I'm doing a lot of work lately with data that requires a large degree
of transactional consistency. One pattern I've found that makes some
of the pain of HRD eventuality go away is to add an extra entity that
uses your query field as a natural key. This really requires global
transactions to work (as announced, it's in trusted testing, wheee!)
but here's an example:
Say you associate a facebook id with an account. In M/S, you'd
probably have something like this:
class User {
@Id Long id;
long fbId;
...
}
...and then when a request arrives with a facebook id, you would query
for the user record. No user record? Create one. With eventual
consistency, this creates a larger window (with M/S it was small)
where you can get duplicate Users for the same fbId.
The solution to transactional integrity and strong consistency is to
add a FbId entity:
class FbId {
@Id String fbId;
long userId;
}
I've now got several of these mapping entities in place now. Using
global transactions to create the FbId and the User at the same time,
it seems to solve consistency issues entirely. I don't know how it
will perform yet under load, but obviously there's not heavy
contention in this situation so I would be surprised if the 2pc hurt
much.
I'm starting to notice several of these FbId-type mapping objects
showing up in my code as a way to force queries (for unique items)
into strong consistency. I'm guessing you could do this for
multi-item queries using a list property instead:
Instead of query(Thing.class).filter("color", someColor), you could
instead keep updating an entity like this:
class ColorThings {
@Id String color;
List<Key<Thing>> things;
}
...which feels upside-down but really has a lot of advantages. If you
put ColorThings in memcache, it's like a query cache which actually
updates properly.
Is anyone else noticing their code being pushed into this pattern by the HRD?
Jeff
On Tue, Sep 20, 2011 at 10:10 AM, Ikai Lan (Google) <ika...@google.com> wrote:
> Well, indexes are just Bigtable rows, so replication lag does apply to them
> as well.
> --
> Ikai Lan
> Developer Programs Engineer, Google App Engine
> plus.ikailan.com | twitter.com/ikai
>
>
> On Tue, Sep 20, 2011 at 7:42 AM, Mike Wesner <mbwes...@gmail.com> wrote:
>>
>> And then I went and used the word replication... i meant index lag.
>>
>> On Sep 20, 9:40 am, Mike Wesner <mbwes...@gmail.com> wrote:
>> > I don't think Ikai read your post...
>> >
>> > Robert and I wanted to write a little HRD status site to track this
>> > and get real data, but we haven't done so yet. I have never seen the
>> > replication take more than about 1s. I think 1s will cover about four
>> > 9's, but that is just an educated guess. Until we (the users)
>> > actually measure this over time I don't think we can know for sure.
>> >
>> > -Mike
>> >
>> > On Sep 19, 7:16 pm, Jeff Schnitzer <j...@infohazard.org> wrote:
>> >
>> >
>> >
>> >
>> >
>> >
>> >
>> > > I know that an index update in the HRD will typically be visible
>> > > within a couple seconds. That's the average case. What is the
>> > > worst-case?
>> >
>> > > Assuming something in the datacenter goes wacky, how long might it
>> > > take for an index to update? Tens of seconds, minutes, hours, days?
>> >
>> > > Thanks,
>> > > Jeff
>>
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