![]() ![]() (".level", "5")įor Databricks Runtime 9.1 LTS and above, you can change the default schema inference behavior in Avro by providing the mergeSchema option when reading files. You can set these properties in the cluster Spark configuration or at runtime using (). If the compression codec is deflate, you can set the compression level with: .level.To configure compression when writing, set the following Spark properties: avro extension when reading, you can set the parameter .without.extension in the Hadoop configuration. You can change the behavior of an Avro data source using various configuration parameters. Record names: Record name and namespace by passing a map of parameters with recordName and recordNamespace.Īlso see Read and write streaming Avro data.The supported types are uncompressed, snappy, and deflate. Compression: Compression to use when writing Avro out to disk. ![]() Partitioning: Easily reading and writing partitioned data without any extra configuration.Schema conversion: Automatic conversion between Apache Spark SQL and Avro records.Code generation as an optional optimization, only worth implementing for statically typed languages. Code generation is not required to read or write data files nor to use or implement RPC protocols. Simple integration with dynamic languages.A container file, to store persistent data.Apache Avro is a data serialization system. ![]()
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