Hi Anuj,
I think that there may be a fundamental misunderstanding about the role of
a schema registry in Kafka. So let me first clarify that.
In each Avro/Parquet file, all records have the same schema. The schema is
stored within the file, such that we can always retrieve the writer schema
for the records.
When Avro was first applied to Kafka, there was the basic question on how
the writer schema for any record is known to the consumer. Storing the
complete schema on each record would mean that each record would be much
larger than needed. Hence, they added the schema registry that assigns a
unique id to schema, which is then embedded into the records.
Now, whenever I update a schema in my producer, I would have old records
with the old schema id and new records with the new schema id.
In my consumer, I'd use a fixed reader schema, such that my application
would not need to worry if the record is written with old or new schema; my
consumer would only see records with the reader schema.
Given that background information, you see that in general, it's impossible
with a generic approach to write the parquet with the same schema as it has
been written in Kafka: the parquet schema needs to be supplied statically
during query compilation while the actual used Avro schema in Kafka is only
known when actually consuming data.
But looking further down the road:
* since you need one schema to write the parquet files, you'd need to
decide: do you want to write with the new or the old schema in case of a
schema update? That should also be the reader schema of your application
for a given event type.
* this decision has further implications: your application need to extract
exactly one specific version of the schema from the schema registry at
query compilation. That could be either a specific schema id or the latest
schema for the event type.
* that means that the output schema is locked until you restart your
application and fetch a new latest schema in case of an update.
* at that point, it might just be easier to use the approach that I
outlined previously by bundling a specific schema with your application.
If you want to extract the latest schema for a subject:
var registryClient = new CachedSchemaRegistryClient(<schemaRepoUrls>, 1000);
var versions = registryClient.getAllVersions(<subject>);
var schema = registryClient.getSchemaById(versions.get(versions.size() - 1));
On Sat, Jan 18, 2020 at 5:22 PM aj <ajainje...@gmail.com> wrote:
> Thanks, Arvid.
>
> I do not fully understand the above approach,
> so currently, I am thinking to go with the envelope approach that you
> suggested.
>
> One more question I have if I do not want to keep schema in my consumer
> project even its a single envelope schema. I want it to be fetched from the
> schema registry and pass to my parquet-sink so that I always use the same
> schema that is used by the producer. Can you provide a sample code how can
> i infer the schema from the generic record or get it from schema registry?
>
>
> Regards,
> Anuj
>
>
>
> On Sat, Jan 18, 2020 at 6:55 PM Arvid Heise <ar...@ververica.com> wrote:
>
>> (Readded user mailing list)
>>
>> Hi Anuj,
>>
>> since I'd still recommend going with distinct sources/sinks, let me try
>> to solve your issues in this mail. If that doesn't work out, I'd address
>> your concerns about the envelope format later.
>>
>> In Flink, you can have several subtopologies in the same application.
>>
>> Thus, for each event type, you can add
>> AvroSource(eventType) -> generic transformation/validation ->
>> AvroSink(eventType)
>> for each event.
>>
>> I'd put all avro schema in one project and use an avro plugin to generate
>> the respective Java Classes. Then I'd simply create a map of Avro Schema
>> (GeneratedClassA::SCHEMA, GeneratedClassB::SCHEMA...) to topic name
>> (event-a, event-b, ...).
>> Next, I'd iterate over the list to add the respective subtopologies to
>> env.
>> Finally, execute everything.
>>
>> You have one project where all validations reside. But you'd have almost
>> no overhead to process a given source of eventType. The downside of that
>> approach is of course, that each new event type would require a
>> redeployment, but that seems like what you'd want to do anyhow.
>>
>> Best,
>>
>> Arvid
>>
>> On Sat, Jan 18, 2020 at 2:08 PM aj <ajainje...@gmail.com> wrote:
>>
>>> Thanks, Arvid.
>>>
>>> 1. I like your approach as I can write a single consumer and put the
>>> data in S3 in parquet format. The only challenge is there are extra columns
>>> that always going to be null as at a time I will get one type of event.
>>>
>>> 2. if I go with a separate schema I am not sure how I can solve it using
>>> a single generalize consumer. Till now what my understanding is I have to
>>> write a consumer for each type of event. Each consumer will read the whole
>>> data then filter the respective events from this and then I can pass this
>>> stream to sink. But this does not look scalable solution as the new events
>>> keep growing then I have to write a consumer for each new type.
>>>
>>>
>>> DataStreamSource<GenericRecord> input = env
>>> .addSource(
>>> new FlinkKafkaConsumer010<GenericRecord>(topics,
>>> new
>>> KafkaGenericAvroDeserializationSchema(schemaRegistryUrl),
>>> config).setStartFromEarliest());
>>>
>>> Example :
>>>
>>> * 1st Consumer:*
>>> DataStreamSource<GenericRecord> input = env.addSource(
>>> new FlinkKafkaConsumer010<GenericRecord>(topics,
>>> new
>>> KafkaGenericAvroDeserializationSchema(schemaRegistryUrl),
>>> config).setStartFromEarliest());
>>> * DataStream<GenericRecord> aInput =
>>> input.filter("event_name"= "a")*
>>>
>>> * 2nd Consumer:*
>>> DataStreamSource<GenericRecord> input = env.addSource(
>>> new FlinkKafkaConsumer010<GenericRecord>(topics,
>>> new
>>> KafkaGenericAvroDeserializationSchema(schemaRegistryUrl),
>>> config).setStartFromEarliest());
>>> * DataStream<GenericRecord> bInput =
>>> input.filter("event_name"= "b")*
>>>
>>>
>>> Can you help me How I solve this using a single consumer as I do not
>>> want to write a separate consumer for each type of schema?
>>>
>>> For example, this is my consumer that contains different types of
>>> records.
>>>
>>> DataStreamSource<GenericRecord> input = env
>>> .addSource(
>>> new FlinkKafkaConsumer010<GenericRecord>(topics,
>>> new
>>> KafkaGenericAvroDeserializationSchema(schemaRegistryUrl),
>>> config).setStartFromEarliest());
>>>
>>> Now I can not write this stream directly as there is no common schema of
>>> records in this stream. So possible way I am thinking is
>>>
>>> 1. Can I create multiple streams from this stream using the key by on
>>> *"event_name"
>>> *and then write each stream separately.
>>>
>>> Just wanna know is this possible ??
>>>
>>>
>>> Thanks,
>>> Anuj
>>>
>>> On Fri, Jan 17, 2020 at 3:20 PM Arvid Heise <ar...@ververica.com> wrote:
>>>
>>>> Hi Anuj,
>>>>
>>>> I originally understood that you would like to store data in the same
>>>> Kafka topic and also want to store it in the same parquet file. In the
>>>> past, I mostly used schema registry with Kafka Streams, where you could
>>>> only store a schema for a key and value respectively. To use different
>>>> record types in the same kafka topic, you had to disable schema
>>>> compatibility checks and just stored the schemas as different versions
>>>> under the same subject.
>>>>
>>>> Your approach is much better. You can ensure full schema compatibility.
>>>> Nevertheless, it still shares the same drawback that consumption is much
>>>> harder (using GenericRecord is proof of that) if you want to read/write
>>>> everything into the same place. Also you will never be able to write one
>>>> consistent file, as they can only have one schema (both on Avro and
>>>> Parquet).
>>>> So you only have two options:
>>>> * keep schemas separated, but then you also need to write separate
>>>> files per record type.
>>>> * have a common schema (either my outlined approach or any other
>>>> wrapper schema).
>>>> The approach with a common schema makes only sense if you want to write
>>>> it into one table/kafka topic.
>>>>
>>>> However, in the last mail you pointed out that you actually want to
>>>> store the record types separately. Then, you should keep everything
>>>> separated. Then you should have a sink for each type each getting the
>>>> respective schema. Note that you'd need to fetch the schema manually from
>>>> the schema registry when creating the query as you would need to pass it to
>>>> the sink.
>>>>
>>>> Btw, do you actually have a need to write all events into one Kafka
>>>> topic? The only real use case is to preserve the time order per key.
>>>> Everything else is much more complicated then storing events individually.
>>>>
>>>> Best,
>>>>
>>>> Arvid
>>>>
>>>>
>>>> On Thu, Jan 16, 2020 at 3:39 PM aj <ajainje...@gmail.com> wrote:
>>>>
>>>>> Hi Arvid,
>>>>> Thanks for the quick response. I am new to this Avro design so can you
>>>>> please help me understand and design for my use case.
>>>>>
>>>>> I have use case like this :
>>>>>
>>>>> 1. we have an app where a lot of action happened from the user side.
>>>>> 2. for each action we collect some set of information that
>>>>> defined using some key-value pairs. This information we want to define as
>>>>> proper schemas so that we maintain the proper format and not push random
>>>>> data.
>>>>> 3. So we are defining for each action a schema and register in the
>>>>> schema registry using topic+record.name as the subject .
>>>>> 4. So I do not think the producer side has any issue as whenever we
>>>>> push the event to Kafka we register a new schema with the above subject.
>>>>>
>>>>> Example :
>>>>>
>>>>> {
>>>>> event_name : "a"
>>>>> "timestamp":
>>>>> "properties" :[
>>>>> "key-1 : "val-1"
>>>>> "key-2 : "val-2"
>>>>> ]
>>>>> }
>>>>>
>>>>> {
>>>>> event_name : "b"
>>>>> "timestamp":
>>>>> "properties" :[
>>>>> "key-3 : "val-3"
>>>>> "key-4 : "val-4"
>>>>> ]
>>>>> }
>>>>>
>>>>> Now I have a consumer that will parse the data by fetching the schema
>>>>> from schema registry and deserialize in the generic record streams.
>>>>>
>>>>> Why you think it will break as I am always deserializing with writer
>>>>> schema only.
>>>>>
>>>>> As you suggested to keep an envelope Avro schema and not separate
>>>>> schema for each type of event that I am generating. I have some doubts
>>>>> about that:
>>>>>
>>>>> 1. How I enforce a schema on each event as it subtypes in the main
>>>>> schema. so when I am getting a JSON event of type "a" how I enforce and
>>>>> convert it to subschema type of "a" and push to Kafka.
>>>>> 2. I want to create a separate hive table for each of the events so
>>>>> when I write this data and lets says I have 20 events than for 19 columns
>>>>> I
>>>>> am getting null values always in data.
>>>>>
>>>>> Please help me in doing this right way. It will be a great help and
>>>>> learning for me.
>>>>>
>>>>> Thanks,
>>>>> Anuj
>>>>>
>>>>>
>>>>>
>>>>>
>>>>>
>>>>>
>>>>>
>>>>> On Thu, Jan 16, 2020 at 7:30 PM Arvid Heise <ar...@ververica.com>
>>>>> wrote:
>>>>>
>>>>>> Hi Anuj,
>>>>>>
>>>>>> you should always avoid having records with different schemas in the
>>>>>> same topic/dataset. You will break the compatibility features of the
>>>>>> schema
>>>>>> registry and your consumer/producer code is always hard to maintain.
>>>>>>
>>>>>> A common and scalable way to avoid it is to use some kind of envelope
>>>>>> format.
>>>>>>
>>>>>> {
>>>>>> "namespace": "example",
>>>>>> "name": "Envelope",
>>>>>> "type": "record",
>>>>>> "fields": [
>>>>>> {
>>>>>> "name": "type1",
>>>>>> "type": ["null", {
>>>>>> "type": "record",
>>>>>> "fields": [ ... ]
>>>>>> }],
>>>>>> "default": null
>>>>>> },
>>>>>> {
>>>>>> "name": "type2",
>>>>>> "type": ["null", {
>>>>>> "type": "record",
>>>>>> "fields": [ ... ]
>>>>>> }],
>>>>>> "default": null
>>>>>> }
>>>>>> ]
>>>>>> }
>>>>>>
>>>>>> This envelope is evolvable (arbitrary addition/removal of wrapped
>>>>>> types, which by themselves can be evolved), and adds only a little
>>>>>> overhead
>>>>>> (1 byte per subtype). The downside is that you cannot enforce that
>>>>>> exactly
>>>>>> one of the subtypes is set.
>>>>>>
>>>>>> This schema is fully compatible with the schema registry, so no need
>>>>>> to parse anything manually.
>>>>>>
>>>>>> This schema can easily be used with Parquet. If you can't change the
>>>>>> input format anymore, you can at least use that approach on your output.
>>>>>>
>>>>>> Best,
>>>>>>
>>>>>> Arvid
>>>>>>
>>>>>> On Thu, Jan 16, 2020 at 2:53 PM aj <ajainje...@gmail.com> wrote:
>>>>>>
>>>>>>> Hi All,
>>>>>>>
>>>>>>> I have a use case where I am getting a different set of Avro records
>>>>>>> in Kafka. I am using the schema registry to store Avro schema. One topic
>>>>>>> can also have different types of records.
>>>>>>>
>>>>>>> Now I have created a GenericRecord Stream using kafkaAvroDeseralizer
>>>>>>> by defining custom
>>>>>>> Deserializer class like this
>>>>>>>
>>>>>>> @Override
>>>>>>> public GenericRecord deserialize(
>>>>>>> byte[] messageKey, byte[] message, String topic, int partition, long
>>>>>>> offset) {
>>>>>>> checkInitialized();
>>>>>>> return (GenericRecord) inner.deserialize(topic, message);
>>>>>>> }
>>>>>>>
>>>>>>> private void checkInitialized() {
>>>>>>> if (inner == null) {
>>>>>>> Map<String, Object> props = new HashMap<>();
>>>>>>> props.put(AbstractKafkaAvroSerDeConfig.SCHEMA_REGISTRY_URL_CONFIG,
>>>>>>> registryUrl);
>>>>>>> props.put(KafkaAvroDeserializerConfig.SPECIFIC_AVRO_READER_CONFIG,
>>>>>>> false);
>>>>>>> SchemaRegistryClient client =
>>>>>>> new CachedSchemaRegistryClient(
>>>>>>> registryUrl,
>>>>>>> AbstractKafkaAvroSerDeConfig.MAX_SCHEMAS_PER_SUBJECT_DEFAULT);
>>>>>>> inner = new KafkaAvroDeserializer(client, props);
>>>>>>> }
>>>>>>> }
>>>>>>>
>>>>>>>
>>>>>>> And this is my consumer code :
>>>>>>>
>>>>>>> DataStreamSource<GenericRecord> input = env
>>>>>>> .addSource(
>>>>>>> new FlinkKafkaConsumer010<GenericRecord>(topics,
>>>>>>> new
>>>>>>> KafkaGenericAvroDeserializationSchema(schemaRegistryUrl),
>>>>>>> config).setStartFromEarliest());
>>>>>>>
>>>>>>> Now I want to write this stream partition on
>>>>>>> *event_name="a"/year=/month=/day=* in parquet format so that I can
>>>>>>> expose hive tables directly on top of this data.
>>>>>>> event_name is common field for all types of records that I am
>>>>>>> getting in Kafka.
>>>>>>> I am stuck as parquet writer needs a schema to write but my
>>>>>>> different records have different schemas So how do I write this stream
>>>>>>> in
>>>>>>> s3 in above partition format.
>>>>>>>
>>>>>>>
>>>>>>> Thanks & Regards,
>>>>>>> Anuj Jain
>>>>>>>
>>>>>>>
>>>>>>>
>>>>>>> <http://www.cse.iitm.ac.in/%7Eanujjain/>
>>>>>>>
>>>>>>
>>>>>
>>>>> --
>>>>> Thanks & Regards,
>>>>> Anuj Jain
>>>>> Mob. : +91- 8588817877
>>>>> Skype : anuj.jain07
>>>>> <http://www.oracle.com/>
>>>>>
>>>>>
>>>>> <http://www.cse.iitm.ac.in/%7Eanujjain/>
>>>>>
>>>>
>>>
>>> --
>>> Thanks & Regards,
>>> Anuj Jain
>>> Mob. : +91- 8588817877
>>> Skype : anuj.jain07
>>> <http://www.oracle.com/>
>>>
>>>
>>> <http://www.cse.iitm.ac.in/%7Eanujjain/>
>>>
>>
>
> --
> Thanks & Regards,
> Anuj Jain
> Mob. : +91- 8588817877
> Skype : anuj.jain07
> <http://www.oracle.com/>
>
>
> <http://www.cse.iitm.ac.in/%7Eanujjain/>
>
