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/>
>
