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  • MySQL reserves too much RAM

    - by Buddy
    I have a cheap VPS with 128Mb RAM and 256Mb burst. MySQL starts and reserves about 110Mb, but uses not more than 20Mb of them. My VPS Control Panel shows, that I use 127Mb (I also running nginx and sphinx), I know, that it shows reserved RAM, but when I reach over 128Mb, my VPS reboots automatically every 4 hours. So I want to force MySQL to reserve less RAM. How can i do that? I did some tweaks with my.conf but it helped not so much. top output: PID USER PR NI VIRT RES SHR S %CPU %MEM TIME+ COMMAND 1 root 15 0 2156 668 572 S 0.0 0.3 0:00.03 init 11311 root 15 0 11212 356 228 S 0.0 0.1 0:00.00 vzctl 11312 root 18 0 3712 1484 1248 S 0.0 0.6 0:00.01 bash 11347 root 18 0 2284 916 732 R 0.0 0.3 0:00.00 top 13978 root 17 -4 2248 552 344 S 0.0 0.2 0:00.00 udevd 14262 root 15 0 1812 564 472 S 0.0 0.2 0:00.03 syslogd 14293 sphinx 15 0 11816 1172 672 S 0.0 0.4 0:00.07 searchd 14305 root 25 0 7192 1036 636 S 0.0 0.4 0:00.00 sshd 14321 root 25 0 2832 836 668 S 0.0 0.3 0:00.00 xinetd 15389 root 18 0 3708 1300 1132 S 0.0 0.5 0:00.00 mysqld_safe 15441 mysql 15 0 113m 16m 4440 S 0.0 6.4 0:00.15 mysqld 15489 root 21 0 13056 1456 340 S 0.0 0.6 0:00.00 nginx 15490 nginx 18 0 13328 2388 992 S 0.0 0.9 0:00.06 nginx 15507 nginx 25 0 19520 5888 4244 S 0.0 2.2 0:00.00 php-cgi 15508 nginx 18 0 19636 4876 2748 S 0.0 1.9 0:00.12 php-cgi 15509 nginx 15 0 19668 4872 2716 S 0.0 1.9 0:00.11 php-cgi 15518 root 18 0 4492 1116 568 S 0.0 0.4 0:00.01 crond MySQL tuner: >> MySQLTuner 1.0.1 - Major Hayden <[email protected]> >> Bug reports, feature requests, and downloads at http://mysqltuner.com/ >> Run with '--help' for additional options and output filtering Please enter your MySQL administrative login: root Please enter your MySQL administrative password: -------- General Statistics -------------------------------------------------- [--] Skipped version check for MySQLTuner script [OK] Currently running supported MySQL version 5.0.77 [OK] Operating on 32-bit architecture with less than 2GB RAM -------- Storage Engine Statistics ------------------------------------------- [--] Status: -Archive -BDB -Federated +InnoDB -ISAM -NDBCluster [--] Data in InnoDB tables: 1M (Tables: 1) [OK] Total fragmented tables: 0 -------- Performance Metrics ------------------------------------------------- [--] Up for: 38m 43s (37 q [0.016 qps], 20 conn, TX: 4M, RX: 3K) [--] Reads / Writes: 100% / 0% [--] Total buffers: 28.1M global + 832.0K per thread (100 max threads) [OK] Maximum possible memory usage: 109.4M (42% of installed RAM) [OK] Slow queries: 0% (0/37) [OK] Highest usage of available connections: 1% (1/100) [OK] Key buffer size / total MyISAM indexes: 128.0K/64.0K [OK] Query cache efficiency: 42.1% (8 cached / 19 selects) [OK] Query cache prunes per day: 0 [!!] Temporary tables created on disk: 27% (3 on disk / 11 total) [!!] Thread cache is disabled [OK] Table cache hit rate: 57% (8 open / 14 opened) [OK] Open file limit used: 1% (12/1K) [OK] Table locks acquired immediately: 100% (22 immediate / 22 locks) [!!] Connections aborted: 10% [OK] InnoDB data size / buffer pool: 1.5M/8.0M -------- Recommendations ----------------------------------------------------- General recommendations: MySQL started within last 24 hours - recommendations may be inaccurate Enable the slow query log to troubleshoot bad queries When making adjustments, make tmp_table_size/max_heap_table_size equal Reduce your SELECT DISTINCT queries without LIMIT clauses Set thread_cache_size to 4 as a starting value Your applications are not closing MySQL connections properly Variables to adjust: tmp_table_size (> 32M) max_heap_table_size (> 16M) thread_cache_size (start at 4) I think if I do what MySQLtuner says, MySQL will use more RAM.

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  • Using linked servers, OPENROWSET and OPENQUERY

    - by BuckWoody
    SQL Server has a few mechanisms to reach out to another server (even another server type) and query data from within a Transact-SQL statement. Among them are a set of stored credentials and information (called a Linked Server), a statement that uses a linked server called called OPENQUERY, another called OPENROWSET, and one called OPENDATASOURCE. This post isn’t about those particular functions or statements – hit the links for more if you’re new to those topics. I’m actually more concerned about where I see these used than the particular method. In many cases, a Linked server isn’t another Relational Database Management System (RDMBS) like Oracle or DB2 (which is possible with a linked server), but another SQL Server. My concern is that linked servers are the new Data Transformation Services (DTS) from SQL Server 2000 – something that was designed for one purpose but which is being morphed into something much more. In the case of DTS, most of us turned that feature into a full-fledged job system. What was designed as a simple data import and export system has been pressed into service doing logic, routing and timing. And of course we all know how painful it was to move off of a complex DTS system onto SQL Server Integration Services. In the case of linked servers, what should be used as a method of running a simple query or two on another server where you have occasional connection or need a quick import of a small data set is morphing into a full federation strategy. In some cases I’ve seen a complex web of linked servers, and when credentials, names or anything else changes there are huge problems. Now don’t get me wrong – linked servers and other forms of distributing queries is a fantastic set of tools that we have to move data around. I’m just saying that when you start having lots of workarounds and when things get really complicated, you might want to step back a little and ask if there’s a better way. Are you able to tolerate some latency? Perhaps you’re able to use Service Broker. Would you like to be platform-independent on the data source? Perhaps a middle-tier might make more sense, abstracting the queries there and sending them to the proper server. Designed properly, I’ve seen these systems scale further and be more resilient than loading up on linked servers. Share this post: email it! | bookmark it! | digg it! | reddit! | kick it! | live it!

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  • Google Search Parameter Question

    - by Brian
    I've been trying to determine different parameters used by Google in their search queries. In particular, the usg parameter is what is giving me troubles. Here is an example value given for it, which is from an actual Google query: usg=0_zDqudnCN52ATGjAl3tignXNtBo4%3D Does anyone know what it could be for / recognize it? I've done a bit of digging, but haven't found any confirmation as to what it could be. Here is the link that I took a look at: http://www.webmasterworld.com/google/3892573.htm

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  • ADNOC talks about 50x increase in performance

    - by KLaker
    If you are still wondering about how Exadata can revolutionise your business then I would recommend watching this great video which was recorded at this year's OpenWorld. First a little background...The Abu Dhabi National Oil Company for Distribution (ADNOC) is an integrated energy company that was founded in 1973. ADNOC Distribution markets and distributes petroleum products and services within the United Arab Emirates and internationally. As one of the largest and most innovative government-owned petroleum companies in the Arab Gulf, ADNOC Distribution is renowned and respected for the exceptional quality and reliability of its products and services. Its five corporate divisions include more than 200 filling stations (a number that is growing at 8% annually), more than 150 convenience stores, 10 vehicle inspection stations, as well as wholesale and retail sales of bulk fuel, gas, oil, diesel, and lubricants. ADNOC selected Oracle Exadata Database Machine after extensive research because it provided them with a single platform that can run mixed workloads in a single unified machine: "We chose Oracle Exadata Database Machine because it.offered a fully integrated and highly engineered system that was ready to deploy. With our infrastructure running all the same technology, we can operate any type of Oracle Database without restrictions and be prepared for business growth," said Ali Abdul Aziz Al-Ali, IT division manager, ADNOC Distribution. ".....we could consolidate our transaction processing and business intelligence onto one platform. Competing solutions are just not capable of doing that." - Awad Ahmed Ali El-Sidiq, Senior Database Administrator, ADNOC Distribution In this new video Awad Ahmen Ali El Sidddig, Senior DBA at ADNOC, talks about the impact that Exadata has had on his team and the whole business. ADNOC is using our engineered systems to drive and manage all their workloads: from transaction systems to payments system to data warehouse to BI environment. A true Disk-to-Dashboard revolution using Engineered Systems. This engineered approach is delivering 50x improvement in performance with one queries running 100x faster! The IT has even revolutionised some of their data warehouse related processes with the help of Exadata and now jobs that were taking over 4 hours now run in a few minutes.  To watch the video click on the image below which will take you to our Oracle YouTube page: (if the above link does not work, click here: http://www.youtube.com/watch?v=zcRpxc6u5Ic) Now that queries are running 100x faster and jobs are completing in minutes not hours, what is next for the IT team at ADNOC? Like many of our customers ADNOC is now looking to take advantage of big data to help them better align their business operations with customer behaviour and customer insights. To help deliver this next level of insight the IT team is looking at the new features in Oracle Database 12c such as the new in-memory feature to deliver even more performance gains.  The great news is that Awad Ahmen Ali El Sidddig was awarded DBA of the Year - EMEA within our Data Warehouse Global Leaders programme and you can see the badge for this award pop-up at the start of video. Well done to everyone at ADNOC and thanks for spending the time with us at OOW to create this great video.

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  • T-SQL Equivalents for Microsoft Access VBA Functions

    If you need to migrate your Access application to SQL Server, don't count on The SQL Server Upsize Wizard in Microsoft Access to automatically convert your VBA functions. If you want to push the complex query processing done by your Access queries to the back end, you'll have to rewrite them in T-SQL.

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  • An XEvent a Day (13 of 31) – The system_health Session

    - by Jonathan Kehayias
    Today’s post was originally planned for this coming weekend, but seems I’ve caught whatever bug my kids had over the weekend so I am changing up today’s blog post with one that is easier to cover and shorter.  If you’ve been running some of the queries from the posts in this series, you have no doubt come across an Event Session running on your server with the name of system_health.  In today’s post I’ll go over this session and provide links to references related to it. When Extended Events...(read more)

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  • Basic Spatial Data with SQL Server and Entity Framework 5.0

    - by Rick Strahl
    In my most recent project we needed to do a bit of geo-spatial referencing. While spatial features have been in SQL Server for a while using those features inside of .NET applications hasn't been as straight forward as could be, because .NET natively doesn't support spatial types. There are workarounds for this with a few custom project like SharpMap or a hack using the Sql Server specific Geo types found in the Microsoft.SqlTypes assembly that ships with SQL server. While these approaches work for manipulating spatial data from .NET code, they didn't work with database access if you're using Entity Framework. Other ORM vendors have been rolling their own versions of spatial integration. In Entity Framework 5.0 running on .NET 4.5 the Microsoft ORM finally adds support for spatial types as well. In this post I'll describe basic geography features that deal with single location and distance calculations which is probably the most common usage scenario. SQL Server Transact-SQL Syntax for Spatial Data Before we look at how things work with Entity framework, lets take a look at how SQL Server allows you to use spatial data to get an understanding of the underlying semantics. The following SQL examples should work with SQL 2008 and forward. Let's start by creating a test table that includes a Geography field and also a pair of Long/Lat fields that demonstrate how you can work with the geography functions even if you don't have geography/geometry fields in the database. Here's the CREATE command:CREATE TABLE [dbo].[Geo]( [id] [int] IDENTITY(1,1) NOT NULL, [Location] [geography] NULL, [Long] [float] NOT NULL, [Lat] [float] NOT NULL ) Now using plain SQL you can insert data into the table using geography::STGeoFromText SQL CLR function:insert into Geo( Location , long, lat ) values ( geography::STGeomFromText ('POINT(-121.527200 45.712113)', 4326), -121.527200, 45.712113 ) insert into Geo( Location , long, lat ) values ( geography::STGeomFromText ('POINT(-121.517265 45.714240)', 4326), -121.517265, 45.714240 ) insert into Geo( Location , long, lat ) values ( geography::STGeomFromText ('POINT(-121.511536 45.714825)', 4326), -121.511536, 45.714825) The STGeomFromText function accepts a string that points to a geometric item (a point here but can also be a line or path or polygon and many others). You also need to provide an SRID (Spatial Reference System Identifier) which is an integer value that determines the rules for how geography/geometry values are calculated and returned. For mapping/distance functionality you typically want to use 4326 as this is the format used by most mapping software and geo-location libraries like Google and Bing. The spatial data in the Location field is stored in binary format which looks something like this: Once the location data is in the database you can query the data and do simple distance computations very easily. For example to calculate the distance of each of the values in the database to another spatial point is very easy to calculate. Distance calculations compare two points in space using a direct line calculation. For our example I'll compare a new point to all the points in the database. Using the Location field the SQL looks like this:-- create a source point DECLARE @s geography SET @s = geography:: STGeomFromText('POINT(-121.527200 45.712113)' , 4326); --- return the ids select ID, Location as Geo , Location .ToString() as Point , @s.STDistance( Location) as distance from Geo order by distance The code defines a new point which is the base point to compare each of the values to. You can also compare values from the database directly, but typically you'll want to match a location to another location and determine the difference for which you can use the geography::STDistance function. This query produces the following output: The STDistance function returns the straight line distance between the passed in point and the point in the database field. The result for SRID 4326 is always in meters. Notice that the first value passed was the same point so the difference is 0. The other two points are two points here in town in Hood River a little ways away - 808 and 1256 meters respectively. Notice also that you can order the result by the resulting distance, which effectively gives you results that are ordered radially out from closer to further away. This is great for searches of points of interest near a central location (YOU typically!). These geolocation functions are also available to you if you don't use the Geography/Geometry types, but plain float values. It's a little more work, as each point has to be created in the query using the string syntax, but the following code doesn't use a geography field but produces the same result as the previous query.--- using float fields select ID, geography::STGeomFromText ('POINT(' + STR (long, 15,7 ) + ' ' + Str(lat ,15, 7) + ')' , 4326), geography::STGeomFromText ('POINT(' + STR (long, 15,7 ) + ' ' + Str(lat ,15, 7) + ')' , 4326). ToString(), @s.STDistance( geography::STGeomFromText ('POINT(' + STR(long ,15, 7) + ' ' + Str(lat ,15, 7) + ')' , 4326)) as distance from geo order by distance Spatial Data in the Entity Framework Prior to Entity Framework 5.0 on .NET 4.5 consuming of the data above required using stored procedures or raw SQL commands to access the spatial data. In Entity Framework 5 however, Microsoft introduced the new DbGeometry and DbGeography types. These immutable location types provide a bunch of functionality for manipulating spatial points using geometry functions which in turn can be used to do common spatial queries like I described in the SQL syntax above. The DbGeography/DbGeometry types are immutable, meaning that you can't write to them once they've been created. They are a bit odd in that you need to use factory methods in order to instantiate them - they have no constructor() and you can't assign to properties like Latitude and Longitude. Creating a Model with Spatial Data Let's start by creating a simple Entity Framework model that includes a Location property of type DbGeography: public class GeoLocationContext : DbContext { public DbSet<GeoLocation> Locations { get; set; } } public class GeoLocation { public int Id { get; set; } public DbGeography Location { get; set; } public string Address { get; set; } } That's all there's to it. When you run this now against SQL Server, you get a Geography field for the Location property, which looks the same as the Location field in the SQL examples earlier. Adding Spatial Data to the Database Next let's add some data to the table that includes some latitude and longitude data. An easy way to find lat/long locations is to use Google Maps to pinpoint your location, then right click and click on What's Here. Click on the green marker to get the GPS coordinates. To add the actual geolocation data create an instance of the GeoLocation type and use the DbGeography.PointFromText() factory method to create a new point to assign to the Location property:[TestMethod] public void AddLocationsToDataBase() { var context = new GeoLocationContext(); // remove all context.Locations.ToList().ForEach( loc => context.Locations.Remove(loc)); context.SaveChanges(); var location = new GeoLocation() { // Create a point using native DbGeography Factory method Location = DbGeography.PointFromText( string.Format("POINT({0} {1})", -121.527200,45.712113) ,4326), Address = "301 15th Street, Hood River" }; context.Locations.Add(location); location = new GeoLocation() { Location = CreatePoint(45.714240, -121.517265), Address = "The Hatchery, Bingen" }; context.Locations.Add(location); location = new GeoLocation() { // Create a point using a helper function (lat/long) Location = CreatePoint(45.708457, -121.514432), Address = "Kaze Sushi, Hood River" }; context.Locations.Add(location); location = new GeoLocation() { Location = CreatePoint(45.722780, -120.209227), Address = "Arlington, OR" }; context.Locations.Add(location); context.SaveChanges(); } As promised, a DbGeography object has to be created with one of the static factory methods provided on the type as the Location.Longitude and Location.Latitude properties are read only. Here I'm using PointFromText() which uses a "Well Known Text" format to specify spatial data. In the first example I'm specifying to create a Point from a longitude and latitude value, using an SRID of 4326 (just like earlier in the SQL examples). You'll probably want to create a helper method to make the creation of Points easier to avoid that string format and instead just pass in a couple of double values. Here's my helper called CreatePoint that's used for all but the first point creation in the sample above:public static DbGeography CreatePoint(double latitude, double longitude) { var text = string.Format(CultureInfo.InvariantCulture.NumberFormat, "POINT({0} {1})", longitude, latitude); // 4326 is most common coordinate system used by GPS/Maps return DbGeography.PointFromText(text, 4326); } Using the helper the syntax becomes a bit cleaner, requiring only a latitude and longitude respectively. Note that my method intentionally swaps the parameters around because Latitude and Longitude is the common format I've seen with mapping libraries (especially Google Mapping/Geolocation APIs with their LatLng type). When the context is changed the data is written into the database using the SQL Geography type which looks the same as in the earlier SQL examples shown. Querying Once you have some location data in the database it's now super easy to query the data and find out the distance between locations. A common query is to ask for a number of locations that are near a fixed point - typically your current location and order it by distance. Using LINQ to Entities a query like this is easy to construct:[TestMethod] public void QueryLocationsTest() { var sourcePoint = CreatePoint(45.712113, -121.527200); var context = new GeoLocationContext(); // find any locations within 5 kilometers ordered by distance var matches = context.Locations .Where(loc => loc.Location.Distance(sourcePoint) < 5000) .OrderBy( loc=> loc.Location.Distance(sourcePoint) ) .Select( loc=> new { Address = loc.Address, Distance = loc.Location.Distance(sourcePoint) }); Assert.IsTrue(matches.Count() > 0); foreach (var location in matches) { Console.WriteLine("{0} ({1:n0} meters)", location.Address, location.Distance); } } This example produces: 301 15th Street, Hood River (0 meters)The Hatchery, Bingen (809 meters)Kaze Sushi, Hood River (1,074 meters)   The first point in the database is the same as my source point I'm comparing against so the distance is 0. The other two are within the 5 mile radius, while the Arlington location which is 65 miles or so out is not returned. The result is ordered by distance from closest to furthest away. In the code, I first create a source point that is the basis for comparison. The LINQ query then selects all locations that are within 5km of the source point using the Location.Distance() function, which takes a source point as a parameter. You can either use a pre-defined value as I'm doing here, or compare against another database DbGeography property (say when you have to points in the same database for things like routes). What's nice about this query syntax is that it's very clean and easy to read and understand. You can calculate the distance and also easily order by the distance to provide a result that shows locations from closest to furthest away which is a common scenario for any application that places a user in the context of several locations. It's now super easy to accomplish this. Meters vs. Miles As with the SQL Server functions, the Distance() method returns data in meters, so if you need to work with miles or feet you need to do some conversion. Here are a couple of helpers that might be useful (can be found in GeoUtils.cs of the sample project):/// <summary> /// Convert meters to miles /// </summary> /// <param name="meters"></param> /// <returns></returns> public static double MetersToMiles(double? meters) { if (meters == null) return 0F; return meters.Value * 0.000621371192; } /// <summary> /// Convert miles to meters /// </summary> /// <param name="miles"></param> /// <returns></returns> public static double MilesToMeters(double? miles) { if (miles == null) return 0; return miles.Value * 1609.344; } Using these two helpers you can query on miles like this:[TestMethod] public void QueryLocationsMilesTest() { var sourcePoint = CreatePoint(45.712113, -121.527200); var context = new GeoLocationContext(); // find any locations within 5 miles ordered by distance var fiveMiles = GeoUtils.MilesToMeters(5); var matches = context.Locations .Where(loc => loc.Location.Distance(sourcePoint) <= fiveMiles) .OrderBy(loc => loc.Location.Distance(sourcePoint)) .Select(loc => new { Address = loc.Address, Distance = loc.Location.Distance(sourcePoint) }); Assert.IsTrue(matches.Count() > 0); foreach (var location in matches) { Console.WriteLine("{0} ({1:n1} miles)", location.Address, GeoUtils.MetersToMiles(location.Distance)); } } which produces: 301 15th Street, Hood River (0.0 miles)The Hatchery, Bingen (0.5 miles)Kaze Sushi, Hood River (0.7 miles) Nice 'n simple. .NET 4.5 Only Note that DbGeography and DbGeometry are exclusive to Entity Framework 5.0 (not 4.4 which ships in the same NuGet package or installer) and requires .NET 4.5. That's because the new DbGeometry and DbGeography (and related) types are defined in the 4.5 version of System.Data.Entity which is a CLR assembly and is only updated by major versions of .NET. Why this decision was made to add these types to System.Data.Entity rather than to the frequently updated EntityFramework assembly that would have possibly made this work in .NET 4.0 is beyond me, especially given that there are no native .NET framework spatial types to begin with. I find it also odd that there is no native CLR spatial type. The DbGeography and DbGeometry types are specific to Entity Framework and live on those assemblies. They will also work for general purpose, non-database spatial data manipulation, but then you are forced into having a dependency on System.Data.Entity, which seems a bit silly. There's also a System.Spatial assembly that's apparently part of WCF Data Services which in turn don't work with Entity framework. Another example of multiple teams at Microsoft not communicating and implementing the same functionality (differently) in several different places. Perplexed as a I may be, for EF specific code the Entity framework specific types are easy to use and work well. Working with pre-.NET 4.5 Entity Framework and Spatial Data If you can't go to .NET 4.5 just yet you can also still use spatial features in Entity Framework, but it's a lot more work as you can't use the DbContext directly to manipulate the location data. You can still run raw SQL statements to write data into the database and retrieve results using the same TSQL syntax I showed earlier using Context.Database.ExecuteSqlCommand(). Here's code that you can use to add location data into the database:[TestMethod] public void RawSqlEfAddTest() { string sqlFormat = @"insert into GeoLocations( Location, Address) values ( geography::STGeomFromText('POINT({0} {1})', 4326),@p0 )"; var sql = string.Format(sqlFormat,-121.527200, 45.712113); Console.WriteLine(sql); var context = new GeoLocationContext(); Assert.IsTrue(context.Database.ExecuteSqlCommand(sql,"301 N. 15th Street") > 0); } Here I'm using the STGeomFromText() function to add the location data. Note that I'm using string.Format here, which usually would be a bad practice but is required here. I was unable to use ExecuteSqlCommand() and its named parameter syntax as the longitude and latitude parameters are embedded into a string. Rest assured it's required as the following does not work:string sqlFormat = @"insert into GeoLocations( Location, Address) values ( geography::STGeomFromText('POINT(@p0 @p1)', 4326),@p2 )";context.Database.ExecuteSqlCommand(sql, -121.527200, 45.712113, "301 N. 15th Street") Explicitly assigning the point value with string.format works however. There are a number of ways to query location data. You can't get the location data directly, but you can retrieve the point string (which can then be parsed to get Latitude and Longitude) and you can return calculated values like distance. Here's an example of how to retrieve some geo data into a resultset using EF's and SqlQuery method:[TestMethod] public void RawSqlEfQueryTest() { var sqlFormat = @" DECLARE @s geography SET @s = geography:: STGeomFromText('POINT({0} {1})' , 4326); SELECT Address, Location.ToString() as GeoString, @s.STDistance( Location) as Distance FROM GeoLocations ORDER BY Distance"; var sql = string.Format(sqlFormat, -121.527200, 45.712113); var context = new GeoLocationContext(); var locations = context.Database.SqlQuery<ResultData>(sql); Assert.IsTrue(locations.Count() > 0); foreach (var location in locations) { Console.WriteLine(location.Address + " " + location.GeoString + " " + location.Distance); } } public class ResultData { public string GeoString { get; set; } public double Distance { get; set; } public string Address { get; set; } } Hopefully you don't have to resort to this approach as it's fairly limited. Using the new DbGeography/DbGeometry types makes this sort of thing so much easier. When I had to use code like this before I typically ended up retrieving data pks only and then running another query with just the PKs to retrieve the actual underlying DbContext entities. This was very inefficient and tedious but it did work. Summary For the current project I'm working on we actually made the switch to .NET 4.5 purely for the spatial features in EF 5.0. This app heavily relies on spatial queries and it was worth taking a chance with pre-release code to get this ease of integration as opposed to manually falling back to stored procedures or raw SQL string queries to return spatial specific queries. Using native Entity Framework code makes life a lot easier than the alternatives. It might be a late addition to Entity Framework, but it sure makes location calculations and storage easy. Where do you want to go today? ;-) Resources Download Sample Project© Rick Strahl, West Wind Technologies, 2005-2012Posted in ADO.NET  Sql Server  .NET   Tweet !function(d,s,id){var js,fjs=d.getElementsByTagName(s)[0];if(!d.getElementById(id)){js=d.createElement(s);js.id=id;js.src="//platform.twitter.com/widgets.js";fjs.parentNode.insertBefore(js,fjs);}}(document,"script","twitter-wjs"); (function() { var po = document.createElement('script'); po.type = 'text/javascript'; po.async = true; po.src = 'https://apis.google.com/js/plusone.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(po, s); })();

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  • Is Berkeley DB a NoSQL solution?

    - by Gregory Burd
    Berkeley DB is a library. To use it to store data you must link the library into your application. You can use most programming languages to access the API, the calls across these APIs generally mimic the Berkeley DB C-API which makes perfect sense because Berkeley DB is written in C. The inspiration for Berkeley DB was the DBM library, a part of the earliest versions of UNIX written by AT&T's Ken Thompson in 1979. DBM was a simple key/value hashtable-based storage library. In the early 1990s as BSD UNIX was transitioning from version 4.3 to 4.4 and retrofitting commercial code owned by AT&T with unencumbered code, it was the future founders of Sleepycat Software who wrote libdb (aka Berkeley DB) as the replacement for DBM. The problem it addressed was fast, reliable local key/value storage. At that time databases almost always lived on a single node, even the most sophisticated databases only had simple fail-over two node solutions. If you had a lot of data to store you would choose between the few commercial RDBMS solutions or to write your own custom solution. Berkeley DB took the headache out of the custom approach. These basic market forces inspired other DBM implementations. There was the "New DBM" (ndbm) and the "GNU DBM" (GDBM) and a few others, but the theme was the same. Even today TokyoCabinet calls itself "a modern implementation of DBM" mimicking, and improving on, something first created over thirty years ago. In the mid-1990s, DBM was the name for what you needed if you were looking for fast, reliable local storage. Fast forward to today. What's changed? Systems are connected over fast, very reliable networks. Disks are cheep, fast, and capable of storing huge amounts of data. CPUs continued to follow Moore's Law, processing power that filled a room in 1990 now fits in your pocket. PCs, servers, and other computers proliferated both in business and the personal markets. In addition to the new hardware entire markets, social systems, and new modes of interpersonal communication moved onto the web and started evolving rapidly. These changes cause a massive explosion of data and a need to analyze and understand that data. Taken together this resulted in an entirely different landscape for database storage, new solutions were needed. A number of novel solutions stepped up and eventually a category called NoSQL emerged. The new market forces inspired the CAP theorem and the heated debate of BASE vs. ACID. But in essence this was simply the market looking at what to trade off to meet these new demands. These new database systems shared many qualities in common. There were designed to address massive amounts of data, millions of requests per second, and scale out across multiple systems. The first large-scale and successful solution was Dynamo, Amazon's distributed key/value database. Dynamo essentially took the next logical step and added a twist. Dynamo was to be the database of record, it would be distributed, data would be partitioned across many nodes, and it would tolerate failure by avoiding single points of failure. Amazon did this because they recognized that the majority of the dynamic content they provided to customers visiting their web store front didn't require the services of an RDBMS. The queries were simple, key/value look-ups or simple range queries with only a few queries that required more complex joins. They set about to use relational technology only in places where it was the best solution for the task, places like accounting and order fulfillment, but not in the myriad of other situations. The success of Dynamo, and it's design, inspired the next generation of Non-SQL, distributed database solutions including Cassandra, Riak and Voldemort. The problem their designers set out to solve was, "reliability at massive scale" so the first focal point was distributed database algorithms. Underneath Dynamo there is a local transactional database; either Berkeley DB, Berkeley DB Java Edition, MySQL or an in-memory key/value data structure. Dynamo was an evolution of local key/value storage onto networks. Cassandra, Riak, and Voldemort all faced similar design decisions and one, Voldemort, choose Berkeley DB Java Edition for it's node-local storage. Riak at first was entirely in-memory, but has recently added write-once, append-only log-based on-disk storage similar type of storage as Berkeley DB except that it is based on a hash table which must reside entirely in-memory rather than a btree which can live in-memory or on disk. Berkeley DB evolved too, we added high availability (HA) and a replication manager that makes it easy to setup replica groups. Berkeley DB's replication doesn't partitioned the data, every node keeps an entire copy of the database. For consistency, there is a single node where writes are committed first - a master - then those changes are delivered to the replica nodes as log records. Applications can choose to wait until all nodes are consistent, or fire and forget allowing Berkeley DB to eventually become consistent. Berkeley DB's HA scales-out quite well for read-intensive applications and also effectively eliminates the central point of failure by allowing replica nodes to be elected (using a PAXOS algorithm) to mastership if the master should fail. This implementation covers a wide variety of use cases. MemcacheDB is a server that implements the Memcache network protocol but uses Berkeley DB for storage and HA to replicate the cache state across all the nodes in the cache group. Google Accounts, the user authentication layer for all Google properties, was until recently running Berkeley DB HA. That scaled to a globally distributed system. That said, most NoSQL solutions try to partition (shard) data across nodes in the replication group and some allow writes as well as reads at any node, Berkeley DB HA does not. So, is Berkeley DB a "NoSQL" solution? Not really, but it certainly is a component of many of the existing NoSQL solutions out there. Forgetting all the noise about how NoSQL solutions are complex distributed databases when you boil them down to a single node you still have to store the data to some form of stable local storage. DBMs solved that problem a long time ago. NoSQL has more to do with the layers on top of the DBM; the distributed, sometimes-consistent, partitioned, scale-out storage that manage key/value or document sets and generally have some form of simple HTTP/REST-style network API. Does Berkeley DB do that? Not really. Is Berkeley DB a "NoSQL" solution today? Nope, but it's the most robust solution on which to build such a system. Re-inventing the node-local data storage isn't easy. A lot of people are starting to come to appreciate the sophisticated features found in Berkeley DB, even mimic them in some cases. Could Berkeley DB grow into a NoSQL solution? Absolutely. Our key/value API could be extended over the net using any of a number of existing network protocols such as memcache or HTTP/REST. We could adapt our node-local data partitioning out over replicated nodes. We even have a nice query language and cost-based query optimizer in our BDB XML product that we could reuse were we to build out a document-based NoSQL-style product. XML and JSON are not so different that we couldn't adapt one to work with the other interchangeably. Without too much effort we could add what's missing, we could jump into this No SQL market withing a single product development cycle. Why isn't Berkeley DB already a NoSQL solution? Why aren't we working on it? Why indeed...

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  • NHibernate Tools

    - by Ricardo Peres
    Felice Pollano is the author of a two great new tools for working with NHibernate: NH Workbench: an IDE for writing HQL queries against a model db2hbm: generation of .hbm.xml files from a database (currently only SQL Server, more to come) I suggest you give them a try and give Felix your feedback!

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  • SQL Server Scripts I Use

    - by Bill Graziano
    When I get to a new client I usually find myself using the same set of scripts for maintenance and troubleshooting.  These are all drop in solutions for various maintenance issues. Reindexing.  I use Michelle Ufford’s (SQLFool) re-indexing script.  I like that it has a throttle and only re-indexes when needed.  She also has a variety of other interesting scripts on her blog too. Server Activity.  Adam Machanic is up to version 10 of sp_WhoIsActive.  It’s a great replacement for the sp_who* stored procedures and does so much more.  If a server is acting funny this is one of the first tools I use. Backups.  Tara Kizer has a great little T-SQL script for SQL Server backups.  Wait Stats.  Paul Randal has a great script to display wait stats.  The biggest benefit for me is that his script filters out at least three dozen wait stats that I just don’t care about (for example LAZYWRITER_SLEEP). Update Statistics.  I didn’t find anything I liked so I wrote a simple script to update stats myself.  The big need for me was that it had to run inside a time window and update the oldest statistics first.  Is there a better one? Diagnostic Queries.  Glenn Berry has a huge collection of DMV queries available.  He also just highlighted five of them including two I really like dealing with unused indexes and suggested indexes. Single Use Query Plans.  Kim Tripp has a script that counts the number of single-use query plans.  This should guide you in whether to enable the Optimize for Adhoc Workloads option in SQL Server 2008. Granting Permissions to Developers.  This is one of those scripts I didn’t even know I needed until I needed it.  Kendra Little wrote it to grant a login read-only permission to all the databases.  It also grants view server state and a few other handy permissions.   What else do you use?  What should I add to my list?

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  • Reinventing the Wheel – Automating Data Consistency Checks with Powershell

    - by Jonathan Kehayias
    When I started in my current position at the beginning of the year, one of the first things that I did was to schedule a sit down with the various teams of Analysts that exist in our organization to find out more about their systems.  One thing I am always interested in is the manual processes that people do routinely that might be able to be automated.   A couple of the analyst mentioned that they routinely run queries in their systems to identify issues so that they can proactively...(read more)

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  • Reinventing the Wheel – Automating Data Consistency Checks with Powershell

    - by Jonathan Kehayias
    When I started in my current position at the beginning of the year, one of the first things that I did was to schedule a sit down with the various teams of Analysts that exist in our organization to find out more about their systems.  One thing I am always interested in is the manual processes that people do routinely that might be able to be automated.   A couple of the analyst mentioned that they routinely run queries in their systems to identify issues so that they can proactively...(read more)

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  • Working with Temporal Data in SQL Server

    - by Dejan Sarka
    My third Pluralsight course, Working with Temporal Data in SQL Server, is published. I am really proud on the second part of the course, where I discuss optimization of temporal queries. This was a nearly impossible task for decades. First solutions appeared only lately. I present all together six solutions (and one more that is not a solution), and I invented four of them. http://pluralsight.com/training/Courses/TableOfContents/working-with-temporal-data-sql-server

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  • Showplan Operator of the Week – BookMark/Key Lookup

    Fabiano continues in his mission to describe the major Showplan Operators used by SQL Server's Query Optimiser. This week he meets a star, the Key Lookup, a stalwart performer, but most famous for its role in ill-performing queries where an index does not 'cover' the data required to execute the query. If you understand why, and in what circumstances, key lookups are slow, it helps greatly with optimising query performance.

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  • Speed-start your Linux App: Using DB2 and the DB2 Control Center

    This article guides you through setting up and using IBM DB2 7.2 with the Command Line Processor. You'll also learn to use the graphical Control Center, which helps you explore and control your databases, and the graphical Command Center, which helps you generate SQL queries. Other topics covered include Java runtime environment setup, useful Linux utility functions, and bash profile customization.

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  • An XEvent a Day (27 of 31) – The Future - Tracking Page Splits in SQL Server Denali CTP1

    - by Jonathan Kehayias
    Nearly two years ago Kalen Delaney blogged about Splitting a page into multiple pages , showing how page splits occur inside of SQL Server.  Following her blog post, Michael Zilberstein wrote a post, Monitoring Page Splits with Extended Events , that showed how to see the sqlserver.page_split Events using Extended Events.  Eladio Rincón also blogged about Using XEvents (Extended Events) in SQL Server 2008 to detect which queries are causing Page Splits , but not in relation to Kalen’s blog...(read more)

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  • Have you really fixed that problem?

    - by DavidWimbush
    The day before yesterday I saw our main live server's CPU go up to constantly 100% with just the occasional short drop to a lower level. The exact opposite of what you'd want to see. We're log shipping every 15 minutes and part of that involves calling WinRAR to compress the log backups before copying them over. (We're on SQL2005 so there's no native compression and we have bandwidth issues with the connection to our remote site.) I realised the log shipping jobs were taking about 10 minutes and that most of that was spent shipping a 'live' reporting database that is completely rebuilt every 20 minutes. (I'm just trying to keep this stuff alive until I can improve it.) We can rebuild this database in minutes if we have to fail over so I disabled log shipping of that database. The log shipping went down to less than 2 minutes and I went off to the SQL Social evening in London feeling quite pleased with myself. It was a great evening - fun, educational and thought-provoking. Thanks to Simon Sabin & co for laying that on, and thanks too to the guests for making the effort when they must have been pretty worn out after doing DevWeek all day first. The next morning I came down to earth with a bump: CPU still at 100%. WTF? I looked in the activity monitor but it was confusing because some sessions have been running for a long time so it's not a good guide what's using the CPU now. I tried the standard reports showing queries by CPU (average and total) but they only show the top 10 so they just show my big overnight archiving and data cleaning stuff. But the Profiler showed it was four queries used by our new website usage tracking system. Four simple indexes later the CPU was back where it should be: about 20% with occasional short spikes. So the moral is: even when you're convinced you've found the cause and fixed the problem, you HAVE to go back and confirm that the problem has gone. And, yes, I have checked the CPU again today and it's still looking sweet.

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  • No search data in Google Analytics or Webmasters

    - by cjk
    I have a domain that has been registered in Google Webmasters and using Google Analytics for over 4 months. I get lots of analytics data, but am getting no information on Google searches in Webmasters, or Queries in Search Engine Optimisation in Analytics, even though I am getting keywords for traffic coming to my site from search engines. I have a test sub-domain with the same setup (except not HTTPS) that is getting some of this information through, even with much less data and visits. What could be wrong to stop me getting this information?

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  • Oracle Coherence 3.5 : Create Internet-scale applications using Oracle's high-performance data grid

    - by frederic.michiara
    Oracle Coherence Coherence provides replicated and distributed (partitioned) data management and caching services on top of a reliable, highly scalable peer-to-peer clustering protocol. Coherence has no single points of failure; it automatically and transparently fails over and redistributes its clustered data management services when a server becomes inoperative or is disconnected from the network. When a new server is added, or when a failed server is restarted, it automatically joins the cluster and Coherence fails back services to it, transparently redistributing the cluster load. Coherence includes network-level fault tolerance features and transparent soft re-start capability to enable servers to self-heal. For the ones looking at an easy reading and first good approach to Oracle Coherence, I would recommend reading the following book : Overview of Oracle Coherence 3.5 Build scalable web sites and Enterprise applications using a market-leading data grid product Design and implement your domain objects to work most effectively with Coherence and apply Domain Driven Designs (DDD) to Coherence applications Leverage Coherence events and continuous queries to provide real-time updates to client applications Successfully integrate various persistence technologies, such as JDBC, Hibernate, or TopLink, with Coherence Filled with numerous examples that provide best practice guidance, and a number of classes you can readily reuse within your own applications This book is targeted to Architects and developers, and as in our team we're more about Solutions Architects than developers I found interest in this book as it help to understand better Oracle Coherence and its value. The only point I may not agree with the authors is that Oracle Coherence is not an alternative to Oracle RAC in providing High Availability, but combining both Oracle RAC and Oracle Coherence will help Architects and Customers to reach higher level of service and high-availability. This book is available on https://www.packtpub.com/oracle-coherence-3-5/book Need to find out about Table of contents : https://www.packtpub.com/toc/oracle-coherence-35-table-contents Discover a sample chapter : https://www.packtpub.com/sites/default/files/6125_Oracle%20Coherence_SampleChapter.pdf Read also articles from the Authors on http://www.packtpub.com/ : Working with Aggregators in Oracle Coherence 3.5 Working with Value Extractors and Simplifying Queries in Oracle Coherence 3.5 Querying the Data Grid in Coherence 3.5: Obtaining Query Results and Using Indexes Installing Coherence 3.5 and Accessing the Data Grid: Part 1 Installing Coherence 3.5 and Accessing the Data Grid: Part 2 For more information on Oracle Coherence : What Oracle Coherence Can Do for You... : http://www.oracle.com/technology/products/coherence/coherencedatagrid/coherence_solutions.html Oracle Coherence on OTN : http://www.oracle.com/technology/products/coherence/index.html Oracle Coherence Knowledge Base : http://coherence.oracle.com/display/COH/Oracle+Coherence+Knowledge+Base+Home

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  • SPARC T4-4 Beats 8-CPU IBM POWER7 on TPC-H @3000GB Benchmark

    - by Brian
    Oracle's SPARC T4-4 server delivered a world record TPC-H @3000GB benchmark result for systems with four processors. This result beats eight processor results from IBM (POWER7) and HP (x86). The SPARC T4-4 server also delivered better performance per core than these eight processor systems from IBM and HP. Comparisons below are based upon system to system comparisons, highlighting Oracle's complete software and hardware solution. This database world record result used Oracle's Sun Storage 2540-M2 arrays (rotating disk) connected to a SPARC T4-4 server running Oracle Solaris 11 and Oracle Database 11g Release 2 demonstrating the power of Oracle's integrated hardware and software solution. The SPARC T4-4 server based configuration achieved a TPC-H scale factor 3000 world record for four processor systems of 205,792 QphH@3000GB with price/performance of $4.10/QphH@3000GB. The SPARC T4-4 server with four SPARC T4 processors (total of 32 cores) is 7% faster than the IBM Power 780 server with eight POWER7 processors (total of 32 cores) on the TPC-H @3000GB benchmark. The SPARC T4-4 server is 36% better in price performance compared to the IBM Power 780 server on the TPC-H @3000GB Benchmark. The SPARC T4-4 server is 29% faster than the IBM Power 780 for data loading. The SPARC T4-4 server is up to 3.4 times faster than the IBM Power 780 server for the Refresh Function. The SPARC T4-4 server with four SPARC T4 processors is 27% faster than the HP ProLiant DL980 G7 server with eight x86 processors on the TPC-H @3000GB benchmark. The SPARC T4-4 server is 52% faster than the HP ProLiant DL980 G7 server for data loading. The SPARC T4-4 server is up to 3.2 times faster than the HP ProLiant DL980 G7 for the Refresh Function. The SPARC T4-4 server achieved a peak IO rate from the Oracle database of 17 GB/sec. This rate was independent of the storage used, as demonstrated by the TPC-H @3000TB benchmark which used twelve Sun Storage 2540-M2 arrays (rotating disk) and the TPC-H @1000TB benchmark which used four Sun Storage F5100 Flash Array devices (flash storage). [*] The SPARC T4-4 server showed linear scaling from TPC-H @1000GB to TPC-H @3000GB. This demonstrates that the SPARC T4-4 server can handle the increasingly larger databases required of DSS systems. [*] The SPARC T4-4 server benchmark results demonstrate a complete solution of building Decision Support Systems including data loading, business questions and refreshing data. Each phase usually has a time constraint and the SPARC T4-4 server shows superior performance during each phase. [*] The TPC believes that comparisons of results published with different scale factors are misleading and discourages such comparisons. Performance Landscape The table lists the leading TPC-H @3000GB results for non-clustered systems. TPC-H @3000GB, Non-Clustered Systems System Processor P/C/T – Memory Composite(QphH) $/perf($/QphH) Power(QppH) Throughput(QthH) Database Available SPARC Enterprise M9000 3.0 GHz SPARC64 VII+ 64/256/256 – 1024 GB 386,478.3 $18.19 316,835.8 471,428.6 Oracle 11g R2 09/22/11 SPARC T4-4 3.0 GHz SPARC T4 4/32/256 – 1024 GB 205,792.0 $4.10 190,325.1 222,515.9 Oracle 11g R2 05/31/12 SPARC Enterprise M9000 2.88 GHz SPARC64 VII 32/128/256 – 512 GB 198,907.5 $15.27 182,350.7 216,967.7 Oracle 11g R2 12/09/10 IBM Power 780 4.1 GHz POWER7 8/32/128 – 1024 GB 192,001.1 $6.37 210,368.4 175,237.4 Sybase 15.4 11/30/11 HP ProLiant DL980 G7 2.27 GHz Intel Xeon X7560 8/64/128 – 512 GB 162,601.7 $2.68 185,297.7 142,685.6 SQL Server 2008 10/13/10 P/C/T = Processors, Cores, Threads QphH = the Composite Metric (bigger is better) $/QphH = the Price/Performance metric in USD (smaller is better) QppH = the Power Numerical Quantity QthH = the Throughput Numerical Quantity The following table lists data load times and refresh function times during the power run. TPC-H @3000GB, Non-Clustered Systems Database Load & Database Refresh System Processor Data Loading(h:m:s) T4Advan RF1(sec) T4Advan RF2(sec) T4Advan SPARC T4-4 3.0 GHz SPARC T4 04:08:29 1.0x 67.1 1.0x 39.5 1.0x IBM Power 780 4.1 GHz POWER7 05:51:50 1.5x 147.3 2.2x 133.2 3.4x HP ProLiant DL980 G7 2.27 GHz Intel Xeon X7560 08:35:17 2.1x 173.0 2.6x 126.3 3.2x Data Loading = database load time RF1 = power test first refresh transaction RF2 = power test second refresh transaction T4 Advan = the ratio of time to T4 time Complete benchmark results found at the TPC benchmark website http://www.tpc.org. Configuration Summary and Results Hardware Configuration: SPARC T4-4 server 4 x SPARC T4 3.0 GHz processors (total of 32 cores, 128 threads) 1024 GB memory 8 x internal SAS (8 x 300 GB) disk drives External Storage: 12 x Sun Storage 2540-M2 array storage, each with 12 x 15K RPM 300 GB drives, 2 controllers, 2 GB cache Software Configuration: Oracle Solaris 11 11/11 Oracle Database 11g Release 2 Enterprise Edition Audited Results: Database Size: 3000 GB (Scale Factor 3000) TPC-H Composite: 205,792.0 QphH@3000GB Price/performance: $4.10/QphH@3000GB Available: 05/31/2012 Total 3 year Cost: $843,656 TPC-H Power: 190,325.1 TPC-H Throughput: 222,515.9 Database Load Time: 4:08:29 Benchmark Description The TPC-H benchmark is a performance benchmark established by the Transaction Processing Council (TPC) to demonstrate Data Warehousing/Decision Support Systems (DSS). TPC-H measurements are produced for customers to evaluate the performance of various DSS systems. These queries and updates are executed against a standard database under controlled conditions. Performance projections and comparisons between different TPC-H Database sizes (100GB, 300GB, 1000GB, 3000GB, 10000GB, 30000GB and 100000GB) are not allowed by the TPC. TPC-H is a data warehousing-oriented, non-industry-specific benchmark that consists of a large number of complex queries typical of decision support applications. It also includes some insert and delete activity that is intended to simulate loading and purging data from a warehouse. TPC-H measures the combined performance of a particular database manager on a specific computer system. The main performance metric reported by TPC-H is called the TPC-H Composite Query-per-Hour Performance Metric (QphH@SF, where SF is the number of GB of raw data, referred to as the scale factor). QphH@SF is intended to summarize the ability of the system to process queries in both single and multiple user modes. The benchmark requires reporting of price/performance, which is the ratio of the total HW/SW cost plus 3 years maintenance to the QphH. A secondary metric is the storage efficiency, which is the ratio of total configured disk space in GB to the scale factor. Key Points and Best Practices Twelve Sun Storage 2540-M2 arrays were used for the benchmark. Each Sun Storage 2540-M2 array contains 12 15K RPM drives and is connected to a single dual port 8Gb FC HBA using 2 ports. Each Sun Storage 2540-M2 array showed 1.5 GB/sec for sequential read operations and showed linear scaling, achieving 18 GB/sec with twelve Sun Storage 2540-M2 arrays. These were stand alone IO tests. The peak IO rate measured from the Oracle database was 17 GB/sec. Oracle Solaris 11 11/11 required very little system tuning. Some vendors try to make the point that storage ratios are of customer concern. However, storage ratio size has more to do with disk layout and the increasing capacities of disks – so this is not an important metric in which to compare systems. The SPARC T4-4 server and Oracle Solaris efficiently managed the system load of over one thousand Oracle Database parallel processes. Six Sun Storage 2540-M2 arrays were mirrored to another six Sun Storage 2540-M2 arrays on which all of the Oracle database files were placed. IO performance was high and balanced across all the arrays. The TPC-H Refresh Function (RF) simulates periodical refresh portion of Data Warehouse by adding new sales and deleting old sales data. Parallel DML (parallel insert and delete in this case) and database log performance are a key for this function and the SPARC T4-4 server outperformed both the IBM POWER7 server and HP ProLiant DL980 G7 server. (See the RF columns above.) See Also Transaction Processing Performance Council (TPC) Home Page Ideas International Benchmark Page SPARC T4-4 Server oracle.com OTN Oracle Solaris oracle.com OTN Oracle Database 11g Release 2 Enterprise Edition oracle.com OTN Sun Storage 2540-M2 Array oracle.com OTN Disclosure Statement TPC-H, QphH, $/QphH are trademarks of Transaction Processing Performance Council (TPC). For more information, see www.tpc.org. SPARC T4-4 205,792.0 QphH@3000GB, $4.10/QphH@3000GB, available 5/31/12, 4 processors, 32 cores, 256 threads; IBM Power 780 QphH@3000GB, 192,001.1 QphH@3000GB, $6.37/QphH@3000GB, available 11/30/11, 8 processors, 32 cores, 128 threads; HP ProLiant DL980 G7 162,601.7 QphH@3000GB, $2.68/QphH@3000GB available 10/13/10, 8 processors, 64 cores, 128 threads.

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  • Using OpenQuery

    - by Derek Dieter
    The OPENQUERY command is used to initiate an ad-hoc distributed query using a linked-server. It is initiated by specifying OPENQUERY as the table name in the from clause. Essentially, it opens a linked server, then executes a query as if executing from that server. While executing queries directly and receiving data directly in this [...]

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  • Fun with Aggregates

    - by Paul White
    There are interesting things to be learned from even the simplest queries.  For example, imagine you are given the task of writing a query to list AdventureWorks product names where the product has at least one entry in the transaction history table, but fewer than ten. One possible query to meet that specification is: SELECT p.Name FROM Production.Product AS p JOIN Production.TransactionHistory AS th ON p.ProductID = th.ProductID GROUP BY p.ProductID, p.Name HAVING COUNT_BIG(*) < 10; That query correctly returns 23 rows (execution plan and data sample shown below): The execution plan looks a bit different from the written form of the query: the base tables are accessed in reverse order, and the aggregation is performed before the join.  The general idea is to read all rows from the history table, compute the count of rows grouped by ProductID, merge join the results to the Product table on ProductID, and finally filter to only return rows where the count is less than ten. This ‘fully-optimized’ plan has an estimated cost of around 0.33 units.  The reason for the quote marks there is that this plan is not quite as optimal as it could be – surely it would make sense to push the Filter down past the join too?  To answer that, let’s look at some other ways to formulate this query.  This being SQL, there are any number of ways to write logically-equivalent query specifications, so we’ll just look at a couple of interesting ones.  The first query is an attempt to reverse-engineer T-SQL from the optimized query plan shown above.  It joins the result of pre-aggregating the history table to the Product table before filtering: SELECT p.Name FROM ( SELECT th.ProductID, cnt = COUNT_BIG(*) FROM Production.TransactionHistory AS th GROUP BY th.ProductID ) AS q1 JOIN Production.Product AS p ON p.ProductID = q1.ProductID WHERE q1.cnt < 10; Perhaps a little surprisingly, we get a slightly different execution plan: The results are the same (23 rows) but this time the Filter is pushed below the join!  The optimizer chooses nested loops for the join, because the cardinality estimate for rows passing the Filter is a bit low (estimate 1 versus 23 actual), though you can force a merge join with a hint and the Filter still appears below the join.  In yet another variation, the < 10 predicate can be ‘manually pushed’ by specifying it in a HAVING clause in the “q1” sub-query instead of in the WHERE clause as written above. The reason this predicate can be pushed past the join in this query form, but not in the original formulation is simply an optimizer limitation – it does make efforts (primarily during the simplification phase) to encourage logically-equivalent query specifications to produce the same execution plan, but the implementation is not completely comprehensive. Moving on to a second example, the following query specification results from phrasing the requirement as “list the products where there exists fewer than ten correlated rows in the history table”: SELECT p.Name FROM Production.Product AS p WHERE EXISTS ( SELECT * FROM Production.TransactionHistory AS th WHERE th.ProductID = p.ProductID HAVING COUNT_BIG(*) < 10 ); Unfortunately, this query produces an incorrect result (86 rows): The problem is that it lists products with no history rows, though the reasons are interesting.  The COUNT_BIG(*) in the EXISTS clause is a scalar aggregate (meaning there is no GROUP BY clause) and scalar aggregates always produce a value, even when the input is an empty set.  In the case of the COUNT aggregate, the result of aggregating the empty set is zero (the other standard aggregates produce a NULL).  To make the point really clear, let’s look at product 709, which happens to be one for which no history rows exist: -- Scalar aggregate SELECT COUNT_BIG(*) FROM Production.TransactionHistory AS th WHERE th.ProductID = 709;   -- Vector aggregate SELECT COUNT_BIG(*) FROM Production.TransactionHistory AS th WHERE th.ProductID = 709 GROUP BY th.ProductID; The estimated execution plans for these two statements are almost identical: You might expect the Stream Aggregate to have a Group By for the second statement, but this is not the case.  The query includes an equality comparison to a constant value (709), so all qualified rows are guaranteed to have the same value for ProductID and the Group By is optimized away. In fact there are some minor differences between the two plans (the first is auto-parameterized and qualifies for trivial plan, whereas the second is not auto-parameterized and requires cost-based optimization), but there is nothing to indicate that one is a scalar aggregate and the other is a vector aggregate.  This is something I would like to see exposed in show plan so I suggested it on Connect.  Anyway, the results of running the two queries show the difference at runtime: The scalar aggregate (no GROUP BY) returns a result of zero, whereas the vector aggregate (with a GROUP BY clause) returns nothing at all.  Returning to our EXISTS query, we could ‘fix’ it by changing the HAVING clause to reject rows where the scalar aggregate returns zero: SELECT p.Name FROM Production.Product AS p WHERE EXISTS ( SELECT * FROM Production.TransactionHistory AS th WHERE th.ProductID = p.ProductID HAVING COUNT_BIG(*) BETWEEN 1 AND 9 ); The query now returns the correct 23 rows: Unfortunately, the execution plan is less efficient now – it has an estimated cost of 0.78 compared to 0.33 for the earlier plans.  Let’s try adding a redundant GROUP BY instead of changing the HAVING clause: SELECT p.Name FROM Production.Product AS p WHERE EXISTS ( SELECT * FROM Production.TransactionHistory AS th WHERE th.ProductID = p.ProductID GROUP BY th.ProductID HAVING COUNT_BIG(*) < 10 ); Not only do we now get correct results (23 rows), this is the execution plan: I like to compare that plan to quantum physics: if you don’t find it shocking, you haven’t understood it properly :)  The simple addition of a redundant GROUP BY has resulted in the EXISTS form of the query being transformed into exactly the same optimal plan we found earlier.  What’s more, in SQL Server 2008 and later, we can replace the odd-looking GROUP BY with an explicit GROUP BY on the empty set: SELECT p.Name FROM Production.Product AS p WHERE EXISTS ( SELECT * FROM Production.TransactionHistory AS th WHERE th.ProductID = p.ProductID GROUP BY () HAVING COUNT_BIG(*) < 10 ); I offer that as an alternative because some people find it more intuitive (and it perhaps has more geek value too).  Whichever way you prefer, it’s rather satisfying to note that the result of the sub-query does not exist for a particular correlated value where a vector aggregate is used (the scalar COUNT aggregate always returns a value, even if zero, so it always ‘EXISTS’ regardless which ProductID is logically being evaluated). The following query forms also produce the optimal plan and correct results, so long as a vector aggregate is used (you can probably find more equivalent query forms): WHERE Clause SELECT p.Name FROM Production.Product AS p WHERE ( SELECT COUNT_BIG(*) FROM Production.TransactionHistory AS th WHERE th.ProductID = p.ProductID GROUP BY () ) < 10; APPLY SELECT p.Name FROM Production.Product AS p CROSS APPLY ( SELECT NULL FROM Production.TransactionHistory AS th WHERE th.ProductID = p.ProductID GROUP BY () HAVING COUNT_BIG(*) < 10 ) AS ca (dummy); FROM Clause SELECT q1.Name FROM ( SELECT p.Name, cnt = ( SELECT COUNT_BIG(*) FROM Production.TransactionHistory AS th WHERE th.ProductID = p.ProductID GROUP BY () ) FROM Production.Product AS p ) AS q1 WHERE q1.cnt < 10; This last example uses SUM(1) instead of COUNT and does not require a vector aggregate…you should be able to work out why :) SELECT q.Name FROM ( SELECT p.Name, cnt = ( SELECT SUM(1) FROM Production.TransactionHistory AS th WHERE th.ProductID = p.ProductID ) FROM Production.Product AS p ) AS q WHERE q.cnt < 10; The semantics of SQL aggregates are rather odd in places.  It definitely pays to get to know the rules, and to be careful to check whether your queries are using scalar or vector aggregates.  As we have seen, query plans do not show in which ‘mode’ an aggregate is running and getting it wrong can cause poor performance, wrong results, or both. © 2012 Paul White Twitter: @SQL_Kiwi email: [email protected]

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  • No search data in Goolge Analytics or Webmasters

    - by cjk
    I have a domain that has been registered in Google Webmasters and using Google Analytics for over 4 months. I get lots of analytics data, but am getting no information on Google searches in Webmasters, or Queries in Search Engine Optimisation in Analytics, even though I am getting keywords for traffic coming to my site from search engines. I have a test sub-domain with the same setup (except not HTTPS) that is getting some of this information through, even with much less data and visits. What could be wrong to stop me getting this information?

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  • Some of my favourite Visual Studio 2012 things&ndash;Teams

    - by Aaron Kowall
    Getting the balance right for when and how many team projects to create has always been a bit of a balance.  On large initiatives, there are often teams who work toward a common system.  These teams often have quite a bit of autonomy, but need to roll up to some higher level initiative.  In TFS 2010, people were often tempted to create separate Team Projects for each of the sub-teams and then do some magic with reporting and cross-team queries to get the consolidated view.  My recommendation was always to use Areas as a means of separating work across the team, but that always resulted in a large number of queries that need to be maintained and just seemed confusing.  When doing anything you had to remember to filter the query or view by Area in order to get correct results. Along with the awesome web access portal that comes in TFS 2012 (which I will cover details of in another post) the product group has introduced the concept of Teams.  A team is a sub-group within a TFS 2012 Team Project which allows us to more easily divide work along team boundaries. Technically, a Team is defined by an Area Path and a TFS Group, both of which could be done in TFS 2012.  However, by allowing for creation of a ‘Team’ in TFS 2012, the web portal is able to do a bunch of ‘magic’ for us.  We can view the project site (backlog, taskboard, etc) for the the team, we can assign items to the team and we can view the burndown for the team.  Basically, all the stuff that we had to prepare manually we now get created and managed for us with a nice UI. When you create a Team Project in TFS 2012, a ‘Default’ team is created with the same name as the Team Project.  So, if you only have 1 team working on the project, you are set.  If you want to divide the work into additional teams, you can create teams by using the Team Web Client. Teams are created using the ‘Administer Server’ icon in the top right of the web site.   You can select the team site by using the team chooser: Once you have selected a team, the Product Backlog, TaskBoard, Burndown Charts, etc. are all filtered to that team. NOTE: You always have the ability to choose the ‘Default’ team to see items for the entire project. PS: It’s been a long while since I shared on this blog.  To help with that I’m in a blogging challenge with some other developer and agilist friends.  Please check out their blogs as well: Steve Rogalsky: http://winnipegagilist.blogspot.ca Dylan Smith: http://www.geekswithblogs.net/optikal Tyler Doerkson: http://blog.tylerdoerksen.com David Alpert: http://www.spinthemoose.com Dave White: http://www.agileramblings.com   Technorati Tags: TFS 2012,Agile,Team

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