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  • How to increase thread-pool threads on IIS 7.0

    - by Xaqron
    Environment: Windows Server 2008 Enterprise, IIS 7.0, ASP.NET 2.0 (CLR), .NET 4.0 I have an ASP.NET application with no page and no session(HttpHandler). It a streaming server. I use two threads for processing each request so if there are 100 connected clients, then 200 threads are used. This is a dedicated server and there's no more application on the server. The problem is after 200 clients are connected (under stress testing) application refuses new clients, but if I increase the worker threads of application pool (create a web garden) then I can have 200 new happy clients per w3wp process. I feel .NET thread pool limit reaches at that point and need to increase it. Thanks

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  • (Serving PHP) Does Apache2 will create new thread on every connection?

    - by apasajja
    Based on many online sources, in serving static files, Apache2 will create new thread on every different connection... results in resource hungry But how about serving PHP through Apache2 (mod_php, MPM worker, etc)? Does apache will also open new thread like serving static files? (AFAIK, in nginx php-fpm, we can set the max thread, but I dont know how many connection per thread) I'm planning to use Apache2 in serving PHP, and hope it will be same as nginx PHP-FPM or even better in resource usage and performance.

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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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  • value types in the vm

    - by john.rose
    value types in the vm p.p1 {margin: 0.0px 0.0px 0.0px 0.0px; font: 14.0px Times} p.p2 {margin: 0.0px 0.0px 14.0px 0.0px; font: 14.0px Times} p.p3 {margin: 0.0px 0.0px 12.0px 0.0px; font: 14.0px Times} p.p4 {margin: 0.0px 0.0px 15.0px 0.0px; font: 14.0px Times} p.p5 {margin: 0.0px 0.0px 0.0px 0.0px; font: 14.0px Courier} p.p6 {margin: 0.0px 0.0px 0.0px 0.0px; font: 14.0px Courier; min-height: 17.0px} p.p7 {margin: 0.0px 0.0px 0.0px 0.0px; font: 14.0px Times; min-height: 18.0px} p.p8 {margin: 0.0px 0.0px 0.0px 36.0px; text-indent: -36.0px; font: 14.0px Times; min-height: 18.0px} p.p9 {margin: 0.0px 0.0px 12.0px 0.0px; font: 14.0px Times; min-height: 18.0px} p.p10 {margin: 0.0px 0.0px 12.0px 0.0px; font: 14.0px Times; color: #000000} li.li1 {margin: 0.0px 0.0px 0.0px 0.0px; font: 14.0px Times} li.li7 {margin: 0.0px 0.0px 0.0px 0.0px; font: 14.0px Times; min-height: 18.0px} span.s1 {font: 14.0px Courier} span.s2 {color: #000000} span.s3 {font: 14.0px Courier; color: #000000} ol.ol1 {list-style-type: decimal} Or, enduring values for a changing world. Introduction A value type is a data type which, generally speaking, is designed for being passed by value in and out of methods, and stored by value in data structures. The only value types which the Java language directly supports are the eight primitive types. Java indirectly and approximately supports value types, if they are implemented in terms of classes. For example, both Integer and String may be viewed as value types, especially if their usage is restricted to avoid operations appropriate to Object. In this note, we propose a definition of value types in terms of a design pattern for Java classes, accompanied by a set of usage restrictions. We also sketch the relation of such value types to tuple types (which are a JVM-level notion), and point out JVM optimizations that can apply to value types. This note is a thought experiment to extend the JVM’s performance model in support of value types. The demonstration has two phases.  Initially the extension can simply use design patterns, within the current bytecode architecture, and in today’s Java language. But if the performance model is to be realized in practice, it will probably require new JVM bytecode features, changes to the Java language, or both.  We will look at a few possibilities for these new features. An Axiom of Value In the context of the JVM, a value type is a data type equipped with construction, assignment, and equality operations, and a set of typed components, such that, whenever two variables of the value type produce equal corresponding values for their components, the values of the two variables cannot be distinguished by any JVM operation. Here are some corollaries: A value type is immutable, since otherwise a copy could be constructed and the original could be modified in one of its components, allowing the copies to be distinguished. Changing the component of a value type requires construction of a new value. The equals and hashCode operations are strictly component-wise. If a value type is represented by a JVM reference, that reference cannot be successfully synchronized on, and cannot be usefully compared for reference equality. A value type can be viewed in terms of what it doesn’t do. We can say that a value type omits all value-unsafe operations, which could violate the constraints on value types.  These operations, which are ordinarily allowed for Java object types, are pointer equality comparison (the acmp instruction), synchronization (the monitor instructions), all the wait and notify methods of class Object, and non-trivial finalize methods. The clone method is also value-unsafe, although for value types it could be treated as the identity function. Finally, and most importantly, any side effect on an object (however visible) also counts as an value-unsafe operation. A value type may have methods, but such methods must not change the components of the value. It is reasonable and useful to define methods like toString, equals, and hashCode on value types, and also methods which are specifically valuable to users of the value type. Representations of Value Value types have two natural representations in the JVM, unboxed and boxed. An unboxed value consists of the components, as simple variables. For example, the complex number x=(1+2i), in rectangular coordinate form, may be represented in unboxed form by the following pair of variables: /*Complex x = Complex.valueOf(1.0, 2.0):*/ double x_re = 1.0, x_im = 2.0; These variables might be locals, parameters, or fields. Their association as components of a single value is not defined to the JVM. Here is a sample computation which computes the norm of the difference between two complex numbers: double distance(/*Complex x:*/ double x_re, double x_im,         /*Complex y:*/ double y_re, double y_im) {     /*Complex z = x.minus(y):*/     double z_re = x_re - y_re, z_im = x_im - y_im;     /*return z.abs():*/     return Math.sqrt(z_re*z_re + z_im*z_im); } A boxed representation groups component values under a single object reference. The reference is to a ‘wrapper class’ that carries the component values in its fields. (A primitive type can naturally be equated with a trivial value type with just one component of that type. In that view, the wrapper class Integer can serve as a boxed representation of value type int.) The unboxed representation of complex numbers is practical for many uses, but it fails to cover several major use cases: return values, array elements, and generic APIs. The two components of a complex number cannot be directly returned from a Java function, since Java does not support multiple return values. The same story applies to array elements: Java has no ’array of structs’ feature. (Double-length arrays are a possible workaround for complex numbers, but not for value types with heterogeneous components.) By generic APIs I mean both those which use generic types, like Arrays.asList and those which have special case support for primitive types, like String.valueOf and PrintStream.println. Those APIs do not support unboxed values, and offer some problems to boxed values. Any ’real’ JVM type should have a story for returns, arrays, and API interoperability. The basic problem here is that value types fall between primitive types and object types. Value types are clearly more complex than primitive types, and object types are slightly too complicated. Objects are a little bit dangerous to use as value carriers, since object references can be compared for pointer equality, and can be synchronized on. Also, as many Java programmers have observed, there is often a performance cost to using wrapper objects, even on modern JVMs. Even so, wrapper classes are a good starting point for talking about value types. If there were a set of structural rules and restrictions which would prevent value-unsafe operations on value types, wrapper classes would provide a good notation for defining value types. This note attempts to define such rules and restrictions. Let’s Start Coding Now it is time to look at some real code. Here is a definition, written in Java, of a complex number value type. @ValueSafe public final class Complex implements java.io.Serializable {     // immutable component structure:     public final double re, im;     private Complex(double re, double im) {         this.re = re; this.im = im;     }     // interoperability methods:     public String toString() { return "Complex("+re+","+im+")"; }     public List<Double> asList() { return Arrays.asList(re, im); }     public boolean equals(Complex c) {         return re == c.re && im == c.im;     }     public boolean equals(@ValueSafe Object x) {         return x instanceof Complex && equals((Complex) x);     }     public int hashCode() {         return 31*Double.valueOf(re).hashCode()                 + Double.valueOf(im).hashCode();     }     // factory methods:     public static Complex valueOf(double re, double im) {         return new Complex(re, im);     }     public Complex changeRe(double re2) { return valueOf(re2, im); }     public Complex changeIm(double im2) { return valueOf(re, im2); }     public static Complex cast(@ValueSafe Object x) {         return x == null ? ZERO : (Complex) x;     }     // utility methods and constants:     public Complex plus(Complex c)  { return new Complex(re+c.re, im+c.im); }     public Complex minus(Complex c) { return new Complex(re-c.re, im-c.im); }     public double abs() { return Math.sqrt(re*re + im*im); }     public static final Complex PI = valueOf(Math.PI, 0.0);     public static final Complex ZERO = valueOf(0.0, 0.0); } This is not a minimal definition, because it includes some utility methods and other optional parts.  The essential elements are as follows: The class is marked as a value type with an annotation. The class is final, because it does not make sense to create subclasses of value types. The fields of the class are all non-private and final.  (I.e., the type is immutable and structurally transparent.) From the supertype Object, all public non-final methods are overridden. The constructor is private. Beyond these bare essentials, we can observe the following features in this example, which are likely to be typical of all value types: One or more factory methods are responsible for value creation, including a component-wise valueOf method. There are utility methods for complex arithmetic and instance creation, such as plus and changeIm. There are static utility constants, such as PI. The type is serializable, using the default mechanisms. There are methods for converting to and from dynamically typed references, such as asList and cast. The Rules In order to use value types properly, the programmer must avoid value-unsafe operations.  A helpful Java compiler should issue errors (or at least warnings) for code which provably applies value-unsafe operations, and should issue warnings for code which might be correct but does not provably avoid value-unsafe operations.  No such compilers exist today, but to simplify our account here, we will pretend that they do exist. A value-safe type is any class, interface, or type parameter marked with the @ValueSafe annotation, or any subtype of a value-safe type.  If a value-safe class is marked final, it is in fact a value type.  All other value-safe classes must be abstract.  The non-static fields of a value class must be non-public and final, and all its constructors must be private. Under the above rules, a standard interface could be helpful to define value types like Complex.  Here is an example: @ValueSafe public interface ValueType extends java.io.Serializable {     // All methods listed here must get redefined.     // Definitions must be value-safe, which means     // they may depend on component values only.     List<? extends Object> asList();     int hashCode();     boolean equals(@ValueSafe Object c);     String toString(); } //@ValueSafe inherited from supertype: public final class Complex implements ValueType { … The main advantage of such a conventional interface is that (unlike an annotation) it is reified in the runtime type system.  It could appear as an element type or parameter bound, for facilities which are designed to work on value types only.  More broadly, it might assist the JVM to perform dynamic enforcement of the rules for value types. Besides types, the annotation @ValueSafe can mark fields, parameters, local variables, and methods.  (This is redundant when the type is also value-safe, but may be useful when the type is Object or another supertype of a value type.)  Working forward from these annotations, an expression E is defined as value-safe if it satisfies one or more of the following: The type of E is a value-safe type. E names a field, parameter, or local variable whose declaration is marked @ValueSafe. E is a call to a method whose declaration is marked @ValueSafe. E is an assignment to a value-safe variable, field reference, or array reference. E is a cast to a value-safe type from a value-safe expression. E is a conditional expression E0 ? E1 : E2, and both E1 and E2 are value-safe. Assignments to value-safe expressions and initializations of value-safe names must take their values from value-safe expressions. A value-safe expression may not be the subject of a value-unsafe operation.  In particular, it cannot be synchronized on, nor can it be compared with the “==” operator, not even with a null or with another value-safe type. In a program where all of these rules are followed, no value-type value will be subject to a value-unsafe operation.  Thus, the prime axiom of value types will be satisfied, that no two value type will be distinguishable as long as their component values are equal. More Code To illustrate these rules, here are some usage examples for Complex: Complex pi = Complex.valueOf(Math.PI, 0); Complex zero = pi.changeRe(0);  //zero = pi; zero.re = 0; ValueType vtype = pi; @SuppressWarnings("value-unsafe")   Object obj = pi; @ValueSafe Object obj2 = pi; obj2 = new Object();  // ok List<Complex> clist = new ArrayList<Complex>(); clist.add(pi);  // (ok assuming List.add param is @ValueSafe) List<ValueType> vlist = new ArrayList<ValueType>(); vlist.add(pi);  // (ok) List<Object> olist = new ArrayList<Object>(); olist.add(pi);  // warning: "value-unsafe" boolean z = pi.equals(zero); boolean z1 = (pi == zero);  // error: reference comparison on value type boolean z2 = (pi == null);  // error: reference comparison on value type boolean z3 = (pi == obj2);  // error: reference comparison on value type synchronized (pi) { }  // error: synch of value, unpredictable result synchronized (obj2) { }  // unpredictable result Complex qq = pi; qq = null;  // possible NPE; warning: “null-unsafe" qq = (Complex) obj;  // warning: “null-unsafe" qq = Complex.cast(obj);  // OK @SuppressWarnings("null-unsafe")   Complex empty = null;  // possible NPE qq = empty;  // possible NPE (null pollution) The Payoffs It follows from this that either the JVM or the java compiler can replace boxed value-type values with unboxed ones, without affecting normal computations.  Fields and variables of value types can be split into their unboxed components.  Non-static methods on value types can be transformed into static methods which take the components as value parameters. Some common questions arise around this point in any discussion of value types. Why burden the programmer with all these extra rules?  Why not detect programs automagically and perform unboxing transparently?  The answer is that it is easy to break the rules accidently unless they are agreed to by the programmer and enforced.  Automatic unboxing optimizations are tantalizing but (so far) unreachable ideal.  In the current state of the art, it is possible exhibit benchmarks in which automatic unboxing provides the desired effects, but it is not possible to provide a JVM with a performance model that assures the programmer when unboxing will occur.  This is why I’m writing this note, to enlist help from, and provide assurances to, the programmer.  Basically, I’m shooting for a good set of user-supplied “pragmas” to frame the desired optimization. Again, the important thing is that the unboxing must be done reliably, or else programmers will have no reason to work with the extra complexity of the value-safety rules.  There must be a reasonably stable performance model, wherein using a value type has approximately the same performance characteristics as writing the unboxed components as separate Java variables. There are some rough corners to the present scheme.  Since Java fields and array elements are initialized to null, value-type computations which incorporate uninitialized variables can produce null pointer exceptions.  One workaround for this is to require such variables to be null-tested, and the result replaced with a suitable all-zero value of the value type.  That is what the “cast” method does above. Generically typed APIs like List<T> will continue to manipulate boxed values always, at least until we figure out how to do reification of generic type instances.  Use of such APIs will elicit warnings until their type parameters (and/or relevant members) are annotated or typed as value-safe.  Retrofitting List<T> is likely to expose flaws in the present scheme, which we will need to engineer around.  Here are a couple of first approaches: public interface java.util.List<@ValueSafe T> extends Collection<T> { … public interface java.util.List<T extends Object|ValueType> extends Collection<T> { … (The second approach would require disjunctive types, in which value-safety is “contagious” from the constituent types.) With more transformations, the return value types of methods can also be unboxed.  This may require significant bytecode-level transformations, and would work best in the presence of a bytecode representation for multiple value groups, which I have proposed elsewhere under the title “Tuples in the VM”. But for starters, the JVM can apply this transformation under the covers, to internally compiled methods.  This would give a way to express multiple return values and structured return values, which is a significant pain-point for Java programmers, especially those who work with low-level structure types favored by modern vector and graphics processors.  The lack of multiple return values has a strong distorting effect on many Java APIs. Even if the JVM fails to unbox a value, there is still potential benefit to the value type.  Clustered computing systems something have copy operations (serialization or something similar) which apply implicitly to command operands.  When copying JVM objects, it is extremely helpful to know when an object’s identity is important or not.  If an object reference is a copied operand, the system may have to create a proxy handle which points back to the original object, so that side effects are visible.  Proxies must be managed carefully, and this can be expensive.  On the other hand, value types are exactly those types which a JVM can “copy and forget” with no downside. Array types are crucial to bulk data interfaces.  (As data sizes and rates increase, bulk data becomes more important than scalar data, so arrays are definitely accompanying us into the future of computing.)  Value types are very helpful for adding structure to bulk data, so a successful value type mechanism will make it easier for us to express richer forms of bulk data. Unboxing arrays (i.e., arrays containing unboxed values) will provide better cache and memory density, and more direct data movement within clustered or heterogeneous computing systems.  They require the deepest transformations, relative to today’s JVM.  There is an impedance mismatch between value-type arrays and Java’s covariant array typing, so compromises will need to be struck with existing Java semantics.  It is probably worth the effort, since arrays of unboxed value types are inherently more memory-efficient than standard Java arrays, which rely on dependent pointer chains. It may be sufficient to extend the “value-safe” concept to array declarations, and allow low-level transformations to change value-safe array declarations from the standard boxed form into an unboxed tuple-based form.  Such value-safe arrays would not be convertible to Object[] arrays.  Certain connection points, such as Arrays.copyOf and System.arraycopy might need additional input/output combinations, to allow smooth conversion between arrays with boxed and unboxed elements. Alternatively, the correct solution may have to wait until we have enough reification of generic types, and enough operator overloading, to enable an overhaul of Java arrays. Implicit Method Definitions The example of class Complex above may be unattractively complex.  I believe most or all of the elements of the example class are required by the logic of value types. If this is true, a programmer who writes a value type will have to write lots of error-prone boilerplate code.  On the other hand, I think nearly all of the code (except for the domain-specific parts like plus and minus) can be implicitly generated. Java has a rule for implicitly defining a class’s constructor, if no it defines no constructors explicitly.  Likewise, there are rules for providing default access modifiers for interface members.  Because of the highly regular structure of value types, it might be reasonable to perform similar implicit transformations on value types.  Here’s an example of a “highly implicit” definition of a complex number type: public class Complex implements ValueType {  // implicitly final     public double re, im;  // implicitly public final     //implicit methods are defined elementwise from te fields:     //  toString, asList, equals(2), hashCode, valueOf, cast     //optionally, explicit methods (plus, abs, etc.) would go here } In other words, with the right defaults, a simple value type definition can be a one-liner.  The observant reader will have noticed the similarities (and suitable differences) between the explicit methods above and the corresponding methods for List<T>. Another way to abbreviate such a class would be to make an annotation the primary trigger of the functionality, and to add the interface(s) implicitly: public @ValueType class Complex { … // implicitly final, implements ValueType (But to me it seems better to communicate the “magic” via an interface, even if it is rooted in an annotation.) Implicitly Defined Value Types So far we have been working with nominal value types, which is to say that the sequence of typed components is associated with a name and additional methods that convey the intention of the programmer.  A simple ordered pair of floating point numbers can be variously interpreted as (to name a few possibilities) a rectangular or polar complex number or Cartesian point.  The name and the methods convey the intended meaning. But what if we need a truly simple ordered pair of floating point numbers, without any further conceptual baggage?  Perhaps we are writing a method (like “divideAndRemainder”) which naturally returns a pair of numbers instead of a single number.  Wrapping the pair of numbers in a nominal type (like “QuotientAndRemainder”) makes as little sense as wrapping a single return value in a nominal type (like “Quotient”).  What we need here are structural value types commonly known as tuples. For the present discussion, let us assign a conventional, JVM-friendly name to tuples, roughly as follows: public class java.lang.tuple.$DD extends java.lang.tuple.Tuple {      double $1, $2; } Here the component names are fixed and all the required methods are defined implicitly.  The supertype is an abstract class which has suitable shared declarations.  The name itself mentions a JVM-style method parameter descriptor, which may be “cracked” to determine the number and types of the component fields. The odd thing about such a tuple type (and structural types in general) is it must be instantiated lazily, in response to linkage requests from one or more classes that need it.  The JVM and/or its class loaders must be prepared to spin a tuple type on demand, given a simple name reference, $xyz, where the xyz is cracked into a series of component types.  (Specifics of naming and name mangling need some tasteful engineering.) Tuples also seem to demand, even more than nominal types, some support from the language.  (This is probably because notations for non-nominal types work best as combinations of punctuation and type names, rather than named constructors like Function3 or Tuple2.)  At a minimum, languages with tuples usually (I think) have some sort of simple bracket notation for creating tuples, and a corresponding pattern-matching syntax (or “destructuring bind”) for taking tuples apart, at least when they are parameter lists.  Designing such a syntax is no simple thing, because it ought to play well with nominal value types, and also with pre-existing Java features, such as method parameter lists, implicit conversions, generic types, and reflection.  That is a task for another day. Other Use Cases Besides complex numbers and simple tuples there are many use cases for value types.  Many tuple-like types have natural value-type representations. These include rational numbers, point locations and pixel colors, and various kinds of dates and addresses. Other types have a variable-length ‘tail’ of internal values. The most common example of this is String, which is (mathematically) a sequence of UTF-16 character values. Similarly, bit vectors, multiple-precision numbers, and polynomials are composed of sequences of values. Such types include, in their representation, a reference to a variable-sized data structure (often an array) which (somehow) represents the sequence of values. The value type may also include ’header’ information. Variable-sized values often have a length distribution which favors short lengths. In that case, the design of the value type can make the first few values in the sequence be direct ’header’ fields of the value type. In the common case where the header is enough to represent the whole value, the tail can be a shared null value, or even just a null reference. Note that the tail need not be an immutable object, as long as the header type encapsulates it well enough. This is the case with String, where the tail is a mutable (but never mutated) character array. Field types and their order must be a globally visible part of the API.  The structure of the value type must be transparent enough to have a globally consistent unboxed representation, so that all callers and callees agree about the type and order of components  that appear as parameters, return types, and array elements.  This is a trade-off between efficiency and encapsulation, which is forced on us when we remove an indirection enjoyed by boxed representations.  A JVM-only transformation would not care about such visibility, but a bytecode transformation would need to take care that (say) the components of complex numbers would not get swapped after a redefinition of Complex and a partial recompile.  Perhaps constant pool references to value types need to declare the field order as assumed by each API user. This brings up the delicate status of private fields in a value type.  It must always be possible to load, store, and copy value types as coordinated groups, and the JVM performs those movements by moving individual scalar values between locals and stack.  If a component field is not public, what is to prevent hostile code from plucking it out of the tuple using a rogue aload or astore instruction?  Nothing but the verifier, so we may need to give it more smarts, so that it treats value types as inseparable groups of stack slots or locals (something like long or double). My initial thought was to make the fields always public, which would make the security problem moot.  But public is not always the right answer; consider the case of String, where the underlying mutable character array must be encapsulated to prevent security holes.  I believe we can win back both sides of the tradeoff, by training the verifier never to split up the components in an unboxed value.  Just as the verifier encapsulates the two halves of a 64-bit primitive, it can encapsulate the the header and body of an unboxed String, so that no code other than that of class String itself can take apart the values. Similar to String, we could build an efficient multi-precision decimal type along these lines: public final class DecimalValue extends ValueType {     protected final long header;     protected private final BigInteger digits;     public DecimalValue valueOf(int value, int scale) {         assert(scale >= 0);         return new DecimalValue(((long)value << 32) + scale, null);     }     public DecimalValue valueOf(long value, int scale) {         if (value == (int) value)             return valueOf((int)value, scale);         return new DecimalValue(-scale, new BigInteger(value));     } } Values of this type would be passed between methods as two machine words. Small values (those with a significand which fits into 32 bits) would be represented without any heap data at all, unless the DecimalValue itself were boxed. (Note the tension between encapsulation and unboxing in this case.  It would be better if the header and digits fields were private, but depending on where the unboxing information must “leak”, it is probably safer to make a public revelation of the internal structure.) Note that, although an array of Complex can be faked with a double-length array of double, there is no easy way to fake an array of unboxed DecimalValues.  (Either an array of boxed values or a transposed pair of homogeneous arrays would be reasonable fallbacks, in a current JVM.)  Getting the full benefit of unboxing and arrays will require some new JVM magic. Although the JVM emphasizes portability, system dependent code will benefit from using machine-level types larger than 64 bits.  For example, the back end of a linear algebra package might benefit from value types like Float4 which map to stock vector types.  This is probably only worthwhile if the unboxing arrays can be packed with such values. More Daydreams A more finely-divided design for dynamic enforcement of value safety could feature separate marker interfaces for each invariant.  An empty marker interface Unsynchronizable could cause suitable exceptions for monitor instructions on objects in marked classes.  More radically, a Interchangeable marker interface could cause JVM primitives that are sensitive to object identity to raise exceptions; the strangest result would be that the acmp instruction would have to be specified as raising an exception. @ValueSafe public interface ValueType extends java.io.Serializable,         Unsynchronizable, Interchangeable { … public class Complex implements ValueType {     // inherits Serializable, Unsynchronizable, Interchangeable, @ValueSafe     … It seems possible that Integer and the other wrapper types could be retro-fitted as value-safe types.  This is a major change, since wrapper objects would be unsynchronizable and their references interchangeable.  It is likely that code which violates value-safety for wrapper types exists but is uncommon.  It is less plausible to retro-fit String, since the prominent operation String.intern is often used with value-unsafe code. We should also reconsider the distinction between boxed and unboxed values in code.  The design presented above obscures that distinction.  As another thought experiment, we could imagine making a first class distinction in the type system between boxed and unboxed representations.  Since only primitive types are named with a lower-case initial letter, we could define that the capitalized version of a value type name always refers to the boxed representation, while the initial lower-case variant always refers to boxed.  For example: complex pi = complex.valueOf(Math.PI, 0); Complex boxPi = pi;  // convert to boxed myList.add(boxPi); complex z = myList.get(0);  // unbox Such a convention could perhaps absorb the current difference between int and Integer, double and Double. It might also allow the programmer to express a helpful distinction among array types. As said above, array types are crucial to bulk data interfaces, but are limited in the JVM.  Extending arrays beyond the present limitations is worth thinking about; for example, the Maxine JVM implementation has a hybrid object/array type.  Something like this which can also accommodate value type components seems worthwhile.  On the other hand, does it make sense for value types to contain short arrays?  And why should random-access arrays be the end of our design process, when bulk data is often sequentially accessed, and it might make sense to have heterogeneous streams of data as the natural “jumbo” data structure.  These considerations must wait for another day and another note. More Work It seems to me that a good sequence for introducing such value types would be as follows: Add the value-safety restrictions to an experimental version of javac. Code some sample applications with value types, including Complex and DecimalValue. Create an experimental JVM which internally unboxes value types but does not require new bytecodes to do so.  Ensure the feasibility of the performance model for the sample applications. Add tuple-like bytecodes (with or without generic type reification) to a major revision of the JVM, and teach the Java compiler to switch in the new bytecodes without code changes. A staggered roll-out like this would decouple language changes from bytecode changes, which is always a convenient thing. A similar investigation should be applied (concurrently) to array types.  In this case, it seems to me that the starting point is in the JVM: Add an experimental unboxing array data structure to a production JVM, perhaps along the lines of Maxine hybrids.  No bytecode or language support is required at first; everything can be done with encapsulated unsafe operations and/or method handles. Create an experimental JVM which internally unboxes value types but does not require new bytecodes to do so.  Ensure the feasibility of the performance model for the sample applications. Add tuple-like bytecodes (with or without generic type reification) to a major revision of the JVM, and teach the Java compiler to switch in the new bytecodes without code changes. That’s enough musing me for now.  Back to work!

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  • Class Loading Deadlocks

    - by tomas.nilsson
    Mattis follows up on his previous post with one more expose on Class Loading Deadlocks As I wrote in a previous post, the class loading mechanism in Java is very powerful. There are many advanced techniques you can use, and when used wrongly you can get into all sorts of trouble. But one of the sneakiest deadlocks you can run into when it comes to class loading doesn't require any home made class loaders or anything. All you need is classes depending on each other, and some bad luck. First of all, here are some basic facts about class loading: 1) If a thread needs to use a class that is not yet loaded, it will try to load that class 2) If another thread is already loading the class, the first thread will wait for the other thread to finish the loading 3) During the loading of a class, one thing that happens is that the <clinit method of a class is being run 4) The <clinit method initializes all static fields, and runs any static blocks in the class. Take the following class for example: class Foo { static Bar bar = new Bar(); static { System.out.println("Loading Foo"); } } The first time a thread needs to use the Foo class, the class will be initialized. The <clinit method will run, creating a new Bar object and printing "Loading Foo" But what happens if the Bar object has never been used before either? Well, then we will need to load that class as well, calling the Bar <clinit method as we go. Can you start to see the potential problem here? A hint is in fact #2 above. What if another thread is currently loading class Bar? The thread loading class Foo will have to wait for that thread to finish loading. But what happens if the <clinit method of class Bar tries to initialize a Foo object? That thread will have to wait for the first thread, and there we have the deadlock. Thread one is waiting for thread two to initialize class Bar, thread two is waiting for thread one to initialize class Foo. All that is needed for a class loading deadlock is static cross dependencies between two classes (and a multi threaded environment): class Foo { static Bar b = new Bar(); } class Bar { static Foo f = new Foo(); } If two threads cause these classes to be loaded at exactly the same time, we will have a deadlock. So, how do you avoid this? Well, one way is of course to not have these circular (static) dependencies. On the other hand, it can be very hard to detect these, and sometimes your design may depend on it. What you can do in that case is to make sure that the classes are first loaded single threadedly, for example during an initialization phase of your application. The following program shows this kind of deadlock. To help bad luck on the way, I added a one second sleep in the static block of the classes to trigger the unlucky timing. Notice that if you uncomment the "//Foo f = new Foo();" line in the main method, the class will be loaded single threadedly, and the program will terminate as it should. public class ClassLoadingDeadlock { // Start two threads. The first will instansiate a Foo object, // the second one will instansiate a Bar object. public static void main(String[] arg) { // Uncomment next line to stop the deadlock // Foo f = new Foo(); new Thread(new FooUser()).start(); new Thread(new BarUser()).start(); } } class FooUser implements Runnable { public void run() { System.out.println("FooUser causing class Foo to be loaded"); Foo f = new Foo(); System.out.println("FooUser done"); } } class BarUser implements Runnable { public void run() { System.out.println("BarUser causing class Bar to be loaded"); Bar b = new Bar(); System.out.println("BarUser done"); } } class Foo { static { // We are deadlock prone even without this sleep... // The sleep just makes us more deterministic try { Thread.sleep(1000); } catch(InterruptedException e) {} } static Bar b = new Bar(); } class Bar { static { try { Thread.sleep(1000); } catch(InterruptedException e) {} } static Foo f = new Foo(); }

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  • How many threads should an Android game use?

    - by kvance
    At minimum, an OpenGL Android game has a UI thread and a Renderer thread created by GLSurfaceView. Renderer.onDrawFrame() should be doing a minimum of work to get the higest FPS. The physics, AI, etc. don't need to run every frame, so we can put those in another thread. Now we have: Renderer thread - Update animations and draw polys Game thread - Logic & periodic physics, AI, etc. updates UI thread - Android UI interaction only Since you don't ever want to block the UI thread, I run one more thread for the game logic. Maybe that's not necessary though? Is there ever a reason to run game logic in the renderer thread?

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  • BitmapFrame in another thread

    - by Lasse Lindström
    Hi I am using a WPF BackgroundWorker to create thumbnails. My worker function looks like: private void work(object sender, DoWorkEventArgs e) { try { var paths = e.Argument as string[]; var boxList = new List(); foreach (string path in paths) { if (!string.IsNullOrEmpty(path)) { FileInfo info = new FileInfo(path); if (info.Exists && info.Length 0) { BitmapImage bi = new BitmapImage(); bi.BeginInit(); bi.DecodePixelWidth = 200; bi.CacheOption = BitmapCacheOption.OnLoad; bi.UriSource = new Uri(info.FullName); bi.EndInit(); var item = new BoxItem(); item.FilePath = path; MemoryStream ms = new MemoryStream(); PngBitmapEncoder encoder = new PngBitmapEncoder(); encoder.Frames.Add(BitmapFrame.Create(bi)); encoder.Save(ms); item.ThumbNail = ms.ToArray(); ms.Close(); boxList.Add(item); } } } e.Result = boxList; } catch (Exception ex) { //nerver comes here } } When this fuction is finnished and before the BackgroundWorker "Completed" function is started, I can see on the output window on Vs2008, that a exception is generated. It looks like: A first chance exception of type 'System.NotSupportedException' occurred in PresentationCore.dll The number of exceptions generates equals the number of thumbnails to be generated. Using "trial and error" I have isolated the problem to: BitmapFrame.Create(bi) Removing that line (makes my function useless) also removes the exception. I have not found any explanation to this,,, or a better method to create thumbnails i a background thread. Can anyone help me? //lasse

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  • Thread Message Loop Hangs in Delphi

    - by erikjw
    Hello all. I have a simple Delphi program that I'm working on, in which I am attempting to use threading to separate the functionality of the program from its GUI, and to keep the GUI responsive during more lengthy tasks, etc. Basically, I have a 'controller' TThread, and a 'view' TForm. The view knows the controller's handle, which it uses to send the controller messages via PostThreadMessage. I have had no problem in the past using this sort of model for forms which are not the main form, but for some reason, when I attempt to use this model for the main form, the message loop of the thread just quits. Here is my code for the threads message loop: procedure TController.Execute; var Msg : TMsg; begin while not Terminated do begin if (Integer(GetMessage(Msg, hwnd(0), 0, 0)) = -1) then begin Synchronize(Terminate); end; TranslateMessage(Msg); DispatchMessage(Msg); case Msg.message of // ...call different methods based on message end; end; To set up the controller, I do this: Controller := TController.Create(true); // Create suspended Controller.FreeOnTerminate := True; Controller.Resume; For processing the main form's messages, I have tried using both Application.Run and the following loop (immediately after Controller.Resume) while not Application.Terminated do begin Application.ProcessMessages; end; I've run stuck here - any help would be greatly appreciated.

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  • Java: Exception in thread main java.lang.NoClassDefFoundError

    - by cath
    I am trying to get the Red5 Flash Media Server working on my computer. I have installed it, but when I run the server I get this error Exception in thread "main" java.lang.NoClassDefFoundError: org/red5/server/Bootstrap Caused by: java.lang.ClassNotFoundException: org.red5.server.Bootstrap at java.net.URLClassLoader$1.run(URLClassLoader.java:217) at java.security.AccessController.doPrivileged(Native Method) at java.net.URLClassLoader.findClass(URLClassLoader.java:205) at java.lang.ClassLoader.loadClass(ClassLoader.java:321) at sun.misc.Launcher$AppClassLoader.loadClass(Launcher.java:294) at java.lang.ClassLoader.loadClass(ClassLoader.java:266) at java.lang.ClassLoader.loadClassInternal(ClassLoader.java:334) Could not find the main class: org.red5.server.Bootstrap. Program will exit. I came across this link where someone had the same issue: http://trac.red5.org/ticket/762 It looks like they ran this command: export CLASSPATH=3D$RED5_HOME/lib/slf4j-api-1.5.10.jar:$RED5_HOME/lib/logback- core-0.9.18.jar:$RED5_HOME/lib/logback-classic-0.9.18.jar I have red5 installed in /usr/share/red5, so I ran this: export CLASSPATH=3D$/usr/share/red5/lib/slf4j-api-1.5.10.jar:$/usr/share/red5/lib/logback-core-0.9.18.jar:$/usr/share/red5/lib/logback-classic-0.9.18.jar Yet despite all this I am still seeing the same error message.

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  • My Thread Programs Block

    - by user315378
    I wrote a program that worked as a server. Knowing that "accept" was blocking the program. I wanted to launch a thread with this statement to prevent precisely that the program crashes, but this still happens. Can anybody help? Post code Thanks -(IBAction)Connetti{ if(switchConnessione.on){ int port = [fieldPort.text intValue]; labelStatus.text = [[NSString alloc] initWithFormat:@"Il Server è attivo"]; server_len = sizeof(server); server.sin_family = AF_INET; server.sin_port = htons((u_short)port); server.sin_addr.s_addr = INADDR_ANY; sd = socket (AF_INET, SOCK_STREAM, 0); bind(sd, (struct sockaddr*)&server, sizeof(server)); listen(sd, 1); [NSThread detachNewThreadSelector:@selector(startThreadAccept) toTarget:self withObject:nil]; } else { labelStatus.text = [[NSString alloc] initWithFormat:@"Server non attivo"]; switchChat.on = FALSE; switchChat.enabled = FALSE; } } -(void)startThreadAccept{ NSAutoreleasePool *pool = [[NSAutoreleasePool alloc]init]; [self performSelectorOnMainThread:@selector(acceptConnection) withObject:nil waitUntilDone:NO]; [pool release]; } -(void)acceptConnection{ new_sd = accept(sd, (struct sockaddr*)&server, &server_len); labelStatus.text = [[NSString alloc] initWithFormat:@"Ho accettato una connessione:%d", new_sd]; switchChat.enabled = TRUE; }

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  • How to mult-thread this?

    - by WilliamKF
    I wish to have two threads. The first thread1 occasionally calls the following pseudo function: void waitForThread2() { if (thread2 is not idle) { return; } notifyThread2IamReady(); while (thread2IsExclusive) { } } The second thread2 is forever in the following pseudo loop: for (;;) { Notify thread1 I am idle. while (!thread1IsReady()) { } Notify thread1 I am exclusive. Do some work while thread1 is blocked. Notify thread1 I am busy. Do some work in parallel with thread1. } What is the best way to write this such that both thread1 and thread2 are kept as busy as possible on a machine with multiple cores. I would like to avoid long delays between notification in one thread and detection by the other. I tried using pthread condition variables but found the delay between thread2 doing 'notify thread1 I am busy' and the loop in waitForThread2() on thear2IsExclusive() can be up to almost one second delay. I then tried using a volatile sig_atomic_t shared variable to control the same, but something is going wrong, so I must not be doing it correctly.

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  • C# Process Binary File, Multi-Thread Processing

    - by washtik
    I have the following code that processes a binary file. I want to split the processing workload by using threads and assigning each line of the binary file to threads in the ThreadPool. Processing time for each line is only small but when dealing with files that might contain hundreds of lines, it makes sense to split the workload. My question is regarding the BinaryReader and thread safety. First of all, is what I am doing below acceptable. I have a feeling it would be better to pass only the binary for each line to the PROCESS_Binary_Return_lineData method. Please note the code below is conceptual. I looking for a but of guidance on this as my knowledge of multi-threading is in its infancy. Perhaps there is a better way to achieve the same result, i.e. split processing of each binary line. var dic = new Dictionary<DateTime, Data>(); var resetEvent = new ManualResetEvent(false); using (var b = new BinaryReader(File.Open(Constants.dataFile, FileMode.Open, FileAccess.Read, FileShare.Read))) { var lByte = b.BaseStream.Length; var toProcess = 0; while (lByte >= DATALENGTH) { b.BaseStream.Position = lByte; lByte = lByte - AB_DATALENGTH; ThreadPool.QueueUserWorkItem(delegate { Interlocked.Increment(ref toProcess); var lineData = PROCESS_Binary_Return_lineData(b); lock(dic) { if (!dic.ContainsKey(lineData.DateTime)) { dic.Add(lineData.DateTime, lineData); } } if (Interlocked.Decrement(ref toProcess) == 0) resetEvent.Set(); }, null); } } resetEvent.WaitOne();

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  • Two column layout, navigation div on the right, solution from previous thread didn't seem to work

    - by Tom
    I tried the solution from this thread, but I must be missing something because it doesn't work: <div style="float:left;margin-right:200px"> <p>Lorem ipsum dolor sit amet, consectetur adipisicing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua. Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat. Duis aute irure dolor in reprehenderit in voluptate velit esse cillum dolore eu fugiat nulla pariatur. Excepteur sint occaecat cupidatat non proident, sunt in culpa qui officia deserunt mollit anim id est laborum.</p> </div> <div style="float:right;width:200px"> <p>navigation</p> </div> It works when the text in the content div (the left one) is short, but when it's long then the div takes up the whole width of the browser and the margin is there, but the right div is pushed below the first one nevertheless. What am I missing? Edit: The goal is to have a fix sized navigation column on the right of the browser window and the left div should get all the space left by the right navigation column (liquid layout).

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  • Calling into a saved java object via JNI from a different thread

    - by Drake Amara
    I have a java object which calls into a C++ shared object via JNI. In C++, I am saving a reference to the JNIEnv and jObject. JavaVM * jvm; JNIEnv * myEnv; jobject myobj; JNIEXPORT void JNICALL Java_org_api_init (JNIEnv *env, jobject jObj) { myEnv = env; myobj = jObj; } I also have a GLSurface renderer and it eventually calls the C++ shared object mentioned above on a different thread, the GLThread. I am then trying to call back into my original Java object using the jobject I saved initially, but I think because I am on the GLThread, I get the following error. W/dalvikvm(16101): JNI WARNING: 0x41ded218 is not a valid JNI reference I/dalvikvm(16101): "GLThread 981" prio=5 tid=15 RUNNABLE I/dalvikvm(16101): | group="main" sCount=0 dsCount=0 obj=0x41d6e220 self=0x5cb11078 I/dalvikvm(16101): | sysTid=16133 nice=0 sched=0/0 cgrp=apps handle=1555429136 I/dalvikvm(16101): | schedstat=( 0 0 0 ) utm=42 stm=32 core=1 The code calling back into Java : void setData() { jvm->AttachCurrentThread(&myEnv, 0); jclass javaClass = myEnv->FindClass("com/myapp/myClass"); if(javaClass == NULL){ LOGD("ERROR - cant find class"); } jmethodID method = myEnv->GetMethodID(javaClass, "updateDataModel", "()V"); if(method == NULL){ LOGD("ERROR - cant access method"); } // this works, but its a new java object //jobject myobj2 = myEnv->NewObject(javaClass, method); //this is where the crash occurs myEnv->CallVoidMethod(myobj, method, NULL); } If instead I create a new jObject using env-NewObject, I can succuessfully call back into Java, but it is a new object and I dont want that. I need to get back to my original Java Object. Is it a matter of switching threads before I call back into Java? If so, how do I do so ?

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  • How do I get events to execute on the "main thread"

    - by William DiStefano
    I have 2 classes, one is frmMain a windows form and the other is a class in vb.NET 2003. frmMain contains a start button which executes the monitor function in the other class. My question is, I am manually adding the event handlers -- when the events are executed how do I get them to excute on the "main thread". Because when the tooltip balloon pops up at the tray icon it displays a second tray icon instead of popping up at the existing tray icon. I can confirm that this is because the events are firing on new threads because if I try to display a balloon tooltip from frmMain it will display on the existing tray icon. Here is a part of the second class (not the entire thing): Friend Class monitorFolders Private _watchFolder As System.IO.FileSystemWatcher = New System.IO.FileSystemWatcher Private _eType As evtType Private Enum evtType changed created deleted renamed End Enum Friend Sub monitor(ByVal path As String) _watchFolder.Path = path 'Add a list of Filter to specify _watchFolder.NotifyFilter = IO.NotifyFilters.DirectoryName _watchFolder.NotifyFilter = _watchFolder.NotifyFilter Or IO.NotifyFilters.FileName _watchFolder.NotifyFilter = _watchFolder.NotifyFilter Or IO.NotifyFilters.Attributes 'Add event handlers for each type of event that can occur AddHandler _watchFolder.Changed, AddressOf change AddHandler _watchFolder.Created, AddressOf change AddHandler _watchFolder.Deleted, AddressOf change AddHandler _watchFolder.Renamed, AddressOf Rename 'Start watching for events _watchFolder.EnableRaisingEvents = True End Sub Private Sub change(ByVal source As Object, ByVal e As System.IO.FileSystemEventArgs) If e.ChangeType = IO.WatcherChangeTypes.Changed Then _eType = evtType.changed notification() End If If e.ChangeType = IO.WatcherChangeTypes.Created Then _eType = evtType.created notification() End If If e.ChangeType = IO.WatcherChangeTypes.Deleted Then _eType = evtType.deleted notification() End If End Sub Private Sub notification() _mainForm.NotifyIcon1.ShowBalloonTip(500, "Folder Monitor", "A file has been " + [Enum].GetName(GetType(evtType), _eType), ToolTipIcon.Info) End Sub End Class

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  • Reload images in a UIWebView after they have been downloaded by a background thread

    - by dantastic
    I have an application that frequently checks in with a server and downloads a batch of articles to the iphone. The articles are in html and just stored using core data. An article has 0-n images on the page. Downloading all associated images at the same time as the text will be too slow and take too much bandwidth. Users are not likely to open every article. If they open an article once it is likely they will open it several times. So I want to download and store the images locally when they are needed. These articles are listed in a UITableView. When you tap an article you pop open a UIWebView that displays the article. I have a function that checks if I have downloaded the images associated with the article already. If I have I just pop open the the UIWebView - everything works fine. If I don't have the images downloaded I go off and download them and store them to my Documents directory. Although this i working, the app is hanging while the images are downloading. Not very tidy. I want the article to open in a snap and download the images with the article open. So what I've done is I check if the images are downloaded, if they aren't I go ahead and just "touch" the files I need and load the webview. The UIWebView opens up but the images referenced contain no data. Then in a background thread I download the images and overwrite the "dummy" ones. This will save the images and everything but it won't reload the images in my current UIWebView. I have to go back out of the article back back in again to see the images. Are there any ways around this? reloading just an image in a UIWebView?

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  • "Thread was being aborted" 0n large dataset

    - by Donaldinio
    I am trying to process 114,000 rows in a dataset (populated from an oracle database). I am hitting an error at around the 600 mark - "Thread was being aborted". All I am doing is reading the dataset, and I still hit the issue. Is this too much data for a dataset? It seems to load into the dataset ok though. I welcome any better ways to process this amount of data. rootTermsTable = entKw.GetRootKeywordsByCategory(catID); for (int k = 0; k < rootTermsTable.Rows.Count; k++) { string keywordID = rootTermsTable.Rows[k]["IK_DBKEY"].ToString(); ... } public DataTable GetKeywordsByCategory(string categoryID) { DbProviderFactory provider = DbProviderFactories.GetFactory(connectionProvider); DbConnection con = provider.CreateConnection(); con.ConnectionString = connectionString; DbCommand com = provider.CreateCommand(); com.Connection = con; com.CommandText = string.Format("Select * From icm_keyword WHERE (IK_IC_DBKEY = {0})",categoryID); com.CommandType = CommandType.Text; DataSet ds = new DataSet(); DbDataAdapter ad = provider.CreateDataAdapter(); ad.SelectCommand = com; con.Open(); ad.Fill(ds); con.Close(); DataTable dt = new DataTable(); dt = ds.Tables[0]; return dt; //return ds.Tables[0].DefaultView; }

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  • Writing a synchronized thread-safety wrapper for NavigableMap

    - by polygenelubricants
    java.util.Collections currently provide the following utility methods for creating synchronized wrapper for various collection interfaces: synchronizedCollection(Collection<T> c) synchronizedList(List<T> list) synchronizedMap(Map<K,V> m) synchronizedSet(Set<T> s) synchronizedSortedMap(SortedMap<K,V> m) synchronizedSortedSet(SortedSet<T> s) Analogously, it also has 6 unmodifiedXXX overloads. The glaring omission here are the utility methods for NavigableMap<K,V>. It's true that it extends SortedMap, but so does SortedSet extends Set, and Set extends Collection, and Collections have dedicated utility methods for SortedSet and Set. Presumably NavigableMap is a useful abstraction, or else it wouldn't have been there in the first place, and yet there are no utility methods for it. So the questions are: Is there a specific reason why Collections doesn't provide utility methods for NavigableMap? How would you write your own synchronized wrapper for NavigableMap? Glancing at the source code for OpenJDK version of Collections.java seems to suggest that this is just a "mechanical" process Is it true that in general you can add synchronized thread-safetiness feature like this? If it's such a mechanical process, can it be automated? (Eclipse plug-in, etc) Is this code repetition necessary, or could it have been avoided by a different OOP design pattern?

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  • StructureMap Configuration Per Thread/Request for the Full Dependency Chain

    - by Phil Sandler
    I've been using Structuremap for a few months now, and it has worked great on some smaller (green field) projects. Most of the configurations I have had to set up involve a single implementation per interface. In the cases where I needed to choose a specific implementation at runtime, I created a factory class that used ObjectFactory.GetNamedInstance<(). In the smaller projects, there were few enough of these cases where I was comfortable with the references to ObjectFactory. My understanding is that you want to limit these references as much as possible, and ideally only reference the ObjectFactory once. I am working to refactor a larger codebase to use IOC/StructureMap, and am finding that I may need many of these factory classes with ObjectFactory references to get what I need. Essentially, I am creating a "root service" with the ObjectFactory, so that everything in the dependency chain is managed by the container. The root service is created by name (i.e. "BuildCar", "BuildTruck"), and the services needed deeper in the dependency chain could also be constructed using the same name--so the "IAttachWheels" service could vary based on whether a car or truck is being built. Since the class that depends on IAttachWheels is the same in both configurations, I don't think I can use ConstructedBy in the registry to choose the implementation. Also, to be clear, the IAttachWheels implementations need to be managed by the container as well, because the dependency chain runs fairly deep. I looked briefly at Profiles as an option, but read (here on StackOverflow) that changing profiles essentially changes implementations for all threads. Is there a feature that is similar to profiles that is thread/request specific? Is the factory class that references ObjectFactory approach the right way to go? Any thoughts would be appreciated.

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  • How to multi-thread this?

    - by WilliamKF
    I wish to have two threads. The first thread1 occasionally calls the following pseudo function: void waitForThread2() { if (thread2 is not idle) { return; } notifyThread2IamReady(); while (thread2IsExclusive) { } } The second thread2 is forever in the following pseudo loop: for (;;) { Notify thread1 I am idle. while (!thread1IsReady()) { } Notify thread1 I am exclusive. Do some work while thread1 is blocked. Notify thread1 I am busy. Do some work in parallel with thread1. } What is the best way to write this such that both thread1 and thread2 are kept as busy as possible on a machine with multiple cores. I would like to avoid long delays between notification in one thread and detection by the other. I tried using pthread condition variables but found the delay between thread2 doing 'notify thread1 I am busy' and the loop in waitForThread2() on thear2IsExclusive() can be up to almost one second delay. I then tried using a volatile sig_atomic_t shared variable to control the same, but something is going wrong, so I must not be doing it correctly.

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  • How to show and update popup in 1 thread

    - by user3713986
    I have 1 app. 2 Forms are MainFrm and PopupFrm, 1 thread to update some information to PopupFrm Now to update PopupFrm i use: In MainFrm.cs private PopupFrm mypop; MainFrm() { .... PopupFrm mypop= new PopupFrm(); mypop.Show(); } MyThread() { Process GetData();... mypop.Update(); ... } In PopupFrm.cs public void Update() { this.Invoke((MethodInvoker)delegate .... }); } Problem here that mypopup alway display when MainFrm display (Start application not when has data to update). So i change MainFrm.cs to : private PopupFrm mypop; private bool firstdisplay=false; MainFrm() { .... PopupFrm mypop= new PopupFrm(); //mypop.Show(); } MyThread() { Process GetData();... if(!firstdisplay) { mypop.Show(); firstdisplay=true; } mypop.Update(); ... } But it can not update Popup GUI. So how can i fix this issue ? Thanks all.

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  • bluetooth BluetoothSocket.connect() thread. how to close this thread

    - by Hia
    I am trying to make an android app that makes connection with bluetooth device. It works fine but when I call BluetoothSocket.connect() and it is not able to connect to devise its blocking. The thread and does not throw any exception. So when I try to close application while connect() is running its not responding. How can I cancel it? Used BluetoothSocket.close() in ... but still its not working for me. protected void simpleComm(Integer port) { // The documents tell us to cancel the discovery process. try { Method m = mmDevice.getClass().getMethod("createRfcommSocket", new Class[] { int.class }); mmSocket = (BluetoothSocket) m.invoke(mmDevice, port); mmSocket.connect(); // <== blocks untill is not connected Log.d(TAG, " connection success==="); }catch(Exception e){ if (!abort) { connectionFailed(); // Close the socket try { mmSocket.close(); // Start the service over to restart listening mode BluetoothService.this.start(); } catch (IOException e2) { Log.e(TAG,"unable to close() socket during connection failure", e2); } } return; } } public void cancel() { try { abort = true; mmSocket.close(); } catch (IOException e) { Log.e(TAG, "close() of connect socket failed", e); } }

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  • "Unable to initialize module" warning after update PHP on CentOS 5.4

    - by ohho
    After upgrading PHP from 5.1x to 5.2.10, there are a lot of warning when php -v: [root@localhost ~]# php -v PHP Warning: PHP Startup: fileinfo: Unable to initialize module Module compiled with module API=20050922, debug=0, thread-safety=0 PHP compiled with module API=20060613, debug=0, thread-safety=0 These options need to match in Unknown on line 0 PHP Warning: PHP Startup: mcrypt: Unable to initialize module Module compiled with module API=20050922, debug=0, thread-safety=0 PHP compiled with module API=20060613, debug=0, thread-safety=0 These options need to match in Unknown on line 0 PHP Warning: PHP Startup: memcache: Unable to initialize module Module compiled with module API=20050922, debug=0, thread-safety=0 PHP compiled with module API=20060613, debug=0, thread-safety=0 These options need to match in Unknown on line 0 PHP Warning: PHP Startup: mhash: Unable to initialize module Module compiled with module API=20050922, debug=0, thread-safety=0 PHP compiled with module API=20060613, debug=0, thread-safety=0 These options need to match in Unknown on line 0 PHP Warning: PHP Startup: mssql: Unable to initialize module Module compiled with module API=20050922, debug=0, thread-safety=0 PHP compiled with module API=20060613, debug=0, thread-safety=0 These options need to match in Unknown on line 0 PHP Warning: PHP Startup: readline: Unable to initialize module Module compiled with module API=20050922, debug=0, thread-safety=0 PHP compiled with module API=20060613, debug=0, thread-safety=0 These options need to match in Unknown on line 0 PHP Warning: PHP Startup: tidy: Unable to initialize module Module compiled with module API=20050922, debug=0, thread-safety=0 PHP compiled with module API=20060613, debug=0, thread-safety=0 These options need to match in Unknown on line 0 PHP 5.2.10 (cli) (built: Nov 13 2009 11:24:03) Copyright (c) 1997-2009 The PHP Group Zend Engine v2.2.0, Copyright (c) 1998-2009 Zend Technologies How can I fix that? Thanks!

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  • If I'm updating a DataRow, do I lock the entire DataTable or just the DataRow?

    - by Dan Tao
    Suppose I'm accessing a DataTable from multiple threads. If I want to access a particular row, I suspect I need to lock that operation (I could be mistaken about this, but at least I know this way I'm safe): // this is a strongly-typed table OrdersRow row = null; lock (orderTable.Rows.SyncRoot) { row = orderTable.FindByOrderId(myOrderId); } But then, if I want to update that row, should I lock the table (or rather, the table's Rows.SyncRoot object) again, or can I simply lock the row?

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