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  • Using SSIS to send a HTML E-Mail Message with built-in table of Counts.

    - by Kevin Shyr
    For the record, this can be just as easily done with a .NET class with a DLL call.  The two major reasons for this ending up as a SSIS package are: There are a lot of SQL resources for maintenance, but not as many .NET developers. There is an existing automated process that links up SQL Jobs (more on that in the next post), and this is part of that process.   To start, this is what the SSIS looks like: The first part of the control flow is just for the override scenario.   In the Execute SQL Task, it calls a stored procedure, which already formats the result into XML by using "FOR XML PATH('Row'), ROOT(N'FieldingCounts')".  The result XML string looks like this: <FieldingCounts>   <Row>     <CellId>M COD</CellId>     <Mailed>64</Mailed>     <ReMailed>210</ReMailed>     <TotalMail>274</TotalMail>     <EMailed>233</EMailed>     <TotalSent>297</TotalSent>   </Row>   <Row>     <CellId>M National</CellId>     <Mailed>11</Mailed>     <ReMailed>59</ReMailed>     <TotalMail>70</TotalMail>     <EMailed>90</EMailed>     <TotalSent>101</TotalSent>   </Row>   <Row>     <CellId>U COD</CellId>     <Mailed>91</Mailed>     <ReMailed>238</ReMailed>     <TotalMail>329</TotalMail>     <EMailed>291</EMailed>     <TotalSent>382</TotalSent>   </Row>   <Row>     <CellId>U National</CellId>     <Mailed>63</Mailed>     <ReMailed>286</ReMailed>     <TotalMail>349</TotalMail>     <EMailed>374</EMailed>     <TotalSent>437</TotalSent>   </Row> </FieldingCounts>  This result is saved into an internal SSIS variable with the following settings on the General tab and the Result Set tab:   Now comes the trickier part.  We need to use the XML Task to format the XML string result into an HTML table, and I used Direct input XSLT And here is the code of XSLT: <xsl:stylesheet version="1.0" xmlns:xsl="http://www.w3.org/1999/XSL/Transform"> <xsl:output method="html" indent="yes"/>   <xsl:template match="/ROOT">         <table border="1" cellpadding="6">           <tr>             <td></td>             <td>Mailed</td>             <td>Re-mailed</td>             <td>Total Mail (Mailed, Re-mailed)</td>             <td>E-mailed</td>             <td>Total Sent (Mailed, E-mailed)</td>           </tr>           <xsl:for-each select="FieldingCounts/Row">             <tr>               <xsl:for-each select="./*">                 <td>                   <xsl:value-of select="." />                 </td>               </xsl:for-each>             </tr>           </xsl:for-each>         </table>   </xsl:template> </xsl:stylesheet>    Then a script task is used to send out an HTML email (as we are all painfully aware that SSIS Send Mail Task only sends plain text) Normal 0 false false false EN-US X-NONE X-NONE MicrosoftInternetExplorer4 using System; using System.Data; using Microsoft.SqlServer.Dts.Runtime; using System.Windows.Forms; using System.Net.Mail; using System.Net;   namespace ST_b829a2615e714bcfb55db0ce97be3901.csproj {     [System.AddIn.AddIn("ScriptMain", Version = "1.0", Publisher = "", Description = "")]     public partial class ScriptMain : Microsoft.SqlServer.Dts.Tasks.ScriptTask.VSTARTScriptObjectModelBase     {           #region VSTA generated code         enum ScriptResults         {             Success = Microsoft.SqlServer.Dts.Runtime.DTSExecResult.Success,             Failure = Microsoft.SqlServer.Dts.Runtime.DTSExecResult.Failure         };         #endregion           public void Main()         {             String EmailMsgBody = String.Format("<HTML><BODY><P>{0}</P><P>{1}</P></BODY></HTML>"                                                 , Dts.Variables["Config_SMTP_MessageSourceText"].Value.ToString()                                                 , Dts.Variables["InternalStr_CountResultAfterXSLT"].Value.ToString());             MailMessage EmailCountMsg = new MailMessage(Dts.Variables["Config_SMTP_From"].Value.ToString().Replace(";", ",")                                                         , Dts.Variables["Config_SMTP_Success_To"].Value.ToString().Replace(";", ",")                                                         , Dts.Variables["Config_SMTP_SubjectLinePrefix"].Value.ToString() + " " + Dts.Variables["InternalStr_FieldingDate"].Value.ToString()                                                         , EmailMsgBody);             //EmailCountMsg.From.             EmailCountMsg.CC.Add(Dts.Variables["Config_SMTP_Success_CC"].Value.ToString().Replace(";", ","));             EmailCountMsg.IsBodyHtml = true;               SmtpClient SMTPForCount = new SmtpClient(Dts.Variables["Config_SMTP_ServerAddress"].Value.ToString());             SMTPForCount.Credentials = CredentialCache.DefaultNetworkCredentials;               SMTPForCount.Send(EmailCountMsg);               Dts.TaskResult = (int)ScriptResults.Success;         }     } } Note on this code: notice the email list has Replace(";", ",").  This is only here because the list is configurable in the SQL Job Step at Set Values, which does not react well with colons as email separator, but system.Net.Mail only handles comma as email separator, hence the extra replace in the string. The result is a nicely formatted email message with count information:

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  • External File Upload Optimizations for Windows Azure

    - by rgillen
    [Cross posted from here: http://rob.gillenfamily.net/post/External-File-Upload-Optimizations-for-Windows-Azure.aspx] I’m wrapping up a bit of the work we’ve been doing on data movement optimizations for cloud computing and the latest set of data yielded some interesting points I thought I’d share. The work done here is not really rocket science but may, in some ways, be slightly counter-intuitive and therefore seemed worthy of posting. Summary: for those who don’t like to read detailed posts or don’t have time, the synopsis is that if you are uploading data to Azure, block your data (even down to 1MB) and upload in parallel. Set your block size based on your source file size, but if you must choose a fixed value, use 1MB. Following the above will result in significant performance gains… upwards of 10x-24x and a reduction in overall file transfer time of upwards of 90% (eg, uploading a 1GB file averaged 46.37 minutes prior to optimizations and averaged 1.86 minutes afterwards). Detail: For those of you who want more detail, or think that the claims at the end of the preceding paragraph are over-reaching, what follows is information and code supporting these claims. As the title would indicate, these tests were run from our research facility pointing to the Azure cloud (specifically US North Central as it is physically closest to us) and do not represent intra-cloud results… we have performed intra-cloud tests and the overall results are similar in notion but the data rates are significantly different as well as the tipping points for the various block sizes… this will be detailed separately). We started by building a very simple console application that would loop through a directory and upload each file to Azure storage. This application used the shipping storage client library from the 1.1 version of the azure tools. The only real variation from the client library is that we added code to collect and record the duration (in ms) and size (in bytes) for each file transferred. The code is available here. We then created a directory that had a collection of files for the following sizes: 2KB, 32KB, 64KB, 128KB, 512KB, 1MB, 5MB, 10MB, 25MB, 50MB, 100MB, 250MB, 500MB, 750MB, and 1GB (50 files for each size listed). These files contained randomly-generated binary data and do not benefit from compression (a separate discussion topic). Our file generation tool is available here. The baseline was established by running the application described above against the directory containing all of the data files. This application uploads the files in a random order so as to avoid transferring all of the files of a given size sequentially and thereby spreading the affects of periodic Internet delays across the collection of results.  We then ran some scripts to split the resulting data and generate some reports. The raw data collected for our non-optimized tests is available via the links in the Related Resources section at the bottom of this post. For each file size, we calculated the average upload time (and standard deviation) and the average transfer rate (and standard deviation). As you likely are aware, transferring data across the Internet is susceptible to many transient delays which can cause anomalies in the resulting data. It is for this reason that we randomized the order of source file processing as well as executed the tests 50x for each file size. We expect that these steps will yield a sufficiently balanced set of results. Once the baseline was collected and analyzed, we updated the test harness application with some methods to split the source file into user-defined block sizes and then to upload those blocks in parallel (using the PutBlock() method of Azure storage). The parallelization was handled by simply relying on the Parallel Extensions to .NET to provide a Parallel.For loop (see linked source for specific implementation details in Program.cs, line 173 and following… less than 100 lines total). Once all of the blocks were uploaded, we called PutBlockList() to assemble/commit the file in Azure storage. For each block transferred, the MD5 was calculated and sent ensuring that the bits that arrived matched was was intended. The timer for the blocked/parallelized transfer method wraps the entire process (source file splitting, block transfer, MD5 validation, file committal). A diagram of the process is as follows: We then tested the affects of blocking & parallelizing the transfers by running the updated application against the same source set and did a parameter sweep on the block size including 256KB, 512KB, 1MB, 2MB, and 4MB (our assumption was that anything lower than 256KB wasn’t worth the trouble and 4MB is the maximum size of a block supported by Azure). The raw data for the parallel tests is available via the links in the Related Resources section at the bottom of this post. This data was processed and then compared against the single-threaded / non-optimized transfer numbers and the results were encouraging. The Excel version of the results is available here. Two semi-obvious points need to be made prior to reviewing the data. The first is that if the block size is larger than the source file size you will end up with a “negative optimization” due to the overhead of attempting to block and parallelize. The second is that as the files get smaller, the clock-time cost of blocking and parallelizing (overhead) is more apparent and can tend towards negative optimizations. For this reason (and is supported in the raw data provided in the linked worksheet) the charts and dialog below ignore source file sizes less than 1MB. (click chart for full size image) The chart above illustrates some interesting points about the results: When the block size is smaller than the source file, performance increases but as the block size approaches and then passes the source file size, you see decreasing benefit to the point of negative gains (see the values for the 1MB file size) For some of the moderately-sized source files, small blocks (256KB) are best As the size of the source file gets larger (see values for 50MB and up), the smallest block size is not the most efficient (presumably due, at least in part, to the increased number of blocks, increased number of individual transfer requests, and reassembly/committal costs). Once you pass the 250MB source file size, the difference in rate for 1MB to 4MB blocks is more-or-less constant The 1MB block size gives the best average improvement (~16x) but the optimal approach would be to vary the block size based on the size of the source file.    (click chart for full size image) The above is another view of the same data as the prior chart just with the axis changed (x-axis represents file size and plotted data shows improvement by block size). It again highlights the fact that the 1MB block size is probably the best overall size but highlights the benefits of some of the other block sizes at different source file sizes. This last chart shows the change in total duration of the file uploads based on different block sizes for the source file sizes. Nothing really new here other than this view of the data highlights the negative affects of poorly choosing a block size for smaller files.   Summary What we have found so far is that blocking your file uploads and uploading them in parallel results in significant performance improvements. Further, utilizing extension methods and the Task Parallel Library (.NET 4.0) make short work of altering the shipping client library to provide this functionality while minimizing the amount of change to existing applications that might be using the client library for other interactions.   Related Resources Source code for upload test application Source code for random file generator ODatas feed of raw data from non-optimized transfer tests Experiment Metadata Experiment Datasets 2KB Uploads 32KB Uploads 64KB Uploads 128KB Uploads 256KB Uploads 512KB Uploads 1MB Uploads 5MB Uploads 10MB Uploads 25MB Uploads 50MB Uploads 100MB Uploads 250MB Uploads 500MB Uploads 750MB Uploads 1GB Uploads Raw Data OData feeds of raw data from blocked/parallelized transfer tests Experiment Metadata Experiment Datasets Raw Data 256KB Blocks 512KB Blocks 1MB Blocks 2MB Blocks 4MB Blocks Excel worksheet showing summarizations and comparisons

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  • Benchmarking MySQL Replication with Multi-Threaded Slaves

    - by Mat Keep
    0 0 1 1145 6530 Homework 54 15 7660 14.0 Normal 0 false false false EN-US JA X-NONE /* Style Definitions */ table.MsoNormalTable {mso-style-name:"Table Normal"; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-priority:99; mso-style-parent:""; mso-padding-alt:0cm 5.4pt 0cm 5.4pt; mso-para-margin:0cm; mso-para-margin-bottom:.0001pt; mso-pagination:widow-orphan; font-size:12.0pt; font-family:Cambria; mso-ascii-font-family:Cambria; mso-ascii-theme-font:minor-latin; mso-hansi-font-family:Cambria; mso-hansi-theme-font:minor-latin; mso-ansi-language:EN-US;} The objective of this benchmark is to measure the performance improvement achieved when enabling the Multi-Threaded Slave enhancement delivered as a part MySQL 5.6. As the results demonstrate, Multi-Threaded Slaves delivers 5x higher replication performance based on a configuration with 10 databases/schemas. For real-world deployments, higher replication performance directly translates to: · Improved consistency of reads from slaves (i.e. reduced risk of reading "stale" data) · Reduced risk of data loss should the master fail before replicating all events in its binary log (binlog) The multi-threaded slave splits processing between worker threads based on schema, allowing updates to be applied in parallel, rather than sequentially. This delivers benefits to those workloads that isolate application data using databases - e.g. multi-tenant systems deployed in cloud environments. Multi-Threaded Slaves are just one of many enhancements to replication previewed as part of the MySQL 5.6 Development Release, which include: · Global Transaction Identifiers coupled with MySQL utilities for automatic failover / switchover and slave promotion · Crash Safe Slaves and Binlog · Optimized Row Based Replication · Replication Event Checksums · Time Delayed Replication These and many more are discussed in the “MySQL 5.6 Replication: Enabling the Next Generation of Web & Cloud Services” Developer Zone article  Back to the benchmark - details are as follows. Environment The test environment consisted of two Linux servers: · one running the replication master · one running the replication slave. Only the slave was involved in the actual measurements, and was based on the following configuration: - Hardware: Oracle Sun Fire X4170 M2 Server - CPU: 2 sockets, 6 cores with hyper-threading, 2930 MHz. - OS: 64-bit Oracle Enterprise Linux 6.1 - Memory: 48 GB Test Procedure Initial Setup: Two MySQL servers were started on two different hosts, configured as replication master and slave. 10 sysbench schemas were created, each with a single table: CREATE TABLE `sbtest` (    `id` int(10) unsigned NOT NULL AUTO_INCREMENT,    `k` int(10) unsigned NOT NULL DEFAULT '0',    `c` char(120) NOT NULL DEFAULT '',    `pad` char(60) NOT NULL DEFAULT '',    PRIMARY KEY (`id`),    KEY `k` (`k`) ) ENGINE=InnoDB DEFAULT CHARSET=latin1 10,000 rows were inserted in each of the 10 tables, for a total of 100,000 rows. When the inserts had replicated to the slave, the slave threads were stopped. The slave data directory was copied to a backup location and the slave threads position in the master binlog noted. 10 sysbench clients, each configured with 10 threads, were spawned at the same time to generate a random schema load against each of the 10 schemas on the master. Each sysbench client executed 10,000 "update key" statements: UPDATE sbtest set k=k+1 WHERE id = <random row> In total, this generated 100,000 update statements to later replicate during the test itself. Test Methodology: The number of slave workers to test with was configured using: SET GLOBAL slave_parallel_workers=<workers> Then the slave IO thread was started and the test waited for all the update queries to be copied over to the relay log on the slave. The benchmark clock was started and then the slave SQL thread was started. The test waited for the slave SQL thread to finish executing the 100k update queries, doing "select master_pos_wait()". When master_pos_wait() returned, the benchmark clock was stopped and the duration calculated. The calculated duration from the benchmark clock should be close to the time it took for the SQL thread to execute the 100,000 update queries. The 100k queries divided by this duration gave the benchmark metric, reported as Queries Per Second (QPS). Test Reset: The test-reset cycle was implemented as follows: · the slave was stopped · the slave data directory replaced with the previous backup · the slave restarted with the slave threads replication pointer repositioned to the point before the update queries in the binlog. The test could then be repeated with identical set of queries but a different number of slave worker threads, enabling a fair comparison. The Test-Reset cycle was repeated 3 times for 0-24 number of workers and the QPS metric calculated and averaged for each worker count. MySQL Configuration The relevant configuration settings used for MySQL are as follows: binlog-format=STATEMENT relay-log-info-repository=TABLE master-info-repository=TABLE As described in the test procedure, the slave_parallel_workers setting was modified as part of the test logic. The consequence of changing this setting is: 0 worker threads:    - current (i.e. single threaded) sequential mode    - 1 x IO thread and 1 x SQL thread    - SQL thread both reads and executes the events 1 worker thread:    - sequential mode    - 1 x IO thread, 1 x Coordinator SQL thread and 1 x Worker thread    - coordinator reads the event and hands it to the worker who executes 2+ worker threads:    - parallel execution    - 1 x IO thread, 1 x Coordinator SQL thread and 2+ Worker threads    - coordinator reads events and hands them to the workers who execute them Results Figure 1 below shows that Multi-Threaded Slaves deliver ~5x higher replication performance when configured with 10 worker threads, with the load evenly distributed across our 10 x schemas. This result is compared to the current replication implementation which is based on a single SQL thread only (i.e. zero worker threads). Figure 1: 5x Higher Performance with Multi-Threaded Slaves The following figure shows more detailed results, with QPS sampled and reported as the worker threads are incremented. The raw numbers behind this graph are reported in the Appendix section of this post. Figure 2: Detailed Results As the results above show, the configuration does not scale noticably from 5 to 9 worker threads. When configured with 10 worker threads however, scalability increases significantly. The conclusion therefore is that it is desirable to configure the same number of worker threads as schemas. Other conclusions from the results: · Running with 1 worker compared to zero workers just introduces overhead without the benefit of parallel execution. · As expected, having more workers than schemas adds no visible benefit. Aside from what is shown in the results above, testing also demonstrated that the following settings had a very positive effect on slave performance: relay-log-info-repository=TABLE master-info-repository=TABLE For 5+ workers, it was up to 2.3 times as fast to run with TABLE compared to FILE. Conclusion As the results demonstrate, Multi-Threaded Slaves deliver significant performance increases to MySQL replication when handling multiple schemas. This, and the other replication enhancements introduced in MySQL 5.6 are fully available for you to download and evaluate now from the MySQL Developer site (select Development Release tab). You can learn more about MySQL 5.6 from the documentation  Please don’t hesitate to comment on this or other replication blogs with feedback and questions. Appendix – Detailed Results

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  • Possible SWITCH Optimization in DAX – #powerpivot #dax #tabular

    - by Marco Russo (SQLBI)
    In one of the Advanced DAX Workshop I taught this year, I had an interesting discussion about how to optimize a SWITCH statement (which could be frequently used checking a slicer, like in the Parameter Table pattern). Let’s start with the problem. What happen when you have such a statement? Sales :=     SWITCH (         VALUES ( Period[Period] ),         "Current", [Internet Total Sales],         "MTD", [MTD Sales],         "QTD", [QTD Sales],         "YTD", [YTD Sales],          BLANK ()     ) The SWITCH statement is in reality just syntax sugar for a nested IF statement. When you place such a measure in a pivot table, for every cell of the pivot table the IF options are evaluated. In order to optimize performance, the DAX engine usually does not compute cell-by-cell, but tries to compute the values in bulk-mode. However, if a measure contains an IF statement, every cell might have a different execution path, so the current implementation might evaluate all the possible IF branches in bulk-mode, so that for every cell the result from one of the branches will be already available in a pre-calculated dataset. The price for that could be high. If you consider the previous Sales measure, the YTD Sales measure could be evaluated for all the cells where it’s not required, and also when YTD is not selected at all in a Pivot Table. The actual optimization made by the DAX engine could be different in every build, and I expect newer builds of Tabular and Power Pivot to be better than older ones. However, we still don’t live in an ideal world, so it could be better trying to help the engine finding a better execution plan. One student (Niek de Wit) proposed this approach: Selection := IF (     HASONEVALUE ( Period[Period] ),     VALUES ( Period[Period] ) ) Sales := CALCULATE (     [Internet Total Sales],     FILTER (         VALUES ( 'Internet Sales'[Order Quantity] ),         'Internet Sales'[Order Quantity]             = IF (                 [Selection] = "Current",                 'Internet Sales'[Order Quantity],                 -1             )     ) )     + CALCULATE (         [MTD Sales],         FILTER (             VALUES ( 'Internet Sales'[Order Quantity] ),             'Internet Sales'[Order Quantity]                 = IF (                     [Selection] = "MTD",                     'Internet Sales'[Order Quantity],                     -1                 )         )     )     + CALCULATE (         [QTD Sales],         FILTER (             VALUES ( 'Internet Sales'[Order Quantity] ),             'Internet Sales'[Order Quantity]                 = IF (                     [Selection] = "QTD",                     'Internet Sales'[Order Quantity],                     -1                 )         )     )     + CALCULATE (         [YTD Sales],         FILTER (             VALUES ( 'Internet Sales'[Order Quantity] ),             'Internet Sales'[Order Quantity]                 = IF (                     [Selection] = "YTD",                     'Internet Sales'[Order Quantity],                     -1                 )         )     ) At first sight, you might think it’s impossible that this approach could be faster. However, if you examine with the profiler what happens, there is a different story. Every original IF’s execution branch is now a separate CALCULATE statement, which applies a filter that does not execute the required measure calculation if the result of the FILTER is empty. I used the ‘Internet Sales’[Order Quantity] column in this example just because in Adventure Works it has only one value (every row has 1): in the real world, you should use a column that has a very low number of distinct values, or use a column that has always the same value for every row (so it will be compressed very well!). Because the value –1 is never used in this column, the IF comparison in the filter discharge all the values iterated in the filter if the selection does not match with the desired value. I hope to have time in the future to write a longer article about this optimization technique, but in the meantime I’ve seen this optimization has been useful in many other implementations. Please write your feedback if you find scenarios (in both Power Pivot and Tabular) where you obtain performance improvements using this technique!

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  • Scheduling thread tiles with C++ AMP

    - by Daniel Moth
    This post assumes you are totally comfortable with, what some of us call, the simple model of C++ AMP, i.e. you could write your own matrix multiplication. We are now ready to explore the tiled model, which builds on top of the non-tiled one. Tiling the extent We know that when we pass a grid (which is just an extent under the covers) to the parallel_for_each call, it determines the number of threads to schedule and their index values (including dimensionality). For the single-, two-, and three- dimensional cases you can go a step further and subdivide the threads into what we call tiles of threads (others may call them thread groups). So here is a single-dimensional example: extent<1> e(20); // 20 units in a single dimension with indices from 0-19 grid<1> g(e);      // same as extent tiled_grid<4> tg = g.tile<4>(); …on the 3rd line we subdivided the single-dimensional space into 5 single-dimensional tiles each having 4 elements, and we captured that result in a concurrency::tiled_grid (a new class in amp.h). Let's move on swiftly to another example, in pictures, this time 2-dimensional: So we start on the left with a grid of a 2-dimensional extent which has 8*6=48 threads. We then have two different examples of tiling. In the first case, in the middle, we subdivide the 48 threads into tiles where each has 4*3=12 threads, hence we have 2*2=4 tiles. In the second example, on the right, we subdivide the original input into tiles where each has 2*2=4 threads, hence we have 4*3=12 tiles. Notice how you can play with the tile size and achieve different number of tiles. The numbers you pick must be such that the original total number of threads (in our example 48), remains the same, and every tile must have the same size. Of course, you still have no clue why you would do that, but stick with me. First, we should see how we can use this tiled_grid, since the parallel_for_each function that we know expects a grid. Tiled parallel_for_each and tiled_index It turns out that we have additional overloads of parallel_for_each that accept a tiled_grid instead of a grid. However, those overloads, also expect that the lambda you pass in accepts a concurrency::tiled_index (new in amp.h), not an index<N>. So how is a tiled_index different to an index? A tiled_index object, can have only 1 or 2 or 3 dimensions (matching exactly the tiled_grid), and consists of 4 index objects that are accessible via properties: global, local, tile_origin, and tile. The global index is the same as the index we know and love: the global thread ID. The local index is the local thread ID within the tile. The tile_origin index returns the global index of the thread that is at position 0,0 of this tile, and the tile index is the position of the tile in relation to the overall grid. Confused? Here is an example accompanied by a picture that hopefully clarifies things: array_view<int, 2> data(8, 6, p_my_data); parallel_for_each(data.grid.tile<2,2>(), [=] (tiled_index<2,2> t_idx) restrict(direct3d) { /* todo */ }); Given the code above and the picture on the right, what are the values of each of the 4 index objects that the t_idx variables exposes, when the lambda is executed by T (highlighted in the picture on the right)? If you can't work it out yourselves, the solution follows: t_idx.global       = index<2> (6,3) t_idx.local          = index<2> (0,1) t_idx.tile_origin = index<2> (6,2) t_idx.tile             = index<2> (3,1) Don't move on until you are comfortable with this… the picture really helps, so use it. Tiled Matrix Multiplication Example – part 1 Let's paste here the C++ AMP matrix multiplication example, bolding the lines we are going to change (can you guess what the changes will be?) 01: void MatrixMultiplyTiled_Part1(vector<float>& vC, const vector<float>& vA, const vector<float>& vB, int M, int N, int W) 02: { 03: 04: array_view<const float,2> a(M, W, vA); 05: array_view<const float,2> b(W, N, vB); 06: array_view<writeonly<float>,2> c(M, N, vC); 07: parallel_for_each(c.grid, 08: [=](index<2> idx) restrict(direct3d) { 09: 10: int row = idx[0]; int col = idx[1]; 11: float sum = 0.0f; 12: for(int i = 0; i < W; i++) 13: sum += a(row, i) * b(i, col); 14: c[idx] = sum; 15: }); 16: } To turn this into a tiled example, first we need to decide our tile size. Let's say we want each tile to be 16*16 (which assumes that we'll have at least 256 threads to process, and that c.grid.extent.size() is divisible by 256, and moreover that c.grid.extent[0] and c.grid.extent[1] are divisible by 16). So we insert at line 03 the tile size (which must be a compile time constant). 03: static const int TS = 16; ...then we need to tile the grid to have tiles where each one has 16*16 threads, so we change line 07 to be as follows 07: parallel_for_each(c.grid.tile<TS,TS>(), ...that means that our index now has to be a tiled_index with the same characteristics as the tiled_grid, so we change line 08 08: [=](tiled_index<TS, TS> t_idx) restrict(direct3d) { ...which means, without changing our core algorithm, we need to be using the global index that the tiled_index gives us access to, so we insert line 09 as follows 09: index<2> idx = t_idx.global; ...and now this code just works and it is tiled! Closing thoughts on part 1 The process we followed just shows the mechanical transformation that can take place from the simple model to the tiled model (think of this as step 1). In fact, when we wrote the matrix multiplication example originally, the compiler was doing this mechanical transformation under the covers for us (and it has additional smarts to deal with the cases where the total number of threads scheduled cannot be divisible by the tile size). The point is that the thread scheduling is always tiled, even when you use the non-tiled model. But with this mechanical transformation, we haven't gained anything… Hint: our goal with explicitly using the tiled model is to gain even more performance. In the next post, we'll evolve this further (beyond what the compiler can automatically do for us, in this first release), so you can see the full usage of the tiled model and its benefits… Comments about this post by Daniel Moth welcome at the original blog.

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  • laptop crashed: why?

    - by sds
    my linux (ubuntu 12.04) laptop crashed, and I am trying to figure out why. # last sds pts/4 :0 Tue Sep 4 10:01 still logged in sds pts/3 :0 Tue Sep 4 10:00 still logged in reboot system boot 3.2.0-29-generic Tue Sep 4 09:43 - 11:23 (01:40) sds pts/8 :0 Mon Sep 3 14:23 - crash (19:19) this seems to indicate a crash at 09:42 (= 14:23+19:19). as per another question, I looked at /var/log: auth.log: Sep 4 09:17:02 t520sds CRON[32744]: pam_unix(cron:session): session closed for user root Sep 4 09:43:17 t520sds lightdm: pam_unix(lightdm:session): session opened for user lightdm by (uid=0) no messages file syslog: Sep 4 09:24:19 t520sds kernel: [219104.819975] CPU0: Package power limit normal Sep 4 09:43:16 t520sds kernel: imklog 5.8.6, log source = /proc/kmsg started. kern.log: Sep 4 09:24:19 t520sds kernel: [219104.819969] CPU1: Package power limit normal Sep 4 09:24:19 t520sds kernel: [219104.819971] CPU2: Package power limit normal Sep 4 09:24:19 t520sds kernel: [219104.819974] CPU3: Package power limit normal Sep 4 09:24:19 t520sds kernel: [219104.819975] CPU0: Package power limit normal Sep 4 09:43:16 t520sds kernel: imklog 5.8.6, log source = /proc/kmsg started. Sep 4 09:43:16 t520sds kernel: [ 0.000000] Initializing cgroup subsys cpuset Sep 4 09:43:16 t520sds kernel: [ 0.000000] Initializing cgroup subsys cpu I had a computation running until 9:24, but the system crashed 18 minutes later! kern.log has many pages of these: Sep 4 09:43:16 t520sds kernel: [ 0.000000] total RAM covered: 8086M Sep 4 09:43:16 t520sds kernel: [ 0.000000] gran_size: 64K chunk_size: 64K num_reg: 10 lose cover RAM: 38M Sep 4 09:43:16 t520sds kernel: [ 0.000000] gran_size: 64K chunk_size: 128K num_reg: 10 lose cover RAM: 38M Sep 4 09:43:16 t520sds kernel: [ 0.000000] gran_size: 64K chunk_size: 256K num_reg: 10 lose cover RAM: 38M Sep 4 09:43:16 t520sds kernel: [ 0.000000] gran_size: 64K chunk_size: 512K num_reg: 10 lose cover RAM: 38M Sep 4 09:43:16 t520sds kernel: [ 0.000000] gran_size: 64K chunk_size: 1M num_reg: 10 lose cover RAM: 38M Sep 4 09:43:16 t520sds kernel: [ 0.000000] gran_size: 64K chunk_size: 2M num_reg: 10 lose cover RAM: 38M Sep 4 09:43:16 t520sds kernel: [ 0.000000] gran_size: 64K chunk_size: 4M num_reg: 10 lose cover RAM: 38M Sep 4 09:43:16 t520sds kernel: [ 0.000000] gran_size: 64K chunk_size: 8M num_reg: 10 lose cover RAM: 38M Sep 4 09:43:16 t520sds kernel: [ 0.000000] gran_size: 64K chunk_size: 16M num_reg: 10 lose cover RAM: 38M Sep 4 09:43:16 t520sds kernel: [ 0.000000] *BAD*gran_size: 64K chunk_size: 32M num_reg: 10 lose cover RAM: -16M Sep 4 09:43:16 t520sds kernel: [ 0.000000] *BAD*gran_size: 64K chunk_size: 64M num_reg: 10 lose cover RAM: -16M Sep 4 09:43:16 t520sds kernel: [ 0.000000] gran_size: 64K chunk_size: 128M num_reg: 10 lose cover RAM: 0G Sep 4 09:43:16 t520sds kernel: [ 0.000000] gran_size: 64K chunk_size: 256M num_reg: 10 lose cover RAM: 0G Sep 4 09:43:16 t520sds kernel: [ 0.000000] gran_size: 64K chunk_size: 512M num_reg: 10 lose cover RAM: 0G Sep 4 09:43:16 t520sds kernel: [ 0.000000] gran_size: 64K chunk_size: 1G num_reg: 10 lose cover RAM: 0G Sep 4 09:43:16 t520sds kernel: [ 0.000000] *BAD*gran_size: 64K chunk_size: 2G num_reg: 10 lose cover RAM: -1G does this mean that my RAM is bad?! it also says Sep 4 09:43:16 t520sds kernel: [ 2.944123] EXT4-fs (sda1): INFO: recovery required on readonly filesystem Sep 4 09:43:16 t520sds kernel: [ 2.944126] EXT4-fs (sda1): write access will be enabled during recovery Sep 4 09:43:16 t520sds kernel: [ 3.088001] firewire_core: created device fw0: GUID f0def1ff8fbd7dff, S400 Sep 4 09:43:16 t520sds kernel: [ 8.929243] EXT4-fs (sda1): orphan cleanup on readonly fs Sep 4 09:43:16 t520sds kernel: [ 8.929249] EXT4-fs (sda1): ext4_orphan_cleanup: deleting unreferenced inode 658984 ... Sep 4 09:43:16 t520sds kernel: [ 9.343266] EXT4-fs (sda1): ext4_orphan_cleanup: deleting unreferenced inode 525343 Sep 4 09:43:16 t520sds kernel: [ 9.343270] EXT4-fs (sda1): 56 orphan inodes deleted Sep 4 09:43:16 t520sds kernel: [ 9.343271] EXT4-fs (sda1): recovery complete Sep 4 09:43:16 t520sds kernel: [ 9.645799] EXT4-fs (sda1): mounted filesystem with ordered data mode. Opts: (null) does this mean my HD is bad? As per FaultyHardware, I tried smartctl -l selftest, which uncovered no errors: smartctl 5.41 2011-06-09 r3365 [x86_64-linux-3.2.0-30-generic] (local build) Copyright (C) 2002-11 by Bruce Allen, http://smartmontools.sourceforge.net === START OF INFORMATION SECTION === Model Family: Seagate Momentus 7200.4 Device Model: ST9500420AS Serial Number: 5VJE81YK LU WWN Device Id: 5 000c50 0440defe3 Firmware Version: 0003LVM1 User Capacity: 500,107,862,016 bytes [500 GB] Sector Size: 512 bytes logical/physical Device is: In smartctl database [for details use: -P show] ATA Version is: 8 ATA Standard is: ATA-8-ACS revision 4 Local Time is: Mon Sep 10 16:40:04 2012 EDT SMART support is: Available - device has SMART capability. SMART support is: Enabled === START OF READ SMART DATA SECTION === SMART overall-health self-assessment test result: PASSED See vendor-specific Attribute list for marginal Attributes. General SMART Values: Offline data collection status: (0x82) Offline data collection activity was completed without error. Auto Offline Data Collection: Enabled. Self-test execution status: ( 0) The previous self-test routine completed without error or no self-test has ever been run. Total time to complete Offline data collection: ( 0) seconds. Offline data collection capabilities: (0x7b) SMART execute Offline immediate. Auto Offline data collection on/off support. Suspend Offline collection upon new command. Offline surface scan supported. Self-test supported. Conveyance Self-test supported. Selective Self-test supported. SMART capabilities: (0x0003) Saves SMART data before entering power-saving mode. Supports SMART auto save timer. Error logging capability: (0x01) Error logging supported. General Purpose Logging supported. Short self-test routine recommended polling time: ( 1) minutes. Extended self-test routine recommended polling time: ( 109) minutes. Conveyance self-test routine recommended polling time: ( 2) minutes. SCT capabilities: (0x103b) SCT Status supported. SCT Error Recovery Control supported. SCT Feature Control supported. SCT Data Table supported. SMART Attributes Data Structure revision number: 10 Vendor Specific SMART Attributes with Thresholds: ID# ATTRIBUTE_NAME FLAG VALUE WORST THRESH TYPE UPDATED WHEN_FAILED RAW_VALUE 1 Raw_Read_Error_Rate 0x000f 117 099 034 Pre-fail Always - 162843537 3 Spin_Up_Time 0x0003 100 100 000 Pre-fail Always - 0 4 Start_Stop_Count 0x0032 100 100 020 Old_age Always - 571 5 Reallocated_Sector_Ct 0x0033 100 100 036 Pre-fail Always - 0 7 Seek_Error_Rate 0x000f 069 060 030 Pre-fail Always - 17210154023 9 Power_On_Hours 0x0032 095 095 000 Old_age Always - 174362787320258 10 Spin_Retry_Count 0x0013 100 100 097 Pre-fail Always - 0 12 Power_Cycle_Count 0x0032 100 100 020 Old_age Always - 571 184 End-to-End_Error 0x0032 100 100 099 Old_age Always - 0 187 Reported_Uncorrect 0x0032 100 100 000 Old_age Always - 0 188 Command_Timeout 0x0032 100 100 000 Old_age Always - 1 189 High_Fly_Writes 0x003a 100 100 000 Old_age Always - 0 190 Airflow_Temperature_Cel 0x0022 061 043 045 Old_age Always In_the_past 39 (0 11 44 26) 191 G-Sense_Error_Rate 0x0032 100 100 000 Old_age Always - 84 192 Power-Off_Retract_Count 0x0032 100 100 000 Old_age Always - 20 193 Load_Cycle_Count 0x0032 099 099 000 Old_age Always - 2434 194 Temperature_Celsius 0x0022 039 057 000 Old_age Always - 39 (0 15 0 0) 195 Hardware_ECC_Recovered 0x001a 041 041 000 Old_age Always - 162843537 196 Reallocated_Event_Count 0x000f 095 095 030 Pre-fail Always - 4540 (61955, 0) 197 Current_Pending_Sector 0x0012 100 100 000 Old_age Always - 0 198 Offline_Uncorrectable 0x0010 100 100 000 Old_age Offline - 0 199 UDMA_CRC_Error_Count 0x003e 200 200 000 Old_age Always - 0 254 Free_Fall_Sensor 0x0032 100 100 000 Old_age Always - 0 SMART Error Log Version: 1 No Errors Logged SMART Self-test log structure revision number 1 Num Test_Description Status Remaining LifeTime(hours) LBA_of_first_error # 1 Extended offline Completed without error 00% 4545 - SMART Selective self-test log data structure revision number 1 SPAN MIN_LBA MAX_LBA CURRENT_TEST_STATUS 1 0 0 Not_testing 2 0 0 Not_testing 3 0 0 Not_testing 4 0 0 Not_testing 5 0 0 Not_testing Selective self-test flags (0x0): After scanning selected spans, do NOT read-scan remainder of disk. If Selective self-test is pending on power-up, resume after 0 minute delay. Googling for the messages proved inconclusive, I can't even figure out whether the messages are routine or catastrophic. So, what do I do now?

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  • The True Cost of a Solution

    - by D'Arcy Lussier
    I had a Twitter chat recently with someone suggesting Oracle and SQL Server were losing out to OSS (Open Source Software) in the enterprise due to their issues with scaling or being too generic (one size fits all). I challenged that a bit, as my experience with enterprise sized clients has been different – adverse to OSS but receptive to an established vendor. The response I got was: Found it easier to influence change by showing how X can’t solve our problems or X is extremely costly to scale. Money talks. I think this is definitely the right approach for anyone pitching an alternate or alien technology as part of a solution: identify the issue, identify the solution, then present pros and cons including a cost/benefit analysis. What can happen though is we get tunnel vision and don’t present a full view of the costs associated with a solution. An “Acura”te Example (I’m so clever…) This is my dream vehicle, a Crystal Black Pearl coloured Acura MDX with the SH-AWD package! We’re a family of 4 (5 if my daughters ever get their wish of adding a dog), and I’ve always wanted a luxury type of vehicle, so this is a perfect replacement in a few years when our Rav 4 has hit the 8 – 10 year mark. MSRP – $62,890 But as we all know, that’s not *really* the cost of the vehicle. There’s taxes and fees added on, there’s the extended warranty if I choose to purchase it, there’s the finance rate that needs to be factored in… MSRP –   $62,890 Taxes –      $7,546 Warranty - $2,500 SubTotal – $72,936 Finance Charge – $ 1094.04 Grand Total – $74,030 Well! Glad we did that exercise – we discovered an extra $11k added on to the MSRP! Well now we have our true price…or do we? Lifetime of the Vehicle I’m expecting to have this vehicle for 7 – 10 years. While the hard cost of the vehicle is known and dealt with, the costs to run and maintain the vehicle are on top of this. I did some research, and here’s what I’ve found: Fuel and Mileage Gas prices are high as it is for regular fuel, but getting into an MDX will require that I *only* purchase premium fuel, which comes at a premium price. I need to expect my bill at the pump to be higher. Comparing the MDX to my 2007 Rav4 also shows I’ll be gassing up more often. The Rav4 has a city MPG of 21, while the MDX plummets to 16! The MDX does have a bigger fuel tank though, so all in all the number of times I hit the pumps might even out. Still, I estimate I’ll be spending approximately $8000 – $10000 more on gas over a 10 year period than my current Rav4. Service Options Limited Although I have options with my Toyota here in Winnipeg (we have 4 Toyota dealerships), I do go to my original dealer for any service work. Still, I like the fact that I have options. However, there’s only one Acura dealership in all of Winnipeg! So if, for whatever reason, I’m not satisfied with the level of service I’m stuck. Non Warranty Service Work Also let’s not forget that there’s a bulk of work required every year that is *not* covered under warranty – oil changes, tire rotations, brake pads, etc. I expect I’ll need to get new tires at the 5 years mark as well, which can easily be $1200 – $1500 (I just paid $1000 for new tires for the Rav4 and we’re at the 5 year mark). Now these aren’t going to be *new* costs that I’m not used to from our existing vehicles, but they should still be factored in. I’d budget $500/year, or $5000 over the 10 years I’ll own the vehicle. Final Assessment So let’s re-assess the true cost of my dream MDX: MSRP                    $62,890 Taxes                       $7,546 Warranty                 $2,500 Finance Charge         $1094 Gas                        $10,000 Service Work            $5000 Grand Total           $89,030 So now I have a better idea of 10 year cost overall, and I’ve identified some concerns with local service availability. And there’s now much more to consider over the original $62,890 price tag. Tying This Back to Technology Solutions The process that we just went through is no different than what organizations do when considering implementing a new system, technology, or technology based solution, within their environments. It’s easy to tout the short term cost savings of particular product/platform/technology in a vacuum. But its when you consider the wider impact that the true cost comes into play. Let’s create a scenario: A company is not happy with its current data reporting suite. An employee suggests moving to an open source solution. The selling points are: - Because its open source its free - The organization would have access to the source code so they could alter it however they wished - It provided features not available with the current reporting suite At first this sounds great to the management and executive, but then they start asking some questions and uncover more information: - The OSS product is built on a technology not used anywhere within the organization - There are no vendors offering product support for the OSS product - The OSS product requires a specific server platform to operate on, one that’s not standard in the organization All of a sudden, the true cost of implementing this solution is starting to become clearer. The company might save money on licensing costs, but their training costs would increase significantly – developers would need to learn how to develop in the technology the OSS solution was built on, IT staff must learn how to set up and maintain a new server platform within their existing infrastructure, and if a problem was found there was no vendor to contact for support. The true cost of implementing a “free” OSS solution is actually spinning up a project to implement it within the organization – no small cost. And that’s just the short-term cost. Now the organization must ensure they maintain trained staff who can make changes to the OSS reporting solution and IT staff that will stay knowledgeable in the new server platform. If those skills are very niche, then higher labour costs could be incurred if those people are hard to find or if trained employees use that knowledge as leverage for higher pay. Maybe a vendor exists that will contract out support, but then there are those costs to consider as well. And let’s not forget end-user training – in our example, anyone that runs reports will need to be trained on how to use the new system. Here’s the Point We still tend to look at software in an “off the shelf” kind of way. It’s very easy to say “oh, this product is better than vendor x’s product – and its free because its OSS!” but the reality is that implementing any new technology within an organization has a cost regardless of the retail price of the product. Training, integration, support – these are real costs that impact an organization and span multiple departments. Whether you’re pitching an improved business process, a new system, or a new technology, you need to consider the bigger picture costs of implementation. What you define as success (in our example, having better reporting functionality) might not be what others define as success if implementing your solution causes them issues. A true enterprise solution needs to consider the entire enterprise.

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  • BI Applications overview

    - by sv744
    Welcome to Oracle BI applications blog! This blog will talk about various features, general roadmap, description of functionality and implementation steps related to Oracle BI applications. In the first post we start with an overview of the BI apps and will delve deeper into some of the topics below in the upcoming weeks and months. If there are other topics you would like us to talk about, pl feel free to provide feedback on that. The Oracle BI applications are a set of pre-built applications that enable pervasive BI by providing role-based insight for each functional area, including sales, service, marketing, contact center, finance, supplier/supply chain, HR/workforce, and executive management. For example, Sales Analytics includes role-based applications for sales executives, sales management, as well as front-line sales reps, each of whom have different needs. The applications integrate and transform data from a range of enterprise sources—including Siebel, Oracle, PeopleSoft, SAP, and others—into actionable intelligence for each business function and user role. This blog  starts with the key benefits and characteristics of Oracle BI applications. In a series of subsequent blogs, each of these points will be explained in detail. Why BI apps? Demonstrate the value of BI to a business user, show reports / dashboards / model that can answer their business questions as part of the sales cycle. Demonstrate technical feasibility of BI project and significantly lower risk and improve success Build Vs Buy benefit Don’t have to start with a blank sheet of paper. Help consolidate disparate systems Data integration in M&A situations Insulate BI consumers from changes in the OLTP Present OLTP data and highlight issues of poor data / missing data – and improve data quality and accuracy Prebuilt Integrations BI apps support prebuilt integrations against leading ERP sources: Fusion Applications, E- Business Suite, Peoplesoft, JD Edwards, Siebel, SAP Co-developed with inputs from functional experts in BI and Applications teams. Out of the box dimensional model to source model mappings Multi source and Multi Instance support Rich Data Model    BI apps have a very rich dimensionsal data model built over 10 years that incorporates best practises from BI modeling perspective as well as reflect the source system complexities  Thanks for reading a long post, and be on the lookout for future posts.  We will look forward to your valuable feedback on these topics as well as suggestions on what other topics would you like us to cover. I Conformed dimensional model across all business subject areas allows cross functional reporting, e.g. customer / supplier 360 Over 360 fact tables across 7 product areas CRM – 145, SCM – 47, Financials – 28, Procurement – 20, HCM – 27, Projects – 18, Campus Solutions – 21, PLM - 56 Supported by 300 physical dimensions Support for extensive calendars; Gregorian, enterprise and ledger based Conformed data model and metrics for real time vs warehouse based reporting  Multi-tenant enabled Extensive BI related transformations BI apps ETL and data integration support various transformations required for dimensional models and reporting requirements. All these have been distilled into common patterns and abstracted logic which can be readily reused across different modules Slowly Changing Dimension support Hierarchy flattening support Row / Column Hybrid Hierarchy Flattening As Is vs. As Was hierarchy support Currency Conversion :-  Support for 3 corporate, CRM, ledger and transaction currencies UOM conversion Internationalization / Localization Dynamic Data translations Code standardization (Domains) Historical Snapshots Cycle and process lifecycle computations Balance Facts Equalization of GL accounting chartfields/segments Standardized values for categorizing GL accounts Reconciliation between GL and subledgers to track accounted/transferred/posted transactions to GL Materialization of data only available through costly and complex APIs e.g. Fusion Payroll, EBS / Fusion Accruals Complex event Interpretation of source data – E.g. o    What constitutes a transfer o    Deriving supervisors via position hierarchy o    Deriving primary assignment in PSFT o    Categorizing and transposition to measures of Payroll Balances to specific metrics to support side by side comparison of measures of for example Fixed Salary, Variable Salary, Tax, Bonus, Overtime Payments. o    Counting of Events – E.g. converting events to fact counters so that for example the number of hires can easily be added up and compared alongside the total transfers and terminations. Multi pass processing of multiple sources e.g. headcount, salary, promotion, performance to allow side to side comparison. Adding value to data to aid analysis through banding, additional domain classifications and groupings to allow higher level analytical reporting and data discovery Calculation of complex measures examples: o    COGs, DSO, DPO, Inventory turns  etc o    Transfers within a Hierarchy or out of / into a hierarchy relative to view point in hierarchy. Configurability and Extensibility support  BI apps offer support for extensibility for various entities as automated extensibility or part of extension methodology Key Flex fields and Descriptive Flex support  Extensible attribute support (JDE)  Conformed Domains ETL Architecture BI apps offer a modular adapter architecture which allows support of multiple product lines into a single conformed model Multi Source Multi Technology Orchestration – creates load plan taking into account task dependencies and customers deployment to generate a plan based on a customers of multiple complex etl tasks Plan optimization allowing parallel ETL tasks Oracle: Bit map indexes and partition management High availability support    Follow the sun support. TCO BI apps support several utilities / capabilities that help with overall total cost of ownership and ensure a rapid implementation Improved cost of ownership – lower cost to deploy On-going support for new versions of the source application Task based setups flows Data Lineage Functional setup performed in Web UI by Functional person Configuration Test to Production support Security BI apps support both data and object security enabling implementations to quickly configure the application as per the reporting security needs Fine grain object security at report / dashboard and presentation catalog level Data Security integration with source systems  Extensible to support external data security rules Extensive Set of KPIs Over 7000 base and derived metrics across all modules Time series calculations (YoY, % growth etc) Common Currency and UOM reporting Cross subject area KPIs (analyzing HR vs GL data, drill from GL to AP/AR, etc) Prebuilt reports and dashboards 3000+ prebuilt reports supporting a large number of industries Hundreds of role based dashboards Dynamic currency conversion at dashboard level Highly tuned Performance The BI apps have been tuned over the years for both a very performant ETL and dashboard performance. The applications use best practises and advanced database features to enable the best possible performance. Optimized data model for BI and analytic queries Prebuilt aggregates& the ability for customers to create their own aggregates easily on warehouse facts allows for scalable end user performance Incremental extracts and loads Incremental Aggregate build Automatic table index and statistics management Parallel ETL loads Source system deletes handling Low latency extract with Golden Gate Micro ETL support Bitmap Indexes Partitioning support Modularized deployment, start small and add other subject areas seamlessly Source Specfic Staging and Real Time Schema Support for source specific operational reporting schema for EBS, PSFT, Siebel and JDE Application Integrations The BI apps also allow for integration with source systems as well as other applications that provide value add through BI and enable BI consumption during operational decision making Embedded dashboards for Fusion, EBS and Siebel applications Action Link support Marketing Segmentation Sales Predictor Dashboard Territory Management External Integrations The BI apps data integration choices include support for loading extenral data External data enrichment choices : UNSPSC, Item class etc. Extensible Spend Classification Broad Deployment Choices Exalytics support Databases :  Oracle, Exadata, Teradata, DB2, MSSQL ETL tool of choice : ODI (coming), Informatica Extensible and Customizable Extensible architecture and Methodology to add custom and external content Upgradable across releases

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  • ?Oracle????SELECT????UNDO

    - by Liu Maclean(???)
    ????????Oracle?????(dirty read),?Oracle??????Asktom????????Oracle???????, ???undo??????????(before image)??????Consistent, ???????????????Oracle????????????? ????????? ??,??,Oracle?????????????RDBMS,???????????? ?????????2?????: _offline_rollback_segments or _corrupted_rollback_segments ?2?????????Oracle???????????ORA-600[4XXX]???????????????,???2??????Undo??Corruption????????????,?????2????????????????? ??????????????_offline_rollback_segments ? _corrupted_rollback_segments ?2?????: ???????(FORCE OPEN DATABASE) ????????????(consistent read & delayed block cleanout) ??????rollback segment??? ?????:???????Oracle????????,??????????2?????,?????????????!! _offline_rollback_segments ? _corrupted_rollback_segments ???????????: ??2???????Undo Segments(???/???)????????online ?UNDO$???????????OFFLINE??? ???instance??????????????????? ??????Undo Segments????????active transaction????????????dead??SMON???(????????SMON??(?):Recover Dead transaction) _OFFLINE_ROLLBACK_SEGMENTS(offline undo segment list)????(hidden parameter)?????: ???startup???open database???????_OFFLINE_ROLLBACK_SEGMENTS????Undo segments(???/???),?????undo segments????????alert.log???TRACE?????,???????startup?? ?????????????,?ITL?????undo segments?: ???undo segments?transaction table?????????????????? ???????????commit,?????CR??? ????undo segments????(???corrupted??,???missed??)???????????alert.log,??????? ?DML?????????????????????????????????CPU,????????????????????? _CORRUPTED_ROLLBACK_SEGMENTS(corrupted undo segment list)??????????: ?????startup?open database???_CORRUPTED_ROLLBACK_SEGMENTS????undo segments(???/???)???????? ???????_CORRUPTED_ROLLBACK_SEGMENTS???undo segments????????????commit,???undo segments???drop??? ??????????? ??????????????????,?????????????????? ??bootstrap???????????,?????????ORA-00704: bootstrap process failure??,???????????(???Oracle????:??ORA-00600:[4000] ORA-00704: bootstrap process failure????) ??????_CORRUPTED_ROLLBACK_SEGMENTS????????????????????,??????????????? Oracle???????TXChecker??????????? ???????2?????,??????????????_CORRUPTED_ROLLBACK_SEGMENTS?????SELECT????UNDO???????: SQL> alter system set event= '10513 trace name context forever, level 2' scope=spfile; System altered. SQL> alter system set "_in_memory_undo"=false scope=spfile; System altered. 10513 level 2 event????SMON ??rollback ??? dead transaction _in_memory_undo ?? in memory undo ?? SQL> startup force; ORACLE instance started. Total System Global Area 3140026368 bytes Fixed Size 2232472 bytes Variable Size 1795166056 bytes Database Buffers 1325400064 bytes Redo Buffers 17227776 bytes Database mounted. Database opened. session A: SQL> conn maclean/maclean Connected. SQL> create table maclean tablespace users as select 1 t1 from dual connect by level exec dbms_stats.gather_table_stats('','MACLEAN'); PL/SQL procedure successfully completed. SQL> set autotrace on; SQL> select sum(t1) from maclean; SUM(T1) ---------- 501 Execution Plan ---------------------------------------------------------- Plan hash value: 1679547536 ------------------------------------------------------------------------------ | Id | Operation | Name | Rows | Bytes | Cost (%CPU)| Time | ------------------------------------------------------------------------------ | 0 | SELECT STATEMENT | | 1 | 3 | 3 (0)| 00:00:01 | | 1 | SORT AGGREGATE | | 1 | 3 | | | | 2 | TABLE ACCESS FULL| MACLEAN | 501 | 1503 | 3 (0)| 00:00:01 | ------------------------------------------------------------------------------ Statistics ---------------------------------------------------------- 1 recursive calls 0 db block gets 3 consistent gets 0 physical reads 0 redo size 515 bytes sent via SQL*Net to client 492 bytes received via SQL*Net from client 2 SQL*Net roundtrips to/from client 0 sorts (memory) 0 sorts (disk) 1 rows processe ???????????,????current block, ????????,consistent gets??3? SQL> update maclean set t1=0; 501 rows updated. SQL> alter system checkpoint; System altered. ??session A?commit; ???? session: SQL> conn maclean/maclean Connected. SQL> SQL> set autotrace on; SQL> select sum(t1) from maclean; SUM(T1) ---------- 501 Execution Plan ---------------------------------------------------------- Plan hash value: 1679547536 ------------------------------------------------------------------------------ | Id | Operation | Name | Rows | Bytes | Cost (%CPU)| Time | ------------------------------------------------------------------------------ | 0 | SELECT STATEMENT | | 1 | 3 | 3 (0)| 00:00:01 | | 1 | SORT AGGREGATE | | 1 | 3 | | | | 2 | TABLE ACCESS FULL| MACLEAN | 501 | 1503 | 3 (0)| 00:00:01 | ------------------------------------------------------------------------------ Statistics ---------------------------------------------------------- 0 recursive calls 0 db block gets 505 consistent gets 0 physical reads 108 redo size 515 bytes sent via SQL*Net to client 492 bytes received via SQL*Net from client 2 SQL*Net roundtrips to/from client 0 sorts (memory) 0 sorts (disk) 1 rows processed ?????? ?????????undo??CR?,???consistent gets??? 505 [oracle@vrh8 ~]$ ps -ef|grep LOCAL=YES |grep -v grep oracle 5841 5839 0 09:17 ? 00:00:00 oracleG10R25 (DESCRIPTION=(LOCAL=YES)(ADDRESS=(PROTOCOL=beq))) [oracle@vrh8 ~]$ kill -9 5841 ??session A???Server Process????,???dead transaction ????smon?? select ktuxeusn, to_char(sysdate, 'DD-MON-YYYY HH24:MI:SS') "Time", ktuxesiz, ktuxesta from x$ktuxe where ktuxecfl = 'DEAD'; KTUXEUSN Time KTUXESIZ KTUXESTA ---------- -------------------- ---------- ---------------- 2 06-AUG-2012 09:20:45 7 ACTIVE ???1?active rollback segment SQL> conn maclean/maclean Connected. SQL> set autotrace on; SQL> select sum(t1) from maclean; SUM(T1) ---------- 501 Execution Plan ---------------------------------------------------------- Plan hash value: 1679547536 ------------------------------------------------------------------------------ | Id | Operation | Name | Rows | Bytes | Cost (%CPU)| Time | ------------------------------------------------------------------------------ | 0 | SELECT STATEMENT | | 1 | 3 | 3 (0)| 00:00:01 | | 1 | SORT AGGREGATE | | 1 | 3 | | | | 2 | TABLE ACCESS FULL| MACLEAN | 501 | 1503 | 3 (0)| 00:00:01 | ------------------------------------------------------------------------------ Statistics ---------------------------------------------------------- 0 recursive calls 0 db block gets 411 consistent gets 0 physical reads 108 redo size 515 bytes sent via SQL*Net to client 492 bytes received via SQL*Net from client 2 SQL*Net roundtrips to/from client 0 sorts (memory) 0 sorts (disk) 1 rows processed ????? ????kill?? ???smon ??dead transaction , ???????????? ?????undo??????? ????active?rollback segment??? SQL> select segment_name from dba_rollback_segs where segment_id=2; SEGMENT_NAME ------------------------------ _SYSSMU2$ SQL> alter system set "_corrupted_rollback_segments"='_SYSSMU2$' scope=spfile; System altered. ? _corrupted_rollback_segments ?? ???2?rollback segment, ????????undo SQL> startup force; ORACLE instance started. Total System Global Area 3140026368 bytes Fixed Size 2232472 bytes Variable Size 1795166056 bytes Database Buffers 1325400064 bytes Redo Buffers 17227776 bytes Database mounted. Database opened. SQL> conn maclean/maclean Connected. SQL> set autotrace on; SQL> select sum(t1) from maclean; SUM(T1) ---------- 94 Execution Plan ---------------------------------------------------------- Plan hash value: 1679547536 ------------------------------------------------------------------------------ | Id | Operation | Name | Rows | Bytes | Cost (%CPU)| Time | ------------------------------------------------------------------------------ | 0 | SELECT STATEMENT | | 1 | 3 | 3 (0)| 00:00:01 | | 1 | SORT AGGREGATE | | 1 | 3 | | | | 2 | TABLE ACCESS FULL| MACLEAN | 501 | 1503 | 3 (0)| 00:00:01 | ------------------------------------------------------------------------------ Statistics ---------------------------------------------------------- 228 recursive calls 0 db block gets 29 consistent gets 5 physical reads 116 redo size 514 bytes sent via SQL*Net to client 492 bytes received via SQL*Net from client 2 SQL*Net roundtrips to/from client 4 sorts (memory) 0 sorts (disk) 1 rows processed SQL> / SUM(T1) ---------- 94 Execution Plan ---------------------------------------------------------- Plan hash value: 1679547536 ------------------------------------------------------------------------------ | Id | Operation | Name | Rows | Bytes | Cost (%CPU)| Time | ------------------------------------------------------------------------------ | 0 | SELECT STATEMENT | | 1 | 3 | 3 (0)| 00:00:01 | | 1 | SORT AGGREGATE | | 1 | 3 | | | | 2 | TABLE ACCESS FULL| MACLEAN | 501 | 1503 | 3 (0)| 00:00:01 | ------------------------------------------------------------------------------ Statistics ---------------------------------------------------------- 0 recursive calls 0 db block gets 3 consistent gets 0 physical reads 0 redo size 514 bytes sent via SQL*Net to client 492 bytes received via SQL*Net from client 2 SQL*Net roundtrips to/from client 0 sorts (memory) 0 sorts (disk) 1 rows processed ?????? consistent gets???3,?????????????????,??ITL???UNDO SEGMENTS?_corrupted_rollback_segments????,???????????COMMIT??,????UNDO? ???????,?????????????????????????(????????????????????),????????????????? ???? , ?????

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  • how to use 3D map Actionscript class in mxml file for display map.

    - by nemade-vipin
    hello friends, I have created the application in which I have to use 3D map Action Script class in mxml file to display a map in form. that is in tab navigator last tab. My ActionScript 3D map class is(FlyingDirections):- package src.SBTSCoreObject { import src.SBTSCoreObject.JSONDecoder; import com.google.maps.InfoWindowOptions; import com.google.maps.LatLng; import com.google.maps.LatLngBounds; import com.google.maps.Map3D; import com.google.maps.MapEvent; import com.google.maps.MapOptions; import com.google.maps.MapType; import com.google.maps.MapUtil; import com.google.maps.View; import com.google.maps.controls.NavigationControl; import com.google.maps.geom.Attitude; import com.google.maps.interfaces.IPolyline; import com.google.maps.overlays.Marker; import com.google.maps.overlays.MarkerOptions; import com.google.maps.services.Directions; import com.google.maps.services.DirectionsEvent; import com.google.maps.services.Route; import flash.display.Bitmap; import flash.display.DisplayObject; import flash.display.DisplayObjectContainer; import flash.display.Loader; import flash.display.LoaderInfo; import flash.display.Sprite; import flash.events.Event; import flash.events.IOErrorEvent; import flash.events.MouseEvent; import flash.events.TimerEvent; import flash.filters.DropShadowFilter; import flash.geom.Point; import flash.net.URLLoader; import flash.net.URLRequest; import flash.net.navigateToURL; import flash.text.TextField; import flash.text.TextFieldAutoSize; import flash.text.TextFormat; import flash.utils.Timer; import flash.utils.getTimer; public class FlyingDirections extends Map3D { /** * Panoramio home page. */ private static const PANORAMIO_HOME:String = "http://www.panoramio.com/"; /** * The icon for the car. */ [Embed("assets/car-icon-24px.png")] private static const Car:Class; /** * The Panoramio icon. */ [Embed("assets/iw_panoramio.png")] private static const PanoramioIcon:Class; /** * We animate a zoom in to the start the route before the car starts * to move. This constant sets the time in seconds over which this * zoom occurs. */ private static const LEAD_IN_DURATION:Number = 3; /** * Duration of the trip in seconds. */ private static const TRIP_DURATION:Number = 40; /** * Constants that define the geometry of the Panoramio image markers. */ private static const BORDER_T:Number = 3; private static const BORDER_L:Number = 10; private static const BORDER_R:Number = 10; private static const BORDER_B:Number = 3; private static const GAP_T:Number = 2; private static const GAP_B:Number = 1; private static const IMAGE_SCALE:Number = 1; /** * Trajectory that the camera follows over time. Each element is an object * containing properties used to generate parameter values for flyTo(..). * fraction = 0 corresponds to the start of the trip; fraction = 1 * correspondsto the end of the trip. */ private var FLY_TRAJECTORY:Array = [ { fraction: 0, zoom: 6, attitude: new Attitude(0, 0, 0) }, { fraction: 0.2, zoom: 8.5, attitude: new Attitude(30, 30, 0) }, { fraction: 0.5, zoom: 9, attitude: new Attitude(30, 40, 0) }, { fraction: 1, zoom: 8, attitude: new Attitude(50, 50, 0) }, { fraction: 1.1, zoom: 8, attitude: new Attitude(130, 50, 0) }, { fraction: 1.2, zoom: 8, attitude: new Attitude(220, 50, 0) }, ]; /** * Number of panaramio photos for which we load data. We&apos;ll select a * subset of these approximately evenly spaced along the route. */ private static const NUM_GEOTAGGED_PHOTOS:int = 50; /** * Number of panaramio photos that we actually show. */ private static const NUM_SHOWN_PHOTOS:int = 7; /** * Scaling between real trip time and animation time. */ private static const SCALE_TIME:Number = 0.001; /** * getTimer() value at the instant that we start the trip. If this is 0 then * we have not yet started the car moving. */ private var startTimer:int = 0; /** * The current route. */ private var route:Route; /** * The polyline for the route. */ private var polyline:IPolyline; /** * The car marker. */ private var marker:Marker; /** * The cumulative duration in seconds over each step in the route. * cumulativeStepDuration[0] is 0; cumulativeStepDuration[1] adds the * duration of step 0; cumulativeStepDuration[2] adds the duration * of step 1; etc. */ private var cumulativeStepDuration:/*Number*/Array = []; /** * The cumulative distance in metres over each vertex in the route polyline. * cumulativeVertexDistance[0] is 0; cumulativeVertexDistance[1] adds the * distance to vertex 1; cumulativeVertexDistance[2] adds the distance to * vertex 2; etc. */ private var cumulativeVertexDistance:Array; /** * Array of photos loaded from Panoramio. This array has the same format as * the &apos;photos&apos; property within the JSON returned by the Panoramio API * (see http://www.panoramio.com/api/), with additional properties added to * individual photo elements to hold the loader structures that fetch * the actual images. */ private var photos:Array = []; /** * Array of polyline vertices, where each element is in world coordinates. * Several computations can be faster if we can use world coordinates * instead of LatLng coordinates. */ private var worldPoly:/*Point*/Array; /** * Whether the start button has been pressed. */ private var startButtonPressed:Boolean = false; /** * Saved event from onDirectionsSuccess call. */ private var directionsSuccessEvent:DirectionsEvent = null; /** * Start button. */ private var startButton:Sprite; /** * Alpha value used for the Panoramio image markers. */ private var markerAlpha:Number = 0; /** * Index of the current driving direction step. Used to update the * info window content each time we progress to a new step. */ private var currentStepIndex:int = -1; /** * The fly directions map constructor. * * @constructor */ public function FlyingDirections() { key="ABQIAAAA7QUChpcnvnmXxsjC7s1fCxQGj0PqsCtxKvarsoS-iqLdqZSKfxTd7Xf-2rEc_PC9o8IsJde80Wnj4g"; super(); addEventListener(MapEvent.MAP_PREINITIALIZE, onMapPreinitialize); addEventListener(MapEvent.MAP_READY, onMapReady); } /** * Handles map preintialize. Initializes the map center and zoom level. * * @param event The map event. */ private function onMapPreinitialize(event:MapEvent):void { setInitOptions(new MapOptions({ center: new LatLng(-26.1, 135.1), zoom: 4, viewMode: View.VIEWMODE_PERSPECTIVE, mapType:MapType.PHYSICAL_MAP_TYPE })); } /** * Handles map ready and looks up directions. * * @param event The map event. */ private function onMapReady(event:MapEvent):void { enableScrollWheelZoom(); enableContinuousZoom(); addControl(new NavigationControl()); // The driving animation will be updated on every frame. addEventListener(Event.ENTER_FRAME, enterFrame); addStartButton(); // We start the directions loading now, so that we&apos;re ready to go when // the user hits the start button. var directions:Directions = new Directions(); directions.addEventListener( DirectionsEvent.DIRECTIONS_SUCCESS, onDirectionsSuccess); directions.addEventListener( DirectionsEvent.DIRECTIONS_FAILURE, onDirectionsFailure); directions.load("48 Pirrama Rd, Pyrmont, NSW to Byron Bay, NSW"); } /** * Adds a big blue start button. */ private function addStartButton():void { startButton = new Sprite(); startButton.buttonMode = true; startButton.addEventListener(MouseEvent.CLICK, onStartClick); startButton.graphics.beginFill(0x1871ce); startButton.graphics.drawRoundRect(0, 0, 150, 100, 10, 10); startButton.graphics.endFill(); var startField:TextField = new TextField(); startField.autoSize = TextFieldAutoSize.LEFT; startField.defaultTextFormat = new TextFormat("_sans", 20, 0xffffff, true); startField.text = "Start!"; startButton.addChild(startField); startField.x = 0.5 * (startButton.width - startField.width); startField.y = 0.5 * (startButton.height - startField.height); startButton.filters = [ new DropShadowFilter() ]; var container:DisplayObjectContainer = getDisplayObject() as DisplayObjectContainer; container.addChild(startButton); startButton.x = 0.5 * (container.width - startButton.width); startButton.y = 0.5 * (container.height - startButton.height); var panoField:TextField = new TextField(); panoField.autoSize = TextFieldAutoSize.LEFT; panoField.defaultTextFormat = new TextFormat("_sans", 11, 0x000000, true); panoField.text = "Photos provided by Panoramio are under the copyright of their owners."; container.addChild(panoField); panoField.x = container.width - panoField.width - 5; panoField.y = 5; } /** * Handles directions success. Starts flying the route if everything * is ready. * * @param event The directions event. */ private function onDirectionsSuccess(event:DirectionsEvent):void { directionsSuccessEvent = event; flyRouteIfReady(); } /** * Handles click on the start button. Starts flying the route if everything * is ready. */ private function onStartClick(event:MouseEvent):void { startButton.removeEventListener(MouseEvent.CLICK, onStartClick); var container:DisplayObjectContainer = getDisplayObject() as DisplayObjectContainer; container.removeChild(startButton); startButtonPressed = true; flyRouteIfReady(); } /** * If we have loaded the directions and the start button has been pressed * start flying the directions route. */ private function flyRouteIfReady():void { if (!directionsSuccessEvent || !startButtonPressed) { return; } var directions:Directions = directionsSuccessEvent.directions; // Extract the route. route = directions.getRoute(0); // Draws the polyline showing the route. polyline = directions.createPolyline(); addOverlay(directions.createPolyline()); // Creates a car marker that is moved along the route. var car:DisplayObject = new Car(); marker = new Marker(route.startGeocode.point, new MarkerOptions({ icon: car, iconOffset: new Point(-car.width / 2, -car.height) })); addOverlay(marker); transformPolyToWorld(); createCumulativeArrays(); // Load Panoramio data for the region covered by the route. loadPanoramioData(directions.bounds); var duration:Number = route.duration; // Start a timer that will trigger the car moving after the lead in time. var leadInTimer:Timer = new Timer(LEAD_IN_DURATION * 1000, 1); leadInTimer.addEventListener(TimerEvent.TIMER, onLeadInDone); leadInTimer.start(); var flyTime:Number = -LEAD_IN_DURATION; // Set up the camera flight trajectory. for each (var flyStep:Object in FLY_TRAJECTORY) { var time:Number = flyStep.fraction * duration; var center:LatLng = latLngAt(time); var scaledTime:Number = time * SCALE_TIME; var zoom:Number = flyStep.zoom; var attitude:Attitude = flyStep.attitude; var elapsed:Number = scaledTime - flyTime; flyTime = scaledTime; flyTo(center, zoom, attitude, elapsed); } } /** * Loads Panoramio data for the route bounds. We load data about more photos * than we need, then select a subset lying along the route. * @param bounds Bounds within which to fetch images. */ private function loadPanoramioData(bounds:LatLngBounds):void { var params:Object = { order: "popularity", set: "full", from: "0", to: NUM_GEOTAGGED_PHOTOS.toString(10), size: "small", minx: bounds.getWest(), miny: bounds.getSouth(), maxx: bounds.getEast(), maxy: bounds.getNorth() }; var loader:URLLoader = new URLLoader(); var request:URLRequest = new URLRequest( "http://www.panoramio.com/map/get_panoramas.php?" + paramsToString(params)); loader.addEventListener(Event.COMPLETE, onPanoramioDataLoaded); loader.addEventListener(IOErrorEvent.IO_ERROR, onPanoramioDataFailed); loader.load(request); } /** * Transforms the route polyline to world coordinates. */ private function transformPolyToWorld():void { var numVertices:int = polyline.getVertexCount(); worldPoly = new Array(numVertices); for (var i:int = 0; i < numVertices; ++i) { var vertex:LatLng = polyline.getVertex(i); worldPoly[i] = fromLatLngToPoint(vertex, 0); } } /** * Returns the time at which the route approaches closest to the * given point. * @param world Point in world coordinates. * @return Route time at which the closest approach occurs. */ private function getTimeOfClosestApproach(world:Point):Number { var minDistSqr:Number = Number.MAX_VALUE; var numVertices:int = worldPoly.length; var x:Number = world.x; var y:Number = world.y; var minVertex:int = 0; for (var i:int = 0; i < numVertices; ++i) { var dx:Number = worldPoly[i].x - x; var dy:Number = worldPoly[i].y - y; var distSqr:Number = dx * dx + dy * dy; if (distSqr < minDistSqr) { minDistSqr = distSqr; minVertex = i; } } return cumulativeVertexDistance[minVertex]; } /** * Returns the array index of the first element that compares greater than * the given value. * @param ordered Ordered array of elements. * @param value Value to use for comparison. * @return Array index of the first element that compares greater than * the given value. */ private function upperBound(ordered:Array, value:Number, first:int=0, last:int=-1):int { if (last < 0) { last = ordered.length; } var count:int = last - first; var index:int; while (count > 0) { var step:int = count >> 1; index = first + step; if (value >= ordered[index]) { first = index + 1; count -= step - 1; } else { count = step; } } return first; } /** * Selects up to a given number of photos approximately evenly spaced along * the route. * @param ordered Array of photos, each of which is an object with * a property &apos;closestTime&apos;. * @param number Number of photos to select. */ private function selectEvenlySpacedPhotos(ordered:Array, number:int):Array { var start:Number = cumulativeVertexDistance[0]; var end:Number = cumulativeVertexDistance[cumulativeVertexDistance.length - 2]; var closestTimes:Array = []; for each (var photo:Object in ordered) { closestTimes.push(photo.closestTime); } var selectedPhotos:Array = []; for (var i:int = 0; i < number; ++i) { var idealTime:Number = start + ((end - start) * (i + 0.5) / number); var index:int = upperBound(closestTimes, idealTime); if (index < 1) { index = 0; } else if (index >= ordered.length) { index = ordered.length - 1; } else { var errorToPrev:Number = Math.abs(idealTime - closestTimes[index - 1]); var errorToNext:Number = Math.abs(idealTime - closestTimes[index]); if (errorToPrev < errorToNext) { --index; } } selectedPhotos.push(ordered[index]); } return selectedPhotos; } /** * Handles completion of loading the Panoramio index data. Selects from the * returned photo indices a subset of those that lie along the route and * initiates load of each of these. * @param event Load completion event. */ private function onPanoramioDataLoaded(event:Event):void { var loader:URLLoader = event.target as URLLoader; var decoder:JSONDecoder = new JSONDecoder(loader.data as String); var allPhotos:Array = decoder.getValue().photos; for each (var photo:Object in allPhotos) { var latLng:LatLng = new LatLng(photo.latitude, photo.longitude); photo.closestTime = getTimeOfClosestApproach(fromLatLngToPoint(latLng, 0)); } allPhotos.sortOn("closestTime", Array.NUMERIC); photos = selectEvenlySpacedPhotos(allPhotos, NUM_SHOWN_PHOTOS); for each (photo in photos) { var photoLoader:Loader = new Loader(); // The images aren&apos;t on panoramio.com: we can&apos;t acquire pixel access // using "new LoaderContext(true)". photoLoader.load( new URLRequest(photo.photo_file_url)); photo.loader = photoLoader; // Save the loader info: we use this to find the original element when // the load completes. photo.loaderInfo = photoLoader.contentLoaderInfo; photoLoader.contentLoaderInfo.addEventListener( Event.COMPLETE, onPhotoLoaded); } } /** * Creates a MouseEvent listener function that will navigate to the given * URL in a new window. * @param url URL to which to navigate. */ private function createOnClickUrlOpener(url:String):Function { return function(event:MouseEvent):void { navigateToURL(new URLRequest(url)); }; } /** * Handles completion of loading an individual Panoramio image. * Adds a custom marker that displays the image. Initially this is made * invisible so that it can be faded in as needed. * @param event Load completion event. */ private function onPhotoLoaded(event:Event):void { var loaderInfo:LoaderInfo = event.target as LoaderInfo; // We need to find which photo element this image corresponds to. for each (var photo:Object in photos) { if (loaderInfo == photo.loaderInfo) { var imageMarker:Sprite = createImageMarker(photo.loader, photo.owner_name, photo.owner_url); var options:MarkerOptions = new MarkerOptions({ icon: imageMarker, hasShadow: true, iconAlignment: MarkerOptions.ALIGN_BOTTOM | MarkerOptions.ALIGN_LEFT }); var latLng:LatLng = new LatLng(photo.latitude, photo.longitude); var marker:Marker = new Marker(latLng, options); photo.marker = marker; addOverlay(marker); // A hack: we add the actual image after the overlay has been added, // which creates the shadow, so that the shadow is valid even if we // don&apos;t have security privileges to generate the shadow from the // image. marker.foreground.visible = false; marker.shadow.alpha = 0; var imageHolder:Sprite = new Sprite(); imageHolder.addChild(photo.loader); imageHolder.buttonMode = true; imageHolder.addEventListener( MouseEvent.CLICK, createOnClickUrlOpener(photo.photo_url)); imageMarker.addChild(imageHolder); return; } } trace("An image was loaded which could not be found in the photo array."); } /** * Creates a custom marker showing an image. */ private function createImageMarker(child:DisplayObject, ownerName:String, ownerUrl:String):Sprite { var content:Sprite = new Sprite(); var panoramioIcon:Bitmap = new PanoramioIcon(); var iconHolder:Sprite = new Sprite(); iconHolder.addChild(panoramioIcon); iconHolder.buttonMode = true; iconHolder.addEventListener(MouseEvent.CLICK, onPanoramioIconClick); panoramioIcon.x = BORDER_L; panoramioIcon.y = BORDER_T; content.addChild(iconHolder); // NOTE: we add the image as a child only after we&apos;ve added the marker // to the map. Currently the API requires this if it&apos;s to generate the // shadow for unprivileged content. // Shrink the image, so that it doesn&apos;t obcure too much screen space. // Ideally, we&apos;d subsample, but we don&apos;t have pixel level access. child.scaleX = IMAGE_SCALE; child.scaleY = IMAGE_SCALE; var imageW:Number = child.width; var imageH:Number = child.height; child.x = BORDER_L + 30; child.y = BORDER_T + iconHolder.height + GAP_T; var authorField:TextField = new TextField(); authorField.autoSize = TextFieldAutoSize.LEFT; authorField.defaultTextFormat = new TextFormat("_sans", 12); authorField.text = "author:"; content.addChild(authorField); authorField.x = BORDER_L; authorField.y = BORDER_T + iconHolder.height + GAP_T + imageH + GAP_B; var ownerField:TextField = new TextField(); ownerField.autoSize = TextFieldAutoSize.LEFT; var textFormat:TextFormat = new TextFormat("_sans", 14, 0x0e5f9a); ownerField.defaultTextFormat = textFormat; ownerField.htmlText = "<a href=\"" + ownerUrl + "\" target=\"_blank\">" + ownerName + "</a>"; content.addChild(ownerField); ownerField.x = BORDER_L + authorField.width; ownerField.y = BORDER_T + iconHolder.height + GAP_T + imageH + GAP_B; var totalW:Number = BORDER_L + Math.max(imageW, ownerField.width + authorField.width) + BORDER_R; var totalH:Number = BORDER_T + iconHolder.height + GAP_T + imageH + GAP_B + ownerField.height + BORDER_B; content.graphics.beginFill(0xffffff); content.graphics.drawRoundRect(0, 0, totalW, totalH, 10, 10); content.graphics.endFill(); var marker:Sprite = new Sprite(); marker.addChild(content); content.x = 30; content.y = 0; marker.graphics.lineStyle(); marker.graphics.beginFill(0xff0000); marker.graphics.drawCircle(0, totalH + 30, 3); marker.graphics.endFill(); marker.graphics.lineStyle(2, 0xffffff); marker.graphics.moveTo(30 + 10, totalH - 10); marker.graphics.lineTo(0, totalH + 30); return marker; } /** * Handles click on the Panoramio icon. */ private function onPanoramioIconClick(event:MouseEvent):void { navigateToURL(new URLRequest(PANORAMIO_HOME)); } /** * Handles failure of a Panoramio image load. */ private function onPanoramioDataFailed(event:IOErrorEvent):void { trace("Load of image failed: " + event); } /** * Returns a string containing cgi query parameters. * @param Associative array mapping query parameter key to value. * @return String containing cgi query parameters. */ private static function paramsToString(params:Object):String { var result:String = ""; var separator:String = ""; for (var key:String in params) { result += separator + encodeURIComponent(key) + "=" + encodeURIComponent(params[key]); separator = "&"; } return result; } /** * Called once the lead-in flight is done. Starts the car driving along * the route and starts a timer to begin fade in of the Panoramio * images in 1.5 seconds. */ private function onLeadInDone(event:Event):void { // Set startTimer non-zero so that the car starts to move. startTimer = getTimer(); // Start a timer that will fade in the Panoramio images. var fadeInTimer:Timer = new Timer(1500, 1); fadeInTimer.addEventListener(TimerEvent.TIMER, onFadeInTimer); fadeInTimer.start(); } /** * Handles the fade in timer&apos;s TIMER event. Sets markerAlpha above zero * which causes the frame enter handler to fade in the markers. */ private function onFadeInTimer(event:Event):void { markerAlpha = 0.01; } /** * The end time of the flight. */ private function get endTime():Number { if (!cumulativeStepDuration || cumulativeStepDuration.length == 0) { return startTimer; } return startTimer + cumulativeStepDuration[cumulativeStepDuration.length - 1]; } /** * Creates the cumulative arrays, cumulativeStepDuration and * cumulativeVertexDistance. */ private function createCumulativeArrays():void { cumulativeStepDuration = new Array(route.numSteps + 1); cumulativeVertexDistance = new Array(polyline.getVertexCount() + 1); var polylineTotal:Number = 0; var total:Number = 0; var numVertices:int = polyline.getVertexCount(); for (var stepIndex:int = 0; stepIndex < route.numSteps; ++stepIndex) { cumulativeStepDuration[stepIndex] = total; total += route.getStep(stepIndex).duration; var startVertex:int = stepIndex >= 0 ? route.getStep(stepIndex).polylineIndex : 0; var endVertex:int = stepIndex < (route.numSteps - 1) ? route.getStep(stepIndex + 1).polylineIndex : numVertices; var duration:Number = route.getStep(stepIndex).duration; var stepVertices:int = endVertex - startVertex; var latLng:LatLng = polyline.getVertex(startVertex); for (var vertex:int = startVertex; vertex < endVertex; ++vertex) { cumulativeVertexDistance[vertex] = polylineTotal; if (vertex < numVertices - 1) { var nextLatLng:LatLng = polyline.getVertex(vertex + 1); polylineTotal += nextLatLng.distanceFrom(latLng); } latLng = nextLatLng; } } cumulativeStepDuration[stepIndex] = total; } /** * Opens the info window above the car icon that details the given * step of the driving directions. * @param stepIndex Index of the current step. */ private function openInfoForStep(stepIndex:int):void { // Sets the content of the info window. var content:String; if (stepIndex >= route.numSteps) { content = "<b>" + route.endGeocode.address + "</b>" + "<br /><br />" + route.summaryHtml; } else { content = "<b>" + stepIndex + ".</b> " + route.getStep(stepIndex).descriptionHtml; } marker.openInfoWindow(new InfoWindowOptions({ contentHTML: content })); } /** * Displays the driving directions step appropriate for the given time. * Opens the info window showing the step instructions each time we * progress to a new step. * @param time Time for which to display the step. */ private function displayStepAt(time:Number):void { var stepIndex:int = upperBound(cumulativeStepDuration, time) - 1; var minStepIndex:int = 0; var maxStepIndex:int = route.numSteps - 1; if (stepIndex >= 0 && stepIndex <= maxStepIndex && currentStepIndex != stepIndex) { openInfoForStep(stepIndex); currentStepIndex = stepIndex; } } /** * Returns the LatLng at which the car should be positioned at the given * time. * @param time Time for which LatLng should be found. * @return LatLng. */ private function latLngAt(time:Number):LatLng { var stepIndex:int = upperBound(cumulativeStepDuration, time) - 1; var minStepIndex:int = 0; var maxStepIndex:int = route.numSteps - 1; if (stepIndex < minStepIndex) { return route.startGeocode.point; } else if (stepIndex > maxStepIndex) { return route.endGeocode.point; } var stepStart:Number = cumulativeStepDuration[stepIndex]; var stepEnd:Number = cumulativeStepDuration[stepIndex + 1]; var stepFraction:Number = (time - stepStart) / (stepEnd - stepStart); var startVertex:int = route.getStep(stepIndex).polylineIndex; var endVertex:int = (stepIndex + 1) < route.numSteps ? route.getStep(stepIndex + 1).polylineIndex : polyline.getVertexCount(); var stepVertices:int = endVertex - startVertex; var stepLeng

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  • datagrid binding

    - by abcdd007
    using System; using System.Data; using System.Configuration; using System.Collections; using System.Web; using System.Web.Security; using System.Web.UI; using System.Web.UI.WebControls; using System.Web.UI.WebControls.WebParts; using System.Web.UI.HtmlControls; using System.Data.SqlClient; public partial class OrderMaster : System.Web.UI.Page { BLLOrderMaster objMaster = new BLLOrderMaster(); protected void Page_Load(object sender, EventArgs e) { if (!Page.IsPostBack) { SetInitialRow(); string OrderNumber = objMaster.SelectDetails().ToString(); if (OrderNumber != "") { txtOrderNo.Text = OrderNumber.ToString(); txtOrderDate.Focus(); } } } private void InsertEmptyRow() { DataTable dt = new DataTable(); DataRow dr = null; dt.Columns.Add(new DataColumn("ItemCode", typeof(string))); dt.Columns.Add(new DataColumn("Description", typeof(string))); dt.Columns.Add(new DataColumn("Unit", typeof(string))); dt.Columns.Add(new DataColumn("Qty", typeof(string))); dt.Columns.Add(new DataColumn("Rate", typeof(string))); dt.Columns.Add(new DataColumn("Disc", typeof(string))); dt.Columns.Add(new DataColumn("Amount", typeof(string))); for (int i = 0; i < 5; i++) { dr = dt.NewRow(); dr["ItemCode"] = string.Empty; dr["Description"] = string.Empty; dr["Unit"] = string.Empty; dr["Qty"] = string.Empty; dr["Rate"] = string.Empty; dr["Disc"] = string.Empty; dr["Amount"] = string.Empty; dt.Rows.Add(dr); } //GridView1.DataSource = dt; //GridView1.DataBind(); } private void SetInitialRow() { DataTable dt = new DataTable(); DataRow dr = null; dt.Columns.Add(new DataColumn("RowNumber", typeof(string))); dt.Columns.Add(new DataColumn("ItemCode", typeof(string))); dt.Columns.Add(new DataColumn("Description", typeof(string))); dt.Columns.Add(new DataColumn("Unit", typeof(string))); dt.Columns.Add(new DataColumn("Qty", typeof(string))); dt.Columns.Add(new DataColumn("Rate", typeof(string))); dt.Columns.Add(new DataColumn("Disc", typeof(string))); dt.Columns.Add(new DataColumn("Amount", typeof(string))); dr = dt.NewRow(); dr["RowNumber"] = 1; dr["ItemCode"] = string.Empty; dr["Description"] = string.Empty; dr["Unit"] = string.Empty; dr["Qty"] = string.Empty; dr["Rate"] = string.Empty; dr["Disc"] = string.Empty; dr["Amount"] = string.Empty; dt.Rows.Add(dr); //Store DataTable ViewState["OrderDetails"] = dt; Gridview1.DataSource = dt; Gridview1.DataBind(); } protected void AddNewRowToGrid() { int rowIndex = 0; if (ViewState["OrderDetails"] != null) { DataTable dtCurrentTable = (DataTable)ViewState["OrderDetails"]; DataRow drCurrentRow = null; if (dtCurrentTable.Rows.Count > 0) { for (int i = 1; i <= dtCurrentTable.Rows.Count; i++) { //extract the TextBox values TextBox box1 = (TextBox)Gridview1.Rows[rowIndex].Cells[1].FindControl("txtItemCode"); TextBox box2 = (TextBox)Gridview1.Rows[rowIndex].Cells[2].FindControl("txtdescription"); TextBox box3 = (TextBox)Gridview1.Rows[rowIndex].Cells[3].FindControl("txtunit"); TextBox box4 = (TextBox)Gridview1.Rows[rowIndex].Cells[4].FindControl("txtqty"); TextBox box5 = (TextBox)Gridview1.Rows[rowIndex].Cells[5].FindControl("txtRate"); TextBox box6 = (TextBox)Gridview1.Rows[rowIndex].Cells[6].FindControl("txtdisc"); TextBox box7 = (TextBox)Gridview1.Rows[rowIndex].Cells[7].FindControl("txtamount"); drCurrentRow = dtCurrentTable.NewRow(); drCurrentRow["RowNumber"] = i + 1; drCurrentRow["ItemCode"] = box1.Text; drCurrentRow["Description"] = box2.Text; drCurrentRow["Unit"] = box3.Text; drCurrentRow["Qty"] = box4.Text; drCurrentRow["Rate"] = box5.Text; drCurrentRow["Disc"] = box6.Text; drCurrentRow["Amount"] = box7.Text; rowIndex++; } //add new row to DataTable dtCurrentTable.Rows.Add(drCurrentRow); //Store the current data to ViewState ViewState["OrderDetails"] = dtCurrentTable; //Rebind the Grid with the current data Gridview1.DataSource = dtCurrentTable; Gridview1.DataBind(); } } else { // } //Set Previous Data on Postbacks SetPreviousData(); } private void SetPreviousData() { int rowIndex = 0; if (ViewState["OrderDetails"] != null) { DataTable dt = (DataTable)ViewState["OrderDetails"]; if (dt.Rows.Count > 0) { for (int i = 1; i < dt.Rows.Count; i++) { TextBox box1 = (TextBox)Gridview1.Rows[rowIndex].Cells[1].FindControl("txtItemCode"); TextBox box2 = (TextBox)Gridview1.Rows[rowIndex].Cells[2].FindControl("txtdescription"); TextBox box3 = (TextBox)Gridview1.Rows[rowIndex].Cells[3].FindControl("txtunit"); TextBox box4 = (TextBox)Gridview1.Rows[rowIndex].Cells[4].FindControl("txtqty"); TextBox box5 = (TextBox)Gridview1.Rows[rowIndex].Cells[5].FindControl("txtRate"); TextBox box6 = (TextBox)Gridview1.Rows[rowIndex].Cells[6].FindControl("txtdisc"); TextBox box7 = (TextBox)Gridview1.Rows[rowIndex].Cells[7].FindControl("txtamount"); box1.Text = dt.Rows[i]["ItemCode"].ToString(); box2.Text = dt.Rows[i]["Description"].ToString(); box3.Text = dt.Rows[i]["Unit"].ToString(); box4.Text = dt.Rows[i]["Qty"].ToString(); box5.Text = dt.Rows[i]["Rate"].ToString(); box6.Text = dt.Rows[i]["Disc"].ToString(); box7.Text = dt.Rows[i]["Amount"].ToString(); rowIndex++; } dt.AcceptChanges(); } ViewState["OrderDetails"] = dt; } } protected void BindOrderDetails() { DataTable dtOrderDetails = new DataTable(); if (ViewState["OrderDetails"] != null) { dtOrderDetails = (DataTable)ViewState["OrderDetails"]; } else { dtOrderDetails.Columns.Add(""); dtOrderDetails.Columns.Add(""); dtOrderDetails.Columns.Add(""); dtOrderDetails.Columns.Add(""); dtOrderDetails.Columns.Add(""); dtOrderDetails.Columns.Add(""); dtOrderDetails.AcceptChanges(); DataRow dr = dtOrderDetails.NewRow(); dtOrderDetails.Rows.Add(dr); ViewState["OrderDetails"] = dtOrderDetails; } if (dtOrderDetails != null) { Gridview1.DataSource = dtOrderDetails; Gridview1.DataBind(); if (Gridview1.Rows.Count > 0) { ((LinkButton)Gridview1.Rows[Gridview1.Rows.Count - 1].FindControl("btnDelete")).Visible = false; } } } protected void btnSave_Click(object sender, EventArgs e) { if (txtOrderDate.Text != "" && txtOrderNo.Text != "" && txtPartyName.Text != "" && txttotalAmount.Text !="") { BLLOrderMaster bllobj = new BLLOrderMaster(); DataTable dtdetails = new DataTable(); UpdateItemDetailRow(); dtdetails = (DataTable)ViewState["OrderDetails"]; SetValues(bllobj); int k = 0; k = bllobj.Insert_Update_Delete(1, bllobj, dtdetails); if (k > 0) { ScriptManager.RegisterStartupScript(this, this.GetType(), "Login Denied", "<Script>alert('Order Code Alraddy Exist');</Script>", false); } else { ScriptManager.RegisterStartupScript(this, this.GetType(), "Login Denied", "<Script>alert('Record Saved Successfully');</Script>", false); } dtdetails.Clear(); SetInitialRow(); txttotalAmount.Text = ""; txtOrderNo.Text = ""; txtPartyName.Text = ""; txtOrderDate.Text = ""; txttotalQty.Text = ""; string OrderNumber = objMaster.SelectDetails().ToString(); if (OrderNumber != "") { txtOrderNo.Text = OrderNumber.ToString(); txtOrderDate.Focus(); } } else { txtOrderNo.Text = ""; } } public void SetValues(BLLOrderMaster bllobj) { if (txtOrderNo.Text != null && txtOrderNo.Text.ToString() != "") { bllobj.OrNumber = Convert.ToInt16(txtOrderNo.Text); } if (txtOrderDate.Text != null && txtOrderDate.Text.ToString() != "") { bllobj.Date = DateTime.Parse(txtOrderDate.Text.ToString()).ToString("dd/MM/yyyy"); } if (txtPartyName.Text != null && txtPartyName.Text.ToString() != "") { bllobj.PartyName = txtPartyName.Text; } bllobj.TotalBillAmount = txttotalAmount.Text == "" ? 0 : int.Parse(txttotalAmount.Text); bllobj.TotalQty = txttotalQty.Text == "" ? 0 : int.Parse(txttotalQty.Text); } protected void txtdisc_TextChanged(object sender, EventArgs e) { double total = 0; double totalqty = 0; foreach (GridViewRow dgvr in Gridview1.Rows) { TextBox tb = (TextBox)dgvr.Cells[7].FindControl("txtamount"); double sum; if (double.TryParse(tb.Text.Trim(), out sum)) { total += sum; } TextBox tb1 = (TextBox)dgvr.Cells[4].FindControl("txtqty"); double qtysum; if (double.TryParse(tb1.Text.Trim(), out qtysum)) { totalqty += qtysum; } } txttotalAmount.Text = total.ToString(); txttotalQty.Text = totalqty.ToString(); AddNewRowToGrid(); Gridview1.TabIndex = 1; } public void UpdateItemDetailRow() { DataTable dt = new DataTable(); if (ViewState["OrderDetails"] != null) { dt = (DataTable)ViewState["OrderDetails"]; } if (dt.Rows.Count > 0) { for (int i = 0; i < Gridview1.Rows.Count; i++) { dt.Rows[i]["ItemCode"] = (Gridview1.Rows[i].FindControl("txtItemCode") as TextBox).Text.ToString(); if (dt.Rows[i]["ItemCode"].ToString() == "") { dt.Rows[i].Delete(); break; } else { dt.Rows[i]["Description"] = (Gridview1.Rows[i].FindControl("txtdescription") as TextBox).Text.ToString(); dt.Rows[i]["Unit"] = (Gridview1.Rows[i].FindControl("txtunit") as TextBox).Text.ToString(); dt.Rows[i]["Qty"] = (Gridview1.Rows[i].FindControl("txtqty") as TextBox).Text.ToString(); dt.Rows[i]["Rate"] = (Gridview1.Rows[i].FindControl("txtRate") as TextBox).Text.ToString(); dt.Rows[i]["Disc"] = (Gridview1.Rows[i].FindControl("txtdisc") as TextBox).Text.ToString(); dt.Rows[i]["Amount"] = (Gridview1.Rows[i].FindControl("txtamount") as TextBox).Text.ToString(); } } dt.AcceptChanges(); } ViewState["OrderDetails"] = dt; } }

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  • Why can't HTC Droid running OTA 2.1 communicate with RFCOMM?

    - by Brad Hein
    Yesterday we received OTA Android 2.1 on my wife's HTC Droid - HOORAY!!! I am finally able to load my carputer app on her phone. Well we loaded it, but it doesn't work. Specifically, it connects but sees no I/O!!! I paired, re-paired, and re-paired again, every time its the same problem: connect() says we connected successfully, but any attempt to send or receive data appears to work but no data ever arrives in the input buffer. The device I'm connecting to uses AT commands. ATI should respond with a device ID. That works fine when I run the app on my Moto Droid, but on the HTC droid, no data is ever present in the inputstream/buffer. Personally, I'm feeling pretty sure it's a bug or limitation in this release for the HTC (because the app works great on my Moto A855 Droid). Can anybody comment on the issue? Obligatory code snippets: Member variable defining my RFCOMM UUID static final UUID UUID_RFCOMM_GENERIC = UUID.fromString("00001101-0000-1000-8000-00805F9B34FB"); Parts of my connect() // make sure peer is defined as a valid device based on their MAC. If not then do it. if (mBTDevice == null) mBTDevice = mBTAdapter.getRemoteDevice(mPeerMAC); // Make an RFCOMM binding. try {mBTSocket = mBTDevice.createRfcommSocketToServiceRecord(UUID_RFCOMM_GENERIC); } catch (Exception e1) { msg ("connect(): Failed to bind to RFCOMM by UUID. msg=" + e1.getMessage()); return false; } msg ("connect(): Try to connect."); try { mBTSocket.connect(); } catch (Exception e) { msg ("connect(): Exception thrown during connect: " + e.getMessage()); return false; // there was a problem connecting... make a note of the particulars and move on. } msg ("connect(): CONNECTED!"); try { mBTOutputStream = mBTSocket.getOutputStream(); mBTInputStream = new BufferedInputStream (mBTSocket.getInputStream(),INPUT_BUFFER_SIZE); //msg ("Connecting non-buffered input stream..."); //mBTInputStream = mBTSocket.getInputStream(); } catch (Exception e) { msg ("connect(): Error attaching i/o streams to socket. msg=" + e.getMessage()); return false; } resetErrorCounters(); setConnected(true); return true; } Then I send "ATI\r" and expect something like "CAN OBD II" but I get nothing. mBTInputStream.available(), it seems, is ALWAYS zero, even when data should be in the input buffer. There are GOBS of trace messages being generated by the OS as viewed with adb logcat -v time Some of the more interesting ones: 05-17 19:44:21.447 D/BluetoothSppPort( 5809): connected to device service! 05-17 19:44:21.447 D/BluetoothSppPort( 5809): Creating a BluetoothSpp proxy object 05-17 19:44:21.467 D/BluetoothSppService( 74): createPort called! 05-17 19:44:21.467 D/BluetoothSppService( 74): createPort checking uuid 05-17 19:44:21.467 D/BluetoothSppService( 74): createPort UUID=00001101-0000-1000-8000-00805f9b34fb auth=true encrypt=true 05-17 19:44:21.467 D/BluetoothSppService( 74): createPort enforcing bluetooth perm 05-17 19:44:21.467 D/BluetoothSppService( 74): createPort creating a jbtlspp object 05-17 19:44:21.467 D/BluetoothSppService( 74): createPort checking if the btl spp object is valid 05-17 19:44:21.467 D/BluetoothSppService( 74): createPort try to create an spp container 05-17 19:44:21.467 D/BluetoothSppService( 74): createPort try to create security params 05-17 19:44:21.467 D/BluetoothSppService( 74): createPort Set Security L2 05-17 19:44:21.467 D/BluetoothSppService( 74): createPort spp port create 05-17 19:44:21.467 D/JBtlSpp ( 74): create: Entered 05-17 19:44:21.467 D/JBtlSpp ( 74): Calling NativeJBtlSpp_Create 05-17 19:44:21.467 D/JBtlSppNative( 74): NativeJBtlSpp_Create: Entered 05-17 19:44:21.467 D/JBtlSppNative( 74): NativeJBtlSpp_Create: Calling BTL_SPP_Remote_Create 05-17 19:44:21.477 D/JBtlSppNative( 74): NativeJBtlSpp_Create: BTL_SPP_Remote_Create returned 0, context:18 05-17 19:44:21.477 D/JBtlSppNative( 74): NativeJBtlSpp_Create: Setting context value in jContext out parm 05-17 19:44:21.477 D/JBtlSppNative( 74): NativeJBtlSpp_Create: Calling Java setValue(0x18) in context's class 05-17 19:44:21.477 D/JBtlProfileContext( 74): setValue: setValue called, value:24 05-17 19:44:21.477 D/JBtlSppNative( 74): create_spp_port_data: will use context struct 0 for the port 24 05-17 19:44:21.477 D/JBtlSppNative( 74): create_spp_port_data: spp port context 0 added 05-17 19:44:21.477 D/JBtlSppNative( 74): NativeJBtlSpp_Create:Exiting Successfully 05-17 19:44:21.477 D/JBtlSpp ( 74): After NativeJBtlSpp_Create, status=SUCCESS, Context = 24 05-17 19:44:21.477 D/JBtlRbtlServices( 74): addUser: Entered, userRefCount = 1 05-17 19:44:21.477 D/BluetoothSppService( 74): port create returned status SUCCESS 05-17 19:44:21.477 D/JBtlSpp ( 74): enable: Entered 05-17 19:44:21.477 D/JBtlSpp ( 74): enable: UUID=00001101-0000-1000-8000-00805f9b34fb 05-17 19:44:21.477 D/JBtlSppNative( 74): NativeJBtlSpp_Enable: Entered 05-17 19:44:21.487 D/JBtlSppNative( 74): NativeJBtlSpp_Enable: BTL_SPP_Enable returned 0 05-17 19:44:21.487 D/JBtlSppNative( 74): NativeJBtlSpp_Enable:Exiting 05-17 19:44:21.487 D/JBtlSpp ( 74): After NativeJBtlSpp_Enable, status=SUCCESS 05-17 19:44:21.487 D/JBtlSpp ( 74): enable: Exiting 05-17 19:44:21.487 D/BluetoothSppService( 74): port enable returned status SUCCESS 05-17 19:44:21.487 D/BluetoothSppService( 74): connectPort called! 05-17 19:44:21.497 D/BluetoothSppService( 74): connectPort received bdaddress:00:18:E4:1D:23:9B 05-17 19:44:21.527 D/BluetoothSppService( 74): Trying to connect to 00:18:E4:1D:23:9B 05-17 19:44:21.527 D/JBtlSpp ( 74): setServiceName: Entered 05-17 19:44:21.527 D/JBtlSppNative( 74): NativeJBtlSpp_SetServiceName: Entered 05-17 19:44:21.547 D/JBtlSppNative( 74): NativeJBtlSpp_SetServiceName: native func returned 0 05-17 19:44:21.547 D/JBtlSppNative( 74): NativeJBtlSpp_SetServiceName:Exiting 05-17 19:44:21.547 D/JBtlSpp ( 74): After setServiceName, status=SUCCESS 05-17 19:44:21.547 D/JBtlSpp ( 74): setServiceName: Exiting 05-17 19:44:21.557 D/BluetoothSppService( 74): port setServiceName returned status SUCCESS 05-17 19:44:21.587 D/JBtlSpp ( 74): connect: Entered connecting to 00:18:E4:1D:23:9B 05-17 19:44:21.587 D/JBtlSppNative( 74): NativeJBtlSpp_Connect: Entered 05-17 19:44:21.597 D/JBtlSppNative( 74): NativeJBtlSpp_Connect: BTL_SPP_Connect returned 2 05-17 19:44:21.597 D/JBtlSppNative( 74): NativeJBtlSpp_Connect:Exiting 05-17 19:44:21.597 D/JBtlSpp ( 74): After NativeJBtlSpp_Connect, status=PENDING 05-17 19:44:21.747 D/AK8973 ( 61): Compass CLOSE 05-17 19:44:21.887 W/Process ( 74): Unable to open /proc/5749/status 05-17 19:44:21.917 I/ActivityManager( 74): Displayed activity com.gtosoft.dash/.Dash: 1279 ms (total 1279 ms) 05-17 19:44:24.047 D/ ( 74): signal_BTEVENT_ACCESSIBLE_CHANGE: Entered 05-17 19:44:24.047 D/ ( 74): signal_BTEVENT_ACCESSIBLE_CHANGE: Calling Java Accessible Change callback 05-17 19:44:24.047 D/JBtlBmg ( 74): nativeAccessibleChange 05-17 19:44:24.087 D/BluetoothService( 74): Callback - accessbileChange, btErrCode = NO_ERROR, mode = CONNECTABLE_ONLY 05-17 19:44:24.087 D/BluetoothService( 74): Sending ACTION_SCAN_MODE_CHANGED intent, mode = 21 05-17 19:44:24.087 D/ ( 74): signal_BTEVENT_ACCESSIBLE_CHANGE: Exiting 05-17 19:44:24.097 D/ ( 74): signal_BTEVENT_LINK_CONNECT_CNF: Entered 05-17 19:44:24.097 D/ ( 74): signal_BTEVENT_LINK_CONNECT_CNF: context: 1, errCode: 0 05-17 19:44:24.097 D/ ( 74): signal_BTEVENT_LINK_CONNECT_CNF: Calling Java Link Connect Confirmation callback 05-17 19:44:24.097 D/JBtlBmg ( 74): nativeLinkConnectCnf 05-17 19:44:24.107 D/BluetoothService( 74): Callback - linkConnectCnf, btErrCode = NO_ERROR, bdAddr = 00:18:E4:1D:23:9B 05-17 19:44:24.117 D/JBtlBmg ( 74): getKnownDeviceInfo: Entered 05-17 19:44:24.117 D/JBtlBmg ( 74): getKnownDeviceInfo: Calling NativeJBtlBmg_GetKnownDeviceInfo 05-17 19:44:24.137 D/ ( 74): NativeJBtlBmg_GetKnownDeviceInfo: Entered 05-17 19:44:24.137 D/ ( 74): NativeJBtlBmg_GetKnownDeviceInfo: Calling BTL_BMG_GetKnownDeviceInfo 05-17 19:44:24.227 D/JBtlBmgJniKnownDeviceInfo( 74): setValues: Entered 05-17 19:44:24.227 D/ ( 74): NativeJBtlBmg_GetKnownDeviceInfo:Exiting 05-17 19:44:24.227 D/JBtlBmg ( 74): getKnownDeviceInfo: After NativeJBtlBmg_GetKnownDeviceInfo, status=SUCCESS 05-17 19:44:24.227 D/JBtlBmg ( 74): getKnownDeviceInfo: Exiting 05-17 19:44:24.227 D/BluetoothService( 74): onRemoteDeviceConnected, device 00:18:E4:1D:23:9B is Paired 05-17 19:44:24.227 D/BluetoothService( 74): Sending ACTION_ACL_CONNECTED intent, address = 00:18:E4:1D:23:9B 05-17 19:44:24.227 D/BluetoothA2dpService( 74): Received intent with action: android.bluetooth.device.action.ACL_CONNECTED 05-17 19:44:24.227 D/ ( 74): signal_BTEVENT_LINK_CONNECT_CNF: Exiting 05-17 19:44:24.757 D/JBtlAg ( 163): setIndicatorValue: entered 05-17 19:44:24.767 I/JBtlAg ( 163): After NativeJBtlAg_SetIndicatorValue, status = SUCCESS 05-17 19:44:24.767 D/JBtlAg ( 163): setIndicatorValue: exiting 05-17 19:44:24.807 D/JBtlSppNative( 74): signal_SPP_EVENT_OPEN: Entered 05-17 19:44:24.807 D/JBtlSppNative( 74): signal_SPP_EVENT_OPEN: status: 0 context:24 05-17 19:44:24.827 D/JBtlSpp ( 74): nativeCb_open: Entered from 00:18:E4:1D:23:9B 05-17 19:44:24.827 D/JBtlSpp ( 74): nativeCb_open: Calling callback 05-17 19:44:24.827 D/BluetoothSppService( 74): connected called! 05-17 19:44:24.847 D/JBtlSpp ( 74): connect: Exiting 05-17 19:44:24.847 D/BluetoothSppService( 74): port connect returned status SUCCESS 05-17 19:44:24.847 D/JBtlSppNative( 74): signal_SPP_EVENT_OPEN: Exiting 05-17 19:44:24.847 D/JBtlSppNative( 74): signal_SPP_EVENT_MODEM_STATUS_IND: Entered 05-17 19:44:24.847 D/JBtlSppNative( 74): signal_SPP_EVENT_MODEM_STATUS_IND: Exiting 05-17 19:44:25.424 D/BluetoothSppService( 74): writeSync called! 05-17 19:44:25.424 D/JBtlSpp ( 74): write: Entered 05-17 19:44:25.427 D/JBtlSppNative( 74): NativeJBtlSpp_WriteNative: Entered 05-17 19:44:25.427 D/JBtlSppNative( 74): NativeJBtlSpp_WriteNative: BTL_SPP_WriteSync returned 0 written: 6 total: 0/6 05-17 19:44:25.437 D/JBtlSppNative( 74): signal_SPP_EVENT_TX_DATA_COMPLETE: Entered 05-17 19:44:25.437 D/JBtlSppNative( 74): signal_SPP_EVENT_TX_DATA_COMPLETE: status: 0 context:24 txDataLen:6 05-17 19:44:25.437 D/JBtlSppNative( 74): signal_SPP_EVENT_TX_DATA_COMPLETE: Exiting ok 05-17 19:44:25.437 D/JBtlSppNative( 74): NativeJBtlSpp_WriteNative: written 6 05-17 19:44:25.437 D/JBtlSppNative( 74): NativeJBtlSpp_WriteNative:Exiting with 0 05-17 19:44:25.437 D/JBtlSppNative( 74): NativeJBtlSpp_WriteNative: returning 6 bytes 05-17 19:44:25.437 D/JBtlSpp ( 74): After write, status=SUCCESS 05-17 19:44:25.437 D/JBtlSpp ( 74): write: Exiting 05-17 19:44:25.437 D/BluetoothSppPort( 5809): written 6 bytes 05-17 19:44:25.467 D/JBtlSppNative( 74): signal_SPP_EVENT_RX_DATA_IND: Entered 05-17 19:44:25.467 D/JBtlSppNative( 74): signal_SPP_EVENT_RX_DATA_IND: status: 0 context: 24 rxDataLen: 1 05-17 19:44:25.467 D/JBtlSppNative( 74): signal_SPP_EVENT_RX_DATA_IND: Exiting 05-17 19:44:25.477 D/JBtlSppNative( 74): signal_SPP_EVENT_RX_DATA_IND: Entered 05-17 19:44:25.477 D/JBtlSppNative( 74): signal_SPP_EVENT_RX_DATA_IND: status: 0 context: 24 rxDataLen: 5 05-17 19:44:25.477 D/JBtlSppNative( 74): signal_SPP_EVENT_RX_DATA_IND: Exiting 05-17 19:44:25.487 D/JBtlSppNative( 74): signal_SPP_EVENT_RX_DATA_IND: Entered 05-17 19:44:25.487 D/JBtlSppNative( 74): signal_SPP_EVENT_RX_DATA_IND: status: 0 context: 24 rxDataLen: 10 05-17 19:44:25.487 D/JBtlSppNative( 74): signal_SPP_EVENT_RX_DATA_IND: Exiting 05-17 19:44:25.497 D/JBtlSppNative( 74): signal_SPP_EVENT_RX_DATA_IND: Entered 05-17 19:44:25.497 D/JBtlSppNative( 74): signal_SPP_EVENT_RX_DATA_IND: status: 0 context: 24 rxDataLen: 7 05-17 19:44:25.497 D/JBtlSppNative( 74): signal_SPP_EVENT_RX_DATA_IND: Exiting 05-17 19:44:27.930 W/ActivityManager( 74): Activity destroy timeout for HistoryRecord{447e0d48 com.gtosoft.dash/.Dash} 05-17 19:44:29.907 D/dalvikvm( 448): GC freed 78 objects / 3664 bytes in 153ms 05-17 19:44:29.917 D/BluetoothSppService( 74): writeSync called! 05-17 19:44:29.917 D/JBtlSpp ( 74): write: Entered 05-17 19:44:29.917 D/JBtlSppNative( 74): NativeJBtlSpp_WriteNative: Entered 05-17 19:44:29.927 D/JBtlSppNative( 74): NativeJBtlSpp_WriteNative: BTL_SPP_WriteSync returned 0 written: 6 total: 0/6 05-17 19:44:29.937 D/JBtlSppNative( 74): signal_SPP_EVENT_TX_DATA_COMPLETE: Entered 05-17 19:44:29.937 D/JBtlSppNative( 74): signal_SPP_EVENT_TX_DATA_COMPLETE: status: 0 context:24 txDataLen:6 05-17 19:44:29.937 D/JBtlSppNative( 74): signal_SPP_EVENT_TX_DATA_COMPLETE: Exiting ok 05-17 19:44:29.937 D/JBtlSppNative( 74): NativeJBtlSpp_WriteNative: written 6 05-17 19:44:29.937 D/JBtlSppNative( 74): NativeJBtlSpp_WriteNative:Exiting with 0 05-17 19:44:29.937 D/JBtlSppNative( 74): NativeJBtlSpp_WriteNative: returning 6 bytes 05-17 19:44:29.937 D/JBtlSpp ( 74): After write, status=SUCCESS 05-17 19:44:29.937 D/JBtlSpp ( 74): write: Exiting

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  • Help Optimizing MySQL Table (~ 500,000 records) and PHP Code.

    - by Pyrite
    I have a MySQL table that collects player data from various game servers (Urban Terror). The bot that collects the data runs 24/7, and currently the table is up to about 475,000+ records. Because of this, querying this table from PHP has become quite slow. I wonder what I can do on the database side of things to make it as optomized as possible, then I can focus on the application to query the database. The table is as follows: CREATE TABLE IF NOT EXISTS `people` ( `id` bigint(20) unsigned NOT NULL AUTO_INCREMENT, `name` varchar(40) NOT NULL, `ip` int(4) unsigned NOT NULL, `guid` varchar(32) NOT NULL, `server` int(4) unsigned NOT NULL, `date` int(11) NOT NULL, PRIMARY KEY (`id`), UNIQUE KEY `Person` (`name`,`ip`,`guid`), KEY `server` (`server`), KEY `date` (`date`), KEY `PlayerName` (`name`) ) ENGINE=MyISAM DEFAULT CHARSET=latin1 COMMENT='People that Play on Servers' AUTO_INCREMENT=475843 ; I'm storying the IPv4 (ip and server) as 4 byte integers, and using the MySQL functions NTOA(), etc to encode and decode, I heard that this way is faster, rather than varchar(15). The guid is a md5sum, 32 char hex. Date is stored as unix timestamp. I have a unique key on name, ip and guid, as to avoid duplicates of the same player. Do I have my keys setup right? Is the way I'm storing data efficient? Here is the code to query this table. You search for a name, ip, or guid, and it grabs the results of the query and cross references other records that match the name, ip, or guid from the results of the first query, and does it for each field. This is kind of hard to explain. But basically, if I search for one player by name, I'll see every other name he has used, every IP he has used and every GUID he has used. <form action="<?php echo $_SERVER['PHP_SELF']; ?>" method="post"> Search: <input type="text" name="query" id="query" /><input type="submit" name="btnSubmit" value="Submit" /> </form> <?php if (!empty($_POST['query'])) { ?> <table cellspacing="1" id="1up_people" class="tablesorter" width="300"> <thead> <tr> <th>ID</th> <th>Player Name</th> <th>Player IP</th> <th>Player GUID</th> <th>Server</th> <th>Date</th> </tr> </thead> <tbody> <?php function super_unique($array) { $result = array_map("unserialize", array_unique(array_map("serialize", $array))); foreach ($result as $key => $value) { if ( is_array($value) ) { $result[$key] = super_unique($value); } } return $result; } if (!empty($_POST['query'])) { $query = trim($_POST['query']); $count = 0; $people = array(); $link = mysql_connect('localhost', 'mysqluser', 'yea right!'); if (!$link) { die('Could not connect: ' . mysql_error()); } mysql_select_db("1up"); $sql = "SELECT id, name, INET_NTOA(ip) AS ip, guid, INET_NTOA(server) AS server, date FROM 1up_people WHERE (name LIKE \"%$query%\" OR INET_NTOA(ip) LIKE \"%$query%\" OR guid LIKE \"%$query%\")"; $result = mysql_query($sql, $link); if (!$result) { die(mysql_error()); } // Now take the initial results and parse each column into its own array while ($row = mysql_fetch_array($result, MYSQL_NUM)) { $name = htmlspecialchars($row[1]); $people[] = array( 'id' => $row[0], 'name' => $name, 'ip' => $row[2], 'guid' => $row[3], 'server' => $row[4], 'date' => $row[5] ); } // now for each name, ip, guid in results, find additonal records $people2 = array(); foreach ($people AS $person) { $ip = $person['ip']; $sql = "SELECT id, name, INET_NTOA(ip) AS ip, guid, INET_NTOA(server) AS server, date FROM 1up_people WHERE (ip = \"$ip\")"; $result = mysql_query($sql, $link); while ($row = mysql_fetch_array($result, MYSQL_NUM)) { $name = htmlspecialchars($row[1]); $people2[] = array( 'id' => $row[0], 'name' => $name, 'ip' => $row[2], 'guid' => $row[3], 'server' => $row[4], 'date' => $row[5] ); } } $people3 = array(); foreach ($people AS $person) { $guid = $person['guid']; $sql = "SELECT id, name, INET_NTOA(ip) AS ip, guid, INET_NTOA(server) AS server, date FROM 1up_people WHERE (guid = \"$guid\")"; $result = mysql_query($sql, $link); while ($row = mysql_fetch_array($result, MYSQL_NUM)) { $name = htmlspecialchars($row[1]); $people3[] = array( 'id' => $row[0], 'name' => $name, 'ip' => $row[2], 'guid' => $row[3], 'server' => $row[4], 'date' => $row[5] ); } } $people4 = array(); foreach ($people AS $person) { $name = $person['name']; $sql = "SELECT id, name, INET_NTOA(ip) AS ip, guid, INET_NTOA(server) AS server, date FROM 1up_people WHERE (name = \"$name\")"; $result = mysql_query($sql, $link); while ($row = mysql_fetch_array($result, MYSQL_NUM)) { $name = htmlspecialchars($row[1]); $people4[] = array( 'id' => $row[0], 'name' => $name, 'ip' => $row[2], 'guid' => $row[3], 'server' => $row[4], 'date' => $row[5] ); } } // Combine people and people2 into just people $people = array_merge($people, $people2); $people = array_merge($people, $people3); $people = array_merge($people, $people4); $people = super_unique($people); foreach ($people AS $person) { $date = ($person['date']) ? date("M d, Y", $person['date']) : 'Before 8/1/10'; echo "<tr>\n"; echo "<td>".$person['id']."</td>"; echo "<td>".$person['name']."</td>"; echo "<td>".$person['ip']."</td>"; echo "<td>".$person['guid']."</td>"; echo "<td>".$person['server']."</td>"; echo "<td>".$date."</td>"; echo "</tr>\n"; $count++; } // Find Total Records //$result = mysql_query("SELECT id FROM 1up_people", $link); //$total = mysql_num_rows($result); mysql_close($link); } ?> </tbody> </table> <p> <?php echo $count." Records Found for \"".$_POST['query']."\" out of $total"; ?> </p> <?php } $time_stop = microtime(true); print("Done (ran for ".round($time_stop-$time_start)." seconds)."); ?> Any help at all is appreciated! Thank you.

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  • Help Optimizing MySQL Table (~ 500,000 records).

    - by Pyrite
    I have a MySQL table that collects player data from various game servers (Urban Terror). The bot that collects the data runs 24/7, and currently the table is up to about 475,000+ records. Because of this, querying this table from PHP has become quite slow. I wonder what I can do on the database side of things to make it as optomized as possible, then I can focus on the application to query the database. The table is as follows: CREATE TABLE IF NOT EXISTS `people` ( `id` bigint(20) unsigned NOT NULL AUTO_INCREMENT, `name` varchar(40) NOT NULL, `ip` int(4) unsigned NOT NULL, `guid` varchar(32) NOT NULL, `server` int(4) unsigned NOT NULL, `date` int(11) NOT NULL, PRIMARY KEY (`id`), UNIQUE KEY `Person` (`name`,`ip`,`guid`), KEY `server` (`server`), KEY `date` (`date`), KEY `PlayerName` (`name`) ) ENGINE=MyISAM DEFAULT CHARSET=latin1 COMMENT='People that Play on Servers' AUTO_INCREMENT=475843 ; I'm storying the IPv4 (ip and server) as 4 byte integers, and using the MySQL functions NTOA(), etc to encode and decode, I heard that this way is faster, rather than varchar(15). The guid is a md5sum, 32 char hex. Date is stored as unix timestamp. I have a unique key on name, ip and guid, as to avoid duplicates of the same player. Do I have my keys setup right? Is the way I'm storing data efficient? Here is the code to query this table. You search for a name, ip, or guid, and it grabs the results of the query and cross references other records that match the name, ip, or guid from the results of the first query, and does it for each field. This is kind of hard to explain. But basically, if I search for one player by name, I'll see every other name he has used, every IP he has used and every GUID he has used. <form action="<?php echo $_SERVER['PHP_SELF']; ?>" method="post"> Search: <input type="text" name="query" id="query" /><input type="submit" name="btnSubmit" value="Submit" /> </form> <?php if (!empty($_POST['query'])) { ?> <table cellspacing="1" id="1up_people" class="tablesorter" width="300"> <thead> <tr> <th>ID</th> <th>Player Name</th> <th>Player IP</th> <th>Player GUID</th> <th>Server</th> <th>Date</th> </tr> </thead> <tbody> <?php function super_unique($array) { $result = array_map("unserialize", array_unique(array_map("serialize", $array))); foreach ($result as $key => $value) { if ( is_array($value) ) { $result[$key] = super_unique($value); } } return $result; } if (!empty($_POST['query'])) { $query = trim($_POST['query']); $count = 0; $people = array(); $link = mysql_connect('localhost', 'mysqluser', 'yea right!'); if (!$link) { die('Could not connect: ' . mysql_error()); } mysql_select_db("1up"); $sql = "SELECT id, name, INET_NTOA(ip) AS ip, guid, INET_NTOA(server) AS server, date FROM 1up_people WHERE (name LIKE \"%$query%\" OR INET_NTOA(ip) LIKE \"%$query%\" OR guid LIKE \"%$query%\")"; $result = mysql_query($sql, $link); if (!$result) { die(mysql_error()); } // Now take the initial results and parse each column into its own array while ($row = mysql_fetch_array($result, MYSQL_NUM)) { $name = htmlspecialchars($row[1]); $people[] = array( 'id' => $row[0], 'name' => $name, 'ip' => $row[2], 'guid' => $row[3], 'server' => $row[4], 'date' => $row[5] ); } // now for each name, ip, guid in results, find additonal records $people2 = array(); foreach ($people AS $person) { $ip = $person['ip']; $sql = "SELECT id, name, INET_NTOA(ip) AS ip, guid, INET_NTOA(server) AS server, date FROM 1up_people WHERE (ip = \"$ip\")"; $result = mysql_query($sql, $link); while ($row = mysql_fetch_array($result, MYSQL_NUM)) { $name = htmlspecialchars($row[1]); $people2[] = array( 'id' => $row[0], 'name' => $name, 'ip' => $row[2], 'guid' => $row[3], 'server' => $row[4], 'date' => $row[5] ); } } $people3 = array(); foreach ($people AS $person) { $guid = $person['guid']; $sql = "SELECT id, name, INET_NTOA(ip) AS ip, guid, INET_NTOA(server) AS server, date FROM 1up_people WHERE (guid = \"$guid\")"; $result = mysql_query($sql, $link); while ($row = mysql_fetch_array($result, MYSQL_NUM)) { $name = htmlspecialchars($row[1]); $people3[] = array( 'id' => $row[0], 'name' => $name, 'ip' => $row[2], 'guid' => $row[3], 'server' => $row[4], 'date' => $row[5] ); } } $people4 = array(); foreach ($people AS $person) { $name = $person['name']; $sql = "SELECT id, name, INET_NTOA(ip) AS ip, guid, INET_NTOA(server) AS server, date FROM 1up_people WHERE (name = \"$name\")"; $result = mysql_query($sql, $link); while ($row = mysql_fetch_array($result, MYSQL_NUM)) { $name = htmlspecialchars($row[1]); $people4[] = array( 'id' => $row[0], 'name' => $name, 'ip' => $row[2], 'guid' => $row[3], 'server' => $row[4], 'date' => $row[5] ); } } // Combine people and people2 into just people $people = array_merge($people, $people2); $people = array_merge($people, $people3); $people = array_merge($people, $people4); $people = super_unique($people); foreach ($people AS $person) { $date = ($person['date']) ? date("M d, Y", $person['date']) : 'Before 8/1/10'; echo "<tr>\n"; echo "<td>".$person['id']."</td>"; echo "<td>".$person['name']."</td>"; echo "<td>".$person['ip']."</td>"; echo "<td>".$person['guid']."</td>"; echo "<td>".$person['server']."</td>"; echo "<td>".$date."</td>"; echo "</tr>\n"; $count++; } // Find Total Records //$result = mysql_query("SELECT id FROM 1up_people", $link); //$total = mysql_num_rows($result); mysql_close($link); } ?> </tbody> </table> <p> <?php echo $count." Records Found for \"".$_POST['query']."\" out of $total"; ?> </p> <?php } $time_stop = microtime(true); print("Done (ran for ".round($time_stop-$time_start)." seconds)."); ?> Any help at all is appreciated! Thank you.

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  • JqGrid addJSONData + ASP.NET 2.0 WS

    - by MilosC
    Dear community ! I am a bit lost. I' ve tried to implement a solution based on JqGrid and tried to use function as datatype. I've setted all by the book i guess, i get WS invoked and get JASON back, I got succes on clientside in ajaf call and i "bind" jqGrid using addJSONData but grid remains empty. I do not have any glue now... other "local" samples on same pages works without a problem (jsonstring ...) My WS method looks like : [WebMethod] [ScriptMethod(ResponseFormat = ResponseFormat.Json)] public string GetGridData() { // Load a list InitSessionVariables(); SA.DB.DenarnaEnota.DenarnaEnotaDB db = new SAOP.SA.DB.DenarnaEnota.DenarnaEnotaDB(); DataSet ds = db.GetLookupForDenarnaEnota(SAOP.FW.DB.RecordStatus.All); // Turn into HTML friendly format GetGridData summaryList = new GetGridData(); summaryList.page = "1"; summaryList.total = "10"; summaryList.records = "160"; int i = 0; foreach (DataRow dr in ds.Tables[0].Rows) { GridRows row = new GridRows(); row.id = dr["DenarnaEnotaID"].ToString(); row.cell = "[" + "\"" + dr["DenarnaEnotaID"].ToString() + "\"" + "," + "\"" + dr["Kratica"].ToString() + "\"" + "," + "\"" + dr["Naziv"].ToString() + "\"" + "," + "\"" + dr["Sifra"].ToString() + "\"" + "]"; summaryList.rows.Add(row); } return JsonConvert.SerializeObject(summaryList); } my ASCX code is this: jQuery(document).ready(function(){ jQuery("#list").jqGrid({ datatype : function (postdata) { jQuery.ajax({ url:'../../AjaxWS/TemeljnicaEdit.asmx/GetGridData', data:'{}', dataType:'json', type: 'POST', contentType: "application/json; charset=utf-8", complete: function(jsondata,stat){ if(stat=="success") { var clearJson = jsondata.responseText; var thegrid = jQuery("#list")[0]; var myjsongrid = eval('('+clearJson+')'); alfs thegrid.addJSONData(myjsongrid.replace(/\\/g,'')); } } } ); }, colNames:['DenarnaEnotaID','Kratica', 'Sifra', 'Naziv'], colModel:[ {name:'DenarnaEnotaID',index:'DenarnaEnotaID', width:100}, {name:'Kratica',index:'Kratica', width:100}, {name:'Sifra',index:'Sifra', width:100}, {name:'Naziv',index:'Naziv', width:100}], rowNum:15, rowList:[15,30,100], pager: jQuery('#pager'), sortname: 'id', // loadtext:"Nalagam zapise...", // viewrecords: true, sortorder: "desc", // caption:"Vrstice", // width:"800", imgpath: "../Scripts/JGrid/themes/basic/images"}); }); from WS i GET JSON like this: {”page”:”1?,”total”:”10?,”records”:”160?,”rows”:[{"id":"18","cell":"["18","BAM","Konvertibilna marka","977"]“},{”id”:”19?,”cell”:”["19","RSD","Srbski dinar","941"]“},{”id”:”20?,”cell”:”["20","AFN","Afgani","971"]“},{”id”:”21?,”cell”:”["21","ALL","Lek","008"]“},{”id”:”22?,”cell”:”["22","DZD","Alžirski dinar","012"]“},{”id”:”23?,”cell”:”["23","AOA","Kvanza","973"]“},{”id”:”24?,”cell”:”["24","XCD","Vzhodnokaribski dolar","951"]“},{”id”:”25?,”cell”:” ……………… ["13","PLN","Poljski zlot","985"]“},{”id”:”14?,”cell”:”["14","SEK","Švedska krona","752"]“},{”id”:”15?,”cell”:”["15","SKK","Slovaška krona","703"]“},{”id”:”16?,”cell”:”["16","USD","Ameriški dolar","840"]“},{”id”:”17?,”cell”:”["17","XXX","Nobena valuta","000"]“},{”id”:”1?,”cell”:”["1","SIT","Slovenski tolar","705"]“}]} i have registered this js : clientSideScripts.RegisterClientScriptFile("prototype.js", CommonFunctions.FixupUrlWithoutSessionID("~/WebUI/Scripts/prototype-1.6.0.2.js")); clientSideScripts.RegisterClientScriptFile("jquery.js", CommonFunctions.FixupUrlWithoutSessionID("~/WebUI/Scripts/JGrid/jquery.js")); clientSideScripts.RegisterClientScriptFile("jquery.jqGrid.js", CommonFunctions.FixupUrlWithoutSessionID("~/WebUI/Scripts/JGrid/jquery.jqGrid.js")); clientSideScripts.RegisterClientScriptFile("jqModal.js", CommonFunctions.FixupUrlWithoutSessionID("~/WebUI/Scripts/JGrid/js/jqModal.js")); clientSideScripts.RegisterClientScriptFile("jqDnR.js", CommonFunctions.FixupUrlWithoutSessionID("~/WebUI/Scripts/JGrid/js/jqDnR.js")); Basical i think it must be something stupid ...but i can figure it out now... Help wanted.

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  • Proxmox VE cluster not working

    - by JJ56
    I have been using a Proxmox VE 2.0 cluster (2 servers) for months. Today, I had the "bright idea" to install a GUI on one of the servers. I used tasksel, and selected the graphical desktop environment (Gnome). When I rebooted the server, neither of the servers on the cluster could see each other (shows a red light by the server on the sidebar, no statistics). The VMs on each server are working fine, individually. pvecm status on the broken server shows cman_tool: cannot open connection to cman, is it running ?. Running it on the other server outputs a lot of lines (here: http://pastebin.com/HpQfUHTU), but I assume the important bit is expected votes:2 total votes: 1 Trying pvecm delnode (othernode) on either server outputs: cluster not ready - no quorum? Any suggestions as to how I can fix this? Thanks in advance!

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  • Android RatingBar weirdness: Whenever I add a RatingBar to my layout, a bunch of the generated tags,

    - by Rben
    Whenever I use a RatingBar view in my layout, I suddenly get all kinds of compile errors. I'm using Android 2.0, but I've also tried 2.0.1, and 2.1, without joy. I also get a message: Shader 'android.graphics.BitmapShader' is not supported in Layout Editor, and an odd warning which may or maynot be related: warning: Ignoring InnerClasses attribute for an anonymous inner class that doesn't come with an associated EnclosingMethod attribute. I've tried using the RatingBar both within a tablelayout and outside it, but it behaves the same way. This is very puzzling and frustrating. Please help if you can. Sincerely, Ray Here's the XML: <!-- Created By --> <TableRow android:layout_height="wrap_content" android:layout_width="fill_parent" > <TextView android:text="Created by: " android:id="@+id/gi_created_label" android:layout_width="wrap_content" android:layout_height="wrap_content" android:gravity="right" /> <TextView android:text="Slartibartfast" android:id="@+id/gi_created" android:layout_width="fill_parent" android:layout_height="wrap_content" /> </TableRow> <!-- Verification --> <TableRow android:layout_height="wrap_content" android:layout_width="fill_parent" > <TextView android:id="@+id/gi_verification_label" android:layout_width="wrap_content" android:layout_height="wrap_content" android:gravity="right" android:text="@string/GameInfoVerificationLabelText" /> <TextView android:id="@+id/gi_verification" android:layout_width="fill_parent" android:layout_height="wrap_content" android:text="HonorSystem" /> </TableRow> <!-- Player Rating Label --> <TableRow android:layout_height="wrap_content" android:layout_width="fill_parent" > <TextView android:layout_width="fill_parent" android:layout_height="wrap_content" android:gravity="right" android:text="@string/GameInfoPlayerRatingLabel" /> <TextView android:layout_width="fill_parent" android:layout_height="wrap_content" android:text=" " /> </TableRow> -- <!-- Times played --> <TableRow android:layout_height="wrap_content" android:layout_width="fill_parent" > <TextView android:id="@+id/gi_times_played_label" android:layout_width="wrap_content" android:layout_height="wrap_content" android:gravity="right" android:text="@string/GameInfoTimesPlayedLabel" /> <TextView android:id="@+id/gi_times_played" android:layout_width="fill_parent" android:layout_height="wrap_content" android:text="999" /> </TableRow> <!-- Total Players --> <TableRow android:layout_height="wrap_content" android:layout_width="fill_parent" > <TextView android:id="@+id/gi_total_players_label" android:layout_width="wrap_content" android:layout_height="wrap_content" android:gravity="right" android:text="@string/GameInfoTotalPlayerCountLabel" /> <TextView android:id="@+id/gi_total_players" android:layout_width="fill_parent" android:layout_height="wrap_content" android:text="999" /> </TableRow> <!-- Total Cancelations --> <TableRow android:layout_height="wrap_content" android:layout_width="fill_parent" > <TextView android:id="@+id/gi_total_cancelations_label" android:layout_width="wrap_content" android:layout_height="wrap_content" android:gravity="right" android:text="@string/GameInfoTotalCancelsLabel" /> <TextView android:id="@+id/gi_total_cancels" android:layout_width="fill_parent" android:layout_height="wrap_content" android:text="999" /> </TableRow> <RatingBar android:id="@+/gi_player_rating" style="?android:attr/ratingBarStyleSmall" android:layout_width="wrap_content" android:layout_height="wrap_content" android:layout_span="2" android:isIndicator="true" android:numStars="5" android:rating="3" android:stepSize="1" android:layout_gravity="center_vertical" /> </TableRow>

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  • Rotate haproxy logs

    - by Jagbir
    I tried few things but still not able to rotate haproxy logs efficiently. I need to rotate logs when log files crosses 500 MB size. Considering haproxy is serving large no. of static tcp connections, I can not restart haproxy process though a reload is doable. Daily haproxy log file size normally crosses 3 GB on my machine. Here's sample from one of newer machine where log file size is growing beyond limit set: ubuntu@server:/mnt/log/haproxy$ ls -lsh total 4.3G 85M -rw-r----- 1 syslog adm 85M Jun 2 07:13 haproxy.log 2.9G -rw-r----- 1 syslog adm 2.9G Jun 2 06:37 haproxy.log.1 460M -rw-r----- 1 syslog adm 460M Jun 1 06:32 haproxy.log.2.gz 469M -rw-r----- 1 syslog adm 469M May 31 06:42 haproxy.log.3.gz 384M -rw-r----- 1 syslog adm 384M May 30 06:49 haproxy.log.4.gz ubuntu@server:/mnt/log/haproxy$ cat /etc/logrotate.d/haproxy /mnt/log/haproxy/haproxy.log { missingok copytruncate notifempty rotate 50 size 500M compress delaycompress }

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  • Elapsed time display in foobar 2000

    - by tripleowl
    The elapsed and total time display in Foobar 2000 with the Metro skin (download here) I use, is not aligned properly. I can't see the minutes track has played, which comes first. The time to the right of the track name is chopped. However on the seekbar it is ok. Click for a full sized image There are scripts in the ELPlaylist settings window which I get when I right click on the playlist window. But unfortunately I don't know which parameter if any in the script should be changed to set a proper time format for the track duration, which would allow me to see the elapsed time. Relevant ELplaylist code Track list Groupheader Per second

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  • Recommendation for PHP-FPM pm.max_children, PHP-FPM pm.start_servers and others

    - by jaypabs
    I have the following server: Intel® Xeon® E3-1270 v2 Single Processor - Quad Core Dedicated Server CPU Speed: 4 x 3.5 Ghz w/ 8MB Smart Cache Motherboard: SuperMicro X9SCM-F Total Cores: 4 Cores + 8 Threads RAM: 32 GB DDR3 1333 ECC Hard Drive: 120GB Smart Cache: 8MB I am using ubuntu 12.04 - nginx, php, mysql with ISPConfig 3. Under ISPConfig 3 website settings: I have this default value: PHP-FPM pm.max_children = 10 PHP-FPM pm.start_servers = 2 PHP-FPM pm.min_spare_servers = 1 PHP-FPM pm.max_spare_servers = 5 PHP-FPM pm.max_requests = 0 My question is what is the recommended settings for the above variable? Because I found some using a different settings.

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  • Apache2 worker mpm too many processes

    - by delerious010
    I've got Apache installed with the worker mpm which seems to have too many processes active in spite of the configurations in place. I'll detail the configs below : StartServers 2 MinSpareThreads 10 MaxSpareThreads 25 ThreadsPerChild 25 MaxClients 150 Based on these settings, we should be seeing a maximum of 1 Apache control process (uid:root) and 6 Apache client processes (uid:www). This being due to MaxClients/ThreadsPerChild. However, I'm seeing a total of 1 Apache control process and 9 Apache client processes. init -- apache2(root) -- -- apache2(www) -- -- apache2(www) -- 1 thread -- -- apache2(www) -- 26 threads -- -- apache2(www) -- 26 threads init -- apache2(www) -- 2 threads -- apache2(www) -- apache2(www) -- apache2(www) We do not make it a habit of restarting Apache nor the Server, and will perform a reload 2-3 times a day at times so as to add new VHOSTs. Would anyone be able to enlighten me as to what might be causing this ? enter code here

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  • Why does OS X insist on spinning up all external drives when loading a file from the local drive?

    - by Phillip Oldham
    Why does OS X insist on spinning up all the attached external drives (firewire, usb) when loading a file from the local (internal) drive? It's driving me insane that I have to wait for 3 attached drives (1 back-up, 2 media) to spin up -- a total of 20s -- to access a file that is located only on my local/internal drive. There is no obvious need to access the other drives; nothing is being read from them and nothing need be written. Examples: Quicktime X opening a file from the local HDD. Starting Caffeine, an app which doesn't access any other files at all. Can I tell OS X to only spin those drives up when actually accessing them?

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  • Fixing predicated NSFetchedResultsController/NSFetchRequest performance with SQLite backend?

    - by Jaanus
    I have a series of NSFetchedResultsControllers powering some table views, and their performance on device was abysmal, on the order of seconds. Since it all runs on main thread, it's blocking my app at startup, which is not great. I investigated and turns out the predicate is the problem: NSPredicate *somePredicate = [NSPredicate predicateWithFormat:@"ANY somethings == %@", something]; [fetchRequest setPredicate:somePredicate]; I.e the fetch entity, call it "things", has a many-to-many relation with entity "something". This predicate is a filter that limits the results to only things that have a relation with a particular "something". When I removed the predicate for testing, fetch time (the initial performFetch: call) dropped (for some extreme cases) from 4 seconds to around 100ms or less, which is acceptable. I am troubled by this, though, as it negates a lot of the benefit I was hoping to gain with Core Data and NSFRC, which otherwise seems like a powerful tool. So, my question is, how can I optimize this performance? Am I using the predicate wrong? Should I modify the model/schema somehow? And what other ways there are to fix this? Is this kind of degraded performance to be expected? (There are on the order of hundreds of <1KB objects.) EDIT WITH DETAILS: Here's the code: [fetchRequest setFetchLimit:200]; NSLog(@"before fetch"); BOOL success = [frc performFetch:&error]; if (!success) { NSLog(@"Fetch request error: %@", error); } NSLog(@"after fetch"); Updated logs (previously, I had some application inefficiencies degrading the performance here. These are the updated logs that should be as close to optimal as you can get under my current environment): 2010-02-05 12:45:22.138 Special Ppl[429:207] before fetch 2010-02-05 12:45:22.144 Special Ppl[429:207] CoreData: sql: SELECT DISTINCT 0, t0.Z_PK, t0.Z_OPT, <model fields> FROM ZTHING t0 LEFT OUTER JOIN Z_1THINGS t1 ON t0.Z_PK = t1.Z_2THINGS WHERE t1.Z_1SOMETHINGS = ? ORDER BY t0.ZID DESC LIMIT 200 2010-02-05 12:45:22.663 Special Ppl[429:207] CoreData: annotation: sql connection fetch time: 0.5094s 2010-02-05 12:45:22.668 Special Ppl[429:207] CoreData: annotation: total fetch execution time: 0.5240s for 198 rows. 2010-02-05 12:45:22.706 Special Ppl[429:207] after fetch If I do the same fetch without predicate (by commenting out the two lines in the beginning of the question): 2010-02-05 12:44:10.398 Special Ppl[414:207] before fetch 2010-02-05 12:44:10.405 Special Ppl[414:207] CoreData: sql: SELECT 0, t0.Z_PK, t0.Z_OPT, <model fields> FROM ZTHING t0 ORDER BY t0.ZID DESC LIMIT 200 2010-02-05 12:44:10.426 Special Ppl[414:207] CoreData: annotation: sql connection fetch time: 0.0125s 2010-02-05 12:44:10.431 Special Ppl[414:207] CoreData: annotation: total fetch execution time: 0.0262s for 200 rows. 2010-02-05 12:44:10.457 Special Ppl[414:207] after fetch 20-fold difference in times. 500ms is not that great, and there does not seem to be a way to do it in background thread or otherwise optimize that I can think of. (Apart from going to a binary store where this becomes a non-issue, so I might do that. Binary store performance is consistently ~100ms for the above 200-object predicated query.) (I nested another question here previously, which I now moved away).

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  • Sum and chart in excel

    - by Chris Lively
    I have data like the following: Both Hour and Count are columns in an excel file. Hour Count 17 79 18 122 19 123 20 142 21 150 22 78 23 15 13 33 14 33 15 40 16 33 17 56 18 46 19 35 20 67 21 65 22 45 23 36 What I want is to create a chart that shows over a period of 1 to 24 (hours) the total count. What's the easiest way to do this. The chart should have a horizontal axis that runs from 1 to 24; and a vertical axis that goes from 0 on up. In the case above the values should be combined like: 1 - 0 2 - 0 3 - 0 4 - 0 5 - 0 6 - 0 7 - 0 8 - 0 9 - 0 10 - 0 11 - 0 12 - 0 13 - 33 14 - 33 15 - 40 16 - 33 17 - 135 18 - 168 19 - 158 20 - 209 21 - 215 22 - 123 23 - 51 24 - 0

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  • How is htop "Swp" calculated?

    - by Thomas
    When I run htop (on OS X 10.6.8), I see something like this : 1 [||||||| 20.0%] Tasks: 70 total, 0 running 2 [||| 7.2%] Load average: 1.11 0.79 0.64 3 [|||||||||||||||||||||||||||81.3%] Uptime: 00:30:42 4 [|| 5.8%] Mem[|||||||||||||||||||||3872/4096MB] Swp[ 0/0MB] PID USER PRI NI VIRT RES SHR S CPU% MEM% TIME+ Command 284 501 57 0 15.3G 1064M 0 S 0.0 6.5 0:01.26 /Applications/Firefox.app/Contents/MacOS/firefox -psn_0_90134 437 501 57 0 14.8G 785M 0 S 0.0 4.8 0:00.18 /Applications/Thunderbird.app/Contents/MacOS/thunderbird -psn_0_114716 428 501 63 0 12.8G 351M 0 S 1.0 2.1 0:00.51 /Applications/Firefox.app/Contents/MacOS/plugin-container.app/Contents/MacOS/ 696 501 63 0 11.7G 175M 0 S 0.0 1.1 0:00.02 /System/Library/Frameworks/QuickLook.framework/Resources/quicklookd.app/Conte 38 0 33 0 11.1G 422M 0 S 0.0 2.6 0:00.59 /System/Library/Frameworks/CoreServices.framework/Frameworks/Metadata.framewo 183 501 48 0 10.9G 137M 0 S 0.0 0.8 0:00.03 /System/Library/CoreServices/Finder.app/Contents/MacOS/Finder How can I have Processes using Gigabytes of VIRT memory and still 0MB of Swap used ?

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