has slammed the door on ever supporting Word or PDF in Crucible. I've tested several workaround methods to make Crucible work for documents without success. (The Confluence/Crucible plug-in was deprecated by Atlassian, so that option is out, too.) I haven't found a plugin for Crucible that adds this functionality, so short of writing my own plug-in, Crucible for documents is unworkable. Word Track Changes doesn't provide a method for true collaboration and commenting. Adobe PDF Comment and Markup is interesting, but doesn't provide a great way to keep a permanent quality record of the conversation. We can't go cloud-based, our documents must be locally hosted on our own server only. We're only on Sharepoint 2007. Help! Anyone have a suggestion?

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• How should I generate and store the boundries of a cave?

  - by Bob Roberts

  I am making a small cave copter game (seriously, where did this type of game come from anyway) and I am trying to figure out how to make and store the procedural generated walls. I am thinking about creating the walls by randomly picking two points away from the center of the screen. They will be no closer than the height of helicopter and no further than the edge of the screen, weighted to prefer to go in the same direction as the point prior so I end up with stalactites and stalagmites and not just noise, at set intervals of distance. To store, perhaps parallel arrays/lists, one for distance from center to top screen and one for distance from center to bottom. Am I way off base with my thinking? I just want the cave to be varied and challenging, I just have never worked with generating data like this. Edit: Woah, I just realized that my idea would lead to a player being able to stay in the middle of the screen and win. That isn't right at all. So the very basis of how I was going to generate is wrong. Edit 2: I also realized I left out a very crucial point. Part of the mechanics of the game will let the player go backwards therefor the data structure should be continuous.

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• Regulating how much to draw based on how much was drawn last frame.

  - by Mike Howard

  I have a 3D game world on an iPhone (limited graphics speed), and I'm already regulating whether I draw each shape on the screen based on it's size and distance from the camera. Something like... if (how_big_it_looks_from_the_camera > constant) then draw What I want to do now is also take into account how many shapes are being drawn, so that in busier areas of the game world I can draw less than I otherwise would. I tried to do this by dividing how_big_it_looks by the number of shapes that were drawn last frame (well, the square root of this but I'm simplifying - the problem is the same). if (how_big_it_looks / shapes_drawn > constant2) then draw But the check happens at the level of objects which represent many drawn shapes, and if an object containing many shapes is switched on, it increases shapes_drawn lots and switches itself back off the next frame. It flickers on and off. I tried keeping a kind of weighted average of previous values, by each frame doing something like shapes_drawn_recently = 0.9 * shapes_drawn_recently + 0.1 * shapes_just_drawn, but of course it only slows the flickering down because of the nature of the feedback loop. Is there a good way of solving this? My project is in Objective-C, but a general algorithm or pseudo-code is good too. Thanks.

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• algorithm for Virtual Machine(VM) Consolidation in Cloud

  - by devansh dalal

  PROBLEM: We have N physical machines(PMs) each with ram Ri, cpu Ci and a set of currently scheduled VMs each with ram requirement ri and ci respectively Moving(Migrating) any VM from one PM to other has a cost associated which depends on its ram ri. A PM with no VMs is shut down to save power. Our target is to minimize the weighted sum of (N,migration cost) by migrating some VMs i.e. minimize the number of working PMs as well as not to degrade the service level due to excessive migrations. My Approach: Brute Force approach is choosing the minimum loaded PM and try to fit its VMs to other PMs by First Fit Decreasing algorithm or we can select the victim PMs and target PMs based on their loading level and shut down victims if possible by moving their VMs to targets. I tried this Greedy approach on the Data of Baadal(IIT-D cloud) but It isn't giving promising results. I have also tried to study the Ant colony optimization for dynamic VM consolidating but was unable to understand very much. I used the links. http://dumas.ccsd.cnrs.fr/docs/00/72/52/15/PDF/Esnault.pdf http://hal.archives-ouvertes.fr/docs/00/72/38/56/PDF/RR-8032.pdf Would anyone please explain the solution or suggest any new approach for better performance soon. Thanks in advance.

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• Efficient algorithm for Virtual Machine(VM) Consolidation in Cloud

  - by devansh dalal

  PROBLEM: We have N physical machines(PMs) each with ram Ri, cpu Ci and a set of currently scheduled VMs each with ram requirement ri and ci respectively Moving(Migrating) any VM from one PM to other has a cost associated which depends on its ram ri. A PM with no VMs is shut down to save power. Our target is to minimize the weighted sum of (N,migration cost) by migrating some VMs i.e. minimize the number of working PMs as well as not to degrade the service level due to excessive migrations. My Approach: Brute Force approach is choosing the minimum loaded PM and try to fit its VMs to other PMs by First Fit Decreasing algorithm or we can select the victim PMs and target PMs based on their loading level and shut down victims if possible by moving their VMs to targets. I tried this Greedy approach on the Data of Baadal(IIT-D cloud) but It isn't giving promising results. I have also tried to study the Ant colony optimization for dynamic VM consolidating but was unable to understand very much. I used the links. http://dumas.ccsd.cnrs.fr/docs/00/72/52/15/PDF/Esnault.pdf http://hal.archives-ouvertes.fr/docs/00/72/38/56/PDF/RR-8032.pdf Would anyone please clarify the solution or suggest any new approach/resources for better performance. I am basically searching for the algorithms not the physical optimizations and I also know that many commercial organizations have provided these solution but I just wanted to know more the underlying algorithms. Thanks in advance.

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• Matlab: Optimization by perturbing variable

  - by S_H

  My main script contains following code: %# Grid and model parameters nModel=50; nModel_want=1; nI_grid1=5; Nth=1; nRow.Scale1=5; nCol.Scale1=5; nRow.Scale2=5^2; nCol.Scale2=5^2; theta = 90; % degrees a_minor = 2; % range along minor direction a_major = 5; % range along major direction sill = var(reshape(Deff_matrix_NthModel,nCell.Scale1,1)); % variance of the coarse data matrix of size nRow.Scale1 X nCol.Scale1 %# Covariance computation % Scale 1 for ihRow = 1:nRow.Scale1 for ihCol = 1:nCol.Scale1 [cov.Scale1(ihRow,ihCol),heff.Scale1(ihRow,ihCol)] = general_CovModel(theta, ihCol, ihRow, a_minor, a_major, sill, 'Exp'); end end % Scale 2 for ihRow = 1:nRow.Scale2 for ihCol = 1:nCol.Scale2 [cov.Scale2(ihRow,ihCol),heff.Scale2(ihRow,ihCol)] = general_CovModel(theta, ihCol/(nCol.Scale2/nCol.Scale1), ihRow/(nRow.Scale2/nRow.Scale1), a_minor, a_major, sill/(nRow.Scale2*nCol.Scale2), 'Exp'); end end %# Scale-up of fine scale values by averaging [covAvg.Scale2,var_covAvg.Scale2,varNorm_covAvg.Scale2] = general_AverageProperty(nRow.Scale2/nRow.Scale1,nCol.Scale2/nCol.Scale1,1,nRow.Scale1,nCol.Scale1,1,cov.Scale2,1); I am using two functions, general_CovModel() and general_AverageProperty(), in my main script which are given as following: function [cov,h_eff] = general_CovModel(theta, hx, hy, a_minor, a_major, sill, mod_type) % mod_type should be in strings angle_rad = theta*(pi/180); % theta in degrees, angle_rad in radians R_theta = [sin(angle_rad) cos(angle_rad); -cos(angle_rad) sin(angle_rad)]; h = [hx; hy]; lambda = a_minor/a_major; D_lambda = [lambda 0; 0 1]; h_2prime = D_lambda*R_theta*h; h_eff = sqrt((h_2prime(1)^2)+(h_2prime(2)^2)); if strcmp(mod_type,'Sph')==1 || strcmp(mod_type,'sph') ==1 if h_eff<=a cov = sill - sill.*(1.5*(h_eff/a_minor)-0.5*((h_eff/a_minor)^3)); else cov = sill; end elseif strcmp(mod_type,'Exp')==1 || strcmp(mod_type,'exp') ==1 cov = sill-(sill.*(1-exp(-(3*h_eff)/a_minor))); elseif strcmp(mod_type,'Gauss')==1 || strcmp(mod_type,'gauss') ==1 cov = sill-(sill.*(1-exp(-((3*h_eff)^2/(a_minor^2))))); end and function [PropertyAvg,variance_PropertyAvg,NormVariance_PropertyAvg]=... general_AverageProperty(blocksize_row,blocksize_col,blocksize_t,... nUpscaledRow,nUpscaledCol,nUpscaledT,PropertyArray,omega) % This function computes average of a property and variance of that averaged % property using power averaging PropertyAvg=zeros(nUpscaledRow,nUpscaledCol,nUpscaledT); %# Average of property for k=1:nUpscaledT, for j=1:nUpscaledCol, for i=1:nUpscaledRow, sum=0; for a=1:blocksize_row, for b=1:blocksize_col, for c=1:blocksize_t, sum=sum+(PropertyArray((i-1)*blocksize_row+a,(j-1)*blocksize_col+b,(k-1)*blocksize_t+c).^omega); % add all the property values in 'blocksize_x','blocksize_y','blocksize_t' to one variable end end end PropertyAvg(i,j,k)=(sum/(blocksize_row*blocksize_col*blocksize_t)).^(1/omega); % take average of the summed property end end end %# Variance of averageed property variance_PropertyAvg=var(reshape(PropertyAvg,... nUpscaledRow*nUpscaledCol*nUpscaledT,1),1,1); %# Normalized variance of averageed property NormVariance_PropertyAvg=variance_PropertyAvg./(var(reshape(... PropertyArray,numel(PropertyArray),1),1,1)); Question: Using Matlab, I would like to optimize covAvg.Scale2 such that it matches closely with cov.Scale1 by perturbing/varying any (or all) of the following variables 1) a_minor 2) a_major 3) theta Thanks.

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• Linq2SQL vs NHibernate performance (have I gone mad?)

  - by HeavyWave

  I have written the following tests to compare performance of Linq2SQL and NHibernate and I find results to be somewhat strange. Mappings are straight forward and identical for both. Both are running against a live DB. Although I'm not deleting Campaigns in case of Linq, but that shouldn't affect performance by more than 10 ms. Linq: [Test] public void Test1000ReadsWritesToAgentStateLinqPrecompiled() { Stopwatch sw = new Stopwatch(); Stopwatch swIn = new Stopwatch(); sw.Start(); for (int i = 0; i < 1000; i++) { swIn.Reset(); swIn.Start(); ReadWriteAndDeleteAgentStateWithLinqPrecompiled(); swIn.Stop(); Console.WriteLine("Run ReadWriteAndDeleteAgentState: " + swIn.ElapsedMilliseconds + " ms"); } sw.Stop(); Console.WriteLine("Total Time: " + sw.ElapsedMilliseconds + " ms"); Console.WriteLine("Average time to execute queries: " + sw.ElapsedMilliseconds / 1000 + " ms"); } private static readonly Func<AgentDesktop3DataContext, int, EntityModel.CampaignDetail> GetCampaignById = CompiledQuery.Compile<AgentDesktop3DataContext, int, EntityModel.CampaignDetail>( (ctx, sessionId) => (from cd in ctx.CampaignDetails join a in ctx.AgentCampaigns on cd.CampaignDetailId equals a.CampaignDetailId where a.AgentStateId == sessionId select cd).FirstOrDefault()); private void ReadWriteAndDeleteAgentStateWithLinqPrecompiled() { int id = 0; using (var ctx = new AgentDesktop3DataContext()) { EntityModel.AgentState agentState = new EntityModel.AgentState(); var campaign = new EntityModel.CampaignDetail { CampaignName = "Test" }; var campaignDisposition = new EntityModel.CampaignDisposition { Code = "123" }; campaignDisposition.Description = "abc"; campaign.CampaignDispositions.Add(campaignDisposition); agentState.CallState = 3; campaign.AgentCampaigns.Add(new AgentCampaign { AgentState = agentState }); ctx.CampaignDetails.InsertOnSubmit(campaign); ctx.AgentStates.InsertOnSubmit(agentState); ctx.SubmitChanges(); id = agentState.AgentStateId; } using (var ctx = new AgentDesktop3DataContext()) { var dbAgentState = ctx.GetAgentStateById(id); Assert.IsNotNull(dbAgentState); Assert.AreEqual(dbAgentState.CallState, 3); var campaignDetails = GetCampaignById(ctx, id); Assert.AreEqual(campaignDetails.CampaignDispositions[0].Description, "abc"); } using (var ctx = new AgentDesktop3DataContext()) { ctx.DeleteSessionById(id); } } NHibernate (the loop is the same): private void ReadWriteAndDeleteAgentState() { var id = WriteAgentState().Id; StartNewTransaction(); var dbAgentState = agentStateRepository.Get(id); Assert.IsNotNull(dbAgentState); Assert.AreEqual(dbAgentState.CallState, 3); Assert.AreEqual(dbAgentState.Campaigns[0].Dispositions[0].Description, "abc"); var campaignId = dbAgentState.Campaigns[0].Id; agentStateRepository.Delete(dbAgentState); NHibernateSession.Current.Transaction.Commit(); Cleanup(campaignId); NHibernateSession.Current.BeginTransaction(); } Results: NHibernate: Total Time: 9469 ms Average time to execute 13 queries: 9 ms Linq: Total Time: 127200 ms Average time to execute 13 queries: 127 ms Linq lost by 13.5 times! Event with precompiled queries (both read queries are precompiled). This can't be right, although I expected NHibernate to be faster, this is just too big of a difference, considering mappings are identical and NHibernate actually executes more queries against the DB.

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• What are good CLI tools for JSON?

  - by jasonmp85

  General Problem Though I may be diagnosing the root cause of an event, determining how many users it affected, or distilling timing logs in order to assess the performance and throughput impact of a recent code change, my tools stay the same: grep, awk, sed, tr, uniq, sort, zcat, tail, head, join, and split. To glue them all together, Unix gives us pipes, and for fancier filtering we have xargs. If these fail me, there's always perl -e. These tools are perfect for processing CSV files, tab-delimited files, log files with a predictable line format, or files with comma-separated key-value pairs. In other words, files where each line has next to no context. XML Analogues I recently needed to trawl through Gigabytes of XML to build a histogram of usage by user. This was easy enough with the tools I had, but for more complicated queries the normal approaches break down. Say I have files with items like this: <foo user="me"> <baz key="zoidberg" value="squid" /> <baz key="leela" value="cyclops" /> <baz key="fry" value="rube" /> </foo> And let's say I want to produce a mapping from user to average number of <baz>s per <foo>. Processing line-by-line is no longer an option: I need to know which user's <foo> I'm currently inspecting so I know whose average to update. Any sort of Unix one liner that accomplishes this task is likely to be inscrutable. Fortunately in XML-land, we have wonderful technologies like XPath, XQuery, and XSLT to help us. Previously, I had gotten accustomed to using the wonderful XML::XPath Perl module to accomplish queries like the one above, but after finding a TextMate Plugin that could run an XPath expression against my current window, I stopped writing one-off Perl scripts to query XML. And I just found out about XMLStarlet which is installing as I type this and which I look forward to using in the future. JSON Solutions? So this leads me to my question: are there any tools like this for JSON? It's only a matter of time before some investigation task requires me to do similar queries on JSON files, and without tools like XPath and XSLT, such a task will be a lot harder. If I had a bunch of JSON that looked like this: { "firstName": "Bender", "lastName": "Robot", "age": 200, "address": { "streetAddress": "123", "city": "New York", "state": "NY", "postalCode": "1729" }, "phoneNumber": [ { "type": "home", "number": "666 555-1234" }, { "type": "fax", "number": "666 555-4567" } ] } And wanted to find the average number of phone numbers each person had, I could do something like this with XPath: fn:avg(/fn:count(phoneNumber)) Questions Are there any command-line tools that can "query" JSON files in this way? If you have to process a bunch of JSON files on a Unix command line, what tools do you use? Heck, is there even work being done to make a query language like this for JSON? If you do use tools like this in your day-to-day work, what do you like/dislike about them? Are there any gotchas? I'm noticing more and more data serialization is being done using JSON, so processing tools like this will be crucial when analyzing large data dumps in the future. Language libraries for JSON are very strong and it's easy enough to write scripts to do this sort of processing, but to really let people play around with the data shell tools are needed. Related Questions Grep and Sed Equivalent for XML Command Line Processing Is there a query language for JSON? JSONPath or other XPath like utility for JSON/Javascript; or Jquery JSON

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• Tuning Red Gate: #3 of Lots

  - by Grant Fritchey

  I'm drilling down into the metrics about SQL Server itself available to me in the Analysis tab of SQL Monitor to see what's up with our two problematic servers. In the previous post I'd noticed that rg-sql01 had quite a few CPU spikes. So one of the first things I want to check there is how much CPU is getting used by SQL Server itself. It's possible we're looking at some other process using up all the CPU Nope, It's SQL Server. I compared this to the rg-sql02 server: You can see that there is a more, consistently low set of CPU counters there. I clearly need to look at rg-sql01 and capture more specific data around the queries running on it to identify which ones are causing these CPU spikes. I always like to look at the Batch Requests/sec on a server, not because it's an indication of a problem, but because it gives you some idea of the load. Just how much is this server getting hit? Here are rg-sql01 and rg-sql02: Of the two, clearly rg-sql01 has a lot of activity. Remember though, that's all this is a measure of, activity. It doesn't suggest anything other than what it says, the number of requests coming in. But it's the kind of thing you want to know in order to understand how the system is used. Are you seeing a correlation between the number of requests and the CPU usage, or a reverse correlation, the number of requests drops as the CPU spikes? See, it's useful. Some of the details you can look at are Compilations/sec, Compilations/Batch and Recompilations/sec. These give you some idea of how the cache is getting used within the system. None of these showed anything interesting on either server. One metric that I like (even though I know it can be controversial) is the Page Life Expectancy. On the average server I expect see a series of mountains as the PLE climbs then drops due to a data load or something along those lines. That's not the case here: Those spikes back in January suggest that the servers weren't really being used much. The PLE on the rg-sql01 seems to be somewhat consistent growing to 3 hours or so then dropping, but the rg-sql02 PLE looks like it might be all over the map. Instead of continuing to look at this high level gathering data view, I'm going to drill down on rg-sql02 and see what it's done for the last week: And now we begin to see where we might have an issue. Memory on this system is getting flushed every 1/2 hour or so. I'm going to check another metric, scans: Whoa! I'm going back to the system real quick to look at some disk information again for rg-sql02. Here is the average disk queue length on the server: and the transfers Right, I think I have a guess as to what's up here. We're seeing memory get flushed constantly and we're seeing lots of scans. The disks are queuing, especially that F drive, and there are lots of requests that correspond to the scans and the memory flushes. In short, we've got queries that are scanning the data, a lot, so we either have bad queries or bad indexes. I'm going back to the server overview for rg-sql02 and check the Top 10 expensive queries. I'm modifying it to show me the last 3 days and the totals, so I'm not looking at some maintenance routine that ran 10 minutes ago and is skewing the results: OK. I need to look into these queries that are getting executed this much. They're generating a lot of reads, but which queries are generating the most reads: Ow, all still going against the same database. This is where I'm going to temporarily leave SQL Monitor. What I want to do is connect up to the server, validate that the Warehouse database is using the F:\ drive (which I'll put money down it is) and then start seeing what's up with these queries. Part 1 of the Series Part 2 of the Series

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• The enterprise vendor con - connecting SSD's using SATA 2 (3Gbits) thus limiting there performance

  - by tonyrogerson

  When comparing SSD against Hard drive performance it really makes me cross when folk think comparing an array of SSD running on 3GBits/sec to hard drives running on 6GBits/second is somehow valid. In a paper from DELL (http://www.dell.com/downloads/global/products/pvaul/en/PowerEdge-PowerVaultH800-CacheCade-final.pdf) on increasing database performance using the DELL PERC H800 with Solid State Drives they compare four SSD drives connected at 3Gbits/sec against ten 10Krpm drives connected at 6Gbits [Tony slaps forehead while shouting DOH!]. It is true in the case of hard drives it probably doesn’t make much difference 3Gbit or 6Gbit because SAS and SATA are both end to end protocols rather than shared bus architecture like SCSI, so the hard drive doesn’t share bandwidth and probably can’t get near the 600MiBytes/second throughput that 6Gbit gives unless you are doing contiguous reads, in my own tests on a single 15Krpm SAS disk using IOMeter (8 worker threads, queue depth of 16 with a stripe size of 64KiB, an 8KiB transfer size on a drive formatted with an allocation size of 8KiB for a 100% sequential read test) I only get 347MiBytes per second sustained throughput at an average latency of 2.87ms per IO equating to 44.5K IOps, ok, if that was 3GBits it would be less – around 280MiBytes per second, oh, but wait a minute [...fingers tap desk] You’ll struggle to find in the commodity space an SSD that doesn’t have the SATA 3 (6GBits) interface, SSD’s are fast not only low latency and high IOps but they also offer a very large sustained transfer rate, consider the OCZ Agility 3 it so happens that in my masters dissertation I did the same test but on a difference box, I got 374MiBytes per second at an average latency of 2.67ms per IO equating to 47.9K IOps – cost of an 240GB Agility 3 is £174.24 (http://www.scan.co.uk/products/240gb-ocz-agility-3-ssd-25-sata-6gb-s-sandforce-2281-read-525mb-s-write-500mb-s-85k-iops), but that same drive set in a box connected with SATA 2 (3Gbits) would only yield around 280MiBytes per second thus losing almost 100MiBytes per second throughput and a ton of IOps too. So why the hell are “enterprise” vendors still only connecting SSD’s at 3GBits? Well, my conspiracy states that they have no interest in you moving to SSD because they’ll lose so much money, the argument that they use SATA 2 doesn’t wash, SATA 3 has been out for some time now and all the commodity stuff you buy uses it now. Consider the cost, not in terms of price per GB but price per IOps, SSD absolutely thrash Hard Drives on that, it was true that the opposite was also true that Hard Drives thrashed SSD’s on price per GB, but is that true now, I’m not so sure – a 300GByte 2.5” 15Krpm SAS drive costs £329.76 ex VAT (http://www.scan.co.uk/products/300gb-seagate-st9300653ss-savvio-15k3-25-hdd-sas-6gb-s-15000rpm-64mb-cache-27ms) which equates to £1.09 per GB compared to a 480GB OCZ Agility 3 costing £422.10 ex VAT (http://www.scan.co.uk/products/480gb-ocz-agility-3-ssd-25-sata-6gb-s-sandforce-2281-read-525mb-s-write-410mb-s-30k-iops) which equates to £0.88 per GB. Ok, I compared an “enterprise” hard drive with a “commodity” SSD, ok, so things get a little more complicated here, most “enterprise” SSD’s are SLC and most commodity are MLC, SLC gives more performance and wear, I’ll talk about that another day. For now though, don’t get sucked in by vendor marketing, SATA 2 (3Gbit) just doesn’t cut it, SSD need 6Gbit to breath and even that SSD’s are pushing. Alas, SSD’s are connected using SATA so all the controllers I’ve seen thus far from HP and DELL only do SATA 2 – deliberate? Well, I’ll let you decide on that one.

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• WebCenter Customer Spotlight: Texas Industries, Inc.

  - by me

  Author: Peter Reiser - Social Business Evangelist, Oracle WebCenter  Solution SummaryTexas Industries, Inc. (TXI) is a leading supplier of cement, aggregate, and consumer product building materials for residential, commercial, and public works projects. TXI is based in Dallas and employs around 2,000 employees. The customer had the challenge of decentralized and manual processes for entering 180,000 vendor invoices annually.  Invoice entry was a time- and resource-intensive process that entailed significant personnel requirements. TXI implemented a centralized solution leveraging Oracle WebCenter Imaging, a smart routing solution that enables users to capture invoices electronically with Oracle WebCenter Capture and Oracle WebCenter Forms Recognition to send  the invoices through to Oracle Financials for approvals and processing.  TXI significantly lowered resource needs for payable processing,  increase productivity by 80% and reduce invoice processing cycle times by 84%—from 20 to 30 days to just 3 to 5 days, on average. Company OverviewTexas Industries, Inc. (TXI) is a leading supplier of cement, aggregate, and consumer product building materials for residential, commercial, and public works projects. With operating subsidiaries in six states, TXI is the largest producer of cement in Texas and a major producer in California. TXI is a major supplier of stone, sand, gravel, and expanded shale and clay products, and one of the largest producers of bagged cement and concrete  products in the Southwest. Business ChallengesTXI had the challenge of decentralized and manual processes for entering 180,000 vendor invoices annually.  Invoice entry was a time- and resource-intensive process that entailed significant personnel requirements. Their business objectives were: Increase the efficiency of core business processes, such as invoice processing, to support the organization’s desire to maintain its role as the Southwest’s leader in delivering high-quality, low-cost products to the construction industry Meet the audit and regulatory requirements for achieving Sarbanes-Oxley (SOX) compliance Streamline entry of 180,000 invoices annually to accelerate processing, reduce errors, cut invoice storage and routing costs, and increase visibility into payables liabilities Solution DeployedTXI replaced a resource-intensive, paper-based, decentralized process for invoice entry with a centralized solution leveraging Oracle WebCenter Imaging 11g. They worked with the Oracle Partner Keste LLC to develop a smart routing solution that enables users to capture invoices electronically with Oracle WebCenter Capture and then uses Oracle WebCenter Forms Recognition and the Oracle WebCenter Imaging workflow to send the invoices through to Oracle Financials for approvals and processing. Business Results Significantly lowered resource needs for payable processing through centralization and improved efficiency  Enabled the company to process invoices faster and pay bills earlier, allowing it to take advantage of additional vendor discounts Tracked to increase productivity by 80% and reduce invoice processing cycle times by 84%—from 20 to 30 days to just 3 to 5 days, on average Achieved a 25% reduction in paper invoice storage costs now that invoices are captured digitally, and enabled a 50% reduction in shipping costs, as the company no longer has to send paper invoices between headquarters and production facilities for approvals “Entering and manually processing more than 180,000 vendor invoices annually was time and labor intensive. With Oracle Imaging and Process Management, we have automated and centralized invoice entry and processing at our corporate office, improving productivity by 80% and reducing invoice processing cycle times by 84%—a very important efficiency gain.” Terry Marshall, Vice President of Information Services, Texas Industries, Inc. Additional Information TXI Customer Snapshot Oracle WebCenter Content Oracle WebCenter Capture Oracle WebCenter Forms Recognition

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• Thoughts on ODTUG Kscope12

  - by thatjeffsmith

  The rodeo rocked! This wasn’t my first rodeo, and it wasn’t my first Kscope, but it was probably my favorite one. What is Kscope? It’s the annual conference for the Oracle Development Tools User Group. 1,000+ attendees from 20+ countries with an average Jeff Klout score of 65. I just made that metric and score up, but this conference attracts the best and brightest in the Oracle database space. I’m not just talking about the speakers either. The attendees are all top notch. They actively participate in sessions, make an effort to get to know their fellow conference mates, and often turn into volunteers and speakers. Developers that enjoy unit testing, understand the importance of modeling your data, and are eager to understand the Oracle CBO – these are traits that describe the ‘average’ ODTUG developer. 2012′s event was held in San Antonio. Yes, it was very hot. But this might have been the nicest Marriott property I’ve ever visited, and I’ve stayed at some nice ones in Hawaii and St. Thomas. They had free WiFi everywhere – the rooms, the Conference Center, the lobby, bars, everywhere. And it worked. The after hours events were very fun. I embarrassed myself several times, but that’s OK. The rodeo was an awesome event and the Thirsty Games experience was something I hope does not make it onto YouTube or Facebook — talking to you Chet Justice. I finally got to meet and spend some time with some folks I’ve always wanted to get to know better, @timothyjgorman, @alexgorbachev, @lj_dobson, @dschleis, @kentGraziano, @chriscmuir, @GaloBalda, @patch72, and many, many more! I even made some new friends thanks to the Mentor program and @carol_finn. 2013′s event will be in New Orleans. If you haven’t joined ODTUG or haven’t made it to Kscope, go ahead and mark your calendars. I had 3 presentations this year. Sunday’s was not a good performance, and I want to apologize to anyone who was there and was hoping for more. My Tips and Debugging sessions on Monday and Tuesday were more to my liking, and I enjoyed them as a presenter. I hope you enjoyed them as an attendee. I understand that my slidedecks were corrupted on the ODTUG site, and I’m working with the coordinator now to get those fixed ASAP. Apparently the 2 most well-received Tips was the /*CSV*/ formatting hint and recalling your previous SQL history via the keyboard. I’ll be doing a follow-up webinar with ODTUG in a few weeks for those members that weren’t able to see my Tips and Debugger sessions in San Antonio. I’ll be sure to post details on that here when I have the details. My next scheduled conference is Oracle Open World, and I may have a couple of shows after that to round out 2012.

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• PASS Summit 2011 &ndash; Part II

  - by Tara Kizer

  I arrived in Seattle last Monday afternoon to attend PASS Summit 2011.  I had really wanted to attend Gail Shaw’s (blog|twitter) and Grant Fritchey’s (blog|twitter) pre-conference seminar “All About Execution Plans” on Monday, but that would have meant flying out on Sunday which I couldn’t do.  On Tuesday, I attended Allan Hirt’s (blog|twitter) pre-conference seminar entitled “A Deep Dive into AlwaysOn: Failover Clustering and Availability Groups”.  Allan is a great speaker, and his seminar was packed with demos and information about AlwaysOn in SQL Server 2012.  Unfortunately, I have lost my notes from this seminar and the presentation materials are only available on the pre-con DVD.  Hmpf! On Wednesday, I attended Gail Shaw’s “Bad Plan! Sit!”, Andrew Kelly’s (blog|twitter) “SQL 2008 Query Statistics”, Dan Jones’ (blog|twitter) “Improving your PowerShell Productivity”, and Brent Ozar’s (blog|twitter) “BLITZ! The SQL – More One Hour SQL Server Takeovers”.  In Gail’s session, she went over how to fix bad plans and bad query patterns.  Update your stale statistics! How to fix bad plans Use local variables – optimizer can’t sniff it, so it’ll optimize for “average” value Use RECOMPILE (at the query or stored procedure level) – CPU hit OPTIMIZE FOR hint – most common value you’ll pass How to fix bad query patterns Don’t use them – ha! Catch-all queries Use dynamic SQL OPTION (RECOMPILE) Multiple execution paths Split into multiple stored procedures OPTION (RECOMPILE) Modifying parameter values Use local variables Split into outer and inner procedure OPTION (RECOMPILE) She also went into “last resort” and “very last resort” options, but those are risky unless you know what you are doing.  For the average Joe, she wouldn’t recommend these.  Examples are query hints and plan guides. While I enjoyed Andrew’s session, I didn’t take any notes as it was familiar material.  Andrew is a great speaker though, and I’d highly recommend attending his sessions in the future. Next up was Dan’s PowerShell session.  I need to look into profiles, manifests, function modules, and function import scripts more as I just didn’t quite grasp these concepts.  I am attending a PowerShell training class at the end of November, so maybe that’ll help clear it up.  I really enjoyed the Excel integration demo.  It was very cool watching PowerShell build the spreadsheet in real-time.  I must look into this more!  On a side note, I am jealous of Dan’s hair.  Fabulous hair! Brent’s session showed us how to quickly gather information about a server that you will be taking over database administration duties for.  He wrote a script to do a fast health check and then later wrapped it into a stored procedure, sp_Blitz.  I can’t wait to use this at my work even on systems where I’ve been the primary DBA for years, maybe there’s something I’ve overlooked.  We are using EPM to help standardize our environment and uncover problems, but sp_Blitz will definitely still help us out.  He even provides a cloud-based update feature, sp_BlitzUpdate, for sp_Blitz so you don’t have to constantly update it when he makes a change.  I think I’ll utilize his update code for some other challenges that we face at my work.

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• SQLAuthority News – Advantages of Distance Learning

  - by Pinal Dave

  Distance education is extremely popular – almost overnight, it seems.  Almost everyone has taken an online course, or knows someone who has, or is considering joining an online school.  There are many advantages and disadvantages to attending an online school – but the same can be said of attending a physical school!  Let’s take a look at the top reasons to use distance education. 1) Flexibility.  Physical universities are usually willing to make some concessions to student – like night classes, study hours, and online networks.  However, nothing is going to beat the flexibility of distance education.  You can attend classes and take notes anytime, anywhere, wearing anything you’d like! 2) Affordability.  We don’t need to get into hard numbers to understand how an expensive university can be.  Students are taking on more and more debt just to get an education.  Many of these fees pay for room, board, and facilities.   Distance education cuts out all these costs, and makes attending school much more affordable for the average student. 3) Try before you buy.  Did you know that the average college student changes his or her major 10 times before they graduate?  You can imagine that this kind of indecision plays a huge part in WHEN you graduate – not being able to make up your mind can cost you big bucks if you have to stay in school for extra years!  Distance education allows you to take different classes from a wide range of disciplines.  Do you want to study forensic science or English literature?  Now you don’t have to pay for classes you can’t afford just to find out. 4) Pace yourself.  Some students struggle in a traditional classroom setting – classes can be taught too fast, too slow, or there are too many distractions.  Distance education allows mature students to set the pace themselves.  They can rewatch lectures they didn’t catch the first time, or go through classes quickly if they are already familiar with the material – cutting out the chance of burning out or getting bored. 5) Lifelong learning.  Maybe you already have a degree, but would like to learn more about your field, or a related field, or maybe even about something completely unrelated – just because you are curious!  Distance education allows you to learn whatever you want ,whenever you want (and yes, wearing anything you’d like!). 6) Attend whatever college you want.  Because of the popularity of distance education, physical campuses are getting in on the game by offering online courses – often just uploaded versions of classes already taught at their campus.  Ever wanted to attend Harvard, but knew you couldn’t get in?  Take a class online!  Of course, you probably should not attempt to lie and say you have a Harvard degree, but Ivy League colleges are prestigious because they are the best in their field – take advantage of the best by taking an online course! I am a big believer in continuing education, whether it is online courses, returning to school, or even take informal classes online.  Distance education can be a great way to accomplish these goals and become a lifelong learner. My friends at provides training through virtual classrooms for students who want to avoid travelling. Distance learning course allows IT aspirants to connect with trainers using the internet.  I encourage everyone to check it out! Reference: Pinal Dave (http://blog.sqlauthority.com) Filed under: PostADay, SQL, SQL Authority, SQL Query, SQL Server, SQL Tips and Tricks, SQL Training, T SQL, Technology

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• New Analytic settings for the new code

  - by Steve Tunstall

  If you have upgraded to the new 2011.1.3.0 code, you may find some very useful settings for the Analytics. If you didn't already know, the analytic datasets have the potential to fill up your OS hard drives. The more datasets you use and create, that faster this can happen. Since they take a measurement every second, forever, some of these metrics can get in the multiple GB size in a matter of weeks. The traditional 'fix' was that you had to go into Analytics -> Datasets about once a month and clean up the largest datasets. You did this by deleting them. Ouch. Now you lost all of that historical data that you might have wanted to check out many months from now. Or, you had to export each metric individually to a CSV file first. Not very easy or fun. You could also suspend a dataset, and have it not collect data at all. Well, that fixed the problem, didn't it? of course you now had no data to go look at. Hmmmm.... All of this is no longer a concern. Check out the new Settings tab under Analytics... Now, I can tell the ZFSSA to keep every second of data for, say, 2 weeks, and then average those 60 seconds of each minute into a single 'minute' value. I can go even further and ask it to average those 60 minutes of data into a single 'hour' value.  This allows me to effectively shrink my older datasets by a factor of 1/3600 !!! Very cool. I can now allow my datasets to go forever, and really never have to worry about them filling up my OS drives. That's great going forward, but what about those huge datasets you already have? No problem. Another new feature in 2011.1.3.0 is the ability to shrink the older datasets in the same way. Check this out. I have here a dataset called "Disk: I/O opps per second" that is about 6.32M on disk (You need not worry so much about the "In Core" value, as that is in RAM, and it fluctuates all the time. Once you stop viewing a particular metric, you will see that shrink over time, just relax).  When one clicks on the trash can icon to the right of the dataset, it used to delete the whole thing, and you would have to re-create it from scratch to get the data collecting again. Now, however, it gives you this prompt: As you can see, this allows you to once again shrink the dataset by averaging the second data into minutes or hours. Here is my new dataset size after I do this. So it shrank from 6.32MB down to 2.87MB, but i can still see my metrics going back to the time I began the dataset. Now, you do understand that once you do this, as you look back in time to the minute or hour data metrics, that you are going to see much larger time values, right? You will need to decide what size of granularity you can live with, and for how long. Check this out. Here is my Disk: Percent utilized from 5-21-2012 2:42 pm to 4:22 pm: After I went through the delete process to change everything older than 1 week to "Minutes", the same date and time looks like this: Just understand what this will do and how you want to use it. Right now, I'm thinking of keeping the last 6 weeks of data as "seconds", and then the last 3 months as "Minutes", and then "Hours" forever after that. I'll check back in six months and see how the sizes look. Steve 

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• How can a developer realize the full value of his work [closed]

  - by Jubbat

  I, honestly, don't want to work as a developer in a company anymore after all I have seen. I want to continue developing software, yes, but not in the way I see it all around me. And I'm in London, a city that congregates lots of great developers from the whole world, so it shouldn't be a problem of location. So, what are my concerns? First of all, best case scenario: you are paying managers salary out of yours. You are consistently underpaid by making up for the average manager negative net return plus his whole salary. Typical scenario. I am a reasonably good developer with common sense who cares for readable code with attention to basic principles. I have found way too often, overconfident and arrogant developers with a severe lack of common sense. Personally, I don't want to follow TDD or Agile practices like all the cool kids nowadays. I would read about them, form my own opinion and take what I feel is useful, but don't follow it sheepishly. I want to work with people who understand that you have to design good interfaces, you absolutely have to document your code, that readability is at the top of your priorities. Also people who don't have a cargo cult mentality too. For instance, the same person who asked me about design patterns in a job interview, later told me that something like a List of Map of Vector of Map of Set (in Java) is very readable. Why would someone ask me about design patterns if they can't even grasp encapsulation? These kind of things are the norm. I've seen many examples. I've seen worse than that too, from very well paid senior devs, by the way. Every second that you spend working with people with such lack of common sense and clear thinking, you are effectively losing money by being terribly inefficient with your time. Yet, with all these inefficiencies, the average developer earns a high salary. So I tried working on my own then, although I don't like the idea. I prefer healthy exchange of opinions and ideas and task division. I then did a bit of online freelancing for a while but I think working in a sweatshop might be more enjoyable. Also, I studied computer engineering and you are in an environment in which your client will presume you don't have any formal education because there is no way to prove it. Again, you are undervalued. You could try building a product, yes. But, of course, luck is a big factor. I wonder if there is a way to work in something you can do well, software development, and be valued for the quality of your work and be paid accordingly, and where you and only you get fairly paid for the value you generate. I know that what I have written seems somehow unlikely but I strongly feel this way. Hopefully someone will understand me and has already figured this out. I don't think I'm alone in this kind of feeling.

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• Basic collision direction detection on 2d objects

  - by Osso Buko

  I am trying to develop a platform game for Android by using ANdroid GL Engine (ANGLE). And I am having trouble with collision detection. I have two objects which is shaped as rectangular. And no change in rotation. Here is a scheme of attributes of objects. What i am trying to do is when objects collide they block each other's movement on that direction. Every object has 4 boolean (bTop, bBottom, bRight, bLeft). For example when bBottom is true object can't advance on that direction. I came up with a solution but it seems it only works on one dimensional. Bottom and top or right and left. public void collisionPlatform (MyObject a, MyObject b) { // first obj is player and second is a wall or a platform Vector p1 = a.mPosition; // p1 = middle point of first object Vector d1 = a.mPosition2; // width(mX) and height of first object Vector mSpeed1 = a.mSpeed; // speed vector of first object Vector p2 = b.mPosition; // p1 = middle point of second object Vector d2 = b.mPosition2; // width(mX) and height of second object Vector mSpeed2 = b.mSpeed; // speed vector of second object float xDist, yDist; // distant between middle of two object float width , height; // this is average of two objects measurements width=(width1+width2)/2 xDist=(p1.mX - p2.mX); // calculate distance // if positive first object is at the right yDist=(p1.mY - p2.mY); // if positive first object is below width = d1.mX + d2.mX; // average measurements calculate height = d1.mY + d2.mY; width/=2; height/=2; if (Math.abs(xDist) < width && Math.abs(yDist) < height) { // Two object is collided if(p1.mY>p2.mY) { // first object is below second one a.bTop = true; if(a.mSpeed.mY<0) a.mSpeed.mY=0; b.bBottom = true; if(b.mSpeed.mY>0) b.mSpeed.mY=0; } else { a.bBottom = true; if(a.mSpeed.mY>0) a.mSpeed.mY=0; b.bTop = true; if(b.mSpeed.mY<0) b.mSpeed.mY=0; } } As seen in my code it simply will not work. when object comes from right or left it doesn't work. I tried couple of ways other than this one but none worked. I am guessing right method will include mSpeed vector. But I have no idea how to do it. I really appreciate if you could help. Sorry for my bad english.

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• Pinterest and the Rising Power of Imagery

  - by Mike Stiles

  If images keep you glued to a screen, you’re hardly alone. Countless social users are letting their eyes do the walking, waiting for that special photo to grab their attention. And perhaps more than any other social network, Pinterest has been giving those eyes plenty of room to walk. Pinterest came along in 2010. Its play was that users could simply create topic boards and pin pictures to the appropriate boards for sharing. Yes there are some words, captions mostly, but not many. The speed of its growth raised eyebrows. Traffic quadrupled in the last quarter of 2011, with 7.51 million unique visitors in December alone. It now gets 1.9 billion monthly page views. And it was sticky. In the US, the average time a user spends strolling through boards and photos on Pinterest is 15 minutes, 50 seconds. Proving the concept of browsing a catalogue is not dead, it became a top 5 referrer for several apparel retailers like Land’s End, Nordstrom, and Bergdorfs. Now a survey of online shoppers by BizRate Insights says that Pinterest is responsible for more purchases online than Facebook. Over 70% of its users are going there specifically to keep up with trends and get shopping ideas. And when they buy, the average order value is $179. Pinterest is also scoring better in terms of user engagement. 66% of pinners regularly follow and repin retailers, whereas 17% of Facebook fans turn to that platform for purchase ideas. (Facebook still wins when it comes to reach and driving traffic to 3rd-party sites by the way). Social posting best practices have consistently shown that posts with photos are rewarded with higher engagement levels. You may be downright Shakespearean in your writing, but what makes images in the digital world so much more powerful than prose? 1. They transcend language barriers. 2. They’re fun and addictive to look at. 3. They can be consumed in fractions of a second, important considering how fast users move through their social content (admit it, you do too). 4. They’re efficient gateways. A good picture might get them to the headline. A good headline might then get them to the written content. 5. The audience for them surpasses demographic limitations. 6. They can effectively communicate and trigger an emotion. 7. With mobile use soaring, photos are created on those devices and easily consumed and shared on them. Pinterest’s iPad app hit #1 in the Apple store in 1 day. Even as far back as 2009, over 2.5 billion devices with cameras were on the streets generating in just 1 year, 10% of the number of photos taken…ever. But let’s say you’re not a retailer. What if you’re a B2B whose products or services aren’t visual? Should you worry about your presence on Pinterest? As with all things, you need a keen awareness of who your audience is, where they reside online, and what they want to do there. If it doesn’t make sense to put a tent stake in Pinterest, fine. But ignore the power of pictures at your own peril. If not visually, how are you going to attention-grab social users scrolling down their News Feeds at top speed? You’re competing with every other cool image out there from countless content sources. Bore us and we’ll fly right past you.

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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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• Worse is better. Is there an example?

  - by J.F. Sebastian

  Is there a widely-used algorithm that has time complexity worse than that of another known algorithm but it is a better choice in all practical situations (worse complexity but better otherwise)? An acceptable answer might be in a form: There are algorithms A and B that have O(N**2) and O(N) time complexity correspondingly, but B has such a big constant that it has no advantages over A for inputs less then a number of atoms in the Universe. Examples highlights from the answers: Simplex algorithm -- worst-case is exponential time -- vs. known polynomial-time algorithms for convex optimization problems. A naive median of medians algorithm -- worst-case O(N**2) vs. known O(N) algorithm. Backtracking regex engines -- worst-case exponential vs. O(N) Thompson NFA -based engines. All these examples exploit worst-case vs. average scenarios. Are there examples that do not rely on the difference between the worst case vs. average case scenario? Related: The Rise of ``Worse is Better''. (For the purpose of this question the "Worse is Better" phrase is used in a narrower (namely -- algorithmic time-complexity) sense than in the article) Python's Design Philosophy: The ABC group strived for perfection. For example, they used tree-based data structure algorithms that were proven to be optimal for asymptotically large collections (but were not so great for small collections). This example would be the answer if there were no computers capable of storing these large collections (in other words large is not large enough in this case). Coppersmith–Winograd algorithm for square matrix multiplication is a good example (it is the fastest (2008) but it is inferior to worse algorithms). Any others? From the wikipedia article: "It is not used in practice because it only provides an advantage for matrices so large that they cannot be processed by modern hardware (Robinson 2005)."

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• SQL Server 2008 Running trigger after Insert, Update locks original table

  - by Polity

  Hi Folks, I have a serious performance problem. I have a database with (related to this problem), 2 tables. 1 Table contains strings with some global information. The second table contains the string stripped down to each individual word. So the string is like indexed in the second table, word by word. The validity of the data in the second table is of less important then the validity of the data in the first table. Since the first table can grow like towards 1*10^6 records and the second table having an average of like 10 words for 1 string can grow like 1*10^7 records, i use a nolock in order to read the second this leaves me free for inserting new records without locking it (Expect many reads on both tables). I have a script which keeps on adding and updating rows to the first table in a MERGE statement. On average, the data beeing merged are like 20 strings a time and the scripts runs like ones every 5 seconds. On the first table, i have a trigger which is beeing invoked on a Insert or Update, which takes the newly inserted or updated data and calls a stored procedure on it which makes sure the data is indexed in the second table. (This takes some significant time). The problem is that when having the trigger disbaled, Reading the first table happens in a few ms. However, when enabling the trigger and your in bad luck of trying to read the first table while this is beeing updated, Our webserver gives you a timeout after 10 seconds (which is way to long anyways). I can quess from this part that when running the trigger, the first table is kept (partially) in a lock untill the trigger is completed. What do you think, if i'm right, is there a easy way around this? Thanks in advance! Cheers, Koen

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