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From Arvid Heise <arvid.he...@gmail.com>
Subject Re: Managed State Custom Serializer with Avro
Date Tue, 20 Feb 2018 12:04:44 GMT
Hi Aljoscha, hi Till,

@Aljoscha, the new AvroSerializer is almost what I wanted except that
it does not use the schema of the snapshot while reading. In fact,
this version will fail with the same error as before when a field is
added or removed.
https://github.com/apache/flink/blob/f3a2197a23524048200ae2b4712d6ed833208124/flink-formats/flink-avro/src/main/java/org/apache/flink/formats/avro/typeutils/AvroSerializer.java#L265
needs to use the schema from
https://github.com/apache/flink/blob/f3a2197a23524048200ae2b4712d6ed833208124/flink-formats/flink-avro/src/main/java/org/apache/flink/formats/avro/typeutils/AvroSerializer.java#L188
as the first parameter. Accordingly, a readSchema field need to be set
in #ensureCompatibility and relayed in #duplicate.
Should I add a ticket for that as well?

@Till concerning the poor man's migration. The doc of
#ensureCompatibility in 1.3.2 states:

<li>{@link CompatibilityResult#compatible()}: this signals Flink that
this serializer is compatible, or
*     has been reconfigured to be compatible, to continue reading
previous data, and that the
*     serialization schema remains the same. No migration needs to be
performed.</li>

The important part is the reconfiguration, which is also mentioned on
the big documentation. The default avro and kryo serializers actually
try to reconfigure themselves.

@Aljoscha, I will open a ticket for the RocksDB thingy. I pinned the
problem down and will try to come up with an easy solution. It's a tad
hard to compare the different versions (since I'm deep into the
debugger), so I just might write a 1.3.2 ticket.

@Till, thanks for reminding me that we are not talking about
incremental checkpoints ;) That makes it indeed much easier to
understand the whole state recovery with evolution.

Best,

Arvid

On Tue, Feb 20, 2018 at 12:27 PM, Aljoscha Krettek <aljoscha@apache.org> wrote:
> Hi Arvid,
>
> Did you check out the most recent AvroSerializer code?
> https://github.com/apache/flink/blob/f3a2197a23524048200ae2b4712d6ed833208124/flink-formats/flink-avro/src/main/java/org/apache/flink/formats/avro/typeutils/AvroSerializer.java#L185
> I think this does what you're suggesting.
>
> Regarding the integration tests, if this is in fact the case it is not good
> and I would be very happy about a Jira Issue/PR there.
>
> Regarding your last point, I think that the RockDB backend stores the
> metadata, which includes the type serialiser snapshot once, and not for all
> keys or key groups.
>
> Best,
> Aljoscha
>
>
> On 20. Feb 2018, at 11:40, Arvid Heise <arvid.heise@gmail.com> wrote:
>
> Hi guys,
>
> just wanted to write about that topic on my own.
>
> The FF talk of Tzu-Li gave me also the impression that by just using
> AvroSerializer, we get some kind of state evolution for free.
> https://www.slideshare.net/FlinkForward/flink-forward-berlin-2017-tzuli-gordon-tai-managing-state-in-apache-flink
>
> However, I discovered two issues on 1.3.2:
>
> 1. The AvroSerializer does not use read/write schema. The snapshot
> stores type information instead of the more plausible schema
> information.
> However, the actual type should not matter as long as a compatible
> type is used for state restoration.
> I have rewritten the AvroSerializer to store the schema in the
> snapshot config and actually uses it as a read schema during the
> initialization of the DatumReader.
>
> 2. During integration tests, it turns out that the current
> implementation of the StateDescriptor always returns copies of the
> serializer through #getSerializer. So #ensureCompatibility is invoked
> on a different serializer than the actual #deserialize method. So
> although my AvroSerializer sets the correct read schema, it is not
> used, since it is set on the wrong instance.
> I propose to make sure that #ensureCompatibility is invoked on the
> original serializer in the state descriptor. Otherwise all adjustments
> to the serializer are lost.
>
> I can provide tests and patches if needed.
>
> One related question:
>
> If I do an incremental snapshot with RocksDB backend and keyed state
> backend, is the snapshot config attached to all keys? So would the
> following work:
> * Write (key1, value1) and (key2, value2) with schema1. Do cancel with
> snapshot.
> * Read (key1, value1) with schema1->schema2 and write with (key1,
> value1). Do cancel with snapshot.
> <Now we have two different schemas in the snapshots>
> * Read (key1, value1) with schema2 and read with (key2, value2) with
> schema1->schema2.
>
> Thanks for any feedback
>
> Arvid
>
> On Mon, Feb 19, 2018 at 7:17 PM, Niels Denissen <nielsdenissen@gmail.com>
> wrote:
>
> Hi Till,
>
> Thanks for the quick reply, I'm using 1.3.2 atm.
>
> Cheers,
> Niels
>
> On Feb 19, 2018 19:10, "Till Rohrmann" <trohrmann@apache.org> wrote:
>
>
> Hi Niels,
>
> which version of Flink are you using? Currently, Flink does not support to
> upgrade the TypeSerializer itself, if I'm not mistaken. As you've described,
> it will try to use the old serializer stored in the checkpoint stream to
> restore state.
>
> I've pulled Gordon into the conversation who can tell you a little bit
> more about the current capability and limitations of state evolution.
>
> Cheers,
> Till
>
> On Mon, Feb 19, 2018 at 4:14 PM, Niels <[hidden email]> wrote:
>
>
> Hi all,
>
> I'm currently trying to use Avro in order to evolve our data present in
> Flink's Managed State. I've extended the TypeSerializer class
> successfully
> for this purpose, but still have issues using Schema Evolution.
>
> *The problem:*
> When we try to read data (deserialize from savepoint) with a new
> serialiser
> and a new schema, Flink seems to use the old schema of the old serializer
> (written to the savepoint). This results in an old GenericRecord that
> doesn't adhere to the new Avro schema.
>
> *What seems to happen to me is the following* (Say we evolve from dataV1
> to
> dataV2):
> - State containing dataV1 is serialized with avro schema V1 to a
> check/savepoint. Along with the data, the serializer itself is written.
> - Upon restore, the old serializer is retrieved from the data (therefore
> needs to be on the classpath). Data is restored using this old
> serializer.
> The new serializer provided is only used for writes.
>
> If this is indeed the case it explains our aforementioned problem. If you
> have any pointers as to whether this is true and what a possible solution
> would be that would be very much appreciated!
>
> Thanks!
> Niels
>
>
>
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