I'm trying to track down intermittent "bursts" of timeouts using the StackExchange Redis library. Here's a bit about our setup: Our API is written in C# and runs on Windows 2008 and IIS. We have 4 API servers in production, and we have 4 Redis machines (Running Linux latest LTS), each with 2 instances of Redis (one master on port 7000, one slave on port 7001). I've looked at pretty much every aspect of the Redis servers and they look fantastic. No errors in the logs, CPU and network is great, everything with the server side of things seem fantastic. I can tail -f the Redis logs while this is happening and don't see anything out of the ordinary (such as rewriting AOF files or anything). I don't think the problem is with Redis.
Here's what I know so far:
We see these timeout exceptions several times an hour. Usually between 40-50 timeouts in a minute, sometimes up to 80-90. Then, they'll go away for several minutes. There were about 5,000 of these events in the past 24 hours, and they happen in bursts from a single API client.
These timeouts only happen against Redis master nodes, never against slave nodes. However, they happen with various Redis commands such as GETs and SETs.
When a burst of these timeouts happen, the calls are coming from a single API server but happen talking to various Redis nodes. For example, API3 might have a bunch of timeouts trying to call Cache1, Cache2 and Cache3. This is strong evidence that the issue is related to the API servers, not the Redis servers.
The Redis master nodes have 108 connected clients. I log current connections, and this number remains stable. There are no big spikes in connections, and it doesn't look like there's any bad code creating too many connections or not sharing ConnectionMultiplexer instances (I have one and it's static)
The Redis slave nodes have 58 connected clients, and this also looks completely stable as well.
We're using StackExchange.Redis version 1.2.6
Redis is using AOF mode, and size on disk is about 195MB
Here's an example timeout exception. Most look pretty much the same as this:
Type=StackExchange.Redis.RedisTimeoutException,Message=Timeout
performing GET limeade:allActivities, inst: 1, mgr: ExecuteSelect,
err: never, queue: 0, qu: 0, qs: 0, qc: 0, wr: 0, wq: 0, in: 0, ar: 0,
clientName: LIMEADEAPI4, serverEndpoint: 10.xx.xx.11:7000,
keyHashSlot: 1295, IOCP: (Busy=0,Free=1000,Min=2,Max=1000), WORKER:
(Busy=9,Free=32758,Min=2,Max=32767) (Please take a look at this
article for some common client-side issues that can cause timeouts:
http://stackexchange.github.io/StackExchange.Redis/Timeouts),StackTrace=
at
StackExchange.Redis.ConnectionMultiplexer.ExecuteSyncImpl[T](Message
message, ResultProcessor1 processor, ServerEndPoint server) at
StackExchange.Redis.ConnectionMultiplexer.ExecuteSyncImpl[T](Message
message, ResultProcessor1 processor, ServerEndPoint server) at
StackExchange.Redis.RedisBase.ExecuteSync[T](Message message,
ResultProcessor1 processor, ServerEndPoint server) at
StackExchange.Redis.RedisDatabase.StringGet(RedisKey key, CommandFlags
flags) at Limeade.Caching.Providers.RedisCacheProvider1.Get[T](K
cacheKey, CacheItemVersion& cacheItemVersion) in ...
I've done a bit of research on tracing down these timeout exceptions, but what's rather surprising is all the numbers are all zeros. Nothing in the queue, nothing waiting to be processed, I have tons of threads free and not doing anything. Everything looks great.
Anyone have any ideas on how to fix this? The problem is these bursts of cache timeouts cause our database to be hit more, and in certain circumstances this is a bad thing. I'm happy to add any more info that anyone would find helpful.
Update: Connection Code
The code to connect to Redis is part of a fairly complex system that supports various cache environments and configuration, but I can probably boil it down to the basics. First, there's a CacheFactory class:
public class CacheFactory : ICacheFactory
{
private static readonly ILogger log = LoggerManager.GetLogger(typeof(CacheFactory));
private static readonly ICacheProvider<CacheKey> cache;
static CacheFactory()
{
ICacheFactory<CacheKey> configuredFactory = CacheFactorySection.Current?.CreateConfiguredFactory<CacheKey>();
if (configuredFactory == null)
{
// Some error handling, not important
}
cache = configuredFactory.GetDefaultCache();
}
// ...
}
The ICacheProvider is what implements a way to talk to a certain cache system, which can be configured. In this case, the configuredFactory is a RedisCacheFactory which looks like this:
public class RedisCacheFactory<T> : ICacheFactory<T> where T : CacheKey, ICacheKeyRepository
{
private RedisCacheProvider<T> provider;
private readonly RedisConfiguration configuration;
public RedisCacheFactory(RedisConfiguration config)
{
this.configuration = config;
}
public ICacheProvider<T> GetDefaultCache()
{
return provider ?? (provider = new RedisCacheProvider<T>(configuration));
}
}
The GetDefaultCache method is called once, in the static constructor, and returns a RedisCacheProvider. This class is what actually connects to Redis:
public class RedisCacheProvider<K> : ICacheProvider<K> where K : CacheKey, ICacheKeyRepository
{
private readonly ConnectionMultiplexer redisConnection;
private readonly IDatabase db;
private readonly RedisCacheSerializer serializer;
private static readonly ILog log = Logging.RedisCacheProviderLog<K>();
private readonly CacheMonitor<K> cacheMonitor;
private readonly TimeSpan defaultTTL;
private int connectionErrors;
public RedisCacheProvider(RedisConfiguration options)
{
redisConnection = ConnectionMultiplexer.Connect(options.EnvironmentOverride ?? options.Connection);
db = redisConnection.GetDatabase();
serializer = new RedisCacheSerializer(options.SerializationBinding);
cacheMonitor = new CacheMonitor<K>();
defaultTTL = options.DefaultTTL;
IEnumerable<string> hosts = options.Connection.EndPoints.Select(e => (e as DnsEndPoint)?.Host);
log.InfoFormat("Created Redis ConnectionMultiplexer connection. Hosts=({0})", String.Join(",", hosts));
}
// ...
}
The constructor creates a ConnectionMultiplexer based on the configured Redis endpoints (which are in some config file). I also log every time I create a connection. We don't see any excessive number of these log statements, and the connections to Redis remains stable.
In global.asax, in try adding:
protected void Application_Start(object sender, EventArgs e)
{
ThreadPool.SetMinThreads(200, 200);
}
For us, this reduced errors from ~50-100 daily to zero. I believe there is no general rule for what numbers to set as it's system dependant (200 works for us) so might require some experimenting on your end.
I also believe this has improved the performance of the site.
Related
I have a piece of code that is essentially executing the following with Infinispan in embedded mode, using version 13.0.0 of the -core and -clustered-lock modules:
#Inject
lateinit var lockManager: ClusteredLockManager
private fun getLock(lockName: String): ClusteredLock {
lockManager.defineLock(lockName)
return lockManager.get(lockName)
}
fun createSession(sessionId: String) {
tryLockCounter.increment()
logger.debugf("Trying to start session %s. trying to acquire lock", sessionId)
Future.fromCompletionStage(getLock(sessionId).lock()).map {
acquiredLockCounter.increment()
logger.debugf("Starting session %s. Got lock", sessionId)
}.onFailure {
logger.errorf(it, "Failed to start session %s", sessionId)
}
}
I take this piece of code and deploy it to kubernetes. I then run it in six pods distributed over six nodes in the same region. The code exposes createSession with random Guids through an API. This API is called and creates sessions in chunks of 500, using a k8s service in front of the pods which means the load gets balanced over the pods. I notice that the execution time to acquire a lock grows linearly with the amount of sessions. In the beginning it's around 10ms, when there's about 20_000 sessions it takes about 100ms and the trend continues in a stable fashion.
I then take the same code and run it, but this time with twelve pods on twelve nodes. To my surprise I see that the performance characteristics are almost identical to when I had six pods. I've been digging in to the code but still haven't figured out why this is, I'm wondering if there's a good reason why infinispan here doesn't seem to perform better with more nodes?
For completeness the configuration of the locks are as follows:
val global = GlobalConfigurationBuilder.defaultClusteredBuilder()
global.addModule(ClusteredLockManagerConfigurationBuilder::class.java)
.reliability(Reliability.AVAILABLE)
.numOwner(1)
and looking at the code the clustered locks is using DIST_SYNC which should spread out the load of the cache onto the different nodes.
UPDATE:
The two counters in the code above are simply micrometer counters. It is through them and prometheus that I can see how the lock creation starts to slow down.
It's correctly observed that there's one lock created per session id, this is per design what we'd like. Our use case is that we want to ensure that a session is running in at least one place. Without going to deep into detail this can be achieved by ensuring that we at least have two pods that are trying to acquire the same lock. The Infinispan library is great in that it tells us directly when the lock holder dies without any additional extra chattiness between pods, which means that we have a "cheap" way of ensuring that execution of the session continues when one pod is removed.
After digging deeper into the code I found the following in CacheNotifierImpl in the core library:
private CompletionStage<Void> doNotifyModified(K key, V value, Metadata metadata, V previousValue,
Metadata previousMetadata, boolean pre, InvocationContext ctx, FlagAffectedCommand command) {
if (clusteringDependentLogic.running().commitType(command, ctx, extractSegment(command, key), false).isLocal()
&& (command == null || !command.hasAnyFlag(FlagBitSets.PUT_FOR_STATE_TRANSFER))) {
EventImpl<K, V> e = EventImpl.createEvent(cache.wired(), CACHE_ENTRY_MODIFIED);
boolean isLocalNodePrimaryOwner = isLocalNodePrimaryOwner(key);
Object batchIdentifier = ctx.isInTxScope() ? null : Thread.currentThread();
try {
AggregateCompletionStage<Void> aggregateCompletionStage = null;
for (CacheEntryListenerInvocation<K, V> listener : cacheEntryModifiedListeners) {
// Need a wrapper per invocation since converter could modify the entry in it
configureEvent(listener, e, key, value, metadata, pre, ctx, command, previousValue, previousMetadata);
aggregateCompletionStage = composeStageIfNeeded(aggregateCompletionStage,
listener.invoke(new EventWrapper<>(key, e), isLocalNodePrimaryOwner));
}
The lock library uses a clustered Listener on the entry modified event, and this one uses a filter to only notify when the key for the lock is modified. It seems to me the core library still has to check this condition on every registered listener, which of course becomes a very big list as the number of sessions grow. I suspect this to be the reason and if it is it would be really really awesome if the core library supported a kind of key filter so that it could use a hashmap for these listeners instead of going through a whole list with all listeners.
I believe you are creating a clustered lock per session id. Is this what you need ? what is the acquiredLockCounter? We are about to deprecate the "lock" method in favour of "tryLock" with timeout since the lock method will block forever if the clustered lock is never acquired. Do you ever unlock the clustered lock in another piece of code? If you shared a complete reproducer of the code will be very helpful for us. Thanks!
I make some performance testing of my PoC. What I saw is my actor is not receiving all messages that are sent to him and the performance is very low. I sent around 150k messages to my app, and it causes a peak on my processor to reach 100% utilization. But when I stop sending requests 2/3 of messages are not delivered to the actor. Here is a simple metrics from app insights:
To prove I have almost the same number of event persistent in mongo that my actor received messages.
Secondly, performance of processing messages is very disappointing. I get around 300 messages per second.
I know Akka.NET message delivery is at most once by default but I don't get any error saying that message were dropped.
Here is code:
Cluster shard registration:
services.AddSingleton<ValueCoordinatorProvider>(provider =>
{
var shardRegion = ClusterSharding.Get(_actorSystem).Start(
typeName: "values-actor",
entityProps: _actorSystem.DI().Props<ValueActor>(),
settings: ClusterShardingSettings.Create(_actorSystem),
messageExtractor: new ValueShardMsgRouter());
return () => shardRegion;
});
Controller:
[ApiController]
[Route("api/[controller]")]
public class ValueController : ControllerBase
{
private readonly IActorRef _valueCoordinator;
public ValueController(ValueCoordinatorProvider valueCoordinatorProvider)
{
_valueCoordinator = valuenCoordinatorProvider();
}
[HttpPost]
public Task<IActionResult> PostAsync(Message message)
{
_valueCoordinator.Tell(message);
return Task.FromResult((IActionResult)Ok());
}
}
Actor:
public class ValueActor : ReceivePersistentActor
{
public override string PersistenceId { get; }
private decimal _currentValue;
public ValueActor()
{
PersistenceId = Context.Self.Path.Name;
Command<Message>(Handle);
}
private void Handle(Message message)
{
Context.IncrementMessagesReceived();
var accepted = new ValueAccepted(message.ValueId, message.Value);
Persist(accepted, valueAccepted =>
{
_currentValue = valueAccepted.BidValue;
});
}
}
Message router.
public sealed class ValueShardMsgRouter : HashCodeMessageExtractor
{
public const int DefaultShardCount = 1_000_000_000;
public ValueShardMsgRouter() : this(DefaultShardCount)
{
}
public ValueShardMsgRouter(int maxNumberOfShards) : base(maxNumberOfShards)
{
}
public override string EntityId(object message)
{
return message switch
{
IWithValueId valueMsg => valueMsg.ValueId,
_ => null
};
}
}
akka.conf
akka {
stdout-loglevel = ERROR
loglevel = ERROR
actor {
debug {
unhandled = on
}
provider = cluster
serializers {
hyperion = "Akka.Serialization.HyperionSerializer, Akka.Serialization.Hyperion"
}
serialization-bindings {
"System.Object" = hyperion
}
deployment {
/valuesRouter {
router = consistent-hashing-group
routees.paths = ["/values"]
cluster {
enabled = on
}
}
}
}
remote {
dot-netty.tcp {
hostname = "desktop-j45ou76"
port = 5054
}
}
cluster {
seed-nodes = ["akka.tcp://valuessystem#desktop-j45ou76:5054"]
}
persistence {
journal {
plugin = "akka.persistence.journal.mongodb"
mongodb {
class = "Akka.Persistence.MongoDb.Journal.MongoDbJournal, Akka.Persistence.MongoDb"
connection-string = "mongodb://localhost:27017/akkanet"
auto-initialize = off
plugin-dispatcher = "akka.actor.default-dispatcher"
collection = "EventJournal"
metadata-collection = "Metadata"
legacy-serialization = off
}
}
snapshot-store {
plugin = "akka.persistence.snapshot-store.mongodb"
mongodb {
class = "Akka.Persistence.MongoDb.Snapshot.MongoDbSnapshotStore, Akka.Persistence.MongoDb"
connection-string = "mongodb://localhost:27017/akkanet"
auto-initialize = off
plugin-dispatcher = "akka.actor.default-dispatcher"
collection = "SnapshotStore"
legacy-serialization = off
}
}
}
}
So there are two issues going on here: actor performance and missing messages.
It's not clear from your writeup, but I'm going to make an assumption: 100% of these messages are going to a single actor.
Actor Performance
The end-to-end throughput of a single actor depends on:
The amount of work it takes to route the message to the actor (i.e. through the sharding system, hierarchy, over the network, etc)
The amount of time it takes the actor to process a single message, as this determines the rate at which a mailbox can be emptied; and
Any flow control that affects which messages can be processed when - i.e. if an actor uses stashing and behavior switching, the amount of time an actor spends stashing messages while waiting for its state to change will have a cumulative impact on the end-to-end processing time for all stashed messages.
You will have poor performance due to item 3 on this list. The design that you are implementing calls Persist and blocks the actor from doing any additional processing until the message is successfully persisted. All other messages sent to the actor are stashed internally until the previous one is successfully persisted.
Akka.Persistence offers four options for persisting messages from the point of view of a single actor:
Persist - highest consistency (no other messages can be processed until persistence is confirmed), lowest performance;
PersistAsync - lower consistency, much higher performance. Doesn't wait for the message to be persisted before processing the next message in the mailbox. Allows multiple messages from a single persistent actor to be processed concurrently in-flight - the order in which those events are persisted will be preserved (because they're sent to the internal Akka.Persistence journal IActorRef in that order) but the actor will continue to process additional messages before the persisted ones are confirmed. This means you probably have to modify your actor's in-memory state before you call PersistAsync and not after the fact.
PersistAll - high consistency, but batches multiple persistent events at once. Same ordering and control flow semantics as Persist - but you're just persisting an array of messages together.
PersistAllAsync - highest performance. Same semantics as PersistAsync but it's an atomic batch of messages in an array being persisted together.
To get an idea as to how the performance characteristics of Akka.Persistence changes with each of these methods, take a look at the detailed benchmark data the Akka.NET organization has put together around Akka.Persistence.Linq2Db, the new high performance RDBMS Akka.Persistence library: https://github.com/akkadotnet/Akka.Persistence.Linq2Db#performance - it's a difference between 15,000 per second and 250 per second on SQL; the write performance is likely even higher in a system like MongoDB.
One of the key properties of Akka.Persistence is that it intentionally routes all of the persistence commands through a set of centralized "journal" and "snapshot" actors on each node in a cluster - so messages from multiple persistent actors can be batched together across a small number of concurrent database connections. There are many users running hundreds of thousands of persistent actors simultaneously - if each actor had their own unique connection to the database it would melt even the most robustly vertically scaled database instances on Earth. This connection pooling / sharing is why the individual persistent actors rely on flow control.
You'll see similar performance using any persistent actor framework (i.e. Orleans, Service Fabric) because they all employ a similar design for the same reasons Akka.NET does.
To improve your performance, you will need to either batch received messages together and persist them in a group with PersistAll (think of this as de-bouncing) or use asynchronous persistence semantics using PersistAsync.
You'll also see better aggregate performance if you spread your workload out across many concurrent actors with different entity ids - that way you can benefit from actor concurrency and parallelism.
Missing Messages
There could be any number of reasons why this might occur - most often it's going to be the result of:
Actors being terminated (not the same as restarting) and dumping all of their messages into the DeadLetter collection;
Network disruptions resulting in dropped connections - this can happen when nodes are sitting at 100% CPU - messages that are queued for delivery at the time can be dropped; and
The Akka.Persistence journal receiving timeouts back from the database will result in persistent actors terminating themselves due to loss of consistency.
You should look for the following in your logs:
DeadLetter warnings / counts
OpenCircuitBreakerExceptions coming from Akka.Persistence
You'll usually see both of those appear together - I suspect that's what is happening to your system. The other possibility could be Akka.Remote throwing DisassociationExceptions, which I would also look for.
You can fix the Akka.Remote issues by changing the heartbeat values for the Akka.Cluster failure-detector in configuration https://getakka.net/articles/configuration/akka.cluster.html:
akka.cluster.failure-detector {
# FQCN of the failure detector implementation.
# It must implement akka.remote.FailureDetector and have
# a public constructor with a com.typesafe.config.Config and
# akka.actor.EventStream parameter.
implementation-class = "Akka.Remote.PhiAccrualFailureDetector, Akka.Remote"
# How often keep-alive heartbeat messages should be sent to each connection.
heartbeat-interval = 1 s
# Defines the failure detector threshold.
# A low threshold is prone to generate many wrong suspicions but ensures
# a quick detection in the event of a real crash. Conversely, a high
# threshold generates fewer mistakes but needs more time to detect
# actual crashes.
threshold = 8.0
# Number of the samples of inter-heartbeat arrival times to adaptively
# calculate the failure timeout for connections.
max-sample-size = 1000
# Minimum standard deviation to use for the normal distribution in
# AccrualFailureDetector. Too low standard deviation might result in
# too much sensitivity for sudden, but normal, deviations in heartbeat
# inter arrival times.
min-std-deviation = 100 ms
# Number of potentially lost/delayed heartbeats that will be
# accepted before considering it to be an anomaly.
# This margin is important to be able to survive sudden, occasional,
# pauses in heartbeat arrivals, due to for example garbage collect or
# network drop.
acceptable-heartbeat-pause = 3 s
# Number of member nodes that each member will send heartbeat messages to,
# i.e. each node will be monitored by this number of other nodes.
monitored-by-nr-of-members = 9
# After the heartbeat request has been sent the first failure detection
# will start after this period, even though no heartbeat mesage has
# been received.
expected-response-after = 1 s
}
Bump the acceptable-heartbeat-pause = 3 s value to something larger like 10,20,30 if needed.
Sharding Configuration
One last thing I want to point out with your code - the shard count is way too high. You should have about ~10 shards per node. Reduce it to something reasonable.
I am currently working on an API service that allows 1 or more users to download 1 or more items from an S3 bucket and return the contents to the user. While the downloading is fine, the time taken to download several files is pretty much 100-150 ms * the number of files.
I have tried a few approaches to speeding this up - parallelStream() instead of stream() (which, considering the amount of simultaneous downloads, is at a serious risk of running out of threads), as well as CompleteableFutures, and even creating an ExecutorService, doing the downloads then shutting down the pool. Typically I would only want a few concurrent tasks e.g. 5 at the same time, per request to try and cut down on the number of active threads.
I have tried integrating Spring #Cacheable to store the downloaded files to Redis (the files are readonly) - while this certainly cuts down response times (several ms to retrieve files compared to 100-150 ms), the benefits are only there once the file has been previously retrieved.
What is the best way to handle waiting on multiple async tasks to finish then getting the results, also considering I don't want (or don't think I could) have hundreds of threads opening http connections and downloading all at once?
You're right to be concerned about tying up the common fork/join pool used by default in parallel streams, since I believe it is used for other things like sort operations outside of the Stream api. Rather than saturating the common fork/join pool with an I/O-bound parallel stream, you can create your own fork/join pool for the Stream. See this question to find out how to create an ad hoc ForkJoinPool with the size you want and run a parallel stream in it.
You could also create an ExecutorService with a fixed-size thread pool that would also be independent of the common fork/join pool, and would throttle the requests, using only the threads in the pool. It also lets you specify the number of threads to dedicate:
ExecutorService executor = Executors.newFixedThreadPool(MAX_THREADS_FOR_DOWNLOADS);
try {
List<CompletableFuture<Path>> downloadTasks = s3Paths
.stream()
.map(s3Path -> completableFuture.supplyAsync(() -> mys3Downloader.downloadAndGetPath(s3Path), executor))
.collect(Collectors.toList());
// at this point, all requests are enqueued, and threads will be assigned as they become available
executor.shutdown(); // stops accepting requests, does not interrupt threads,
// items in queue will still get threads when available
// wait for all downloads to complete
CompletableFuture.allOf(downloadTasks.toArray(new CompletableFuture[downloadTasks.size()])).join();
// at this point, all downloads are finished,
// so it's safe to shut down executor completely
} catch (InterruptedException | ExecutionException e) {
e.printStackTrace();
} finally {
executor.shutdownNow(); // important to call this when you're done with the executor.
}
Following #Hank D's lead, you can encapsulate the creation of the executor service to ensure that you do, indeed, call ExecutorService::shutdownNow after using said executor:
private static <VALUE> VALUE execute(
final int nThreads,
final Function<ExecutorService, VALUE> function
) {
ExecutorService executorService = Executors.newFixedThreadPool(nThreads);
try {
return function.apply(executorService);
} catch (final InterruptedException | ExecutionException exception) {
exception.printStackTrace();
} finally {
executorService .shutdownNow(); // important to call this when you're done with the executor service.
}
}
public static void main(final String... arguments) {
// define variables
final List<CompletableFuture<Path>> downloadTasks = execute(
MAX_THREADS_FOR_DOWNLOADS,
executor -> s3Paths
.stream()
.map(s3Path -> completableFuture.supplyAsync(
() -> mys3Downloader.downloadAndGetPath(s3Path),
executor
))
.collect(Collectors.toList())
);
// use downloadTasks
}
I am trying to use sentinal redis to get/set keys from redis. I was trying to stress test my setup with about 2000 concurrent requests.
i used sentinel to put a single key on redis and then I executed 1000 concurrent get requests from redis.
But the underlying jedis used my sentinel is blocking call on getResource() (pool size is 500) and the overall average response time that I am achieving is around 500 ms, but my target was about 10 ms.
I am attaching sample of jvisualvm snapshot here
redis.clients.jedis.JedisSentinelPool.getResource() 98.02227 4.0845232601E7 ms 4779
redis.clients.jedis.BinaryJedis.get() 1.6894469 703981.381 ms 141
org.apache.catalina.core.ApplicationFilterChain.doFilter() 0.12820946 53424.035 ms 6875
org.springframework.core.serializer.support.DeserializingConverter.convert() 0.046286926 19287.457 ms 4
redis.clients.jedis.JedisSentinelPool.returnResource() 0.04444578 18520.263 ms 4
org.springframework.aop.framework.CglibAopProxy$DynamicAdvisedInterceptor.intercept() 0.035538 14808.45 ms 11430
May anyone help to debug further into the issue?
From JedisSentinelPool implementation of getResource() from Jedis sources (2.6.2):
#Override
public Jedis getResource() {
while (true) {
Jedis jedis = super.getResource();
jedis.setDataSource(this);
// get a reference because it can change concurrently
final HostAndPort master = currentHostMaster;
final HostAndPort connection = new HostAndPort(jedis.getClient().getHost(), jedis.getClient()
.getPort());
if (master.equals(connection)) {
// connected to the correct master
return jedis;
} else {
returnBrokenResource(jedis);
}
}
}
Note the while(true) and the returnBrokenResource(jedis), it means that it tries to get a jedis resource randomly from the pool that is indeed connected to the correct master and retries if it is not the good one. It is a dirty check and also a blocking call.
The super.getResource() call refers to JedisPool traditionnal implementation that is actually based on Apache Commons Pool (2.0). It does a lot to get an object from the pool, and I think it even repairs fail connections for instance. With a lot of contention on your pool, as probably in your stress test, it can probably take a lot of time to get a resource from the pool, just to see it is not connected to the correct master, so you end up calling it again, adding contention, slowing getting the resource etc...
You should check all the jedis instances in your pool to see if there's a lot of 'bad' connections.
Maybe you should give up using a common pool for your stress test (only create Jedis instances manually connected to the correct node, and close them nicely), or setting multiple ones to mitigate the cost of looking to "dirty" unchecked jedis resources.
Also with a pool of 500 jedis instances, you can't emulate 1000 concurrent queries, you need at least 1000.
I want to create a WCF service which uses an MSMQ binding as I have a high volume of notifications the service is to process. It is important that clients are not held up by the service and that the notifications are processed in the order they are raised, hence the queue implementation.
Another consideration is resilience. I know I could cluster MSMQ itself to make the queue more robust, but I want to be able to run an instance of my service on different servers, so if a server crashes notifications do not build up in the queue but another server carries on processing.
I have experimented with the MSMQ binding and found that you can have multiple instances of a service listening on the same queue, and left to themselves they end up doing a sort of round-robin with the load spread across the available services. This is great, but I end up losing the sequencing of the queue as different instances take a different amount of time to process the request.
I've been using a simple console app to experiment, which is the epic code dump below. When it's run I get an output like this:
host1 open
host2 open
S1: 01
S1: 03
S1: 05
S2: 02
S1: 06
S1: 08
S1: 09
S2: 04
S1: 10
host1 closed
S2: 07
host2 closed
What I want to happen is:
host1 open
host2 open
S1: 01
<pause while S2 completes>
S2: 02
S1: 03
<pause while S2 completes>
S2: 04
S1: 05
S1: 06
etc.
I would have thought that as S2 has not completed, it might still fail and return the message it was processing to the queue. Therefore S1 should not be allowed to pull another message off of the queue. My queue us transactional and I have tried setting TransactionScopeRequired = true on the service but to no avail.
Is this even possible? Am I going about it the wrong way? Is there some other way to build a failover service without some kind of central synchronisation mechanism?
class WcfMsmqProgram
{
private const string QueueName = "testq1";
static void Main()
{
// Create a transactional queue
string qPath = ".\\private$\\" + QueueName;
if (!MessageQueue.Exists(qPath))
MessageQueue.Create(qPath, true);
else
new MessageQueue(qPath).Purge();
// S1 processes as fast as it can
IService s1 = new ServiceImpl("S1");
// S2 is slow
IService s2 = new ServiceImpl("S2", 2000);
// MSMQ binding
NetMsmqBinding binding = new NetMsmqBinding(NetMsmqSecurityMode.None);
// Host S1
ServiceHost host1 = new ServiceHost(s1, new Uri("net.msmq://localhost/private"));
ConfigureService(host1, binding);
host1.Open();
Console.WriteLine("host1 open");
// Host S2
ServiceHost host2 = new ServiceHost(s2, new Uri("net.msmq://localhost/private"));
ConfigureService(host2, binding);
host2.Open();
Console.WriteLine("host2 open");
// Create a client
ChannelFactory<IService> factory = new ChannelFactory<IService>(binding, new EndpointAddress("net.msmq://localhost/private/" + QueueName));
IService client = factory.CreateChannel();
// Periodically call the service with a new number
int counter = 1;
using (Timer t = new Timer(o => client.EchoNumber(counter++), null, 0, 500))
{
// Enter to stop
Console.ReadLine();
}
host1.Close();
Console.WriteLine("host1 closed");
host2.Close();
Console.WriteLine("host2 closed");
// Wait for exit
Console.ReadLine();
}
static void ConfigureService(ServiceHost host, NetMsmqBinding binding)
{
var endpoint = host.AddServiceEndpoint(typeof(IService), binding, QueueName);
}
[ServiceContract]
interface IService
{
[OperationContract(IsOneWay = true)]
void EchoNumber(int number);
}
[ServiceBehavior(InstanceContextMode = InstanceContextMode.Single)]
class ServiceImpl : IService
{
public ServiceImpl(string name, int sleep = 0)
{
this.name = name;
this.sleep = sleep;
}
private string name;
private int sleep;
public void EchoNumber(int number)
{
Thread.Sleep(this.sleep);
Console.WriteLine("{0}: {1:00}", this.name, number);
}
}
}
batwad,
You are trying to manually create a service bus. Why don't you try to use an existing one?
NServiceBus, MassTransit, ServiceStack
At least 2 of those work with MSMQ.
Furthermore, if you absolutely need order it may actually be for another reason - you want to be able to send a message and you don't want dependent messages to be processed before the first message. You are looking for the Saga Pattern. NServiceBus and MassTransit both will allow you to manage Sagas easily, they will both allow you to simply trigger the initial message and then trigger the remaining messages based on conditions. It will allow you to implement the plumping of your distributed application a snap.
You can then even scale up to thousands of clients, queue servers and message processors without having to write a single line of code nor have any issues.
We tried to implement our own service bus over msmq here, we gave up because another issue kept creeping up. We went with NServiceBus but MassTransit is also an excellent product (it's 100% open source, NServiceBus isn't). ServiceStack is awesome at making APIs and using Message Queues - I'm sure you could use it to make Services that act as Queue front-ends in minutes.
Oh, did I mention that in the case of NSB and MT both only require under 10 lines of code to fully implement queues, senders and handlers?
----- ADDED -----
Udi Dahan (one of the main contributers of NServiceBus) talks about this in:
"In-Order Messaging a Myth" by Udi Dahan
"Message Ordering: Is it Cost Effective?" with Udi Dahan
Chris Patterson (one of the main contributers of Mass Transit)
"Using Sagas to ensure proper sequential message order" question
StackOverflow questions/answers:
"Preserve message order when consuming MSMQ messages in a WCF application"
----- QUESTION -----
I must say that I'm baffled as to why you need to guarantee message order - would you be in the same position if you were using an HTTP/SOAP protocol? My guess is no, then why is it a problem in MSMQ?
Good luck, hope this helps,
Ensuring in-order delivery of messages is one of the de-facto sticky issues with high volume messaging.
In an ideal world, your message destinations should be able to handle out-of-order messaging. This can be achieved by ensuring that your message source includes some kind of sequencing information. Again ideally this takes the form of some kind of x-of-n batch stamp (message 1 of 10, 2 of 10, etc). Your message destination is then required to assemble the data into order once it has been delivered.
However, in the real world there often is no scope for changing downstream systems to handle messages arriving out of order. In this instance you have two choices:
Go entirely single threaded - actually you can usually find some kind of 'grouping id' which means you can go single-threaded in a for-each-group sense, meaning you still have concurrency across different message groups.
Implement a re-sequencer wrapper around each of your consumer systems you want to receive in-order messages.
Neither solution is very nice, but that's the only way I think you can have concurrency and in-order message delivery.