So, my investigation starts at StreamProcessor.java. Line 294 in method
onNewJobModel() logs an INFO message that it's starting a container. This
message never appears.
I see that ZkJobCoordinator calls onNewJobModel from its onNewJobModelConfirmed
method which also logs an info message stating "version X of the job model got
confirmed". I never see this message either, so I go up the chain some more.
I DO see:
2018-03-16 21:43:58 logback 20498 [ZkClient-EventThread-13-10.0.127.114:2181]
INFO o.apache.samza.zk.ZkJobCoordinator - ZkJobCoordinator::onBecomeLeader - I
became the leader!
And
2018-03-16 21:44:18 logback 40712 [debounce-thread-0] INFO
o.apache.samza.zk.ZkJobCoordinator -
pid=91e07d20-ae33-4156-a5f3-534a95642133Generated new Job Model. Version = 1
Which led me to method onDoProcessorChange line 210. I see that line, but not
the line below " Published new Job Model. Version =" so something in here is
not completing:
LOG.info("pid=" + processorId + "Generated new Job Model. Version = " +
nextJMVersion);
// Publish the new job model
zkUtils.publishJobModel(nextJMVersion, jobModel);
// Start the barrier for the job model update
barrier.create(nextJMVersion, currentProcessorIds);
// Notify all processors about the new JobModel by updating JobModel
Version number
zkUtils.publishJobModelVersion(currentJMVersion, nextJMVersion);
LOG.info("pid=" + processorId + "Published new Job Model. Version = " +
nextJMVersion);
As I mentioned, after the line "Generated new Job Model. Version = 1" I just
get repeated zk ping responses.. no more application logging.
The very next thing that's run is zkUtils.publishJobModel() which only has two
lines before another log statement (which I don't see):
public void publishJobModel(String jobModelVersion, JobModel jobModel) {
try {
ObjectMapper mmapper = SamzaObjectMapper.getObjectMapper();
String jobModelStr =
mmapper.writerWithDefaultPrettyPrinter().writeValueAsString(jobModel);
LOG.info("jobModelAsString=" + jobModelStr);
...
Could it really be getting hung up on one of these two lines? (seems like it
must be, but I don't see anything there that seems like it would just hang).
I'll keep troubleshooting, maybe add some more debug logging and try again.
Thanks for any guidance you all might have.
-Thunder
-----Original Message-----
From: Thunder Stumpges [mailto:tstump...@ntent.com]
Sent: Friday, March 16, 2018 14:43
To: dev@samza.apache.org; Jagadish Venkatraman <jagadish1...@gmail.com>
Cc: t...@recursivedream.com; yi...@linkedin.com; Yi Pan <nickpa...@gmail.com>
Subject: RE: Old style "low level" Tasks with alternative deployment model(s)
Well I have my stand-alone application in docker and running in kubernetes. I
think something isn't wired up all the way though, because my task never
actually gets invoked. I see no errors, however I'm not getting the usual
startup logs (checking existing offsets, "entering run loop"...) My logs look
like this:
2018-03-16 21:05:55 logback 50797 [debounce-thread-0] INFO
kafka.utils.VerifiableProperties - Verifying properties
2018-03-16 21:05:55 logback 50797 [debounce-thread-0] INFO
kafka.utils.VerifiableProperties - Property client.id is overridden to
samza_admin-test_stream_task-1
2018-03-16 21:05:55 logback 50798 [debounce-thread-0] INFO
kafka.utils.VerifiableProperties - Property metadata.broker.list is overridden
to test-kafka-kafka.test-svc:9092
2018-03-16 21:05:55 logback 50798 [debounce-thread-0] INFO
kafka.utils.VerifiableProperties - Property request.timeout.ms is overridden to
30000
2018-03-16 21:05:55 logback 50799 [debounce-thread-0] INFO
kafka.client.ClientUtils$ - Fetching metadata from broker
BrokerEndPoint(0,test-kafka-kafka.test-svc,9092) with correlation id 0 for 1
topic(s) Set(dev_k8s.samza.test.topic)
2018-03-16 21:05:55 logback 50800 [debounce-thread-0] DEBUG
kafka.network.BlockingChannel - Created socket with SO_TIMEOUT = 30000
(requested 30000), SO_RCVBUF = 179680 (requested -1), SO_SNDBUF = 102400
(requested 102400), connectTimeoutMs = 30000.
2018-03-16 21:05:55 logback 50800 [debounce-thread-0] INFO
kafka.producer.SyncProducer - Connected to test-kafka-kafka.test-svc:9092 for
producing
2018-03-16 21:05:55 logback 50804 [debounce-thread-0] INFO
kafka.producer.SyncProducer - Disconnecting from test-kafka-kafka.test-svc:9092
2018-03-16 21:05:55 logback 50804 [debounce-thread-0] DEBUG
kafka.client.ClientUtils$ - Successfully fetched metadata for 1 topic(s)
Set(dev_k8s.samza.test.topic)
2018-03-16 21:05:55 logback 50813 [debounce-thread-0] INFO
o.a.s.coordinator.JobModelManager$ - SystemStreamPartitionGrouper
org.apache.samza.container.grouper.stream.GroupByPartition@1a7158cc has grouped
the SystemStreamPartitions into 10 tasks with the following taskNames:
[Partition 1, Partition 0, Partition 3, Partition 2, Partition 5, Partition 4,
Partition 7, Partition 6, Partition 9, Partition 8]
2018-03-16 21:05:55 logback 50818 [debounce-thread-0] INFO
o.a.s.coordinator.JobModelManager$ - New task Partition 0 is being assigned
changelog partition 0.
2018-03-16 21:05:55 logback 50819 [debounce-thread-0] INFO
o.a.s.coordinator.JobModelManager$ - New task Partition 1 is being assigned
changelog partition 1.
2018-03-16 21:05:55 logback 50820 [debounce-thread-0] INFO
o.a.s.coordinator.JobModelManager$ - New task Partition 2 is being assigned
changelog partition 2.
2018-03-16 21:05:55 logback 50820 [debounce-thread-0] INFO
o.a.s.coordinator.JobModelManager$ - New task Partition 3 is being assigned
changelog partition 3.
2018-03-16 21:05:55 logback 50820 [debounce-thread-0] INFO
o.a.s.coordinator.JobModelManager$ - New task Partition 4 is being assigned
changelog partition 4.
2018-03-16 21:05:55 logback 50820 [debounce-thread-0] INFO
o.a.s.coordinator.JobModelManager$ - New task Partition 5 is being assigned
changelog partition 5.
2018-03-16 21:05:55 logback 50820 [debounce-thread-0] INFO
o.a.s.coordinator.JobModelManager$ - New task Partition 6 is being assigned
changelog partition 6.
2018-03-16 21:05:55 logback 50820 [debounce-thread-0] INFO
o.a.s.coordinator.JobModelManager$ - New task Partition 7 is being assigned
changelog partition 7.
2018-03-16 21:05:55 logback 50820 [debounce-thread-0] INFO
o.a.s.coordinator.JobModelManager$ - New task Partition 8 is being assigned
changelog partition 8.
2018-03-16 21:05:55 logback 50820 [debounce-thread-0] INFO
o.a.s.coordinator.JobModelManager$ - New task Partition 9 is being assigned
changelog partition 9.
2018-03-16 21:05:55 logback 50838 [main-SendThread(10.0.127.114:2181)] DEBUG
org.apache.zookeeper.ClientCnxn - Reading reply sessionid:0x1622c8b5fc01ac7,
packet:: clientPath:null serverPath:null finished:false header:: 23,4
replyHeader:: 23,14024,0 request::
'/app-test_stream_task-1/dev_test_stream_task-1-coordinationData/JobModelGeneration/jobModelVersion,T
response:: ,s{13878,13878,1521234010089,1521234010089,0,0,0,0,0,0,13878}
2018-03-16 21:05:55 logback 50838 [debounce-thread-0] INFO
o.apache.samza.zk.ZkJobCoordinator -
pid=a14a0434-a238-4ff6-935b-c78d906fe80dGenerated new Job Model. Version = 1
2018-03-16 21:06:05 logback 60848 [main-SendThread(10.0.127.114:2181)] DEBUG
org.apache.zookeeper.ClientCnxn - Got ping response for sessionid:
0x1622c8b5fc01ac7 after 2ms
2018-03-16 21:06:15 logback 70856 [main-SendThread(10.0.127.114:2181)] DEBUG
org.apache.zookeeper.ClientCnxn - Got ping response for sessionid:
0x1622c8b5fc01ac7 after 1ms
2018-03-16 21:06:25 logback 80865 [main-SendThread(10.0.127.114:2181)] DEBUG
org.apache.zookeeper.ClientCnxn - Got ping response for sessionid:
0x1622c8b5fc01ac7 after 2ms ...
The zk ping responses continue every 10 seconds, but no other activity or
messages occur.
It looks like it gets as far as confirming the JobModel and grouping the
partitions, but nothing actually starts up.
Any ideas?
Thanks in advance!
Thunder
-----Original Message-----
From: Thunder Stumpges [mailto:tstump...@ntent.com]
Sent: Thursday, March 15, 2018 16:35
To: Jagadish Venkatraman <jagadish1...@gmail.com>
Cc: dev@samza.apache.org; t...@recursivedream.com; yi...@linkedin.com; Yi Pan
<nickpa...@gmail.com>
Subject: RE: Old style "low level" Tasks with alternative deployment model(s)
Thanks a lot for the info. I have something basically working at this point! I
have not integrated it with Docker nor Kubernetes yet, but it does run from my
local machine.
I have determined that LocalApplicationRunner does NOT do config rewriting. I
had to write my own little “StandAloneApplicationRunner” that handles the
“main” entrypoint. It does command parsing using CommandLine, load config from
ConfigFactory, and perform rewriting before creating the new instance of
LocalApplicationRunner. This is all my StandAloneApplicationRunner contains:
object StandAloneSamzaRunner extends App with LazyLogging {
// parse command line args just like JobRunner.
val cmdline = new ApplicationRunnerCommandLine
val options = cmdline.parser.parse(args: _*)
val config = cmdline.loadConfig(options)
val runner = new LocalApplicationRunner(Util.rewriteConfig(config))
runner.runTask()
runner.waitForFinish()
}
The only config settings I needed to make to use this runner were (easily
configured due to our central Consul config system and our rewriter) :
# use the ZK based job coordinator
job.coordinator.factory=org.apache.samza.zk.ZkJobCoordinatorFactory
# need to use GroupByContainerIds instead of GroupByContainerCount
task.name.grouper.factory=org.apache.samza.container.grouper.task.GroupByContainerIdsFactory
# ZKJC config
job.coordinator.zk.connect=<our_zk_connection>
I did run into one potential problem; as you see above, I have started the task
using runTask() and then to prevent my main method from returning, I have
called waitForFinish(). The first time I ran it, the job itself failed because
I had forgotten to override the task grouper, and container count was pulled
from our staging environment. There are some failures logged and it appears the
JobCoordinator fails, but it never returns from waitForFinish. Stack trace and
continuation of log is below:
2018-03-15 22:34:32 logback 77786 [debounce-thread-0] ERROR
o.a.s.zk.ScheduleAfterDebounceTime - Execution of action: OnProcessorChange
failed.
java.lang.IllegalArgumentException: Your container count (4) is larger than
your task count (2). Can't have containers with nothing to do, so aborting.
at
org.apache.samza.container.grouper.task.GroupByContainerCount.validateTasks(GroupByContainerCount.java:212)
at
org.apache.samza.container.grouper.task.GroupByContainerCount.group(GroupByContainerCount.java:62)
at
org.apache.samza.container.grouper.task.TaskNameGrouper.group(TaskNameGrouper.java:56)
at
org.apache.samza.coordinator.JobModelManager$.readJobModel(JobModelManager.scala:266)
at
org.apache.samza.coordinator.JobModelManager.readJobModel(JobModelManager.scala)
at
org.apache.samza.zk.ZkJobCoordinator.generateNewJobModel(ZkJobCoordinator.java:306)
at
org.apache.samza.zk.ZkJobCoordinator.doOnProcessorChange(ZkJobCoordinator.java:197)
at
org.apache.samza.zk.ZkJobCoordinator$LeaderElectorListenerImpl.lambda$onBecomingLeader$0(ZkJobCoordinator.java:318)
at
org.apache.samza.zk.ScheduleAfterDebounceTime.lambda$getScheduleableAction$0(ScheduleAfterDebounceTime.java:134)
at
java.util.concurrent.Executors$RunnableAdapter.call$$$capture(Executors.java:511)
at java.util.concurrent.Executors$RunnableAdapter.call(Executors.java)
at java.util.concurrent.FutureTask.run$$$capture(FutureTask.java:266)
at java.util.concurrent.FutureTask.run(FutureTask.java)
at
java.util.concurrent.ScheduledThreadPoolExecutor$ScheduledFutureTask.access$201(ScheduledThreadPoolExecutor.java:180)
at
java.util.concurrent.ScheduledThreadPoolExecutor$ScheduledFutureTask.run(ScheduledThreadPoolExecutor.java:293)
at
java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142)
at
java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617)
at java.lang.Thread.run(Thread.java:745)
2018-03-15 22:34:32 logback 77787 [debounce-thread-0] DEBUG
o.a.samza.processor.StreamProcessor - Container is not instantiated yet.
2018-03-15 22:34:32 logback 77787 [debounce-thread-0] DEBUG
org.I0Itec.zkclient.ZkClient - Closing ZkClient...
2018-03-15 22:34:32 logback 77789 [ZkClient-EventThread-15-10.0.127.114:2181]
INFO org.I0Itec.zkclient.ZkEventThread - Terminate ZkClient event thread.
And then the application continues on with metric reporters, and other debug
logging (not actually running the task though)
Thanks in advance for the guidance, this has been easier than I imagined! I’ll
report back when I get more of the Dockerization/Kubernetes running and test it
a bit more.
Cheers,
Thunder
From: Jagadish Venkatraman [mailto:jagadish1...@gmail.com]
Sent: Thursday, March 15, 2018 14:46
To: Thunder Stumpges <tstump...@ntent.com>
Cc: dev@samza.apache.org; t...@recursivedream.com; yi...@linkedin.com; Yi Pan
<nickpa...@gmail.com>
Subject: Re: Old style "low level" Tasks with alternative deployment model(s)
>> Thanks for the info on the tradeoffs. That makes a lot of sense. I am
>> on-board with using ZkJobCoordinator, sounds like some good benefits over
>> just the Kafka high-level consumer.
This certainly looks like the simplest alternative.
For your other questions, please find my answers inline.
>> Q1: If I use LocalApplicationRunner, It does not use "ProcessJobFactory" (or
>> any StreamJob or *Job classes) correct?
Your understanding is correct. It directly instantiates the StreamProcessor,
which in-turn creates and runs the SamzaContainer.
>> Q2: If I use LocalApplicationRunner, I will need to code myself the loading
>> and rewriting of the Config that is currently handled by JobRunner, correct?
I don't think you'll need to do this. IIUC, the LocalApplicationRunner should
automatically invoke rewriters and do the right thing.
>> Q3: Do I need to also handle coordinator stream(s) and storing of config
>> that is done in JobRunner (I don’t think so as the ?
I don't think this is necessary either. The creation of coordinator stream and
persisting configuration happens in the LocalApplicationRunner (more
specifically in StreamManager#createStreams).
>> Q4: Where/How do I specify the Container ID for each instance? Is there a
>> config setting that I can pass, (or pull from an env variable and add to the
>> config) ? I am assuming it is my responsibility to ensure that each instance
>> is started with a unique container ID..?
Nope, If you are using the ZkJobCoordinator, you need not have to worry about
assigning IDs for each instance. The framework will automatically take care of
generating IDs and reaching consensus by electing a leader. If you are curious
please take a look at implementations of the ProcessorIdGenerator interface.
Please let us know should you have further questions!
Best,
Jagdish
On Thu, Mar 15, 2018 at 11:48 AM, Thunder Stumpges
<tstump...@ntent.com<mailto:tstump...@ntent.com>> wrote:
Thanks for the info on the tradeoffs. That makes a lot of sense. I am on-board
with using ZkJobCoordinator, sounds like some good benefits over just the Kafka
high-level consumer.
To that end, I have made some notes on possible approaches based on the
previous thread, and from my look into the code. I’d love to get feedback.
Approach 1. Configure jobs to use “ProcessJobFactory” and run instances of the
job using run-job.sh or using JobRunner directly.
I don’t think this makes sense from what I can see for a few reasons:
* JobRunner is concerned with stuff I don't *think* we need:
* coordinatorSystemProducer|Consumer,
* writing/reading the configuration to the coordinator streams
* ProcessJobFactory hard-codes the ID to “0” so I don’t think that will
work for multiple instances.
Approach 2. Configure ZkJobCoordinator, GroupByContainerIds, and invoke
LocalApplicationRunner.runTask()
Q1: If I use LocalApplicationRunner, It does not use "ProcessJobFactory"
(or any StreamJob or *Job classes) correct?
Q2: If I use LocalApplicationRunner, I will need to code myself the loading
and rewriting of the Config that is currently handled by JobRunner, correct?
Q3: Do I need to also handle coordinator stream(s) and storing of config
that is done in JobRunner (I don’t think so as the ?
Q4: Where/How do I specify the Container ID for each instance? Is there a
config setting that I can pass, (or pull from an env variable and add to the
config) ? I am assuming it is my responsibility to ensure that each instance is
started with a unique container ID..?
I am getting started on the above (Approach 2.), and looking closer at the code
so I may have my own answers to my questions, but figured I should go ahead and
ask now anyway. Thanks!
-Thunder
From: Jagadish Venkatraman
[mailto:jagadish1...@gmail.com<mailto:jagadish1...@gmail.com>]
Sent: Thursday, March 15, 2018 1:41
To: dev@samza.apache.org<mailto:dev@samza.apache.org>; Thunder Stumpges
<tstump...@ntent.com<mailto:tstump...@ntent.com>>;
t...@recursivedream.com<mailto:t...@recursivedream.com>
Cc: yi...@linkedin.com<mailto:yi...@linkedin.com>; Yi Pan
<nickpa...@gmail.com<mailto:nickpa...@gmail.com>>
Subject: Re: Old style "low level" Tasks with alternative deployment model(s)
>> You are correct that this is focused on the higher-level API but
>> doesn't
preclude using the lower-level API. I was at the same point you were not long
ago, in fact, and had a very productive conversation on the list
Thanks Tom for linking the thread, and I'm glad that you were able to get
Kubernetes integration working with Samza.
>> If it is helpful for everyone, once I get the low-level API +
>> ZkJobCoordinator + Docker +
K8s working, I'd be glad to formulate an additional sample for hello-samza.
@Thunder Stumpges:
We'd be thrilled to receive your contribution. Examples, demos, tutorials etc.
contribute a great deal to improving the ease of use of Apache Samza. I'm happy
to shepherd design discussions/code-reviews in the open-source including
answering any questions you may have.
>> One thing I'm still curious about, is what are the drawbacks or
>> complexities of leveraging the Kafka High-level consumer +
>> PassthroughJobCoordinator in a stand-alone setup like this? We do
>> have Zookeeper (because of kafka) so I think either would work. The
>> Kafka High-level consumer comes with other nice tools for monitoring
>> offsets, lag, etc
@Thunder Stumpges:
Samza uses a "Job-Coordinator" to assign your input-partitions among the
different instances of your application s.t. they don't overlap. A typical way
to solve this "partition distribution"
problem is to have a single instance elected as a "leader" and have the leader
assign partitions to the group.
The ZkJobCoordinator uses Zk primitives to achieve this, while the YarnJC
relies on Yarn's guarantee that there will be a singleton-AppMaster to achieve
this.
A key difference that separates the PassthroughJC from the Yarn/Zk variants is
that it does _not_ attempt to solve the "partition distribution" problem. As a
result, there's no leader-election involved. Instead, it pushes the problem of
"partition distribution" to the underlying consumer.
The PassThroughJc supports these 2 scenarios:
1. Consumer-managed partition distribution: When using the Kafka high-level
consumer (or an AWS KinesisClientLibrary consumer) with Samza, the consumer
manages partitions internally.
2. Static partition distribution: Alternately, partitions can be managed
statically using configuration. You can achieve static partition assignment by
implementing a custom
SystemStreamPartitionGrouper<https://samza.apache.org/learn/documentation/0.8/api/javadocs/org/apache/samza/container/grouper/stream/SystemStreamPartitionGrouper.html>
and
TaskNameGrouper<https://github.com/apache/samza/blob/master/samza-core/src/main/java/org/apache/samza/container/grouper/task/TaskNameGrouper.java>.
Solutions in this category will typically require you to distinguish the
various processors in the group by providing an "id" for each.
Once the "id"s are decided, you can then statically compute assignments using a
function (eg: modulo N).
You can rely on the following mechanisms to provide this id:
- Configure each instance differently to have its own id
- Obtain the id from the cluster-manager. For instance, Kubernetes will
provide each POD an unique id in the range [0,N). AWS ECS should expose similar
capabilities via a REST end-point.
>> One thing I'm still curious about, is what are the drawbacks or complexities
>> of leveraging the Kafka High-level consumer + PassthroughJobCoordinator in a
>> stand-alone setup like this?
Leveraging the Kafka High-level consumer:
The Kafka high-level consumer is not integrated into Samza just yet. Instead,
Samza's integration with Kafka uses the low-level consumer because
i) It allows for greater control in fetching data from individual brokers. It
is simple and performant in-terms of the threading model to have one-thread
pull from each broker.
ii) It is efficient in memory utilization since it does not do
internal-buffering of messages.
iii) There's no overhead like Kafka-controller heart-beats that are driven by
consumer.poll
Since there's no built-in integration, you will have to build a new
SystemConsumer if you need to integrate with the Kafka High-level consumer.
Further, there's more a fair bit of complexity to manage in checkpointing.
>> The Kafka High-level consumer comes with other nice tools for
>> monitoring offsets, lag, etc
Samza
exposes<https://github.com/apache/samza/blob/master/samza-kafka/src/main/scala/org/apache/samza/system/kafka/KafkaSystemConsumerMetrics.scala>
the below metrics for lag-monitoring:
- The current log-end offset for each partition
- The last check-pointed offset for each partition
- The number of messages behind the highwatermark of the partition
Please let us know if you need help discovering these or integrating these with
other systems/tools.
Leveraging the Passthrough JobCoordinator:
It's helpful to split this discussion on tradeoffs with PassthroughJC into 2
parts:
1. PassthroughJC + consumer managed partitions:
- In this model, Samza has no control over partition-assignment since it's
managed by the consumer. This means that stateful operations like joins that
rely on partitions being co-located on the same task will not work.
Simple stateless operations (eg: map, filter, remote lookups) are fine.
- A key differentiator between Samza and other frameworks is our support for
"host
affinity<https://samza.apache.org/learn/documentation/0.14/yarn/yarn-host-affinity.html>".
Samza achieves this by assigning partitions to hosts taking data-locality into
account. If the consumer can arbitrarily shuffle partitions, it'd be hard to
support this affinity/locality. Often this is a key optimization when dealing
with large stateful jobs.
2. PassthroughJC + static partitions:
- In this model, it is possible to make stateful processing (including host
affinity) work by carefully choosing how "id"s are assigned and computed.
Recommendation:
- Owing to the above subtleties, I would recommend that we give the
ZkJobCoordinator + the built-in low-level Kafka integration a try.
- If we hit snags down this path, we can certainly explore the approach with
PassthroughJC + static partitions.
- Using the PassthroughJC + consumer-managed distribution would be least
preferable owing to the subtleties I outlined above.
Please let us know should you have more questions.
Best,
Jagdish
On Wed, Mar 14, 2018 at 9:24 PM, Thunder Stumpges
<tstump...@ntent.com<mailto:tstump...@ntent.com>> wrote:
Wow, what great timing, and what a great thread! I definitely have some good
starters to go off of here.
If it is helpful for everyone, once I get the low-level API + ZkJobCoordinator
+ Docker + K8s working, I'd be glad to formulate an additional sample for
hello-samza.
One thing I'm still curious about, is what are the drawbacks or complexities of
leveraging the Kafka High-level consumer + PassthroughJobCoordinator in a
stand-alone setup like this? We do have Zookeeper (because of kafka) so I think
either would work. The Kafka High-level consumer comes with other nice tools
for monitoring offsets, lag, etc....
Thanks guys!
-Thunder
-----Original Message-----
From: Tom Davis [mailto:t...@recursivedream.com<mailto:t...@recursivedream.com>]
Sent: Wednesday, March 14, 2018 17:50
To: dev@samza.apache.org<mailto:dev@samza.apache.org>
Subject: Re: Old style "low level" Tasks with alternative deployment model(s)
Hey there!
You are correct that this is focused on the higher-level API but doesn't
preclude using the lower-level API. I was at the same point you were not long
ago, in fact, and had a very productive conversation on the list:
you should look for "Question about custom StreamJob/Factory" in the list
archive for the past couple months.
I'll quote Jagadish Venkatraman from that thread:
> For the section on the low-level API, can you use
> LocalApplicationRunner#runTask()? It basically creates a new
> StreamProcessor and runs it. Remember to provide task.class and set it
> to your implementation of StreamTask or AsyncStreamTask. Please note
> that this is an evolving API and hence, subject to change.
I ended up just switching to the high-level API because I don't have any
existing Tasks and the Kubernetes story is a little more straight forward there
(there's only one container/configuration to deploy).
Best,
Tom
Thunder Stumpges <tstump...@ntent.com<mailto:tstump...@ntent.com>> writes:
> Hi all,
>
> We are using Samza (0.12.0) in about 2 dozen jobs implementing several
> processing pipelines. We have also begun a significant move of other
> services within our company to Docker/Kubernetes. Right now our
> Hadoop/Yarn cluster has a mix of stream and batch "Map Reduce" jobs (many
> reporting and other batch processing jobs). We would really like to move our
> stream processing off of Hadoop/Yarn and onto Kubernetes.
>
> When I just read about some of the new progress in .13 and .14 I got
> really excited! We would love to have our jobs run as simple libraries
> in our own JVM, and use the Kafka High-Level-Consumer for partition
> distribution and such. This would let us "dockerfy" our application and
> run/scale in kubernetes.
>
> However as I read it, this new deployment model is ONLY for the
> new(er) High Level API, correct? Is there a plan and/or resources for
> adapting this back to existing low-level tasks ? How complicated of a task is
> that? Do I have any other options to make this transition easier?
>
> Thanks in advance.
> Thunder
--
Jagadish V,
Graduate Student,
Department of Computer Science,
Stanford University
--
Jagadish V,
Graduate Student,
Department of Computer Science,
Stanford University
