Our goal is to generate reports in streaming mode.

At some point we need to deal with data streams (e.g. xml streams for xslt transformations). Often a nature of report demands several passes through the data. To increase performance we have defined a class named StreamResource. This class encapsulates input data, reads it once and caches it into a temp file; thus data can be traversed many times. StreamResource can read data lazily or in a eager way thus releasing resources early. This class can be used as a variation of PipeStream, which never blocks, as if a size of a buffer is not limited, and which can be read many times.

The API looks like this:

public class StreamResource: IDisposable
{
  /// <summary>
  /// Creates a StreamSource instance.
  /// </summary>
  /// <param name="source">
  /// A function that returns source as an input stream.
  /// </param>
  /// <param name="settings">Optional settings.</param>
  public StreamResource(Func<Stream> source, Settings settings = null);

  /// <summary>
  /// Creates a StreamSource instance.
  /// </summary>
  /// <param name="source">
  /// A function that writes source data into an output stream.
  /// </param>
  /// <param name="settings">Optional settings.</param>
  public StreamResource(Action<Stream> source, Settings settings = null);

  /// <summary>
  /// Gets an input stream.
  /// </summary>
  /// <param name="shared">
  /// Indicates that this StreamResouce should be disposed when returned
  /// stream is closed and there are no more currently opened cache streams.
  /// </param>
  /// <returns>A input stream.</returns>
  public Stream GetStream(bool shared = false);
}

The use pattern is following:

// Acquire resource.
using(var resource = new StreamResource(() => CallService(params...)))
{
  // Read stream.
  using(var stream = resource.GetStream())
  {
    ...
  }

  ...

  // Read stream again.
  using(var stream = resource.GetStream())
  {
    ...
  }
}

StreamResource is efficient even if you need to process content only once, as it monitors timings of reading of source data and compares it with timings of data consumption. If the difference exceeds some threshold then StreamResource caches source greedily, otherwise source is pooled lazily. Thus, input resources can be released promptly. This is important, for example, when the source depends on a database connection.

The use pattern is following:

// Acquire resource and get shared stream.
using(var stream = new StreamResource(() => CallService(params...)).GetStream(true))
{
  ...
}

Finally, StreamResource allows to process data in a pipe stream mode. This is when you have a generator function Action<Stream> that can write to a stream, and you want to read that data. The advantage of StreamResource over real pipe stream is that it can work without blocking of generator, thus releasing resources early.

The use pattern is similar to the previous one:

using(var stream = new StreamResource(output => Generate(output, params...)).GetStream(true))
{
  ...
}

The source of the class can be found at Streaming.zip.

Two monthes ago we have started a process of changing column type from smallint to int in a big database.

This was splitted in two phases:

1. Change tables and internal stored procedures and functions.
2. Change interface API and update all clients.

The first part took almost two monthes to complete. Please read earlier post about the technique we have selected for the implementation. In total we have transferred about 15 billion rows. During this time database was online.

The second part was short but the problem was that we did not control all clients, so could not arbitrary change types of parameters and of result columns.

All our clients use Entity Framework 4 to access the database. All access is done though stored procedures. So suppose there was a procedure:

create procedure Data.GetReports(@type smallint) as
begin
  select Type, ... from Data.Report where Type = @type;
end;

where column "Type" was of type smallint. Now we were going to change it to:

create procedure Data.GetReports(@type int) as
begin
  select Type, ... from Data.Report where Type = @type;
end;

where "Type" column became of type int.

Our tests have shown that EF bears with change of types of input parameters, but throws exceptions when column type has been changed, even when a value fits the range.  The reason is that EF uses method SqlDataReader.GetInt16 to access the column value. This method has a remark: "No conversions are performed; therefore, the data retrieved must already be a 16-bit signed integer."

Fortunately, we have found that EF allows additional columns in the result set. This helped us to formulate the solution. We have updated the procedure definition like this:

create procedure Data.GetReports(@type int) as
begin
  select
    cast(Type as smallint) Type, -- deprecated
    Type TypeEx, ...
  from
    Data.Report
  where
    Type = @type;
end;

This way:

• result column "Type" is declared as deprecated;
• old clients still work;
• all clients should be updated to use "TypeEx" column;
• after all clients will be updated we shall remove "Type" column from the result set.

So there is a clear migration process.

P.S. we don't understand why SqlDataReader doesn't support value conversion.

If you deal with web applications you probably have already dealt with export data to Excel. There are several options to prepare data for Excel:

• generate CSV;
• generate HTML that excel understands;
• generate XML in Spreadsheet 2003 format;
• generate data using Open XML SDK or some other 3rd party libraries;
• generate data in XLSX format, according to Open XML specification.

You may find a good article with pros and cons of each solution here. We, in our turn, would like to share our experience in this field. Let's start from requirements:

• Often we have to export huge data-sets.
• We should be able to format, parametrize and to apply different styles to the exported data.
• There are cases when exported data may contain more than one table per sheet or even more than one sheet.
• Some exported data have to be illustrated with charts.

All these requirements led us to a solution based on XSLT processing of streamed data. The advantage of this solution is that the result is immediately forwarded to a client as fast as XSLT starts to generate output. Such approach is much productive than generating of XLSX using of Open XML SDK or any other third party library, since it avoids keeping a huge data-sets in memory on the server side.

Another advantage - is simple maintenance, as we achieve clear separation of data and presentation layers. On each request to change formatting or apply another style to a cell you just have to modify xslt file(s) that generate variable parts of XLSX.

As result, our clients get XLSX files according with Open XML specifications. The details of implementations of our solution see in our next posts.

Recently we had a discussion with DBA regarding optimization strategey we have selected for some queries.

We have a table in our database. These are facts about that table:

• the table is partitioned by date;
• each partition contains a month worth of data;
• the table contains at present about 110 million rows;
• the table ever grows;
• the table is most accessed in the database;
• the most accessed part of the data is related to last 2-3 days, which is about 150000 rows.

The way we have optimized access to that table was a core of the dispute.

We have created filtered index that includes data for the last 3 days.

To achieve desired effect we had to:

• create a job that recreates that index once a day, as filter condition is moving;
• adjust queries that access the table, as we had to use several access pathes to the table depending on date.

As result we can see that under the load, stored procedures that access that table became almost 50% faster. On the other hand maintainance became more complicated.

DBA who didn't like the database complications had to agree that there are speed improvements. He said that there should be a better way to achieve the same effect but could not find it.

Are there a better way to optimize access to this table?

We're implementing UDT changes in the big database. Earlier, that User Defined Type was based on smallint, and now we have to use int as the base.

The impact here is manyfold:

1. Clients of the database should be prepared to use wider types.
2. All stored procedures, functions, triggers, and views should be updated accordingly.
3. Impact on the database size should be analyzed.
4. Types of columns in tables should be changed.
5. Performance impact should be minimal.

Now, we're trying to address (3), (5) and to implement (4), while trying to keep interface with clients using old types.

As for database size impact, we have found that an index fragmentation is a  primary disk space waster (see Reorganize index in SQL Server). We have performed some partial index reorganization and can see now that we can gain back hundreds of GB of a disk space. On the other hand we use page compression, so we expect that change of types will not increase sizes of tables considerably. Indeed, our measurments show that tables will only be ~1-3% bigger.

The change of types of columns is untrivial task. The problem is that if you try to change column's type (which is part of clustered index) directly then you should temporary remove foreign keys, and to rebuild all indices. This won't work neither due to disk space required for the operation (a huge transaction log is required), nor due to availability of tables (we're talking about days or even weeks to rebuild indices).

To work-around the problem we have selected another way. For each target table T we performed the following:

• Renamed table T to T_old;
• Created a table T_new with required type changes;
• Created a view named T, which is union of T_old for the dates before a split date and T_new for the dates after the split date;
• Created instead of insert/update/delete triggers for the view T.
• Created a procedures that move data in bulks from T_old to the T_new, update split date in view definitions, and delete data from T_old.

Note that:

• the new view uses wider column types, so we had to change stored procedures that clients use to cast those columns back to shorter types to prevent side effects (fortunately all access to this database is through stored procedures and functions);
• the procedures that transfer data between new and old tables may work online;
• the quality of execution plans did not degrade due to switch from table to a view;
• all data related to the date after the split date are inserted into T_new table.

After transfer will be complete we shall drop T_old tables, and T views, and will rename T_new tables into T.

This will complete part 4 of the whole task. Our estimations are that it will take a month or even more to complete the transfer. However solution is rather slow, the database will stay online whole this period, which is required condition.

The next task is to deal with type changes in parameters of stored procedures and column types of output result sets. We're not sure yet what's the best way to deal with it, and probably shall complain about in in next posts.

Back in 2006 and 2007 we have defined dbo.Numbers function: Numbers table in SQL Server 2005, Parade of numbers. Such construct is very important in a set based programming. E.g. XPath 2 contains a range expression like this: "1 to 10" to return a sequence of numbers. Unfortunately neither SQL Server 2008 R2, nor SQL Server 2012 support such construct, so dbo.Numbers function is still actual.

After all these years the function evolved a little bit to achieve a better performance. Here is its source:

-- Returns numbers table.
-- Table has a following structure: table(value int not null);
-- value is an integer number that contains numbers from 1 to a specified value.
create function dbo.Numbers
(
  -- Number of rows to return.
  @count int
)
returns table
as
return
  with Number8 as
  (
    select
      *
    from
      (
        values
          (0), (0), (0), (0), (0), (0), (0), (0), (0), (0), (0), (0), (0), (0), (0), (0),
          (0), (0), (0), (0), (0), (0), (0), (0), (0), (0), (0), (0), (0), (0), (0), (0),
          (0), (0), (0), (0), (0), (0), (0), (0), (0), (0), (0), (0), (0), (0), (0), (0),
          (0), (0), (0), (0), (0), (0), (0), (0), (0), (0), (0), (0), (0), (0), (0), (0),
          (0), (0), (0), (0), (0), (0), (0), (0), (0), (0), (0), (0), (0), (0), (0), (0),
          (0), (0), (0), (0), (0), (0), (0), (0), (0), (0), (0), (0), (0), (0), (0), (0),
          (0), (0), (0), (0), (0), (0), (0), (0), (0), (0), (0), (0), (0), (0), (0), (0),
          (0), (0), (0), (0), (0), (0), (0), (0), (0), (0), (0), (0), (0), (0), (0), (0),
          (0), (0), (0), (0), (0), (0), (0), (0), (0), (0), (0), (0), (0), (0), (0), (0),
          (0), (0), (0), (0), (0), (0), (0), (0), (0), (0), (0), (0), (0), (0), (0), (0),
          (0), (0), (0), (0), (0), (0), (0), (0), (0), (0), (0), (0), (0), (0), (0), (0),
          (0), (0), (0), (0), (0), (0), (0), (0), (0), (0), (0), (0), (0), (0), (0), (0),
          (0), (0), (0), (0), (0), (0), (0), (0), (0), (0), (0), (0), (0), (0), (0), (0),
          (0), (0), (0), (0), (0), (0), (0), (0), (0), (0), (0), (0), (0), (0), (0), (0),
          (0), (0), (0), (0), (0), (0), (0), (0), (0), (0), (0), (0), (0), (0), (0), (0),
          (0), (0), (0), (0), (0), (0), (0), (0), (0), (0), (0), (0), (0), (0), (0), (0)
      ) N(Value)
  ),
  Number32(Value) as
  (
    select
      0
    from
      Number8 N1
      left join
      Number8 N2
      on
        @count > 0x100
      left join
      Number8 N3
      left join
      Number8 N4
      on
        @count > 0x1000000
      on
        @count > 0x10000
  )
  select top(@count) row_number() over(order by @count) Value from Number32;

We're working with an online database, which is ever populated with a new data. Database activity is mostly around recent data. Activity against older data declines with increasing the distance from today. The ratio of an amount of a new data, say for a last month, to the whole data, at present stays at ~1%. The size of database is measured in TBs.

While we're developers and not DBA's,  you will see from a later blog posts why we're bothered with the database size. In short we're planning to change some UDF type from smallint to int. This will impact on many tables, and the task now is to estimate that impact.

Our first attempts to measure the difference between table sizes before and after type change showed that a data fragmentation often masks the difference, so we started to look at a way to reduce fragmentation.

Internet is full with recomentations. An advice can be found in BOL at Reorganize and Rebuild Indexes.

So, our best help in this task is the function sys.dm_db_index_physical_stats, which reports statistics about fragmentation.

Analysing what that function has given to us we could see that we had a highly fragmented data. There was no reason to bear with that taking into an account that the most of the data stored in the database is historical, which is rarely accessed and even more rarely updated.

The next simplest instument adviced is:

alter index { index_name | ALL } on <object> reorganize [ PARTITION = partition_number ];

The less trivial but often more efficient instrument is the use of online index rebuild and index reorganize depending on index type and a level of fragmentation.

All in all our estimation is that rebuilding or reorganizing indices frees ~100-200GBs of disk space. While, it's only a small percent of total database size, it gives us several monthes worth of a disk space!

Earlier we overlooked SQL Server API to monitor fragmentation, rebuild, and reorganize indices, and now we're going to create a job that will regulary defragment the database.

We have a large table in the form:

create table dbo.Data
(
  Date date not null,
  Type int not null,
  Value nvarchar(50) null,
  primary key clustered(Date, Type)
);

create unique nonclustered index IX_Data on dbo.Data(Type, Date);

Among other queries we often need a snapshot of data per each Type for a latest Date available:

select
  max(Date) Date,
  Type
from
  dbo.Data
group by
  Type

We have found that the above select does not run well on our data set. In fact dbo.Data grows with time, while snapshot we need stays more or less of the same size. The best solution to such query is to precalculate it. One way would be to create an indexed view, but SQL Server does not support max() aggregate in indexed views.

So, we have decided to add additional bit field dbo.Data.Last indicating that a row belongs to a last date snapshot, and to create filtered index to access that snapshot:

create table dbo.Data
(
  Date date not null,
  Type int not null,
  Value nvarchar(50) null,
  Last bit not null default 0,
  primary key clustered(Date, Type)
);

create unique nonclustered index IX_Data on dbo.Data(Type, Date);

create unique nonclustered index IX_Data_Last on dbo.Data(Type)
include(Date)
where Last = 1;

One way to support Last indicator is to create a trigger that will adjust Last value:

create trigger dbo.Data_Update on dbo.Data
after insert,delete,update
as
begin
  if (trigger_nestlevel(@@procid) < 2)
  begin
    set nocount on;

    with D as
    (
      select Date, Type from deleted
      union
      select Date, Type from inserted
    ),
    U as
    (
      select
        V.Date, V.Type
      from
        D
        inner join
        dbo.Data V
        on
          (V.Last = 1) and
          (V.Type = D.Type)
      union
      select
        max(V.Date) Date,
        V.Type
      from
        D
        inner join
        dbo.Data V
        on
          V.Type = D.Type
      group by
        V.Type
    ),
    V as
    (
      select
        rank() over(partition by V.Type order by V.Date desc) Row,
        V.*
      from
        dbo.Data V
        inner join
        U
        on
          (V.Date = U.Date) and
          (V.Type = U.Type)
    )
    update V set Last = 1 - cast(Row - 1 as bit);
  end;
end;

With Last indicator in action, our original query has been transformed to:

select Date, Type from dbo.Data where Last = 1

Execution plan shows that a new filtered index IX_Data_Last is used. Execution speed has increased considerably. As our actual table contains other bit fields, so Last indicator did not increase the table size, as SQL Server packs each 8 bit fields in one byte.

Earlier we have shown how to build streaming xml reader from business data and have reminded about ForwardXPathNavigator which helps to create a streaming xslt transformation. Now we want to show how to stream content produced with xslt out of WCF service.

To achieve streaming in WCF one needs:

1. To configure service to use streaming. Description on how to do this can be found in the internet. See web.config of the sample Streaming.zip for the details.

2. Create a service with a method returning Stream:

[ServiceContract(Namespace = "http://www.nesterovsky-bros.com")]
[AspNetCompatibilityRequirements(RequirementsMode = AspNetCompatibilityRequirementsMode.Allowed)]
public class Service
{
  [OperationContract]
  [WebGet(RequestFormat = WebMessageFormat.Json)]
  public Stream GetPeopleHtml(int count, int seed)
  {
    ...
  }
}

2. Return a Stream from xsl transformation.

Unfortunately (we mentioned it already), XslCompiledTransform generates its output into XmlWriter (or into output Stream) rather than exposes result as XmlReader, while WCF gets input stream and passes it to a client.

We could generate xslt output into a file or a memory Stream and then return that content as input Stream, but this will defeat a goal of streaming, as client would have started to get data no earlier that the xslt completed its work. What we need instead is a pipe that form xslt output Stream to an input Stream returned from WCF.

.NET implements pipe streams, so our task is trivial. We have defined a utility method that creates an input Stream from a generator populating an output Stream:

public static Stream GetPipedStream(Action<Stream> generator)
{
  var output = new AnonymousPipeServerStream();
  var input = new AnonymousPipeClientStream(
    output.GetClientHandleAsString());

  Task.Factory.StartNew(
    () =>
    {
      using(output)
      {
        generator(output);
        output.WaitForPipeDrain();
      }
    },
    TaskCreationOptions.LongRunning);

  return input;
}

We wrapped xsl transformation as such a generator:

[OperationContract]
[WebGet(RequestFormat = WebMessageFormat.Json)]
public Stream GetPeopleHtml(int count, int seed)
{
  var context = WebOperationContext.Current;

  context.OutgoingResponse.ContentType = "text/html";
  context.OutgoingResponse.Headers["Content-Disposition"] =
    "attachment;filename=reports.html";

  var cache = HttpRuntime.Cache;
  var path = HttpContext.Current.Server.MapPath("~/People.xslt");
  var transform = cache[path] as XslCompiledTransform;

  if (transform == null)
  {
    transform = new XslCompiledTransform();
    transform.Load(path);
    cache.Insert(path, transform, new CacheDependency(path));
  }

  return Extensions.GetPipedStream(
    output =>
    {
      // We have a streamed business data.
      var people = Data.CreateRandomData(count, seed, 0, count);

      // We want to see it as streamed xml data.
      using(var stream =
        people.ToXmlStream("people", "http://www.nesterovsky-bros.com"))
      using(var reader = XmlReader.Create(stream))
      {
        // XPath forward navigator is used as an input source.
        transform.Transform(
          new ForwardXPathNavigator(reader),
          new XsltArgumentList(),
          output);
      }
    });
}

This way we have build a code that streams data directly from business data to a client in a form of report. A set of utility functions and classes helped us to overcome .NET's limitations and to build simple code that one can easily support.

The sources can be found at Streaming.zip.

In the previous post about streaming we have dropped at the point where we have XmlReader in hands, which continously gets data from IEnumerable<Person> source. Now we shall remind about ForwardXPathNavigator - a class we have built back in 2002, which adds streaming transformations to .NET's xslt processor.

While XslCompiledTransform is desperately obsolete, and no upgrade will possibly follow; still it's among the fastest xslt 1.0 processors. With ForwardXPathNavigator we add ability to transform input data of arbitrary size to this processor.

We find it interesting that xslt 3.0 Working Draft defines streaming processing in a way that closely matches rules for ForwardXPathNavigator:

The only significant difference between ForwardXPathNavigator and xlst 3.0 streaming is in that we reported violations of rules for streamability at runtime, while xslt 3.0 attempts to perform this analysis at compile time.

Here the C# code for the xslt streamed transformation:

var transform = new XslCompiledTransform();

transform.Load("People.xslt");

// We have a streamed business data.
var people = Data.CreateRandomData(10000, 0, 0, 10000);

// We want to see it as streamed xml data.
using(var stream =
  people.ToXmlStream("people", "http://www.nesterovsky-bros.com"))
using(var reader = XmlReader.Create(stream))
using(var output = File.Create("people.html"))
{
  // XPath forward navigator is used as an input source.
  transform.Transform(
    new ForwardXPathNavigator(reader),
    new XsltArgumentList(),
    output);
}

Notice how XmlReader is wrapped into ForwardXPathNavigator.

To complete the picture we need xslt that follows the streaming rules:

<xsl:stylesheet version="1.0" xmlns:xsl="http://www.w3.org/1999/XSL/Transform"
  xmlns:msxsl="urn:schemas-microsoft-com:xslt"
  xmlns:d="http://www.nesterovsky-bros.com"
  exclude-result-prefixes="msxsl d">

  <xsl:output method="html" indent="yes"/>

  <!-- Root template processed in the streaming mode. -->
  <xsl:template match="/d:people">
    <html>
      <head>
        <title>List of persons</title>
        <style type="text/css">
          .even
          {
          }

          .odd
          {
            background: #d0d0d0;
          }
        </style>
      </head>
      <body>
        <table border="1">
          <tr>
            <th>ID</th>
            <th>First name</th>
            <th>Last name</th>
            <th>City</th>
            <th>Title</th>
            <th>Age</th>
          </tr>

          <xsl:for-each select="d:person">
            <!--
              Get element snapshot.
              A snapshot allows arbitrary access to the element's content.
            -->
            <xsl:variable name="person">
              <xsl:copy-of select="."/>
            </xsl:variable>

            <xsl:variable name="position" select="position()"/>

            <xsl:apply-templates mode="snapshot" select="msxsl:node-set($person)/d:person">
              <xsl:with-param name="position" select="$position"/>
            </xsl:apply-templates>
          </xsl:for-each>
        </table>
      </body>
    </html>
  </xsl:template>

  <xsl:template mode="snapshot" match="d:person">
    <xsl:param name="position"/>

    <tr>
      <xsl:attribute name="class">
        <xsl:choose>
          <xsl:when test="$position mod 2 = 1">
            <xsl:text>odd</xsl:text>
          </xsl:when>
          <xsl:otherwise>
            <xsl:text>even</xsl:text>
          </xsl:otherwise>
        </xsl:choose>
      </xsl:attribute>

      <td>
        <xsl:value-of select="d:Id"/>
      </td>
      <td>
        <xsl:value-of select="d:FirstName"/>
      </td>
      <td>
        <xsl:value-of select="d:LastName"/>
      </td>
      <td>
        <xsl:value-of select="d:City"/>
      </td>
      <td>
        <xsl:value-of select="d:Title"/>
      </td>
      <td>
        <xsl:value-of select="d:Age"/>
      </td>
    </tr>
  </xsl:template>

</xsl:stylesheet>

So, we have started with a streamed entity data, proceeded to the streamed XmlReader and reached to the streamed xslt transformation.

But at the final post about streaming we shall remind a simple way of building WCF service returning html stream from our xslt transformation.

The sources can be found at Streaming.zip.

If you're using .NET's IDictionary<K, V> you have probably found its access API too boring. Indeed at each access point you have to write a code like this:

MyValueType value;
var hasValue = dictionary.TryGetValue(key, out value);
...

In many, if not in most, cases the value is of a reference type, and you do not usually store null values, so it would be fine if dictionary returned null when value does not exist for the key.

To deal with this small nuisance we have declared a couple of accessor extension methods:

public static class Extensions
{
  public static V Get<K, V>(this IDictionary<K, V> dictionary, K key)
    where V: class
  {
    V value;

    if (key == null)
    {
      value = null;
    }
    else
    {
      dictionary.TryGetValue(key, out value);
    }

    return value;
  }

  public static V Get<K, V>(this IDictionary<K, V> dictionary, K? key)
    where V: class
    where K: struct
  {
    V value;

    if (key == null)
    {
      value = null;
    }
    else
    {
      dictionary.TryGetValue(key.GetValueOrDefault(), out value);
    }

    return value;
  }
}

These methods simplify dictionary access to:

var value = dictionary.Get(key);
...

For some reason neither .NET's XmlSerializer nor DataContractSerializer allow reading data through an XmlReader. These APIs work other way round writing data into an XmlWriter. To get data through XmlReader one needs to write it to some destination like a file or memory stream, and then to read it using XmlReader. This complicates streaming design considerably.

In fact the very same happens with other .NET APIs.

We think the reason of why .NET designers preferred XmlWriter to XmlReader in those APIs is that XmlReader's implementation is a state machine like, while XmlWriter's implementation looks like a regular procedure. It's much harder to manually write and to support a correct state machine logic than a procedure.

If history would have gone slightly different way, and if yield return, lambda, and Enumerator API appeared before XmlReader, and XmlWriter then, we think, both these classes looked differently. Xml source would have been described with a IEnumerable<XmlEvent> instead of XmlReader, and XmlWriter must be looked like a function receiving IEnumerable<XmlEvent>. Implementing XmlReader would have meant a creating a enumerator. Yield return and Enumerable API would have helped to implement it in a procedural way.

But in our present we have to deal with the fact that DataContractSerializer should write the data into XmlWriter, so let's assume we have a project that uses Entity Framework to access the database, and that you have a data class Person, and data access method GetPeople():

[DataContract(Name = "person", Namespace = "http://www.nesterovsky-bros.com")]
public class Person
{
  [DataMember] public int Id { get; set; }
  [DataMember] public string FirstName { get; set; }
  [DataMember] public string LastName { get; set; }
  [DataMember] public string City { get; set; }
  [DataMember] public string Title { get; set; }
  [DataMember] public DateTime BirthDate { get; set; }
  [DataMember] public int Age { get; set; }
}

public static IEnumerable<Person> GetPeople() { ... }

And your goal is to expose result of GetPeople() as XmlReader. We achieve result with three simple steps:

1. Define JoinedStream - an input Stream implementation that reads data from a enumeration of streams (IEnumerable<Stream>).
2. Build xml parts in the form of IEnumerable<Stream>.
3. Combine parts into final xml stream.

The code is rather simple, so here we qoute its essential part:

public static class Extensions
{
  public static Stream JoinStreams(this IEnumerable<Stream> streams, bool closeStreams = true)
  {
    return new JoinedStream(streams, closeStreams);
  }

  public static Stream ToXmlStream<T>(
    this IEnumerable<T> items,
    string rootName = null,
    string rootNamespace = null)
  {
    return items.ToXmlStreamParts<T>(rootName, rootNamespace).
      JoinStreams(false);
  }

  private static IEnumerable<Stream> ToXmlStreamParts<T>(
    this IEnumerable<T> items,
    string rootName = null,
    string rootNamespace = null)
  {
    if (rootName == null)
    {
      rootName = "ArrayOfItems";
    }

    if (rootNamespace == null)
    {
      rootNamespace = "";
    }

    var serializer = new DataContractSerializer(typeof(T));
    var stream = new MemoryStream();
    var writer = XmlDictionaryWriter.CreateTextWriter(stream);

    writer.WriteStartDocument();
    writer.WriteStartElement(rootName, rootNamespace);
    writer.WriteXmlnsAttribute("s", XmlSchema.Namespace);
    writer.WriteXmlnsAttribute("i", XmlSchema.InstanceNamespace);

    foreach(var item in items)
    {
      serializer.WriteObject(writer, item);
      writer.WriteString(" ");

      writer.Flush();
      stream.Position = 0;

      yield return stream;

      stream.Position = 0;
      stream.SetLength(0);
    }

    writer.WriteEndElement();
    writer.WriteEndDocument();

    writer.Flush();
    stream.Position = 0;

    yield return stream;
  }

  private class JoinedStream: Stream
  {
    public JoinedStream(IEnumerable<Stream> streams, bool closeStreams = true)
    ...
  }
}

The use is even more simple:

// We have a streamed business data.
var people = GetPeople();

// We want to see it as streamed xml data.
using(var stream = people.ToXmlStream("persons", "http://www.nesterovsky-bros.com"))
using(var reader = XmlReader.Create(stream))
{
  ...
}

We have packed the sample into the project Streaming.zip.

In the next post we're going to remind about streaming processing in xslt.

For some reason KendoUI DataSource does not allow to access current ajax request. Indeed, it seems quite natural to have a way to cancel running request.

To achieve a desired effect we have made a small set of changes in the RemoteTransport class:

var RemoteTransport_setup = kendo.RemoteTransport.fn.setup;

kendo.RemoteTransport.fn.setup = function()
{
  var that = this,
      options = RemoteTransport_setup.apply(that, arguments),
      beforeSend = options.beforeSend;

  options.beforeSend = functions(request, options)
  {
    that.abort();

    that._request = request;

    if (beforeSend && (beforeSend.apply(this, arguments) === false))
    {
      that._request = null;

      return false;
    }

    request.always(function() { that._request = null; });
  } 

  return options;
}

kendo.RemoteTransport.fn.request = function()
{
  return this._request;
}

kendo.RemoteTransport.fn.abort = function()
{
  var request = this._request;

  if (request)
  {
    this._request = null;
    request.abort();
  }
}

These changes allow to get an ajax request instance: grid.dataSource.request(), or to cancel a request grid.dataSource.abort().

We're pleased to work with Kendo UI. Its design is good, however we find here and there things we would wish be done better. Here is a list of problems in a no particular order we would like to be addressed in the next release:

• RTL is not supported (including correct scroll bar position see Tunning KendoUI).
• Templates and binding should support a context information along with the data source. (Why do they use with statement?)
• attr binding should use jquery.attr() method; there should be prop binding which is analogous to attr binding.
• There should be custom binding that allows any json object to bind to different aspects of a widget or an element.
• One should be able to use format/parse functions during binding. (Allow binding to express as a triple json object?)
• parseExact(value, format, culture) method should be rewritten, as it has nothing in common with parsing data string according to exact format.
• Type inference during binding is poor (parseOption() method). It works neither for string "1,2", nor json " { x: 0 } ", nor for date.
• Binding is not implemented for many components: splitter, grid.
• Splitter's pane should support size="auto".
• Drid does not support totals in group headers, nor it supports header selection.
• DataSource does not works after remote error, neither it allows to cancel request.
• innerHtml is used all over the code, thus one cannot rely on jquery.data().
• Grid does not support customization (localization) of a column filter.
• Grid should support data binding of its content.
• One should be able to destroy any widget.

Trying to make KendoUI to work with Hebrew or more generally in RTL environment we had to find a way to guess the position of scroll bar when direction is rtl.

The problem exists due to the fact that some browsers (Chrome one of them) always put scroll bars to the right. That's utterly wrong. Consider a label and a listbox:

Chrome	IE

You can see that the scroll bar appears between the label (on the right) and the data in the list box (on the left) in Chrome, and on the left side of the list box in the IE.

We came up with the following test that calculates a scroll bar position in rtl mode:

<script type="text/javascript">
var _scrollbar;

function scrollbar()
{
  if (!_scrollbar)
  {
    var div = document.createElement("div");

    div.style.cssText = "overflow:scroll;zoom:1;clear:both;direction:rtl";
    div.innerHTML = "<div>&nbsp;</div>";
    document.body.appendChild(div);

    _scrollbar =
    {
      size: div.offsetWidth - div.scrollWidth,
      rtlPosition: div.offsetLeft < div.firstChild.offsetLeft ? "left" : "right"
    };

    document.body.removeChild(div);
  }

  return _scrollbar;
}
</script>

In conjuction with an approach described in How to create a <style> tag with Javascript we were able to define rtl css classes for kendo controls and in particular for the grid, combobox, dropdownlist, and datepicker.