Hey AJ,
Depending on your control messages and what you’re trying to accomplish you can
simplify the application even further by stripping out the second broadcast and
letting operator chaining guarantee that control messages flow appropriately.
This results in
_______________ ____________
| Schema Source | | Event Source |
----------------------- -------------------
| |
Broadcast |
| __________ |
----- | Processor | -----------
| |
---------------
|
_______
| Sink |
-----------
Whatever your final topology ends up being, your sink needs to know about the
updated schema. If you aren’t using a schema registry, this means you need some
way to distinguish between events and control messages in the invoke method of
your sink, for example:
@Override
public void invoke(ValueType value, Context ctx) throws Exception {
if (value.isControlMessage()) {
updateSchema(value);
return;
}
writeValue(value)
}
I don’t know what types you’re using etc. and how you’re storing output, so I
can’t be much more concrete than this, but if it helps, here’s a simplified
version of what I’m doing (I’m reading in avro events and writing them out in
bucketed files):
@Override
public void invoke(GenericRecord value, Context ctx) throws Exception {
if (value != null && value.getSchema().equals(CONTROL_SCHEMA)) {
/*
* My schema update control messages don’t actually contain a new
schema. This function
* fetches the schema from a remote location, performs various checks
and updates various
* systems, and then sets the working schema to the new schema.
*/
updateSchema(value);
return;
}
// … determining the correct bucket for the provided value and initializing
the bucketer if necessary …
if (!bucketState.schema.equals(this.schema)) {
bucketState.refreshSchema(this.schema, this.schemaTimestamp);
openNewPartFile(bucketPath, bucketState);
} else if (shouldRoll(bucketState, currentProcessingTime)) {
openNewPartFile(bucketPath, bucketState);
}
bucketState.fileWriter.append(value);
}
I’ve tried to use the method names that match the BucketingSink
interface<https://github.com/apache/flink/blob/release-1.11/flink-connectors/flink-connector-filesystem/src/main/java/org/apache/flink/streaming/connectors/fs/bucketing/BucketingSink.java>
as much as possible to aid comprehension. I’ve also stripped out a lot of
internal logic, instrumentation, sanity checks, etc. The main idea is that
1. Your sink needs to keep track of your schema and update it in response to
control messages (in the simplest case, the control messages can just contain
the new schema)
2. You need some way to refresh the schema used by your actual writers,
which may involve closing current outputs and creating new ones
3. Your schema needs to be serializable so that it can be snapshotted in
checkpoints and restored, or you need some way to determine the correct schema
in the open() or initializeState() methods
I hope this helps!
For anyone reading this who might be using Avro GenericRecords, note that Flink
doesn’t serialize GenericRecords efficiently. You’ll want to write a custom
serializer on the pattern of
http://apache-flink-user-mailing-list-archive.2336050.n4.nabble.com/Serialization-performance-td12019.html,
the eBay GenericAvroSerializer for Spark, or the many other GenericRecord
serializers out there that replace the full schema with the schema’s
fingerprint. If your schema is very large, you’ll want to go further and work
on schema hashcodes instead of fingerprints (with appropriate safeguards!)
because fingerprinting and checking RecordSchemas for equality quickly becomes
expensive. In the sample code above, I include the check
`(!bucketState.schema.equals(this.schema))`. Make sure that you’re actually
comparing schema fingerprints or the like instead of directly calling
schema.equals(otherSchema).
Julian
From: aj <ajainje...@gmail.com>
Date: Friday, December 4, 2020 at 7:20 AM
To: Piotr Nowojski <pnowoj...@apache.org>
Cc: "Jaffe, Julian" <julianja...@activision.com>, "user@flink.apache.org"
<user@flink.apache.org>
Subject: Re: Broadcasting control messages to a sink
Hi Jafee,
Can u please help me out with the sample code how you have written the custom
sink and how you using this broadcast pattern to update schema at run time. It
will help me.
On Sat, Oct 17, 2020 at 1:55 PM Piotr Nowojski
<pnowoj...@apache.org<mailto:pnowoj...@apache.org>> wrote:
Hi Julian,
Glad to hear it worked! And thanks for coming back to us :)
Best,
Piotrek
sob., 17 paź 2020 o 04:22 Jaffe, Julian
<julianja...@activision.com<mailto:julianja...@activision.com>> napisał(a):
Hey Piotr,
Thanks for your help! The main thing I was missing was the .broadcast partition
operation on a stream (searching for “broadcasting” obviously brought up the
broadcast state pattern). This coupled with my misunderstanding of an error in
my code as being an error in Flink code resulted in me making this a much
harder problem than it needed to be.
For anyone who may find this in the future, Piotr’s suggestion is pretty
spot-on. I wound up broadcasting (as in the partitioning strategy) my schema
stream and connecting it to my event stream. I then processed those using a
CoProcessFunction, using the schema messages to update the parsing for the
events. I also emitted a side output message when I processed a new schema,
using the same type as my main output messages. I once again
broadcast-as-in-partitioning the side output stream, unioned it with my
processed output from the CoProcessFunction and passed it to my sink, making
sure to handle control messages before attempting to do any bucketing.
In poor ASCII art, it looks something like the below:
_______________ ____________
| Schema Source | | Event Source |
----------------------- -------------------
| |
Broadcast |
| __________ |
----- | Processor | -----------
| | ----------- • Control message
side output
--------------- |
| |
| Broadcast
| |
Union --------------
|
_______
| Sink |
-----------
I hope this is helpful to someone.
Julian
From: Piotr Nowojski <pnowoj...@apache.org<mailto:pnowoj...@apache.org>>
Date: Wednesday, October 14, 2020 at 11:22 PM
To: "Jaffe, Julian"
<julianja...@activision.com<mailto:julianja...@activision.com>>
Cc: "user@flink.apache.org<mailto:user@flink.apache.org>"
<user@flink.apache.org<mailto:user@flink.apache.org>>
Subject: Re: Broadcasting control messages to a sink
Hi Julian,
I think the problem is that BroadcastProcessFunction and SinkFunction will be
executed by separate operators, so they won't be able to share state. If you
can not split your logic into two, I think you will have to workaround this
problem differently.
1. Relay on operator chaining and wire both of them together.
If you set up your BroadcastProcessFunction and SinkFunction one after another,
with the same parallelism, with the default chaining, without any
rebalance/keyBy in between, you can be sure they will be chained together. So
the output type of your record between BroadcastProcessFunction and
SinkFunction, can be a Union type, of a) your actual payload, b) broadcasted
message. Upon initialization/before processing first record, if you have any
broadcast state, you would need to forward it's content to the downstream
SinkFunction as well.
2. Another solution is that maybe you can try to embed SinkFunction inside the
BroadcastProcessFunction? This will require some careful proxying and wrapping
calls.
3. As always, you can also write a custom operator that will be doing the same
thing.
For the 2. and 3. I'm not entirely sure if there are some gotchas that I
haven't thought through (state handling?), so if you can make 1. work for you,
it will probably be a safer route.
Best,
Piotrek
śr., 14 paź 2020 o 19:42 Jaffe, Julian
<julianja...@activision.com<mailto:julianja...@activision.com>> napisał(a):
Thanks for the suggestion Piotr!
The problem is that the sink needs to have access to the schema (so that it can
write the schema only once per file instead of record) and thus needs to know
when the schema has been updated. In this proposed architecture, I think the
sink would still need to check each record to see if the current schema matches
the new record or not? The main problem I encountered when playing around with
broadcast state was that I couldn’t figure out how to access the broadcast
state within the sink, but perhaps I just haven’t thought about it the right
way. I’ll meditate on the docs further 🙂
Julian
From: Piotr Nowojski <pnowoj...@apache.org<mailto:pnowoj...@apache.org>>
Date: Wednesday, October 14, 2020 at 6:35 AM
To: "Jaffe, Julian"
<julianja...@activision.com<mailto:julianja...@activision.com>>
Cc: "user@flink.apache.org<mailto:user@flink.apache.org>"
<user@flink.apache.org<mailto:user@flink.apache.org>>
Subject: Re: Broadcasting control messages to a sink
Hi Julian,
Have you seen Broadcast State [1]? I have never used it personally, but it
sounds like something you want. Maybe your job should look like:
1. read raw messages from Kafka, without using the schema
2. read schema changes and broadcast them to 3. and 5.
3. deserialize kafka records in BroadcastProcessFunction by using combined 1.
and 2.
4. do your logic o
5. serialize records using schema in another BroadcastProcessFunction by using
combined 4. and 2.
6. write raw records using BucketingSink
?
Best,
Piotrek
[1]
https://ci.apache.org/projects/flink/flink-docs-stable/dev/stream/state/broadcast_state.html<https://urldefense.proofpoint.com/v2/url?u=https-3A__ci.apache.org_projects_flink_flink-2Ddocs-2Dstable_dev_stream_state_broadcast-5Fstate.html&d=DwMFaQ&c=qE8EibqjfXM-zBfebVhd4gtjNZbrDcrKYXvb1gt38s4&r=zKznthi6OTKpoJID9dIcyiJ28NX59JIQ2bD246nnMac&m=0fL33mv_n-SUiL8AARIrGXmY1d8pdhu4ivDeRjg5f84&s=RjsXnxEVCBz2BGLxe89FU_SpbtfTlRkjsT5J-gbvqFI&e=>
śr., 14 paź 2020 o 11:01 Jaffe, Julian
<julianja...@activision.com<mailto:julianja...@activision.com>> napisał(a):
Hey all,
I’m building a Flink app that pulls in messages from a Kafka topic and writes
them out to disk using a custom bucketed sink. Each message needs to be parsed
using a schema that is also needed when writing in the sink. This schema is
read from a remote file on a distributed file system (it could also be fetched
from a service). The schema will be updated very infrequently.
In order to support schema evolution, I have created a custom source that
occasionally polls for updates and if it finds one parses the new schema and
sends a message containing the serialized schema. I’ve connected these two
streams and then use a RichCoFlatMapFunction to flatten them back into a single
output stream (schema events get used to update the parser, messages get parsed
using the parser and emitted).
However, I need some way to communicate the updated schema to every task of the
sink. Simply emitting a control message that is ignored when writing to disk
means that only one sink partition will receive the message and thus update the
schema. I thought about sending the control message as side output and then
broadcasting the resulting stream to the sink alongside the processed event
input but I couldn’t figure out a way to do so. For now, I’m bundling the
schema used to parse each event with the event, storing the schema in the sink,
and then checking every event’s schema against the stored schema but this is
fairly inefficient. Also, I’d like to eventually increase the types of control
messages I can send to the sink, some of which may not be idempotent. Is there
a better way to handle this pattern?
(Bonus question: ideally, I’d like to be able to perform an action when all
sink partitions have picked up the new schema. I’m not aware of any way to emit
metadata of this sort from Flink tasks beyond abusing the metrics system. This
approach still leaves open the possibility of tasks picking up the new schema
and then crashing for unrelated reasons thus inflating the count of tasks using
a specific schema and moreover requires tracking at least the current level of
parallelism and probably also Flink task state outside of Flink. Are there any
patterns for reporting metadata like this to the job manager?)
I’m using Flink 1.8.
--
Thanks & Regards,
Anuj Jain
Mob. : +91- 8588817877
Skype : anuj.jain07
