If the “value” field that contains your data is in Avro, you could use from_avro() to extract your data, enrich it, clean it, and then push it downstream to Kafka again or write it out to a file.Kafka key-value record will be augmented with some metadata, such as the ingestion timestamp into Kafka, the offset in Kafka, etc. Using Avro record as columns are useful when reading from or writing to a streaming source like Kafka. Both functions transform one column toĪnother column, and the input/output SQL data type can be complex type or primitive type. The Avro package provides function to_avro to encode a column as binary in Avroįormat, and from_avro() to decode Avro binary data into a column. Write.df (select (df, 'name', 'favorite_color' ), 'namesAndFavColors.avro', 'avro' ) to_avro() and from_avro() To load/save data in Avro format, you need to specify the data source option format as avro(or .avro).ĭf <- read.df ( 'examples/src/main/resources/users.avro', 'avro' ) Since spark-avro module is external, there is no. See Application Submission Guide for more details about submitting applications with external dependencies. įor experimenting on spark-shell, you can also use -packages to add :spark-avro_2.11 and its dependencies directly. spark-avro_2.11Īnd its dependencies can be directly added to spark-submit using -packages, such as. The spark-avro module is external and not included in spark-submit or spark-shell by default.Īs with any Spark applications, spark-submit is used to launch your application. Since Spark 2.4 release, Spark SQL provides built-in support for reading and writing Apache Avro data. Supported types for Spark SQL -> Avro conversion.Supported types for Avro -> Spark SQL conversion.Compatibility with Databricks spark-avro.

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