Hey all, figured I'd chime in here since I've been doing some work on evaluating options for performance testing over the last few weeks.
The tl;dr version is that there are a number of tools out there, but that the best one I was able to find was a tool called PerfKit Benchmarker (PKB)[1]. I'll have a doc out to the mailing list tomorrow with the full results of my investigation to bring everyone up to speed with what I've found, but there are a few clear wins with PKB. As it turns out, they already had the ability to benchmark Spark (I have a PR out to extend the Spark functionality[2] and a couple more improvements in the works), and I've put together some additional work in a branch on my repository[3] to enable proof-of-concept Dataflow Java benchmarks. I'm pretty excited about it overall. Specifically regarding to data stores, there's two things that make PKB attractive: they already support both container orchestration systems we're considering (Mesos / Marathon & Kubernetes), and they have built-in benchmark phases for spin-up and spin-down of resources that are isolated from the actual benchmark itself, so you get unpolluted data out of the box without having to do any log parsing or building multiple phases yourself. This means that once we have docker images set up it'd be pretty much turn-key to spin up / down instances of data stores for our tests. Let me know what you think. As mentioned, I'll also have an email out tomorrow with a doc evaluating the performance testing options I found; please chime in there too if you're interested! [1] https://github.com/GoogleCloudPlatform/PerfKitBenchmarker [2] https://github.com/GoogleCloudPlatform/PerfKitBenchmarker/pull/1214 [3] https://github.com/jasonkuster/PerfKitBenchmarker/tree/beam On Wed, Nov 23, 2016 at 3:33 PM, Stephen Sisk <s...@google.com.invalid> wrote: > Since this thread has been active recently and I feel like we're > mid-discussion, I just wanted to let folks know that I won't be checking > mail thursday/friday (US thanksgiving holiday) - I'll be back next monday. > > Thanks! > Stephen > > On Wed, Nov 23, 2016 at 10:03 AM Stephen Sisk <s...@google.com> wrote: > > > It's great to hear more experiences. > > > > I'm also glad to hear that people see real value in the high > > volume/performance benchmark tests. I tried to capture that in the > Testing > > doc I shared, under "Reasons for Beam Test Strategy". [1] > > > > It does generally sound like we're in agreement here. Areas of discussion > > I see: > > 1. People like the idea of bringing up fresh instances for each test > > rather than keeping instances running all the time, since that ensures no > > contamination between tests. That seems reasonable to me. If we see > > flakiness in the tests or we note that setting up/tearing down instances > is > > taking a lot of time, > > 2. Deciding on cluster management software/orchestration software - I > want > > to make sure we land on the right tool here since choosing the wrong tool > > could result in administration of the instances taking more work. I > suspect > > that's a good place for a follow up discussion, so I'll start a separate > > thread on that. I'm happy with whatever tool we choose, but I want to > make > > sure we take a moment to consider different options and have a reason for > > choosing one. > > > > Etienne - thanks for being willing to port your creation/other scripts > > over. You might be a good early tester of whether this system works well > > for everyone. > > > > Stephen > > > > [1] Reasons for Beam Test Strategy - > > https://docs.google.com/document/d/153J9jPQhMCNi_eBzJfhAg- > NprQ7vbf1jNVRgdqeEE8I/edit?ts=58349aec# > > > > > > > > On Wed, Nov 23, 2016 at 12:48 AM Jean-Baptiste Onofré <j...@nanthrax.net> > > wrote: > > > > I second Etienne there. > > > > We worked together on the ElasticsearchIO and definitely, the high > > valuable test we did were integration tests with ES on docker and high > > volume. > > > > I think we have to distinguish the two kinds of tests: > > 1. utests are located in the IO itself and basically they should cover > > the core behaviors of the IO > > 2. itests are located as contrib in the IO (they could be part of the IO > > but executed by the integration-test plugin or a specific profile) that > > deals with "real" backend and high volumes. The resources required by > > the itest can be bootstrapped by Jenkins (for instance using > > Mesos/Marathon and docker images as already discussed, and it's what I'm > > doing on my own "server"). > > > > It's basically what Stephen described. > > > > We have to not relay only on itest: utests are very important and they > > validate the core behavior. > > > > My $0.01 ;) > > > > Regards > > JB > > > > On 11/23/2016 09:27 AM, Etienne Chauchot wrote: > > > Hi Stephen, > > > > > > I like your proposition very much and I also agree that docker + some > > > orchestration software would be great ! > > > > > > On the elasticsearchIO (PR to be created this week) there is docker > > > container creation scripts and logstash data ingestion script for IT > > > environment available in contrib directory alongside with integration > > > tests themselves. I'll be happy to make them compliant to new IT > > > environment. > > > > > > What you say bellow about the need for external IT environment is > > > particularly true. As an example with ES what came out in first > > > implementation was that there were problems starting at some high > volume > > > of data (timeouts, ES windowing overflow...) that could not have be > seen > > > on embedded ES version. Also there where some particularities to > > > external instance like secondary (replica) shards that where not > visible > > > on embedded instance. > > > > > > Besides, I also favor bringing up instances before test because it > > > allows (amongst other things) to be sure to start on a fresh dataset > for > > > the test to be deterministic. > > > > > > Etienne > > > > > > > > > Le 23/11/2016 à 02:00, Stephen Sisk a écrit : > > >> Hi, > > >> > > >> I'm excited we're getting lots of discussion going. There are many > > >> threads > > >> of conversation here, we may choose to split some of them off into a > > >> different email thread. I'm also betting I missed some of the > > >> questions in > > >> this thread, so apologies ahead of time for that. Also apologies for > the > > >> amount of text, I provided some quick summaries at the top of each > > >> section. > > >> > > >> Amit - thanks for your thoughts. I've responded in detail below. > > >> Ismael - thanks for offering to help. There's plenty of work here to > go > > >> around. I'll try and think about how we can divide up some next steps > > >> (probably in a separate thread.) The main next step I see is deciding > > >> between kubernetes/mesos+marathon/docker swarm - I'm working on that, > > but > > >> having lots of different thoughts on what the advantages/disadvantages > > of > > >> those are would be helpful (I'm not entirely sure of the protocol for > > >> collaborating on sub-projects like this.) > > >> > > >> These issues are all related to what kind of tests we want to write. I > > >> think a kubernetes/mesos/swarm cluster could support all the use cases > > >> we've discussed here (and thus should not block moving forward with > > >> this), > > >> but understanding what we want to test will help us understand how the > > >> cluster will be used. I'm working on a proposed user guide for testing > > IO > > >> Transforms, and I'm going to send out a link to that + a short summary > > to > > >> the list shortly so folks can get a better sense of where I'm coming > > >> from. > > >> > > >> > > >> > > >> Here's my thinking on the questions we've raised here - > > >> > > >> Embedded versions of data stores for testing > > >> -------------------- > > >> Summary: yes! But we still need real data stores to test against. > > >> > > >> I am a gigantic fan of using embedded versions of the various data > > >> stores. > > >> I think we should test everything we possibly can using them, and do > the > > >> majority of our correctness testing using embedded versions + the > direct > > >> runner. However, it's also important to have at least one test that > > >> actually connects to an actual instance, so we can get coverage for > > >> things > > >> like credentials, real connection strings, etc... > > >> > > >> The key point is that embedded versions definitely can't cover the > > >> performance tests, so we need to host instances if we want to test > that. > > >> > > >> I consider the integration tests/performance benchmarks to be costly > > >> things > > >> that we do only for the IO transforms with large amounts of community > > >> support/usage. A random IO transform used by a few users doesn't > > >> necessarily need integration & perf tests, but for heavily used IO > > >> transforms, there's a lot of community value in these tests. The > > >> maintenance proposal below scales with the amount of community support > > >> for > > >> a particular IO transform. > > >> > > >> > > >> > > >> Reusing data stores ("use the data stores across executions.") > > >> ------------------ > > >> Summary: I favor a hybrid approach: some frequently used, very small > > >> instances that we keep up all the time + larger multi-container data > > >> store > > >> instances that we spin up for perf tests. > > >> > > >> I don't think we need to have a strong answer to this question, but I > > >> think > > >> we do need to know what range of capabilities we need, and use that to > > >> inform our requirements on the hosting infrastructure. I think > > >> kubernetes/mesos + docker can support all the scenarios I discuss > below. > > >> > > >> I had been thinking of a hybrid approach - reuse some instances and > > don't > > >> reuse others. Some tests require isolation from other tests (eg. > > >> performance benchmarking), while others can easily re-use the same > > >> database/data store instance over time, provided they are written in > the > > >> correct manner (eg. a simple read or write correctness integration > > tests) > > >> > > >> To me, the question of whether to use one instance over time for a > > >> test vs > > >> spin up an instance for each test comes down to a trade off between > > these > > >> factors: > > >> 1. Flakiness of spin-up of an instance - if it's super flaky, we'll > > >> want to > > >> keep more instances up and running rather than bring them up/down. > (this > > >> may also vary by the data store in question) > > >> 2. Frequency of testing - if we are running tests every 5 minutes, it > > may > > >> be wasteful to bring machines up/down every time. If we run tests > once a > > >> day or week, it seems wasteful to keep the machines up the whole time. > > >> 3. Isolation requirements - If tests must be isolated, it means we > > either > > >> have to bring up the instances for each test, or we have to have some > > >> sort > > >> of signaling mechanism to indicate that a given instance is in use. I > > >> strongly favor bringing up an instance per test. > > >> 4. Number/size of containers - if we need a large number of machines > > >> for a > > >> particular test, keeping them running all the time will use more > > >> resources. > > >> > > >> > > >> The major unknown to me is how flaky it'll be to spin these up. I'm > > >> hopeful/assuming they'll be pretty stable to bring up, but I think the > > >> best > > >> way to test that is to start doing it. > > >> > > >> I suspect the sweet spot is the following: have a set of very small > data > > >> store instances that stay up to support small-data-size post-commit > > >> end to > > >> end tests (post-commits run frequently and the data size means the > > >> instances would not use many resources), combined with the ability to > > >> spin > > >> up larger instances for once a day/week performance benchmarks (these > > use > > >> up more resources and are used less frequently.) That's the mix I'll > > >> propose in my docs on testing IO transforms. If spinning up new > > >> instances > > >> is cheap/non-flaky, I'd be fine with the idea of spinning up instances > > >> for > > >> each test. > > >> > > >> > > >> > > >> Management ("what's the overhead of managing such a deployment") > > >> -------------------- > > >> Summary: I propose that anyone can contribute scripts for setting up > > data > > >> store instances + integration/perf tests, but if the community doesn't > > >> maintain a particular data store's tests, we disable the tests and > > >> turn off > > >> the data store instances. > > >> > > >> Management of these instances is a crucial question. First, let's > break > > >> down what tasks we'll need to do on a recurring basis: > > >> 1. Ongoing maintenance (update to new versions, both instance & > > >> dependencies) - we don't want to have a lot of old versions vulnerable > > to > > >> attacks/buggy > > >> 2. Investigate breakages/regressions > > >> (I'm betting there will be more things we'll discover - let me know if > > >> you > > >> have suggestions) > > >> > > >> There's a couple goals I see: > > >> 1. We should only do sys admin work for things that give us a lot of > > >> benefit. (ie, don't build IT/perf/data store set up scripts for data > > >> stores > > >> without a large community) > > >> 2. We should do as much as possible of testing via in-memory/embedded > > >> testing (as you brought up). > > >> 3. Reduce the amount of manual administration overhead > > >> > > >> As I discussed above, I think that integration tests/performance > > >> benchmarks > > >> are costly things that we should do only for the IO transforms with > > large > > >> amounts of community support/usage. Thus, I propose that we limit the > IO > > >> transforms that get integration tests & performance benchmarks to > those > > >> that have community support for maintaining the data store instances. > > >> > > >> We can enforce this organically using some simple rules: > > >> 1. Investigating breakages/regressions: if a given integration/perf > test > > >> starts failing and no one investigates it within a set period of time > (a > > >> week?), we disable the tests and shut off the data store instances if > we > > >> have instances running. When someone wants to step up and support it > > >> again, > > >> they can fix the test, check it in, and re-enable the test. > > >> 2. Ongoing maintenance: every N months, file a jira issue that is just > > >> "is > > >> the IO Transform X data store up to date?" - if the jira is not > > >> resolved in > > >> a set period of time (1 month?), the perf/integration tests are > > disabled, > > >> and the data store instances shut off. > > >> > > >> This is pretty flexible - > > >> * If a particular person or organization wants to support an IO > > >> transform, > > >> they can. If a group of people all organically organize to keep the > > tests > > >> running, they can. > > >> * It can be mostly automated - there's not a lot of central organizing > > >> work > > >> that needs to be done. > > >> > > >> Exposing the information about what IO transforms currently have > running > > >> IT/perf benchmarks on the website will let users know what IO > transforms > > >> are well supported. > > >> > > >> I like this solution, but I also recognize this is a tricky problem. > > This > > >> is something the community needs to be supportive of, so I'm open to > > >> other > > >> thoughts. > > >> > > >> > > >> Simulating failures in real nodes ("programmatic tests to simulate > > >> failure") > > >> ----------------- > > >> Summary: 1) Focus our testing on the code in Beam 2) We should > > >> encourage a > > >> design pattern separating out network/retry logic from the main IO > > >> transform logic > > >> > > >> We *could* create instance failure in any container management > software > > - > > >> we can use their programmatic APIs to determine which containers are > > >> running the instances, and ask them to kill the container in > question. A > > >> slow node would be trickier, but I'm sure we could figure it out - for > > >> example, add a network proxy that would delay responses. > > >> > > >> However, I would argue that this type of testing doesn't gain us a > > >> lot, and > > >> is complicated to set up. I think it will be easier to test network > > >> errors > > >> and retry behavior in unit tests for the IO transforms. > > >> > > >> Part of the way to handle this is to separate out the read code from > the > > >> network code (eg. bigtable has BigtableService). If you put the > "handle > > >> errors/retry logic" code in a separate MySourceService class, you can > > >> test > > >> MySourceService on the wide variety of networks errors/data store > > >> problems, > > >> and then your main IO transform tests focus on the read behavior and > > >> handling the small set of errors the MySourceService class will > return. > > >> > > >> I also think we should focus on testing the IO Transform, not the data > > >> store - if we kill a node in a data store, it's that data store's > > >> problem, > > >> not beam's problem. As you were pointing out, there are a *large* > > >> number of > > >> possible ways that a particular data store can fail, and we would like > > to > > >> support many different data stores. Rather than try to test that each > > >> data > > >> store behaves well, we should ensure that we handle generic/expected > > >> errors > > >> in a graceful manner. > > >> > > >> > > >> > > >> > > >> > > >> > > >> Ismaeal had a couple other quick comments/questions, I'll answer here > - > > >> We can use this to test other runners running on multiple machines - I > > >> agree. This is also necessary for a good performance benchmark test. > > >> > > >> "providing the test machines to mount the cluster" - we can discuss > this > > >> further, but one possible option is that google may be willing to > donate > > >> something to support this. > > >> > > >> "IO Consistency" - let's follow up on those questions in another > thread. > > >> That's as much about the public interface we provide to users as > > anything > > >> else. I agree with your sentiment that a user should be able to expect > > >> predictable behavior from the different IO transforms. > > >> > > >> Thanks for everyone's questions/comments - I really am excited to see > > >> that > > >> people care about this :) > > >> > > >> Stephen > > >> > > >> On Tue, Nov 22, 2016 at 7:59 AM Ismaël Mejía <ieme...@gmail.com> > wrote: > > >> > > >>> Hello, > > >>> > > >>> @Stephen Thanks for your proposal, it is really interesting, I would > > >>> really > > >>> like to help with this. I have never played with Kubernetes but this > > >>> seems > > >>> a really nice chance to do something useful with it. > > >>> > > >>> We (at Talend) are testing most of the IOs using simple container > > images > > >>> and in some particular cases ‘clusters’ of containers using > > >>> docker-compose > > >>> (a little bit like Amit’s (2) proposal). It would be really nice to > > have > > >>> this at the Beam level, in particular to try to test more complex > > >>> semantics, I don’t know how programmable kubernetes is to achieve > > >>> this for > > >>> example: > > >>> > > >>> Let’s think we have a cluster of Cassandra or Kafka nodes, I would > > >>> like to > > >>> have programmatic tests to simulate failure (e.g. kill a node), or > > >>> simulate > > >>> a really slow node, to ensure that the IO behaves as expected in the > > >>> Beam > > >>> pipeline for the given runner. > > >>> > > >>> Another related idea is to improve IO consistency: Today the > > >>> different IOs > > >>> have small differences in their failure behavior, I really would like > > >>> to be > > >>> able to predict with more precision what will happen in case of > errors, > > >>> e.g. what is the correct behavior if I am writing to a Kafka node and > > >>> there > > >>> is a network partition, does the Kafka sink retries or no ? and what > > >>> if it > > >>> is the JdbcIO ?, will it work the same e.g. assuming checkpointing? > > >>> Or do > > >>> we guarantee exactly once writes somehow?, today I am not sure about > > >>> what > > >>> happens (or if the expected behavior depends on the runner), but well > > >>> maybe > > >>> it is just that I don’t know and we have tests to ensure this. > > >>> > > >>> Of course both are really hard problems, but I think with your > > >>> proposal we > > >>> can try to tackle them, as well as the performance ones. And apart of > > >>> the > > >>> data stores, I think it will be also really nice to be able to test > the > > >>> runners in a distributed manner. > > >>> > > >>> So what is the next step? How do you imagine such integration tests? > > >>> ? Who > > >>> can provide the test machines so we can mount the cluster? > > >>> > > >>> Maybe my ideas are a bit too far away for an initial setup, but it > > >>> will be > > >>> really nice to start working on this. > > >>> > > >>> Ismael > > >>> > > >>> > > >>> On Tue, Nov 22, 2016 at 11:00 AM, Amit Sela <amitsel...@gmail.com> > > >>> wrote: > > >>> > > >>>> Hi Stephen, > > >>>> > > >>>> I was wondering about how we plan to use the data stores across > > >>> executions. > > >>>> Clearly, it's best to setup a new instance (container) for every > test, > > >>>> running a "standalone" store (say HBase/Cassandra for example), and > > >>>> once > > >>>> the test is done, teardown the instance. It should also be agnostic > to > > >>> the > > >>>> runtime environment (e.g., Docker on Kubernetes). > > >>>> I'm wondering though what's the overhead of managing such a > deployment > > >>>> which could become heavy and complicated as more IOs are supported > and > > >>> more > > >>>> test cases introduced. > > >>>> > > >>>> Another way to go would be to have small clusters of different data > > >>> stores > > >>>> and run against new "namespaces" (while lazily evicting old ones), > > >>>> but I > > >>>> think this is less likely as maintaining a distributed instance > (even > > a > > >>>> small one) for each data store sounds even more complex. > > >>>> > > >>>> A third approach would be to to simply have an "embedded" in-memory > > >>>> instance of a data store as part of a test that runs against it > > >>>> (such as > > >>> an > > >>>> embedded Kafka, though not a data store). > > >>>> This is probably the simplest solution in terms of orchestration, > > >>>> but it > > >>>> depends on having a proper "embedded" implementation for an IO. > > >>>> > > >>>> Does this make sense to you ? have you considered it ? > > >>>> > > >>>> Thanks, > > >>>> Amit > > >>>> > > >>>> On Tue, Nov 22, 2016 at 8:20 AM Jean-Baptiste Onofré < > j...@nanthrax.net > > > > > >>>> wrote: > > >>>> > > >>>>> Hi Stephen, > > >>>>> > > >>>>> as already discussed a bit together, it sounds great ! Especially I > > >>> like > > >>>>> it as a both integration test platform and good coverage for IOs. > > >>>>> > > >>>>> I'm very late on this but, as said, I will share with you my > Marathon > > >>>>> JSON and Mesos docker images. > > >>>>> > > >>>>> By the way, I started to experiment a bit kubernetes and swamp but > > >>>>> it's > > >>>>> not yet complete. I will share what I have on the same github repo. > > >>>>> > > >>>>> Thanks ! > > >>>>> Regards > > >>>>> JB > > >>>>> > > >>>>> On 11/16/2016 11:36 PM, Stephen Sisk wrote: > > >>>>>> Hi everyone! > > >>>>>> > > >>>>>> Currently we have a good set of unit tests for our IO Transforms - > > >>>> those > > >>>>>> tend to run against in-memory versions of the data stores. > However, > > >>>> we'd > > >>>>>> like to further increase our test coverage to include running them > > >>>>> against > > >>>>>> real instances of the data stores that the IO Transforms work > > against > > >>>>> (e.g. > > >>>>>> cassandra, mongodb, kafka, etc…), which means we'll need to have > > real > > >>>>>> instances of various data stores. > > >>>>>> > > >>>>>> Additionally, if we want to do performance regression detection, > > it's > > >>>>>> important to have instances of the services that behave > > >>> realistically, > > >>>>>> which isn't true of in-memory or dev versions of the services. > > >>>>>> > > >>>>>> > > >>>>>> Proposed solution > > >>>>>> ------------------------- > > >>>>>> If we accept this proposal, we would create an infrastructure for > > >>>> running > > >>>>>> real instances of data stores inside of containers, using > container > > >>>>>> management software like mesos/marathon, kubernetes, docker swarm, > > >>> etc… > > >>>>> to > > >>>>>> manage the instances. > > >>>>>> > > >>>>>> This would enable us to build integration tests that run against > > >>> those > > >>>>> real > > >>>>>> instances and performance tests that run against those real > > instances > > >>>>> (like > > >>>>>> those that Jason Kuster is proposing elsewhere.) > > >>>>>> > > >>>>>> > > >>>>>> Why do we need one centralized set of instances vs just having > > >>> various > > >>>>>> people host their own instances? > > >>>>>> ------------------------- > > >>>>>> Reducing flakiness of tests is key. By not having dependencies > from > > >>> the > > >>>>>> core project on external services/instances of data stores we have > > >>>>>> guaranteed access to the services and the group can fix issues > that > > >>>>> arise. > > >>>>>> An exception would be something that has an ops team supporting it > > >>> (eg, > > >>>>>> AWS, Google Cloud or other professionally managed service) - those > > we > > >>>>> trust > > >>>>>> will be stable. > > >>>>>> > > >>>>>> > > >>>>>> There may be a lot of different data stores needed - how will we > > >>>> maintain > > >>>>>> them? > > >>>>>> ------------------------- > > >>>>>> It will take work above and beyond that of a normal set of unit > > tests > > >>>> to > > >>>>>> build and maintain integration/performance tests & their data > store > > >>>>>> instances. > > >>>>>> > > >>>>>> Setup & maintenance of the data store containers and data store > > >>>> instances > > >>>>>> on it must be automated. It also has to be as simple of a setup as > > >>>>>> possible, and we should avoid hand tweaking the containers - > > >>> expecting > > >>>>>> checked in scripts/dockerfiles is key. > > >>>>>> > > >>>>>> Aligned with the community ownership approach of Apache, as > members > > >>> of > > >>>>> the > > >>>>>> community are excited to contribute & maintain those tests and the > > >>>>>> integration/performance tests, people will be able to step up and > do > > >>>>> that. > > >>>>>> If there is no longer support for maintaining a particular set of > > >>>>>> integration & performance tests and their data store instances, > then > > >>> we > > >>>>> can > > >>>>>> disable those tests. We may document on the website what IO > > >>> Transforms > > >>>>> have > > >>>>>> current integration/performance tests so users know what level of > > >>>> testing > > >>>>>> the various IO Transforms have. > > >>>>>> > > >>>>>> > > >>>>>> What about requirements for the container management software > > itself? > > >>>>>> ------------------------- > > >>>>>> * We should have the data store instances themselves in Docker. > > >>> Docker > > >>>>>> allows new instances to be spun up in a quick, reproducible way > and > > >>> is > > >>>>>> fairly platform independent. It has wide support from a variety of > > >>>>>> different container management services. > > >>>>>> * As little admin work required as possible. Crashing instances > > >>> should > > >>>> be > > >>>>>> restarted, setup should be simple, everything possible should be > > >>>>>> scripted/scriptable. > > >>>>>> * Logs and test output should be on a publicly available website, > > >>>> without > > >>>>>> needing to log into test execution machine. Centralized capture of > > >>>>>> monitoring info/logs from instances running in the containers > would > > >>>>> support > > >>>>>> this. Ideally, this would just be supported by the container > > software > > >>>> out > > >>>>>> of the box. > > >>>>>> * It'd be useful to have good persistent volume in the container > > >>>>> management > > >>>>>> software so that databases don't have to reload large data sets > > every > > >>>>> time. > > >>>>>> * The containers may be a place to execute runners themselves if > we > > >>>> need > > >>>>>> larger runner instances, so it should play well with Spark, Flink, > > >>> etc… > > >>>>>> As I discussed earlier on the mailing list, it looks like hosting > > >>>> docker > > >>>>>> containers on kubernetes, docker swarm or mesos+marathon would be > a > > >>>> good > > >>>>>> solution. > > >>>>>> > > >>>>>> Thanks, > > >>>>>> Stephen Sisk > > >>>>>> > > >>>>> -- > > >>>>> Jean-Baptiste Onofré > > >>>>> jbono...@apache.org > > >>>>> http://blog.nanthrax.net > > >>>>> Talend - http://www.talend.com > > >>>>> > > > > > > > -- > > Jean-Baptiste Onofré > > jbono...@apache.org > > http://blog.nanthrax.net > > Talend - http://www.talend.com > > > > > -- ------- Jason Kuster Apache Beam (Incubating) / Google Cloud Dataflow
