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  • Optimizing MySQL, Improving Performance of Database Servers

    - by Antoinette O'Sullivan
    Optimization involves improving the performance of a database server and queries that run against it. Optimization reduces query execution time and optimized queries benefit everyone that uses the server. When the server runs more smoothly and processes more queries with less, it performs better as a whole. To learn more about how a MySQL developer can make a difference with optimization, take the MySQL Developers training course. This 5-day instructor-led course is available as: Live-Virtual Event: Attend a live class from your own desk - no travel required. Choose from a selection of events on the schedule to suit different timezones. In-Class Event: Travel to an education center to attend an event. Below is a selection of the events on the schedule.  Location  Date  Delivery Language  Vienna, Austria  17 November 2014  German  Brussels, Belgium  8 December 2014  English  Sao Paulo, Brazil  14 July 2014  Brazilian Portuguese London, English  29 September 2014  English   Belfast, Ireland  6 October 2014  English  Dublin, Ireland  27 October 2014  English  Milan, Italy  10 November 2014  Italian  Rome, Italy  21 July 2014  Italian  Nairobi, Kenya  14 July 2014  English  Petaling Jaya, Malaysia  25 August 2014  English  Utrecht, Netherlands  21 July 2014  English  Makati City, Philippines  29 September 2014  English  Warsaw, Poland  25 August 2014  Polish  Lisbon, Portugal  13 October 2014  European Portuguese  Porto, Portugal  13 October 2014  European Portuguese  Barcelona, Spain  7 July 2014  Spanish  Madrid, Spain  3 November 2014  Spanish  Valencia, Spain  24 November 2014  Spanish  Basel, Switzerland  4 August 2014  German  Bern, Switzerland  4 August 2014  German  Zurich, Switzerland  4 August 2014  German The MySQL for Developers course helps prepare you for the MySQL 5.6 Developers OCP certification exam. To register for an event, request an additional event or learn more about the authentic MySQL curriculum, go to http://education.oracle.com/mysql.

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  • Workshop CUOA-Oracle Hyperion "Pianificazione economico-finanziaria, reporting e performance management" - Altavilla Vicentina, 25/10/2012

    - by Edilio Rossi
    Più di 100 professsionisti -  manager della funzione Amministrazione, Finanza e Controllo in azienda e consulenti del settore - hanno partecipato al Workshop, organizzato da CUOA e Oracle Hyperion, in collaborazione con Adacta Studio Associato. E' stata un'occasione unica per approfondire i temi della pianificazione "estesa" e del controllo di gestione nelle imprese italiane - piccole, medie e grandi - alternando chiavi di lettura diverse (accademica, consulenziale, tecnologico-applicativa e utenti) ma tutte legate dal filo conduttore dell'evoluzione dei modelli, degli strumenti e dell'utilizzo dei sistemi evoluti di planning e budgeting economico-finanziario e patrimoniale. Una particolare attenzione è stata posta sul rapporto banca-impresa alla luce dell'attuale crisi e di come i sistemi innovativi di performance management e business intelligence possono aiutare il management nel ridisegno del sistema di finanziamento delle aziende e nella negoziazione con i diversi stakeholders. Grazie alle testimonianze dei casi aziendali GIV (Gruppo Italiano Vini) e Datalogic si è potuto "toccare con mano" l'utlizzo dei modelli e degli strumenti di pianificazione e controllo in realtà aziendali diverse ma che affrontano entrambe alcune delle sfide che i mercati oggi pongono alle imprese italiane.  Le presentazioni sono disponibili su richiesta inviando una mail a: paolo.leveghi-AT-oracle.com

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  • Honing Performance Tuning Skills on MySQL

    - by Antoinette O'Sullivan
    Get hands-on experience with techniques for tuning a MySQL Server with the Authorized MySQL Performance Tuning course.  This course is designed for database administrators, database developers and system administrators who are responsible for managing, optimizing, and tuning a MySQL Server. You can follow this live instructor led training: From your desk. Choose from among the 800+ events on the live-virtual training schedule. In a classroom. A selection of events/locations listed below  Location  Date  Delivery Language  Prague, Czech Republic  1 October 2012  Czech  Warsaw, Poland  9 July 2012  Polish  London, UK  19 November 2012  English  Rome, Italy  23 October 2012  Italian  Lisbon, Portugal  17 September 2012  European Portugese  Aix-en-Provence, France  4 September 2012  French  Strasbourg, France  16 October 2012  French  Nieuwegein, Netherlands  3 September 2012  Dutch  Madrid, Spain  6 August 2012  Spanish  Mechelen, Belgium  1 October 2012  English  Riga, Latvia  10 December 2012  Latvian  Petaling Jaya, Malaysia  10 September 2012  English  Edmonton, Canada  27 August 2012  English  Vancouver, Canada  27 August 2012  English  Ottawa, Canada  26 November 2012  English  Toronto, Canada  26 November 2012  English  Montreal, Canada  26 November 2012  English  Mexico City, Mexico  9 July 2012  Spanish  Sao Paulo, Brazil  2 July 2012  Brazilian Portugese To find a virtual or in-class event that suits you, go or http://oracle.com/education and choose a course and delivery type in your location.  

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  • How to improve Minecraft-esque voxel world performance?

    - by SomeXnaChump
    After playing Minecraft I marveled a bit at its large worlds but at the same time I found them extremely slow to navigate, even with a quad core and meaty graphics card. Now I assume Minecraft is fairly slow because: A) It's written in Java, and as most of the spatial partitioning and memory management activities happen in there, it would naturally be slower than a native C++ version. B) It doesn't partition its world very well. I could be wrong on both assumptions; however it got me thinking about the best way to manage large voxel worlds. As it is a true 3D world, where a block can exist in any part of the world, it is basically a big 3D array [x][y][z], where each block in the world has a type (i.e BlockType.Empty = 0, BlockType.Dirt = 1 etc.) Now, I am assuming to make this sort of world perform well you would need to: A) Use a tree of some variety (oct/kd/bsp) to split all the cubes out; it seems like an oct/kd would be the better option as you can just partition on a per cube level not a per triangle level. B) Use some algorithm to work out which blocks can currently be seen, as blocks closer to the user could obfuscate the blocks behind, making it pointless to render them. C) Keep the block object themselves lightweight, so it is quick to add and remove them from the trees. I guess there is no right answer to this, but I would be interested to see peoples' opinions on the subject. How would you improve performance in a large voxel-based world?

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  • Minimum percentage of free physical memory that Linux require for optimal performance

    - by csoto
    Recently, we have been getting questions about this percentage of free physical memory that OS require for optimal performance, mainly applicable to physical compute nodes. Under normal conditions you may see that at the nodes without any application running the OS take (for example) between 24 and 25 GB of memory. The Linux system reports the free memory in a different way, and most of those 25gbs (of the example) are available for user processes. IE: Mem: 99191652k total, 23785732k used, 75405920k free, 173320k buffers The MOS Doc Id. 233753.1 - "Analyzing Data Provided by '/proc/meminfo'" - explains it (section 4 - "Final Remarks"): Free Memory and Used Memory Estimating the resource usage, especially the memory consumption of processes is by far more complicated than it looks like at a first glance. The philosophy is an unused resource is a wasted resource.The kernel therefore will use as much RAM as it can to cache information from your local and remote filesystems/disks. This builds up over time as reads and writes are done on the system trying to keep the data stored in RAM as relevant as possible to the processes that have been running on your system. If there is free RAM available, more caching will be performed and thus more memory 'consumed'. However this doesn't really count as resource usage, since this cached memory is available in case some other process needs it. The cache is reclaimed, not at the time of process exit (you might start up another process soon that needs the same data), but upon demand. That said, focusing more specifically on the percentage question, apart from this memory that OS takes, how much should be the minimum free memory that must be available every node so that they operate normally? The answer is: As a rule of thumb 80% memory utilization is a good threshold, anything bigger than that should be investigated and remedied.

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  • Trouble with a query

    - by Mark Allison
    Hi there, I'm having trouble with a query in SQL Server 2008 on some forex trading data. I have a trades table and an orders table. A trade needs to comprise of 2 or more orders. DDL schema and sample data below. What I want to do is write a query that shows the profit/loss in pips for each trade. A pip is 1/1000th of a currency. So the difference between USD 1.3441 and 1.3442 is 1 pip in forex-speak. A trade usually has one entry order and multiple exit orders. So for example if I buy 3 lots of the currency pair GBP/USD at the exchange rate of 1.6100 and then sell 1 lot at 1.6150, 1 lot at 1.6200 and 1 lot at 1.6250 then the profit is (1.6150 - 1.6100) + (1.6200 - 1.6100) + (1.6250 - 1.6100), or 50 + 100 + 150 = 300 pips profit. The trade could also go the other way (Shorting). For example the currency pair can be sold first before it's bought back later at a cheaper price. I would like a query that returns the following: tradeId, currencyPair, profitInPips It seems like a pretty straightforward query, but it's eluding me right now. Here's my DDL and sample data: CREATE TABLE [dbo].[trades]( [tradeId] [int] IDENTITY(1,1) NOT NULL, [currencyPair] [char](6) NOT NULL, CONSTRAINT [PK_trades] PRIMARY KEY CLUSTERED ( [tradeId] ASC )WITH (PAD_INDEX = OFF, STATISTICS_NORECOMPUTE = OFF, IGNORE_DUP_KEY = OFF, ALLOW_ROW_LOCKS = ON, ALLOW_PAGE_LOCKS = ON) ON [PRIMARY] ) ON [PRIMARY] GO SET ANSI_PADDING OFF GO SET IDENTITY_INSERT [dbo].[trades] ON INSERT [dbo].[trades] ([tradeId], [currencyPair]) VALUES (1, N'GBPUSD') INSERT [dbo].[trades] ([tradeId], [currencyPair]) VALUES (2, N'GBPUSD') INSERT [dbo].[trades] ([tradeId], [currencyPair]) VALUES (3, N'GBPUSD') INSERT [dbo].[trades] ([tradeId], [currencyPair]) VALUES (4, N'GBPUSD') INSERT [dbo].[trades] ([tradeId], [currencyPair]) VALUES (5, N'GBPUSD') INSERT [dbo].[trades] ([tradeId], [currencyPair]) VALUES (6, N'GBPUSD') INSERT [dbo].[trades] ([tradeId], [currencyPair]) VALUES (7, N'GBPUSD') INSERT [dbo].[trades] ([tradeId], [currencyPair]) VALUES (8, N'GBPUSD') INSERT [dbo].[trades] ([tradeId], [currencyPair]) VALUES (9, N'GBPUSD') INSERT [dbo].[trades] ([tradeId], [currencyPair]) VALUES (10, N'GBPUSD') SET IDENTITY_INSERT [dbo].[trades] OFF GO CREATE TABLE [dbo].[orders]( [orderId] [int] IDENTITY(1,1) NOT NULL, [tradeId] [int] NOT NULL, [amount] [decimal](18, 1) NOT NULL, [buySell] [char](1) NOT NULL, [rate] [decimal](18, 6) NOT NULL, [orderDateTime] [datetime] NOT NULL, CONSTRAINT [PK_orders] PRIMARY KEY CLUSTERED ( [orderId] ASC )WITH (PAD_INDEX = OFF, STATISTICS_NORECOMPUTE = OFF, IGNORE_DUP_KEY = OFF, ALLOW_ROW_LOCKS = ON, ALLOW_PAGE_LOCKS = ON) ON [PRIMARY] ) ON [PRIMARY] GO SET ANSI_PADDING OFF GO SET IDENTITY_INSERT [dbo].[orders] ON INSERT [dbo].[orders] ([orderId], [tradeId], [amount], [buySell], [rate], [orderDateTime]) VALUES (1, 1, CAST(3.0 AS Decimal(18, 1)), N'S', CAST(1.606500 AS Decimal(18, 6)), CAST(0x00009CF40083D600 AS DateTime)) INSERT [dbo].[orders] ([orderId], [tradeId], [amount], [buySell], [rate], [orderDateTime]) VALUES (2, 1, CAST(3.0 AS Decimal(18, 1)), N'B', CAST(1.615500 AS Decimal(18, 6)), CAST(0x00009CF400A4CB80 AS DateTime)) INSERT [dbo].[orders] ([orderId], [tradeId], [amount], [buySell], [rate], [orderDateTime]) VALUES (3, 2, CAST(3.0 AS Decimal(18, 1)), N'S', CAST(1.608000 AS Decimal(18, 6)), CAST(0x00009CF500000000 AS DateTime)) INSERT [dbo].[orders] ([orderId], [tradeId], [amount], [buySell], [rate], [orderDateTime]) VALUES (4, 2, CAST(1.0 AS Decimal(18, 1)), N'B', CAST(1.603000 AS Decimal(18, 6)), CAST(0x00009CF50083D600 AS DateTime)) INSERT [dbo].[orders] ([orderId], [tradeId], [amount], [buySell], [rate], [orderDateTime]) VALUES (5, 2, CAST(2.0 AS Decimal(18, 1)), N'B', CAST(1.605500 AS Decimal(18, 6)), CAST(0x00009CF50107AC00 AS DateTime)) INSERT [dbo].[orders] ([orderId], [tradeId], [amount], [buySell], [rate], [orderDateTime]) VALUES (6, 3, CAST(3.0 AS Decimal(18, 1)), N'S', CAST(1.595500 AS Decimal(18, 6)), CAST(0x00009CF70083D600 AS DateTime)) INSERT [dbo].[orders] ([orderId], [tradeId], [amount], [buySell], [rate], [orderDateTime]) VALUES (7, 3, CAST(1.0 AS Decimal(18, 1)), N'B', CAST(1.590500 AS Decimal(18, 6)), CAST(0x00009CF700C5C100 AS DateTime)) INSERT [dbo].[orders] ([orderId], [tradeId], [amount], [buySell], [rate], [orderDateTime]) VALUES (8, 3, CAST(2.0 AS Decimal(18, 1)), N'B', CAST(1.594500 AS Decimal(18, 6)), CAST(0x00009CF701499700 AS DateTime)) INSERT [dbo].[orders] ([orderId], [tradeId], [amount], [buySell], [rate], [orderDateTime]) VALUES (9, 4, CAST(3.0 AS Decimal(18, 1)), N'B', CAST(1.611000 AS Decimal(18, 6)), CAST(0x00009CFB0083D600 AS DateTime)) INSERT [dbo].[orders] ([orderId], [tradeId], [amount], [buySell], [rate], [orderDateTime]) VALUES (10, 4, CAST(1.0 AS Decimal(18, 1)), N'S', CAST(1.616000 AS Decimal(18, 6)), CAST(0x00009CFB00A4CB80 AS DateTime)) INSERT [dbo].[orders] ([orderId], [tradeId], [amount], [buySell], [rate], [orderDateTime]) VALUES (11, 4, CAST(2.0 AS Decimal(18, 1)), N'S', CAST(1.611500 AS Decimal(18, 6)), CAST(0x00009CFB0107AC00 AS DateTime)) INSERT [dbo].[orders] ([orderId], [tradeId], [amount], [buySell], [rate], [orderDateTime]) VALUES (12, 5, CAST(3.0 AS Decimal(18, 1)), N'B', CAST(1.613000 AS Decimal(18, 6)), CAST(0x00009CFC0083D600 AS DateTime)) INSERT [dbo].[orders] ([orderId], [tradeId], [amount], [buySell], [rate], [orderDateTime]) VALUES (13, 5, CAST(1.0 AS Decimal(18, 1)), N'S', CAST(1.618000 AS Decimal(18, 6)), CAST(0x00009CFC0107AC00 AS DateTime)) INSERT [dbo].[orders] ([orderId], [tradeId], [amount], [buySell], [rate], [orderDateTime]) VALUES (14, 5, CAST(1.0 AS Decimal(18, 1)), N'S', CAST(1.623000 AS Decimal(18, 6)), CAST(0x00009CFC0083D600 AS DateTime)) INSERT [dbo].[orders] ([orderId], [tradeId], [amount], [buySell], [rate], [orderDateTime]) VALUES (15, 5, CAST(1.0 AS Decimal(18, 1)), N'S', CAST(1.628000 AS Decimal(18, 6)), CAST(0x00009CFD00C5C100 AS DateTime)) INSERT [dbo].[orders] ([orderId], [tradeId], [amount], [buySell], [rate], [orderDateTime]) VALUES (16, 6, CAST(3.0 AS Decimal(18, 1)), N'B', CAST(1.632000 AS Decimal(18, 6)), CAST(0x00009D020083D600 AS DateTime)) INSERT [dbo].[orders] ([orderId], [tradeId], [amount], [buySell], [rate], [orderDateTime]) VALUES (17, 6, CAST(1.0 AS Decimal(18, 1)), N'S', CAST(1.637000 AS Decimal(18, 6)), CAST(0x00009D0200A4CB80 AS DateTime)) INSERT [dbo].[orders] ([orderId], [tradeId], [amount], [buySell], [rate], [orderDateTime]) VALUES (18, 6, CAST(2.0 AS Decimal(18, 1)), N'S', CAST(1.630000 AS Decimal(18, 6)), CAST(0x00009D0200C5C100 AS DateTime)) INSERT [dbo].[orders] ([orderId], [tradeId], [amount], [buySell], [rate], [orderDateTime]) VALUES (19, 7, CAST(3.0 AS Decimal(18, 1)), N'B', CAST(1.634500 AS Decimal(18, 6)), CAST(0x00009D0201499700 AS DateTime)) INSERT [dbo].[orders] ([orderId], [tradeId], [amount], [buySell], [rate], [orderDateTime]) VALUES (20, 7, CAST(1.0 AS Decimal(18, 1)), N'S', CAST(1.639500 AS Decimal(18, 6)), CAST(0x00009D0300000000 AS DateTime)) INSERT [dbo].[orders] ([orderId], [tradeId], [amount], [buySell], [rate], [orderDateTime]) VALUES (21, 7, CAST(1.0 AS Decimal(18, 1)), N'S', CAST(1.644500 AS Decimal(18, 6)), CAST(0x00009D030083D600 AS DateTime)) INSERT [dbo].[orders] ([orderId], [tradeId], [amount], [buySell], [rate], [orderDateTime]) VALUES (22, 7, CAST(1.0 AS Decimal(18, 1)), N'S', CAST(1.637500 AS Decimal(18, 6)), CAST(0x00009D0300C5C100 AS DateTime)) INSERT [dbo].[orders] ([orderId], [tradeId], [amount], [buySell], [rate], [orderDateTime]) VALUES (23, 8, CAST(3.0 AS Decimal(18, 1)), N'S', CAST(1.625000 AS Decimal(18, 6)), CAST(0x00009D0400C5C100 AS DateTime)) INSERT [dbo].[orders] ([orderId], [tradeId], [amount], [buySell], [rate], [orderDateTime]) VALUES (24, 8, CAST(1.0 AS Decimal(18, 1)), N'B', CAST(1.620000 AS Decimal(18, 6)), CAST(0x00009D050083D600 AS DateTime)) INSERT [dbo].[orders] ([orderId], [tradeId], [amount], [buySell], [rate], [orderDateTime]) VALUES (25, 8, CAST(1.0 AS Decimal(18, 1)), N'B', CAST(1.615000 AS Decimal(18, 6)), CAST(0x00009D0500A4CB80 AS DateTime)) INSERT [dbo].[orders] ([orderId], [tradeId], [amount], [buySell], [rate], [orderDateTime]) VALUES (26, 8, CAST(1.0 AS Decimal(18, 1)), N'S', CAST(1.623000 AS Decimal(18, 6)), CAST(0x00009D050107AC00 AS DateTime)) INSERT [dbo].[orders] ([orderId], [tradeId], [amount], [buySell], [rate], [orderDateTime]) VALUES (27, 9, CAST(3.0 AS Decimal(18, 1)), N'S', CAST(1.618000 AS Decimal(18, 6)), CAST(0x00009D0600C5C100 AS DateTime)) INSERT [dbo].[orders] ([orderId], [tradeId], [amount], [buySell], [rate], [orderDateTime]) VALUES (28, 9, CAST(1.0 AS Decimal(18, 1)), N'B', CAST(1.613000 AS Decimal(18, 6)), CAST(0x00009D0600D63BC0 AS DateTime)) INSERT [dbo].[orders] ([orderId], [tradeId], [amount], [buySell], [rate], [orderDateTime]) VALUES (29, 9, CAST(1.0 AS Decimal(18, 1)), N'B', CAST(1.608000 AS Decimal(18, 6)), CAST(0x00009D0600E6B680 AS DateTime)) INSERT [dbo].[orders] ([orderId], [tradeId], [amount], [buySell], [rate], [orderDateTime]) VALUES (30, 9, CAST(1.0 AS Decimal(18, 1)), N'B', CAST(1.613300 AS Decimal(18, 6)), CAST(0x00009D0601391C40 AS DateTime)) INSERT [dbo].[orders] ([orderId], [tradeId], [amount], [buySell], [rate], [orderDateTime]) VALUES (31, 10, CAST(3.0 AS Decimal(18, 1)), N'B', CAST(1.614500 AS Decimal(18, 6)), CAST(0x00009D090083D600 AS DateTime)) INSERT [dbo].[orders] ([orderId], [tradeId], [amount], [buySell], [rate], [orderDateTime]) VALUES (32, 10, CAST(1.0 AS Decimal(18, 1)), N'S', CAST(1.619500 AS Decimal(18, 6)), CAST(0x00009D090107AC00 AS DateTime)) INSERT [dbo].[orders] ([orderId], [tradeId], [amount], [buySell], [rate], [orderDateTime]) VALUES (33, 10, CAST(1.0 AS Decimal(18, 1)), N'S', CAST(1.624500 AS Decimal(18, 6)), CAST(0x00009D0901499700 AS DateTime)) INSERT [dbo].[orders] ([orderId], [tradeId], [amount], [buySell], [rate], [orderDateTime]) VALUES (34, 10, CAST(1.0 AS Decimal(18, 1)), N'S', CAST(1.619000 AS Decimal(18, 6)), CAST(0x00009D0A0083D600 AS DateTime)) SET IDENTITY_INSERT [dbo].[orders] OFF /****** Object: ForeignKey [FK_orders_trades] Script Date: 04/02/2010 15:05:31 ******/ ALTER TABLE [dbo].[orders] WITH CHECK ADD CONSTRAINT [FK_orders_trades] FOREIGN KEY([tradeId]) REFERENCES [dbo].[trades] ([tradeId]) GO ALTER TABLE [dbo].[orders] CHECK CONSTRAINT [FK_orders_trades] GO Thanks in advance for any help!

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  • Function inserted not all records

    - by user1799459
    I wrote the following code by data transfer from Access to Firebird def FirebirdDatetime(dt): return '\'%s.%s.%s\'' % (str(dt.day).rjust(2,'0'), str(dt.month).rjust(2,'0'), str(dt.year).rjust(4,'0')) def SelectFromAccessTable(tablename): return 'select * from [' + tablename+']' def InsertToFirebirdTable(tablename, row): values='' i=0 for elem in row: i+=1 #print type(elem) if type(elem) == int: temp = str(elem) elif (type(elem) == str) or (type(elem)==unicode): temp = '\'%s\'' % (elem,) elif type(elem) == datetime.datetime: temp =FirebirdDatetime(elem) elif type(elem) == decimal.Decimal: temp = str(elem) elif elem==None: temp='null' if (i<len(row)): values+=temp+', ' else: values+=temp return 'insert into '+tablename+' values ('+values+')' def AccessToFirebird(accesstablename, firebirdtablename, accesscursor, firebirdcursor): SelectSql=SelectFromAccessTable(accesstablename) for row in accesscursor.execute(SelectSql): InsertSql=InsertToFirebirdTable(firebirdtablename, row) InsertSql=InsertSql print InsertSql firebirdcursor.execute(InsertSql) In the main module there is an AccessToFirebird function call conAcc = pyodbc.connect('DRIVER={Microsoft Access Driver (*.mdb, *.accdb)};DBQ=D:\ThirdTask\Northwind.accdb') SqlAccess=conAcc.cursor(); conn.begin() cur=conn.cursor() sql.AccessToFirebird('Customers', 'CLIENTS', SqlAccess, cur) conn.commit() conn.begin() cur=conn.cursor() sql.AccessToFirebird('??????????', 'EMPLOYEES', SqlAccess, cur) sql.AccessToFirebird('????', 'ROLES', SqlAccess, cur) sql.AccessToFirebird('???? ???????????', 'EMPLOYEES_ROLES', SqlAccess, cur) sql.AccessToFirebird('????????', 'DELIVERY', SqlAccess, cur) sql.AccessToFirebird('??????????', 'SUPPLIERS', SqlAccess, cur) sql.AccessToFirebird('????????? ?????? ???????', 'TAX_STATUS_OF_ORDERS', SqlAccess, cur) sql.AccessToFirebird('????????? ???????? ? ??????', 'STATE_ORDER_DETAILS', SqlAccess, cur) sql.AccessToFirebird('????????? ???????', 'CONDITION_OF_ORDERS', SqlAccess, cur) sql.AccessToFirebird('??????', 'ORDERS', SqlAccess, cur) sql.AccessToFirebird('?????', 'BILLS', SqlAccess, cur) sql.AccessToFirebird('????????? ?????? ?? ????????????', 'STATUS_PURCHASE_ORDER', SqlAccess, cur) sql.AccessToFirebird('?????? ?? ????????????', 'ORDERS_FOR_ACQUISITION', SqlAccess, cur) sql.AccessToFirebird('???????? ? ?????? ?? ????????????', 'INFORMPURCHASEORDER', SqlAccess, cur) sql.AccessToFirebird('??????', 'PRODUCTS', SqlAccess, cur) conn.commit() conAcc.commit() conn.close() conAcc.close() But as a result, not all records have been inserted into the table Products (Table Goods - Northwind database), for example, does not work request insert into PRODUCTS values ('4', 1, 'NWTB-1', '?????????? ???', null, 13.5000, 18.0000, 10, 40, '10 ??????? ?? 20 ?????????', '10 ??????? ?? 20 ?????????', 10, '???????', '') In ibexpert to this request message issued can't format message 13:587 -- message file C:\Windows\firebird.msg not found. conversion error from string "10 ?????????±???? ???? 20 ???°???µ?‚????????". Worked only requests insert into PRODUCTS values ('1', 82, 'NWTC-82', '???????', null, 2.0000, 4.0000, 20, 100, null, null, null, '????', '') insert into PRODUCTS values ('9', 83, 'NWTCS-83', '???????????? ?????', null, 0.5000, 1.8000, 30, 200, null, null, null, '????? ? ???????', '') insert into PRODUCTS values ('1', 97, 'NWTC-82', '???????', null, 3.0000, 5.0000, 50, 200, null, null, null, '????', '') insert into PRODUCTS values ('6', 98, 'NWTSO-98', '??????? ???', null, 1.0000, 1.8900, 100, 200, null, null, null, '????', '') insert into PRODUCTS values ('6', 99, 'NWTSO-99', '??????? ??????', null, 1.0000, 1.9500, 100, 200, null, null, null, '????', '') other records were not inserted.

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  • MS Access 2003 - Unbound Form uses INSERT statement to save to table; what about subforms?

    - by Justin
    So I have an unbound form that I use to save data to a table on button click. Is there a way I can have subforms for entry that will allow me to save data to the table within that same button click? Basically I want to add more entry options for the user, and while I know other ways to do it, I am particularly curious about doing it this way (if it can be done). So lets say the 'parent form' is frmMain. And there are two child forms "sub1" and "sub2". Just for example sake lets say on frmMain there are two text boxes: txtTitle & txtAuthor. sub1 and sub2 both have a text Box on them that represent something like prices. The idea is Title & author of a book, and then a price at each store (simplified). So I tried this (because I thought it was worth a shot): Dim db as DAO.database Dim sql as String sql = "INSERT INTO (Title, Author, PriceA, PriceB) VALUES (" if not isnull(me.txtTitle) then sql = sql & """" & me.txtTitle & """," Else sql = sql & " NULL," End If if not IsNull(me.txtAuthor) then sql = sql & " """ & me.txtAuthor & """," else sql = sql & " NULL," end if if not IsNull (forms!sub1.txtPrice) then sql = sql & " """ & forms!sub1.txtPrice & """," else sql = sql & " NULL," end if without finishing the code, i think you may see the GOTCHA i am headed for. I tried this and got an "Access cannot find the form "" ". I think I can pretty much see why on this approach too, because when I click the button that calls the new sub form into the parent form, the values that were just entered are not held/saved as sub1 closes and sub2 opens. I should mention that the idea above is not intended to be a one or the other approach, rather both sub forms used everytime. so this is an example. i want to use this method (if possible) to have about 7 different sub form choices in one form, and be able to save to a table via a SQL statement. I realize that there may be better ways, but I am just wondering if I can get there with this approach out of curiousity. Thanks as always!

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  • What's the best way to measure and track performance over various calls at runtime?

    - by bitcruncher
    Hello. I'm trying to optimize the performance of my code, but I'm not familiar with xcode's debuggers or debuggers in general. Is it possible to track the execution time and frequency of calls being made at runtime? Imagine a chain of events with some recursive calls over a fraction of a second. What's the best way to track where the CPU spends most of its time? Many thanks. Edit: Maybe this is better asked by saying, how do I use the xcode debug tools to do a stack trace?

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  • Are there ways to improve NHibernate's performance regarding entity instantiation?

    - by denny_ch
    Hi folks, while profiling NHibernate with NHProf I noticed that a lot of time is spend for entity building or at least spend outside the query duration (database roundtrip). The project I'm currently working on prefetches some static data (which goes into the 2nd level cache) at application start. There are about 3000 rows in the result set (and maybe 30 columns) that is queried in 75 ms. The overall duration observed by NHProf is about 13 SECONDS! Is this typical beheviour? I know that NHibernate shouldn't be used for bulk operations, but I didn't thought that entity instantiation would be so expensive. Are there ways to improve performance in such situations or do I have to live with it? Thx, denny_ch

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  • What is the performance impact of tracing in C# and ASP.NET?

    - by SkippyFire
    I found this in some production login code I was looking at recently... HttpContext.Current.Trace.Write(query + ": " + username + ", " + password)); ...where query is a short SQL query to grab matching users. Does this have any sort of performance impact? I assume its very small. Also, what is the purpose of this exact type of trace, using the HTTP Context? Where does this data get traced to? Thanks in advance!

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  • Low cost way to host a large table yet keep the performance scalable?

    - by Leo Liang
    I have a growing table storing time series data, 500M entries now, and 200K new records every day. The total size is around 15GB for now. My clients are querying the table via a PHP script mostly, and the size of the result set is around 10K records (not very large). select * from T where timestamp > X and timestamp < Y and additionFilters And I want this operation cheap. Currently my table is hosting in Postgres 7, on a single 16G memory Box, and I would love to see some good suggestion for me to host this in low cost and also allow me to scale up for performance if needed. The table serves: 1. Query: 90% 2. Insert: 9.9% 2. Update: 0.1% <-- very rare.

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  • Having all Views in the Shared folder - works but is throwing "caught exceptions". Performance conc

    - by Scott
    Hi everyone, I have a simple but heavily used app done in VS2010/MVC2. I didn't like having separate folders for each view/controller and so have all the views in the Shared folder. It's working fine but while debugging in VS, I noticed that it's throwing IO "caught exceptions" since it seems to be looking in the [FolderName]/[ViewName] folder before going down to the Shared folder. Again, the app runs fine but I'm concerned that all these "caught exceptions" will have a minor performance impact since they do have a cost in via the CLR. Is there any way I can configure the Routing so that it will only look in the Shared folder? Thanks.

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  • How to test the performance of a user's PC in/for Flash?

    - by Jan P.
    Hey, I'm a developer on nice space MMO using Flash. On new PCs performance is quite good, but some features shouldn't be enabled on older PCs because the framerate drops to shit if we do. Flash wasn't made for this, but hey, pushing boundaries is fun. An example is fullscreen mode. Of course every user can manually enable it, but "advertising" it to a user with and oldie PC would be a bad idea - but for the Alienware crowd it would be dumb not to. So I want to find out how "capable" a user's PC is to decide if I should enable or disable some features for him. Any ideas? Thanks, Sujan

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  • When to trash hashmap contents to avoid performance degradation?

    - by Jack
    Hello, I'm woking on Java with a large (millions) hashmap that is actually built with a capacity of 10.000.000 and a load factor of .75 and it's used to cache some values since cached values become useless with time (not accessed anymore) but I can't remove useless ones while on the way I would like to entirely empty the cache when its performance starts to degrade. How can I decide when it's good to do it? For example, with 10 millions capacity and .75 should I empty it when it reaches 7.5 millions of elements? Because I tried various threshold values but I would like to have an analytic one. I've already tested the fact that emping it when it's quite full is a boost for perfomance (first 2-3 algorithm iterations after the wipe just fill it back, then it starts running faster than before the wipe) Thanks

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  • Is there a linear-time performance guarantee with using an Iterator?

    - by polygenelubricants
    If all that you're doing is a simple one-pass iteration (i.e. only hasNext() and next(), no remove()), are you guaranteed linear time performance and/or amortized constant cost per operation? Is this specified in the Iterator contract anywhere? Are there data structures/Java Collection which cannot be iterated in linear time? java.util.Scanner implements Iterator<String>. A Scanner is hardly a data structure (e.g. remove() makes absolutely no sense). Is this considered a design blunder? Is something like PrimeGenerator implements Iterator<Integer> considered bad design, or is this exactly what Iterator is for? (hasNext() always returns true, next() computes the next number on demand, remove() makes no sense). Similarly, would it have made sense for java.util.Random implements Iterator<Double>?

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  • What are the performance characteristics of SignalR at scale?

    - by Joel Martinez
    I'm interested in the performance characteristics of SignalR at scale ... particularly, how it behaves at the fringes of capability. When a server is at capacity, what happens? Does it drop messages? Do some clients not get notified? Are messages queued until all are delivered? And if so, will the queue eventually overflow and crash the server? I ask because conducting such a test myself would be impractical, and I'm hoping someone could point me to documentation speaking to this ... or perhaps someone could comment that has seen how SignalR behaves at scale. Thanks! note: I'm familiar with this other stackoverflow question on the stability and scalability of SignalR. But I believe my question is asking a slightly different question in that I'm not concerned with the theoretical scaling limits, I want to know how it behaves when it reaches the limits ... so I know what to be on the lookout for.

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  • Why do debug symbols so adversely affect the performance of threaded applications on Linux?

    - by fluffels
    Hi. I'm writing a ray tracer. Recently, I added threading to the program to exploit the additional cores on my i5 Quad Core. In a weird turn of events the debug version of the application is now running slower, but the optimized build is running faster than before I added threading. I'm passing the "-g -pg" flags to gcc for the debug build and the "-O3" flag for the optimized build. Host system: Ubuntu Linux 10.4 AMD64. I know that debug symbols add significant overhead to the program, but the relative performance has always been maintained. I.e. a faster algorithm will always run faster in both debug and optimization builds. Any idea why I'm seeing this behavior?

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  • Performance Impact of Generating 100's of Dynamic Methods in Ruby?

    - by viatropos
    What are the performance issues associated with generating 100's of dynamic methods in Ruby? I've been interested in using the Ruby Preferences Gem and noticed that it generates a bunch of helper methods for each preference you set. For instance: class User < ActiveRecord::Base preference :hot_salsa end ...generates something like: user.prefers_hot_salsa? # => false user.prefers_hot_salsa # => false If there are 100's of preferences like this, how does this impact the application? I assume it's not really a big deal but I'm just wondering, theoretically.

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  • Improving the performance of an nHibernate Data Access Layer.

    - by Amitabh
    I am working on improving the performance of DataAccess Layer of an existing Asp.Net Web Application. The scenerios are. Its a web based application in Asp.Net. DataAccess layer is built using NHibernate 1.2 and exposed as WCF Service. The Entity class is marked with DataContract. Lazy loading is not used and because of the eager-fetching of the relations there is huge no of database objects are loaded in the memory. No of hits to the database is also high. For example I profiled the application using NHProfiler and there were about 50+ sql calls to load one of the Entity object using the primary key. I also can not change code much as its an existing live application with no NUnit test cases at all. Please can I get some suggestions here?

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  • Can using non primitive Integer/ Long datatypes too frequently in the application, hurt the performance??

    - by Marcos
    I am using Long/Integer data types very frequently in my application, to build Generic datatypes. I fear that using these wrapper objects instead of primitive data types may be harmful for performance since each time it needs to create objects which is an expensive operation. but also it seems that I have no other choice(when I have to use primtives with generics) rather than just using them. However, still it would be great if you can suggest if there is anything I could do to make it better. or any way if I could just avoid it ?? Also What may be the downsides of this ? Suggestions welcomed!

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  • What's the most performance effective way to have a webbrowser inside a class library ?

    - by Xaqron
    I'm developing a class library. Need some data from internet and this cannot be done with HttpWebRequest in my case so I wanna use WebBrowser component. WebBrowser is used for opening a single page and fetch some data from it, so WebBrowser life-time is very short. Running thread is MTA and no message pump or STA thread is available by default (class library is used by an ASP.NET application). How to create a WebBrowser object, run it with a STA thread, fetch data from a web page and finally dispose it with the least performance impact on the application ? I just need the idea/concept and will find details myself. Thanks guys

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  • JavaScript: String Concatenation slow performance? Array.join('')?

    - by NickNick
    I've read that if I have a for loop, I should not use string concation because it's slow. Such as: for (i=0;i<10000000;i++) { str += 'a'; } And instead, I should use Array.join(), since it's much faster: var tmp = []; for (i=0;i<10000000;i++) { tmp.push('a'); } var str = tmp.join(''); However, I have also read that string concatention is ONLY a problem for Internet Explorer and that browsers such as Safari/Chrome, which use Webkit, actually perform FASTER is using string concatention than Array.join(). I've attempting to find a performance comparison between all major browser of string concatenation vs Array.join() and haven't been able to find one. As such, what is faster and more efficient JavaScript code? Using string concatenation or Array.join()?

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  • Cardinality Estimation Bug with Lookups in SQL Server 2008 onward

    - by Paul White
    Cost-based optimization stands or falls on the quality of cardinality estimates (expected row counts).  If the optimizer has incorrect information to start with, it is quite unlikely to produce good quality execution plans except by chance.  There are many ways we can provide good starting information to the optimizer, and even more ways for cardinality estimation to go wrong.  Good database people know this, and work hard to write optimizer-friendly queries with a schema and metadata (e.g. statistics) that reduce the chances of poor cardinality estimation producing a sub-optimal plan.  Today, I am going to look at a case where poor cardinality estimation is Microsoft’s fault, and not yours. SQL Server 2005 SELECT th.ProductID, th.TransactionID, th.TransactionDate FROM Production.TransactionHistory AS th WHERE th.ProductID = 1 AND th.TransactionDate BETWEEN '20030901' AND '20031231'; The query plan on SQL Server 2005 is as follows (if you are using a more recent version of AdventureWorks, you will need to change the year on the date range from 2003 to 2007): There is an Index Seek on ProductID = 1, followed by a Key Lookup to find the Transaction Date for each row, and finally a Filter to restrict the results to only those rows where Transaction Date falls in the range specified.  The cardinality estimate of 45 rows at the Index Seek is exactly correct.  The table is not very large, there are up-to-date statistics associated with the index, so this is as expected. The estimate for the Key Lookup is also exactly right.  Each lookup into the Clustered Index to find the Transaction Date is guaranteed to return exactly one row.  The plan shows that the Key Lookup is expected to be executed 45 times.  The estimate for the Inner Join output is also correct – 45 rows from the seek joining to one row each time, gives 45 rows as output. The Filter estimate is also very good: the optimizer estimates 16.9951 rows will match the specified range of transaction dates.  Eleven rows are produced by this query, but that small difference is quite normal and certainly nothing to worry about here.  All good so far. SQL Server 2008 onward The same query executed against an identical copy of AdventureWorks on SQL Server 2008 produces a different execution plan: The optimizer has pushed the Filter conditions seen in the 2005 plan down to the Key Lookup.  This is a good optimization – it makes sense to filter rows out as early as possible.  Unfortunately, it has made a bit of a mess of the cardinality estimates. The post-Filter estimate of 16.9951 rows seen in the 2005 plan has moved with the predicate on Transaction Date.  Instead of estimating one row, the plan now suggests that 16.9951 rows will be produced by each clustered index lookup – clearly not right!  This misinformation also confuses SQL Sentry Plan Explorer: Plan Explorer shows 765 rows expected from the Key Lookup (it multiplies a rounded estimate of 17 rows by 45 expected executions to give 765 rows total). Workarounds One workaround is to provide a covering non-clustered index (avoiding the lookup avoids the problem of course): CREATE INDEX nc1 ON Production.TransactionHistory (ProductID) INCLUDE (TransactionDate); With the Transaction Date filter applied as a residual predicate in the same operator as the seek, the estimate is again as expected: We could also force the use of the ultimate covering index (the clustered one): SELECT th.ProductID, th.TransactionID, th.TransactionDate FROM Production.TransactionHistory AS th WITH (INDEX(1)) WHERE th.ProductID = 1 AND th.TransactionDate BETWEEN '20030901' AND '20031231'; Summary Providing a covering non-clustered index for all possible queries is not always practical, and scanning the clustered index will rarely be optimal.  Nevertheless, these are the best workarounds we have today. In the meantime, watch out for poor cardinality estimates when a predicate is applied as part of a lookup. The worst thing is that the estimate after the lookup join in the 2008+ plans is wrong.  It’s not hopelessly wrong in this particular case (45 versus 16.9951 is not the end of the world) but it easily can be much worse, and there’s not much you can do about it.  Any decisions made by the optimizer after such a lookup could be based on very wrong information – which can only be bad news. If you think this situation should be improved, please vote for this Connect item. © 2012 Paul White – All Rights Reserved twitter: @SQL_Kiwi email: [email protected]

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