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From Zhou Fang <zhouf...@google.com.INVALID>
Subject Re: Airflow DAG Serialisation
Date Mon, 29 Jul 2019 22:59:18 GMT
Hi Kevin,

Yes. DAG persistence in DB is definitely the way to go. I referred to the
aysnc dag loader because it may alleviate your current problem (since it is
code ready).

It actually reduces the time to 15min, because DAGs are refreshed by the
background process in a streaming way and you don't need to restart
webserver per 20min.



Thanks,
Zhou


On Mon, Jul 29, 2019 at 3:14 PM Kevin Yang <yrqls21@gmail.com> wrote:

> Hi Zhou,
>
> Thank you for the pointer. This solves the issue gunicorn restart rate
> throttles webserver refresh rate but not the long DAG parsing time issue,
> right? Worst case scenario we still wait 30 mins for the change to show up,
> comparing to the previous 35 mins( I was wrong on the number, it should be
> 35 mins instead of 55 mins as the clock starts whenever the webserver
> restarts). I believe in the previous discussion, we firstly proposed this
> local webserver DAG parsing optimization to use the same DAG parsing logic
> in scheduler to speed up the parsing. Then the stateless webserver proposal
> came up and we were brought in that it is a better idea to persist DAGs
> into the DB and read directly from the DB, better DAG def consistency and
> webserver cluster consistency. I'm all supportive on the proposed structure
> in AIP-24 but -1 on just feed webserver with a single subprocess parsing
> the DAGs. I would image there won't be too many additional work to fetch
> from DB instead of a subprocess, would there?( haven't look into the
> serialization format part but assuming they are the same/similar)
>
> Cheers,
> Kevin Y
>
> On Mon, Jul 29, 2019 at 2:18 PM Zhou Fang <zhoufang@google.com> wrote:
>
>> Hi Kevin,
>>
>> The problem that DAG parsing takes a long time can be solved by
>> Asynchronous DAG loading: https://github.com/apache/airflow/pull/5594
>>
>> The idea is the a background process parses DAG files, and sends DAGs to
>> webserver process every [webserver] dagbag_sync_interval = 10s.
>>
>> We have launched it in Composer, so our users can set webserver worker
>> restart interval to 1 hour (or longer). The background DAG parsing
>> processing refresh all DAGs per [webserver] = collect_dags_interval = 30s
>> .
>>
>> If parsing all DAGs take 15min, you can see DAGs being gradually freshed
>> with this feature.
>>
>> Thanks,
>> Zhou
>>
>>
>> On Sat, Jul 27, 2019 at 2:43 AM Kevin Yang <yrqls21@gmail.com> wrote:
>>
>>> Nice job Zhou!
>>>
>>> Really excited, exactly what we wanted for the webserver scaling issue.
>>> Want to add another big drive for Airbnb to start think about this
>>> previously to support the effort: it can not only bring consistency
>>> between
>>> webservers but also bring consistency between webserver and
>>> scheduler/workers. It may be less of a problem if total DAG parsing time
>>> is
>>> small, but for us the total DAG parsing time is 15+ mins and we had to
>>> set
>>> the webserver( gunicorn subprocesses) restart interval to 20 mins, which
>>> leads to a worst case 15+20+15=50 mins delay between scheduler start to
>>> schedule things and users can see their deployed DAGs/changes...
>>>
>>> I'm not so sure about the scheduler performance improvement: currently we
>>> already feed the main scheduler process with SimpleDag through
>>> DagFileProcessorManager running in a subprocess--in the future we feed it
>>> with data from DB, which is likely slower( tho the diff should have
>>> negligible impact to the scheduler performance). In fact if we'd keep the
>>> existing behavior, try schedule only fresh parsed DAGs, then we may need
>>> to
>>> deal with some consistency issue--dag processor and the scheduler race
>>> for
>>> updating the flag indicating if the DAG is newly parsed. No big deal
>>> there
>>> but just some thoughts on the top of my head and hopefully can be
>>> helpful.
>>>
>>> And good idea on pre-rendering the template, believe template rendering
>>> was
>>> the biggest concern in the previous discussion. We've also chose the
>>> pre-rendering+JSON approach in our smart sensor API
>>> <
>>> https://cwiki.apache.org/confluence/display/AIRFLOW/AIP-17+Airflow+sensor+optimization
>>> >
>>> and
>>> seems to be working fine--a supporting case for ur proposal ;) There's a
>>> WIP
>>> PR <https://github.com/apache/airflow/pull/5499> for it just in case you
>>> are interested--maybe we can even share some logics.
>>>
>>> Thumbs-up again for this and please don't heisitate to reach out if you
>>> want to discuss further with us or need any help from us.
>>>
>>>
>>> Cheers,
>>> Kevin Y
>>>
>>> On Sat, Jul 27, 2019 at 12:54 AM Driesprong, Fokko <fokko@driesprong.frl
>>> >
>>> wrote:
>>>
>>> > Looks great Zhou,
>>> >
>>> > I have one thing that pops in my mind while reading the AIP; should
>>> keep
>>> > the caching on the webserver level. As the famous quote goes: *"There
>>> are
>>> > only two hard things in Computer Science: cache invalidation and naming
>>> > things." -- Phil Karlton*
>>> >
>>> > Right now, the fundamental change that is being proposed in the AIP is
>>> > fetching the DAGs from the database in a serialized format, and not
>>> parsing
>>> > the Python files all the time. This will give already a great
>>> performance
>>> > improvement on the webserver side because it removes a lot of the
>>> > processing. However, since we're still fetching the DAGs from the
>>> database
>>> > in a regular interval, cache it in the local process, so we still have
>>> the
>>> > two issues that Airflow is suffering from right now:
>>> >
>>> >    1. No snappy UI because it is still polling the database in a
>>> regular
>>> >    interval.
>>> >    2. Inconsistency between webservers because they might poll in a
>>> >    different interval, I think we've all seen this:
>>> >    https://www.youtube.com/watch?v=sNrBruPS3r4
>>> >
>>> > As I also mentioned in the Slack channel, I strongly feel that we
>>> should be
>>> > able to render most views from the tables in the database, so without
>>> > touching the blob. For specific views, we could just pull the blob
>>> from the
>>> > database. In this case we always have the latest version, and we
>>> tackle the
>>> > second point above.
>>> >
>>> > To tackle the first one, I also have an idea. We should change the DAG
>>> > parser from a loop to something that uses inotify
>>> > https://pypi.org/project/inotify_simple/. This will change it from
>>> polling
>>> > to an event-driven design, which is much more performant and less
>>> resource
>>> > hungry. But this would be an AIP on its own.
>>> >
>>> > Again, great design and a comprehensive AIP, but I would include the
>>> > caching on the webserver to greatly improve the user experience in the
>>> UI.
>>> > Looking forward to the opinion of others on this.
>>> >
>>> > Cheers, Fokko
>>> >
>>> >
>>> >
>>> >
>>> >
>>> >
>>> >
>>> >
>>> > Op za 27 jul. 2019 om 01:44 schreef Zhou Fang
>>> <zhoufang@google.com.invalid
>>> > >:
>>> >
>>> > > Hi Kaxi,
>>> > >
>>> > > Just sent out the AIP:
>>> > >
>>> > >
>>> >
>>> https://cwiki.apache.org/confluence/display/AIRFLOW/AIP-24+DAG+Persistence+in+DB+using+JSON+for+Airflow+Webserver+and+%28optional%29+Scheduler
>>> > >
>>> > > Thanks!
>>> > > Zhou
>>> > >
>>> > >
>>> > > On Fri, Jul 26, 2019 at 1:33 PM Zhou Fang <zhoufang@google.com>
>>> wrote:
>>> > >
>>> > > > Hi Kaxil,
>>> > > >
>>> > > > We are also working on persisting DAGs into DB using JSON for
>>> Airflow
>>> > > > webserver in Google Composer. We target at minimizing the change
>>> to the
>>> > > > current Airflow code. Happy to get synced on this!
>>> > > >
>>> > > > Here is our progress:
>>> > > > (1) Serializing DAGs using Pickle to be used in webserver
>>> > > > It has been launched in Composer. I am working on the PR to
>>> upstream
>>> > it:
>>> > > > https://github.com/apache/airflow/pull/5594
>>> > > > Currently it does not support non-Airflow operators and we are
>>> working
>>> > on
>>> > > > a fix.
>>> > > >
>>> > > > (2) Caching Pickled DAGs in DB to be used by webserver
>>> > > > We have a proof-of-concept implementation, working on an AIP now.
>>> > > >
>>> > > > (3) Using JSON instead of Pickle in (1) and (2)
>>> > > > Decided to use JSON because Pickle is not secure and human
>>> readable.
>>> > The
>>> > > > serialization approach is very similar to (1).
>>> > > >
>>> > > > I will update the RP (https://github.com/apache/airflow/pull/5594)
>>> to
>>> > > > replace Pickle by JSON, and send our design of (2) as an AIP next
>>> week.
>>> > > > Glad to check together whether our implementation makes sense
and
>>> do
>>> > > > improvements on that.
>>> > > >
>>> > > > Thanks!
>>> > > > Zhou
>>> > > >
>>> > > >
>>> > > > On Fri, Jul 26, 2019 at 7:37 AM Kaxil Naik <kaxilnaik@gmail.com>
>>> > wrote:
>>> > > >
>>> > > >> Hi all,
>>> > > >>
>>> > > >> We, at Astronomer, are going to spend time working on DAG
>>> > Serialisation.
>>> > > >> There are 2 AIPs that are somewhat related to what we plan
to
>>> work on:
>>> > > >>
>>> > > >>    - AIP-18 Persist all information from DAG file in DB
>>> > > >>    <
>>> > > >>
>>> > >
>>> >
>>> https://cwiki.apache.org/confluence/display/AIRFLOW/AIP-18+Persist+all+information+from+DAG+file+in+DB
>>> > > >> >
>>> > > >>    - AIP-19 Making the webserver stateless
>>> > > >>    <
>>> > > >>
>>> > >
>>> >
>>> https://cwiki.apache.org/confluence/display/AIRFLOW/AIP-19+Making+the+webserver+stateless
>>> > > >> >
>>> > > >>
>>> > > >> We plan to use JSON as the Serialisation format and store
it as a
>>> blob
>>> > > in
>>> > > >> metadata DB.
>>> > > >>
>>> > > >> *Goals:*
>>> > > >>
>>> > > >>    - Make Webserver Stateless
>>> > > >>    - Use the same version of the DAG across Webserver &
Scheduler
>>> > > >>    - Keep backward compatibility and have a flag (globally
& at
>>> DAG
>>> > > level)
>>> > > >>    to turn this feature on/off
>>> > > >>    - Enable DAG Versioning (extended Goal)
>>> > > >>
>>> > > >>
>>> > > >> We will be preparing a proposal (AIP) after some research
and some
>>> > > initial
>>> > > >> work and open it for the suggestions of the community.
>>> > > >>
>>> > > >> We already had some good brain-storming sessions with Twitter
>>> folks
>>> > > (DanD
>>> > > >> &
>>> > > >> Sumit), folks from GoDataDriven (Fokko & Bas) & Alex
(from Uber)
>>> which
>>> > > >> will
>>> > > >> be a good starting point for us.
>>> > > >>
>>> > > >> If anyone in the community is interested in it or has some
>>> experience
>>> > > >> about
>>> > > >> the same and want to collaborate please let me know and join
>>> > > >> #dag-serialisation channel on Airflow Slack.
>>> > > >>
>>> > > >> Regards,
>>> > > >> Kaxil
>>> > > >>
>>> > > >
>>> > >
>>> >
>>>
>>

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