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[SPARK-24772][SQL] Avro: support logical date type #21984

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[SPARK-24772][SQL] Avro: support logical date type #21984

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16e0357
support logical date type
gengliangwang Aug 3, 2018
c6b997b
fix typo
gengliangwang Aug 6, 2018
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5 changes: 5 additions & 0 deletions external/avro/src/main/scala/org/apache/spark/sql/avro/AvroDeserializer.scala
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Expand Up @@ -84,6 +84,9 @@ class AvroDeserializer(rootAvroType: Schema, rootCatalystType: DataType) {
case (INT, IntegerType) => (updater, ordinal, value) =>
updater.setInt(ordinal, value.asInstanceOf[Int])

case (INT, DateType) => (updater, ordinal, value) =>
updater.setInt(ordinal, value.asInstanceOf[Int])

case (LONG, LongType) => (updater, ordinal, value) =>
updater.setLong(ordinal, value.asInstanceOf[Long])

Expand All @@ -100,6 +103,8 @@ class AvroDeserializer(rootAvroType: Schema, rootCatalystType: DataType) {
s"Cannot convert Avro logical type ${other} to Catalyst Timestamp type.")
}

// Before we upgrade Avro to 1.8 for logical type support, spark-avo converts Long to Date.

@viirya viirya Aug 5, 2018

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typo: spark-avo -> spark-avro.

// For backward compatibility, we still keep this conversion.
case (LONG, DateType) => (updater, ordinal, value) =>
updater.setInt(ordinal, (value.asInstanceOf[Long] / DateTimeUtils.MILLIS_PER_DAY).toInt)

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2 changes: 1 addition & 1 deletion external/avro/src/main/scala/org/apache/spark/sql/avro/AvroSerializer.scala
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Expand Up @@ -92,7 +92,7 @@ class AvroSerializer(rootCatalystType: DataType, rootAvroType: Schema, nullable:
case BinaryType =>
(getter, ordinal) => ByteBuffer.wrap(getter.getBinary(ordinal))
case DateType =>
(getter, ordinal) => getter.getInt(ordinal) * DateTimeUtils.MILLIS_PER_DAY
(getter, ordinal) => getter.getInt(ordinal)

@gengliangwang gengliangwang Aug 3, 2018

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For the write path, let's drop the previous conversion to Long

@HyukjinKwon HyukjinKwon Aug 4, 2018 •
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Does this cause a behaviour change comparing to the third party one?

@gengliangwang gengliangwang Aug 5, 2018 •
edited
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I don't think it is a real behavior change. The only concern is that the Avro file with date type column is written with this built-in package, and read by third party one with user specify schema. The case should be very trivial and we can ignore that.

@cloud-fan cloud-fan Aug 7, 2018

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There are 2 kinds of compatibilities:

  1. the file written by old avro data source can be read by the new avro data source
  2. the file written by new avro data source can be read by the old avro data source

I think we should focus on 1) and ignore 2)

case TimestampType => avroType.getLogicalType match {
case _: TimestampMillis => (getter, ordinal) => getter.getLong(ordinal) / 1000
case _: TimestampMicros => (getter, ordinal) => getter.getLong(ordinal)
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12 changes: 9 additions & 3 deletions external/avro/src/main/scala/org/apache/spark/sql/avro/SchemaConverters.scala
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Expand Up @@ -20,7 +20,7 @@ package org.apache.spark.sql.avro
import scala.collection.JavaConverters._

import org.apache.avro.{LogicalType, LogicalTypes, Schema, SchemaBuilder}
import org.apache.avro.LogicalTypes.{TimestampMicros, TimestampMillis}
import org.apache.avro.LogicalTypes.{Date, TimestampMicros, TimestampMillis}
import org.apache.avro.Schema.Type._

import org.apache.spark.sql.internal.SQLConf.AvroOutputTimestampType
Expand All @@ -38,7 +38,10 @@ object SchemaConverters {
*/
def toSqlType(avroSchema: Schema): SchemaType = {
avroSchema.getType match {
case INT => SchemaType(IntegerType, nullable = false)
case INT => avroSchema.getLogicalType match {
case _: Date => SchemaType(DateType, nullable = false)
case _ => SchemaType(IntegerType, nullable = false)
}
case STRING => SchemaType(StringType, nullable = false)
case BOOLEAN => SchemaType(BooleanType, nullable = false)
case BYTES => SchemaType(BinaryType, nullable = false)
Expand Down Expand Up @@ -121,7 +124,10 @@ object SchemaConverters {
case BooleanType => builder.booleanType()
case ByteType | ShortType | IntegerType => builder.intType()
case LongType => builder.longType()
case DateType => builder.longType()
case DateType => builder
.intBuilder()
.prop(LogicalType.LOGICAL_TYPE_PROP, LogicalTypes.date().getName)
.endInt()
case TimestampType =>
val timestampType = outputTimestampType match {
case AvroOutputTimestampType.TIMESTAMP_MILLIS => LogicalTypes.timestampMillis()
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Binary file added external/avro/src/test/resources/date.avro
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54 changes: 51 additions & 3 deletions external/avro/src/test/scala/org/apache/spark/sql/avro/AvroSuite.scala
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Expand Up @@ -33,6 +33,7 @@ import org.apache.avro.generic.GenericData.{EnumSymbol, Fixed}
import org.apache.commons.io.FileUtils

import org.apache.spark.sql._
import org.apache.spark.sql.catalyst.util.DateTimeUtils
import org.apache.spark.sql.execution.datasources.DataSource
import org.apache.spark.sql.internal.SQLConf
import org.apache.spark.sql.test.{SharedSQLContext, SQLTestUtils}
Expand Down Expand Up @@ -67,6 +68,27 @@ class AvroSuite extends QueryTest with SharedSQLContext with SQLTestUtils {
// writer.close()
val timestampAvro = testFile("timestamp.avro")

// The test file date.avro is generated via following Python code:
// import json
// import avro.schema
// from avro.datafile import DataFileWriter
// from avro.io import DatumWriter
//
// write_schema = avro.schema.parse(json.dumps({
// "namespace": "logical",
// "type": "record",
// "name": "test",
// "fields": [
// {"name": "date", "type": {"type": "int", "logicalType": "date"}}
// ]
// }))
//
// writer = DataFileWriter(open("date.avro", "wb"), DatumWriter(), write_schema)
// writer.append({"date": 7})
// writer.append({"date": 365})
// writer.close()
val dateAvro = testFile("date.avro")

override protected def beforeAll(): Unit = {
super.beforeAll()
spark.conf.set("spark.sql.files.maxPartitionBytes", 1024)
Expand Down Expand Up @@ -350,9 +372,35 @@ class AvroSuite extends QueryTest with SharedSQLContext with SQLTestUtils {
val df = spark.createDataFrame(rdd, schema)
df.write.format("avro").save(dir.toString)
assert(spark.read.format("avro").load(dir.toString).count == rdd.count)
assert(
spark.read.format("avro").load(dir.toString).select("date").collect().map(_(0)).toSet ==
Array(null, 1451865600000L, 1459987200000L).toSet)
checkAnswer(
spark.read.format("avro").load(dir.toString).select("date"),
Seq(Row(null), Row(new Date(1451865600000L)), Row(new Date(1459987200000L))))
}
}

test("Logical type: date") {
val expected = Seq(7, 365).map(t => Row(DateTimeUtils.toJavaDate(t)))
val df = spark.read.format("avro").load(dateAvro)

checkAnswer(df, expected)

val avroSchema = s"""
{
"namespace": "logical",
"type": "record",
"name": "test",
"fields": [
{"name": "date", "type": {"type": "int", "logicalType": "date"}}
]
}
"""

checkAnswer(spark.read.format("avro").option("avroSchema", avroSchema).load(dateAvro),
expected)

withTempPath { dir =>
df.write.format("avro").save(dir.toString)
checkAnswer(spark.read.format("avro").load(dir.toString), expected)
}
}

Expand Down