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Pain free Spark/Avro integration.
Seamlessly convert your Avro records from anywhere (e.g. Kafka, Parquet, HDFS, etc) into Spark Rows.
Convert your Dataframes into Avro records without even specifying a schema.
Seamlessly integrate with Confluent platform, including Schema Registry with all available naming strategies.
Go back-and-forth Spark Avro (since Spark 2.4).
On spark 2.4.x Abris should work without any further requirements.
On Spark 2.3.x you must declare dependency on org.apache.avro:avro:1.8.0 or higher. (Spark 2.3.x uses Avro 1.7.x so you must overwrite this because ABRiS needs Avro 1.8.0+.)
ABRiS API is in it's most basic form almost identical to Spark built-in support for Avro, but it provides additional functionality. Mainly it's support of schema registry and also seamless integration with confluent Avro data format.
The API consists of four Spark SQL expressions:
to_avroandfrom_avroused for normal Avro payloadto_confluent_avroandfrom_confluent_avroused for Confluent Avro data format
Full runnable examples can be found in the za.co.absa.abris.examples package. You can also take a look at unit tests in package za.co.absa.abris.avro.sql.
Old ABRiS API is deprecated, but is still included in the library. Documentation for old API is in ABRiS 2.2.3.
The format of Avro binary data is defined in Avro specification. Confluent format extends it and prepends the schema id before the actual record. The Confluent expressions in this library expect this format and add the id after the Avro data are generated or remove it before they are parsed.
You can find more about Confluent and Schema Registry in Confluent documentation.
import za.co.absa.abris.avro.functions.from_avro
def readAvro(dataFrame: DataFrame, schemaString: String): DataFrame = {
dataFrame.select(from_avro(col("value"), schemaString) as 'data).select("data.*")
}In this example the Avro binary data are in dataFrame inside column the value. The Avro schema is provided as a string schemaString.
After the Avro data are converted to Spark SQL representation they are stored in column the data. This column is immediately flattened in the next select so the result will be a DataFrame containing only the deserialized avro data.
If you want to use Schema Registry you need to provide a configuration:
val schemaRegistryConfig = Map(
SchemaManager.PARAM_SCHEMA_REGISTRY_URL -> "url_to_schema_registry",
SchemaManager.PARAM_SCHEMA_REGISTRY_TOPIC -> "topic_name",
SchemaManager.PARAM_VALUE_SCHEMA_NAMING_STRATEGY -> SchemaManager.SchemaStorageNamingStrategies.{TOPIC_NAME, RECORD_NAME, TOPIC_RECORD_NAME}, // choose a subject name strategy
SchemaManager.PARAM_VALUE_SCHEMA_ID -> "current_schema_id" // set to "latest" if you want the latest schema version to used
)Depending on the selected naming strategy you may also need to provide SchemaManager.PARAM_SCHEMA_NAME_FOR_RECORD_STRATEGY and SchemaManager.PARAM_SCHEMA_NAMESPACE_FOR_RECORD_STRATEGY
import za.co.absa.abris.avro.functions.from_avro
def readAvro(dataFrame: DataFrame, schemaRegistryConfig: Map[String, String]): DataFrame = {
dataFrame.select(from_avro(col("value"), schemaRegistryConfig) as 'data).select("data.*")
}This example is similar to the previous one except for the fact that this time configurations to access Schema Registry are provided instead of the schema itself. The schema must already be available on Schema Registry.
import za.co.absa.abris.avro.functions.from_confluent_avro
def readAvro(dataFrame: DataFrame, schemaString: String): DataFrame = {
dataFrame.select(from_confluent_avro(col("value"), schemaString) as 'data).select("data.*")
}The main difference between from_confluent_avro and from_avro is in whether it expects the schema_id in the Avro payload. The usage is identical to previous examples.
Schema Registry configuration is the same as in previous Schema Registry example.
import za.co.absa.abris.avro.functions.from_confluent_avro
def readAvro(dataFrame: DataFrame, schemaRegistryConfig: Map[String, String]): DataFrame = {
dataFrame.select(from_confluent_avro(col("value"), schemaRegistryConfig) as 'data).select("data.*")
}The only difference is the expression name.
In case we are sending Avro data using Kafka we may want to serialize both the key and the value of the Kafka message. The serialization of Avro data is not really different when we are doing it for key or for value, but Schema Registry handles each of them slightly differently.
The way the library knows whether you are working with key or value is the schema naming strategy.
Use SchemaManager.PARAM_KEY_SCHEMA_NAMING_STRATEGY for key and SchemaManager.PARAM_VALUE_SCHEMA_NAMING_STRATEGY for value. If the configuration contains both of them, it will throw!
This is one way to create the configurations for key and value serialization:
val commonRegistryConfig = Map(
SchemaManager.PARAM_SCHEMA_REGISTRY_TOPIC -> "example_topic",
SchemaManager.PARAM_SCHEMA_REGISTRY_URL -> "http://localhost:8081"
)
val keyRegistryConfig = commonRegistryConfig ++ Map(
SchemaManager.PARAM_KEY_SCHEMA_NAMING_STRATEGY -> "topic.record.name",
SchemaManager.PARAM_KEY_SCHEMA_ID -> "latest",
SchemaManager.PARAM_SCHEMA_NAME_FOR_RECORD_STRATEGY -> "foo",
SchemaManager.PARAM_SCHEMA_NAMESPACE_FOR_RECORD_STRATEGY -> "com.bar"
)
val valueRegistryConfig = commonRegistryConfig ++ Map(
SchemaManager.PARAM_VALUE_SCHEMA_NAMING_STRATEGY -> "topic.name",
SchemaManager.PARAM_VALUE_SCHEMA_ID -> "latest"
)Let's assume that the Avro binary data for key are in the key column and the payload data are in the value column of the same DataFrame.
import za.co.absa.abris.avro.functions.from_confluent_avro
val result: DataFrame = dataFrame.select(
from_confluent_avro(col("key"), keyRegistryConfig) as 'key,
from_confluent_avro(col("value"), valueRegistryConfig) as 'value)We just need to use the right configuration for the right column and that's it.
import za.co.absa.abris.avro.functions.to_avro
def writeAvro(dataFrame: DataFrame): DataFrame = {
val allColumns = struct(dataFrame.columns.head, dataFrame.columns.tail: _*)
dataFrame.select(to_avro(allColumns) as 'value)
}This is the simplest possible usage of the to_avro expression. We just provide the column that we want to serialize and the library will generate the schema automatically from Spark data types.
If you want to serialize more than one column, you have to put them in a Spark struct first, as shown in the example.
import za.co.absa.abris.avro.functions.to_avro
def writeAvro(dataFrame: DataFrame, schemaString: String): DataFrame = {
val allColumns = struct(dataFrame.columns.head, dataFrame.columns.tail: _*)
dataFrame.select(to_avro(allColumns, schemaString) as 'value)
}If you provide the Avro schema as a second argument, ABRiS will use it to convert Spark data into Avro. Please make sure that the data types in Spark DataFrame and in schema are compatible.
First we need to provide the Schema Registry configuration:
val schemaRegistryConfig = Map(
SchemaManager.PARAM_SCHEMA_REGISTRY_URL -> "url_to_schema_registry",
SchemaManager.PARAM_SCHEMA_REGISTRY_TOPIC -> "topic_name",
SchemaManager.PARAM_VALUE_SCHEMA_NAMING_STRATEGY -> SchemaManager.SchemaStorageNamingStrategies.TOPIC_RECORD_NAME,
SchemaManager.PARAM_SCHEMA_NAME_FOR_RECORD_STRATEGY -> "schema_name",
SchemaManager.PARAM_SCHEMA_NAMESPACE_FOR_RECORD_STRATEGY -> "schema_namespace"
)In this example the TOPIC_RECORD_NAME naming strategy is used, therefore we need to provide topic, name and namespace.
import za.co.absa.abris.avro.functions.to_avro
def writeAvro(dataFrame: DataFrame, schemaRegistryConfig: Map[String, String]): DataFrame = {
val allColumns = struct(dataFrame.columns.head, dataFrame.columns.tail: _*)
dataFrame.select(to_avro(allColumns, schemaRegistryConfig) as 'value)
}Since we didn't provide a schema it will be generated automatically and then stored into Schema Registry.
The only difference from previous example is that we have one additional parameter for the schema.
import za.co.absa.abris.avro.functions.to_avro
def writeAvro(dataFrame: DataFrame, schemaString: String, schemaRegistryConfig: Map[String, String]): DataFrame = {
val allColumns = struct(dataFrame.columns.head, dataFrame.columns.tail: _*)
dataFrame.select(to_avro(allColumns, schemaString, schemaRegistryConfig) as 'value)
}Schema Registry configuration is the same as in previous examples.
import za.co.absa.abris.avro.functions.to_confluent_avro
def writeAvro(dataFrame: DataFrame, schemaRegistryConfig: Map[String, String]): DataFrame = {
val allColumns = struct(dataFrame.columns.head, dataFrame.columns.tail: _*)
dataFrame.select(to_confluent_avro(allColumns, schemaRegistryConfig) as 'value)
}The main difference between from_confluent_avro and from_avro is in whether it prepends the schema_id to the Avro payload. The usage is identical to previous examples.
import za.co.absa.abris.avro.functions.to_confluent_avro
def writeAvro(dataFrame: DataFrame, schemaString: String, schemaRegistryConfig: Map[String, String]): DataFrame = {
val allColumns = struct(dataFrame.columns.head, dataFrame.columns.tail: _*)
dataFrame.select(to_confluent_avro(allColumns, schemaString, registryConfig) as 'value)
}As in the reading example, for writing you also have to specify the naming strategy parameter to let ABRiS know if you are using value or key.
val commonRegistryConfig = Map(
SchemaManager.PARAM_SCHEMA_REGISTRY_TOPIC -> "example_topic",
SchemaManager.PARAM_SCHEMA_REGISTRY_URL -> "http://localhost:8081",
SchemaManager.PARAM_SCHEMA_NAME_FOR_RECORD_STRATEGY -> "foo",
SchemaManager.PARAM_SCHEMA_NAMESPACE_FOR_RECORD_STRATEGY -> "com.bar"
)
val keyRegistryConfig = commonRegistryConfig +
(SchemaManager.PARAM_KEY_SCHEMA_NAMING_STRATEGY -> "topic.record.name")
val valueRegistryConfig = commonRegistryConfig +
(SchemaManager.PARAM_VALUE_SCHEMA_NAMING_STRATEGY -> "topic.name")Let's assume that we have the data that we want to serialize in a DataFrame in the key and value columns.
val result: DataFrame = dataFrame.select(
to_confluent_avro(col("key"), keyRegistryConfig) as 'key,
to_confluent_avro(col("value"), valueRegistryConfig) as 'value)After serialization the data are again stored in the columns key and value, but now they are in Avro binary format.
Some settings are required when using Schema Registry, as explained above. However, there is more settings supported by Schema Registry as contained in io.confluent.kafka.serializers.AbstractKafkaAvroSerDeConfig.
Among those are basic.auth.user.info and basic.auth.credentials.source required for user authentication.
To make use of those, all that is required is to add them to the settings map as in the example below:
val commonRegistryConfig = Map(
SchemaManager.PARAM_SCHEMA_REGISTRY_TOPIC -> "example_topic",
SchemaManager.PARAM_SCHEMA_REGISTRY_URL -> "http://localhost:8081",
SchemaManager.PARAM_SCHEMA_NAME_FOR_RECORD_STRATEGY -> "foo",
SchemaManager.PARAM_SCHEMA_NAMESPACE_FOR_RECORD_STRATEGY -> "com.bar"
)
val valueRegistryConfig = commonRegistryConfig +
(SchemaManager.PARAM_VALUE_SCHEMA_NAMING_STRATEGY -> "topic.name")
val securityRegistryConfig = valueRegistryConfig +
("basic.auth.credentials.source" -> "USER_INFO",
"basic.auth.user.info" -> "srkey:srvalue")ABRiS can also be used with PySpark to deserialize Avro payloads from Confluent Kafka. For that, we need to convert Python object into JVM ones. The snippet below shows how it can be achieved.
import logging, traceback
import requests
from pyspark.sql import Column
from pyspark.sql.column import *
jvm_gateway = spark_context._gateway.jvm
abris_avro = jvm_gateway.za.co.absa.abris.avro
naming_strategy = getattr(getattr(abris_avro.read.confluent.SchemaManager, "SchemaStorageNamingStrategies$"), "MODULE$").TOPIC_NAME()
schema_registry_config_dict = {"schema.registry.url": schema_registry_url,
"schema.registry.topic": topic,
"value.schema.id": "latest",
"value.schema.naming.strategy": naming_strategy}
conf_map = getattr(getattr(jvm_gateway.scala.collection.immutable.Map, "EmptyMap$"), "MODULE$")
for k, v in schema_registry_config_dict.items():
conf_map = getattr(conf, "$plus")(jvm_gateway.scala.Tuple2(k, v))
deserialized_df = data_frame.select(Column(abris_avro.functions.from_confluent_avro(data_frame._jdf.col("value"), conf_map))
.alias("data")).select("data.*")This library also provides utility methods for registering schemas with topics into Schema Registry. Below is an example of how it can be done.
val schemaRegistryConfs = Map(
SchemaManager.PARAM_SCHEMA_REGISTRY_URL -> "url_to_schema_registry",
SchemaManager.PARAM_VALUE_SCHEMA_NAMING_STRATEGY -> SchemaManager.SchemaStorageNamingStrategies.{TOPIC_NAME, RECORD_NAME, TOPIC_RECORD_NAME}, // if you are retrieving value schema
SchemaManager.PARAM_KEY_SCHEMA_NAMING_STRATEGY -> SchemaManager.SchemaStorageNamingStrategies.{TOPIC_NAME, RECORD_NAME, TOPIC_RECORD_NAME}, // if your are retrieving key schema
SchemaManager.PARAM_SCHEMA_NAME_FOR_RECORD_STRATEGY -> "schema_name", // if you're using RecordName or TopicRecordName strategies
SchemaManager.PARAM_SCHEMA_NAMESPACE_FOR_RECORD_STRATEGY -> "schema_namespace" // if you're using RecordName or TopicRecordName strategies
)
SchemaManager.configureSchemaRegistry(schemaRegistryConfs)
val topic = "example_topic"
val subject = SchemaManager.getSubjectName(topic, false) // create a subject for the value
val schema = AvroSchemaUtils.load("path_to_the_schema_in_a_file_system")
val schemaId = SchemaManager.register(schema, subject)This library also provides convenient methods to convert between Avro and Spark schemas.
If you have an Avro schema which you want to convert into a Spark SQL one - to generate your Dataframes, for instance - you can do as follows:
val avroSchema: Schema = AvroSchemaUtils.load("path_to_avro_schema")
val sqlSchema: StructType = SparkAvroConversions.toSqlType(avroSchema) You can also do the inverse operation by running:
val sqlSchema = new StructType(new StructField ....
val avroSchema = SparkAvroConversions.toAvroSchema(sqlSchema, avro_schema_name, avro_schema_namespace)The naming strategies RecordName and TopicRecordName allow for a topic to receive different payloads, i.e. payloads containing different schemas that do not have to be compatible, as explained here.
However, currently, there is no way for Spark to change Dataframes schemas on the fly, thus, if incompatible schemas are used on the same topic, the job will fail. Also, it would be cumbersome to write jobs that shift between schemas.
A possible solution would be for ABRiS to create an uber schema from all schemas expected to be part of a topic, which will be investigated in future releases.
Fixed is an alternative way of encoding binary data in Avro. Unlike bytes type the fixed type doesn't store the length of the data in the payload, but in Avro schema itself.
The corresponding data type in Spark is BinaryType, but the inferred schema will always use bytes type for this kind of data. If you want to use the fixed type you must provide the appropriate Avro schema.