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From DImuthu Upeksha <dimuthu.upeks...@gmail.com>
Subject Re: Linked Container Services for Apache Airavata Components - Phase 2 - Initial Prototype
Date Fri, 03 Nov 2017 14:12:55 GMT
To be clear, not implemented part that I have mentioned above is the
resuming part of the Task Scheduler. Up to that point including Task
Scheduler getting feedback form the executing tasks and Event Sinks
persisting the status of the task are already there.


On Fri, Nov 3, 2017 at 5:34 AM, DImuthu Upeksha <dimuthu.upeksha2@gmail.com>
wrote:

> Hi Marlon,
>
> Then Task Executor drops the status message to Task-Event topic. Sample
> status message is like this
>
> <ProcessId>,<TaskId>,<Status>,<Reason>
>
>  This is a broadcasting message and all Task Schedulers receives this
> message and the corresponding Task Scheduler which executes the DAG that
> contains this Task will stop DAG execution. Others will safely ignore this
> message. However you might have an issue like, what if that Task Scheduler
> got killed in between? If you can look at the design diagram, there is an
> another type of microservice (Event-Sink) which is reading from the
> Task-Event topic. What they does is, they read each status event from the
> topic and persist them in the database through the API server. That helps
> us to continue the same same DAG in a new Task Scheduler. However in
> current version, this feature was not implemented.
>
>
> ​
> ​
>
> Thanks
> Dimuthu
>
> On Fri, Nov 3, 2017 at 12:46 AM, Pierce, Marlon <marpierc@iu.edu> wrote:
>
>> Hi Dimuthu,
>>
>>
>>
>> Thanks for the clarification on the first issue.  The second issue is
>> more about how you handle external errors in your approach: the SSH
>> connection times out, for instance, and all the retries fail, so the Task
>> Executor needs to report back that the command failed.
>>
>>
>>
>> Marlon
>>
>>
>>
>>
>>
>> *From: *"dimuthu.upeksha2@gmail.com" <dimuthu.upeksha2@gmail.com>
>> *Reply-To: *"dev@airavata.apache.org" <dev@airavata.apache.org>
>> *Date: *Thursday, November 2, 2017 at 7:33 AM
>> *To: *"dev@airavata.apache.org" <dev@airavata.apache.org>
>> *Subject: *Re: Linked Container Services for Apache Airavata Components
>> - Phase 2 - Initial Prototype
>>
>>
>>
>> Hi Marlon,
>>
>>
>>
>> Thanks for the comments and please find the explanations for those
>> comments below.
>>
>>
>>
>> *Transactionality*
>>
>>
>>
>> Kafka has a rich acknowledgment mechanism. Actually there are several
>> acknowledgement levels. Not like in RabbitMQ where messages are removed
>> from the queue once read, Kafka keeps all the messages of the topic for a
>> given time. Until that time expires, consumers are free to go back and
>> forth on the topic to read previously read messages. However if we want to
>> keep track of the read messages by a particular consumer (actually a
>> consumer group) we can use kafka's Current Offset and Committed Offset to
>> achieve this [1]. A consumer can read more than one messages from a
>> partition of the topic. When it is done, Kafka keeps track of the pointer
>> (offset) where that particular consumer finally read from the partition.
>> That is the current offset. However it does not mean that messages are
>> properly consumed by the consumer. Once the messages are consumed, consumer
>> acknowledges that it has consumed x number of messages of the already read
>> messages. Then Kafka keeps track of the last pointer where that particular
>> consumer has acknowledged. This is the Committed Offset. Let's assume that
>> the particular Consumer goes down and a new Consumer from the same consumer
>> group starts to read from vacant partition. Then it will start to read from
>> the Committed offset of last consumer. So to achieve the behavior that you
>> have mentioned, it can be done by controlling the acknowledgement mechanism
>> of a Consumer. There are mainly 2 acknowledgement mechanisms in Kafka
>>
>>
>>
>> 1. Auto commit - Once a consumer reads a message or set of messages at
>> once, it will auto commit after a given time interval
>>
>> 2. Manual commit - Developer has the handle to decide whether to
>> acknowledge or not for a particular message
>>
>>
>>
>> From above two methods we have to use manual commit as we have to make
>> sure that messages are acknowledged once it was properly handled. Manual
>> commit also has several levels and modes of acknowledgements.
>>
>>
>>
>> 1. Per Message acknowledgement
>>
>> 2. Per Message set acknowledgement
>>
>>
>>
>> 1. Asynchronous Mode
>>
>> 2. Synchronous Mode
>>
>>
>>
>> Here we use per message synchronous acknowledgement as we want the
>> highest level of transactionality. In that case, once we read a message
>> from a Kafka topic, we parse it and do the necessary operations and finally
>> acknowledges once the message has been successfully handled. If something
>> went wrong (consumer failed) and read messages were not acknowledged, new
>> consumer will continue from last acknowledged position. I have implemented
>> PoC code that demonstrates above concept with a single producer and 3
>> consumers [2]. You can find the screen recording of the test from here [3].
>>
>>
>>
>> If  you need 100% transactionality form consumer side, it is also
>> possible with an external transactional scope like database operations.
>> However in our case it is not required as the operations that we are doing
>> inside the message handling operation are not reversible. Let's talk more
>> about this approach in future.
>>
>>
>>
>> *Slow SSH communication*
>>
>>
>>
>> If you are expecting to improve the communication mechanism instead of
>> SSH, one option is to use agents inside comupte resources and communicate
>> with them in a optimized messaging protocol. I have provided an approach in
>> the previous thread. But your concern is more about being the whole
>> pipeline blocked due to a slow task not releasing the message from the
>> topic, there is a solution for that. When we are publishing messages to a
>> Kafka topic, they are distributed among the partitions of the topic.
>> Consumers are reading from those partitions. If a consumer becomes slow,
>> only the message read from that partitions will become slow. Other
>> partitions will work without any issue. So we can have a higher number of
>> partitions in a topic to handle that. Further if we want to go further like
>> grouping slow tasks to a one set and allowing low latency tasks to an other
>> set by using a custom partitioner [5]
>>
>>
>>
>> *Registry*
>>
>>
>>
>> It is already there [6]. Actually in this implementation, API server acts
>> as the registry itself. It stores experiment objects and current status of
>> the cluster. However As Gaurav has pointed out in the next email, that
>> would be better if this is separated into multiple parts. What do you think?
>>
>>
>>
>> [1] https://www.youtube.com/watch?v=kZT8v2_b2XE&index=15&lis
>> t=PLkz1SCf5iB4enAR00Z46JwY9GGkaS2NON
>>
>> [2] https://github.com/DImuthuUpe/kafka-transactionality
>>
>> [3] https://www.youtube.com/watch?v=j6bOVLUlyf4&feature=youtu.be
>>
>> [4] https://www.youtube.com/watch?v=AshMNCxSp3c&list=PLkz1SC
>> f5iB4enAR00Z46JwY9GGkaS2NON&index=17
>>
>> [5] https://www.youtube.com/watch?v=pMDAcNRkWkE&index=10&lis
>> t=PLkz1SCf5iB4enAR00Z46JwY9GGkaS2NON
>>
>> [6] https://github.com/apache/airavata-sandbox/tree/master/a
>> iravata-kubernetes/modules/microservices/api-server/src/main
>> /java/org/apache/airavata/k8s/api/server
>>
>>
>>
>> On Thu, Nov 2, 2017 at 2:52 AM, Pierce, Marlon <marpierc@iu.edu> wrote:
>>
>> Hi Dimuthu,
>>
>>
>>
>> Thanks for sending this very thoughtful document. A couple of comments:
>>
>>
>>
>> * Use of Kafka instead of RabbitMQ is interesting. Can you say more about
>> how this approach can handle Kafka client failures?  For RabbitMQ, for
>> example, there is the simple “Work Queue” approach in which the broker
>> pushes a task to a worker. The task remains in queue until the worker sends
>> an acknowledgement that the job has been handled, not just received.
>> “Handled” may mean for example that the job has been submitted to an
>> external batch scheduler over SSH, which may require some retries, etc.
>> If the worker crashes before the job has been submitted, then the broker
>> can resend the message to another worker.   I’m wondering how your
>> Kafka-based solution would handle the same issue.
>>
>>
>>
>> * A simpler but more common failure is communicating with external
>> resources. A task executor may need to SSH to a remote resource, which can
>> fail (the resource is slow to communicate, usually). How do you handle this
>> case?
>>
>>
>>
>> * Your design focuses on Airavata’s experiment execution handling.
>> Airavata’s registry is another important component: this is where
>> experiment objects get persistently stored. The registry stores metadata
>> about both “live” experiments that are currently executing as well as
>> archived experiments that have completed.
>>
>>
>>
>> How would you extend your architecture to include the registry?
>>
>>
>>
>> Marlon
>>
>>
>>
>>
>>
>> *From: *"dimuthu.upeksha2@gmail.com" <dimuthu.upeksha2@gmail.com>
>> *Reply-To: *"dev@airavata.apache.org" <dev@airavata.apache.org>
>> *Date: *Monday, October 30, 2017 at 10:45 AM
>> *To: *"dev@airavata.apache.org" <dev@airavata.apache.org>
>> *Subject: *Linked Container Services for Apache Airavata Components -
>> Phase 2 - Initial Prototype
>>
>>
>>
>> Hi All,
>>
>>
>>
>> Based on the analysis of Phase 1, within past two weeks I have been
>> working on implementing a task execution workflow following the
>> microservices deployment pattern and Kubernetes as the deployment platform.
>>
>>
>>
>> Please find attached design document that explains the components and
>> messaging interactions between components. Based on that design, I have
>> implemented following components
>>
>>
>>
>> 1. Set of microservices to compose the workflow
>>
>> 2. A simple Web Console to  deploy and monitor workflows on the framework
>>
>>
>>
>> I used Kakfa as the primary messaging medium to communicate among the
>> microservices due to its simplicity and powerful features like partitions
>> and consumer groups.
>>
>>
>>
>> I have attached a user guide so that you can install and try this in your
>> local machine. And source code for each component can be found from [1]
>>
>>
>>
>> Please share you ideas and suggestions.
>>
>>
>>
>> Thanks
>>
>> Dimuthu
>>
>>
>>
>> [1] https://github.com/DImuthuUpe/airavata/tree/master/
>> sandbox/airavata-kubernetes
>>
>> [2] https://docs.google.com/document/d/1R1xrmuPldHiWVDn4xNVa
>> y9Vnxn9FODQZXtF55JxJpSY/edit?usp=sharing
>>
>> [3] https://docs.google.com/document/d/1A5eRIZiuUj4ShZVMS0Nd
>> AxjAxtOTZXculaYDCZ7IMQ8/edit?usp=sharing
>>
>>
>>
>
>

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