I have created an API using .NETCore 2.0 ; This API is connected to an oracle database to retrieve needed data; One of the functions takes too much time so I decided to use caching in order to retrieve data faster;
Function description: Get ranking
Caching period: Data should be renewed in cache memory each Monday
I am using IMemoryCache, but the problem is that data is not being cached for multiple days; It lasts only for one hour, after that data is being retrieved from database and takes too much time (10 s.); Below is my code:
var dateNow = DateTime.Now;
int diff = 7; // if today is Monday then should add 7 days to get next Monday date
if (dateNow.DayOfWeek != DayOfWeek.Monday) {
var daysToStartWeek = dateNow.DayOfWeek - DayOfWeek.Monday;
diff = (7 - (daysToStartWeek)) % 7;
}
var nextMonday = dateNow.AddDays(diff).Date;
var totalDays = (nextMonday - dateNow).TotalDays;
if (_cache.TryGetValue("GetRanking", out IEnumerable<GetRankingStruct> objRanking))
{
return Ok(objRanking);
}
var dp = new DataProvider(Configuration);
var response = dp.GetRanking(userName, asAtDate);
_cache.Set("GetRanking", response, TimeSpan.FromDays(diff));
return Ok(response);
Could be related to the token life Time since it's only 1 hour?
Firstly - have you tried checking to see if your worker process is being restarted? You don't specify how you are hosting your application but, obviously, if the application (worker process) is restarted your memory cache will be empty.
If your worker process / process is restarting then you could load the cache on start up.
Secondly - I believe that the implementation may choose to empty the cache due to inactivity or memory constraints. You can set the priority to never remove - https://learn.microsoft.com/en-us/dotnet/api/microsoft.extensions.caching.memory.cacheitempriority?view=dotnet-plat-ext-3.1
I believe you can set this by passing a MemoryCacheOptions object to the constructor of the memory cache https://learn.microsoft.com/en-us/dotnet/api/microsoft.extensions.caching.memory.memorycache.-ctor?view=dotnet-plat-ext-3.1#Microsoft_Extensions_Caching_Memory_MemoryCache__ctor_Microsoft_Extensions_Options_IOptions_Microsoft_Extensions_Caching_Memory_MemoryCacheOptions__.
Finally - I assume you've made your _cache object static so it is shared by all instances of your class. (Or made the controller, if that's what it is, a singleton).
These are my suggestions.
Good luck.
Related
I am developing a web application with Vapor 4. It would be useful to persist client-made data on the server side for a few minutes at a time in between requests. I want to use sessions to do this. However, I am a bit confused on how the best way to automatically destroy this data after a set time. Should I make a job and have it check periodically? Or is there an easy way to set an expiry time on session creation?
I have used a bit of Middleware to achieve this for some months and it is very reliable.
It compares the timestamp now to the value from the immediate previous request. If the difference is greater than the allowed session timeout, it forces a logout.
I had to give a bit of thought to initialising the timestamp and "BAD" ensures a nil gets returned from trying to initialise a Double, which then gets the current timestamp to start the session 'timer'. I think this is safe as the user can't log in without having made at least one route call beforehand and I have other Middleware that checks to make sure the user is logged in. Try this:
struct SessionTimeoutMiddleware:Middleware
{
func respond(to request:Request, chainingTo next:Responder) -> EventLoopFuture<Response>
{
let lastRequestTimeStamp = Double(request.session.data["lastRequest"] ?? "BAD") ?? Date().timeIntervalSince1970
request.session.data["lastRequest"] = String(Date().timeIntervalSince1970)
if Date().timeIntervalSince1970 - lastRequestTimeStamp > 300.0 // seconds
{
request.auth.logout(User.self)
return request.eventLoop.makeSucceededFuture(request.redirect(to:"/somewhere/safe"))
}
return next.respond(to:request)
}
}
Then, register in configure.swift using:
let userAuthSessionsMW = User.authenticator()
let sessionTimeoutMW = SessionTimeoutMiddleware()
let timed = app.grouped(C.URI.Users).grouped(userAuthSessionsMW, sessionTimeoutMW)
try SecureRoutes(timed)
Just want to get some ideas from anyone who have encountered similar problems and how did you guys come up with the solution.
Basically, we have around 10K documents stored in RavenDB. And we need the ability to allow users to perform filter and search against those documents. I am aware that there is a maximum of 1024 page size within RavenDb. So in order for the filter and search to work, I need to do my own paging. But my solution gives me the following error:
The maximum number of requests (30) allowed for this session has been reached.
I have tried many different ways of disposing the session by wrapping it around using keyword and also explicitly calling Dispose after every call to RavenDb with no success.
Does anyone know how to get around this issue? what's the best practice for this kind of scenario?
var pageSize = 1024;
var skipSize = 0;
var maxSize = 0;
using (_documentSession)
{
maxSize = _documentSession.Query<LogEvent>().Count();
}
while (skipSize < maxSize)
{
using (_documentSession)
{
var events = _documentSession.Query<LogEvent>().Skip(skipSize).Take(pageSize).ToList();
_documentSession.Dispose();
//building finalPredicate codes..... which i am not providing here....
results.AddRange(events.Where(finalPredicate.Compile()).ToList());
skipSize += pageSize;
}
}
Raven limits the number of Request (Load, Query, ...) to 30 per Session. This behavior is documented.
I can see that you dispose the session in your code. But I don't see where you recreating the session. Anyways loading data they way you intend to do is not a good idea.
We're using indexes and paging and never load more than 1024.
If you're expecting thousands of documents or your precicate logic doesn't work as an index and you don't care about how long your query will take use the unbounded results API.
var results = new List<LogEvent>();
var query = session.Query<LogEvent>();
using (var enumerator = session.Advanced.Stream(query))
{
while (enumerator.MoveNext())
{
if (predicate(enumerator.Current.Document)) {
results.Add(enumerator.Current.Document);
}
}
}
Depending on the amount of document this will use a lot of RAM.
I have the task: need to select data from "TABLE_FROM", modify it and insert to the "TABLE_TO". The main problem is script must run on production and shouldn't hurts live site performance, but "TABLE_FROM" contains hundred millions of rows. Going to run the script using nodejs. What techniques are using to resolve such kind of problems? ie. how to make this script running "slowly" or other words "softly" to prevent DB and CPU overload?
Time of script execution is irrelevant. I use Cassandra DB.
Sample code:
var OFFSET = 0;
var BATCHSIZE = 100;
var TIMEOUT = 1000;
function fetchPush() {
// fetch from TABLE_FROM, possibly in batches
rows = fetch(OFFSET, BATCHSIZE);
// push to TABLE_TO
push(rows);
// do next batch in timeout
setTimeout(fetchPush, TIMEOUT);
}
Here I'm assuming the fetch and push are blocking calls, for async processing you could (obviously) use async.
I have a bit of linq to entities code in a web app. It basically keeps a count of how many times an app was downloaded. I'm worried that this might happen:
Session 1 reads the download count (eg. 50)
Session 2 reads the download count (again, 50)
Session 1 increments it and writes it to the db (database stores 51)
Session 2 increments it and writes it to the db (database stores 51)
This is my code:
private void IncreaseHitCountDB()
{
JTF.JTFContainer jtfdb = new JTF.JTFContainer();
var app =
(from a in jtfdb.Apps
where a.Name.Equals(this.Title)
select a).FirstOrDefault();
if (app == null)
{
app = new JTF.App();
app.Name = this.Title;
app.DownloadCount = 1;
jtfdb.AddToApps(app);
}
else
{
app.DownloadCount = app.DownloadCount + 1;
}
jtfdb.SaveChanges();
}
Is it possible that this could happen? How could I prevent it?
Thank you,
Fidel
Entity Framework, by default, uses an optimistic concurrency model. Google says optimistic means "Hopeful and confident about the future", and that's exactly how Entity Framework acts. That is, when you call SaveChanges() it is "hopeful and confident" that no concurrency issue will occur, so it just tries to save your changes.
The other model Entity Framework can use should be called a pessimistic concurrency model ("expecting the worst possible outcome"). You can enable this mode on an entity-by-entity basis. In your case, you would enable it on the App entity. This is what I do:
Step 1. Enabling concurrency checking on an Entity
Right-click the .edmx file and choose Open With...
Choose XML (Text) Editor in the popup dialog, and click OK.
Locate the App entity in the ConceptualModels. I suggest toggling outlining and just expanding tags as necessary. You're looking for something like this:
<edmx:Edmx Version="2.0" xmlns:edmx="http://schemas.microsoft.com/ado/2008/10/edmx">
<!-- EF Runtime content -->
<edmx:Runtime>
<!-- SSDL content -->
...
<!-- CSDL content -->
<edmx:ConceptualModels>
<Schema Namespace="YourModel" Alias="Self" xmlns:annotation="http://schemas.microsoft.com/ado/2009/02/edm/annotation" xmlns="http://schemas.microsoft.com/ado/2008/09/edm">
<EntityType Name="App">
Under the EntityType you should see a bunch of <Property> tags. If one exists with Name="Status" modify it by adding ConcurrencyMode="Fixed". If the property doesn't exist, copy this one in:
<Property Name="Status" Type="Byte" Nullable="false" ConcurrencyMode="Fixed" />
Save the file and double click the .edmx file to go back to the designer view.
Step 2. Handling concurrency when calling SaveChanges()
SaveChanges() will throw one of two exceptions. The familiar UpdateException or an OptimisticConcurrencyException.
if you have made changes to an Entity which has ConcurrencyMode="Fixed" set, Entity Framework will first check the data store for any changes made to it. If there are changes, a OptimisticConcurrencyException will be thrown. If no changes have been made, it will continue normally.
When you catch the OptimisticConcurrencyException you need to call the Refresh() method of your ObjectContext and redo your calculation before trying again. The call to Refresh() updates the Entity(s) and RefreshMode.StoreWins means conflicts will be resolved using the data in the data store. The DownloadCount being changed concurrently is a conflict.
Here's what I'd make your code look like. Note that this is more useful when you have a lot of operations between getting your Entity and calling SaveChanges().
private void IncreaseHitCountDB()
{
JTF.JTFContainer jtfdb = new JTF.JTFContainer();
var app =
(from a in jtfdb.Apps
where a.Name.Equals(this.Title)
select a).FirstOrDefault();
if (app == null)
{
app = new JTF.App();
app.Name = this.Title;
app.DownloadCount = 1;
jtfdb.AddToApps(app);
}
else
{
app.DownloadCount = app.DownloadCount + 1;
}
try
{
try
{
jtfdb.SaveChanges();
}
catch (OptimisticConcurrencyException)
{
jtfdb.Refresh(RefreshMode.StoreWins, app);
app.DownloadCount = app.DownloadCount + 1;
jtfdb.SaveChanges();
}
}
catch (UpdateException uex)
{
// Something else went wrong...
}
}
You can prevent this from happenning if you only query the download count column right before you are about to increment it, the longer the time spent between reading and incrementing the longer the time another session has to read it (and later rewriting - wrongly - incremented number ) and thus messing up the count.
with a single SQL query :
UPDATE Data SET Counter = (Counter+1)
since its Linq To Entities,it means delayed execution,for another session to screw up the Count (increment the same base,losing 1 count there) it would have to try to increment the app.Download count i beleive between the two lines:
else
{
app.DownloadCount += 1; //First line
}
jtfdb.SaveChanges(); //Second line
}
thats means that the window for the change to occur, thus making the previous count old, is so small that for an application like this is virtually impossible.
Since Im no LINQ pro, i dont know whether LINQ actually gets app.DownLoadCount before adding one or just adds one through some SQL command, but in either case you shouldnt have to worry about that imho
You could easily test what would happen in this scenario - start a thread, sleep it, and then start another.
else
{
app.DownloadCount = app.DownloadCount + 1;
}
System.Threading.Thread.Sleep(10000);
jtfdb.SaveChanges();
But the simple answer is that no, Entity Framework does not perform any concurrency checking by default (MSDN - Saving Changes and Managing Concurrency).
That site will provide some background for you.
Your options are
to enable concurrency checking, which will mean that if two users download at the same time and the first updates after the second has read but before the second has updated, you'll get an exception.
create a stored procedure that will increment the value in the table directly, and call the stored procedure from code in a single operation - e.g. IncrementDownloadCounter. This will ensure that there is no 'read' and therefore no possibility of a 'dirty read'.
I am interested in getting statistics on the Ehcache I have running.
I would like to see the number of hits/misses for a given key over a period of time. Perhaps in the form of a map. For example.
For the passed hour (or however long it has been running)
Key A had 30 hits and 2 misses Key
B had 400 hits and 100 misses Key
C had 2 hits and 1 misses Key D
had 150 hits and 10 misses
I have looked through the documentation (SampledCacheStatistics, SampledCacheStatisticsImpl, SampledCacheStatisticsWrapper, etc) and I am having a terrible time figuring this out.
Has anyone else had experience implementing this?
Any help or ideas on this would be MUCH appreciated!
The EhCache Monitor gives you that type of information... http://ehcache.org/documentation/monitor.html
Programmatic access is available as follows:
CacheManager cacheManager = CacheManager.getInstance();
String[] cacheNames = cacheManager.getCacheNames();
for (int i = 0; i < cacheNames.length; i++) {
String cacheName = cacheNames[i];
System.out.println(cacheName+" - "+ cacheManager.getCache(cacheName).getStatistics().toString());
}
You can't track misses on a per-key basis because the statistics are stored on object IN the cache and if there was a miss, there would be no element in the cache to track it. But if you want a hit-count for all the keys in a cache you'd need to do something like:
public Map<Object,long> getKeyHits(Ehcache cache)
{
Map<Object,long> hitMap = new HashMap<Object,long>();
Map<Object,Element> allElements = cache.getAll(cache.getKeys());
for (Object key : allElements.keySet())
{
hitMap.put(key, allElements.get(key).hitCount());
}
return hitMap;
}
If you'd rather see statistics aggregated over an entire cache (or you want to track misses), you can call getStatistics() on the cache. See http://ehcache.org/apidocs/net/sf/ehcache/Ehcache.html.