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  • How are builds deployed into QA->Staging->Production for ASP.NET Web Applications?

    - by CodeToGlory
    Secondary questions are How do we best utilize SCM in the build process? How are code files labed and branched? Should we the .csproj and .sln files for build? How flexible are these when deploying to several environments? I know these are msbuild files. But as we add new files, this can become a bottlenect of updating and maintaining these .csproj files in SCM. How is rollback done in case of failed builds that QA missed testing etc,etc., Are there any good articles on the build process? This is more a question on the process and less on the choice of automated build tools. Please share your build process. I would like to get an end-to-end view from developers checking-in to Going Live.

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  • linked list printing using while loop and combining like terms

    - by C Z
    i have a problem printing out my linked list. My program asks the user to enter many different coefficients and degrees and makes it a polynomial and using mergesort it sorts it and then prints it, now i want to combine like terms and i have a problem doing so. thats part of my function that i don't know what is wrong with it: Term* p; p=poly; if (p==0) cout<<"---empty list---"; while(p !=0) if (p->coef==(p->next)->coef){ cout<<(p->deg)+((p->next)->deg)<<"x^"<<(p->coef)<<endl; p=p->next;} if (p->coef !=(p->next)->coef){ cout<<p->deg<<"x"<<p->coef<<"+"; p=p->next;} cout<<endl; } and thats my struct: struct Term { int deg; float coef; Term *next; }; typedef Term* Poly;

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  • Sorting a Singly Linked List With Pointers

    - by Mark Simson
    I am trying to sort a singly linked list using bubble sort by manipulating ONLY the pointers, no keys. The following gets stuck in the for loop and loops infinitely. I don't understand why this is. Can anybody explain to me why the end of the list is not being found? Node* sort_list(Node* head) { Node * temp; Node * curr; for(bool didSwap = true; didSwap; ) { didSwap = false; for(curr = head; curr->next != NULL; curr = curr->next) { if(curr->key > curr->next->key) { temp = curr; curr = curr->next; curr->next = temp; didSwap = true; } cout << curr->next->key << endl; } } return head; } If I change the code so that the keys (data) are swapped, then the function works properly but for some reason I am not able make it work by manipulating only pointers.

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  • Improving Partitioned Table Join Performance

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

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  • Oracle Utilities Application Framework future feature deprecation

    - by Paula Speranza-Hadley
    From time to time, existing functionality is replaced with alternative features to offer greater flexibility and standardization. In Oracle Utilities Application Framework V4.2.0.0.0 the following features are being announced for deprecation in the next release or have been previously announced and are not being delivered with this version of the Oracle Utilities Application Framework: ·         No SQL Server Support – Oracle Utilities Application Framework V4.2.0.0.0 or above does not ship with any support for SQL Server. ·         No MPL Support – Oracle Utilities Application Framework V4.2.0.0.0 or above does not ship with the Multi-Purpose Listener (MPL) component of the XML Application Integration (XAI) component. Customers using the MPL should migrate to Oracle Service Bus. ·         No provided Crystal Reports/Business Objects Interface – Oracle Utilities Application Framework V4.2.0.0.0 or above does not ship with a supported Crystal Reports/Business Objects Interface. This facility is now available as downloadable customization for existing or new customers. Responsibility for maintenance and new features is now individual customer's responsibility. ·         XAI Servlet deprecation – The XAI Servlet (xaiserver and classicxai) will be removed in the next release of the Oracle Utilities Application Framework. Customers are encouraged to migrate to the native Web Services Support as outlined in XAI Best Practices whitepaper available from My Oracle Support (Doc Id: 942074.1). ·         ConfigLab deprecation – The ConfigLab facility will be removed in the next release of Oracle Utilities Application Framework for products it is shipped with. Customers are recommended to migrate to the Configuration Migration Assistant which provides the same and more functionality.   ·         Archiving deprecation – The inbuilt Archiving has been removed from Oracle Utilities Application Framework V4.2.0.0.0 or above, for products it is shipped with. Customers considering Archiving solution should migrate to the Information Lifecycle Management based solution provided for your product. ·         DISTRIBUTED batch execution mode deprecation – The DISTRIBUTED execution mode used by the batch component of the Oracle Utilities Application Framework will be deprecated in the next release of the Oracle Utilities Application Framework. Customers using DISTRUBUTED mode should migrate to CLUSTERED mode as outlined in the Batch Best Practices For Oracle Utilities Application Framework Based Products whitepaper available from My Oracle Support (Doc Id: 836362.1). ·         XAI Schema Editor deprecation – The XAI Schema Editor which is a component of the Oracle Utilities Software Development Kit will be removed in the next release of the Oracle Utilities Application Framework. Customers should migrate their existing schemas to Business Object based schemas and use the browser based Schema Editor instead.  

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  • StreamInsight and Reactive Framework Challenge

    In his blogpost Roman from the StreamInsight team asked if we could create a Reactive Framework version of what he had done in the post using StreamInsight.  For those who don’t know, the Reactive Framework or Rx to its friends is a library for composing asynchronous and event-based programs using observable collections in the .Net framework.  Yes, there is some overlap between StreamInsight and the Reactive Extensions but StreamInsight has more flexibility and power in its temporal algebra (Windowing, Alteration of event headers) Well here are two alternate ways of doing what Roman did. The first example is a mix of StreamInsight and Rx var rnd = new Random(); var RandomValue = 0; var interval = Observable.Interval(TimeSpan.FromMilliseconds((Int32)rnd.Next(500,3000))) .Select(i => { RandomValue = rnd.Next(300); return RandomValue; }); Server s = Server.Create("Default"); Microsoft.ComplexEventProcessing.Application a = s.CreateApplication("Rx SI Mischung"); var inputStream = interval.ToPointStream(a, evt => PointEvent.CreateInsert( System.DateTime.Now.ToLocalTime(), new { RandomValue = evt}), AdvanceTimeSettings.IncreasingStartTime, "Rx Sample"); var r = from evt in inputStream select new { runningVal = evt.RandomValue }; foreach (var x in r.ToPointEnumerable().Where(e => e.EventKind != EventKind.Cti)) { Console.WriteLine(x.Payload.ToString()); } This next version though uses the Reactive Extensions Only   var rnd = new Random(); var RandomValue = 0; Observable.Interval(TimeSpan.FromMilliseconds((Int32)rnd.Next(500, 3000))) .Select(i => { RandomValue = rnd.Next(300); return RandomValue; }).Subscribe(Console.WriteLine, () => Console.WriteLine("Completed")); Console.ReadKey();   These are very simple examples but both technologies allow us to do a lot more.  The ICEPObservable() design pattern was reintroduced in StreamInsight 1.1 and the more I use it the more I like it.  It is a very useful pattern when wanting to show StreamInsight samples as is the IEnumerable() pattern.

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  • Finding the Twins when Implementing Catmull-Clark subdivision using Half-Edge mesh [migrated]

    - by Ailurus
    Note: The description became a little longer than expected. Do you know a readable implementation of this algorithm using this mesh? Please let me know! I'm trying to implement Catmull-Clark subdivision using Matlab (because later on the results have to be compared with some other stuff already implemented in Matlab). First try was with a Vertex-Face mesh, the algorithm works but it is of course not very efficient (since you need neighbouring information for edges and faces). Therefore, I'm now using a Half-Edge mesh (info), see also the paper of Lutz Kettner. Wikipedia link to the idea behind Catmull-Clark SDV: Wiki. My problem lies in finding the Twin HalfEdges, I'm just not sure how to do this. Below I'm describing my thoughts on the implementation, trying to keep it concise. Half-Edge mesh (using indices to Vertices/HalfEdges/Faces): Vertex (x,y,z,Outgoing_HalfEdge) HalfEdge (HeadVertex (or TailVertex, which one should I use), Next, Face, Twin). Face (HalfEdge) To keep it simple for now, assume that every face is a quadrilateral. The actual mesh is a list of Vertices, HalfEdges and Faces. The new mesh will consist of NewVertices, NewHalfEdges and NewFaces, like this (note: Number_... is the number of ...): NumberNewVertices: Number_Faces + Number_HalfEdges/2 + Number_Vertices NumberNewHalfEdges: 4 * 4 * NumberFaces NumberNewfaces: 4 * NumberFaces Catmull-Clark: Find the FacePoint (centroid) of each Face: --> Just average the x,y,z values of the vertices, save as a NewVertex. Find the EdgePoint of each HalfEdge: --> To prevent duplicates (each HalfEdge has a Twin which would result in the same HalfEdge) --> Only calculate EdgePoints of the HalfEdge which has the lowest index of the Pair. Update old Vertices Ok, now all the new Vertices are calculated (however, their Outgoing_HalfEdge is still unknown). Next step to save the new HalfEdges and Faces. This is the part causing me problems! Loop through each old Face, there are 4 new Faces to be created (because of the quadrilateral assumption) First create the 4 new HalfEdges per New Face, starting at the FacePoint to the Edgepoint Next a new HalfEdge from the EdgePoint to an Updated Vertex Another new one from the Updated Vertex to the next EdgePoint Finally the fourth new HalfEdge from the EdgePoint back to the FacePoint. The HeadVertex of each new HalfEdge is known, the Next HalfEdge too. The Face is also known (since it is the new face you're creating!). Only the Twin HalfEdge is unknown, how should I know this? By the way, while looping through the Vertices of the new Face, assign the Outgoing_HalfEdge to the Vertices. This is probably the place to find out which HalfEdge is the Twin. Finally, after the 4 new HalfEdges are created, save the Face with the HalfVertex index the last newly created HalfVertex. I hope this is clear, if needed I can post my (obviously not-yet-finished) Matlab code.

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  • Unable to make the session state request to the session state server

    - by Angry_IT_Guru
    For about 4-5 months now, I seem to be having this sporadic issue--mainly during our busiest time of the day between 10:30-11:45AM, where all my Windows 2003 web servers in a Microsoft NLB cluster start throwing session state server errors. A sample error is below. System.Web.HttpException: Unable to make the session state request to the session state server. Please ensure that the ASP.NET State service is started and that the client and server ports are the same. If the server is on a remote machine, please ensure that it accepts remote requests by checking the value of HKEY_LOCAL_MACHINE\SYSTEM\CurrentControlSet\Services\aspnet_state\Parameters\AllowRemoteConnection. If the server is on the local machine, and if the before mentioned registry value does not exist or is set to 0, then the state server connection string must use either 'localhost' or '127.0.0.1' as the server name. at System.Web.SessionState.OutOfProcSessionStateStore.MakeRequest(StateProtocolVerb verb, String id, StateProtocolExclusive exclusiveAccess, Int32 extraFlags, Int32 timeout, Int32 lockCookie, Byte[] buf, Int32 cb, Int32 networkTimeout, SessionNDMakeRequestResults& results) at System.Web.SessionState.OutOfProcSessionStateStore.SetAndReleaseItemExclusive(HttpContext context, String id, SessionStateStoreData item, Object lockId, Boolean newItem) at System.Web.SessionState.SessionStateModule.OnReleaseState(Object source, EventArgs eventArgs) at System.Web.HttpApplication.SyncEventExecutionStep.System.Web.HttpApplication.IExecutionStep.Execute() at System.Web.HttpApplication.ExecuteStep(IExecutionStep step, Boolean& completedSynchronously) Now I'm using ASP.NET State service on a centralized back-end Windows 2003 server that all servers communicate to. I was originally using SQL Server state for a couple years as well prior to having this issue. The problem with SQL wqas that when the issue occurred, it created a blocking situation which essentially impacted all users across all servers. The product company recommended that I use the standard ASP.NET State service as that was what they technically supported. Why this would make a difference is beyond me -- but I had no choice but to try it! I have attempted to create multiple application pools, adding additional servers, chaning TCP/IP timeout from 20 to 30 seconds, and even calling Microsoft ASP.NET product support, with very little success. I even recommended that they review whether they are using read-only session state instead of read/write per page request -- as I understand that this basically causes every page to make round-trips to state server even if state isn't being used on the page. Unfortunately, the application is developed by our product company and they insist that it is something with my environment because other clients do not have these sort of issues. However, I've talked to other clients and they tell me when they've seen issues like they, they've basically had to create another web farm. This issue almost seems like I've simply reached some architectural limit within the application... Microsoft's position on the issue is that the session state needs to be reduced and the returncode being reported back from the state server indicates buffers are full. To better understand the scope of issues (rather than wait for customers to call and complain), I installed ELMAH and configured it to send me e-mails when unhandled exceptions occur. I basically get 500-1000 e-mails during the time period of high activity! If any one has any other ideas I could try or better ways to troubleshoot, I'd appreciate it.

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  • Apache URL rewriting in reverse proxy

    - by Jeremy Gooch
    I'm deploying Apache in front of a Karaf-hosted application (Apache and Karaf are on separate servers). I want Apache to operate as a reverse proxy and also to hide part of the URL. The URL to get the log-in page of the application directly from the app server is http://app-server:8181/jellyfish. Pages are served by the Jetty instance running within Karaf. Of course, this behaviour would usually be blocked by the firewall for everything except the reverse proxy server. With the firewall off, if you hit this URL then Jetty loads the log-in page. The browser's address bar correctly changes to http://app-server:8181/jellyfish/login?0 and everything works. What I want is for http://web-server (i.e. from the root) to map to Jetty on the app server with the name of the app (jellyfish) suppressed. e.g. The browser would change to show http://web-server/login?0 in the address bar and all subsequent URLs and content would be served with the web-server's domain and without the jellyfish clutter. I can get Apache to operate as a simple reverse proxy, using the following config (snippet):- ProxyPass /jellyfish http://app-server:8181/jellyfish ProxyPassReverse / http://app-server:8181/ ...but this requires the browser's URL to contain jellyfish and going to the root URL (http://web-server) gives a 404 Not Found. I've spent a lot of time trying to use mod_rewrite with and without its [P] flag to get around this, but without success. I then tried the ProxyPassMatch directive, but I can't seem to get that quite correct either. Here's the current config, as is loaded into /etc/apache2/sites-available/ on the web server. Note that there is a locally-hosted images directory. I've also kept the mod_rewrite proxy exploit protection and am suppressing a couple of mod_security rules that were giving false positives. <VirtualHost *:80> ServerAdmin admin@drummer-server ServerName drummer-server ErrorLog ${APACHE_LOG_DIR}/error.log LogLevel warn CustomLog ${APACHE_LOG_DIR}/access.log combined Alias /images/ "/var/www/images/" RewriteEngine On RewriteCond %{REQUEST_URI} !^$ RewriteCond %{REQUEST_URI} !^/ RewriteRule .* - [R=400,L] ProxyPass /images ! ProxyPassMatch ^/(.*) http://granny-server:8181/jellyfish/$1 ProxyPassReverse / http://granny-server:8181/jellyfish ProxyPreserveHost On SecRuleRemoveById 981059 981060 <Directory "/var/www/images"> Options Indexes MultiViews FollowSymLinks AllowOverride None Order allow,deny Allow from all </Directory> </VirtualHost> If I go to http://web-server, I get redirected to http://web-server/jellyfish/home but this gives a 404, with a complaint about trying to access /jellyfish/jellyfish/home - NB the browser's address bar does not contain the double /jellyfish. HTTP ERROR 404 Problem accessing /jellyfish/jellyfish/home. Reason: Not Found And, if I go to http://web-server/login, I get redirected to http://web-server/jellyfish/login?0 but this gives a 404, with a complaint about trying to access /jellyfish/jellyfish/login. HTTP ERROR 404 Problem accessing /jellyfish/jellyfish/login. Reason: Not Found So, I'm guessing I'm somehow passing through the rules twice. I am also slightly bemused as to where the home bit of the URL comes from in the first example. Can someone point me in the right direction, please? Thanks, J.

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  • Unable to make the session state request to the session state server.

    - by Angry_IT_Guru
    For about 4-5 months now, I seem to be having this sporadic issue--mainly during our busiest time of the day between 10:30-11:45AM, where all my Windows 2003 web servers in a Microsoft NLB cluster start throwing session state server errors. A sample error is below. System.Web.HttpException: Unable to make the session state request to the session state server. Please ensure that the ASP.NET State service is started and that the client and server ports are the same. If the server is on a remote machine, please ensure that it accepts remote requests by checking the value of HKEY_LOCAL_MACHINE\SYSTEM\CurrentControlSet\Services\aspnet_state\Parameters\AllowRemoteConnection. If the server is on the local machine, and if the before mentioned registry value does not exist or is set to 0, then the state server connection string must use either 'localhost' or '127.0.0.1' as the server name. at System.Web.SessionState.OutOfProcSessionStateStore.MakeRequest(StateProtocolVerb verb, String id, StateProtocolExclusive exclusiveAccess, Int32 extraFlags, Int32 timeout, Int32 lockCookie, Byte[] buf, Int32 cb, Int32 networkTimeout, SessionNDMakeRequestResults& results) at System.Web.SessionState.OutOfProcSessionStateStore.SetAndReleaseItemExclusive(HttpContext context, String id, SessionStateStoreData item, Object lockId, Boolean newItem) at System.Web.SessionState.SessionStateModule.OnReleaseState(Object source, EventArgs eventArgs) at System.Web.HttpApplication.SyncEventExecutionStep.System.Web.HttpApplication.IExecutionStep.Execute() at System.Web.HttpApplication.ExecuteStep(IExecutionStep step, Boolean& completedSynchronously) Now I'm using ASP.NET State service on a centralized back-end Windows 2003 server that all servers communicate to. I was originally using SQL Server state for a couple years as well prior to having this issue. The problem with SQL wqas that when the issue occurred, it created a blocking situation which essentially impacted all users across all servers. The product company recommended that I use the standard ASP.NET State service as that was what they technically supported. Why this would make a difference is beyond me -- but I had no choice but to try it! I have attempted to create multiple application pools, adding additional servers, chaning TCP/IP timeout from 20 to 30 seconds, and even calling Microsoft ASP.NET product support, with very little success. I even recommended that they review whether they are using read-only session state instead of read/write per page request -- as I understand that this basically causes every page to make round-trips to state server even if state isn't being used on the page. Unfortunately, the application is developed by our product company and they insist that it is something with my environment because other clients do not have these sort of issues. However, I've talked to other clients and they tell me when they've seen issues like they, they've basically had to create another web farm. This issue almost seems like I've simply reached some architectural limit within the application... Microsoft's position on the issue is that the session state needs to be reduced and the returncode being reported back from the state server indicates buffers are full. To better understand the scope of issues (rather than wait for customers to call and complain), I installed ELMAH and configured it to send me e-mails when unhandled exceptions occur. I basically get 500-1000 e-mails during the time period of high activity! If any one has any other ideas I could try or better ways to troubleshoot, I'd appreciate it.

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  • Handwritten linked list is segfaulting and I don't understand why

    - by Born2Smile
    Hi I was working on a bit of fun, making an interface to run gnuplot from within c++, and for some reason the my linked list implementation fails. The code below fails on the line plots-append(&plot). Stepping through the code I discovered that for some reason the destructor ~John() is called immediately after the constructor John(), and I cannot seem to figure out why. The code included below is a stripped down version operating only on Plot*. Originally I made the linked list as a template class. And it worked fine as ll<int and ll<char* but for some reason it fails as ll<Plot*. Could youp please help me figure out why it fails? and perhaps help me understand how to make it work? In advance: Thanks a heap! //B2S #include <string.h class Plot{ char title[80]; public: Plot(){ } }; class Link{ Plot* element; Link* next; Link* prev; friend class ll; }; class ll{ Link* head; Link* tail; public: ll(){ head = tail = new Link(); head-prev = tail-prev = head-next = tail-next = head; } ~ll(){ while (head!=tail){ tail = tail-prev; delete tail-next; } delete head; } void append(Plot* element){ tail-element = element; tail-next = new Link(); tail-next-prev = tail; tail-next = tail; } }; class John{ ll* plots; public: John(){ plots= new ll(); } ~John(){ delete plots; } John(Plot* plot){ John(); plots-append(plot); } }; int main(){ Plot p; John k(&p); }

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  • how find out the form is submit in JSP?

    - by user261002
    I am trying to create a an online exam with JSP. I want to get the questions one by one and show them on the screen, and create a "Next" button then user is able to to see the next question, but the problem is that I dont know how to find out that the user has clicked on the "Next" button, I know how to do it in PHP : if($_SERVER['REQUEST_METHOD']=='GET') if($_GET['action']=='Next') but I dont know how to do it in JSP. Please help me this is piece of my code: String result = ""; if (database.DatabaseManager.getInstance().connectionOK()) { database.SQLSelectStatement sqlselect = new database.SQLSelectStatement("question", "question", "0"); ResultSet _userExist = sqlselect.executeWithNoCondition(); ResultSetMetaData rsm = _userExist.getMetaData(); result+="<form method='post'>"; result += "<table border=2>"; for (int i = 0; i < rsm.getColumnCount(); i++) { result += "<th>" + rsm.getColumnName(i + 1) + "</th>"; } if (_userExist.next()) { result += "<tr>"; result += "<td>" + _userExist.getInt(1) + "</td>"; result += "<td>" + _userExist.getString(2) + "</td>"; result += "</tr>"; result += "<tr>"; result += "<tr> <td colspan='2'>asdas</td></tr>"; result += "</tr>"; } result += "</table>"; result+="<input type='submit' value='next' name='next'/></form>"; }

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  • stop and split generated sequence at repeats - clojure

    - by fitzsnaggle
    I am trying to make a sequence that will only generate values until it finds the following conditions and return the listed results: case head = 0 - return {:origin [all generated except 0] :pattern 0} 1 - return {:origin nil :pattern [all-generated-values] } repeated-value - {:origin [values-before-repeat] :pattern [values-after-repeat] { ; n = int ; x = int ; hist - all generated values ; Keeps the head below x (defn trim-head [head x] (loop [head head] (if (> head x) (recur (- head x)) head))) ; Generates the next head (defn next-head [head x n] (trim-head (* head n) x)) (defn row [x n] (iterate #(next-head % x n) n)) ; Generates a whole row - ; Rows are a max of x - 1. (take (- x 1) (row 11 3)) Examples of cases to stop before reaching end of row: [9 8 4 5 6 7 4] - '4' is repeated so STOP. Return preceding as origin and rest as pattern. {:origin [9 8] :pattern [4 5 6 7]} [4 5 6 1] - found a '1' so STOP, so return everything as pattern {:origin nil :pattern [4 5 6 1]} [3 0] - found a '0' so STOP {:origin [3] :pattern [0]} :else if the sequences reaches a length of x - 1: {:origin [all values generated] :pattern nil} The Problem I have used partition-by with some success to split the groups at the point where a repeated value is found, but would like to do this lazily. Is there some way I can use take-while, or condp, or the :while clause of the for loop to make a condition that partitions when it finds repeats? Some Attempts (take 2 (partition-by #(= 1 %) (row 11 4))) (for [p (partition-by #(stop-match? %) head) (iterate #(next-head % x n) n) :while (or (not= (last p) (or 1 0 n) (nil? (rest p))] {:origin (first p) :pattern (concat (second p) (last p))})) # Updates What I really want to be able to do is find out if a value has repeated and partition the seq without using the index. Is that possible? Something like this - { (defn row [x n] (loop [hist [n] head (gen-next-head (first hist) x n) steps 1] (if (>= (- x 1) steps) (case head 0 {:origin [hist] :pattern [0]} 1 {:origin nil :pattern (conj hist head)} ; Speculative from here on out (let [p (partition-by #(apply distinct? %) (conj hist head))] (if-not (nil? (next p)) ; One partition if no repeats. {:origin (first p) :pattern (concat (second p) (nth 3 p))} (recur (conj hist head) (gen-next-head head x n) (inc steps))))) {:origin hist :pattern nil}))) }

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  • MVC how to implement two different post actions

    - by AnonyMouse
    I'm developing this really important squirrel application. There is a wizard where squirrels are added to the database. So say there are three screens to this wizard: Squirrel name details Height and weight Nut storage So at each step of the wizard I'm wanting to save the details to the database. The Height and weight view looks like: @model HeightWeightViewModel @{ ViewBag.Title = "Height and weight"; } <h2>Height and weight</h2> @using (Html.BeginForm()) { <h3>Height</h3> <div> @Html.EditorFor(model => model.Squirrel.Height) </div> <h3>Weight</h3> <div> @Html.EditorFor(model => model.Squirrel.Weight) </div> <input type="submit" value="Previous" /> <input type="submit" value="Next" /> } So I'm hoping that Previous and Next buttons will save these details. The Previous button while saving will also take the user to the Squirrel name details page. The Next will save and take the user to the nut storage page. I got the Next button working using: public ActionResult Edit(SquirrelViewModel squirrelViewModel) { _unitOfWork.SaveHeightWeight(squirrelViewModel); return RedirectToAction("Edit", "NutStorage", new { id = squirrelViewModel.Squirrel.Id }); } So the Next button saves the details and sends the user to the NutStorage page. The Previous button does the same as Next but I actually want it to send the user to the first step of the Wizard after saving. I'm not sure how to do this. Would I have another method to post to for Previous? I can't image how to implement this. Maybe I should be using ActionLinks instead of submit buttons but that would not post the details to be saved. Can anyone suggest how to get the previous button to save and send the user to the first page of the wizard while still having the Next functionality working?

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  • how to avoid clutch billiard balls?

    - by Nait87
    I'm working on the simple behaviour of billiard balls in a collision with each other. All works normal, but there was a problem when facing a few easy balls is the effect of coupling balls and they're cool with each other. Tell me how to prevent this. bool MGBilliard::CollisingBall(CCPoint curr_point, CCPoint next_point) { float dx = next_point.x - (curr_point.x + dvdt.x); float dy = next_point.y - (curr_point.y - dvdt.y); float d = dx*dx+dy*dy; return d <= BALL_RADIUS * BALL_RADIUS; } double MGBilliard::angleCollisionBalls(Ball* current, Ball* next) { double na; double dx = fabs(next->location.x - current->location.x); double dy = fabs(next->location.y - current->location.y); na = atan(fabs(dy/dx)); if(atan(fabs(current->location.y/current->location.x)) < atan(fabs(next->location.y/next->location.x))) na = current->angle - na; else if(atan(fabs(current->location.y/current->location.x)) > atan(fabs(next->location.y/next->location.x))) na = current->angle + na; return na; } for(unsigned int i = 0;i<BALL_COUNT;++i) { if(vBalls[i]->speed > 0){ vBalls[i]->speed += vBalls[i]->acceleration; float dsdt = vBalls[i]->speed*dt; dvdt.x = dsdt*cos(vBalls[i]->angle); dvdt.y = dsdt*sin(vBalls[i]->angle); vBalls[i]->location.x += dvdt.x; vBalls[i]->location.y += dvdt.y; for(unsigned int j = 1; j < BALL_COUNT; ++j) { if(i == j) continue; if(CollisingBall(vBalls[i]->spriteBall->getPosition(),vBalls[j]->spriteBall->getPosition())) { vBalls[j]->speed = 600; double angle; angle = angleCollisionBalls(vBalls[i],vBalls[j]); vBalls[i]->angle = (float)-angle; vBalls[j]->angle = (float)angle; } } } }

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  • C++ vector and segmentation faults

    - by Headspin
    I am working on a simple mathematical parser. Something that just reads number = 1 + 2; I have a vector containing these tokens. They store a type and string value of the character. I am trying to step through the vector to build an AST of these tokens, and I keep getting segmentation faults, even when I am under the impression my code should prevent this from happening. Here is the bit of code that builds the AST: struct ASTGen { const vector<Token> &Tokens; unsigned int size, pointer; ASTGen(const vector<Token> &t) : Tokens(t), pointer(0) { size = Tokens.size() - 1; } unsigned int next() { return pointer + 1; } Node* Statement() { if(next() <= size) { switch(Tokens[next()].type) { case EQUALS : Node* n = Assignment_Expr(); return n; } } advance(); } void advance() { if(next() <= size) ++pointer; } Node* Assignment_Expr() { Node* lnode = new Node(Tokens[pointer], NULL, NULL); advance(); Node* n = new Node(Tokens[pointer], lnode, Expression()); return n; } Node* Expression() { if(next() <= size) { advance(); if(Tokens[next()].type == SEMICOLON) { Node* n = new Node(Tokens[pointer], NULL, NULL); return n; } if(Tokens[next()].type == PLUS) { Node* lnode = new Node(Tokens[pointer], NULL, NULL); advance(); Node* n = new Node(Tokens[pointer], lnode, Expression()); return n; } } } }; ... ASTGen AST(Tokens); Node* Tree = AST.Statement(); cout << Tree->Right->Data.svalue << endl; I can access Tree->Data.svalue and get the = Node's token info, so I know that node is getting spawned, and I can also get Tree->Left->Data.svalue and get the variable to the left of the = I have re-written it many times trying out different methods for stepping through the vector, but I always get a segmentation fault when I try to access the = right node (which should be the + node) Any help would be greatly appreciated.

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  • How to store multiple variables from a File Input of unknown size in Java?

    - by AlphaOmegaStrife
    I'm a total beginner with my first programming assignment in Java. For our programming assignment, we will be given a .txt file of students like so: 3 345 Lisa Miller 890238 Y 2 <-(Number of classes) Mathematics MTH345 4 A Physics PHY357 3 B Bill Wilton 798324 N 2 English ENG378 3 B Philosophy PHL534 3 A Dandy Goat 746333 Y 1 History HIS101 3 A" The teacher will give us a .txt file on the day of turning it in with a list of unknown students. My problem is: I have a specific class for turning the data from the file into variables to be used for a different class in printing it to the screen. However, I do not know of a good way to get the variables from the input file for the course numbers, since that number is not predetermined. The only way I can think of to iterate over that unknown amount is using a loop, but that would just overwrite my variables every time. Also, the teacher has requested that we not use any JCL classes (I don't really know what this means.) Sorry if I have done a poor job of explaining this, but I can't think of a better way to conceptualize it. Let me know if I can clarify. Edit: public static void analyzeData() { Scanner inputStream = null; try { inputStream = new Scanner(new FileInputStream("Programming Assignment 1 Data.txt")); } catch (FileNotFoundException e) { System.out.println("File Programming Assignment 1 Data.txt could not be found or opened."); System.exit(0); } int numberOfStudents = inputStream.nextInt(); int tuitionPerHour = inputStream.nextInt(); String firstName = inputStream.next(); String lastname = inputStream.next(); String isTuitionPaid = inputStream.next(); int numberOfCourses = inputStream.nextInt(); String courseName = inputStream.next(); String courseNumber = inputStream.next(); int creditHours = inputStream.nextInt(); String grade = inputStream.next(); To show the methods I am using now, I am just using a Scanner to read from the file and for Scanner inputStream, I am using nextInt() or next() to get variables from the file. Obviously this will not work when I do not know exactly how many classes each student will have.

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  • AJAX: Problems returning multiple variables

    - by fwaokda
    First off sorry if I'm missing something simple just started working with AJAX today. I have an issue where I'm trying to get information from my database, but different records have different amounts of values. For instance, each record has a "features" column. In the features column I store a string. (ex: Feature1~Feature2~Feature3~Feature4... ) When I'm building the object I take apart the string and store all the features into an array. Some objects can have 1 feature others can have up to whatever. So... how do I return this values back to my ajax function from my php page? Below is my ajax function that I was trying and I'll provide a link with my php file. [ next.php : http://pastebin.com/SY74jV7X ] $("a#next").click(function() { $.ajax({ type : 'POST', url : 'next.php', dataType : 'json', data : { nextID : $("a#next").attr("rel") }, success : function ( data ) { var lastID = $("a#next").attr("rel"); var originID = $("a#next").attr("rev"); if(lastID == 1) { lastID = originID; } else { lastID--; } $("img#spotlight").attr("src",data.spotlightimage); $("div#showcase h1").text(data.title); $("div#showcase h2").text(data.subtitle); $("div#showcase p").text(data.description); $("a#next").attr("rel", lastID); for(var i=0; i < data.size; i++) { $("ul#features").append("<li>").text(data.feature+i).append("</li>"); } /* for(var j=1; j < data.picsize; j++) { $("div.thumbnails ul").append("<li>").text(data.image+j).append("</li>"); } */ }, error : function ( XMLHttpRequest, textStatus, errorThrown) { $("div#showcase h1").text("An error has occured: " + errorThrown); } }); });

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  • This Android SDK requires Android Developer Toolkit version 22.0.0 or above. Current version is 21.x.x.

    - by user2626673
    Hi i have a problem with my Eclipse and the SDK (i have download and install the latest ADT Bundle for windows ) when i start my eclipse i get this problem : This Android SDK requires Android Developer Toolkit version 22.0.0 or above. Current version is 20.0.0. please update your SDK tools to the latest version i have tried the option : Help - check for updates But with no new update find then i try this one : How to Update your ADT to Latest Version In Eclipse go to Help Install New Software ---> Add inside Add Repository write the Name: ADT (or whatever you want) and Location: https://dl-ssl.google.com/android/eclipse/ after loading you should get Developer Tools and NDK Plugins check both if you want to use the Native Developer Kit (NDK) in the future or check Developer Tool only click Next Finish But i dont have the option to click next to finish (the back , next and finish options are grey ) Then i try this method : Go here download latest version of ADT-22.0.4.zip (*) At Eclipse > Help > Install new software... > Uncheck Contact all update sites during install to find required software (last bottom preference) that will avoid any unwanted delays during install. then at the same screen (top) Click Add > Archive > select downloaded ADT-X.X.X.zip > follow on screen installation steps But had the same problem when it was to finish the installation.. no option to click ''next'' then i try this one : Help – Install New Software in the ADT menu. Type https://dl-ssl.google.com/android/eclipse/site.xml in “Work with:” and Enter. You can see the “Developer Tools” item. Select it and click Next. Click Next one more. Click Finish accepting the terms of the license agreements. Click OK in the “Security Warning” window. Let the installer restart ADT after installing the tools. But and in this option have the same problem as above.. can click the ''next'' to finish http://i30.photobucket.com/albums/c316/caslor_1978/diafora/atdproblem_zps0d141b7b.jpg i check my version and it is the latest but have the problem http://i30.photobucket.com/albums/c316/caslor_1978/diafora/atdproblem2_zps81de6317.jpg How can i fix this problem ? any suggestion? Win7 / 32bit / java SE Development kit7 update 25

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