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  • New Enhancements for InnoDB Memcached

    - by Calvin Sun
    In MySQL 5.6, we continued our development on InnoDB Memcached and completed a few widely desirable features that make InnoDB Memcached a competitive feature in more scenario. Notablely, they are 1) Support multiple table mapping 2) Added background thread to auto-commit long running transactions 3) Enhancement in binlog performance  Let’s go over each of these features one by one. And in the last section, we will go over a couple of internally performed performance tests. Support multiple table mapping In our earlier release, all InnoDB Memcached operations are mapped to a single InnoDB table. In the real life, user might want to use this InnoDB Memcached features on different tables. Thus being able to support access to different table at run time, and having different mapping for different connections becomes a very desirable feature. And in this GA release, we allow user just be able to do both. We will discuss the key concepts and key steps in using this feature. 1) "mapping name" in the "get" and "set" command In order to allow InnoDB Memcached map to a new table, the user (DBA) would still require to "pre-register" table(s) in InnoDB Memcached “containers” table (there is security consideration for this requirement). If you would like to know about “containers” table, please refer to my earlier blogs in blogs.innodb.com. Once registered, the InnoDB Memcached will then be able to look for such table when they are referred. Each of such registered table will have a unique "registration name" (or mapping_name) corresponding to the “name” field in the “containers” table.. To access these tables, user will include such "registration name" in their get or set commands, in the form of "get @@new_mapping_name.key", prefix "@@" is required for signaling a mapped table change. The key and the "mapping name" are separated by a configurable delimiter, by default, it is ".". So the syntax is: get [@@mapping_name.]key_name set [@@mapping_name.]key_name  or  get @@mapping_name set @@mapping_name Here is an example: Let's set up three tables in the "containers" table: The first is a map to InnoDB table "test/demo_test" table with mapping name "setup_1" INSERT INTO containers VALUES ("setup_1", "test", "demo_test", "c1", "c2", "c3", "c4", "c5", "PRIMARY");  Similarly, we set up table mappings for table "test/new_demo" with name "setup_2" and that to table "mydatabase/my_demo" with name "setup_3": INSERT INTO containers VALUES ("setup_2", "test", "new_demo", "c1", "c2", "c3", "c4", "c5", "secondary_index_x"); INSERT INTO containers VALUES ("setup_3", "my_database", "my_demo", "c1", "c2", "c3", "c4", "c5", "idx"); To switch to table "my_database/my_demo", and get the value corresponding to “key_a”, user will do: get @@setup_3.key_a (this will also output the value that corresponding to key "key_a" or simply get @@setup_3 Once this is done, this connection will switch to "my_database/my_demo" table until another table mapping switch is requested. so it can continue issue regular command like: get key_b  set key_c 0 0 7 These DMLs will all be directed to "my_database/my_demo" table. And this also implies that different connections can have different bindings (to different table). 2) Delimiter: For the delimiter "." that separates the "mapping name" and key value, we also added a configure option in the "config_options" system table with name of "table_map_delimiter": INSERT INTO config_options VALUES("table_map_delimiter", "."); So if user wants to change to a different delimiter, they can change it in the config_option table. 3) Default mapping: Once we have multiple table mapping, there should be always a "default" map setting. For this, we decided if there exists a mapping name of "default", then this will be chosen as default mapping. Otherwise, the first row of the containers table will chosen as default setting. Please note, user tables can be repeated in the "containers" table (for example, user wants to access different columns of the table in different settings), as long as they are using different mapping/configure names in the first column, which is enforced by a unique index. 4) bind command In addition, we also extend the protocol and added a bind command, its usage is fairly straightforward. To switch to "setup_3" mapping above, you simply issue: bind setup_3 This will switch this connection's InnoDB table to "my_database/my_demo" In summary, with this feature, you now can direct access to difference tables with difference session. And even a single connection, you can query into difference tables. Background thread to auto-commit long running transactions This is a feature related to the “batch” concept we discussed in earlier blogs. This “batch” feature allows us batch the read and write operations, and commit them only after certain calls. The “batch” size is controlled by the configure parameter “daemon_memcached_w_batch_size” and “daemon_memcached_r_batch_size”. This could significantly boost performance. However, it also comes with some disadvantages, for example, you will not be able to view “uncommitted” operations from SQL end unless you set transaction isolation level to read_uncommitted, and in addition, this will held certain row locks for extend period of time that might reduce the concurrency. To deal with this, we introduce a background thread that “auto-commits” the transaction if they are idle for certain amount of time (default is 5 seconds). The background thread will wake up every second and loop through every “connections” opened by Memcached, and check for idle transactions. And if such transaction is idle longer than certain limit and not being used, it will commit such transactions. This limit is configurable by change “innodb_api_bk_commit_interval”. Its default value is 5 seconds, and minimum is 1 second, and maximum is 1073741824 seconds. With the help of such background thread, you will not need to worry about long running uncommitted transactions when set daemon_memcached_w_batch_size and daemon_memcached_r_batch_size to a large number. This also reduces the number of locks that could be held due to long running transactions, and thus further increase the concurrency. Enhancement in binlog performance As you might all know, binlog operation is not done by InnoDB storage engine, rather it is handled in the MySQL layer. In order to support binlog operation through InnoDB Memcached, we would have to artificially create some MySQL constructs in order to access binlog handler APIs. In previous lab release, for simplicity consideration, we open and destroy these MySQL constructs (such as THD) for each operations. This required us to set the “batch” size always to 1 when binlog is on, no matter what “daemon_memcached_w_batch_size” and “daemon_memcached_r_batch_size” are configured to. This put a big restriction on our capability to scale, and also there are quite a bit overhead in creating destroying such constructs that bogs the performance down. With this release, we made necessary change that would keep MySQL constructs as long as they are valid for a particular connection. So there will not be repeated and redundant open and close (table) calls. And now even with binlog option is enabled (with innodb_api_enable_binlog,), we still can batch the transactions with daemon_memcached_w_batch_size and daemon_memcached_r_batch_size, thus scale the write/read performance. Although there are still overheads that makes InnoDB Memcached cannot perform as fast as when binlog is turned off. It is much better off comparing to previous release. And we are continuing optimize the solution is this area to improve the performance as much as possible. Performance Study: Amerandra of our System QA team have conducted some performance studies on queries through our InnoDB Memcached connection and plain SQL end. And it shows some interesting results. The test is conducted on a “Linux 2.6.32-300.7.1.el6uek.x86_64 ix86 (64)” machine with 16 GB Memory, Intel Xeon 2.0 GHz CPU X86_64 2 CPUs- 4 Core Each, 2 RAID DISKS (1027 GB,733.9GB). Results are described in following tables: Table 1: Performance comparison on Set operations Connections 5.6.7-RC-Memcached-plugin ( TPS / Qps) with memcached-threads=8*** 5.6.7-RC* X faster Set (QPS) Set** 8 30,000 5,600 5.36 32 59,000 13,000 4.54 128 68,000 8,000 8.50 512 63,000 6.800 9.23 * mysql-5.6.7-rc-linux2.6-x86_64 ** The “set” operation when implemented in InnoDB Memcached involves a couple of DMLs: it first query the table to see whether the “key” exists, if it does not, the new key/value pair will be inserted. If it does exist, the “value” field of matching row (by key) will be updated. So when used in above query, it is a precompiled store procedure, and query will just execute such procedures. *** added “–daemon_memcached_option=-t8” (default is 4 threads) So we can see with this “set” query, InnoDB Memcached can run 4.5 to 9 time faster than MySQL server. Table 2: Performance comparison on Get operations Connections 5.6.7-RC-Memcached-plugin ( TPS / Qps) with memcached-threads=8 5.6.7-RC* X faster Get (QPS) Get 8 42,000 27,000 1.56 32 101,000 55.000 1.83 128 117,000 52,000 2.25 512 109,000 52,000 2.10 With the “get” query (or the select query), memcached performs 1.5 to 2 times faster than normal SQL. Summary: In summary, we added several much-desired features to InnoDB Memcached in this release, allowing user to operate on different tables with this Memcached interface. We also now provide a background commit thread to commit long running idle transactions, thus allow user to configure large batch write/read without worrying about large number of rows held or not being able to see (uncommit) data. We also greatly enhanced the performance when Binlog is enabled. We will continue making efforts in both performance enhancement and functionality areas to make InnoDB Memcached a good demo case for our InnoDB APIs. Jimmy Yang, September 29, 2012

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  • Getting data from array of DataSet objects returned from web service

    - by Sarah Vessels
    I have a web service that I want to access when it is added as a web reference to my C# project. A particular method in the web service takes a SQL query string and returns the results of the query as a custom type. When I add the web service reference, the method shows up as returning DataSet[] instead of the custom type. This is fine provided I can still somehow access the data returned from the query within those DataSet objects. I ran a particular query that should return 6 rows; I got back a DataSet[] array with 6 elements. However, when I iterate over those DataSet objects, none of them has any tables (via the Tables property on the DataSet). What gives? Where is my data? The web service is tested and works when I use it as a data source in a Report Builder 2.0 report. I am able to send an XML SOAP query to the web service and get back XML results containing my data.

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  • MySQL Connection Error in PHP

    - by user309381
    I have set the password for root and grant all privileges for root. Why does it say it is denied? ****mysql_query() [function.mysql-query]: Access denied for user 'SYSTEM'@'localhost' (using password: NO) in C:\wamp\www\photo_gallery\includes\database.php on line 56 Warning: mysql_query() [function.mysql-query]: A link to the server could not be established in C:\wamp\www\photo_gallery\includes\database.php on line 56 The Query has problemAccess denied for user 'SYSTEM'@'localhost' (using password: NO) Code as follows: <?php include("DB_Info.php"); class MySQLDatabase { public $connection; function _construct() { $this->open_connection(); } public function open_connection() { /* $DB_SERVER = "localhost"; $DB_USER = "root"; $DB_PASS = ""; $DB_NAME = "photo_gallery";*/ $this->connection = mysql_connect($DBSERVER,$DBUSER,$DBPASS); if(!$this->connection) { die("Database Connection Failed" . mysql_error()); } else { $db_select = mysql_select_db($DBNAME,$this->connection); if(!$db_select) { die("Database Selection Failed" . mysql_error()); } } } function mysql_prep($value) { if (get_magic_quotes_gpc()) { $value = stripslashes($value); } // Quote if not a number if (!is_numeric($value)) { $value = "'" . mysql_real_escape_string($value) . "'"; } return $value; } public function close_connection() { if(isset($this->connection)) { mysql_close($this->connection); unset($this->connection); } } public function query($sql) { //$sql = "SELECT*FROM users where id = 1"; $result = mysql_query($sql); $this->confirm_query($result); //$found_user = mysql_fetch_assoc($result); //echo $found_user; return $found_user; } private function confirm_query($result) { if(!$result) { die("The Query has problem" . mysql_error()); } } } $database = new MySQLDatabase(); ?>

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  • Google Analytics API and Internal Search question

    - by Am
    I'm trying to use Google Analytics API to query internal searches that happen on my site. I'd like to be able to query the keywords and the number of times that keyword was used in internal search, based on URL of a page on the site. The idea is to find out which keywords direct the user to a particular page. Does anyone know which dimensions and metrics must use to query that information?

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  • Building Private IaaS with SPARC and Oracle Solaris

    - by ferhat
    A superior enterprise cloud infrastructure with high performing systems using built-in virtualization! We are happy to announce the expansion of Oracle Optimized Solution for Enterprise Cloud Infrastructure with Oracle's SPARC T-Series servers and Oracle Solaris.  Designed, tuned, tested and fully documented, the Oracle Optimized Solution for Enterprise Cloud Infrastructure now offers customers looking to upgrade, consolidate and virtualize their existing SPARC-based infrastructure a proven foundation for private cloud-based services which can lower TCO by up to 81 percent(1). Faster time to service, reduce deployment time from weeks to days, and can increase system utilization to 80 percent. The Oracle Optimized Solution for Enterprise Cloud Infrastructure can also be deployed at up to 50 percent lower cost over five years than comparable alternatives(2). The expanded solution announced today combines Oracle’s latest SPARC T-Series servers; Oracle Solaris 11, the first cloud OS; Oracle VM Server for SPARC, Oracle’s Sun ZFS Storage Appliance, and, Oracle Enterprise Manager Ops Center 12c, which manages all Oracle system technologies, streamlining cloud infrastructure management. Thank you to all who stopped by Oracle booth at the CloudExpo Conference in New York. We were also at Cloud Boot Camp: Building Private IaaS with Oracle Solaris and SPARC, discussing how this solution can maximize return on investment and help organizations manage costs for their existing infrastructures or for new enterprise cloud infrastructure design. Designed, tuned, and tested, Oracle Optimized Solution for Enterprise Cloud Infrastructure is a complete cloud infrastructure or any virtualized environment  using the proven documented best practices for deployment and optimization. The solution addresses each layer of the infrastructure stack using Oracle's powerful SPARC T-Series as well as x86 servers with storage, network, virtualization, and management configurations to provide a robust, flexible, and balanced foundation for your enterprise applications and databases.  For more information visit Oracle Optimized Solution for Enterprise Cloud Infrastructure. Solution Brief: Accelerating Enterprise Cloud Infrastructure Deployments White Paper: Reduce Complexity and Accelerate Enterprise Cloud Infrastructure Deployments Technical White Paper: Enterprise Cloud Infrastructure on SPARC (1) Comparison based on current SPARC server customers consolidating existing installations including Sun Fire E4900, Sun Fire V440 and SPARC Enterprise T5240 servers to latest generation SPARC T4 servers. Actual deployments and configurations will vary. (2) Comparison based on solution with SPARC T4-2 servers with Oracle Solaris and Oracle VM Server for SPARC versus HP ProLiant DL380 G7 with VMware and Red Hat Enterprise Linux and IBM Power 720 Express - Power 730 Express with IBM AIX Enterprise Edition and Power VM.

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  • Zend_Paginator - Increase querys

    - by poru
    Hello, I started using Zend_Paginator, it works everything fine but I noticed that there is one more query which slows the load time down. The additional query: SELECT COUNT(1) AS `zend_paginator_row_count` FROM `content` The normal query: SELECT `content`.`id`, `content`.`name` FROM `content` LIMIT 2 PHP: $adapter = new Zend_Paginator_Adapter_DbSelect($table->select()->from($table, array('id', 'name'))); $paginator = new Zend_Paginator($adapter); Could I merge the two querys into one (for better performance)?

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  • Double of Total Problem

    - by Gopal
    Table1 ID | WorkTime ----------------- 001 | 10:50:00 001 | 00:00:00 002 | .... WorkTime Datatype is *varchar(. SELECT ID, CONVERT(varchar(10), TotalSeconds1 / 3600) + ':' + RIGHT('00' + CONVERT(varchar(2), (TotalSeconds1 - TotalSeconds1 / 3600 * 3600) / 60), 2) + ':' + RIGHT('00' + CONVERT(varchar(2), TotalSeconds1 - (TotalSeconds1 / 3600 * 3600 + (TotalSeconds1 - TotalSeconds1 / 3600 * 3600) / 60 * 60)), 2) AS TotalWork From ( SELECT ID, SUM(DATEDIFF(second, CONVERT(datetime, '1/1/1900'), CONVERT(datetime, '1/1/1900 ' + WorkTime))) AS TotalSeconds1 FROM table1 group by ID) AS tab1 where id = '001' The above Query is showing "double the total of time" For Example From table1 i want to calculate the total WorkTime, when i run the above query it is showing ID WorkTime 001 21:40:00 002..., But it should show like this ID Worktime 001 10:50:00 ..., How to avoid the double total of worktime. How to modify my query. Need Query Help

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  • Nonetype object has no attribute '__getitem__'

    - by adohertyd
    I am trying to use an API wrapper downloaded from the net to get results from the new azure Bing API. I'm trying to implement it as per the instructions but getting the runtime error: Traceback (most recent call last): File "bingwrapper.py", line 4, in <module> bingsearch.request("affirmative action") File "/usr/local/lib/python2.7/dist-packages/bingsearch-0.1-py2.7.egg/bingsearch.py", line 8, in request return r.json['d']['results'] TypeError: 'NoneType' object has no attribute '__getitem__' This is the wrapper code: import requests URL = 'https://api.datamarket.azure.com/Data.ashx/Bing/SearchWeb/Web?Query=%(query)s&$top=50&$format=json' API_KEY = 'SECRET_API_KEY' def request(query, **params): r = requests.get(URL % {'query': query}, auth=('', API_KEY)) return r.json['d']['results'] The instructions are: >>> import bingsearch >>> bingsearch.API_KEY='Your-Api-Key-Here' >>> r = bingsearch.request("Python Software Foundation") >>> r.status_code 200 >>> r[0]['Description'] u'Python Software Foundation Home Page. The mission of the Python Software Foundation is to promote, protect, and advance the Python programming language, and to ...' >>> r[0]['Url'] u'http://www.python.org/psf/ This is my code that uses the wrapper (as per the instructions): import bingsearch bingsearch.API_KEY='abcdefghijklmnopqrstuv' r = bingsearch.request("affirmative+action")

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  • How to pass a value from a method to property procedure in c#?

    - by sameer
    Here is my code: The jewellery class is my main class in which i am inheriting a connection string class. class Jewellery : Connectionstr { string lmcode; public string LM_code/**/Here i want to access the value of the method ReadData i.e displaystring and i want to store this value in the insert query below.** { get { return lmcode; } set { lmcode = value; } } string mname; public string M_Name { get { return mname; } set { mname = value; } } string desc; public string Desc { get { return desc; } set { desc = value; } } public string ReadData() { OleDbDataReader dr; string jid = string.Empty; string displayString = string.Empty; String query = "select max(LM_code)from Master_Accounts"; Datamanager.RunExecuteReader(Constr, query); if (dr.Read()) { jid = dr[0].ToString(); if (string.IsNullOrEmpty(jid)) { jid = "AM0000"; } int len = jid.Length; string split = jid.Substring(2, len - 2); int num = Convert.ToInt32(split); num++; displayString = jid.Substring(0, 2) + num.ToString("0000"); dr.Close(); } **return displayString;** I want to pass this value to the above property procedure above i.e LM_code. } public void add() { String query ="insert into Master_Accounts values ('" + LM_code + "','" + M_Name + "'," + "'" + Desc + "')"; Datamanager.RunExecuteNonQuery(Constr , query);// } If possible can u edit this code! Anticipated thanks by sameer

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  • Taming Hopping Windows

    - by Roman Schindlauer
    At first glance, hopping windows seem fairly innocuous and obvious. They organize events into windows with a simple periodic definition: the windows have some duration d (e.g. a window covers 5 second time intervals), an interval or period p (e.g. a new window starts every 2 seconds) and an alignment a (e.g. one of those windows starts at 12:00 PM on March 15, 2012 UTC). var wins = xs     .HoppingWindow(TimeSpan.FromSeconds(5),                    TimeSpan.FromSeconds(2),                    new DateTime(2012, 3, 15, 12, 0, 0, DateTimeKind.Utc)); Logically, there is a window with start time a + np and end time a + np + d for every integer n. That’s a lot of windows. So why doesn’t the following query (always) blow up? var query = wins.Select(win => win.Count()); A few users have asked why StreamInsight doesn’t produce output for empty windows. Primarily it’s because there is an infinite number of empty windows! (Actually, StreamInsight uses DateTimeOffset.MaxValue to approximate “the end of time” and DateTimeOffset.MinValue to approximate “the beginning of time”, so the number of windows is lower in practice.) That was the good news. Now the bad news. Events also have duration. Consider the following simple input: var xs = this.Application                 .DefineEnumerable(() => new[]                     { EdgeEvent.CreateStart(DateTimeOffset.UtcNow, 0) })                 .ToStreamable(AdvanceTimeSettings.IncreasingStartTime); Because the event has no explicit end edge, it lasts until the end of time. So there are lots of non-empty windows if we apply a hopping window to that single event! For this reason, we need to be careful with hopping window queries in StreamInsight. Or we can switch to a custom implementation of hopping windows that doesn’t suffer from this shortcoming. The alternate window implementation produces output only when the input changes. We start by breaking up the timeline into non-overlapping intervals assigned to each window. In figure 1, six hopping windows (“Windows”) are assigned to six intervals (“Assignments”) in the timeline. Next we take input events (“Events”) and alter their lifetimes (“Altered Events”) so that they cover the intervals of the windows they intersect. In figure 1, you can see that the first event e1 intersects windows w1 and w2 so it is adjusted to cover assignments a1 and a2. Finally, we can use snapshot windows (“Snapshots”) to produce output for the hopping windows. Notice however that instead of having six windows generating output, we have only four. The first and second snapshots correspond to the first and second hopping windows. The remaining snapshots however cover two hopping windows each! While in this example we saved only two events, the savings can be more significant when the ratio of event duration to window duration is higher. Figure 1: Timeline The implementation of this strategy is straightforward. We need to set the start times of events to the start time of the interval assigned to the earliest window including the start time. Similarly, we need to modify the end times of events to the end time of the interval assigned to the latest window including the end time. The following snap-to-boundary function that rounds a timestamp value t down to the nearest value t' <= t such that t' is a + np for some integer n will be useful. For convenience, we will represent both DateTime and TimeSpan values using long ticks: static long SnapToBoundary(long t, long a, long p) {     return t - ((t - a) % p) - (t > a ? 0L : p); } How do we find the earliest window including the start time for an event? It’s the window following the last window that does not include the start time assuming that there are no gaps in the windows (i.e. duration < interval), and limitation of this solution. To find the end time of that antecedent window, we need to know the alignment of window ends: long e = a + (d % p); Using the window end alignment, we are finally ready to describe the start time selector: static long AdjustStartTime(long t, long e, long p) {     return SnapToBoundary(t, e, p) + p; } To find the latest window including the end time for an event, we look for the last window start time (non-inclusive): public static long AdjustEndTime(long t, long a, long d, long p) {     return SnapToBoundary(t - 1, a, p) + p + d; } Bringing it together, we can define the translation from events to ‘altered events’ as in Figure 1: public static IQStreamable<T> SnapToWindowIntervals<T>(IQStreamable<T> source, TimeSpan duration, TimeSpan interval, DateTime alignment) {     if (source == null) throw new ArgumentNullException("source");     // reason about DateTime and TimeSpan in ticks     long d = Math.Min(DateTime.MaxValue.Ticks, duration.Ticks);     long p = Math.Min(DateTime.MaxValue.Ticks, Math.Abs(interval.Ticks));     // set alignment to earliest possible window     var a = alignment.ToUniversalTime().Ticks % p;     // verify constraints of this solution     if (d <= 0L) { throw new ArgumentOutOfRangeException("duration"); }     if (p == 0L || p > d) { throw new ArgumentOutOfRangeException("interval"); }     // find the alignment of window ends     long e = a + (d % p);     return source.AlterEventLifetime(         evt => ToDateTime(AdjustStartTime(evt.StartTime.ToUniversalTime().Ticks, e, p)),         evt => ToDateTime(AdjustEndTime(evt.EndTime.ToUniversalTime().Ticks, a, d, p)) -             ToDateTime(AdjustStartTime(evt.StartTime.ToUniversalTime().Ticks, e, p))); } public static DateTime ToDateTime(long ticks) {     // just snap to min or max value rather than under/overflowing     return ticks < DateTime.MinValue.Ticks         ? new DateTime(DateTime.MinValue.Ticks, DateTimeKind.Utc)         : ticks > DateTime.MaxValue.Ticks         ? new DateTime(DateTime.MaxValue.Ticks, DateTimeKind.Utc)         : new DateTime(ticks, DateTimeKind.Utc); } Finally, we can describe our custom hopping window operator: public static IQWindowedStreamable<T> HoppingWindow2<T>(     IQStreamable<T> source,     TimeSpan duration,     TimeSpan interval,     DateTime alignment) {     if (source == null) { throw new ArgumentNullException("source"); }     return SnapToWindowIntervals(source, duration, interval, alignment).SnapshotWindow(); } By switching from HoppingWindow to HoppingWindow2 in the following example, the query returns quickly rather than gobbling resources and ultimately failing! public void Main() {     var start = new DateTimeOffset(new DateTime(2012, 6, 28), TimeSpan.Zero);     var duration = TimeSpan.FromSeconds(5);     var interval = TimeSpan.FromSeconds(2);     var alignment = new DateTime(2012, 3, 15, 12, 0, 0, DateTimeKind.Utc);     var events = this.Application.DefineEnumerable(() => new[]     {         EdgeEvent.CreateStart(start.AddSeconds(0), "e0"),         EdgeEvent.CreateStart(start.AddSeconds(1), "e1"),         EdgeEvent.CreateEnd(start.AddSeconds(1), start.AddSeconds(2), "e1"),         EdgeEvent.CreateStart(start.AddSeconds(3), "e2"),         EdgeEvent.CreateStart(start.AddSeconds(9), "e3"),         EdgeEvent.CreateEnd(start.AddSeconds(3), start.AddSeconds(10), "e2"),         EdgeEvent.CreateEnd(start.AddSeconds(9), start.AddSeconds(10), "e3"),     }).ToStreamable(AdvanceTimeSettings.IncreasingStartTime);     var adjustedEvents = SnapToWindowIntervals(events, duration, interval, alignment);     var query = from win in HoppingWindow2(events, duration, interval, alignment)                 select win.Count();     DisplayResults(adjustedEvents, "Adjusted Events");     DisplayResults(query, "Query"); } As you can see, instead of producing a massive number of windows for the open start edge e0, a single window is emitted from 12:00:15 AM until the end of time: Adjusted Events StartTime EndTime Payload 6/28/2012 12:00:01 AM 12/31/9999 11:59:59 PM e0 6/28/2012 12:00:03 AM 6/28/2012 12:00:07 AM e1 6/28/2012 12:00:05 AM 6/28/2012 12:00:15 AM e2 6/28/2012 12:00:11 AM 6/28/2012 12:00:15 AM e3 Query StartTime EndTime Payload 6/28/2012 12:00:01 AM 6/28/2012 12:00:03 AM 1 6/28/2012 12:00:03 AM 6/28/2012 12:00:05 AM 2 6/28/2012 12:00:05 AM 6/28/2012 12:00:07 AM 3 6/28/2012 12:00:07 AM 6/28/2012 12:00:11 AM 2 6/28/2012 12:00:11 AM 6/28/2012 12:00:15 AM 3 6/28/2012 12:00:15 AM 12/31/9999 11:59:59 PM 1 Regards, The StreamInsight Team

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  • SQL Azure Data Sync

    - by kaleidoscope
    The Microsoft Sync Framework Power Pack for SQL Azure contains a series of components that improve the experience of synchronizing with SQL Azure. This includes runtime components that optimize performance and simplify the process of synchronizing with the cloud. SQL Azure Data Sync allows developers and DBA's to: · Link existing on-premises data stores to SQL Azure. · Create new applications in Windows Azure without abandoning existing on-premises applications. · Extend on-premises data to remote offices, retail stores and mobile workers via the cloud. · Take Windows Azure and SQL Azure based web application offline to provide an “Outlook like” cached-mode experience. The Microsoft Sync Framework Power Pack for SQL Azure is comprised of the following: · SqlAzureSyncProvider · Sql Azure Offline Visual Studio Plug-In · SQL Azure Data Sync Tool for SQL Server · New SQL Azure Events Automated Provisioning Geeta

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  • Caching and cache invalidation in user controls?

    - by Rishabh Ohri
    HI, In our .aspx pages we have many user controls. each user control executes a sql query. The caching mechanism to be followed is to fragment cache each user control on the page and add the query dependency to the respective queries of the user controls. How to achieve query dependency on fragment cached data for invalidation?

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  • Read alphanumeric characters from csv file in C#

    - by Prasad
    I am using the following code to read my csv file: public DataTable ParseCSV(string path) { if (!File.Exists(path)) return null; string full = Path.GetFullPath(path); string file = Path.GetFileName(full); string dir = Path.GetDirectoryName(full); //create the "database" connection string string connString = "Provider=Microsoft.ACE.OLEDB.12.0;" + "Data Source=\"" + dir + "\\\";" + "Extended Properties=\"text;HDR=Yes;FMT=Delimited;IMEX=1\""; //create the database query string query = "SELECT * FROM " + file; //create a DataTable to hold the query results DataTable dTable = new DataTable(); //create an OleDbDataAdapter to execute the query OleDbDataAdapter dAdapter = new OleDbDataAdapter(query, connString); //fill the DataTable dAdapter.Fill(dTable); dAdapter.Dispose(); return dTable; } But the above doesn't reads the alphanumeric value from the csv file. it reads only i either numeric or alpha. Whats the fix i need to make to read the alphanumeric values? Please suggest.

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  • Crashes while playing Mp3 songs

    - by sid
    I have Downloaded and Installed Ubuntu last month while downloading codecs for playing Music and Video Formats my Laptop (Dell XPS) crashed. later i again started the system now the problems i face are 1) After Signing in as User/Admin the wallpaper loads while all other windows disappear no UI (task bar and dock) is displayed even after say 30 min. 2) I uninstalled and reinstalled Ubnutu hence there were no problems but when i play Music files the Laptop crashes and the same sequence as above follows this has happened for last 6 times. 3) Whenever the UI disaapears after logging in the Hard Disk starts to heat up and there is considerable increase in power usage of the system. where in the power drain is notable. Please suggest any changes or rectify the issue. Regards Sid

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  • Little Wheel Is An Atmospheric and Engaging Point-and-Click Adventure

    - by Jason Fitzpatrick
    If you’re a fan of the resurgence of highly stylized and atmospheric adventure games–such as Spirit, World of Goo, and the like–you’ll definitely want to check out this well executed, free, and more than a little bit charming browser-based game. Little Wheel is set in a world of robots where, 10,000 years ago, a terrible accident at the central power plant left all the robots without power. The entire robot world went into a deep sleep and now, thanks to a freak lightning strike, one little robot has woken up. Your job, as that little robot, is to navigate the world of Little Wheel and help bring it back to life. Hit up the link below to play the game for free–the quality of the visual and audio design make going full screen and turning the speakers on a must. Little Wheel [via Freeware Genuis] How to Make Your Laptop Choose a Wired Connection Instead of Wireless HTG Explains: What Is Two-Factor Authentication and Should I Be Using It? HTG Explains: What Is Windows RT and What Does It Mean To Me?

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  • SQLiteException and SQLite error near "(": syntax error with Subsonic ActiveRecord

    - by nvuono
    I ran into an interesting error with the following LiNQ query using LiNQPad and when using Subsonic 3.0.x w/ActiveRecord within my project and wanted to share the error and resolution for anyone else who runs into it. The linq statement below is meant to group entries in the tblSystemsValues collection into their appropriate system and then extract the system with the highest ID. from ksf in KeySafetyFunction where ksf.Unit == 2 && ksf.Condition_ID == 1 join sys in tblSystems on ksf.ID equals sys.KeySafetyFunction join xval in (from t in tblSystemsValues group t by t.tblSystems_ID into groupedT select new { sysId = groupedT.Key, MaxID = groupedT.Max(g=>g.ID), MaxText = groupedT.First(gt2 => gt2.ID == groupedT.Max(g=>g.ID)).TextValue, MaxChecked = groupedT.First(gt2 => gt2.ID == groupedT.Max(g=>g.ID)).Checked }) on sys.ID equals xval.sysId select new {KSFDesc=ksf.Description, sys.Description, xval.MaxText, xval.MaxChecked} On its own, the subquery for grouping into groupedT works perfectly and the query to match up KeySafetyFunctions with their System in tblSystems also works perfectly on its own. However, when trying to run the completed query in linqpad or within my project I kept running into a SQLiteException SQLite Error Near "(" First I tried splitting the queries up within my project because I knew that I could just run a foreach loop over the results if necessary. However, I continued to receive the same exception! I eventually separated the query into three separate parts before I realized that it was the lazy execution of the queries that was killing me. It then became clear that adding the .ToList() specifier after the myProtectedSystem query below was the key to avoiding the lazy execution after combining and optimizing the query and being able to get my results despite the problems I encountered with the SQLite driver. // determine the max Text/Checked values for each system in tblSystemsValue var myProtectedValue = from t in tblSystemsValue.All() group t by t.tblSystems_ID into groupedT select new { sysId = groupedT.Key, MaxID = groupedT.Max(g => g.ID), MaxText = groupedT.First(gt2 => gt2.ID ==groupedT.Max(g => g.ID)).TextValue, MaxChecked = groupedT.First(gt2 => gt2.ID ==groupedT.Max(g => g.ID)).Checked}; // get the system description information and filter by Unit/Condition ID var myProtectedSystem = (from ksf in KeySafetyFunction.All() where ksf.Unit == 2 && ksf.Condition_ID == 1 join sys in tblSystem.All() on ksf.ID equals sys.KeySafetyFunction select new {KSFDesc = ksf.Description, sys.Description, sys.ID}).ToList(); // finally join everything together AFTER forcing execution with .ToList() var joined = from protectedSys in myProtectedSystem join protectedVal in myProtectedValue on protectedSys.ID equals protectedVal.sysId select new {protectedSys.KSFDesc, protectedSys.Description, protectedVal.MaxChecked, protectedVal.MaxText}; // print the gratifying debug results foreach(var protectedItem in joined) { System.Diagnostics.Debug.WriteLine(protectedItem.Description + ", " + protectedItem.KSFDesc + ", " + protectedItem.MaxText + ", " + protectedItem.MaxChecked); }

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  • issue in list of dict

    - by gaggina
    class MyOwnClass: # list who contains the queries queries = [] # a template dict template_query = {} template_query['name'] = 'mat' template_query['age'] = '12' obj = MyOwnClass() query = obj.template_query query['name'] = 'sam' query['age'] = '23' obj.queries.append(query) query2 = obj.template_query query2['name'] = 'dj' query2['age'] = '19' obj.queries.append(query2) print obj.queries It gives me [{'age': '19', 'name': 'dj'}, {'age': '19', 'name': 'dj'}] while I expect to have [{'age': '23' , 'name': 'sam'}, {'age': '19', 'name': 'dj'}] I thought to use a template for this list because I'm gonna to use it very often and there are some default variable who does not need to be changed. Why does doing it the template_query itself changes? I'm new to python and I'm getting pretty confused.

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  • Querying tables based on other column values

    - by blcArmadillo
    Is there a way to query different databases based on the value of a column in the query? Say for example you have the following columns: id part_id attr_id attr_value_ext attr_value_int You then run a query and if the attr_id is '1' is returns the attr_value_int column but if attr_id is greater than '1' it joins data from another table based on the attr_value_ext.

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  • Perform Grouping of Resultsets in Code, not on Database Level

    - by NinjaBomb
    Stackoverflowers, I have a resultset from a SQL query in the form of: Category Column2 Column3 A 2 3.50 A 3 2 B 3 2 B 1 5 ... I need to group the resultset based on the Category column and sum the values for Column2 and Column3. I have to do it in code because I cannot perform the grouping in the SQL query that gets the data due to the complexity of the query (long story). This grouped data will then be displayed in a table. I have it working for specific set of values in the Category column, but I would like a solution that would handle any possible values that appear in the Category column. I know there has to be a straightforward, efficient way to do it but I cannot wrap my head around it right now. How would you accomplish it? EDIT I have attempted to group the result in SQL using the exact same grouping query suggested by Thomas Levesque and both times our entire RDBMS crashed trying to process the query. I was under the impression that Linq was not available until .NET 3.5. This is a .NET 2.0 web application so I did not think it was an option. Am I wrong in thinking that? EDIT Starting a bounty because I believe this would be a good technique to have in the toolbox to use no matter where the different resultsets are coming from. I believe knowing the most concise way to group any 2 somewhat similar sets of data in code (without .NET LINQ) would be beneficial to more people than just me.

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  • Organization &amp; Architecture UNISA Studies &ndash; Chap 5

    - by MarkPearl
    Learning Outcomes Describe the operation of a memory cell Explain the difference between DRAM and SRAM Discuss the different types of ROM Explain the concepts of a hard failure and a soft error respectively Describe SDRAM organization Semiconductor Main Memory The two traditional forms of RAM used in computers are DRAM and SRAM DRAM (Dynamic RAM) Divided into two technologies… Dynamic Static Dynamic RAM is made with cells that store data as charge on capacitors. The presence or absence of charge in a capacitor is interpreted as a binary 1 or 0. Because capacitors have natural tendency to discharge, dynamic RAM requires periodic charge refreshing to maintain data storage. The term dynamic refers to the tendency of the stored charge to leak away, even with power continuously applied. Although the DRAM cell is used to store a single bit (0 or 1), it is essentially an analogue device. The capacitor can store any charge value within a range, a threshold value determines whether the charge is interpreted as a 1 or 0. SRAM (Static RAM) SRAM is a digital device that uses the same logic elements used in the processor. In SRAM, binary values are stored using traditional flip flop logic configurations. SRAM will hold its data as along as power is supplied to it. Unlike DRAM, no refresh is required to retain data. SRAM vs. DRAM DRAM is simpler and smaller than SRAM. Thus it is more dense and less expensive than SRAM. The cost of the refreshing circuitry for DRAM needs to be considered, but if the machine requires a large amount of memory, DRAM turns out to be cheaper than SRAM. SRAMS are somewhat faster than DRAM, thus SRAM is generally used for cache memory and DRAM is used for main memory. Types of ROM Read Only Memory (ROM) contains a permanent pattern of data that cannot be changed. ROM is non volatile meaning no power source is required to maintain the bit values in memory. While it is possible to read a ROM, it is not possible to write new data into it. An important application of ROM is microprogramming, other applications include library subroutines for frequently wanted functions, System programs, Function tables. A ROM is created like any other integrated circuit chip, with the data actually wired into the chip as part of the fabrication process. To reduce costs of fabrication, we have PROMS. PROMS are… Written only once Non-volatile Written after fabrication Another variation of ROM is the read-mostly memory, which is useful for applications in which read operations are far more frequent than write operations, but for which non volatile storage is required. There are three common forms of read-mostly memory, namely… EPROM EEPROM Flash memory Error Correction Semiconductor memory is subject to errors, which can be classed into two categories… Hard failure – Permanent physical defect so that the memory cell or cells cannot reliably store data Soft failure – Random error that alters the contents of one or more memory cells without damaging the memory (common cause includes power supply issues, etc.) Most modern main memory systems include logic for both detecting and correcting errors. Error detection works as follows… When data is to be read into memory, a calculation is performed on the data to produce a code Both the code and the data are stored When the previously stored word is read out, the code is used to detect and possibly correct errors The error checking provides one of 3 possible results… No errors are detected – the fetched data bits are sent out An error is detected, and it is possible to correct the error. The data bits plus error correction bits are fed into a corrector, which produces a corrected set of bits to be sent out An error is detected, but it is not possible to correct it. This condition is reported Hamming Code See wiki for detailed explanation. We will probably need to know how to do a hemming code – refer to the textbook (pg. 188 – 189) Advanced DRAM organization One of the most critical system bottlenecks when using high-performance processors is the interface to main memory. This interface is the most important pathway in the entire computer system. The basic building block of main memory remains the DRAM chip. In recent years a number of enhancements to the basic DRAM architecture have been explored, and some of these are now on the market including… SDRAM (Synchronous DRAM) DDR-DRAM RDRAM SDRAM (Synchronous DRAM) SDRAM exchanges data with the processor synchronized to an external clock signal and running at the full speed of the processor/memory bus without imposing wait states. SDRAM employs a burst mode to eliminate the address setup time and row and column line precharge time after the first access In burst mode a series of data bits can be clocked out rapidly after the first bit has been accessed SDRAM has a multiple bank internal architecture that improves opportunities for on chip parallelism SDRAM performs best when it is transferring large blocks of data serially There is now an enhanced version of SDRAM known as double data rate SDRAM or DDR-SDRAM that overcomes the once-per-cycle limitation of SDRAM

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  • How do I convert tuple of tuples to list in one line (pythonic)?

    - by ThinkCode
    query = 'select mydata from mytable' cursor.execute(query) myoutput = cursor.fetchall() print myoutput (('aa',), ('bb',), ('cc',)) Why is it (cursor.fetchall) returning a tuple of tuples instead of a tuple since my query is asking for only one column of data? What is the best way of converting it to ['aa', 'bb', 'cc'] ? I can do something like this : mylist = [] myoutput = list(myoutput) for each in myoutput: mylist.append(each[0]) I am sure this isn't the best way of doing it. Please enlighten me!

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  • Calling a protected Windows executable with Perl

    - by Jake
    I'm trying to write a perl script that determines which users are currently logged into Windows by using query.exe (c:\Windows\system32\query.exe). Perl is unable to access this file, unable to execute it, even unable to see that it exists, as I've found with the following code: print `dir c:\\windows\\system32\\query*`; This produces the following output: 07/13/2009 05:16 PM 1,363,456 Query.dll 1 File(s) 1,363,456 bytes 0 Dir(s) 183,987,658,752 bytes free I've checked the user executing the script using perl's getlogin function, and it returns the name of a member of the local Administrators group (specifically, me). I've also tried adding read/execute permissions for "Everyone", but windows keeps giving me access denied errors when I try to modify this file's permissions. Finally, I've tried running perl.exe as an administrator but that doesn't fix the problem either. Is this something I can solve by changing some settings in Windows? Do I need to add something to my perl script? Or is there just no way to grant perl access to some of these processes?

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  • How to improve INSERT INTO ... SELECT locking behavior

    - by Artem
    In our production database, we ran the following pseudo-code SQL batch query running every hour: INSERT INTO TemporaryTable (SELECT FROM HighlyContentiousTableInInnoDb WHERE allKindsOfComplexConditions are true) Now this query itself does not need to be fast, but I noticed it was locking up HighlyContentiousTableInInnoDb, even though it was just reading from it. Which was making some other very simple queries take ~25 seconds (that's how long that other query takes). Then I discovered that InnoDB tables in such a case are actually locked by a SELECT! http://www.mysqlperformanceblog.com/2006/07/12/insert-into-select-performance-with-innodb-tables/ But I don't really like the solution in the article of selecting into an OUTFILE, it seems like a hack (temporary files on filesystem seem sucky). Any other ideas? Is there a way to make a full copy of an InnoDB table without locking it in this way during the copy. Then I could just copy the HighlyContentiousTable to another table and do the query there.

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  • exec problem in sql 2005

    - by IordanTanev
    Hi, i have the situation where i have two databases whith same structure. The first have some data in its datatables. I need to create a script that will transfer the data from the first database to the second. I have created this script. DECLARE @table_name nvarchar(MAX), @query nvarchar(MAX) DECLARE @table_cursor CURSOR SET @table_cursor = CURSOR FAST_FORWARD FOR Select TABLE_NAME FROM INFORMATION_SCHEMA.TABLES OPEN @table_cursor FETCH NEXT FROM @table_cursor INTO @table_name WHILE @@FETCH_STATUS = 0 BEGIN SET @query = 'INSERT INTO ' + @table_name + ' SELECT * FROM MyDataBase.dbo.' + @table_name print @query exec @query FETCH NEXT FROM @table_cursor INTO @table_name END CLOSE @table_cursor DEALLOCATE @table_cursor The problem is that when i run th script the "print @query" statement prints statement like this INSERT INTO table SELECT * FROM MyDataBase.dbo.table When i copy this and run it from Management studio it works fine. But when the script trys to run it with exec i get this error Msg 911, Level 16, State 1, Line 21 Could not locate entry in sysdatabases for database 'INSERT INTO table SELECT * FROM MPDEV090314'. No entry found with that name. Make sure that the name is entered correctly. Hope someone can tell me whot is wront with this. Best Regards, Iordan Tanev

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