spark技术分享Avro Data Source
Spark SQL supports structured queries over Avro files as well as in columns (in a DataFrame).
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Note
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Apache Avro is a data serialization format and provides the following features:
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Avro data source is provided by the spark-avro external module. You should include it as a dependency in your Spark application (e.g. spark-submit --packages or in build.sbt).
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org.apache.spark:spark-avro_2.12:2.4.0 |
The following shows how to include the spark-avro module in a spark-shell session.
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$ ./bin/spark-shell --packages org.apache.spark:spark-avro_2.12:2.4.0 |
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Parses an Avro-encoded binary column and converts to a Catalyst value per JSON-encoded Avro schema |
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Converts a column to an Avro-encoded binary column |
After the module is loaded, you should import the org.apache.spark.sql.avro package to have the from_avro and to_avro functions available.
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import org.apache.spark.sql.avro._ |
Converting Column to Avro-Encoded Binary Column — to_avro Method
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to_avro(data: Column): Column |
to_avro creates a Column with the CatalystDataToAvro unary expression (with the Catalyst expression of the given data column).
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import org.apache.spark.sql.avro._ val q = spark.range(1).withColumn("to_avro_id", to_avro('id)) scala> q.show +---+----------+ | id|to_avro_id| +---+----------+ | 0| [00]| +---+----------+ val logicalPlan = q.queryExecution.logical scala> println(logicalPlan.numberedTreeString) 00 'Project [id#33L, catalystdatatoavro('id) AS to_avro_id#35] 01 +- Range (0, 1, step=1, splits=Some(8)) import org.apache.spark.sql.avro.CatalystDataToAvro // Let's use QueryExecution.analyzed instead // https://issues.apache.org/jira/browse/SPARK-26063 val analyzedPlan = q.queryExecution.analyzed val toAvroExpr = analyzedPlan.expressions.drop(1).head.children.head.asInstanceOf[CatalystDataToAvro] scala> println(toAvroExpr.sql) to_avro(`id`, bigint) |
Converting Avro-Encoded Column to Catalyst Value — from_avro Method
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from_avro(data: Column, jsonFormatSchema: String): Column |
from_avro creates a Column with the AvroDataToCatalyst unary expression (with the Catalyst expression of the given data column and the jsonFormatSchema JSON-encoded schema).
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import org.apache.spark.sql.avro._ val data = spark.range(1).withColumn("to_avro_id", to_avro('id)) // Use from_avro to decode to_avro-encoded id column val jsonFormatSchema = s""" |{ | "type": "long", | "name": "id" |} """.stripMargin val q = data.select(from_avro('to_avro_id, jsonFormatSchema) as "id_from_avro") scala> q.show +------------+ |id_from_avro| +------------+ | 0| +------------+ val logicalPlan = q.queryExecution.logical scala> println(logicalPlan.numberedTreeString) 00 'Project [avrodatatocatalyst('to_avro_id, 01 { 02 "type": "long", 03 "name": "id" 04 } 05 ) AS id_from_avro#77] 06 +- Project [id#66L, catalystdatatoavro(id#66L) AS to_avro_id#68] 07 +- Range (0, 1, step=1, splits=Some(8)) import org.apache.spark.sql.avro.AvroDataToCatalyst // Let's use QueryExecution.analyzed instead // https://issues.apache.org/jira/browse/SPARK-26063 val analyzedPlan = q.queryExecution.analyzed val fromAvroExpr = analyzedPlan.expressions.head.children.head.asInstanceOf[AvroDataToCatalyst] scala> println(fromAvroExpr.sql) from_avro(`to_avro_id`, bigint) |
