Avro

Avro format is a row-based storage format for Hadoop, which is widely used as a serialization platform. Avro format stores the schema in JSON format, making it easy to read and interpret by any program. The data itself is stored in a binary format making it compact and efficient.

This Gem allows you to read from or write to an Avro file.

Source

Source Parameters

Parameter	Description	Required	Default
Location	File path where avro files are present	True	None
Schema	Schema to be applied on the loaded data. Can be defined/edited as JSON or inferred using Infer Schema button	True	None
Recursive File Lookup	This is used to recursively load files and it disables partition inferring. If data source explicitly specifies the partitionSpec when recursiveFileLookup is true, an exception will be thrown.	False	False
Path Global Filter	An optional glob pattern to only include files with paths matching the pattern. The syntax follows GlobFilter. It does not change the behavior of partition discovery.	False	None
Modified Before	An optional timestamp to only include files with modification times occurring before the specified Time. The provided timestamp must be in the following form: YYYY-MM-DDTHH:mm:ss (e.g. 2020-06-01T13:00:00)	False	None
Modified After	An optional timestamp to only include files with modification times occurring after the specified Time. The provided timestamp must be in the following form: YYYY-MM-DDTHH:mm:ss (e.g. 2020-06-01T13:00:00)	False	None
Avro Schema	Optional schema in JSON format. See here for more details.	False	None
ignoreExtension	DEPRECATED. Enable to load files without the .avro extension. See caveats here	False	True

Schema Evolution

When reading Avro, the Avro Schema option can be set to a newer evolved schema which is compatible but different from the schema written to storage. The resulting DataFrame will follow the newer, evolved schema. For example, if we set an evolved schema containing one additional column with a default value, the resulting DataFrame will contain the new column too.

Ignoring the File Extension

If the ignoreExtension option is enabled, all files (with and without .avro extension) are loaded. The option has been deprecated, and it will be removed in the future releases. Please pathGlobFilter for filtering file names.

Example

Schema used in example above

Generated Code

Python

def read_avro(spark: SparkSession) -> DataFrame:
    return spark.read\
        .format("avro")\
        .option("ignoreExtension", True)\
        .option(
          "avroSchema",
          "{\"type\":\"record\",\"name\":\"Person\",\"fields\":[{\"name\":\"firstname\",\"type\":\"string\"},{\"name\":\"middlename\",\"type\":\"string\"},{\"name\":\"lastname\",\"type\":\"string\"},{\"name\":\"dob_year\",\"type\":\"int\"},{\"name\":\"dob_month\",\"type\":\"int\"},{\"name\":\"gender\",\"type\":\"string\"},{\"name\":\"salary\",\"type\":\"int\"}]}"
        )\
        .load("dbfs:/FileStore/Users/abhinav/avro/test.avro")

Scala

object read_avro {

  def apply(spark: SparkSession): DataFrame =
    spark.read
        .format("avro")
        .option("ignoreExtension", true)
        .option(
          "avroSchema",
          "{\"type\":\"record\",\"name\":\"Person\",\"fields\":[{\"name\":\"firstname\",\"type\":\"string\"},{\"name\":\"middlename\",\"type\":\"string\"},{\"name\":\"lastname\",\"type\":\"string\"},{\"name\":\"dob_year\",\"type\":\"int\"},{\"name\":\"dob_month\",\"type\":\"int\"},{\"name\":\"gender\",\"type\":\"string\"},{\"name\":\"salary\",\"type\":\"int\"}]}"
        )
        .load("dbfs:/FileStore/Users/abhinav/avro/test.avro")

}

---

Target

Target Parameters

Write data as avro files at the specified path.

Parameter	Description	Required	Default
Location	Locaiton to write the Avro files to present	True	None
Avro Schema	Optional schema provided by a user in JSON format. This option can be set if the expected output Avro schema doesn't match the schema converted by Spark. For example, the expected schema of one column is of enum type, instead of string type in the default converted schema.	False	None
Record Name	Top level record name in write result, which is required in Avro spec.	False	topLevelRecord
Record Namespace	Record namespace in write result.	False	""
Compression	Compression codec used when writing. Currently supported codecs are uncompressed, snappy, deflate, bzip2, xz and zstandard. Defaults to whatever spark.sql.avro.compression.codec is set to.	False	snappy
Write Mode	How to handle existing data. See this table for a list of available options.	True	error
Partition Columns	List of columns to partition the avro files by	False	None

Supported Write Modes

Write Mode	Description
overwrite	If data already exists, overwrite with the contents of the DataFrame
append	If data already exists, append the contents of the DataFrame
ignore	If data already exists, do nothing with the contents of the DataFrame. This is similar to a CREATE TABLE IF NOT EXISTS in SQL.
error	If data already exists, throw an exception.

Example

Generated Code

Python

def write_avro(spark: SparkSession, in0: DataFrame):
    in0.write\
        .format("avro")\
        .mode("overwrite")\
        .partitionBy("dob_year","dob_month")\
        .save("dbfs:/data/test_output.avro")

Scala

object write_avro {
  def apply(spark: SparkSession, in: DataFrame): Unit =
    in.write
        .format("avro")
        .mode("overwrite")
        .partitionBy("dob_year","dob_month")
        .save("dbfs:/data/test_output.avro")
}

info

To know more about tweaking Avro related properties in Spark config click here.