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
