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• how to solve this problem

  - by Surbir

  root@me-desktop:~# sudo apt-get install aircrack-ng Reading package lists... Done Building dependency tree Reading state information... Done The following NEW packages will be installed: aircrack-ng 0 upgraded, 1 newly installed, 0 to remove and 446 not upgraded. 1 not fully installed or removed. Need to get 1,579kB of archives. After this operation, 2,843kB of additional disk space will be used. Get:1 http://archive.ubuntu.com/ubuntu/ maverick/universe aircrack-ng i386 1:1.1-1 [1,579kB] Fetched 1,579kB in 1min 9s (22.7kB/s) Selecting previously deselected package aircrack-ng. (Reading database ... 520739 files and directories currently installed.) Unpacking aircrack-ng (from .../aircrack-ng_1%3a1.1-1_i386.deb) ... Processing triggers for man-db ... Setting up linux-image-3.0.1-030001-generic (3.0.1-030001.201108060905) ... Running depmod. update-initramfs: Generating /boot/initrd.img-3.0.1-030001-generic Warning: No support for locale: en_US.utf8 Examining /etc/kernel/postinst.d. run-parts: executing /etc/kernel/postinst.d/dkms 3.0.1-030001-generic /boot/vmlinuz-3.0.1-030001-generic run-parts: executing /etc/kernel/postinst.d/initramfs-tools 3.0.1-030001-generic /boot/vmlinuz-3.0.1-030001-generic run-parts: executing /etc/kernel/postinst.d/nvidia-common 3.0.1-030001-generic /boot/vmlinuz-3.0.1-030001-generic run-parts: executing /etc/kernel/postinst.d/pm-utils 3.0.1-030001-generic /boot/vmlinuz-3.0.1-030001-generic run-parts: executing /etc/kernel/postinst.d/update-notifier 3.0.1-030001-generic /boot/vmlinuz-3.0.1-030001-generic run-parts: executing /etc/kernel/postinst.d/zz-update-grub 3.0.1-030001-generic /boot/vmlinuz-3.0.1-030001-generic exec: 15: update-grub: not found run-parts: /etc/kernel/postinst.d/zz-update-grub exited with return code 2 Failed to process /etc/kernel/postinst.d at /var/lib/dpkg/info/linux-image-3.0.1-030001-generic.postinst line 1010. dpkg: error processing linux-image-3.0.1-030001-generic (--configure): subprocess installed post-installation script returned error exit status 2 Setting up aircrack-ng (1:1.1-1) ... Errors were encountered while processing: linux-image-3.0.1-030001-generic E: Sub-process /usr/bin/dpkg returned an error code (1) root@me-desktop:~#

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• C++ Parallel Asynchonous task

  - by Doodlemeat

  I am currently building a randomly generated terrain game where terrain is created automatically around the player. I am experiencing lag when the generated process is active, as I am running quite heavy tasks with post-processing and creating physics bodies. Then I came to mind using a parallel asynchronous task to do the post-processing for me. But I have no idea how I am going to do that. I have searched for C++ std::async but I believe that is not what I want. In the examples I found, a task returned something. I want the task to change objects in the main program. This is what I want: // Main program // Chunks that needs to be processed. // NOTE! These chunks are already generated, but need post-processing only! std::vector<Chunk*> unprocessedChunks; And then my task could look something like this, running like a loop constantly checking if there is chunks to process. // Asynced task if(unprocessedChunks.size() > 0) { processChunk(unprocessedChunks.pop()); } I know it's not far from easy as I wrote it, but it would be a huge help for me if you could push me at the right direction. In Java, I could type something like this: asynced_task = startAsyncTask(new PostProcessTask()); And that task would run until I do this: asynced_task.cancel();

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• Customer Highlight: NTT DOCOMO

  - by jeckels

  NTT DOCOMO is the largest mobile operator in Japan, and serves over 13 million smartphone customers. Due to their growing data processing and scalability needs, they turned to Oracle's Cloud Application Foundation products for an integral soultion. At Oracle OpenWorld 2012, we first showcased NTT DOCOMO as a customer who was utilizing Oracle Coherence to process mobile data at a rate of 700,000 events per second (and then using Hadoop for distributed processing of big data). Overall, this Led to a 50% cost reduction due to the ultra-high velocity traffic processing of their customers' events. Recently, on October 7th, 2013, Oracle and NTT DOCOMO were proud to again announce a partnership around another key component of Oracle CAF: WebLogic Server. WebLogic was recently deployed as the application platform of choice to run DOCOMO's mission-critical data system ALADIN, which connects nationwide shops and information centers. ALADIN, which also utilizes Oracle Database and Oracle Tuxedo, is based on Java Platform, Enterprise Edition (Java EE), which has allowed the company to operate smoothly while minimizing additional development and modification associated with the migration of application server products. We look forward to continuing to partner with NTT DOCOMO, and are proud that Oracle Cloud Application Foundation products are providing the mission-critical solutions - at scale - that DOCOMO requires. Want to learn more about how CAF products are working in the real world? Join us for a FREE Virtual Developer Day on November 5th from 9am-1pm Pacific Time!REGISTER NOW

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• "type not defined" exception with WF4 RC

  - by avi1234

  Hi, I`m gettin the following exception while invoking my workflow (dynamically): The following errors were encountered while processing the workflow tree: 'DynamicActivity': The private implementation of activity '1: DynamicActivity' has the following validation error: Compiler error(s) encountered processing expression "TryCast(simplerule_out,OutputBase2)". Type 'OutputBase2' is not defined. 'DynamicActivity': The private implementation of activity '1: DynamicActivity' has the following validation error: Compiler error(s) encountered processing expression "Res". Type 'OutputBase2' is not defined. 'DynamicActivity': The private implementation of activity '1: DynamicActivity' has the following validation error: Compiler error(s) encountered processing expression "Res". Type 'OutputBase2' is not defined. 'DynamicActivity': The private implementation of activity '1: DynamicActivity' has the following validation error: Compiler error(s) encountered processing expression "New List(Of OutputBase2)". Type 'OutputBase2' is not defined. The workflow is very simple and worked fine on VS 2010 beta 2! All I`m trying to do is to create new list of my abstract custom type "OutputBase2". public class OutputBase2 { public OutputBase2() { } public bool Succeeded { get; set; } } class Example { public void Exec() { ActivityBuilder builder = new ActivityBuilder(); builder.Name = "act1"; var res = new DynamicActivityProperty { Name = "Res", Type = typeof(OutArgument<List<OutputBase2>>), Value = new OutArgument<List<OutputBase2>>() }; builder.Properties.Add(res); builder.Implementation = new Sequence(); ((Sequence)builder.Implementation).Activities.Add(new Assign<List<OutputBase2>> { To = new VisualBasicReference<List<OutputBase2>> { ExpressionText = res.Name }, Value = new VisualBasicValue<List<OutputBase2>>("New List(Of OutputBase2)") }); Activity act = getActivity(builder); var res2 = WorkflowInvoker.Invoke(act); } string getXamlStringFromActivityBuilder(ActivityBuilder activityBuilder) { string xamlString; StringBuilder stringBuilder = new StringBuilder(); System.IO.StringWriter stringWriter = new System.IO.StringWriter(stringBuilder); System.Xaml.XamlSchemaContext xamlSchemaContext = new System.Xaml.XamlSchemaContext(); System.Xaml.XamlXmlWriter xamlXmlWriter = new System.Xaml.XamlXmlWriter(stringWriter, xamlSchemaContext); System.Xaml.XamlWriter xamlWriter = System.Activities.XamlIntegration.ActivityXamlServices.CreateBuilderWriter(xamlXmlWriter); System.Xaml.XamlServices.Save(xamlWriter, activityBuilder); xamlString = stringBuilder.ToString(); return xamlString; } public Activity getActivity(ActivityBuilder t) { string xamlString = getXamlStringFromActivityBuilder(t); System.IO.StringReader stringReader = new System.IO.StringReader(xamlString); Activity activity = System.Activities.XamlIntegration.ActivityXamlServices.Load(stringReader); return activity; } } Thanks!

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• Problems related to showing MessageBox from non-GUI threads

  - by Hans Løken

  I'm working on a heavily data-bound Win.Forms application where I've found some strange behavior. The app has separate I/O threads receiving updates through asynchronous web-requests which it then sends to the main/GUI thread for processing and updating of application-wide data-stores (which in turn may be data-bound to various GUI-elements, etc.). The server at the other end of the web-requests requires periodic requests or the session times out. I've gone through several attempted solutions of dealing with thread-issues etc. and I've observed the following behavior: If I use Control.Invoke for sending updates from I/O-thread(s) to main-thread and this update causes a MessageBox to be shown the main form's message pump stops until the user clicks the ok-button. This also blocks the I/O-thread from continuing eventually leading to timeouts on the server. If I use Control.BeginInvoke for sending updates from I/O-thread(s) to main-thread the main form's message pump does not stop, but if the processing of an update leads to a messagebox being shown, the processing of the rest of that update is halted until the user clicks ok. Since the I/O-threads keep running and the message pump keeps processing messages several BeginInvoke's for updates may be called before the one with the message box is finished. This leads to out-of-sequence updates which is unacceptable. I/O-threads add updates to a blocking queue (very similar to http://stackoverflow.com/questions/530211/creating-a-blocking-queuet-in-net/530228#530228). GUI-thread uses a Forms.Timer that periodically applies all updates in the blocking queue. This solution solves both the problem of blocking I/O threads and sequentiality of updates i.e. next update will be never be started until previous is finished. However, there is a small performance cost as well as introducing a latency in showing updates that is unacceptable in the long run. I would like update-processing in the main-thread to be event-driven rather than polling. So to my question. How should I do this to: avoid blocking the I/O-threads guarantee that updates are finished in-sequence keep the main message pump running while showing a message box as a result of an update.

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• ASP.NET Client to Server communication

  - by Nelson

  Can you help me make sense of all the different ways to communicate from browser to client in ASP.NET? I have made this a community wiki so feel free to edit my post to improve it. Specifically, I'm trying to understand in which scenario to use each one by listing how each works. I'm a little fuzzy on UpdatePanel vs CallBack (with ViewState): I know UpdatePanel always returns HTML while CallBack can return JSON. Any other major differences? ...and CallBack (without ViewState) vs WebMethod. CallBack goes through most of the Page lifecycle, WebMethod doesn't. Any other major differences? IHttpHandler Custom handler for anything (page, image, etc.) Only does what you tell it to do (light server processing) Page is an implementation of IHttpHandler If you don't need what Page provides, create a custom IHttpHandler If you are using Page but overriding Render() and not generating HTML, you probably can do it with a custom IHttpHandler (e.g. writing binary data such as images) By default can use the .axd or .ashx file extensions -- both are functionally similar .ashx doesn't have any built-in endpoints, so it's preferred by convention Regular PostBack (System.Web.UI.Page : IHttpHandler) Inherits Page Full PostBack, including ViewState and HTML control values (heavy traffic) Full Page lifecycle (heavy server processing) No JavaScript required Webpage flickers/scrolls since everything is reloaded in browser Returns full page HTML (heavy traffic) UpdatePanel (Control) Control inside Page Full PostBack, including ViewState and HTML control values (heavy traffic) Full Page lifecycle (heavy server processing) Controls outside the UpdatePanel do Render(NullTextWriter) Must use ScriptManager If no client-side JavaScript, it can fall back to regular PostBack with no JavaScript (?) No flicker/scroll since it's an async call, unless it falls back to regular postback. Can be used with master pages and user controls Has built-in support for progress bar Returns HTML for controls inside UpdatePanel (medium traffic) Client CallBack (Page, System.Web.UI.ICallbackEventHandler) Inherits Page Most of Page lifecycle (heavy server processing) Takes only data you specify (light traffic) and optionally ViewState (?) (medium traffic) Client must support JavaScript and use ScriptManager No flicker/scroll since it's an async call Can be used with master pages and user controls Returns only data you specify in format you specify (e.g. JSON, XML...) (?) (light traffic) WebMethod Class implements System.Web.Service.WebService HttpContext available through this.Context Takes only data you specify (light traffic) Server only runs the called method (light server processing) Client must support JavaScript No flicker/scroll since it's an async call Can be used with master pages and user controls Returns only data you specify, typically JSON (light traffic) Can create instance of server control to render HTML and sent back as string, but events, paging in GridView, etc. won't work PageMethods Essentially a WebMethod contained in the Page class, so most of WebMethod's bullet's apply Method must be public static, therefore no Page instance accessible HttpContext available through HttpContext.Current Accessed directly by URL Page.aspx/MethodName (e.g. with XMLHttpRequest directly or with library such as jQuery) Setting ScriptManager property EnablePageMethods="True" generates a JavaScript proxy for each WebMethod Cannot be used directly in user controls with master pages and user controls Any others?

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• .Net Remote Log Querying

  - by jlafay

  I have a Win Service that I'm working on that consists of the service, WF Service (using WorkflowServiceHost), a Workflow (WorkflowApplication) that queries/processes data from a SQL Server DB, and a Comm Marshall class that handles data flow between the service and the WF. The WF does a lot of heavy data processing and the original app (early VB6) logged all the processing and displayed the results on the screen of the host machine. Critical events will be committed to eventlog because I strongly believe that should be common practice because admins naturally will look there and because it already has support for remote viewing. The workflow will also need to write logging events as it processes and iterates according to our business logic. Such as: records queried, records returned, processed records, etc. The data is very critical and we need to log actions as they occur. The logs are currently kept as text files on disk and I think that is ok. Ideally I would like to record log events in XML so it's easier to query and because it is less costly than a DB, especially since our DB servers do a lot of heavy processing anyways. Since we are replacing essentially a VB6 application with a robust windows service (taking advantage of WF 4.0), it has been requested that a remote client also be created. It receives callbacks from the service after subscribing to it and being added to a collection of subscribers. Basic statistics and summaries are updated client side after receiving basic monitoring data of what is going on with the service. We would like to also provide a way to provide details when we need to examine what is going on further because this is a long running data processing service and issues need to be addressed immediately. What is the best way to implement some type of query from the client that is sent to the service and returned to the client? Would it be efficient to implement another method to expose on the service and then have that pass that off to some querying class/object to examine the XML files by whichever specification and then return it to the client? That's the main concern. I don't want the service to processing to bottleneck much while this occurs. It seems that WF already auto-magically threads well for the most part but I want to make sure this is the right way to go about it. Any suggestions/recommendations on how to architect and implement a small log querying framework for a remote service would be awesome.

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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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• E: Sub-process /usr/bin/dpkg returned an error code (1) seems to be choking on kde-runtime-data version issue

  - by BMT

  12.04 LTS, on a dell mini 10. Install stable until about a week ago. Updated about 1x a week, sometimes more often. Several days ago, I booted up and the system was no longer working correctly. All these symptoms occurred simultaneously: Cannot run (exit on opening, every time): Update manager, software center, ubuntuOne, libreOffice. Vinagre autostarts on boot, no explanation, not set to startup with Ubuntu. Using apt-get to fix install results in the following: maura@pandora:~$ sudo apt-get -f install Reading package lists... Done Building dependency tree Reading state information... Done Correcting dependencies... Done The following package was automatically installed and is no longer required: libtelepathy-farstream2 Use 'apt-get autoremove' to remove them. The following extra packages will be installed: gwibber gwibber-service kde-runtime-data software-center Suggested packages: gwibber-service-flickr gwibber-service-digg gwibber-service-statusnet gwibber-service-foursquare gwibber-service-friendfeed gwibber-service-pingfm gwibber-service-qaiku unity-lens-gwibber The following packages will be upgraded: gwibber gwibber-service kde-runtime-data software-center 4 upgraded, 0 newly installed, 0 to remove and 39 not upgraded. 20 not fully installed or removed. Need to get 0 B/5,682 kB of archives. After this operation, 177 kB of additional disk space will be used. Do you want to continue [Y/n]? debconf: Perl may be unconfigured (Can't locate Scalar/Util.pm in @INC (@INC contains: /etc/perl /usr/local/lib/perl/5.14.2 /usr/local/share/perl/5.14.2 /usr/lib/perl5 /usr/share/perl5 /usr/lib/perl/5.14 /usr/share/perl/5.14 /usr/local/lib/site_perl .) at /usr/lib/perl/5.14/Hash/Util.pm line 9. BEGIN failed--compilation aborted at /usr/lib/perl/5.14/Hash/Util.pm line 9. Compilation failed in require at /usr/share/perl/5.14/fields.pm line 122. Compilation failed in require at /usr/share/perl5/Debconf/Log.pm line 10. Compilation failed in require at (eval 1) line 4. BEGIN failed--compilation aborted at (eval 1) line 4. ) -- aborting (Reading database ... 242672 files and directories currently installed.) Preparing to replace gwibber 3.4.1-0ubuntu1 (using .../gwibber_3.4.2-0ubuntu1_i386.deb) ... Could not find platform dependent libraries <exec_prefix> Consider setting $PYTHONHOME to <prefix>[:<exec_prefix>] Traceback (most recent call last): File "/usr/bin/pyclean", line 25, in <module> import logging ImportError: No module named logging Error in sys.excepthook: Traceback (most recent call last): File "/usr/lib/python2.7/dist-packages/apport_python_hook.py", line 64, in apport_excepthook from apport.fileutils import likely_packaged, get_recent_crashes File "/usr/lib/python2.7/dist-packages/apport/__init__.py", line 1, in <module> from apport.report import Report File "/usr/lib/python2.7/dist-packages/apport/report.py", line 16, in <module> from xml.parsers.expat import ExpatError File "/usr/lib/python2.7/xml/parsers/expat.py", line 4, in <module> from pyexpat import * ImportError: No module named pyexpat Original exception was: Traceback (most recent call last): File "/usr/bin/pyclean", line 25, in <module> import logging ImportError: No module named logging dpkg: warning: subprocess old pre-removal script returned error exit status 1 dpkg - trying script from the new package instead ... Could not find platform dependent libraries <exec_prefix> Consider setting $PYTHONHOME to <prefix>[:<exec_prefix>] Traceback (most recent call last): File "/usr/bin/pyclean", line 25, in <module> import logging ImportError: No module named logging Error in sys.excepthook: Traceback (most recent call last): File "/usr/lib/python2.7/dist-packages/apport_python_hook.py", line 64, in apport_excepthook from apport.fileutils import likely_packaged, get_recent_crashes File "/usr/lib/python2.7/dist-packages/apport/__init__.py", line 1, in <module> from apport.report import Report File "/usr/lib/python2.7/dist-packages/apport/report.py", line 16, in <module> from xml.parsers.expat import ExpatError File "/usr/lib/python2.7/xml/parsers/expat.py", line 4, in <module> from pyexpat import * ImportError: No module named pyexpat Original exception was: Traceback (most recent call last): File "/usr/bin/pyclean", line 25, in <module> import logging ImportError: No module named logging dpkg: error processing /var/cache/apt/archives/gwibber_3.4.2-0ubuntu1_i386.deb (--unpack): subprocess new pre-removal script returned error exit status 1 Could not find platform dependent libraries <exec_prefix> Consider setting $PYTHONHOME to <prefix>[:<exec_prefix>] Traceback (most recent call last): File "/usr/bin/pycompile", line 27, in <module> import logging ImportError: No module named logging Error in sys.excepthook: Traceback (most recent call last): File "/usr/lib/python2.7/dist-packages/apport_python_hook.py", line 64, in apport_excepthook from apport.fileutils import likely_packaged, get_recent_crashes File "/usr/lib/python2.7/dist-packages/apport/__init__.py", line 1, in <module> from apport.report import Report File "/usr/lib/python2.7/dist-packages/apport/report.py", line 16, in <module> from xml.parsers.expat import ExpatError File "/usr/lib/python2.7/xml/parsers/expat.py", line 4, in <module> from pyexpat import * ImportError: No module named pyexpat Original exception was: Traceback (most recent call last): File "/usr/bin/pycompile", line 27, in <module> import logging ImportError: No module named logging dpkg: error while cleaning up: subprocess installed post-installation script returned error exit status 1 Preparing to replace gwibber-service 3.4.1-0ubuntu1 (using .../gwibber-service_3.4.2-0ubuntu1_all.deb) ... Could not find platform dependent libraries <exec_prefix> Consider setting $PYTHONHOME to <prefix>[:<exec_prefix>] Traceback (most recent call last): File "/usr/bin/pyclean", line 25, in <module> import logging ImportError: No module named logging Error in sys.excepthook: Traceback (most recent call last): File "/usr/lib/python2.7/dist-packages/apport_python_hook.py", line 64, in apport_excepthook from apport.fileutils import likely_packaged, get_recent_crashes File "/usr/lib/python2.7/dist-packages/apport/__init__.py", line 1, in <module> from apport.report import Report File "/usr/lib/python2.7/dist-packages/apport/report.py", line 16, in <module> from xml.parsers.expat import ExpatError File "/usr/lib/python2.7/xml/parsers/expat.py", line 4, in <module> from pyexpat import * ImportError: No module named pyexpat Original exception was: Traceback (most recent call last): File "/usr/bin/pyclean", line 25, in <module> import logging ImportError: No module named logging dpkg: warning: subprocess old pre-removal script returned error exit status 1 dpkg - trying script from the new package instead ... Could not find platform dependent libraries <exec_prefix> Consider setting $PYTHONHOME to <prefix>[:<exec_prefix>] Traceback (most recent call last): File "/usr/bin/pyclean", line 25, in <module> import logging ImportError: No module named logging Error in sys.excepthook: Traceback (most recent call last): File "/usr/lib/python2.7/dist-packages/apport_python_hook.py", line 64, in apport_excepthook from apport.fileutils import likely_packaged, get_recent_crashes File "/usr/lib/python2.7/dist-packages/apport/__init__.py", line 1, in <module> from apport.report import Report File "/usr/lib/python2.7/dist-packages/apport/report.py", line 16, in <module> from xml.parsers.expat import ExpatError File "/usr/lib/python2.7/xml/parsers/expat.py", line 4, in <module> from pyexpat import * ImportError: No module named pyexpat Original exception was: Traceback (most recent call last): File "/usr/bin/pyclean", line 25, in <module> import logging ImportError: No module named logging dpkg: error processing /var/cache/apt/archives/gwibber-service_3.4.2-0ubuntu1_all.deb (--unpack): subprocess new pre-removal script returned error exit status 1 Could not find platform dependent libraries <exec_prefix> Consider setting $PYTHONHOME to <prefix>[:<exec_prefix>] Traceback (most recent call last): File "/usr/bin/pycompile", line 27, in <module> import logging ImportError: No module named logging Error in sys.excepthook: Traceback (most recent call last): File "/usr/lib/python2.7/dist-packages/apport_python_hook.py", line 64, in apport_excepthook from apport.fileutils import likely_packaged, get_recent_crashes File "/usr/lib/python2.7/dist-packages/apport/__init__.py", line 1, in <module> from apport.report import Report File "/usr/lib/python2.7/dist-packages/apport/report.py", line 16, in <module> from xml.parsers.expat import ExpatError File "/usr/lib/python2.7/xml/parsers/expat.py", line 4, in <module> from pyexpat import * ImportError: No module named pyexpat Original exception was: Traceback (most recent call last): File "/usr/bin/pycompile", line 27, in <module> import logging ImportError: No module named logging dpkg: error while cleaning up: subprocess installed post-installation script returned error exit status 1 Preparing to replace kde-runtime-data 4:4.8.3-0ubuntu0.1 (using .../kde-runtime-data_4%3a4.8.4-0ubuntu0.1_all.deb) ... Unpacking replacement kde-runtime-data ... dpkg: error processing /var/cache/apt/archives/kde-runtime-data_4%3a4.8.4-0ubuntu0.1_all.deb (--unpack): trying to overwrite '/usr/share/sounds', which is also in package sound-theme-freedesktop 0.7.pristine-2 dpkg-deb (subprocess): subprocess data was killed by signal (Broken pipe) dpkg-deb: error: subprocess <decompress> returned error exit status 2 Preparing to replace python-crypto 2.4.1-1 (using .../python-crypto_2.4.1-1_i386.deb) ... Could not find platform dependent libraries <exec_prefix> Consider setting $PYTHONHOME to <prefix>[:<exec_prefix>] Traceback (most recent call last): File "/usr/bin/pyclean", line 25, in <module> import logging ImportError: No module named logging Error in sys.excepthook: Traceback (most recent call last): File "/usr/lib/python2.7/dist-packages/apport_python_hook.py", line 64, in apport_excepthook from apport.fileutils import likely_packaged, get_recent_crashes File "/usr/lib/python2.7/dist-packages/apport/__init__.py", line 1, in <module> from apport.report import Report File "/usr/lib/python2.7/dist-packages/apport/report.py", line 16, in <module> from xml.parsers.expat import ExpatError File "/usr/lib/python2.7/xml/parsers/expat.py", line 4, in <module> from pyexpat import * ImportError: No module named pyexpat Original exception was: Traceback (most recent call last): File "/usr/bin/pyclean", line 25, in <module> import logging ImportError: No module named logging dpkg: warning: subprocess old pre-removal script returned error exit status 1 dpkg - trying script from the new package instead ... Could not find platform dependent libraries <exec_prefix> Consider setting $PYTHONHOME to <prefix>[:<exec_prefix>] Traceback (most recent call last): File "/usr/bin/pyclean", line 25, in <module> import logging ImportError: No module named logging Error in sys.excepthook: Traceback (most recent call last): File "/usr/lib/python2.7/dist-packages/apport_python_hook.py", line 64, in apport_excepthook from apport.fileutils import likely_packaged, get_recent_crashes File "/usr/lib/python2.7/dist-packages/apport/__init__.py", line 1, in <module> from apport.report import Report File "/usr/lib/python2.7/dist-packages/apport/report.py", line 16, in <module> from xml.parsers.expat import ExpatError File "/usr/lib/python2.7/xml/parsers/expat.py", line 4, in <module> from pyexpat import * ImportError: No module named pyexpat Original exception was: Traceback (most recent call last): File "/usr/bin/pyclean", line 25, in <module> import logging ImportError: No module named logging dpkg: error processing /var/cache/apt/archives/python-crypto_2.4.1-1_i386.deb (--unpack): subprocess new pre-removal script returned error exit status 1 No apport report written because MaxReports is reached already Could not find platform dependent libraries <exec_prefix> Consider setting $PYTHONHOME to <prefix>[:<exec_prefix>] Traceback (most recent call last): File "/usr/bin/pycompile", line 27, in <module> import logging ImportError: No module named logging Error in sys.excepthook: Traceback (most recent call last): File "/usr/lib/python2.7/dist-packages/apport_python_hook.py", line 64, in apport_excepthook from apport.fileutils import likely_packaged, get_recent_crashes File "/usr/lib/python2.7/dist-packages/apport/__init__.py", line 1, in <module> from apport.report import Report File "/usr/lib/python2.7/dist-packages/apport/report.py", line 16, in <module> from xml.parsers.expat import ExpatError File "/usr/lib/python2.7/xml/parsers/expat.py", line 4, in <module> from pyexpat import * ImportError: No module named pyexpat Original exception was: Traceback (most recent call last): File "/usr/bin/pycompile", line 27, in <module> import logging ImportError: No module named logging dpkg: error while cleaning up: subprocess installed post-installation script returned error exit status 1 Preparing to replace software-center 5.2.2.2 (using .../software-center_5.2.4_all.deb) ... Could not find platform dependent libraries <exec_prefix> Consider setting $PYTHONHOME to <prefix>[:<exec_prefix>] Traceback (most recent call last): File "/usr/bin/pyclean", line 25, in <module> import logging ImportError: No module named logging Error in sys.excepthook: Traceback (most recent call last): File "/usr/lib/python2.7/dist-packages/apport_python_hook.py", line 64, in apport_excepthook from apport.fileutils import likely_packaged, get_recent_crashes File "/usr/lib/python2.7/dist-packages/apport/__init__.py", line 1, in <module> from apport.report import Report File "/usr/lib/python2.7/dist-packages/apport/report.py", line 16, in <module> from xml.parsers.expat import ExpatError File "/usr/lib/python2.7/xml/parsers/expat.py", line 4, in <module> from pyexpat import * ImportError: No module named pyexpat Original exception was: Traceback (most recent call last): File "/usr/bin/pyclean", line 25, in <module> import logging ImportError: No module named logging dpkg: warning: subprocess old pre-removal script returned error exit status 1 dpkg - trying script from the new package instead ... Could not find platform dependent libraries <exec_prefix> Consider setting $PYTHONHOME to <prefix>[:<exec_prefix>] Traceback (most recent call last): File "/usr/bin/pyclean", line 25, in <module> import logging ImportError: No module named logging Error in sys.excepthook: Traceback (most recent call last): File "/usr/lib/python2.7/dist-packages/apport_python_hook.py", line 64, in apport_excepthook from apport.fileutils import likely_packaged, get_recent_crashes File "/usr/lib/python2.7/dist-packages/apport/__init__.py", line 1, in <module> from apport.report import Report File "/usr/lib/python2.7/dist-packages/apport/report.py", line 16, in <module> from xml.parsers.expat import ExpatError File "/usr/lib/python2.7/xml/parsers/expat.py", line 4, in <module> from pyexpat import * ImportError: No module named pyexpat Original exception was: Traceback (most recent call last): File "/usr/bin/pyclean", line 25, in <module> import logging ImportError: No module named logging dpkg: error processing /var/cache/apt/archives/software-center_5.2.4_all.deb (--unpack): subprocess new pre-removal script returned error exit status 1 No apport report written because MaxReports is reached already Could not find platform dependent libraries <exec_prefix> Consider setting $PYTHONHOME to <prefix>[:<exec_prefix>] Traceback (most recent call last): File "/usr/bin/pycompile", line 27, in <module> import logging ImportError: No module named logging Error in sys.excepthook: Traceback (most recent call last): File "/usr/lib/python2.7/dist-packages/apport_python_hook.py", line 64, in apport_excepthook from apport.fileutils import likely_packaged, get_recent_crashes File "/usr/lib/python2.7/dist-packages/apport/__init__.py", line 1, in <module> from apport.report import Report File "/usr/lib/python2.7/dist-packages/apport/report.py", line 16, in <module> from xml.parsers.expat import ExpatError File "/usr/lib/python2.7/xml/parsers/expat.py", line 4, in <module> from pyexpat import * ImportError: No module named pyexpat Original exception was: Traceback (most recent call last): File "/usr/bin/pycompile", line 27, in <module> import logging ImportError: No module named logging dpkg: error while cleaning up: subprocess installed post-installation script returned error exit status 1 Preparing to replace xdiagnose 2.5 (using .../archives/xdiagnose_2.5_all.deb) ... Could not find platform dependent libraries <exec_prefix> Consider setting $PYTHONHOME to <prefix>[:<exec_prefix>] Traceback (most recent call last): File "/usr/bin/pyclean", line 25, in <module> import logging ImportError: No module named logging Error in sys.excepthook: Traceback (most recent call last): File "/usr/lib/python2.7/dist-packages/apport_python_hook.py", line 64, in apport_excepthook from apport.fileutils import likely_packaged, get_recent_crashes File "/usr/lib/python2.7/dist-packages/apport/__init__.py", line 1, in <module> from apport.report import Report File "/usr/lib/python2.7/dist-packages/apport/report.py", line 16, in <module> from xml.parsers.expat import ExpatError File "/usr/lib/python2.7/xml/parsers/expat.py", line 4, in <module> from pyexpat import * ImportError: No module named pyexpat Original exception was: Traceback (most recent call last): File "/usr/bin/pyclean", line 25, in <module> import logging ImportError: No module named logging dpkg: warning: subprocess old pre-removal script returned error exit status 1 dpkg - trying script from the new package instead ... Could not find platform dependent libraries <exec_prefix> Consider setting $PYTHONHOME to <prefix>[:<exec_prefix>] Traceback (most recent call last): File "/usr/bin/pyclean", line 25, in <module> import logging ImportError: No module named logging Error in sys.excepthook: Traceback (most recent call last): File "/usr/lib/python2.7/dist-packages/apport_python_hook.py", line 64, in apport_excepthook from apport.fileutils import likely_packaged, get_recent_crashes File "/usr/lib/python2.7/dist-packages/apport/__init__.py", line 1, in <module> from apport.report import Report File "/usr/lib/python2.7/dist-packages/apport/report.py", line 16, in <module> from xml.parsers.expat import ExpatError File "/usr/lib/python2.7/xml/parsers/expat.py", line 4, in <module> from pyexpat import * ImportError: No module named pyexpat Original exception was: Traceback (most recent call last): File "/usr/bin/pyclean", line 25, in <module> import logging ImportError: No module named logging dpkg: error processing /var/cache/apt/archives/xdiagnose_2.5_all.deb (--unpack): subprocess new pre-removal script returned error exit status 1 No apport report written because MaxReports is reached already Could not find platform dependent libraries <exec_prefix> Consider setting $PYTHONHOME to <prefix>[:<exec_prefix>] Traceback (most recent call last): File "/usr/bin/pycompile", line 27, in <module> import logging ImportError: No module named logging Error in sys.excepthook: Traceback (most recent call last): File "/usr/lib/python2.7/dist-packages/apport_python_hook.py", line 64, in apport_excepthook from apport.fileutils import likely_packaged, get_recent_crashes File "/usr/lib/python2.7/dist-packages/apport/__init__.py", line 1, in <module> from apport.report import Report File "/usr/lib/python2.7/dist-packages/apport/report.py", line 16, in <module> from xml.parsers.expat import ExpatError File "/usr/lib/python2.7/xml/parsers/expat.py", line 4, in <module> from pyexpat import * ImportError: No module named pyexpat Original exception was: Traceback (most recent call last): File "/usr/bin/pycompile", line 27, in <module> import logging ImportError: No module named logging dpkg: error while cleaning up: subprocess installed post-installation script returned error exit status 1 Errors were encountered while processing: /var/cache/apt/archives/gwibber_3.4.2-0ubuntu1_i386.deb /var/cache/apt/archives/gwibber-service_3.4.2-0ubuntu1_all.deb /var/cache/apt/archives/kde-runtime-data_4%3a4.8.4-0ubuntu0.1_all.deb /var/cache/apt/archives/python-crypto_2.4.1-1_i386.deb /var/cache/apt/archives/software-center_5.2.4_all.deb /var/cache/apt/archives/xdiagnose_2.5_all.deb E: Sub-process /usr/bin/dpkg returned an error code (1) maura@pandora:~$ ^C maura@pandora:~$

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• Framework 4 Features: Support for Timed Jobs

  - by Anthony Shorten

  One of the new features of the Oracle Utilities Application Framework V4 is the ability for the batch framework to support Timed Batch. Traditionally batch is associated with set processing in the background in a fixed time frame. For example, billing customers. Over the last few versions their has been functionality required by the products required a more monitoring style batch process. The monitor is a batch process that looks for specific business events based upon record status or other pieces of data. For example, the framework contains a fact monitor (F1-FCTRN) that can be configured to look for specific status's or other conditions. The batch process then uses the instructions on the object to determine what to do. To support monitor style processing, you need to run the process regularly a number of times a day (for example, every ten minutes). Traditional batch could support this but it was not as optimal as expected (if you are a site using the old Workflow subsystem, you understand what I mean). The Batch framework was extended to add additional facilities to support times (and continuous batch which is another new feature for another blog entry). The new facilities include: The batch control now defines the job as Timed or Not Timed. Non-Timed batch are traditional batch jobs. The timer interval (the interval between executions) can be specified The timer can be made active or inactive. Only active timers are executed. Setting the Timer Active to inactive will stop the job at the next time interval. Setting the Timer Active to Active will start the execution of the timed job. You can specify the credentials, language to view the messages and an email address to send the a summary of the execution to. The email address is optional and requires an email server to be specified in the relevant feature configuration. You can specify the thread limits and commit intervals to be sued for the multiple executions. Once a timer job is defined it will be executed automatically by the Business Application Server process if the DEFAULT threadpool is active. This threadpool can be started using the online batch daemon (for non-production) or externally using the threadpoolworker utility. At that time any batch process with the Timer Active set to Active and Batch Control Type of Timed will begin executing. As Timed jobs are executed automatically then they do not appear in any external schedule or are managed by an external scheduler (except via the DEFAULT threadpool itself of course). Now, if the job has no work to do as the timer interval is being reached then that instance of the job is stopped and the next instance started at the timer interval. If there is still work to complete when the interval interval is reached, the instance will continue processing till the work is complete, then the instance will be stopped and the next instance scheduled for the next timer interval. One of the key ways of optimizing this processing is to set the timer interval correctly for the expected workload. This is an interesting new feature of the batch framework and we anticipate it will come in handy for specific business situations with the monitor processes.

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• How to I do install DB2 ODBC?

  - by Justin

  I have been trying, with no success, to install a IBM DB2 ODBC driver so that my PHP server can connect to a database. I've tried installing the db2_connect and get all sorts of problems, I tried install I Access for Linux and the RPM did not install right nor did using alien breed any useful results. I've also tried the DB2 Runtime v8.1, no success. If I attempt to run the rpm it claims I need dependencies that I can't find in apt-get. Yum is also not very helpful as it appears I don't have any repositories installed or lists... Running the simple RPM gives me this result in terminal: # rpm -ivh iSeriesAccess-7.1.0-1.0.x86_64.rpm rpm: RPM should not be used directly install RPM packages, use Alien instead! rpm: However assuming you know what you are doing... error: Failed dependencies: /bin/ln is needed by iSeriesAccess-7.1.0-1.0.x86_64 /sbin/ldconfig is needed by iSeriesAccess-7.1.0-1.0.x86_64 /bin/rm is needed by iSeriesAccess-7.1.0-1.0.x86_64 /bin/sh is needed by iSeriesAccess-7.1.0-1.0.x86_64 libc.so.6()(64bit) is needed by iSeriesAccess-7.1.0-1.0.x86_64 libc.so.6(GLIBC_2.2.5)(64bit) is needed by iSeriesAccess-7.1.0-1.0.x86_64 libc.so.6(GLIBC_2.3)(64bit) is needed by iSeriesAccess-7.1.0-1.0.x86_64 libdl.so.2()(64bit) is needed by iSeriesAccess-7.1.0-1.0.x86_64 libdl.so.2(GLIBC_2.2.5)(64bit) is needed by iSeriesAccess-7.1.0-1.0.x86_64 libgcc_s.so.1()(64bit) is needed by iSeriesAccess-7.1.0-1.0.x86_64 libm.so.6()(64bit) is needed by iSeriesAccess-7.1.0-1.0.x86_64 libm.so.6(GLIBC_2.2.5)(64bit) is needed by iSeriesAccess-7.1.0-1.0.x86_64 libodbcinst.so.1()(64bit) is needed by iSeriesAccess-7.1.0-1.0.x86_64 libodbc.so.1()(64bit) is needed by iSeriesAccess-7.1.0-1.0.x86_64 libpthread.so.0()(64bit) is needed by iSeriesAccess-7.1.0-1.0.x86_64 libpthread.so.0(GLIBC_2.2.5)(64bit) is needed by iSeriesAccess-7.1.0-1.0.x86_64 libpthread.so.0(GLIBC_2.3.2)(64bit) is needed by iSeriesAccess-7.1.0-1.0.x86_64 librt.so.1()(64bit) is needed by iSeriesAccess-7.1.0-1.0.x86_64 librt.so.1(GLIBC_2.2.5)(64bit) is needed by iSeriesAccess-7.1.0-1.0.x86_64 libstdc++.so.6()(64bit) is needed by iSeriesAccess-7.1.0-1.0.x86_64 libstdc++.so.6(CXXABI_1.3)(64bit) is needed by iSeriesAccess-7.1.0-1.0.x86_64 libstdc++.so.6(GLIBCXX_3.4)(64bit) is needed by iSeriesAccess-7.1.0-1.0.x86_64 Using alien and running the dkpg gives me thes headaque: $ alien iSeriesAccess-7.1.0-1.0.x86_64.rpm --scripts # dpkg -i iseriesaccess_7.1.0-2_amd64.deb (Reading database ... 127664 files and directories currently installed.) Preparing to replace iseriesaccess 7.1.0-2 (using iseriesaccess_7.1.0-2_amd64.deb) ... Unpacking replacement iseriesaccess ... post uninstall processing for iSeriesAccess 1.0...upgrade /var/lib/dpkg/info/iseriesaccess.postrm: line 8: [: upgrade: integer expression expected Setting up iseriesaccess (7.1.0-2) ... post install processing for iSeriesAccess 1.0...configure iSeries Access ODBC Driver has been deleted (if it existed at all) because its usage count became zero odbcinst: Driver installed. Usage count increased to 1. Target directory is /etc odbcinst: Driver installed. Usage count increased to 3. Target directory is /etc Processing triggers for libc-bin ... ldconfig deferred processing now taking place So it seems the files installed right, well my odbc driver shows up but db2cli.ini is no where to be found. So several questions. Is there a better alternative to connect php to db2, say an ubuntu package I can just install? Can someone direct me to the steps that makes my ubuntu server works well with the RPM so I can build my db2 instance? Also remember I'm connection to an I Series remotely. I'm not using the DB2 Express C thing, even if I did try it to get the db2 php functions to work. And I don't have zend but I think I have every other package on the ubuntu repositories. Help, thank you!

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• Oracle TimesTen In-Memory Database Performance on SPARC T4-2

  - by Brian

  The Oracle TimesTen In-Memory Database is optimized to run on Oracle's SPARC T4 processor platforms running Oracle Solaris 11 providing unsurpassed scalability, performance, upgradability, protection of investment and return on investment. The following demonstrate the value of combining Oracle TimesTen In-Memory Database with SPARC T4 servers and Oracle Solaris 11: On a Mobile Call Processing test, the 2-socket SPARC T4-2 server outperforms: Oracle's SPARC Enterprise M4000 server (4 x 2.66 GHz SPARC64 VII+) by 34%. Oracle's SPARC T3-4 (4 x 1.65 GHz SPARC T3) by 2.7x, or 5.4x per processor. Utilizing the TimesTen Performance Throughput Benchmark (TPTBM), the SPARC T4-2 server protects investments with: 2.1x the overall performance of a 4-socket SPARC Enterprise M4000 server in read-only mode and 1.5x the performance in update-only testing. This is 4.2x more performance per processor than the SPARC64 VII+ 2.66 GHz based system. 10x more performance per processor than the SPARC T2+ 1.4 GHz server. 1.6x better performance per processor than the SPARC T3 1.65 GHz based server. In replication testing, the two socket SPARC T4-2 server is over 3x faster than the performance of a four socket SPARC Enterprise T5440 server in both asynchronous replication environment and the highly available 2-Safe replication. This testing emphasizes parallel replication between systems. Performance Landscape Mobile Call Processing Test Performance System Processor Sockets/Cores/Threads Tps SPARC T4-2 SPARC T4, 2.85 GHz 2 16 128 218,400 M4000 SPARC64 VII+, 2.66 GHz 4 16 32 162,900 SPARC T3-4 SPARC T3, 1.65 GHz 4 64 512 80,400 TimesTen Performance Throughput Benchmark (TPTBM) Read-Only System Processor Sockets/Cores/Threads Tps SPARC T3-4 SPARC T3, 1.65 GHz 4 64 512 7.9M SPARC T4-2 SPARC T4, 2.85 GHz 2 16 128 6.5M M4000 SPARC64 VII+, 2.66 GHz 4 16 32 3.1M T5440 SPARC T2+, 1.4 GHz 4 32 256 3.1M TimesTen Performance Throughput Benchmark (TPTBM) Update-Only System Processor Sockets/Cores/Threads Tps SPARC T4-2 SPARC T4, 2.85 GHz 2 16 128 547,800 M4000 SPARC64 VII+, 2.66 GHz 4 16 32 363,800 SPARC T3-4 SPARC T3, 1.65 GHz 4 64 512 240,500 TimesTen Replication Tests System Processor Sockets/Cores/Threads Asynchronous 2-Safe SPARC T4-2 SPARC T4, 2.85 GHz 2 16 128 38,024 13,701 SPARC T5440 SPARC T2+, 1.4 GHz 4 32 256 11,621 4,615 Configuration Summary Hardware Configurations: SPARC T4-2 server 2 x SPARC T4 processors, 2.85 GHz 256 GB memory 1 x 8 Gbs FC Qlogic HBA 1 x 6 Gbs SAS HBA 4 x 300 GB internal disks Sun Storage F5100 Flash Array (40 x 24 GB flash modules) 1 x Sun Fire X4275 server configured as COMSTAR head SPARC T3-4 server 4 x SPARC T3 processors, 1.6 GHz 512 GB memory 1 x 8 Gbs FC Qlogic HBA 8 x 146 GB internal disks 1 x Sun Fire X4275 server configured as COMSTAR head SPARC Enterprise M4000 server 4 x SPARC64 VII+ processors, 2.66 GHz 128 GB memory 1 x 8 Gbs FC Qlogic HBA 1 x 6 Gbs SAS HBA 2 x 146 GB internal disks Sun Storage F5100 Flash Array (40 x 24 GB flash modules) 1 x Sun Fire X4275 server configured as COMSTAR head Software Configuration: Oracle Solaris 11 11/11 Oracle TimesTen 11.2.2.4 Benchmark Descriptions TimesTen Performance Throughput BenchMark (TPTBM) is shipped with TimesTen and measures the total throughput of the system. The workload can test read-only, update-only, delete and insert operations as required. Mobile Call Processing is a customer-based workload for processing calls made by mobile phone subscribers. The workload has a mixture of read-only, update, and insert-only transactions. The peak throughput performance is measured from multiple concurrent processes executing the transactions until a peak performance is reached via saturation of the available resources. Parallel Replication tests using both asynchronous and 2-Safe replication methods. For asynchronous replication, transactions are processed in batches to maximize the throughput capabilities of the replication server and network. In 2-Safe replication, also known as no data-loss or high availability, transactions are replicated between servers immediately emphasizing low latency. For both environments, performance is measured in the number of parallel replication servers and the maximum transactions-per-second for all concurrent processes. See Also SPARC T4-2 Server oracle.com OTN Oracle TimesTen In-Memory Database oracle.com OTN Oracle Solaris oracle.com OTN Oracle Database 11g Release 2 Enterprise Edition oracle.com OTN Disclosure Statement Copyright 2012, Oracle and/or its affiliates. All rights reserved. Oracle and Java are registered trademarks of Oracle and/or its affiliates. Other names may be trademarks of their respective owners. Results as of 1 October 2012.

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• Oracle Announces Oracle Insurance Policy Administration for Life and Annuity 9.4

  - by helen.pitts(at)oracle.com

  Normal 0 false false false EN-US X-NONE X-NONE /* Style Definitions */ table.MsoNormalTable {mso-style-name:"Table Normal"; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-priority:99; mso-style-qformat:yes; mso-style-parent:""; mso-padding-alt:0in 5.4pt 0in 5.4pt; mso-para-margin-top:0in; mso-para-margin-right:0in; mso-para-margin-bottom:10.0pt; mso-para-margin-left:0in; line-height:115%; mso-pagination:widow-orphan; font-size:11.0pt; font-family:"Calibri","sans-serif"; mso-ascii-font-family:Calibri; mso-ascii-theme-font:minor-latin; mso-hansi-font-family:Calibri; mso-hansi-theme-font:minor-latin;} Today's global insurers require the ability to provide higher levels of service and quickly bring to market life insurance and annuity products that not only help them stand out from the competition, but also stay current with local legislation. To succeed, they require agile and flexible core systems that enable them to meet the unique localization requirements of the markets in which they operate, whether in North America, Asia Pacific or the Pan-European Region. The release of Oracle Insurance Policy Administration for Life and Annuity 9.4, announced today, helps insurers meet this need with expanded international market capabilities that enable them to reduce risk and profitably compete wherever their business takes them. It offers expanded multi-language along with unit-linked product and fund processing capabilities that enable regional and global insurers to rapidly configure and deliver localized products – along with providing better service for end users through a single policy admin solution. Key enhancements include: Kanji/Kana language support, pre-defined content, and imperial date processing for the Japanese market New localization flexibility for configuring and managing international mailing addresses along with regional variations for client information Enhanced capability to calculate unit-linked pricing and valuation, in addition to market-based processing and pre-configured unit linked content Expanded role-based security and masking capability to further protect sensitive customer data Enhanced capability to restrict processing specified activities based on time of day and user role, reducing exposure to market timing risks Further capability to eliminate duplicate client records, helping to reduce underwriting risks and enhance servicing through a single view of the client "The ability to leverage a single, rules-driven policy administration system for multiple global operation centers can help insurers realize significant improvements in speed to market, customer service, compliance with regional regulations, and consolidation efforts,” noted Celent's Craig Weber, senior vice president, Insurance. “We believe such initiatives are necessary to help the industry address service and distribution imperatives." Helping our customers meet these mission-critical business imperatives is a key objective for Oracle Insurance. Active, ongoing dialogue with our customers is an important part of the process to help understand how our solutions are and can continue to help them achieve success in the marketplace. I had the opportunity to meet with several of our insurance customers at the Oracle Insurance Policy Administration Client Advisory Board meeting last week in Philadelphia, Penn. (View photos on the Oracle Insurance Facebook page.)   It was a great forum for Oracle Insurance and our clients. Discussion centered on the latest business and IT trends, with opportunities to learn more about the latest release of Oracle Insurance Policy Administration for Life and Annuity and other Oracle Insurance solutions such as data warehousing / business intelligence, while exchanging best practices for product innovation and servicing customers and sales channels. Helen Pitts is senior product marketing manager for Oracle Insurance's life and annuities solutions.

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• SQL SERVER – Partition Parallelism Support in expressor 3.6

  - by pinaldave

  I am very excited to learn that there is a new version of expressor’s data integration platform coming out in March of this year.  It will be version 3.6, and I look forward to using it and telling everyone about it.  Let me describe a little bit more about what will be so great in expressor 3.6: Greatly enhanced user interface Parallel Processing Bulk Artifact Upgrading The User Interface First let me cover the most obvious enhancements. The expressor Studio user interface (UI) has had some significant work done. Kudos to the expressor Engineering team; the entire UI is a visual masterpiece that is very responsive and intuitive. The improvements are more than just eye candy; they provide significant productivity gains when developing expressor Dataflows. Operator shape icons now include a description that identifies the function of each operator, instead of having to guess at the function by the icon. Operator shapes and highlighting depict the current function and status: Disabled, enabled, complete, incomplete, and error. Each status displays an appropriate message in the message panel with correction suggestions. Floating or docking property panels provide descriptive tool tips for each property as well as auto resize when adjusting the canvas, without having to search Help or the need to scroll around to get access to the property. Progress and status indicators let you know when an operation is working. “No limit” canvas with snap-to-grid allows automatic sizing and accurate positioning when you have numerous operators in the Dataflow. The inline tool bar offers quick access to pan, zoom, fit and overview functions. Selecting multiple artifacts with a right click context allows you to easily manage your workspace more efficiently. Partitioning and Parallel Processing Partitioning allows each operator to process multiple subsets of records in parallel as opposed to processing all records that flow through that operator in a single sequential set. This capability allows the user to configure the expressor Dataflow to run in a way that most efficiently utilizes the resources of the hardware where the Dataflow is running. Partitions can exist in most individual operators. Using partitions increases the speed of an expressor data integration application, therefore improving performance and load times. With the expressor 3.6 Enterprise Edition, expressor simplifies enabling parallel processing by adding intuitive partition settings that are easy to configure. Bulk Artifact Upgrading Bulk Artifact Upgrading sounds a bit intimidating, but it actually is not and it is a welcome addition to expressor Studio. In past releases, users were prompted to confirm that they wanted to upgrade their individual artifacts only when opened. This was a cumbersome and repetitive process. Now with bulk artifact upgrading, a user can easily select what artifact or group of artifacts to upgrade all at once. As you can see, there are many new features and upgrade options that will prove to make expressor Studio quicker and more efficient.  I hope I’m not the only one who is excited about all these new upgrades, and that I you try expressor and share your experience with me. Reference: Pinal Dave (http://blog.sqlauthority.com) Filed under: PostADay, SQL, SQL Authority, SQL Performance, SQL Query, SQL Server, SQL Tips and Tricks, SQLServer, T SQL, Technology

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• Ubiquitous BIP

  - by Tim Dexter

  The last number I heard from Mike and the PM team was that BIP is now embedded in more than 40 oracle products. That's a lot of products to keep track of and to help out with new releases, etc. Its interesting to see how internal Oracle product groups have integrated BIP into their products. Just as you might integrate BIP they have had to make a choice about how to integrate. 1. Library level - BIP is a pure java app and at the bottom of the architecture are a group of java libraries that expose APIs that you can use. they fall into three main areas, data extraction, template processing and formatting and delivery. There are post processing capabilities but those APIs are embedded withing the template processing libraries. Taking this integration route you are going to need to manage templates, data extraction and processing. You'll have your own UI to allow users to control all of this for themselves. Ultimate control but some effort to build and maintain. I have been trawling some of the products during a coffee break. I found a great post on the reporting capabilities provided by BIP in the records management product within WebCenter Content 11g. This integration falls into the first category, content manager looks after the report artifacts itself and provides you the UI to manage and run the reports. 2. Web Service level - further up in the stack is the web service layer. This is sitting on the BI Publisher server as a set of services, runReport and scheduleReport are the main protagonists. However, you can also manage the reports and users (locally managed) on the server and the catalog itself via the services layer.Taking this route, you still need to provide the user interface to choose reports and run them but the creation and management of the reports is all handled by the Publisher server. I have worked with a few customer on this approach. The web services provide the ability to retrieve a list of reports the user can access; then the parameters and LOVs for the selected report and finally a service to submit the report on the server. 3. Embedded BIP server UI- the final level is not so well supported yet. You can currently embed a report and its various levels of surrounding  'chrome' inside another html based application using a URL. Check the docs here. The look and feel can be customized but again, not easy, nor documented. I have messed with running the server pages inside an IFRAME, not bad, but not great. Taking this path should present the least amount of effort on your part to get BIP integrated but there are a few gotchas you need to get around. So a reasonable amount of choices with varying amounts of effort involved. There is another option coming soon for all you ADF developers out there, the ability to drop a BIP report into your application pages. But that's for another post.

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• java Processbuilder - exec a file which is not in path on OS X

  - by Jakob

  Okay i'm trying to make ChucK available in exported Processing sketches, i.e. if i export an app from Processing, the ChucK VM binary will be executed from inside the app. So as a user of said app you don't need to worry about ChucK being in your path at all. Right now i'm generating and executing a bash script file, but this way i don't get any console output from ChucK back into Processing: #!/bin/bash cd "[to where the Chuck executable is located]" ./chuck --kill killall chuck # just to make sure ./chuck chuckScript1.ck cuckScriptn.ck then Process p = Runtime.getRuntime().exec("chmod 777 "+scriptPath); p = Runtime.getRuntime().exec(scriptPath); This works but i want to run ChucK directly from Processing instead, but can't get it to execute: String chuckPath = "[folder in which the chuck executable is located]" ProcessBuilder builder = new ProcessBuilder (chuckPath+"/chuck", "test.ck"); final Process process = builder.start(); InputStream is = process.getInputStream(); InputStreamReader isr = new InputStreamReader(is); BufferedReader br = new BufferedReader(isr); String line; while((line = br.readLine()) != null) println(line); println("done chuckin'! exitValue: " + process.exitValue()); Sorry if this is newbie style :D

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• How can I improve my real-time behavior in multi-threaded app using pthreads and condition variables

  - by WilliamKF

  I have a multi-threaded application that is using pthreads. I have a mutex() lock and condition variables(). There are two threads, one thread is producing data for the second thread, a worker, which is trying to process the produced data in a real time fashion such that one chuck is processed as close to the elapsing of a fixed time period as possible. This works pretty well, however, occasionally when the producer thread releases the condition upon which the worker is waiting, a delay of up to almost a whole second is seen before the worker thread gets control and executes again. I know this because right before the producer releases the condition upon which the worker is waiting, it does a chuck of processing for the worker if it is time to process another chuck, then immediately upon receiving the condition in the worker thread, it also does a chuck of processing if it is time to process another chuck. In this later case, I am seeing that I am late processing the chuck many times. I'd like to eliminate this lost efficiency and do what I can to keep the chucks ticking away as close to possible to the desired frequency. Is there anything I can do to reduce the delay between the release condition from the producer and the detection that that condition is released such that the worker resumes processing? For example, would it help for the producer to call something to force itself to be context switched out? Bottom line is the worker has to wait each time it asks the producer to create work for itself so that the producer can muck with the worker's data structures before telling the worker it is ready to run in parallel again. This period of exclusive access by the producer is meant to be short, but during this period, I am also checking for real-time work to be done by the producer on behalf of the worker while the producer has exclusive access. Somehow my hand off back to running in parallel again results in significant delay occasionally that I would like to avoid. Please suggest how this might be best accomplished.

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• I have two choices of Master's classes this fall. Which is the most useful?

  - by ahplummer

  (For background purposes and context): I am a Software Engineer, and manage other Software Engineers currently. I kind of wear two hats right now: one of a programmer, and one as a 'team lead'. In this regard, I've started going back to school to get my Master's degree with an emphasis in Computer Science. I already have a Bachelor's in Computer Science, and have been working in the field for about 13 years. Our primary development environment is a Windows environment, writing in .NET, Delphi, and SQL Server. Choice #1: CST 798 DATA VISUALIZATION Course Description: Basically, this is a course on the "Processing" language: http://processing.org/ Choice #2: CST 711 INFORMATICS Course Description: (From catalog): Informatics is the science of the use and processing of data, information, and knowledge. This course covers a variety of applied issues from information technology, information management at a variety of levels, ranging from simple data entry, to the creation, design and implementation of new information systems, to the development of models. Topics include basic information representation, processing, searching, and organization, evaluation and analysis of information, Internet-based information access tools, ethics and economics of information sharing.

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• activemessaging with stomp and activemq.prefetchSize=1

  - by Clint Miller

  I have a situation where I have a single activemq broker with 2 queues, Q1 and Q2. I have two ruby-based consumers using activemessaging. Let's call them C1 and C2. Both consumers subscribe to each queue. I'm setting activemq.prefetchSize=1 when subscribing to each queue. I'm also setting ack=client. Consider the following sequence of events: 1) A message that triggers a long-running job is published to queue Q1. Call this M1. 2) M1 is dispatched to consumer C1, kicking off a long operation. 3) Two messages that trigger short jobs are published to queue Q2. Call these M2 and M3. 4) M2 is dispatched to C2 which quickly runs the short job. 5) M3 is dispatched to C1, even though C1 is still running M1. It's able to dispatch to C1 because prefetchSize=1 is set on the queue subscription, not on the connection. So the fact that a Q1 message has already been dispatched doesn't stop one Q2 message from being dispatched. Since activemessaging consumers are single-threaded, the net result is that M3 sits and waits on C1 for a long time until C1 finishes processing M1. So, M3 is not processed for a long time, despite the fact that consumer C2 is sitting idle (since it quickly finishes with message M2). Essentially, whenever a long Q1 job is run and then a whole bunch of short Q2 jobs are created, exactly one of the short Q2 jobs gets stuck on a consumer waiting for the long Q1 job to finish. Is there a way to set prefetchSize at the connection level rather than at the subscription level? I really don't want any messages dispatched to C1 while it is processing M1. The other alternative is that I could create a consumer dedicated to processing Q1 and then have other consumers dedicated to processing Q2. But, I'd rather not do that since Q1 messages are infrequent--Q1's dedicated consumers would sit idle most of the day tying up memory.

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• Generate and download a text file in javascript

  - by Mark B

  All my research so far suggests this can't be done, but I'm hoping someone here has some cunning ideas. I have a form on a website which allows users to bulk upload lots of URLs to add to a list on the server. There's quite a lot of server-side processing to do on each URL, so to avoid timeouts and to display progress, I've implemented the upload using jQuery to submit the URLs one at a time using ajax. This is all working nicely. However, part of the processing on each URL is deduplicating it against the complete list. The ajax call returns a status indicating either a successful upload or a rejection due to duplication. As the upload progresses, I tell the user how many URLs have been rejected as duplicates (along with overall progress and ETA). The problem now is how to give the user a complete list of the failed duplicate URLs. I've kept them in an array in my jQuery, and would like the user to be able to click on a link on the form to download a text file containing those URLs. Is this possible just using client-side processing? The server-side processing basically handles a single keyword at a time. I'd rather not have to store the duplicates in a database table with some kind of session key which gets sent with every ajax call, and is then used at the end to generate the text file server-side (and then gets cleaned up some time later). I can see how to do this, but it seems very clunky and a bit 20th century.

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• DB optimization to use it as a queue

  - by anony

  We have a table called worktable which has some columns(key(primary key), ptime, aname, status, content) we have something called producer which puts in rows in this table and we have consumer which does an order-by on the key column and fetches the first row which has status as 'pending'. The consumer does some processing on this row: 1. updates status to "processing" 2. does some processing using content 3. deletes the row we are facing contention issues when we try to run multiple consumers(probably due to the order-by which does a full table scan)... using Advanced queues would be our next step but before we go there we want to check what is the max throughput we can achieve with multiple consumers and producer on the table. What are the optimizations we can do to get the best numbers possible? Can we do an in-memory processing where a consumer fetches 1000 rows at a time processes and deletes? will that improve? What are other possibilities? partitioning of table? parallelization? Index organized tables?...

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• Data Integration Solution?

  - by Shlomo

  At my company we have a number of data feeds and processing that run on any given day. The number of feeds and processing steps is starting to out-number the ability to manage it ad-hoc as it is managed currently. Is there a good solution that helps with logging and managing/scheduling dependencies? For example: A: When file x is FTP dropped into directory D1, kick off processing step B B: Load flat file into DB1 C: When file y is FTP dropped into directory D2, kick off processing Step D D: Load flat file into DB11 E: When B and D are done, churn through the data, and load new data into DB111. F: When Step E is done, launch application process P G: etc... I want those steps to run at the appropriate times, not to mention if B fails, there's no reason to run steps E & F, but I could still run C & D. When I re-run B successfully, it should trigger just E & F to re-run, not C & D. We're a .NET/C#/Sql Server shop, and I'm already familiar with SSIS. Is that really the best there is? That manages steps well, but not external dependencies, or logging. Open source (.NET) preferred, but not required.

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• The Data Scientist

  - by BuckWoody

  A new term - well, perhaps not that new - has come up and I’m actually very excited about it. The term is Data Scientist, and since it’s new, it’s fairly undefined. I’ll explain what I think it means, and why I’m excited about it. In general, I’ve found the term deals at its most basic with analyzing data. Of course, we all do that, and the term itself in that definition is redundant. There is no science that I know of that does not work with analyzing lots of data. But the term seems to refer to more than the common practices of looking at data visually, putting it in a spreadsheet or report, or even using simple coding to examine data sets. The term Data Scientist (as far as I can make out this early in it’s use) is someone who has a strong understanding of data sources, relevance (statistical and otherwise) and processing methods as well as front-end displays of large sets of complicated data. Some - but not all - Business Intelligence professionals have these skills. In other cases, senior developers, database architects or others fill these needs, but in my experience, many lack the strong mathematical skills needed to make these choices properly. I’ve divided the knowledge base for someone that would wear this title into three large segments. It remains to be seen if a given Data Scientist would be responsible for knowing all these areas or would specialize. There are pretty high requirements on the math side, specifically in graduate-degree level statistics, but in my experience a company will only have a few of these folks, so they are expected to know quite a bit in each of these areas. Persistence The first area is finding, cleaning and storing the data. In some cases, no cleaning is done prior to storage - it’s just identified and the cleansing is done in a later step. This area is where the professional would be able to tell if a particular data set should be stored in a Relational Database Management System (RDBMS), across a set of key/value pair storage (NoSQL) or in a file system like HDFS (part of the Hadoop landscape) or other methods. Or do you examine the stream of data without storing it in another system at all? This is an important decision - it’s a foundation choice that deals not only with a lot of expense of purchasing systems or even using Cloud Computing (PaaS, SaaS or IaaS) to source it, but also the skillsets and other resources needed to care and feed the system for a long time. The Data Scientist sets something into motion that will probably outlast his or her career at a company or organization. Often these choices are made by senior developers, database administrators or architects in a company. But sometimes each of these has a certain bias towards making a decision one way or another. The Data Scientist would examine these choices in light of the data itself, starting perhaps even before the business requirements are created. The business may not even be aware of all the strategic and tactical data sources that they have access to. Processing Once the decision is made to store the data, the next set of decisions are based around how to process the data. An RDBMS scales well to a certain level, and provides a high degree of ACID compliance as well as offering a well-known set-based language to work with this data. In other cases, scale should be spread among multiple nodes (as in the case of Hadoop landscapes or NoSQL offerings) or even across a Cloud provider like Windows Azure Table Storage. In fact, in many cases - most of the ones I’m dealing with lately - the data should be split among multiple types of processing environments. This is a newer idea. Many data professionals simply pick a methodology (RDBMS with Star Schemas, NoSQL, etc.) and put all data there, regardless of its shape, processing needs and so on. A Data Scientist is familiar not only with the various processing methods, but how they work, so that they can choose the right one for a given need. This is a huge time commitment, hence the need for a dedicated title like this one. Presentation This is where the need for a Data Scientist is most often already being filled, sometimes with more or less success. The latest Business Intelligence systems are quite good at allowing you to create amazing graphics - but it’s the data behind the graphics that are the most important component of truly effective displays. This is where the mathematics requirement of the Data Scientist title is the most unforgiving. In fact, someone without a good foundation in statistics is not a good candidate for creating reports. Even a basic level of statistics can be dangerous. Anyone who works in analyzing data will tell you that there are multiple errors possible when data just seems right - and basic statistics bears out that you’re on the right track - that are only solvable when you understanding why the statistical formula works the way it does. And there are lots of ways of presenting data. Sometimes all you need is a “yes” or “no” answer that can only come after heavy analysis work. In that case, a simple e-mail might be all the reporting you need. In others, complex relationships and multiple components require a deep understanding of the various graphical methods of presenting data. Knowing which kind of chart, color, graphic or shape conveys a particular datum best is essential knowledge for the Data Scientist. Why I’m excited I love this area of study. I like math, stats, and computing technologies, but it goes beyond that. I love what data can do - how it can help an organization. I’ve been fortunate enough in my professional career these past two decades to work with lots of folks who perform this role at companies from aerospace to medical firms, from manufacturing to retail. Interestingly, the size of the company really isn’t germane here. I worked with one very small bio-tech (cryogenics) company that worked deeply with analysis of complex interrelated data. So  watch this space. No, I’m not leaving Azure or distributed computing or Microsoft. In fact, I think I’m perfectly situated to investigate this role further. We have a huge set of tools, from RDBMS to Hadoop to allow me to explore. And I’m happy to share what I learn along the way.

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• Using Node.js as an accelerator for WCF REST services

  - by Elton Stoneman

  Node.js is a server-side JavaScript platform "for easily building fast, scalable network applications". It's built on Google's V8 JavaScript engine and uses an (almost) entirely async event-driven processing model, running in a single thread. If you're new to Node and your reaction is "why would I want to run JavaScript on the server side?", this is the headline answer: in 150 lines of JavaScript you can build a Node.js app which works as an accelerator for WCF REST services*. It can double your messages-per-second throughput, halve your CPU workload and use one-fifth of the memory footprint, compared to the WCF services direct.   Well, it can if: 1) your WCF services are first-class HTTP citizens, honouring client cache ETag headers in request and response; 2) your services do a reasonable amount of work to build a response; 3) your data is read more often than it's written. In one of my projects I have a set of REST services in WCF which deal with data that only gets updated weekly, but which can be read hundreds of times an hour. The services issue ETags and will return a 304 if the client sends a request with the current ETag, which means in the most common scenario the client uses its local cached copy. But when the weekly update happens, then all the client caches are invalidated and they all need the same new data. Then the service will get hundreds of requests with old ETags, and they go through the full service stack to build the same response for each, taking up threads and processing time. Part of that processing means going off to a database on a separate cloud, which introduces more latency and downtime potential.   We can use ASP.NET output caching with WCF to solve the repeated processing problem, but the server will still be thread-bound on incoming requests, and to get the current ETags reliably needs a database call per request. The accelerator solves that by running as a proxy - all client calls come into the proxy, and the proxy routes calls to the underlying REST service. We could use Node as a straight passthrough proxy and expect some benefit, as the server would be less thread-bound, but we would still have one WCF and one database call per proxy call. But add some smart caching logic to the proxy, and share ETags between Node and WCF (so the proxy doesn't even need to call the servcie to get the current ETag), and the underlying service will only be invoked when data has changed, and then only once - all subsequent client requests will be served from the proxy cache.   I've built this as a sample up on GitHub: NodeWcfAccelerator on sixeyed.codegallery. Here's how the architecture looks:     The code is very simple. The Node proxy runs on port 8010 and all client requests target the proxy. If the client request has an ETag header then the proxy looks up the ETag in the tag cache to see if it is current - the sample uses memcached to share ETags between .NET and Node. If the ETag from the client matches the current server tag, the proxy sends a 304 response with an empty body to the client, telling it to use its own cached version of the data. If the ETag from the client is stale, the proxy looks for a local cached version of the response, checking for a file named after the current ETag. If that file exists, its contents are returned to the client as the body in a 200 response, which includes the current ETag in the header. If the proxy does not have a local cached file for the service response, it calls the service, and writes the WCF response to the local cache file, and to the body of a 200 response for the client. So the WCF service is only troubled if both client and proxy have stale (or no) caches.   The only (vaguely) clever bit in the sample is using the ETag cache, so the proxy can serve cached requests without any communication with the underlying service, which it does completely generically, so the proxy has no notion of what it is serving or what the services it proxies are doing. The relative path from the URL is used as the lookup key, so there's no shared key-generation logic between .NET and Node, and when WCF stores a tag it also stores the "read" URL against the ETag so it can be used for a reverse lookup, e.g:   Key Value /WcfSampleService/PersonService.svc/rest/fetch/3 "28cd4796-76b8-451b-adfd-75cb50a50fa6" "28cd4796-76b8-451b-adfd-75cb50a50fa6" /WcfSampleService/PersonService.svc/rest/fetch/3    In Node we read the cache using the incoming URL path as the key and we know that "28cd4796-76b8-451b-adfd-75cb50a50fa6" is the current ETag; we look for a local cached response in /caches/28cd4796-76b8-451b-adfd-75cb50a50fa6.body (and the corresponding .header file which contains the original service response headers, so the proxy response is exactly the same as the underlying service). When the data is updated, we need to invalidate the ETag cache – which is why we need the reverse lookup in the cache. In the WCF update service, we don't need to know the URL of the related read service - we fetch the entity from the database, do a reverse lookup on the tag cache using the old ETag to get the read URL, update the new ETag against the URL, store the new reverse lookup and delete the old one.   Running Apache Bench against the two endpoints gives the headline performance comparison. Making 1000 requests with concurrency of 100, and not sending any ETag headers in the requests, with the Node proxy I get 102 requests handled per second, average response time of 975 milliseconds with 90% of responses served within 850 milliseconds; going direct to WCF with the same parameters, I get 53 requests handled per second, mean response time of 1853 milliseconds, with 90% of response served within 3260 milliseconds. Informally monitoring server usage during the tests, Node maxed at 20% CPU and 20Mb memory; IIS maxed at 60% CPU and 100Mb memory.   Note that the sample WCF service does a database read and sleeps for 250 milliseconds to simulate a moderate processing load, so this is *not* a baseline Node-vs-WCF comparison, but for similar scenarios where the  service call is expensive but applicable to numerous clients for a long timespan, the performance boost from the accelerator is considerable.     * - actually, the accelerator will work nicely for any HTTP request, where the URL (path + querystring) uniquely identifies a resource. In the sample, there is an assumption that the ETag is a GUID wrapped in double-quotes (e.g. "28cd4796-76b8-451b-adfd-75cb50a50fa6") – which is the default for WCF services. I use that assumption to name the cache files uniquely, but it is a trivial change to adapt to other ETag formats.

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• Can't remove burg theme packages

  - by Lassi

  Today after trying to install and remove BURG and few themes I faced an issue. Now I can't install or remove anything. Here is the output (unfortunately partly in Finnish, I couldn't change language since it also seems to depend on package listings: lassi@lassi-ubuntu:~$ sudo apt-get autoremove Luetaan pakettiluetteloita... Valmis Muodostetaan riippuvuussuhteiden puu Luetaan tilatietoja... Valmis Seuraavat paketit POISTETAAN: burg-theme-fortune burg-theme-gnome burg-theme-picchio 0 päivitetty, 0 uutta asennusta, 3 poistettavaa ja 0 päivittämätöntä. 3 ei asennettu kokonaan tai poistettiin. Toiminnon jälkeen vapautuu 7 180 k t levytilaa. Haluatko jatkaa [K/e]? k (Luetaan tietokantaa... 166462 files and directories currently installed.) Poistetaan pakettia burg-theme-fortune... sudo: update-burg: command not found dpkg: virhe käsiteltäessä burg-theme-fortune (--remove): aliprosessi installed post-removal script palautti virhetilakoodin 1 Poistetaan pakettia burg-theme-gnome... sudo: update-burg: command not found dpkg: virhe käsiteltäessä burg-theme-gnome (--remove): aliprosessi installed post-removal script palautti virhetilakoodin 1 Poistetaan pakettia burg-theme-picchio... sudo: update-burg: command not found dpkg: virhe käsiteltäessä burg-theme-picchio (--remove): aliprosessi installed post-removal script palautti virhetilakoodin 1 Käsittelyssä tapahtui liian monta virhettä: burg-theme-fortune burg-theme-gnome burg-theme-picchio E: Sub-process /usr/bin/dpkg returned an error code (1) Basically what seems to happen is this: It creates the package lists, then tries to remove packet burg-theme-fortune. This fails as update-burg command was not found. Then dpkg reports an error while processing the packet. Same goes with all 3 packages. In the end it claims that there were too many errors, and packages stay installed. I also tried installing burg as it tries to run command update-burg, but appears that it tries to delete these packages always when I try to install or remove or do anything with apt. Any ideas how I could solve this issue? Edit: Here is the output of apt-get install burg (tried installing again to get English output) lassi@lassi-ubuntu:~$ LC_ALL=C sudo apt-get install burg [sudo] password for lassi: Reading package lists... Done Building dependency tree Reading state information... Done burg is already the newest version. 0 upgraded, 0 newly installed, 0 to remove and 0 not upgraded. 3 not fully installed or removed. Need to get 0 B/6169 kB of archives. After this operation, 0 B of additional disk space will be used. Do you want to continue [Y/n]? y (Reading database ... 167497 files and directories currently installed.) Preparing to replace burg-theme-fortune 0.5.0-1 (using .../burg-theme-fortune_0.5.0-1_all.deb) ... Unpacking replacement burg-theme-fortune ... Generating burg.cfg ... /usr/sbin/burg-probe: error: cannot stat `/boot/burg/locale'. No path or device is specified. Try `/usr/sbin/burg-probe --help' for more information. dpkg: warning: subprocess old post-removal script returned error exit status 1 dpkg - trying script from the new package instead ... Generating burg.cfg ... /usr/sbin/burg-probe: error: cannot stat `/boot/burg/locale'. No path or device is specified. Try `/usr/sbin/burg-probe --help' for more information. dpkg: error processing /var/cache/apt/archives/burg-theme-fortune_0.5.0-1_all.deb (--unpack): subprocess new post-removal script returned error exit status 1 Generating burg.cfg ... /usr/sbin/burg-probe: error: cannot stat `/boot/burg/locale'. No path or device is specified. Try `/usr/sbin/burg-probe --help' for more information. dpkg: error while cleaning up: subprocess new post-removal script returned error exit status 1 Preparing to replace burg-theme-gnome 0.5.0-1 (using .../burg-theme-gnome_0.5.0-1_all.deb) ... Unpacking replacement burg-theme-gnome ... Generating burg.cfg ... /usr/sbin/burg-probe: error: cannot stat `/boot/burg/locale'. No path or device is specified. Try `/usr/sbin/burg-probe --help' for more information. dpkg: warning: subprocess old post-removal script returned error exit status 1 dpkg - trying script from the new package instead ... Generating burg.cfg ... /usr/sbin/burg-probe: error: cannot stat `/boot/burg/locale'. No path or device is specified. Try `/usr/sbin/burg-probe --help' for more information. dpkg: error processing /var/cache/apt/archives/burg-theme-gnome_0.5.0-1_all.deb (--unpack): subprocess new post-removal script returned error exit status 1 Generating burg.cfg ... /usr/sbin/burg-probe: error: cannot stat `/boot/burg/locale'. No path or device is specified. Try `/usr/sbin/burg-probe --help' for more information. dpkg: error while cleaning up: subprocess new post-removal script returned error exit status 1 Preparing to replace burg-theme-picchio 0.5.0-1 (using .../burg-theme-picchio_0.5.0-1_all.deb) ... Unpacking replacement burg-theme-picchio ... Generating burg.cfg ... /usr/sbin/burg-probe: error: cannot stat `/boot/burg/locale'. No path or device is specified. Try `/usr/sbin/burg-probe --help' for more information. dpkg: warning: subprocess old post-removal script returned error exit status 1 dpkg - trying script from the new package instead ... Generating burg.cfg ... /usr/sbin/burg-probe: error: cannot stat `/boot/burg/locale'. No path or device is specified. Try `/usr/sbin/burg-probe --help' for more information. dpkg: error processing /var/cache/apt/archives/burg-theme-picchio_0.5.0-1_all.deb (--unpack): subprocess new post-removal script returned error exit status 1 Generating burg.cfg ... /usr/sbin/burg-probe: error: cannot stat `/boot/burg/locale'. No path or device is specified. Try `/usr/sbin/burg-probe --help' for more information. dpkg: error while cleaning up: subprocess new post-removal script returned error exit status 1 Errors were encountered while processing: /var/cache/apt/archives/burg-theme-fortune_0.5.0-1_all.deb /var/cache/apt/archives/burg-theme-gnome_0.5.0-1_all.deb /var/cache/apt/archives/burg-theme-picchio_0.5.0-1_all.deb E: Sub-process /usr/bin/dpkg returned an error code (1) lassi@lassi-ubuntu:~$

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