Мы с вами узнаем, как предполагается использовать TPL Dataflow, рассмотрим плюсы и минусы его внедрения, а так же и особенности использования и настройки под конкретную задачу

1. Control
your
data
right
way
TPL
Dataflow
crash
course
Mikhail
Veselov
Moscow
20
Апрель
2015

2. 2
History
overview
§ TPL
Dataflow
is
another
abstracGon
level
§ ReacGve
Extensions
&
Flow
sync
§ TTB
Flow
Graph
analog
§ AAL
à
CCR
à
TDF
TPL
Dataflow
–
crash
course
IntroducGon
20 Апрель 2015
Data
AcGon
Recycle
Cache
Thread
Pool
Threads

3. 3
Main
idea
-­‐
why
Dataflow?
§ Define
your
applicaGon
dataflow
§ Async
I/O
and
CPU-­‐oriented
code,
high-­‐throughput
&
low-­‐latency
§ Random,
unstructured
data
(compare
with
Parallel
and
PLINQ)
§ Rx
IObservable<T>
support
§ Easy
start:
ActionBlock<T>
&
BufferBlock<T>
TPL
Dataflow
–
crash
course
IntroducGon
20 Апрель 2015
FIFO queue Action
Example:
GZIP
compressing
simple
schema

4. 4
Interfaces
-­‐
IDataflowBlock
public
interface
IDataflowBlock
{
void
Complete();
void
Fault(Exception
error);
Task
Completion
{
get;
}
}
block.Completion.ContinueWith(t
=>
{
if
(t.IsFaulted)
((IDataflowBlock)nextBlock).Fault(t.Exception);
else
nextBlock.Complete();
});
TPL
Dataflow
–
crash
course
Fundamental
Interfaces
20 Апрель 2015
§ IDataflowBlock
–
base
interface,
no
abstract
implementaGon
§ Task
CompleGon
staGc
methods:
§ WhenAll
§ ContinueWith
§ AggregateException
with
previous
block’s
fault
§ CancellationToken

5. 5
Interfaces
-­‐
ITargetBlock
public
interface
ITargetBlock<in
TInput>
:
IDataflowBlock
{
DataflowMessageStatus
OfferMessage(
DataflowMessageHeader
messageHeader,
TInput
messageValue,
ISourceBlock<TInput>
source,
bool
consumeToAccept);
}
§ DataflowMessageStatus:
– Accepted
–
OK,
I’ll
handle
it
– Declined
–
NO,
take
it
back
– Postponed
–
May
Be,
please,
call
back
later
J
– NotAvailable
–
Tried
to
consume
with
no
luck
– DecliningPermanently
–
No,
and
don’t
call
me
anymore
L
§ bool
consumeToAccept:
– Call
ConsumeMessage
synchroniously
TPL
Dataflow
–
crash
course
Fundamental
Interfaces
20 Апрель 2015

6. 6
Interfaces
-­‐
ISourceBlock
public
interface
ISourceBlock<out
TOutput>
:
IDataflowBlock
{
IDisposable
LinkTo(ITargetBlock<TOutput>
target,
bool
unlinkAfterOne);
bool
ReserveMessage(
//
prepare
DataflowMessageHeader
messageHeader,
ITargetBlock<TOutput>
target);
TOutput
ConsumeMessage(
//
commit
DataflowMessageHeader
messageHeader,
ITargetBlock<TOutput>
target,
out
bool
messageConsumed);
void
ReleaseReservation(
//rollback
DataflowMessageHeader
messageHeader,
ITargetBlock<TOutput>
target);
}
§ 2-­‐phase
commit
protocol
(a.k.a.
transacGon)
TPL
Dataflow
–
crash
course
Fundamental
Interfaces
20 Апрель 2015

7. 7
Advanced
Interfaces
§ IPropagatorBlock<TInput,
TOutput>
public
interface
IPropagatorBlock<in
TInput,
out
TOutput>
:
ITargetBlock<TInput>,
ISourceBlock<TOutput>
{
}
§ 1-­‐by-­‐1
linking
vs
1-­‐by-­‐n
linking
§ IReceivableSourceBlock<TOutput>
public
interface
IReceivableSourceBlock<TOutput>
:
ISourceBlock<TOutput>
{
bool
TryReceive(out
TOutput
item,
Predicate<TOutput>
filter);
bool
TryReceiveAll(out
IList<TOutput>
items);
}
§ Easier
data
process
TPL
Dataflow
–
crash
course
Fundamental
Interfaces
20 Апрель 2015

8. 8
Pure
Buffering
Blocks
§ BufferBlock<T>
– FIFO
queue
– Producer/Consumer
BufferBlock<FacebookDTO>
dataToProcess
=
new
BufferBlock<FacebookDTO>();
dataToProcess.PostAsync(newVideo);
dataToProcess.Post(newRepost);
dataToProcess.SendAsync(newLike);
TPL
Dataflow
–
crash
course
Built-­‐in
Dataflow
Blocks
20 Апрель 2015

9. 9
Pure
Buffering
Blocks
§ BroadcastBlock<T>
– Current
overwrite
– No
receivers
–
drop
it
var
bb
=
new
BroadcastBlock<ImageDTO>(i
=>
i);
var
saveToDisk
=
new
ActionBlock<ImageDTO>(item
=>
item.Image.Save(item.Path));
var
showInUi
=
new
ActionBlock<ImageDTO>(item
=>
imagePanel.AddImage(item.Image),
new
DataflowBlockOptions
{
TaskScheduler
=
TaskScheduler.FromCurrentSynchronizationContext()
});
bb.LinkTo(saveToDisk);
bb.LinkTo(showInUi);
TPL
Dataflow
–
crash
course
Built-­‐in
Dataflow
Blocks
20 Апрель 2015

10. 10
Pure
Buffering
Blocks
§ WriteOnceBlock<T>
– Singleton
writeOnce
=
new
WriteOnceBlock<Lazy<Task<T>>>(i
=>
i);
writeOnce.Post(new
Lazy<Task<T>>(()
=>
Task.Run(amadeusConnectionFactory)));
var
lazyValue
=
await
writeOnce.RecieveAsync();
var
taskConnection
=
await
lazyValue.Value;
var
connection
=
taskConnection.Result;
TPL
Dataflow
–
crash
course
Built-­‐in
Dataflow
Blocks
20 Апрель 2015

11. 11
Executor
Blocks
§ ActionBlock<TInput>
var
chooser
=
new
ActionBlock<PostDTO>(post
=>
{
Process(post);
});
var
threeMessageAtOnce
=
new
DataflowBlockOptions
{
BoundedCapacity
=
3,
TaskScheduler
=
TaskScheduler.Current
};
var
threePerTask
=
new
DataflowBlockOptions
{
MaxMessagesPerTask
=
3
};
TPL
Dataflow
–
crash
course
Built-­‐in
Dataflow
Blocks
20 Апрель 2015

12. 12
Executor
Blocks
§ TransformBlock<TInput,
TOutput>
– Output
ordering-­‐safe
queue
var
gzipper
=
new
TransformBlock<byte[],
Task<byte[]>>
(b
=>
Task.Run(()
=>
Compress(b));
var
RSAEncryptor
=
new
TransformBlock<byte[],
byte[]>(z
=>
RSA(z));
gzipper.LinkTo(RSAEncryptor);
TPL
Dataflow
–
crash
course
Built-­‐in
Dataflow
Blocks
20 Апрель 2015

13. 13
Executor
Blocks
§ TransformManyBlock<TInput,
TOutput>
– Produce
zero
or
more
items
per
1
input
message
– Output
can
be
a
Task
//
.SelectMany()
analog
var
tagCloudAggregator
=
new
TransformManyBlock<TagFromPost[],
TagFromPost>
(arrayOfTags
=>
arrayOfTags);
var
filteringTags
=
return
new
TransformManyBlock<T,
T>(async
tag
=>
await
filter(tag)
?
new
[]
{
tag
}
:
Enumerable.Empty<T>());
tagCloudAggregator.LinkTo(filteringTags);
//
provide
info
to
UI
filteringTags.TryRecieveAll(out
tagDataSource);
tagCloudControl.Show(tagDataSource);
TPL
Dataflow
–
crash
course
Built-­‐in
Dataflow
Blocks
20 Апрель 2015

14. 14
Executor
Blocks
§ NullTarget<TInput>
Recycle
bin
TPL
Dataflow
–
crash
course
Built-­‐in
Dataflow
Blocks
20 Апрель 2015

15. 15
Joining
Blocks
§ BatchBlock<T>
– accumulate
and
run
var
batch
=
new
BatchBlock<T>(batchSize:
Int32.MaxValue);
new
Timer(delegate
{
batch.TriggerBatch();
}).Change(1000,
1000);
var
batch
=
new
BatchBlock<T>(batchSize:
100);
TPL
Dataflow
–
crash
course
Built-­‐in
Dataflow
Blocks
20 Апрель 2015

16. 16
Joining
Blocks
§ JoinBlock<T1,
T2,
…>
– make
a
Tuple
– StarvaGon
problem
var
throttle
=
new
JoinBlock<SyntaxTree,
Request>();
for
(int
i
=
0;
i
<
10;
++i)
throttle.Target1.Post(new
SyntaxTree());
var
processor
=
new
TransformBlock<Tuple<SyntaxTree,
Request>,
SyntaxTree>
(pair
=>
{
var
request
=
pair.Item2;
var
resource
=
pair.Item1;
request.ProcessWith(resource);
return
resource;
});
throttle.LinkTo(processor);
processor.LinkTo(throttle.Target1);
TPL
Dataflow
–
crash
course
Built-­‐in
Dataflow
Blocks
20 Апрель 2015

17. 17
Joining
Blocks
§ BatchedJoinBlock<T1,
T2,…>
– accumulate
a
Tuples
and
run
try
{
batchedJoin.Target1.Post(DoWork());
batchedJoin.Target2.Post(default(T2));
}
Catch
(Exception
e)
{
batchJoin.Target2.Post(e);
batchJoin.Target1.Post(default(T1));
}
//
Item1
–
results
from
Target1
//
Item2
–
results
from
Target12
await
batchedJoin.RecieveAsync();
TPL
Dataflow
–
crash
course
Built-­‐in
Dataflow
Blocks
20 Апрель 2015

18. 18
ConfiguraKon
OpKons
–
TPL
support
§ TaskScheduler
&
SynchronizationContext
– TaskScheduler.Default
is
default
– TaskScheduler.Current
is
not
a
default
– ConcurrentExclusiveSchedulerPair
§ MaxDegreeOfParallelism
– ExecuGonDataflowBlockOpGons
– All
operaGons
are
not
concurrent
by
default
§ CancellationToken
TPL
Dataflow
–
crash
course
Advanced topics
20 Апрель 2015

19. 19
ConfiguraKon
OpKons
–
load
balancing
§ MaxMessagesPerTask
– Blocks
try
to
minimize
number
of
Tasks
§ MaxNumberOfGroups
– Grouping
blocks
autocomplete
§ Greedy
– How
to
create
batches
and
join
§ BoundedCapacity
– load
balancing
–
queue
size
var
taskSchedulerPair
=
new
ConcurrentExclusiveSchedulerPair();
var
readerActions
=
from
checkBox
in
new[]
{
checkBox1,
checkBox2,
checkBox3
}
select
new
ActionBlock<int>(milliseconds
=>
{
toggleCheckBox.Post(checkBox);
Thread.Sleep(milliseconds);
toggleCheckBox.Post(checkBox);
);},
new
ExecutionDataflowBlockOptions
{
TaskScheduler
=
taskSchedulerPair.ConcurrentScheduler
});
TPL
Dataflow
–
crash
course
Advanced topics
20 Апрель 2015

20. 20
StaKc
extension
methods
–
data
process
§ Choose
– MulGple
sources
for
an
acGon
– There
will
be
only
one
message
processed
§ OutputAvailableAsync
– Analog
of
Stack.Peek
operaGon
§ Post/SendAsync
– Always
async
data
propagaGon
– You
can
postpone
the
message
with
SendAsync
,
not
with
Post
§ Receive(Async)
TPL
Dataflow
–
crash
course
Advanced topics
20 Апрель 2015

21. 21
StaKc
extension
methods
–
outer
API
§ Encapsulate
(propagator
block
factory
method)
§ LinkTo
– Filter
the
message
propagaGon
– Link
opGons
– Do
not
confuse
with
ISourceBlock<T>
method
§ AsObsevable(er)
– Rx
extension
support,
no
holy
war
here
new
DataflowLinkOptions
{
MaxMessages
=
1,
Append
=
false,
PropagateCompletion
=
true
}
TPL
Dataflow
–
crash
course
Advanced topics
20 Апрель 2015

22. 22
Deep
inside
§ Implement
your
own
block
§ Advanced
debug
info
with
DebuggerDisplayAttribute
§ Chapter
#4
in
Concurrency
in
C#
Cookbook
by
Stephen
Cleary
TPL
Dataflow
–
crash
course
Advanced topics
20 Апрель 2015

23. 23
Gains When to useLosses
TPL
Dataflow
–
crash
course
Ending
20 Апрель 2015
Gathering
it
up
§ Thread
Safety
§ Structured
dataflow
§ Async
TPL-­‐oriented
§ Rx-­‐Extension
support
§ CCR-­‐oriented
code
easy
migraGon
§ Another
abstracGon
layer
§ Hard
debug
when
dataflow
is
complicated
§ Too
many
generics
§ You
have
a
random
data
which
must
be
ordered
§ CPU
and
I/O
operaGons
§ You
can
parallelize
work

24. Your
QR Code
I am at your disposal in case
of any questions or doubts
20 Апрель 2015
Mikhail Veselov
Moscow
MVeselov@luxoft.com
+7 911 951 42 98