I have a web method upload Transaction (ASMX web service) that take the XML file, validate the file and store the file content in SQL server database. we noticed that a certain users can submit the same file twice at the same time. so we can have the same codes again in our database( we cannot use unique index on the database or do anything on database level, don't ask me why). I thought I can use the lock statement on the user id string but i don't know if this will solve the issue. or if I can use a cashed object for storing all user id requests and check if we have 2 requests from the same user Id we will execute the first one and block the second request with an error message
so if anyone have any idea please help
Blocking on strings is bad. Blocking your webserver is bad.
AsyncLocker is a handy class that I wrote to allow locking on any type that behaves nicely as a key in a dictionary. It also requires asynchronous awaiting before entering the critical section (as opposed to the normal blocking behaviour of locks):
public class AsyncLocker<T>
{
private LazyDictionary<T, SemaphoreSlim> semaphoreDictionary =
new LazyDictionary<T, SemaphoreSlim>();
public async Task<IDisposable> LockAsync(T key)
{
var semaphore = semaphoreDictionary.GetOrAdd(key, () => new SemaphoreSlim(1,1));
await semaphore.WaitAsync();
return new ActionDisposable(() => semaphore.Release());
}
}
It depends on the following two helper classes:
LazyDictionary:
public class LazyDictionary<TKey,TValue>
{
//here we use Lazy<TValue> as the value in the dictionary
//to guard against the fact the the initializer function
//in ConcurrentDictionary.AddOrGet *can*, under some conditions,
//run more than once per key, with the result of all but one of
//the runs being discarded.
//If this happens, only uninitialized
//Lazy values are discarded. Only the Lazy that actually
//made it into the dictionary is materialized by accessing
//its Value property.
private ConcurrentDictionary<TKey, Lazy<TValue>> dictionary =
new ConcurrentDictionary<TKey, Lazy<TValue>>();
public TValue GetOrAdd(TKey key, Func<TValue> valueGenerator)
{
var lazyValue = dictionary.GetOrAdd(key,
k => new Lazy<TValue>(valueGenerator));
return lazyValue.Value;
}
}
ActionDisposable:
public sealed class ActionDisposable:IDisposable
{
//useful for making arbitrary IDisposable instances
//that perform an Action when Dispose is called
//(after a using block, for instance)
private Action action;
public ActionDisposable(Action action)
{
this.action = action;
}
public void Dispose()
{
var action = this.action;
if(action != null)
{
action();
}
}
}
Now, if you keep a static instance of this somewhere:
static AsyncLocker<string> userLock = new AsyncLocker<string>();
you can use it in an async method, leveraging the delights of LockAsync's IDisposable return type to write a using statement that neatly wraps the critical section:
using(await userLock.LockAsync(userId))
{
//user with userId only allowed in this section
//one at a time.
}
If we need to wait before entering, it's done asynchronously, freeing up the thread to service other requests, instead of blocking until the wait is over and potentially messing up your server's performance under load.
Of course, when you need to scale to more than one webserver, this approach will no longer work, and you'll need to synchronize using a different means (probably via the DB).
Related
Assuming the following case:
public HashTable map = new HashTable();
public void Cache(String fileName) {
if (!map.ContainsKey(fileName))
{
map.Add(fileName, new Object());
_Cache(fileName);
}
}
}
private void _Cache(String fileName) {
lock (map[fileName])
{
if (File Already Cached)
return;
else {
cache file
}
}
}
When having the following consumers:
Task.Run(()=> {
Cache("A");
});
Task.Run(()=> {
Cache("A");
});
Would it be possible in any ways that the Cache method would throw a Duplicate key exception meaning that both tasks would hit the map.add method and try to add the same key??
Edit:
Would using the following data structure solve this concurrency problem?
public class HashMap<Key, Value>
{
private HashSet<Key> Keys = new HashSet<Key>();
private List<Value> Values = new List<Value>();
public int Count => Keys.Count;
public Boolean Add(Key key, Value value) {
int oldCount = Keys.Count;
Keys.Add(key);
if (oldCount != Keys.Count) {
Values.Add(value);
return true;
}
return false;
}
}
Yes, of course it would be possible. Consider the following fragment:
if (!map.ContainsKey(fileName))
{
map.Add(fileName, new Object());
Thread 1 may execute if (!map.ContainsKey(fileName)) and find that the map does not contain the key, so it will proceed to add it, but before it gets the chance to add it, Thread 2 may also execute if (!map.ContainsKey(fileName)), at which point it will also find that the map does not contain the key, so it will also proceed to add it. Of course, that will fail.
EDIT (after clarifications)
So, the problem seems to be how to keep the main map locked for as little as possible, and how to prevent cached objects from being initialized twice.
This is a complex problem, so I cannot give you a ready-to-run answer that will work, (especially since I do not currently even have a C# development environment handy,) but generally speaking, I think that you should proceed as follows:
Fully guard your map with lock().
Keep your map locked as little as possible; when an object is not found to be in the map, add an empty object to the map and exit the lock immediately. This will ensure that this map will not become a point of contention for all requests coming in to the web server.
After the check-if-present-and-add-if-not fragment, you are holding an object which is guaranteed to be in the map. However, this object may and may not be initialized at this point. That's fine. We will take care of that next.
Repeat the lock-and-check idiom, this time with the cached object: every single incoming request interested in that specific object will need to lock it, check whether it is initialized, and if not, initialize it. Of course, only the first request will suffer the penalty of initialization. Also, any requests that arrive before the object has been fully initialized will have to wait on their lock until the object is initialized. But that's all very fine, that's exactly what you want.
I have a static class which handles the cache read/write for frequently used data.
The code is this:
public static T GetFromCache<T>(double seconds, string cacheId, Func<T> method) where T : class
{
HttpContext ctx = HttpContext.Current;
object temp = null;
temp = ctx.Cache[cacheId];
if (temp == null)
{
lock (Sync)
{
temp = ctx.Cache[cacheId];
if (temp == null)
{
temp = method.Invoke();
AddToCache(temp as T, seconds, cacheId);
return temp as T;
}
}
}
if (temp is T)
{
return (T)temp;
}
return null;
}
The code is used by various callers to read data from and write data to the cache.
Now I have a Sync object (private static readonly object Sync = new object();) which gets locked when data gets written to the cache.
As this code is called by multiple callers, I would like to create a List of Sync objects, one for each caller. (with caller I don't mean the user, but calling code. I then would identify a caller by the signature of the parameter method)
The reason I want this is that every piece of calling code can have it's own lock object; otherwise (I think) every call to this cachecontroller from different callers will use the same lock object. Then, the caching for the list of countries will also lock the caching of the list of states, and with two different lock objects, they will not be in each others way.
I would then use the CacheItemRemovedCallback method to remove the lockitems from the list.
The question is this: How can I do that?
By having one Sync object for each user will defy the purpose of synchronization as each use will hold its own lock and there will be a chance that for the same cacheId you will end up invoking the method multiple times. This might result in data becoming inconsistent.
If you wish to keep one Sync object per user then it's good to make use of session variables or per user cache or something similar.. otherwise each user will virtually end up messing up with each other's cacheId results.
If you have a scenario when there can be Multiple readers of data but at a time a single user can write it then try using ReaderWriterLockSlim. This is very fast compared to lock in a multi user scenario.
Update1
Considering the cacheId is unique and not common among the callers. You can use the following code.
No lock is needed here. Reason, HttpContext.Cache is ThreadSafe. Meaning, you can read/save values to Cache. But, if the value reference itself is being shared among more than one concurrent calls, then please synchronize it.
public static T GetFromCache<T>(double seconds, string cacheId, Func<T> method) where T : class
{
HttpContext ctx = HttpContext.Current;
object temp = null;
temp = ctx.Cache[cacheId];
if (temp == null)
{
temp = method.Invoke();
AddToCache(temp as T, seconds, cacheId);
return temp as T;
}
}
Regards
There are a great number of articles available regarding thread safe caching, here's an example:
private static object _lock = new object();
public void CacheData()
{
SPListItemCollection oListItems;
oListItems = (SPListItemCollection)Cache["ListItemCacheName"];
if(oListItems == null)
{
lock (_lock)
{
// Ensure that the data was not loaded by a concurrent thread
// while waiting for lock.
oListItems = (SPListItemCollection)Cache[“ListItemCacheName”];
if (oListItems == null)
{
oListItems = DoQueryToReturnItems();
Cache.Add("ListItemCacheName", oListItems, ..);
}
}
}
}
However, this example depends on the request for the cache also rebuilding the cache.
I'm looking for a solution where the request and rebuild are separate. Here's the scenario.
I have a web service that I want to monitor for certain types of error. If an error occurs, I create an monitor object and cache - it is updatable and is locked accordingly during update. Alls well so far.
Elsewhere, I check for the existence of the cached object, and the data it contains. This would work straight out of the box except for one particular scenario.
If the cache object is being updated - say a status change, I would like to wait and get the latest info rather than the current info, which if returned, would be out of date. So for my fetch code, I need to check if the object is currently being created/updating, and if so wait, then retry.
As I pointed out, there are many examples of cache locking patterns but I can't seem to find one that for this scenario. Any ideas as to how to go about this would be appreciated?
You can try the following code using two locks. Write lock in the setter is quite simple and protects cache from being written by more than one threads. The getter use a simple double-check lock.
Now, the trick is in Refresh() method, which uses the same lock as the getter. The method uses the lock and in the first step removes list from the cache. It will trigger any getter to fail the first null check and wait for the lock. The method in the meantime gets items, sets cache again and releases the lock.
When it comes back to the getter, it reads the cache again and now it contains the list.
public class CacheData
{
private static object _readLock = new object();
private static object _writeLock = new object();
public SPListItemCollection ListItem
{
get
{
var oListItems = (SPListItemCollection) Cache["ListItemCacheName"];
if (oListItems == null)
{
lock (_readLock)
{
oListItems = (SPListItemCollection)Cache["ListItemCacheName"];
if (oListItems == null)
{
oListItems = DoQueryToReturnItems();
Cache.Add("ListItemCacheName", oListItems, ..);
}
}
}
return oListItems;
}
set
{
lock (_writeLock)
{
Cache.Add("ListItemCacheName", value, ..);
}
}
}
public void Refresh()
{
lock (_readLock)
{
Cache.Remove("ListItemCacheName");
var oListItems = DoQueryToReturnItems();
ListItem = oListItems;
}
}
}
You can make the method and property static, if you do not need CacheData instance.
Code Details:
// Singleton class CollectionObject
public class CollectionObject
{
private static CollectionObject instance = null;
// GetInstance() is not called from multiple threads
public static CollectionObject GetInstance()
{
if (CollectionObject.instance == null)
CollectionObject.instance = new CollectionObject();
return CollectionObject.instance;
}
// Dictionary object contains Service ID (int) as key and Service object as the value
// Dictionary is filled up during initiation, before the method call ReadServiceMatrix detailed underneath
public Dictionary<int, Service> serviceCollectionDictionary = new Dictionary<int,Service>();
public Service GetServiceByIDFromDictionary(int servID)
{
if (this.serviceCollectionDictionary.ContainsKey(servID))
return this.serviceCollectionDictionary[servID];
else
return null;
}
}
DataTable serviceMatrix = new DataTable();
// Fill serviceMatrix data table from the database
private int ReadServiceMatrix()
{
// Access the Singleton class object
CollectionObject collectionObject = CollectionObject.GetInstance();
// Parallel processing of the data table rows
Parallel.ForEach<DataRow>(serviceMatrix.AsEnumerable(), row =>
{
//Access Service ID from the Data table
string servIDStr = row["ServID"].ToString().Trim();
// Access other column details for each row of the data table
string currLocIDStr = row["CurrLocId"].ToString().Trim();
string CurrLocLoadFlagStr = row["CurrLocLoadFlag"].ToString().Trim();
string nextLocIDStr = row["NextLocId"].ToString().Trim();
string nextLocBreakFlagStr = row["NextLocBreakFlag"].ToString().Trim();
string seqStr = row["Seq"].ToString().Trim();
int servID = Int32.Parse(servIDStr);
int currLocID = Int32.Parse(currLocIDStr);
int nextLocID = Int32.Parse(nextLocIDStr);
bool nextLocBreakFlag = Int32.Parse(nextLocBreakFlagStr) > 0 ? true : false;
bool currLocBreakFlag = Int32.Parse(CurrLocLoadFlagStr) > 0 ? true : false;
int seq = Int32.Parse(seqStr);
// Method call leading to the issue (definition in Collection Object class)
// Fetch service object using the Service ID from the DB
Service service = collectionObject.GetServiceByIDFromDictionary(servID);
// Call a Service class method
service.InitLanes.Add(new Service.LaneNode(currLoc.SequentialID, currLocBreakFlag, nextLoc.SequentialID, nextLocBreakFlag, seq));
}
Issue that happens is:
In the code above for all the Service objects in the dictionary, the subsequent method call is not made, leading to issues in further processing. It has to o with fetching the Service object from the dictionary in parallel mode
The db an dictionary contains all the Ids /Service objects, but my understanding is when processing in Parallel mode for the Singleton class, few of the objects are skipped leading to the issue.
In my understanding the service id passed and service object created is local to a thread, so there shouldn't be an issue that I am facing. This kind of issue is only possible, when for a given method call one thread replace service id value of another thread by its, thus both end up with Service object and few are thus skipped, which is strange in my view until and unless I do not understand the Multi threading in this case correctly
Currently I am able to run the same code in non threaded mode by using the foreach loop instead of Parallel.ForEach / Parallel.Invoke
Please review and let me know your view or any pointer that can help me resolve the issue
In my understanding the service id passed and service object created
is local to a thread
Your understanding is incorrect, if two threads request the same service id the two threads will be both working on the same singular object. If you wanted separate objects you would need to put some kind of new Service() call in GetServiceByIDFromDictionary instead of a dictionary of existing values.
Because multiple threads could be using the same service objects I think your problem lies from the fact that service.InitLanes.Add is likely not thread safe.
The easiest fix is to just lock on that single step
//...SNIP...
Service service = collectionObject.GetServiceByIDFromDictionary(servID);
// Call a Service class method, only let one thread do it for this specific service instance,
// other threads locking on other instances will not block, only other threads using the same instance will block
lock(service)
{
service.InitLanes.Add(new Service.LaneNode(currLoc.SequentialID, currLocBreakFlag, nextLoc.SequentialID, nextLocBreakFlag, seq));
}
}
This assumes that this Parallel.Foreach is the only location collectionObject.GetServiceByIDFromDictionary is used concurrently. If it is not, any other locations that could potentially be calling any methods on returned services must also lock on service.
However if Service is under your control and you can somehow modify service.InitLanes.Add to be thread safe (perhaps change InitLanes out with a thread safe collection from the System.Collections.Concurrent namespace) that would be a better solution than locking.
1.Implementing singleton always think about using of it in mulithreaded way. Always use multithreaded singleton pattern variant, one of them - lazy singleton. Use Lazy singleton using System.Lazy with appropriate LazyThreadSafeMode consturctor argument:
public class LazySingleton3
{
// static holder for instance, need to use lambda to enter code here
//construct since constructor private
private static readonly Lazy<LazySingleton3> _instance
= new Lazy<LazySingleton3>(() => new LazySingleton3(),
LazyThreadSafeMode.PublicationOnly);
// private to prevent direct instantiation.
private LazySingleton3()
{
}
// accessor for instance
public static LazySingleton3 Instance
{
get
{
return _instance.Value;
}
}
}
Read about it here
2.Use lock-ing of your service variable in parallel loop body
// Method call leading to the issue (definition in Collection Object class)
// Fetch service object using the Service ID from the DB
Service service = collectionObject.GetServiceByIDFromDictionary(servID);
lock (service)
{
// Call a Service class method
service.InitLanes.Add(new Service.LaneNode(currLoc.SequentialID,
currLocBreakFlag, nextLoc.SequentialID,
nextLocBreakFlag, seq));
}
3.Consider to use multithreading here. Using lock-ing code make your code not so perfomant as synchronous. So make sure you multithreaded/paralelised code gives you advantages
4.Use appropriate concurrent collections instead of reinventing wheel - System.Collections.Concurrent Namespace
Ok I was a little unsure on how best name this problem :) But assume this scenarion, you're
going out and fetching some webpage (with various urls) and caching it locally. The cache part is pretty easy to solve even with multiple threads.
However, imagine that one thread starts fetching an url, and a couple of milliseconds later another want to get the same url. Is there any good pattern for making the seconds thread's method wait on the first one to fetch the page , insert it into the cache and return it so you don't have to do multiple requests. With little enough overhead that it's worth doing even for requests that take about 300-700 ms? And without locking requests for other urls
Basically when requests for identical urls comes in tightly after each other I want the second request to "piggyback" the first request
I had some loose idea of having a dictionary where you insert an object with the key as url when you start fetching a page and lock on it. If there's any matching the key already it get's the object, locks on it and then tries to fetch the url for the actual cache.
I'm a little unsure of the particulars however to make it really thread-safe, using ConcurrentDictionary might be one part of it...
Is there any common pattern and solutions for scenarios like this?
Breakdown wrong behavior:
Thread 1: Checks the cache, it doesnt exists so starts fetching the url
Thread 2: Starts fetching the same url since it still doesn't exist in Cache
Thread 1: finished and inserts into the cache, returns the page
Thread 2: Finishes and also inserts into cache (or discards it), returns the page
Breakdown correct behavior:
Thread 1: Checks the cache, it doesnt exists so starts fetching the url
Thread 2: Wants the same url, but sees it's currently being fetched so waits on thread 1
Thread 1: finished and inserts into the cache, returns the page
Thread 2: Notices that thread 1 is finished and returns the page thread 1 it fetched
EDIT
Most solutions sofar seem to misunderstand the problem and only addressing the caching, as I said that isnt the problem, the problem is when doing an external web fetch to make the second fetch that is done before the first one has cached it to use the result from the first rather then doing a second
You could use a ConcurrentDictionary<K,V> and a variant of double-checked locking:
public static string GetUrlContent(string url)
{
object value1 = _cache.GetOrAdd(url, new object());
if (value1 == null) // null check only required if content
return null; // could legitimately be a null string
var urlContent = value1 as string;
if (urlContent != null)
return urlContent; // got the content
// value1 isn't a string which means that it's an object to lock against
lock (value1)
{
object value2 = _cache[url];
// at this point value2 will *either* be the url content
// *or* the object that we already hold a lock against
if (value2 != value1)
return (string)value2; // got the content
urlContent = FetchContentFromTheWeb(url); // todo
_cache[url] = urlContent;
return urlContent;
}
}
private static readonly ConcurrentDictionary<string, object> _cache =
new ConcurrentDictionary<string, object>();
EDIT: My code is quite a bit uglier now, but uses a separate lock per URL. This allows different URLs to be fetched asynchronously, however each URL will only be fetched once.
public class UrlFetcher
{
static Hashtable cache = Hashtable.Synchronized(new Hashtable());
public static String GetCachedUrl(String url)
{
// exactly 1 fetcher is created per URL
InternalFetcher fetcher = (InternalFetcher)cache[url];
if( fetcher == null )
{
lock( cache.SyncRoot )
{
fetcher = (InternalFetcher)cache[url];
if( fetcher == null )
{
fetcher = new InternalFetcher(url);
cache[url] = fetcher;
}
}
}
// blocks all threads requesting the same URL
return fetcher.Contents;
}
/// <summary>Each fetcher locks on itself and is initilized with null contents.
/// The first thread to call fetcher.Contents will cause the fetch to occur, and
/// block until completion.</summary>
private class InternalFetcher
{
private String url;
private String contents;
public InternalFetcher(String url)
{
this.url = url;
this.contents = null;
}
public String Contents
{
get
{
if( contents == null )
{
lock( this ) // "this" is an instance of InternalFetcher...
{
if( contents == null )
{
contents = FetchFromWeb(url);
}
}
}
return contents;
}
}
}
}
Will the Semaphore please stand up! stand up! stand up!
use Semaphore you can easily synchronize your threads with it.
on both cases where
you are trying to load a page that is currently being cached
you are saving cache to a file where a page is loading from it.
in both scenarios you will face troubles.
it is just like writers and readers problem that is a common problem in Operating System Racing Issues. just when a thread wants to rebuild a cache or start caching a page no thread should read from it. if a thread is reading it it should wait until reading finished and replace the cache, no 2 threads should cache same page in to a same file. hence it is possible for all readers to read from a cache at anytime since no writer is writing on it.
you should read some semaphore using samples on msdn, it is very easy to use. just the thread that wants to do something is call the semaphore and if the resource can granted it do the works otherwise sleeps and wait to be woken up when the resource is ready.
Disclaimer: This might be a n00bish answer. Please pardon me, if it is.
I'd recommend using some shared dictionary object with locks to keep a track of the url being currently fetched or have already been fetched.
At every request, check the url against this object.
If an entry for the url is present, check the cache. (this means one of the threads has either fetched it or is currently fetching it)
If its available in the cache, use it, else put the current thread to sleep for a while and check back again. (if not in cache, some thread is still fetching it, so wait while its done)
If the entry is not found in the dictionary object, add the url to it and send the request. Once it obtains a response, add it to cache.
This logic should work, however, you would need to take care of cache expiration and removal of the entry from the dictionary object.
my solution is use atomicBoolean to control access database when cache is timeout or unexist;
at the same moment, only one thread(i call it read-th) can access database, the other threads spin until the read-th return data and write it into cache;
here codes; implement by java;
public class CacheBreakDownDefender<K, R> {
/**
* false = do not write null to cache when get null value from database;
*/
private final boolean writeNullToCache;
/**
* cache different query key
*/
private final ConcurrentHashMap<K, AtomicBoolean> selectingDBTagMap = new ConcurrentHashMap<>();
public static <K, R> CacheBreakDownDefender<K, R> getInstance(Class<K> keyType, Class<R> resultType) {
return Singleton.get(keyType.getName() + resultType.getName(), () -> new CacheBreakDownDefender<>(false));
}
public static <K, R> CacheBreakDownDefender<K, R> getInstance(Class<K> keyType, Class<R> resultType, boolean writeNullToCache) {
return Singleton.get(keyType.getName() + resultType.getName(), () -> new CacheBreakDownDefender<>(writeNullToCache));
}
private CacheBreakDownDefender(boolean writeNullToCache) {
this.writeNullToCache = writeNullToCache;
}
public R readFromCache(K key, Function<K, ? extends R> getFromCache, Function<K, ? extends R> getFromDB, BiConsumer<K, R> writeCache) throws InterruptedException {
R result = getFromCache.apply(key);
if (result == null) {
final AtomicBoolean selectingDB = selectingDBTagMap.computeIfAbsent(key, x -> new AtomicBoolean(false));
if (selectingDB.compareAndSet(false, true)) {
try {
result = getFromDB.apply(key);
if (result != null || writeNullToCache) {
writeCache.accept(key, result);
}
} finally {
selectingDB.getAndSet(false);
selectingDBTagMap.remove(key);
}
} else {
while (selectingDB.get()) {
TimeUnit.MILLISECONDS.sleep(0L);
//do nothing...
}
return getFromCache.apply(key);
}
}
return result;
}
public static void main(String[] args) throws InterruptedException {
Map<String, String> map = new ConcurrentHashMap<>();
CacheBreakDownDefender<String, String> instance = CacheBreakDownDefender.getInstance(String.class, String.class, true);
for (int i = 0; i < 9; i++) {
int finalI = i;
new Thread(() -> {
String kele = null;
try {
if (finalI == 6) {
kele = instance.readFromCache("kele2", map::get, key -> "helloword2", map::put);
} else
kele = instance.readFromCache("kele", map::get, key -> "helloword", map::put);
} catch (InterruptedException e) {
Thread.currentThread().interrupt();
}
log.info("resut= {}", kele);
}).start();
}
TimeUnit.SECONDS.sleep(2L);
}
}
This is not exactly for concurrent caches but for all caches:
"A cache with a bad policy is another name for a memory leak" (Raymond Chen)