adevore3 commented on issue #9738:
URL: https://github.com/apache/iceberg/issues/9738#issuecomment-1954798452
Hi, I was trying to create a code sample but got distracted. I've tried to
copy all the relevant code pieces here, 1 thing I couldn't do is the protobuf
file. It seems to be failing on the `insertIntoTable` function call based on
the stacktrace
```
import com.indeed.osiris.iceberg.exporter.config.OutputTableDefinition
import com.indeed.osiris.iceberg.exporter.datasources.Datasource
import
com.indeed.osiris.iceberg.exporter.datasources.JobArchiveOsirisDatasource.getClass
import com.indeed.spark.hivesupport.SessionBuilder
import org.apache.hadoop.fs.Path
import org.apache.iceberg.Table
import org.apache.iceberg.spark.SparkCatalog
import org.apache.spark.sql.{DataFrame, SparkSession}
import org.joda.time.DateTime
import org.slf4j.LoggerFactory
import org.springframework.boot.builder.SpringApplicationBuilder
import org.springframework.boot.{CommandLineRunner, WebApplicationType}
import org.springframework.context.annotation.Bean
class OsirisIcebergExporter(
spark: SparkSession,
datasource: Datasource,
inputCheckpointBucket: Option[String],
inputCheckpointMillis: Option[Long],
outputTableDefinition: OutputTableDefinition,
) extends CommandLineRunner {
import OsirisIcebergExporter._
private val newHighWaterMark =
inputCheckpointMillis.getOrElse(System.currentTimeMillis())
private val inputCheckpointPath = inputCheckpointBucket.map(b => new
Path(s"s3a://$b/tmp/osiris_iceberg/jatt3/$newHighWaterMark"))
private val icebergCatalog =
spark.sessionState.catalogManager.catalog("outbox").asInstanceOf[SparkCatalog].icebergCatalog()
override def run(rawArgs: String*): Unit = {
try {
log.info(s"running OsirisIcebergExporter. new high water mark will is
$newHighWaterMark")
val df = inputDf(datasource.dataframe(spark))
df.createOrReplaceTempView("input")
val icebergTable = populateTable(df)
} finally {
// close connections
}
}
def populateTable(df: DataFrame): Table = {
// df not used in this example since we'll read from the temp view
val t = icebergCatalog.loadTable(outputTableDefinition.icebergIdentifier)
mergeIntoTable(spark, "input", t)
t
}
def mergeIntoTable(spark: SparkSession, view: String, icebergTable:
Table): Unit = {
val currentSnapshot = icebergTable.currentSnapshot()
log.info(s"mergeIntoTable >>> SET spark.wap.id =
${newHighWaterMark}_delete")
spark.sql(s"SET spark.wap.id = ${newHighWaterMark}_delete")
deleteFromTable(spark, view)
log.info(s"mergeIntoTable >>> SET spark.wap.id =
${newHighWaterMark}_insert")
spark.sql(s"SET spark.wap.id = ${newHighWaterMark}_insert")
insertIntoTable(spark, view)
log.info("mergeIntoTable >>> RESET spark.wap.id")
spark.sql(s"RESET spark.wap.id")
log.info("mergeIntoTable >>> icebergTable.refresh()")
icebergTable.refresh()
}
def deleteFromTable(spark: SparkSession, view: String): Unit = {
val identifierFieldTuple = "(jobId)"
spark.sql(
s"""
|DELETE FROM outbox.osiris_iceberg.jatt3
|WHERE $identifierFieldTuple in (select $identifierFieldTuple from
$view)
|""".stripMargin)
}
def insertIntoTable(spark: SparkSession, view: String): Unit = {
spark.sql(
s"""
|INSERT INTO (jobId)
|SELECT * FROM $view ORDER BY outbox.bucket(512, jobId), jobId
|""".stripMargin)
}
def inputDf(sourceDf: DataFrame): DataFrame = {
inputCheckpointPath.map(b => {
val fileSystem =
b.getFileSystem(spark.sparkContext.hadoopConfiguration)
if (!fileSystem.exists(b)) {
//write the output, and then read it back
//which seems crazy, but it makes recovery faster if downstream
stages fail
//and an osiris partition needs to be reread (which is super slow)
//dont just checkpoint, cus then wed still only have 256 input
partitions
sourceDf.write
.option("maxRecordsPerFile", 500000) //this should probably
actually be configurable. assume 1k rows, goal is 512-1gb files
.orc(b.toString)
}
spark.createDataFrame(spark.read.orc(b.toString).rdd,
sourceDf.schema)
})
.getOrElse({
//if we didnt checkpoint in s3, we should cache
//not just optimization, needed for correctness
// (in case new records come into osiris between the delete and
insert)
sourceDf.cache()
sourceDf
})
}
}
object OsirisIcebergExporter {
private val log = LoggerFactory.getLogger(getClass)
def main(args: Array[String]): Unit = {
java.security.Security.setProperty("networkaddress.cache.ttl", "60")
new SpringApplicationBuilder(classOf[OsirisIcebergExporter])
.web(WebApplicationType.NONE)
.run(args: _*)
.close()
}
}
@Bean
def spark(): SparkSession = {
val icebergCatalogConfig = Map(
s"spark.sql.catalog.outbox" -> "org.apache.iceberg.spark.SparkCatalog",
s"spark.sql.catalog.outbox.io-impl" ->
"org.apache.iceberg.aws.s3.S3FileIO",
s"spark.sql.catalog.outbox.catalog-impl" ->
"org.apache.iceberg.aws.glue.GlueCatalog",
s"spark.sql.catalog.outbox.glue.id" -> "936411429724",
//"spark.sql.catalog.iceberg.s3.staging-dir" -> "/tmp/" //should this be
configurable? is it even needed?
)
SessionBuilder(s"OsirisIcebergExporter-jatt3")
.withMoreProperties(icebergCatalogConfig)
.build
}
import org.apache.log4j.Level
case class SessionBuilder(
appName: String,
logLevel: Level = Level.INFO,
properties: Map[String, String] = Map.empty,
) {
def withMoreProperties(properties: Map[String, String]): SessionBuilder =
copy(properties = this.properties ++ properties)
def builder: SparkSession.Builder = {
SparkSession.builder
.appName(appName)
.enableHiveSupport
.config("spark.hadoop.hive.exec.dynamic.partition", "true")
.config("spark.hadoop.hive.exec.dynamic.partition.mode", "nonstrict")
.config("spark.hadoop.hive.exec.max.dynamic.partitions", "2048")
.config("spark.hadoop.mapreduce.fileoutputcommitter.marksuccessfuljobs",
"false")
.config("spark.kryoserializer.buffer.max", "1g")
.config("spark.serializer",
"org.apache.spark.serializer.KryoSerializer")
.config("spark.sql.hive.metastorePartitionPruning", "true")
.config("spark.sql.orc.cache.stripe.details.size", "10000")
.config("spark.sql.orc.enabled", "true")
.config("spark.sql.orc.enableVectorizedReader", "true")
.config("spark.sql.orc.filterPushdown", "true")
.config("spark.sql.orc.impl", "native")
.config("spark.sql.orc.splits.include.file.footer", "true")
.config("spark.sql.parquet.filterPushdown", "true")
.config("spark.sql.parquet.mergeSchema", "false")
.config("spark.sql.session.timeZone", "-06:00")
.config("spark.sql.sources.partitionOverwriteMode", "dynamic")
.config("spark.ui.view.acls", "*")
}
def build: SparkSession = {
val sparkSession = builder.getOrCreate
sparkSession.sparkContext.setLogLevel(logLevel.toString)
sparkSession
}
}
@Bean
def datasource(): Datasource = {
val startTime: Long = DateTime.now().getMillis
val inputPartitions = 4
val useDfc = false
JobArchiveOsirisDatasource(startTime,
Option(inputPartitions).map(_.toInt), useDfc)
}
import com.example.proto.JobArchiveEntry
import org.apache.spark.sql.types.{StructField, StructType}
trait Datasource {
def dataframe(spark: SparkSession): DataFrame
def setNullable(df: DataFrame, fieldName: String, nullable: Boolean) :
DataFrame = {
val schema = df.schema
val newSchema = StructType(schema.map {
case StructField( c, t, _, m) if c.equals(fieldName) => StructField(
c, t, nullable = nullable, m)
case y: StructField => y
})
df.sqlContext.createDataFrame( df.rdd, newSchema )
}
}
case class OsirisRowv2(value: Array[Byte])
case class JobArchiveOsirisDatasource(minTs: Long, inputPartitions:
Option[Int], useDfc: Boolean) extends Datasource {
def dataframe(spark: SparkSession): DataFrame = {
import spark.implicits._
val dfcOptions: Map[String, String] =
if (useDfc)
Map(
"osiris.dfc"-> "true",
"osiris.dfc.datadir"-> "/osiris/",
"osiris.dfc.readahead"-> "4",
"osiris.s3.bucket" -> "cmhprod3-cdcosiris"
)
else
Map(
"servers" -> "osirisserver:26238"
)
val rawDf = spark.read
.format("osirisv2")
.option("keys", "jobId")
.option("keysplitter", "vlong-1000")
.option("table", "jobarchive_new")
.option("osiris.s3.region", "us-east-2")
.option("osiris.request.timeout", "180000")
.option("osiris.retry.timeout", "360000")
.option("minTs", minTs)
.options(dfcOptions)
.load()
val df = rawDf
.as[OsirisRowv2]
.map(r => RowTransformer.parseRow(r))
.toDF()
inputPartitions
.map(ps => df.repartition(ps))
.getOrElse(df)
}
}
object RowTransformer extends Serializable {
val transcoder = new ByteArrayTranscoder()
def parseRow(row: OsirisRowv2): JobInfo = {
val bytes = transcoder.toBytes(row.value)
val jobArchiveEntry = JobArchiveEntry.parseFrom(bytes)
JobInfo(
jobArchiveEntry.getJobId,
jobArchiveEntry.getTitleId,
)
}
}
case class JobInfo(
jobId: Long,
titleId: Int,
)
class ByteArrayTranscoder {
def fromBytes(bytes: Array[Byte]) = bytes
def toBytes(bytes: Array[Byte]) = bytes
def equals(o: AnyRef): Boolean = {
if (this eq o) return true
o != null && (getClass eq o.getClass)
}
override def toString = "byte"
}
```
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