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C# Multi-Producer/Multi-Tiered Multi-Consumer Losing Data

I have a built complex application using a multi-tiered producer-consumer pattern, with multiple consumers performing specialized tasks before enqueing data to the next group of consumers. The ultimate job of the application is to break down a raw data file into test records for individual units that that will have been normalized.
The base of the P-C pattern uses Dustin Hyun's pattern from http://dustin-hyun.blogspot.com/2013_07_01_archive.html. I have made numerous modifications because of the multiple tiered approach and others. The code is too complex to post here- perhaps I could post snippets upon request to help clarify and answer questions.
I have employed two tools to speed up how a file gets processed. First is multiple instances of any of the tiers of consumer- there could be eight "index" consumers running whose jobs are to convert the test data from unit IDs and Test Names to Unit Indices and Test Name Indices to normalize the results to load into the DB. Second is the Bundling of units into merged DataTables at two point in the operation.
I have identified that data is lost intermittently, but in a fairly predictable pattern. It appears to be the last, incomplete bundle where the data was expected to have been. After the standard loop pattern, I have a check for a boolean that I use to flag if there is an incoMplete bundle, and it works:
if (dataToSend) // Check if incomplete bundle to process & send prior to ending comsumer operation.
{
UpdateLimitsIndices(bundleNlu);
Enqueue(StdfQType.Func, new BundledNamedTables((N_ParamRes)bundlePR.Copy(), (N_FuncRes)bundleFR.Copy(), numUnitsInCurrBundle));
}
I also have put locks onto everyplace I can see where the any of the p_c entities read or write anything from any of the shared queue members. With just the locks, there appeared to be no real impact. On a whim, I started to play with the sleep time before the loop re-spins So far, Test conditions that caused data loss with a 1ms sleep did not cause data loss during a 100 ms sleep or even a 10 ms sleep during limited testing. Could it be that the longer sleep is allowing the last piece/bundle of data to be properly processed?
I recognize that this question is vague and has few specifics because the application is too complex to post. I do hope I gave enough information for a dialog to start, however. I look for eard to heading your thoughts.
Jeff

I would suggest that because you are not using thread-safe collections (and neither does the author that you are basing your code on) that this may be the basis for losing data due to a concurrent write operation that fails (silently).
Luckily, along with the Task Parallel Library (TPL) .NET 4.0 gives us a whole bunch of concurrent collections which ARE thread-safe for multi-threaded environments.
Have a look at the collections in System.Collections.Concurrent as they are all thread-safe and their locking mechanisms are a lot faster than traditional lock-based objects.

Threading is very difficult to get right, and it appears that you have not gotten it right. Also, why are you (and the author of that blog post) using sleep intervals rather than Monitor.Pulse()?
Rather than trying to implement this yourself, why not use a library that will give you a slightly higher level of abstraction above the underlying thread coordination mechanism?
TPL Dataflow
Reactive Extensions

Database recommendations needed -> Columnar, Embedded (if possible)

EDIT: As result of the answers so far I like to add more focus in what I like to zero in on: A database that allows writing in-memory (could be simple C# code) with persistence to storage options in order to access the data from within R. Redis so far looks the most promising. I also consider to actually use something similar to Lockfree++ or ZeroMQ, in order to avoid writing data concurrently to the database, but rather sending all to be persisted data over a message bus/other implementation and to have one "actor" handle all write operations to an in-memory db or other solution. Any more ideas aside Redis (some mentioned SQLite and I will need to still test its performance). Any other suggestions?
I am searching for the ideal database structure/solution that meets most of my below requirements but so far I utterly failed. Can you please help?
My tasks: I run a process in .Net 4.5 (C#) and generate (generally) value types that I want to use for further analysis in other applications and therefore like to either preserve in-memory or persist on disk. More below. The data is generated within different tasks/threads and thus a row based data format does not lend itself well to match this situation (because the data generated in different threads is generated at different times and is thus not aligned). Thus I thought a columnar data structure may be suitable but please correct me if I am wrong.
Example:
Tasks/Thread #1 generates the following data at given time stamps
datetime.ticks / value of output data
1000000001 233.23
1000000002 233.34
1000000006 234.23
...
Taks/Thread #2 generates the following data at given time stamps
datetime.ticks / value of output data
1000000002 33.32
1000000005 34.34
1000000015 54.32
...
I do not need to align the time stamps at the .Net run-time, I am first and foremost after preserving the data and to process the data within R or Python at a later point.
My requirements:
Fast writes, fast writes, fast writes: It can happen that I generate 100,000- 1,000,000 data points per second and need to persist (worst case) or retain in memory the data. Its ok to run the writes on its own thread so this process can lag the data generation process but limitation is 16gb RAM (64bit code), more below.
Preference is for columnar db format as it lends itself well to how I want to query the data later but I am open to any other structure if it makes sense in regards to the examples above (document/key-value also ok if all other requirements are met, especially in terms of write speed).
API that can be referenced from within .Net. Example: HDF5 may be considered capable by some but I find their .Net port horrible.Something that supports .Net a little better would be a plus but if all other requirements are met then I can deal with something similar to the HDF5 .Net port.
Concurrent writes if possible: As described earlier I like to write data concurrently from different tasks/threads.
I am constrained by 16gb memory (run .Net process in 64bit) and thus I probably look for something that is not purely in-memory as I may sometimes generate more data than that. Something in-memory which persists at times or a pure persistence model is probably preferable.
Preference for embedded but if a server in a client/server solution can run as a windows service then no issue.
In terms of data access I have strong preference for a db solution for which interfaces from R and Python already exist because I like to use the Panda library within Python for time series alignments and other analysis and run analyses within R.
If the API/library supports in addition SQL/SQL-like/Linq/ like queries that would be terrific but generally I just need the absolute bare bones such as load columnar data in between start and end date (given the "key"/index is in such format) because I analyze and run queries within R/Python.
If it comes with a management console or data visualizer that would be a plus but not a must.
Should be open source or priced within "reach" (no, KDB does not qualify in that regards ;-)
OK, here is what I have so far, and again its all I got because most db solution simply fail already on the write performance requirement:
Infobright and Db4o. I like what I read so far but I admit I have not checked into any performance stats
Something done myself. I can easily store value types in binary format and index the data by datetime.ticks , I just would need to somehow write scripts to load/deserialize the data in Python/R. But it would be a massive tasks if I wanted to add concurrency, a query engine, and other goodies. Thus I look for something already out there.

I can't comment -- low rep (I'm new here) -- so you get a full answer instead...
First, are you sure you need a database at all? If fast write speed and portability to R is your biggest concern then have you just considered a flat file mechanism? According to your comments you're willing to batch writes out but you need persistence; if those were my requirements I'd write a straight-to-disck buffering system that was lightning fast then build a separate task that periodically took the disk files and moved them into a data store for R, and that's only if R reading the flat files wasn't sufficient in the first place.
If you can do alignment after-the-fact, then you could write the threads to separate files in your main parallel loop, cutting each file off every so often, and leave the alignment and database loading to the subprocess.
So (in crappy pseudo_code), build a thread process that you'd call with backgroundworker or some such and include a threadname string uniquely identifying each worker and thus each filestream (task/thread):
file_name = threadname + '0001.csv' // or something
open(file_name for writing)
while(generating_data) {
generate_data()
while (buffer_not_full and very_busy) {
write_data_to_buffer
generate_data()
}
flush_buffer_to_disk(file_name)
if(file is big enough or enough time has passed or we're not too busy) {
close(file_name)
move(file_name to bob's folder)
increment file_name
open(file_name for writing)
}
)
Efficient and speedy file I/O and buffering is a straightforward and common problem. Nothing is going to be faster than this. Then you can just write another process to do the database loads and not sweat the performance there:
while(file_name in list of files in bob's folder sorted by date for good measure)
{
read bob's file
load bob's file to database
align dates, make pretty
}
And I wouldn't write that part in C#, I'd batch script it and use the database's native loader which is going to be as fast as anything you can build from scratch.
You'll have to make sure the two loops don't interfere much if you're running on the same hardware. That is, run the task threads at a higher priority, or build in some mutex or performance limiters so that the database load doesn't hog resources while the threads are running. I'd definitely segregate the database server and hardware so that file I/O to the flat files isn't compromised.
FIFO queues would work if you're on Unix, but you're not. :-)
Also, hardware is going to have more of a performance impact for you than the database engine, I'd imagine. If you're on a budget I'm guessing you're on COTS hardware, so springing for a solid state drive may up performance fairly cheaply. As I said, separating the DB storage from the flat file storage would help, and the CPU/RAM for R, the Database, and your Threads should all be segregated ideally.
What I'm saying is that choice of DB vendor probably isn't your biggest issue, unless you have a lot of money to spend. You'll be hardware bound most of the time otherwise. Database tuning is an art, and while you can eek out minor performance gains at the top end, having a good database administrator will keep most databases in the same ballpark for performance. I'd look at what R and Python support well and that you're comfortable with. If you think in columnar fashion then look at R and C#'s support for Cassandra (my vote), Hana, Lucid, HBase, Infobright, Vertica and others and pick one based on price and support. For traditional databases on a single commodity machine, I haven't seen anything that MySQL can't handle.

This is not to answer my own question but to keep track of all data bases which I tested so far and why they have not met my requirements (yet): each time I attempted to write 1 million single objects (1 long, 2 floats) to the database. For ooDBs, I stuck the objects into a collection and wrote the collection itself, similar story for key/value such as Redis but also attempted to write simple ints (1mil) to columnar dbs such as InfoBright.
Db4o, awefully slow writes: 1mil objects within a collection took about 45 seconds. I later optimized the collection structure and also wrote each object individually, not much love here.
InfoBright: Same thing, very slow in terms of write speed, which surprised me quite a bit as it organizes data in columnar format but I think the "knowledge tree" only kicks in when querying data rather than when saving flat data structures/tables-like structures.
Redis (through BookSleeve): Great API for .Net: Full Redis functionality (though couple drawbacks to run the server on Windows machines vs. a Linux or Unix box). Performance was very fast...North of 1 million items per second. I serialized all objects using Protocol Buffers (protobuf-net, both written by Marc Gravell), still need to play a lot more with the library but R and Python both have full access to the Redis DB, which is a big plus. Love it so far. The Async framework that Marc wrote around the Redis base functions is awesome, really neat and it works so far. I wanna spend a little more time to experiment with the Redis Lists/Collection types as well, as I so far only serialized to byte arrays.
SqLite: I ran purely in-memory and managed to write 1 million value type elements in around 3 seconds. Not bad for a pure RDBMS, obviously the in-memory option really speeds things up. I only created one connection, one transaction, created one command, one parameter, and simply adjusted the value of the parameter within a loop and ran the ExecuteNonQuery on each iteration. The transaction commit was then run outside the loop.
HDF5: Though there is a .Net port and there also exists a library to somehow work with HDF5 files out of R, I strongly discourage anyone to do so. Its a pure nightmare. The .Net port is very badly written, heck, the whole HDF5 concept is more than questionable. Its a very old and in my opinion outgrown solution to store vectorized/columnar data. This is 2012 not 1995. If one cannot completely delete datasets and vectors out of the file in which they were stored before then I do not call that an annoyance but a major design flaw. The API in general (not just .Net) is very badly designed and written imho, there are tons of class objects that nobody, without having spent hours and hours of studying the file structure, understands how to use. I think that is somewhat evidenced by the very sparse amount of documentation and example code that is out there. Furthermore, the h5r R library is a drama, an absolute nightmare. Its badly written as well (often the file upon writing is not correctly close due to a faulty flush and it corrupts files), the library has issues to even be properly installed on 32 bit OSs...and it goes on and on. I write the most about HDF5 because I spent the most of my time on this piece of .... and ended up with the most frustration. The idea to have a fast columnar file storage system, accessible from R and .Net was enticing but it just does not deliver what it promised in terms of API integration and usability or lack thereof.
Update: I ditched testing velocityDB simply because there does not seem any adapter to access the db from within R available. I currently contemplate writing my own GUI with charting library which would access the generated data either from a written binary file or have it sent over a broker-less message bus (zeroMQ) or sent through LockFree++ to an "actor" (my gui). I could then call R from within C# and have results returned to my GUI. That would possibly allow me the most flexibility and freedom, but would obviously also be the most tedious to code. I am running into more and more limitations during my tests that with each db test I befriend this idea more and more.
RESULT: Thanks for the participation. In the end I awarded the bounty points to Chipmonkey because he suggested partly what I considered important points to the solution to my problem (though I chose my own, different solution in the end).
I ended up with a hybrid between Redis in memory storage and direct calls out of .Net to the R.dll. Redis allows access to its data stored in memory by different processes. This makes it a convenient solution to quickly store the data as key/value in Redis and to then access the same data out of R. Additionally I directly send data and invoke functions in R through its .dll and the excellent R.Net library. Passing a collection of 1 million value types to R takes about 2.3 seconds on my machine which is fast enough given that I get the convenience to just pass in the data, invoke computational functions within R out of the .Net environment and getting the results back sync or async.

Just a note: I once had a similar problem posted by a fellow in a delphi forum. I could help him with a simple ID-key-value database backend I wrote at that time (kind of a NoSQL engine). Basically, it uses a B-Tree to store triplets (32bit ObjectID, 32bit PropertyKey, 64bit Value). I could manage to save about 500k/sec Values in real time (about 5 years ago). Of course, the data was indexed on all three values (ID, property-ID and value). You could optimize this by ignoring the value index.
The source I still have is in Delphi, but I would think about implementing something like that using C#. I cannot tell you whether it will meet your needs for performance, but if all else fails, give it a try. Using a buffered write should also drastically improve performance.

I would go with way combining persistence storage (I personally prefer db4o, but you can use files as well as mentioned above) and storing objects into memory this way:
use BlockingCollection<T> to store objects in memory (I believe you will achieve better performance then 1000000/s to store objects in memory), and than have one or more processing threads which will consume the objects and store them into persistent database
// Producing thread
for (int i=0; i<1000000; i++)
blockingCollection.Add(myObject);
// Consuming threads
while (true)
{
var myObject = blockingCollection.Take();
db4oSession.Store(myObject); // or write it to the files or whathever
}
BlockingCollection pretty much solves Producer-Consumer workflow, and in case you will use multiple instance of them and use AddToAny/TakeFromAny you can reach any kind of multithreaded performance
each consuming thread could have different db4o session (file) to reach desired performance (db4o is singlethreaded).

Since you want to use ZeroMQ why not use memcache over Redis?
ZeroMQ offers no persistence as far as I know. Memcache also offers no persistence and is a bit faster than Redis.
Or perhaps the other way, if you use Redis why not use beanstalk MQ?
If you want to use Redis (for the persistence) you might want to switch from ZeroMQ to beanstalk MQ (also a fast in memory queue, but also has persistence via logging). Beanstalk also has C# libs.

Threading not possible suggest any other way to do parallel processing

My code in C# which i put in threading is not thread safe as it involves lots of database connections, stored procedure executions. but still i want to minimize the time required for the execution, can anyone suggest me something for parallel or asynchronous processing.. anything from database side or .net side...
I am stuck with threading.. not able to apply...
Thanks

There is not nearly enough information in your question to suggest an optimized solution. However, if you must deal with resources that are not thread safe, one strategy is to spawn separate processes to handle sub-tasks.
Keep in mind, though, that separate processes still would have to handle portions of the overall solution that do not step on each other.

Well . There are couple of things that can be done . However I dont know how much it really fits you.
On database side you can do Query Optimizations , Indexing and other stuff that may help increase the query run time. Use profilers to analyse . See query plans to check if the indexes are properly used.
Use NOLOCKS but only where you see that you can USE NO LOCKS in your selects. (Not always a good practice but is used)
Implement proper Synchronized threading that can process multiple request. There is no other way from .Net Side. You have to review your design properly. However you can do code optimizations as well using profiler. You can use Task Library as well.
Other thing is There could be an issue with your server. Check CPU and Memory utilizations.
(This is my least of the concerns).

You should explain better what you are doing in your code...
If you do a lot of loops, you should try Parallel.For / Parallel.Foreach
http://msdn.microsoft.com/en-us/library/system.threading.tasks.parallel.aspx
In Parallels you can also queue tasks for ordered computation or divide loops into blocks that could improve overall performance...
The best I can say, with so little information.
Hope it helps.

What tools to use as api for building a generic performance profiler for .net applications?

I know a similar question has already been asked here What features should a C#/.NET profiler have? but this thread is not only about the wishlist but also about how to go about implementing that wishlist.
So let me ask you this just once more. I am in the process of building a generic performance profiler. I know I can take in a dll as input and take the usual Stopwatch approach to profile the response times of methods in that dll. But this is very basic stuff. I am willing to use third party api(or do some code on my own too) to extract whatever useful information I can lay my hands on from that dll. I want to know everything that makes it slow. I want to know about it's memory leaks. Anything at all that would help me find bottlenecks of the application. I'd want similar approach to find expensive db operations. But all this, under one application.
So what approach do you suggest? Which tools can I bring under my umbrella so that I can use them in my project?
I want to make a 'single' application that will take generic inputs like dlls, can also take input as source code tree(solution, projects, .cs files) and emit out results in the form of response times, identifying bottlenecks, memory leaks, etc.

Anything at all that would help me find bottlenecks of the
application.
Be careful of the universal profiling assumption that measurements unerringly lead to where the bottlenecks are, because some of them can be found that way, but only some.
Then the remaining bottlenecks sit there consuming time needlessly, but the developer is oblivious to them because profiler measurements did not isolate them.
A simple example could be some sort of dictionary lookup that appears to be optimal, except that the words being looked up are highly non-random. If certain words are looked up much more frequently, that represents an opportunity for optimization, but to detect that you need to know something about the data. Measuring profilers don't look at the program's data.
A more extreme example is any sort of interpreter, whose data is the "instruction set" for another language. The bottlenecks could easily be in that other language, but since it is data the measuring profiler would not see them.
What does see problems of this sort are not measurements but a small number of samples of the program's state, where the developer can fully examine and characterize the content of each sample (call stack and data). This leads to a much better understanding of how and why the program is spending its time than putting measurements on methods or browsing a call graph.

What to make parallel? What will make me better? (.net Web Business Application, MVC+SL)

I'm working on a web application framework, which uses MSSQL for data storage, mostly just does CRUD operations (but on arbitrarly complex structures), provides a WCF interface for rich Silverlight admin and has an MVC3 display (and some basic forms like user settings, etc).
It's getting quite good at being able to load, display, edit and save any (reasonably) complex data structure, in a user-friendly way.
But, I'm looking towards the future, and want to expand my capabilities (and it would be fun to learn new things along the way as well...) - so I've decided (in the light of what's coming for C#5...) to try to get some parallel/async optimalization... Now, I haven't even learned TPL and PLinq yet, so I'm happy for any advice there as well.
So my question is, what are possible areas where parallel processing maybe of help, and where does TPL and PLinq help me on that?
My guts tell me, I could try saving branches of a data structure in a parallel way to the database (this is where I'd expect the biggest peformance optimalization), I could perform some complex operations (file upload, mail sending maybe?) in a multithreaded enviroment, etc. Can I build complex SL UI views in parallel on the client? (Creating 60 data-bound fields on a view can cause "blinking"...) Can I create partial views (menus, category trees, search forms, etc) in MVC at once?
ps: If this turns into "Tell me everything about parallel stuffs" thread, I'm happy to make it community-wiki...

Remember that an asp.net web application is intrinsically a parallel application in any case. Requests can be serviced in parallel and this will all be managed by the asp.net framework. So there are two cases:
You have lots of users all hitting the site at once. In which case the parallel processing capability of the server is probably being used to capacity in any case.
You don't have lots of users all hitting the site at once. In which case the server is probably quite capable of dealing with the responses without parallel processing in a suitable fast response time.
Any time you start thinking about optimising something just because it might be fun, or because you just think you should make stuff faster then you are almost certainly guilty of premature optimization. Your efforts could almost certainly be better spent enriching the functionality of the framework, rather than making what is probably a plenty fast enough solution a little bit faster (at the cost of significantly increase complexity).
In answer to the question of where can TPL and PLINQ really help. In my opinion the main advantage of these technologies is in places in the application where you really do have a lot of long running blocking processes. For example if you have a situation where you call out several times to an external web service - it can be a significant advantage to make these calls in parallel. I would strongly question whether writing to a local database - or even a database on a different box on a local network would count as being a long running blocking process to the extent that this kind of parallelisation is of any significant value.
Pretty much all the examples you list fall in to the category of getting the PC to do something in parallel that it was previously doing in sequence. How many CPUs are on your server - how many are really free when the website is under load. Making something parallel does not necessarily equate to making it faster unless the process involved has some measure of time when you PC is sitting around doing nothing waiting for an external event.

First question is to ask the users / testers which bits seem slow. The only way to know for sure what's slowing you down is to use a profiler like dottrace. The results are sometimes surprising.
If you do find something, parallel processing may not be the answer. You need to remember that there is an overhead in splitting tasks up, so if the task is fairly quick in the first place, it could end up being slower. You also have to consider the added complexity, e.g. what happens if half a task succeeds, and half fails? (Although TPL and PLINQ hide you from this to an extend)
Have fun, but I wondering whether this is a case of 1) solution chasing a problem, and 2) premature optimization.