The Nifi PutParquet Processor with timer driven run schedule of 0 sec with previous processor in stopped status shows ~3000 Tasks for the last 5 minutes.
We are on Nifi 1.9.2.
My expectation would be that this processor only creates tasks if data is in the incoming queue for the processor. Is this some misconfiguration or a bug in the implementation?
The processor is annotated with #TriggerWhenEmpty which lets it execute all the time regardless of data in the incoming queue. The reason for this is because in a kerberized environment, the processor needs a chance to refresh the credentials. It was a common problem with other processors where no data comes in for a long time, say over a weekend, and during that time the kerberos ticket expired, and then when data starts coming in Monday everything fails.
These empty executions shouldn't have a big impact on the system. When the processor executes and no data is available, it just calls yield and returns. The default yield duration is 1 second, but is controllable through the UI.
Related
When I request an API in Nifi, more than one response returns. And the content of these responses is the same. If I don't stop the processor, it keeps coming. I keep turning the processor on and off quickly. Is there a way to restrict this?
Can I have the API return a certain number of times no matter how many requests it sends? For example, return only 3 requests.
NiFi flows are intended to be always-on streams. If you go to the Scheduling tab of a processor's config, you'll see that, by default, it is scheduled to run continuously (0 ms).
If you don't want this style of streaming behaviour, you need to change the Scheduling of the processor.
You can change it to only schedule the processor every X seconds, or you can change it to run based on a CRON expression.
I'm trying to learn writing custom Nifi Processor and from the documentation, the processor should be thread-safe. What I wanted to understand is, if, say - I have 100 flow file records connected to my custom processor, would my processor's onTrigger method ( assume that I haven't enabled #TriggerSerially on this method ) be triggered 100 times and in 100 separate threads ( irrespective of concurrently or not ), or is there a possibility that one flow file is used as input to more than one thread of onTrigger method on my processor.
I apologize if I didn't articulate the question correctly, but essentially, is is possible that the number of times my processor's onTrigger method is triggered, is more than the number of flow files that are connected as input to the processor?
The number of threads executing a processor is based on the number of concurrent tasks on the scheduling tab, which defaults to 1. If you increase this to 2, then 2 threads are concurrently executing the onTrigger method. A single flow file will only be processed by one of these threads.
The #TriggerSerially annotation prevents you from being able to increase the conccurent tasks, so it forces there to never be concurrent execution. A common use case for this would be a source processor that is pulling data from somewhere, typically you wouldn't to concurrently be pulling the same data twice.
Setting the scene
I am working to make a Spark streaming application (Spark 2.2.1 with Scala) run on a Yarn cluster (Hadoop 2.7.4).
So far I managed to submit the application to the Yarn cluster with spark-submit. I can see that the receiver task starts up correctly and fetches a lot of records from the database (Couchbase Server 5.0) and I can also see that the records are divided into batches.
The question
When I look at the Streaming Statistics on the Spark Web UI, I can however see that my batches are never processed. I have seen batches with 0 records process and complete but when a batch with records start processing it never completes. One time it even got stuck on a batch with 0 records.
I even tried simplifying the output operations on the SteamingContext as much as possible. But still with the very simple output operation print() my batches are never processed. The logs does not show any warnings or errors.
Does anyone know what might be wrong? Any suggestions on how to solve this will be much appreciated.
More Info
The main class of the Spark application is built from this example (first one) from the Couchbase Spark Connector documentation combined with this example with checkpoint from the Spark Documentation.
Right now I have 3230 Active Batches (3229 queued and 1 processing) and 1 Completed Batch (that had 0 records) and the application has been running for 4 hours and 30 minutes... and another batch is added every 5 seconds.
If I look at the "thread dump" for the executors I see a lot of WAITING, TIMED WAITING and a few RUNNABLE threads. The list will fill up 3 screenshots, so i will only post it if needed.
Below you will find some screenshots from the Web UI
Executor Overview
Spark Jobs Overview
Node Overview with resources
Capacity Scheduler Overview
Per screenshot, you have 2 cores and 1 is being used for driver and another is being used for receiver. You don't have a core for the actual processing to happen. Please increase the number of cores and try again.
Refer: https://spark.apache.org/docs/latest/streaming-programming-guide.html#input-dstreams-and-receivers
If you are using an input DStream based on a receiver (e.g. sockets, Kafka, Flume, etc.), then the single thread will be used to run the receiver, leaving no thread for processing the received data. Hence, when running locally, always use “local[n]” as the master URL, where n > number of receivers to run (see Spark Properties for information on how to set the master).
In my Spring Boot application, based on the Cron job(runs every 5 minutes) I need to process 2000 products in my database.
Right now the process time of these 2000 products takes more than 5 minutes. I ran into the issue where the second Cron job runs when the first one is not completed yet.
Is there in Spring/Cron out of the box functionality that will allow to synchronize these jobs and wait for the previous job completion before starting the next one?
Please advise how to properly implement such kind of system. Anyway, the following technologies are also available Neo4j, MongoDB, Kafka. Please advise how to properly design/implement this functionality using the Spring/Cron separately or even together with the mentioned technologies.
1) You may try to use #Scheduled(fixedDelay = 5*60*1000). It will guarantee that next invocation will happen strictly in 5 minutes after previous one is finished. But this may break your scheduling requirements
2) You can limit the underlying ThreadExecutor's pool size to 1 thread, so next invocation will have to wait until previous is finished, but this, again, can break the logic, since it would affect all periodic tasks invoked by #Scheduled
3) You can use Quartz instead of spring's native #Scheduled. It's more complicated to configure, but allows to achieve the desired behaviour via #DisallowConcurrentExecution annotation or via setting JobDetail::isConcurrentExectionDisallowed in your job details
My production environment running a java scheduler job using quartz 2.1.4. on weblogic cluster server with 4 machine and only one schedule job execute at one cluster node (node 1) normally for few months, but node 2 sudden find the node 1 fail at take over the executing job last night. In fact, the node 1 without error (according to the server, network, database, application log), this event caused duplicate message created due to 2 process concurrent execute.
What is the mechanism of quartz to detect node fails? By ping scan, or heart beat ping via UCP broadcast, or database respond time other? Any configuration on it?
I have read the quartz configuration guide
http://quartz-scheduler.org/documentation/quartz-2.1.x/configuration/ConfigJDBCJobStoreClustering
, but there is no answer.
I am using JDBCJobstore. After details checking, we found that there is a database (Oracle) statement executing abnormal long (from 5 sec to 30 sec). The incident happened on this period of time. Do you think it related?
my configuration is
`
org.quartz.threadPool.threadCount=10
org.quartz.threadPool.threadPriority=5
org.quartz.jobStore.misfireThreshold = 10000
org.quartz.jobStore.class=org.quartz.impl.jdbcjobstore.JobStoreTX
`
Anyone have this information? Thanks.
I know the answer is very late, but maybe somebody like both of us will still need it.
Short version: it is all handled by DB. Important property would be org.quartz.jobStore.clusterCheckinInterval.
Long version (all credits go to http://flylib.com/books/en/2.65.1.91/1/ ) :
Detecting Failed Scheduler Nodes
When a Scheduler instance performs the check-in routine, it looks to
see if there are other Scheduler instances that didn't check in when
they were supposed to. It does this by inspecting the SCHEDULER_STATE
table and looking for schedulers that have a value in the
LAST_CHECK_TIME column that is older than the property
org.quartz.jobStore.clusterCheckinInterval (discussed in the next
section). If one or more nodes haven't checked in, the running
Scheduler assumes that the other instance(s) have failed.
Additionally the next paragraph might also be important:
Running Nodes on Separate Machines with Unsynchronized Clocks
As you can ascertain by now, if you run nodes on different machines and the
clocks are not synchronized, you can get unexpected results. This is
because a timestamp is being used to inform other instances of the
last time one node checked in. If that node's clock was set for the
future, a running Scheduler might never realize that a node has gone
down. On the other hand, if a clock on one node is set in the past, a
node might assume that the node has gone down and attempt to take over
and rerun its jobs. In either case, it's not the behavior that you
want. When you're using different machines in a cluster (which is the
normal case), be sure to synchronize the clocks. See the section
"Quartz Clustering Cookbook," later in this chapter for details on how
to do this.