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  • Linq Aggregate on object and List

    - by Kris-I
    I do this query with NHibernate: var test = _session.CreateCriteria(typeof(Estimation)) .SetFetchMode("EstimationItems", FetchMode.Eager) .List(); An "Estimation" can have several "EstimationItems" (Quantity, Price and ProductId) I'd like a list of "Estimation" with these constraints : One line by "Estimation" code on the picture (ex : 2011/0001 and 2011/0003) By estimation (means on each line) the number of "EstimationItems" By Estimation (means on each line) the total price (Quantity * Price) for each "EstimationItems" I hope the structure will be clearer with the picture below. Thanks,

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  • Searching For A Record After A LINQ query

    - by Justin
    I'm confused to why this is happening. I'm new to LINQ so I'm clearly missing something here, that is probably pretty easy. I've looked up help on the topic, but I don't really know what to ask so I haven't found any answers that really address my question. This doesn't work It throws an EntityCommandExecutionException when the FirstOrDefault method is executed. var query = from band in context.BandsEntitySet where band.ID == 12345 select band; string venueName = "Willis Park"; foreach (var item in query) { var venue = context.VenueEntitySet.FirstOrDefault(r => r.Venue.Equals(venueName)); } This works var query = from band in context.BandsEntitySet where band.ID == 12345 select band; var bandList = query.toList(); string venueName = "Willis Park"; foreach (var item in bandList) { var venue = context.VenueEntitySet.FirstOrDefault(r => r.Venue.Equals(venueName)); } My question is simple: Why is the exception being thrown? And why does creating a list from the query allow me to use the FirstOrDefault method? Exception Message: A first chance exception of type 'System.Data.EntityCommandExecutionException' occurred in System.Data.Entity.dll I guess I am wrong in my assumption that query is a list? Then what is it exactly? I'm confused because this doesn't throw an exception: foreach (var item in query) { var area = item.VenueArea; } I'd appreciate any help on this issue. thanks, Justin

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  • How to query the SPView object

    - by Hugo Migneron
    I have a SPView object that contains a lot of SPListItem objects (there are many fields in the view). I am only interested in one of these fields. Let's call it specialField Given that view and specialField, I want to know if a value is contained in specialField. Here is a way of doing what I want to do : String specialField = "Special Field"; String specialValue = "value"; SPList list = SPContext.Current.Site.RootWeb.Lists["My List"]; SPView view = list.Views["My View"]; //This is the view I want to query SPQuery query = new SPQuery(); query.Query = view.Query; SPListItemCollection items = list.GetItems(query); foreach(SPListItem item in items) { var value = item[specialField]; if(value != null) && (value.ToString() == specialValue) { //My value is found. This is what I was looking for. //break out of the loop or return } } //My value is not found. However, iterating through each ListItem hardly seems optimal, especially as there might be hundreds of items. This query will be executed often, so I am looking for an efficient way to do this. EDIT I will not always be working with the same view, so my solution cannot be hardcoded (it has to be generic enough that the list, view and specialField can be changed.

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  • ASP.NET MVC image upload store location (db vs filesystem)

    - by adrin
    I am writing web application using ASP.NET MVC + NHibernate + Postres stack. I wonder if images uploaded should be stored in database as binary blobs or on filesystem (and reference only in db). One advantage of db storage I can think of is easy backup/recovery of all data without reverting to filesystem copy tools. On the other hand I suspect that filesystem access may be faster (but is it especially when dealing with many concurrent requests?) What are your suggestions?

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  • Slow T-SQL query, convert to LINQ to Object

    - by yimbot
    I have a T-SQL query which populates a DataSet from an MSSQL database. string qry = "SELECT * FROM EvnLog AS E WHERE TimeDate = (SELECT MAX(TimeDate) From EvnLog WHERE Code = E.Code) AND (Event = 8) AND (TimeDate BETWEEN '" + Start + "' AND '" + Finish + "')" The database is quite large and being the type of nested query it is, the Data Adapter can take a number of minutes to fill the DataSet. I have extended the DataAdapter's timeout value to 480 seconds to combat it, but the client still complains about slow performance and occassional timeouts. To combat this, I was considering executing a simpler query (ie. just taking the date range) and then populating a Generic List which I could then execute a Linq query against. The simple query executes very quickly which is great. However, I cannot seem to build a Linq query which generates the same results as the T-SQL query above. Is this the best solution to this problem? Can anyone provide tips on rewriting the above T-SQL into Linq? I have also considered using a DataView, but cannot seem to get the results from that either.

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  • How To perform a SQL Query to DataTable Operation That Can Be Cancelled

    - by David W
    I tried to make the title as specific as possible. Basically what I have running inside a backgroundworker thread now is some code that looks like: SqlConnection conn = new SqlConnection(connstring); SqlCommand cmd = new SqlCommand(query, conn); conn.Open(); SqlDataAdapter sda = new SqlDataAdapter(cmd); sda.Fill(Results); conn.Close(); sda.Dispose(); Where query is a string representing a large, time consuming query, and conn is the connection object. My problem now is I need a stop button. I've come to realize killing the backgroundworker would be worthless because I still want to keep what results are left over after the query is canceled. Plus it wouldn't be able to check the canceled state until after the query. What I've come up with so far: I've been trying to conceptualize how to handle this efficiently without taking too big of a performance hit. My idea was to use a SqlDataReader to read the data from the query piece at a time so that I had a "loop" to check a flag I could set from the GUI via a button. The problem is as far as I know I can't use the Load() method of a datatable and still be able to cancel the sqlcommand. If I'm wrong please let me know because that would make cancelling slightly easier. In light of what I discovered I came to the realization I may only be able to cancel the sqlcommand mid-query if I did something like the below (pseudo-code): while(reader.Read()) { //check flag status //if it is set to 'kill' fire off the kill thread //otherwise populate the datatable with what was read } However, it would seem to me this would be highly ineffective and possibly costly. Is this the only way to kill a sqlcommand in progress that absolutely needs to be in a datatable? Any help would be appreciated!

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  • Run multiple MySQL queries based on a series of ifs

    - by OldWest
    I am just getting started on this complex query I need to write and was hoping for any suggestions or feedback regarding table structure and the actual query itself.. I've already created my tables and populated test data, and now just trying to sort out how and what is possible within MySQL. Here is an outline of the problem: End result: Listing of rates based on specific queried criteria (see below): Age: [ 27 ] Spouse Age: [ 25 ] Num of Children: [ 3 ] Zip Code: [ 97128 ] The problem I am running into is each company that provides rates has a unique way of dealing with the rate. And I am looking for the best approach for multiple queries based on the company (one query with results for each company more or less all combined into one result set). Here are some facts: - Each company deals with zip code ranges which assist in the query result. - Each company has a different method of calculating the rate based on the Applicant, Spouse, Num of Children: Example, a) Company A determines rate by: Applicant + Spouse + Child(ren) = rate (age is pertinent to the applicant within a range). b) Company B determines the rate by total number of applicants like: 1, 2, 3, 4, 5, 6+ = rate (and age is ignored). First off, what would I call this type of query? Multiple nested query? And should I intertwine php within it to determine the If()s ... I apologize if this thread lacks sufficient data, so please tell me anything you would like to see.

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  • Merge Function In Entity FrameWork?

    - by Ahmed
    In NHibernate there is a merge function that does the following: if there is a persistent instance with the same identifier currently associated with the session, copy the state of the given object onto the persistent instance if there is no persistent instance currently associated with the session, try to load it from the database, or create a new persistent instance the persistent instance is returned Is this possible in EF? I mean this part : copy the state of the given object onto the persistent instance. And if i used ApplyCurrentValues it seemes to be as update behavior or not?

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  • Parent reference in automapped component

    - by asgerhallas
    In Fluent NHibernate, given an automapped component, is there a convention for setting up a parent reference back to the "holder" of the component? By having for example a property named Parent or something like that? I can't seem to find any information about how to do it or issues about it.

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  • EF Query with conditional include that uses Joins

    - by makerofthings7
    This is a follow up to another user's question. I have 5 tables CompanyDetail CompanyContacts FK to CompanyDetail CompanyContactsSecurity FK to CompanyContact UserDetail UserGroupMembership FK to UserDetail How do I return all companies and include the contacts in the same query? I would like to include companies that contain zero contacts. Companies have a 1 to many association to Contacts, however not every user is permitted to see every Contact. My goal is to get a list of every Company regardless of the count of Contacts, but include contact data. Right now I have this working query: var userGroupsQueryable = _entities.UserGroupMembership .Where(ug => ug.UserID == UserID) .Select(a => a.GroupMembership); var contactsGroupsQueryable = _entities.CompanyContactsSecurity;//.Where(c => c.CompanyID == companyID); /// OLD Query that shows permitted contacts /// ... I want to "use this query inside "listOfCompany" /// //var permittedContacts= from c in userGroupsQueryable //join p in contactsGroupsQueryable on c equals p.GroupID //select p; However this is inefficient when I need to get all contacts for all companies, since I use a For..Each loop and query each company individually and update my viewmodel. Question: How do I shoehorn the permittedContacts variable above and insert that into this query: var listOfCompany = from company in _entities.CompanyDetail.Include("CompanyContacts").Include("CompanyContactsSecurity") where company.CompanyContacts.Any( // Insert Query here.... // b => b.CompanyContactsSecurity.Join(/*inner*/,/*OuterKey*/,/*innerKey*/,/*ResultSelector*/) ) select company; My attempt at doing this resulted in: var listOfCompany = from company in _entities.CompanyDetail.Include("CompanyContacts").Include("CompanyContactsSecurity") where company.CompanyContacts.Any( // This is concept only... doesn't work... from grps in userGroupsQueryable join p in company.CompanyContactsSecurity on grps equals p.GroupID select p ) select company;

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  • Getting Wrong Answer in range maximum query [on hold]

    - by user3186829
    I've just learnt range minimum and maximum queries using segment trees.But when I implemented it on my own I'm getting wrong answer.Logically I don't find any mistake in my code but if any one can point it out then I would be really thankful. Code in C++: #include<bits/stdc++.h> using namespace std; #define LL long long #define mp make_pair #define pb push_back #define gc getchar_unlocked #define pc putchar_unlocked #define LD long double #define MAXN 19999999 #define max(a,b) ((a)>(b)?(a):(b)) LL P[MAXN+15]; LL ST[2*MAXN+25]; long N,M,i,A,B,K; void build(long id,long L,long R) { long M=(L+R)>>1L; long LCT=id<<1L; long RCT=LCT+1L; if(L==R) { ST[id]=P[L]; return; } build(LCT,L,M); build(RCT,M+1,R); } //Range Update of segment tree void updateST(long id,long L,long R,long Q1,long Q2,long val) { long M=(L+R)>>1L; long LCT=id<<1L; long RCT=LCT+1L; if(L>Q2||R<Q1) { return; } if(L==Q1&&R==Q2) { ST[id]+=val; return; } if(Q2<=M) { updateST(LCT,L,M,Q1,Q2,val); } else if(Q1>M) { updateST(RCT,M+1,R,Q1,Q2,val); } else { updateST(LCT,L,M,Q1,M,val); updateST(RCT,M+1,R,M+1,Q2,val); } } //Query for finding maximum element in a given range[Q1,Q2] and 1<=Q1,Q2<=N LL query2(long id,long L,long R,long Q1,long Q2) { long M=(L+R)>>1; long LCT=id<<1; long RCT=LCT+1; if(L>Q2||R<Q1) { return 0; } if(L==Q1&&R==Q2) { return ST[id]; } if(Q2<=M) { return query2(LCT,L,M,Q1,Q2); } else if(Q1>M) { return query2(RCT,M+1,R,Q1,Q2); } else { LL G=query2(LCT,L,M,Q1,M); LL H=query2(RCT,M+1,R,M+1,Q2); LL RES=max(G,H); return RES; } } int main() { scanf("%ld %ld",&N,&M); build(1,1,N); for(i=0;i<M;i++) { scanf("%ld %ld %ld",&A,&B,&K); updateST(1,1,N,A,B,K); } //Finding maximum element in range[1,N]] cout<<query2(1,1,N,1,N); return 0; }

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  • variables used in inner queries

    - by wcpro
    im trying to build a query that has something like this select id, (select top 1 create_date from table2 where table1id = t1.id and status = 'success') [last_success_date], (select count(*) from table2 where table1id = t1.id and create_date > [last_success_date]) [failures_since_success] from table1 t1 as you can see the [last_Success_Date] is not within the scope of the second query, and i was wondering how i could access that value in other queries without having to rerun it?

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  • Advanced TSQL Tuning: Why Internals Knowledge Matters

    - by Paul White
    There is much more to query tuning than reducing logical reads and adding covering nonclustered indexes.  Query tuning is not complete as soon as the query returns results quickly in the development or test environments.  In production, your query will compete for memory, CPU, locks, I/O and other resources on the server.  Today’s entry looks at some tuning considerations that are often overlooked, and shows how deep internals knowledge can help you write better TSQL. As always, we’ll need some example data.  In fact, we are going to use three tables today, each of which is structured like this: Each table has 50,000 rows made up of an INTEGER id column and a padding column containing 3,999 characters in every row.  The only difference between the three tables is in the type of the padding column: the first table uses CHAR(3999), the second uses VARCHAR(MAX), and the third uses the deprecated TEXT type.  A script to create a database with the three tables and load the sample data follows: USE master; GO IF DB_ID('SortTest') IS NOT NULL DROP DATABASE SortTest; GO CREATE DATABASE SortTest COLLATE LATIN1_GENERAL_BIN; GO ALTER DATABASE SortTest MODIFY FILE ( NAME = 'SortTest', SIZE = 3GB, MAXSIZE = 3GB ); GO ALTER DATABASE SortTest MODIFY FILE ( NAME = 'SortTest_log', SIZE = 256MB, MAXSIZE = 1GB, FILEGROWTH = 128MB ); GO ALTER DATABASE SortTest SET ALLOW_SNAPSHOT_ISOLATION OFF ; ALTER DATABASE SortTest SET AUTO_CLOSE OFF ; ALTER DATABASE SortTest SET AUTO_CREATE_STATISTICS ON ; ALTER DATABASE SortTest SET AUTO_SHRINK OFF ; ALTER DATABASE SortTest SET AUTO_UPDATE_STATISTICS ON ; ALTER DATABASE SortTest SET AUTO_UPDATE_STATISTICS_ASYNC ON ; ALTER DATABASE SortTest SET PARAMETERIZATION SIMPLE ; ALTER DATABASE SortTest SET READ_COMMITTED_SNAPSHOT OFF ; ALTER DATABASE SortTest SET MULTI_USER ; ALTER DATABASE SortTest SET RECOVERY SIMPLE ; USE SortTest; GO CREATE TABLE dbo.TestCHAR ( id INTEGER IDENTITY (1,1) NOT NULL, padding CHAR(3999) NOT NULL,   CONSTRAINT [PK dbo.TestCHAR (id)] PRIMARY KEY CLUSTERED (id), ) ; CREATE TABLE dbo.TestMAX ( id INTEGER IDENTITY (1,1) NOT NULL, padding VARCHAR(MAX) NOT NULL,   CONSTRAINT [PK dbo.TestMAX (id)] PRIMARY KEY CLUSTERED (id), ) ; CREATE TABLE dbo.TestTEXT ( id INTEGER IDENTITY (1,1) NOT NULL, padding TEXT NOT NULL,   CONSTRAINT [PK dbo.TestTEXT (id)] PRIMARY KEY CLUSTERED (id), ) ; -- ============= -- Load TestCHAR (about 3s) -- ============= INSERT INTO dbo.TestCHAR WITH (TABLOCKX) ( padding ) SELECT padding = REPLICATE(CHAR(65 + (Data.n % 26)), 3999) FROM ( SELECT TOP (50000) n = ROW_NUMBER() OVER (ORDER BY (SELECT 0)) - 1 FROM master.sys.columns C1, master.sys.columns C2, master.sys.columns C3 ORDER BY n ASC ) AS Data ORDER BY Data.n ASC ; -- ============ -- Load TestMAX (about 3s) -- ============ INSERT INTO dbo.TestMAX WITH (TABLOCKX) ( padding ) SELECT CONVERT(VARCHAR(MAX), padding) FROM dbo.TestCHAR ORDER BY id ; -- ============= -- Load TestTEXT (about 5s) -- ============= INSERT INTO dbo.TestTEXT WITH (TABLOCKX) ( padding ) SELECT CONVERT(TEXT, padding) FROM dbo.TestCHAR ORDER BY id ; -- ========== -- Space used -- ========== -- EXECUTE sys.sp_spaceused @objname = 'dbo.TestCHAR'; EXECUTE sys.sp_spaceused @objname = 'dbo.TestMAX'; EXECUTE sys.sp_spaceused @objname = 'dbo.TestTEXT'; ; CHECKPOINT ; That takes around 15 seconds to run, and shows the space allocated to each table in its output: To illustrate the points I want to make today, the example task we are going to set ourselves is to return a random set of 150 rows from each table.  The basic shape of the test query is the same for each of the three test tables: SELECT TOP (150) T.id, T.padding FROM dbo.Test AS T ORDER BY NEWID() OPTION (MAXDOP 1) ; Test 1 – CHAR(3999) Running the template query shown above using the TestCHAR table as the target, we find that the query takes around 5 seconds to return its results.  This seems slow, considering that the table only has 50,000 rows.  Working on the assumption that generating a GUID for each row is a CPU-intensive operation, we might try enabling parallelism to see if that speeds up the response time.  Running the query again (but without the MAXDOP 1 hint) on a machine with eight logical processors, the query now takes 10 seconds to execute – twice as long as when run serially. Rather than attempting further guesses at the cause of the slowness, let’s go back to serial execution and add some monitoring.  The script below monitors STATISTICS IO output and the amount of tempdb used by the test query.  We will also run a Profiler trace to capture any warnings generated during query execution. DECLARE @read BIGINT, @write BIGINT ; SELECT @read = SUM(num_of_bytes_read), @write = SUM(num_of_bytes_written) FROM tempdb.sys.database_files AS DBF JOIN sys.dm_io_virtual_file_stats(2, NULL) AS FS ON FS.file_id = DBF.file_id WHERE DBF.type_desc = 'ROWS' ; SET STATISTICS IO ON ; SELECT TOP (150) TC.id, TC.padding FROM dbo.TestCHAR AS TC ORDER BY NEWID() OPTION (MAXDOP 1) ; SET STATISTICS IO OFF ; SELECT tempdb_read_MB = (SUM(num_of_bytes_read) - @read) / 1024. / 1024., tempdb_write_MB = (SUM(num_of_bytes_written) - @write) / 1024. / 1024., internal_use_MB = ( SELECT internal_objects_alloc_page_count / 128.0 FROM sys.dm_db_task_space_usage WHERE session_id = @@SPID ) FROM tempdb.sys.database_files AS DBF JOIN sys.dm_io_virtual_file_stats(2, NULL) AS FS ON FS.file_id = DBF.file_id WHERE DBF.type_desc = 'ROWS' ; Let’s take a closer look at the statistics and query plan generated from this: Following the flow of the data from right to left, we see the expected 50,000 rows emerging from the Clustered Index Scan, with a total estimated size of around 191MB.  The Compute Scalar adds a column containing a random GUID (generated from the NEWID() function call) for each row.  With this extra column in place, the size of the data arriving at the Sort operator is estimated to be 192MB. Sort is a blocking operator – it has to examine all of the rows on its input before it can produce its first row of output (the last row received might sort first).  This characteristic means that Sort requires a memory grant – memory allocated for the query’s use by SQL Server just before execution starts.  In this case, the Sort is the only memory-consuming operator in the plan, so it has access to the full 243MB (248,696KB) of memory reserved by SQL Server for this query execution. Notice that the memory grant is significantly larger than the expected size of the data to be sorted.  SQL Server uses a number of techniques to speed up sorting, some of which sacrifice size for comparison speed.  Sorts typically require a very large number of comparisons, so this is usually a very effective optimization.  One of the drawbacks is that it is not possible to exactly predict the sort space needed, as it depends on the data itself.  SQL Server takes an educated guess based on data types, sizes, and the number of rows expected, but the algorithm is not perfect. In spite of the large memory grant, the Profiler trace shows a Sort Warning event (indicating that the sort ran out of memory), and the tempdb usage monitor shows that 195MB of tempdb space was used – all of that for system use.  The 195MB represents physical write activity on tempdb, because SQL Server strictly enforces memory grants – a query cannot ‘cheat’ and effectively gain extra memory by spilling to tempdb pages that reside in memory.  Anyway, the key point here is that it takes a while to write 195MB to disk, and this is the main reason that the query takes 5 seconds overall. If you are wondering why using parallelism made the problem worse, consider that eight threads of execution result in eight concurrent partial sorts, each receiving one eighth of the memory grant.  The eight sorts all spilled to tempdb, resulting in inefficiencies as the spilled sorts competed for disk resources.  More importantly, there are specific problems at the point where the eight partial results are combined, but I’ll cover that in a future post. CHAR(3999) Performance Summary: 5 seconds elapsed time 243MB memory grant 195MB tempdb usage 192MB estimated sort set 25,043 logical reads Sort Warning Test 2 – VARCHAR(MAX) We’ll now run exactly the same test (with the additional monitoring) on the table using a VARCHAR(MAX) padding column: DECLARE @read BIGINT, @write BIGINT ; SELECT @read = SUM(num_of_bytes_read), @write = SUM(num_of_bytes_written) FROM tempdb.sys.database_files AS DBF JOIN sys.dm_io_virtual_file_stats(2, NULL) AS FS ON FS.file_id = DBF.file_id WHERE DBF.type_desc = 'ROWS' ; SET STATISTICS IO ON ; SELECT TOP (150) TM.id, TM.padding FROM dbo.TestMAX AS TM ORDER BY NEWID() OPTION (MAXDOP 1) ; SET STATISTICS IO OFF ; SELECT tempdb_read_MB = (SUM(num_of_bytes_read) - @read) / 1024. / 1024., tempdb_write_MB = (SUM(num_of_bytes_written) - @write) / 1024. / 1024., internal_use_MB = ( SELECT internal_objects_alloc_page_count / 128.0 FROM sys.dm_db_task_space_usage WHERE session_id = @@SPID ) FROM tempdb.sys.database_files AS DBF JOIN sys.dm_io_virtual_file_stats(2, NULL) AS FS ON FS.file_id = DBF.file_id WHERE DBF.type_desc = 'ROWS' ; This time the query takes around 8 seconds to complete (3 seconds longer than Test 1).  Notice that the estimated row and data sizes are very slightly larger, and the overall memory grant has also increased very slightly to 245MB.  The most marked difference is in the amount of tempdb space used – this query wrote almost 391MB of sort run data to the physical tempdb file.  Don’t draw any general conclusions about VARCHAR(MAX) versus CHAR from this – I chose the length of the data specifically to expose this edge case.  In most cases, VARCHAR(MAX) performs very similarly to CHAR – I just wanted to make test 2 a bit more exciting. MAX Performance Summary: 8 seconds elapsed time 245MB memory grant 391MB tempdb usage 193MB estimated sort set 25,043 logical reads Sort warning Test 3 – TEXT The same test again, but using the deprecated TEXT data type for the padding column: DECLARE @read BIGINT, @write BIGINT ; SELECT @read = SUM(num_of_bytes_read), @write = SUM(num_of_bytes_written) FROM tempdb.sys.database_files AS DBF JOIN sys.dm_io_virtual_file_stats(2, NULL) AS FS ON FS.file_id = DBF.file_id WHERE DBF.type_desc = 'ROWS' ; SET STATISTICS IO ON ; SELECT TOP (150) TT.id, TT.padding FROM dbo.TestTEXT AS TT ORDER BY NEWID() OPTION (MAXDOP 1, RECOMPILE) ; SET STATISTICS IO OFF ; SELECT tempdb_read_MB = (SUM(num_of_bytes_read) - @read) / 1024. / 1024., tempdb_write_MB = (SUM(num_of_bytes_written) - @write) / 1024. / 1024., internal_use_MB = ( SELECT internal_objects_alloc_page_count / 128.0 FROM sys.dm_db_task_space_usage WHERE session_id = @@SPID ) FROM tempdb.sys.database_files AS DBF JOIN sys.dm_io_virtual_file_stats(2, NULL) AS FS ON FS.file_id = DBF.file_id WHERE DBF.type_desc = 'ROWS' ; This time the query runs in 500ms.  If you look at the metrics we have been checking so far, it’s not hard to understand why: TEXT Performance Summary: 0.5 seconds elapsed time 9MB memory grant 5MB tempdb usage 5MB estimated sort set 207 logical reads 596 LOB logical reads Sort warning SQL Server’s memory grant algorithm still underestimates the memory needed to perform the sorting operation, but the size of the data to sort is so much smaller (5MB versus 193MB previously) that the spilled sort doesn’t matter very much.  Why is the data size so much smaller?  The query still produces the correct results – including the large amount of data held in the padding column – so what magic is being performed here? TEXT versus MAX Storage The answer lies in how columns of the TEXT data type are stored.  By default, TEXT data is stored off-row in separate LOB pages – which explains why this is the first query we have seen that records LOB logical reads in its STATISTICS IO output.  You may recall from my last post that LOB data leaves an in-row pointer to the separate storage structure holding the LOB data. SQL Server can see that the full LOB value is not required by the query plan until results are returned, so instead of passing the full LOB value down the plan from the Clustered Index Scan, it passes the small in-row structure instead.  SQL Server estimates that each row coming from the scan will be 79 bytes long – 11 bytes for row overhead, 4 bytes for the integer id column, and 64 bytes for the LOB pointer (in fact the pointer is rather smaller – usually 16 bytes – but the details of that don’t really matter right now). OK, so this query is much more efficient because it is sorting a very much smaller data set – SQL Server delays retrieving the LOB data itself until after the Sort starts producing its 150 rows.  The question that normally arises at this point is: Why doesn’t SQL Server use the same trick when the padding column is defined as VARCHAR(MAX)? The answer is connected with the fact that if the actual size of the VARCHAR(MAX) data is 8000 bytes or less, it is usually stored in-row in exactly the same way as for a VARCHAR(8000) column – MAX data only moves off-row into LOB storage when it exceeds 8000 bytes.  The default behaviour of the TEXT type is to be stored off-row by default, unless the ‘text in row’ table option is set suitably and there is room on the page.  There is an analogous (but opposite) setting to control the storage of MAX data – the ‘large value types out of row’ table option.  By enabling this option for a table, MAX data will be stored off-row (in a LOB structure) instead of in-row.  SQL Server Books Online has good coverage of both options in the topic In Row Data. The MAXOOR Table The essential difference, then, is that MAX defaults to in-row storage, and TEXT defaults to off-row (LOB) storage.  You might be thinking that we could get the same benefits seen for the TEXT data type by storing the VARCHAR(MAX) values off row – so let’s look at that option now.  This script creates a fourth table, with the VARCHAR(MAX) data stored off-row in LOB pages: CREATE TABLE dbo.TestMAXOOR ( id INTEGER IDENTITY (1,1) NOT NULL, padding VARCHAR(MAX) NOT NULL,   CONSTRAINT [PK dbo.TestMAXOOR (id)] PRIMARY KEY CLUSTERED (id), ) ; EXECUTE sys.sp_tableoption @TableNamePattern = N'dbo.TestMAXOOR', @OptionName = 'large value types out of row', @OptionValue = 'true' ; SELECT large_value_types_out_of_row FROM sys.tables WHERE [schema_id] = SCHEMA_ID(N'dbo') AND name = N'TestMAXOOR' ; INSERT INTO dbo.TestMAXOOR WITH (TABLOCKX) ( padding ) SELECT SPACE(0) FROM dbo.TestCHAR ORDER BY id ; UPDATE TM WITH (TABLOCK) SET padding.WRITE (TC.padding, NULL, NULL) FROM dbo.TestMAXOOR AS TM JOIN dbo.TestCHAR AS TC ON TC.id = TM.id ; EXECUTE sys.sp_spaceused @objname = 'dbo.TestMAXOOR' ; CHECKPOINT ; Test 4 – MAXOOR We can now re-run our test on the MAXOOR (MAX out of row) table: DECLARE @read BIGINT, @write BIGINT ; SELECT @read = SUM(num_of_bytes_read), @write = SUM(num_of_bytes_written) FROM tempdb.sys.database_files AS DBF JOIN sys.dm_io_virtual_file_stats(2, NULL) AS FS ON FS.file_id = DBF.file_id WHERE DBF.type_desc = 'ROWS' ; SET STATISTICS IO ON ; SELECT TOP (150) MO.id, MO.padding FROM dbo.TestMAXOOR AS MO ORDER BY NEWID() OPTION (MAXDOP 1, RECOMPILE) ; SET STATISTICS IO OFF ; SELECT tempdb_read_MB = (SUM(num_of_bytes_read) - @read) / 1024. / 1024., tempdb_write_MB = (SUM(num_of_bytes_written) - @write) / 1024. / 1024., internal_use_MB = ( SELECT internal_objects_alloc_page_count / 128.0 FROM sys.dm_db_task_space_usage WHERE session_id = @@SPID ) FROM tempdb.sys.database_files AS DBF JOIN sys.dm_io_virtual_file_stats(2, NULL) AS FS ON FS.file_id = DBF.file_id WHERE DBF.type_desc = 'ROWS' ; TEXT Performance Summary: 0.3 seconds elapsed time 245MB memory grant 0MB tempdb usage 193MB estimated sort set 207 logical reads 446 LOB logical reads No sort warning The query runs very quickly – slightly faster than Test 3, and without spilling the sort to tempdb (there is no sort warning in the trace, and the monitoring query shows zero tempdb usage by this query).  SQL Server is passing the in-row pointer structure down the plan and only looking up the LOB value on the output side of the sort. The Hidden Problem There is still a huge problem with this query though – it requires a 245MB memory grant.  No wonder the sort doesn’t spill to tempdb now – 245MB is about 20 times more memory than this query actually requires to sort 50,000 records containing LOB data pointers.  Notice that the estimated row and data sizes in the plan are the same as in test 2 (where the MAX data was stored in-row). The optimizer assumes that MAX data is stored in-row, regardless of the sp_tableoption setting ‘large value types out of row’.  Why?  Because this option is dynamic – changing it does not immediately force all MAX data in the table in-row or off-row, only when data is added or actually changed.  SQL Server does not keep statistics to show how much MAX or TEXT data is currently in-row, and how much is stored in LOB pages.  This is an annoying limitation, and one which I hope will be addressed in a future version of the product. So why should we worry about this?  Excessive memory grants reduce concurrency and may result in queries waiting on the RESOURCE_SEMAPHORE wait type while they wait for memory they do not need.  245MB is an awful lot of memory, especially on 32-bit versions where memory grants cannot use AWE-mapped memory.  Even on a 64-bit server with plenty of memory, do you really want a single query to consume 0.25GB of memory unnecessarily?  That’s 32,000 8KB pages that might be put to much better use. The Solution The answer is not to use the TEXT data type for the padding column.  That solution happens to have better performance characteristics for this specific query, but it still results in a spilled sort, and it is hard to recommend the use of a data type which is scheduled for removal.  I hope it is clear to you that the fundamental problem here is that SQL Server sorts the whole set arriving at a Sort operator.  Clearly, it is not efficient to sort the whole table in memory just to return 150 rows in a random order. The TEXT example was more efficient because it dramatically reduced the size of the set that needed to be sorted.  We can do the same thing by selecting 150 unique keys from the table at random (sorting by NEWID() for example) and only then retrieving the large padding column values for just the 150 rows we need.  The following script implements that idea for all four tables: SET STATISTICS IO ON ; WITH TestTable AS ( SELECT * FROM dbo.TestCHAR ), TopKeys AS ( SELECT TOP (150) id FROM TestTable ORDER BY NEWID() ) SELECT T1.id, T1.padding FROM TestTable AS T1 WHERE T1.id = ANY (SELECT id FROM TopKeys) OPTION (MAXDOP 1) ; WITH TestTable AS ( SELECT * FROM dbo.TestMAX ), TopKeys AS ( SELECT TOP (150) id FROM TestTable ORDER BY NEWID() ) SELECT T1.id, T1.padding FROM TestTable AS T1 WHERE T1.id IN (SELECT id FROM TopKeys) OPTION (MAXDOP 1) ; WITH TestTable AS ( SELECT * FROM dbo.TestTEXT ), TopKeys AS ( SELECT TOP (150) id FROM TestTable ORDER BY NEWID() ) SELECT T1.id, T1.padding FROM TestTable AS T1 WHERE T1.id IN (SELECT id FROM TopKeys) OPTION (MAXDOP 1) ; WITH TestTable AS ( SELECT * FROM dbo.TestMAXOOR ), TopKeys AS ( SELECT TOP (150) id FROM TestTable ORDER BY NEWID() ) SELECT T1.id, T1.padding FROM TestTable AS T1 WHERE T1.id IN (SELECT id FROM TopKeys) OPTION (MAXDOP 1) ; SET STATISTICS IO OFF ; All four queries now return results in much less than a second, with memory grants between 6 and 12MB, and without spilling to tempdb.  The small remaining inefficiency is in reading the id column values from the clustered primary key index.  As a clustered index, it contains all the in-row data at its leaf.  The CHAR and VARCHAR(MAX) tables store the padding column in-row, so id values are separated by a 3999-character column, plus row overhead.  The TEXT and MAXOOR tables store the padding values off-row, so id values in the clustered index leaf are separated by the much-smaller off-row pointer structure.  This difference is reflected in the number of logical page reads performed by the four queries: Table 'TestCHAR' logical reads 25511 lob logical reads 000 Table 'TestMAX'. logical reads 25511 lob logical reads 000 Table 'TestTEXT' logical reads 00412 lob logical reads 597 Table 'TestMAXOOR' logical reads 00413 lob logical reads 446 We can increase the density of the id values by creating a separate nonclustered index on the id column only.  This is the same key as the clustered index, of course, but the nonclustered index will not include the rest of the in-row column data. CREATE UNIQUE NONCLUSTERED INDEX uq1 ON dbo.TestCHAR (id); CREATE UNIQUE NONCLUSTERED INDEX uq1 ON dbo.TestMAX (id); CREATE UNIQUE NONCLUSTERED INDEX uq1 ON dbo.TestTEXT (id); CREATE UNIQUE NONCLUSTERED INDEX uq1 ON dbo.TestMAXOOR (id); The four queries can now use the very dense nonclustered index to quickly scan the id values, sort them by NEWID(), select the 150 ids we want, and then look up the padding data.  The logical reads with the new indexes in place are: Table 'TestCHAR' logical reads 835 lob logical reads 0 Table 'TestMAX' logical reads 835 lob logical reads 0 Table 'TestTEXT' logical reads 686 lob logical reads 597 Table 'TestMAXOOR' logical reads 686 lob logical reads 448 With the new index, all four queries use the same query plan (click to enlarge): Performance Summary: 0.3 seconds elapsed time 6MB memory grant 0MB tempdb usage 1MB sort set 835 logical reads (CHAR, MAX) 686 logical reads (TEXT, MAXOOR) 597 LOB logical reads (TEXT) 448 LOB logical reads (MAXOOR) No sort warning I’ll leave it as an exercise for the reader to work out why trying to eliminate the Key Lookup by adding the padding column to the new nonclustered indexes would be a daft idea Conclusion This post is not about tuning queries that access columns containing big strings.  It isn’t about the internal differences between TEXT and MAX data types either.  It isn’t even about the cool use of UPDATE .WRITE used in the MAXOOR table load.  No, this post is about something else: Many developers might not have tuned our starting example query at all – 5 seconds isn’t that bad, and the original query plan looks reasonable at first glance.  Perhaps the NEWID() function would have been blamed for ‘just being slow’ – who knows.  5 seconds isn’t awful – unless your users expect sub-second responses – but using 250MB of memory and writing 200MB to tempdb certainly is!  If ten sessions ran that query at the same time in production that’s 2.5GB of memory usage and 2GB hitting tempdb.  Of course, not all queries can be rewritten to avoid large memory grants and sort spills using the key-lookup technique in this post, but that’s not the point either. The point of this post is that a basic understanding of execution plans is not enough.  Tuning for logical reads and adding covering indexes is not enough.  If you want to produce high-quality, scalable TSQL that won’t get you paged as soon as it hits production, you need a deep understanding of execution plans, and as much accurate, deep knowledge about SQL Server as you can lay your hands on.  The advanced database developer has a wide range of tools to use in writing queries that perform well in a range of circumstances. By the way, the examples in this post were written for SQL Server 2008.  They will run on 2005 and demonstrate the same principles, but you won’t get the same figures I did because 2005 had a rather nasty bug in the Top N Sort operator.  Fair warning: if you do decide to run the scripts on a 2005 instance (particularly the parallel query) do it before you head out for lunch… This post is dedicated to the people of Christchurch, New Zealand. © 2011 Paul White email: @[email protected] twitter: @SQL_Kiwi

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  • Working with Timelines with LINQ to Twitter

    - by Joe Mayo
    When first working with the Twitter API, I thought that using SinceID would be an effective way to page through timelines. In practice it doesn’t work well for various reasons. To explain why, Twitter published an excellent document that is a must-read for anyone working with timelines: Twitter Documentation: Working with Timelines This post shows how to implement the recommended strategies in that document by using LINQ to Twitter. You should read the document in it’s entirety before moving on because my explanation will start at the bottom and work back up to the top in relation to the Twitter document. What follows is an explanation of SinceID, MaxID, and how they come together to help you efficiently work with Twitter timelines. The Role of SinceID Specifying SinceID says to Twitter, “Don’t return tweets earlier than this”. What you want to do is store this value after every timeline query set so that it can be reused on the next set of queries.  The next section will explain what I mean by query set, but a quick explanation is that it’s a loop that gets all new tweets. The SinceID is a backstop to avoid retrieving tweets that you already have. Here’s some initialization code that includes a variable named sinceID that will be used to populate the SinceID property in subsequent queries: // last tweet processed on previous query set ulong sinceID = 210024053698867204; ulong maxID; const int Count = 10; var statusList = new List<status>(); Here, I’ve hard-coded the sinceID variable, but this is where you would initialize sinceID from whatever storage you choose (i.e. a database). The first time you ever run this code, you won’t have a value from a previous query set. Initially setting it to 0 might sound like a good idea, but what if you’re querying a timeline with lots of tweets? Because of the number of tweets and rate limits, your query set might take a very long time to run. A caveat might be that Twitter won’t return an entire timeline back to Tweet #0, but rather only go back a certain period of time, the limits of which are documented for individual Twitter timeline API resources. So, to initialize SinceID at too low of a number can result in a lot of initial tweets, yet there is a limit to how far you can go back. What you’re trying to accomplish in your application should guide you in how to initially set SinceID. I have more to say about SinceID later in this post. The other variables initialized above include the declaration for MaxID, Count, and statusList. The statusList variable is a holder for all the timeline tweets collected during this query set. You can set Count to any value you want as the largest number of tweets to retrieve, as defined by individual Twitter timeline API resources. To effectively page results, you’ll use the maxID variable to set the MaxID property in queries, which I’ll discuss next. Initializing MaxID On your first query of a query set, MaxID will be whatever the most recent tweet is that you get back. Further, you don’t know what MaxID is until after the initial query. The technique used in this post is to do an initial query and then use the results to figure out what the next MaxID will be.  Here’s the code for the initial query: var userStatusResponse = (from tweet in twitterCtx.Status where tweet.Type == StatusType.User && tweet.ScreenName == "JoeMayo" && tweet.SinceID == sinceID && tweet.Count == Count select tweet) .ToList(); statusList.AddRange(userStatusResponse); // first tweet processed on current query maxID = userStatusResponse.Min( status => ulong.Parse(status.StatusID)) - 1; The query above sets both SinceID and Count properties. As explained earlier, Count is the largest number of tweets to return, but the number can be less. A couple reasons why the number of tweets that are returned could be less than Count include the fact that the user, specified by ScreenName, might not have tweeted Count times yet or might not have tweeted at least Count times within the maximum number of tweets that can be returned by the Twitter timeline API resource. Another reason could be because there aren’t Count tweets between now and the tweet ID specified by sinceID. Setting SinceID constrains the results to only those tweets that occurred after the specified Tweet ID, assigned via the sinceID variable in the query above. The statusList is an accumulator of all tweets receive during this query set. To simplify the code, I left out some logic to check whether there were no tweets returned. If  the query above doesn’t return any tweets, you’ll receive an exception when trying to perform operations on an empty list. Yeah, I cheated again. Besides querying initial tweets, what’s important about this code is the final line that sets maxID. It retrieves the lowest numbered status ID in the results. Since the lowest numbered status ID is for a tweet we already have, the code decrements the result by one to keep from asking for that tweet again. Remember, SinceID is not inclusive, but MaxID is. The maxID variable is now set to the highest possible tweet ID that can be returned in the next query. The next section explains how to use MaxID to help get the remaining tweets in the query set. Retrieving Remaining Tweets Earlier in this post, I defined a term that I called a query set. Essentially, this is a group of requests to Twitter that you perform to get all new tweets. A single query might not be enough to get all new tweets, so you’ll have to start at the top of the list that Twitter returns and keep making requests until you have all new tweets. The previous section showed the first query of the query set. The code below is a loop that completes the query set: do { // now add sinceID and maxID userStatusResponse = (from tweet in twitterCtx.Status where tweet.Type == StatusType.User && tweet.ScreenName == "JoeMayo" && tweet.Count == Count && tweet.SinceID == sinceID && tweet.MaxID == maxID select tweet) .ToList(); if (userStatusResponse.Count > 0) { // first tweet processed on current query maxID = userStatusResponse.Min( status => ulong.Parse(status.StatusID)) - 1; statusList.AddRange(userStatusResponse); } } while (userStatusResponse.Count != 0 && statusList.Count < 30); Here we have another query, but this time it includes the MaxID property. The SinceID property prevents reading tweets that we’ve already read and Count specifies the largest number of tweets to return. Earlier, I mentioned how it was important to check how many tweets were returned because failing to do so will result in an exception when subsequent code runs on an empty list. The code above protects against this problem by only working with the results if Twitter actually returns tweets. Reasons why there wouldn’t be results include: if the first query got all the new tweets there wouldn’t be more to get and there might not have been any new tweets between the SinceID and MaxID settings of the most recent query. The code for loading the returned tweets into statusList and getting the maxID are the same as previously explained. The important point here is that MaxID is being reset, not SinceID. As explained in the Twitter documentation, paging occurs from the newest tweets to oldest, so setting MaxID lets us move from the most recent tweets down to the oldest as specified by SinceID. The two loop conditions cause the loop to continue as long as tweets are being read or a max number of tweets have been read.  Logically, you want to stop reading when you’ve read all the tweets and that’s indicated by the fact that the most recent query did not return results. I put the check to stop after 30 tweets are reached to keep the demo from running too long – in the console the response scrolls past available buffer and I wanted you to be able to see the complete output. Yet, there’s another point to be made about constraining the number of items you return at one time. The Twitter API has rate limits and making too many queries per minute will result in an error from twitter that LINQ to Twitter raises as an exception. To use the API properly, you’ll have to ensure you don’t exceed this threshold. Looking at the statusList.Count as done above is rather primitive, but you can implement your own logic to properly manage your rate limit. Yeah, I cheated again. Summary Now you know how to use LINQ to Twitter to work with Twitter timelines. After reading this post, you have a better idea of the role of SinceID - the oldest tweet already received. You also know that MaxID is the largest tweet ID to retrieve in a query. Together, these settings allow you to page through results via one or more queries. You also understand what factors affect the number of tweets returned and considerations for potential error handling logic. The full example of the code for this post is included in the downloadable source code for LINQ to Twitter.   @JoeMayo

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  • Separate Query for Count

    - by Anraiki
    Hello, I am trying to get my query to grab multiple rows while returning the maximum count of that query. My query: SELECT *, COUNT(*) as Max FROM tableA LIMIT 0 , 30 However, it is only outputting 1 record. I would like to return multiple record as it was the following query: SELECT * FROM tableA LIMIT 0 , 30 Do I have to use separate queries?

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  • The query contains the XXXXXName parameter, which is not declared. SSRS2008/MDX query

    - by adolf garlic - SAVE BBC6MUSIC
    Parser: The query contains the XXXXXName parameter, which is not declared. (msmgdsrv) I have no idea why I keep getting this error. It occurs when I change the MDX in the query designer and trying OKing out of the query designer. The strange thing is that the parameter DOES exist, I can see it in the parameters section of the dataset dialog. I am creating it before I do anything else with the query.

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  • Convert JSON query parameters to objects with JAX-RS

    - by deamon
    I have a JAX-RS resource, which gets its paramaters as a JSON string like this: http://some.test/aresource?query={"paramA":"value1", "paramB":"value2"} The reason to use JSON here, is that the query object can be quite complex in real use cases. I'd like to convert the JSON string to a Java object, dto in the example: @GET @Produces("text/plain") public String getIt(@QueryParam("query") DataTransferObject dto ) { ... } Does JAX-RS support such a conversion from JSON passed as a query param to Java objects?

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