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From Thunder Stumpges <tstump...@ntent.com>
Subject RE: Old style "low level" Tasks with alternative deployment model(s)
Date Thu, 15 Mar 2018 23:34:58 GMT
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:jagadish1989@gmail.com]
Sent: Thursday, March 15, 2018 14:46
To: Thunder Stumpges <tstumpges@ntent.com>
Cc: dev@samza.apache.org; tom@recursivedream.com; Yipan@linkedin.com; Yi Pan <nickpan47@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 <tstumpges@ntent.com<mailto:tstumpges@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:jagadish1989@gmail.com<mailto:jagadish1989@gmail.com>]
Sent: Thursday, March 15, 2018 1:41
To: dev@samza.apache.org<mailto:dev@samza.apache.org>; Thunder Stumpges <tstumpges@ntent.com<mailto:tstumpges@ntent.com>>;
tom@recursivedream.com<mailto:tom@recursivedream.com>
Cc: Yipan@linkedin.com<mailto:Yipan@linkedin.com>; Yi Pan <nickpan47@gmail.com<mailto:nickpan47@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 <tstumpges@ntent.com<mailto:tstumpges@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:tom@recursivedream.com<mailto:tom@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 <tstumpges@ntent.com<mailto:tstumpges@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
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