avro

问题 As of now i am doing something like this reading avsc file to get schema value_schema = avro.load('client.avsc') can i do something to get schema from confluent schema registry using topic-name? i found one way but didn't figure out how to use it. https://github.com/marcosschroh/python-schema-registry-client 回答1: Using confluent-kafka-python from confluent_kafka.avro.cached_schema_registry_client import CachedSchemaRegistryClient sr = CachedSchemaRegistryClient({ 'url': 'http://localhost:8081

问题 I'm using Java to convert JSON to Avro and store these to GCS using Google DataFlow. The Avro schema is created on runtime using SchemaBuilder. One of the fields I define in the schema is an optional LONG field, it is defined like this: SchemaBuilder.FieldAssembler<Schema> fields = SchemaBuilder.record(mainName).fields(); Schema concreteType = SchemaBuilder.nullable().longType(); fields.name("key1").type(concreteType).noDefault(); Now when I create a GenericRecord using the schema above, and

问题 I'm using Java to convert JSON to Avro and store these to GCS using Google DataFlow. The Avro schema is created on runtime using SchemaBuilder. One of the fields I define in the schema is an optional LONG field, it is defined like this: SchemaBuilder.FieldAssembler<Schema> fields = SchemaBuilder.record(mainName).fields(); Schema concreteType = SchemaBuilder.nullable().longType(); fields.name("key1").type(concreteType).noDefault(); Now when I create a GenericRecord using the schema above, and

问题 I'm using Java to convert JSON to Avro and store these to GCS using Google DataFlow. The Avro schema is created on runtime using SchemaBuilder. One of the fields I define in the schema is an optional LONG field, it is defined like this: SchemaBuilder.FieldAssembler<Schema> fields = SchemaBuilder.record(mainName).fields(); Schema concreteType = SchemaBuilder.nullable().longType(); fields.name("key1").type(concreteType).noDefault(); Now when I create a GenericRecord using the schema above, and

问题 I have an existing avro schema { "name": "myenum", "type": { "type": "enum", "name": "Suit", "symbols": ["SPADES", "HEARTS", "DIAMONDS", "CLUBS"] }, "default": null } I want to add null to be the default and updating the contract to the following result in backward compatibility error. what can be done to solve this issue { "name": "myenum", "type": [ null, { "type": "enum", "name": "Suit", "symbols": ["SPADES", "HEARTS", "DIAMONDS", "CLUBS"] }], "default": null } 回答1: There is a problem with

问题 I have an existing avro schema { "name": "myenum", "type": { "type": "enum", "name": "Suit", "symbols": ["SPADES", "HEARTS", "DIAMONDS", "CLUBS"] }, "default": null } I want to add null to be the default and updating the contract to the following result in backward compatibility error. what can be done to solve this issue { "name": "myenum", "type": [ null, { "type": "enum", "name": "Suit", "symbols": ["SPADES", "HEARTS", "DIAMONDS", "CLUBS"] }], "default": null } 回答1: There is a problem with

问题 I am trying to read partitioned Avro data which is partitioned based on Year, Month and Day and that seems to be significantly slower than pointing it directly to the path. In the Physical plan I can see that the partition filters are getting passed on, so it is not scanning the entire set of directories but still it is significantly slower. E.g. reading the partitioned data like this profitLossPath="abfss://raw@"+datalakename+".dfs.core.windows.net/datawarehouse/CommercialDM.ProfitLoss/"

问题 I am trying to read partitioned Avro data which is partitioned based on Year, Month and Day and that seems to be significantly slower than pointing it directly to the path. In the Physical plan I can see that the partition filters are getting passed on, so it is not scanning the entire set of directories but still it is significantly slower. E.g. reading the partitioned data like this profitLossPath="abfss://raw@"+datalakename+".dfs.core.windows.net/datawarehouse/CommercialDM.ProfitLoss/"

问题 Closed. This question is not reproducible or was caused by typos. It is not currently accepting answers. Want to improve this question? Update the question so it's on-topic for Stack Overflow. Closed 2 months ago . Improve this question I have access to a project within BigQuery. I'm looking to create a partitioned table by ingestion time, partitioned by day, then set up a BigQuery Data Transfers process that brings avro files in from multiple directories within a Google Cloud Storage Bucket.

问题 Closed. This question is not reproducible or was caused by typos. It is not currently accepting answers. Want to improve this question? Update the question so it's on-topic for Stack Overflow. Closed 2 months ago . Improve this question I have access to a project within BigQuery. I'm looking to create a partitioned table by ingestion time, partitioned by day, then set up a BigQuery Data Transfers process that brings avro files in from multiple directories within a Google Cloud Storage Bucket.