I'm currently playing with Hazelcast Cloud. My use case requires me to upload 50mb of jar file dependencies to Hazelcast Cloud servers. I found out that the upload seems to give up after about a minute or so. I get an upload rate of about 1mb a second, it drops after a while and then stops. I have repeated it a few times and the same thing happens.
Here is the config code I'm using:
Clientconfig config = new ClientConfig();
ClientUserCodeDeploymentConfig clientUserCodeDeploymentConfig =
new ClientUserCodeDeploymentConfig();
// added many jars here...
clientUserCodeDeploymentConfig.addJar("jar dependancy path..");
clientUserCodeDeploymentConfig.addJar("jar dependancy path..");
clientUserCodeDeploymentConfig.addJar("jar dependancy path..");
clientUserCodeDeploymentConfig.setEnabled(true);
config.setUserCodeDeploymentConfig(clientUserCodeDeploymentConfig);
ClientNetworkConfig networkConfig = new ClientNetworkConfig();
networkConfig.setConnectionTimeout(9999999); // i.e. don't timeout
networkConfig.setConnectionAttemptPeriod(9999999); // i.e. don't timeout
config.setNetworkConfig(networkConfig);
Any idea what's the cause, maybe there's a limit on the free cloud cluster?
I'd suggest using the smaller jar because this feature, the client user code upload, was designed for a bit different use cases:
You have objects that run on the cluster via the clients such as Runnable, Callable and Entry Processors.
You have new or amended user domain objects (in-memory format of the IMap set to Object) which need to be deployed into the cluster.
Please see more info here.
Related
I am reading events from an Azure EventHub cluster synchronously via the receiveFromPartition method on the EventHubConsumerClient class.
I create the client once like so:
EventHubConsumerClient eventHubConsumerClient = new EventHubClientBuilder()
.connectionString(eventHubConnectionString)
.consumerGroup(consumerGroup)
.buildConsumerClient());
I then just use a ScheduledExecutorService to retrieve events every 1.5s via:
IterableStream<PartitionEvent> receivedEvents = eventHubConsumerClient.receiveFromPartition(
partitionId, 1, eventPosition);
The equivalent logic in V3 of the SDK worked fine (using PartitionReceivers), but now I am seeing OOMs in my JVM.
Running a profiler against a local version of the logic I see the majority of the heap (90%, mainly in OG) is being taken up by byte[]s, referenced by org.apache.qpid.proton.codex.CompositeReadableBuffer. This pattern is not present when I profile the V3 logic.
What could be causing a leak of the AMQP messages here, do I need to interact with the SDK further, for example close a connection that I'm not aware of after each call?
Any advise would be very appreciated, thanks!
Turns out it was a bug, solved here: https://github.com/Azure/azure-sdk-for-java/issues/13775
I am stuck in one problem which I need to resolve quickly. I have gone through many posts and tutorial about spark cluster deploy mode, but I am clueless about the approach as I am stuck for some days.
My use-case :- I have lots of spark jobs submitted using 'spark2-submit' command and I need to get the application id printed in the console once they are submitted. The spark jobs are submitted using cluster deploy mode. ( In normal client mode , its getting printed )
Points I need to consider while creating solution :- I am not supposed to change code ( as it would take long time, cause there are many applications running ), I can only provide log4j properties or some custom coding.
My approach:-
1) I have tried changing the log4j levels and various log4j parameters but the logging still goes to the centralized log directory.
Part from my log4j.properties:-
log4j.logger.org.apache.spark.scheduler.cluster.YarnClusterSchedulerBackend=ALL,console
log4j.appender.org.apache.spark.scheduler.cluster.YarnClusterSchedulerBackend.Target=System.out
log4j.logger.org.apache.spark.deploy.SparkSubmit=ALL
log4j.appender.org.apache.spark.deploy.SparkSubmit=console
log4j.logger.org.apache.spark.deploy.SparkSubmit=TRACE,console
log4j.additivity.org.apache.spark.deploy.SparkSubmit=false
log4j.logger.org.apache.spark.deploy.yarn.Client=ALL
log4j.appender.org.apache.spark.deploy.yarn.Client=console
log4j.logger.org.apache.spark.SparkContext=WARN
log4j.logger.org.apache.spark.scheduler.DAGScheduler=INFO,console
log4j.logger.org.apache.hadoop.ipc.Client=ALL
2) I have also tried to add custom listener and I am able to get the spark application id after the applications finishes , but not to console.
Code logic :-
public void onApplicationEnd(SparkListenerApplicationEnd arg0)
{
for (Thread t : Thread.getAllStackTraces().keySet())
{
if (t.getName().equals("main"))
{
System.out.println("The current state : "+t.getState());
Configuration config = new Configuration();
ApplicationId appId = ConverterUtils.toApplicationId(getjobUId);
// some logic to write to communicate with the main thread to print the app id to console.
}
}
}
3) I have enabled the spark.eventLog to true and specified a directory in HDFS to write the event logs from spark-submit command .
If anyone could help me in finding an approach to the solution, it would be really helpful. Or if I am doing something very wrong, any insights would help me.
Thanks.
After being stuck at the same place for some days, I was finally able to get a solution to my problem.
After going through the Spark Code for the cluster deploy mode and some blogs, few things got clear. It might help someone else looking to achieve the same result.
In cluster deploy mode, the job is submitted via a Client thread from the machine from which the user is submitting. Actually I was passing the log4j configs to the driver and executors, but missed out on the part that the log 4j configs for the "Client" was missing.
So we need to use :-
SPARK_SUBMIT_OPTS="-Dlog4j.debug=true -Dlog4j.configuration=<location>/log4j.properties" spark-submit <rest of the parameters>
To clarify:
client mode means the Spark driver is running on the same machine you ran spark submit from
cluster mode means the Spark driver is running out on the cluster somewhere
You mentioned that it is getting logged when you run the app in client mode and you can see it in the console. Your output is also getting logged when you run in cluster mode you just can't see it because it is running on a different machine.
Some ideas:
Aggregate the logs from the worker nodes into one place where you can parse them to get the app ID.
Write the appIDs to some shared location like HDFS or a database. You might be able to use a Log4j appender if you want to keep log4j.
I'm new to Hazelcast Jet and have a very basic question. I have a 3-node JET cluster set up. I have a sample code to read from Kafka and drain to an IMap. When I run it from command-line (using jet-submit.sh and use JetBootstrap.getInstance() to acquire JET client instance) it works perfectly fine. When I run the same code (using Jet.newJetClient() to acquire the instance and Run As -> Java application on Eclipse), I get:
java.lang.ClassCastException: cannot assign instance of java.lang.invoke.SerializedLambda to field com.hazelcast.jet.core.ProcessorMetaSupplier.
Could you please let me know where am I going wrong?
One of your lambda functions captures an outside variable, probably defined at class level, and that class is not Serializable or not added to the Job config when submitting from client. This is done automatically when submitting via the script.
Please see http://docs.hazelcast.org/docs/jet/0.6.1/manual/#remember-that-a-jet-job-is-distributed
When you use a client instance to submit the job, you have to add all classes that contain the code called by the job to the JobConfig:
JobConfig config = new JobConfig();
config.addClass(...);
config.addJar(...);
...
client.newJob(pipeline, config);
For example, if you use a lambda for stage.map(), the class containing the lambda has to be added.
The jet-submit.sh script makes this easier by automatically adding the entire submitted .jar file.
I am using Serilog to write to a file and try to get more information about an error that is occurring in my production cluster...
In my local dev cluster the log files are created fine but they are not created in the VM's on my production cluster. I think this may be security related
Has anyone ever had this?
My production cluster has 5 nodes with a Windows 2016 VM on each
Even more strange is that this works on a single node cluster in Azure
public static ILogger ConfigureLogging(string appName, string appVersion)
{
AppDomain.CurrentDomain.ProcessExit += (sender, args) => Log.CloseAndFlush();
var configPackage = FabricRuntime.GetActivationContext().GetConfigurationPackageObject("Config");
var environmentName = configPackage.GetSetting("appSettings", "Inspired.TradingPlatform:EnvironmentName");
var loggerConfiguration = new LoggerConfiguration()
.WriteTo.File(#"D:\SvcFab\applog-" + appName + ".txt", shared: true, rollingInterval: RollingInterval.Day)
.Enrich.WithProperty("AppName", appName)
.Enrich.WithProperty("AppVersion", appVersion)
.Enrich.WithProperty("EnvName", environmentName);
var log = loggerConfiguration.CreateLogger();
log.Information("Starting {AppName} v{AppVersion} application", appName, appVersion);
return Log.Logger = log;
}
Paul
I wouldn't recommend logging into local files in Service Fabric, since your node may be moved to another VM any time and you won't have access to these files. Consider using another sinks which write to external system (database, message bus or logging system like loggly)
It is likely a permission issue. Your service might be trying to log to a folder where it does not have permission.
By default, your services will run under same user as the Fabric.exe process, that run as NetworkService, you can find more information about this on this link.
I would not recommend this approach, because many reasons, a few of them are:
Your services might be moved around the cluster so your files will be incomplete
You have to log on multiple machines to find the logs
The node might be gone with files (Scale up + Down, Failure, Disk error)
Multiple instances on same node trying to access the same file
and so on...
On Service Fabric, the recommended way is to use EventSource(or ETW) + EventFlow + Application Insights. They run smoothly together and bring you many features.
If you want to use stay on Serilog, I would recommend you use Serilog + Application Insights instead, it will give you move flexibility on your monitoring. Take a look at the Application Insights sink for serilog here.
This was actually user error! I was connecting to a different cluster of VMs than the one my service fabric was connected to! Whoops!
I'm trying to set up a robust memcached configuration for a nodejs app with the node-memcached driver, but it does not seem to use the specified failover servers when one server dies.
My local experiment goes as follows:
shell
memcached -p 11212
node
MC = require('memcached')
c = new MC('localhost:11211', //this process does not exist
{failOverServers: ['localhost:11212']})
c.get('foo', console.log) //this will eventually time out
c.get('foo', console.log) //repeat 5 or 6 times to exceed the retries number
//wait until all the connection errors appear in the console
//at this point, the failover server should be in use
c.get('foo', console.log) //this still times out :(
Any ideas of what might we be doing wrong?
It seems that the failover feature is somewhat buggy in node-memcached.
To enable failover you must set the remove options:
c = new MC('localhost:11211', //this process does not exist
{failOverServers: ['localhost:11212'],
remove : true})
Unfortunately, this is not going to work because of the following error:
[depricated] HashRing#replaceServer is removed.
[depricated] the API has no replacement
That is, when trying to replace a dead server with a replacement from the failover list, node-memcached outputs a deprecation error from the HashRing library (which, in turn, is maintained by the same author of node-memcached). IMHO, feel free to open a bug :-)
This is come when your nodejs server not getting any session id from memcached
Please check properly in php.ini file you are setting properly or not for memcached
session.save = 'memcache'
session.path = 'tcp://localhost:11212'