小编给大家分享一下spark delta如何读数据，相信大部分人都还不怎么了解，因此分享这篇文章给大家参考一下，希望大家阅读完这篇文章后大有收获，下面让我们一起去了解一下吧！
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spark 的delta datasource的构建要从DataSource.lookupDataSourceV2开始，之后会流向到loadV1Source，这里会进行dataSource.createRelation进行构建datasource的Relation的构建,直接转到deltaDataSource 的createRelation：
override def createRelation(
sqlContext: SQLContext,
parameters: Map[String, String]): BaseRelation = {
val maybePath = parameters.getOrElse("path", {
throw DeltaErrors.pathNotSpecifiedException
})
// Log any invalid options that are being passed in
DeltaOptions.verifyOptions(CaseInsensitiveMap(parameters))
val timeTravelByParams = DeltaDataSource.getTimeTravelVersion(parameters)
DeltaTableV2(
sqlContext.sparkSession,
new Path(maybePath),
timeTravelOpt = timeTravelByParams).toBaseRelation
}DeltaOptions.verifyOptions进行参数校验,有效的参数如下：
val validOptionKeys : Set[String] = Set( REPLACE_WHERE_OPTION, MERGE_SCHEMA_OPTION, EXCLUDE_REGEX_OPTION, OVERWRITE_SCHEMA_OPTION, USER_METADATA_OPTION, MAX_FILES_PER_TRIGGER_OPTION, IGNORE_FILE_DELETION_OPTION, IGNORE_CHANGES_OPTION, IGNORE_DELETES_OPTION, OPTIMIZE_WRITE_OPTION, DATA_CHANGE_OPTION, "queryName", "checkpointLocation", "path", "timestampAsOf", "versionAsOf" )
DeltaDataSource.getTimeTravelVersion根据指定的timestampAsOf或者versionAsOf获取指定的版本
直接调用DeltaTableV2的toBaseRelation方法：
def toBaseRelation: BaseRelation = {
if (deltaLog.snapshot.version == -1) {
val id = catalogTable.map(ct => DeltaTableIdentifier(table = Some(ct.identifier)))
.getOrElse(DeltaTableIdentifier(path = Some(path.toString)))
throw DeltaErrors.notADeltaTableException(id)
}
val partitionPredicates = DeltaDataSource.verifyAndCreatePartitionFilters(
path.toString, deltaLog.snapshot, partitionFilters)
// TODO(burak): We should pass in the snapshot here
deltaLog.createRelation(partitionPredicates, timeTravelSpec)
}如果存在分区，则DeltaDataSource.verifyAndCreatePartitionFilter创建partitionPredicates
timeTravelSpec，这里优先选择用户指定的timeTravelByParams，否则通过DeltaDataSource.parsePathIdentifier选择path指定的version，格式如:/some/path/partition=1@v1234 或者/some/path/partition=1@yyyyMMddHHmmssSSS
直接调用deltaLog.createRelation:
def createRelation(
partitionFilters: Seq[Expression] = Nil,
timeTravel: Option[DeltaTimeTravelSpec] = None): BaseRelation = {
val versionToUse = timeTravel.map { tt =>
val (version, accessType) = DeltaTableUtils.resolveTimeTravelVersion(
spark.sessionState.conf, this, tt)
val source = tt.creationSource.getOrElse("unknown")
recordDeltaEvent(this, s"delta.timeTravel.$source", data = Map(
"tableVersion" -> snapshot.version,
"queriedVersion" -> version,
"accessType" -> accessType
))
version
}
/** Used to link the files present in the table into the query planner. */
val snapshotToUse = versionToUse.map(getSnapshotAt(_)).getOrElse(snapshot)
val fileIndex = TahoeLogFileIndex(
spark, this, dataPath, snapshotToUse.metadata.schema, partitionFilters, versionToUse)
new HadoopFsRelation(
fileIndex,
partitionSchema = snapshotToUse.metadata.partitionSchema,
dataSchema = snapshotToUse.metadata.schema,
bucketSpec = None,
snapshotToUse.fileFormat,
snapshotToUse.metadata.format.options)(spark) with InsertableRelation {
def insert(data: DataFrame, overwrite: Boolean): Unit = {
val mode = if (overwrite) SaveMode.Overwrite else SaveMode.Append
WriteIntoDelta(
deltaLog = DeltaLog.this,
mode = mode,
new DeltaOptions(Map.empty[String, String], spark.sessionState.conf),
partitionColumns = Seq.empty,
configuration = Map.empty,
data = data).run(spark)
}
}
override def inputFiles: Array[String] = {
getSnapshot(stalenessAcceptable = false).filesForScan(
projection = Nil, partitionFilters).files.map(f => absolutePath(f.path).toString).toArray
}该方法调用了snapshot的filesForScan方法：
def filesForScan(projection: Seq[Attribute], filters: Seq[Expression]): DeltaScan = {
implicit val enc = SingleAction.addFileEncoder
val partitionFilters = filters.flatMap { filter =>
DeltaTableUtils.splitMetadataAndDataPredicates(filter, metadata.partitionColumns, spark)._1
}
val files = DeltaLog.filterFileList(
metadata.partitionSchema,
allFiles.toDF(),
partitionFilters).as[AddFile].collect()
DeltaScan(version = version, files, null, null, null)(null, null, null, null)
}. 通过指定版本获取对应的snapshot
. 构建TahoeLogFileIndex，因为这里构建的是HadoopFsRelation，所以我们关注TahoeLogFileIndex的inputfiles方法：
通过之前文章的分析，我们直到deltalog记录了AddFile和Remove记录，那现在读数据怎么读取呢？全部在allFiles方法。
重点看一下：allFiles方法：
def allFiles: Dataset[AddFile] = {
val implicits = spark.implicits
import implicits._
state.where("add IS NOT NULL").select($"add".as[AddFile])
}这里调用了state方法，而它又调用了stateReconstruction方法，
private lazy val cachedState = cacheDS(stateReconstruction, s"Delta Table State #$version - $redactedPath") /** The current set of actions in this [[Snapshot]]. */ def state: Dataset[SingleAction] = cachedState.getDS
stateReconstruction方法在checkpoint的时用到了，在这里也用到了，主要是重新构造文件状态，合并AddFile和RemoveFile：
private def stateReconstruction: Dataset[SingleAction] = {
...
loadActions.mapPartitions { actions =>
val hdpConf = hadoopConf.value.value
actions.flatMap {
_.unwrap match {
case add: AddFile => Some(add.copy(path = canonicalizePath(add.path, hdpConf)).wrap)
case rm: RemoveFile => Some(rm.copy(path = canonicalizePath(rm.path, hdpConf)).wrap)
case other if other == null => None
case other => Some(other.wrap)
}
}
}
...
.mapPartitions { iter =>
val state = new InMemoryLogReplay(time)
state.append(0, iter.map(_.unwrap))
state.checkpoint.map(_.wrap)
}
}而关键在于InMemoryLogReplay的append方法和checkpoint方法，这里做到了文件状态的合并：
assert(currentVersion == -1 || version == currentVersion + 1,
s"Attempted to replay version $version, but state is at $currentVersion")
currentVersion = version
actions.foreach {
case a: SetTransaction =>
transactions(a.appId) = a
case a: Metadata =>
currentMetaData = a
case a: Protocol =>
currentProtocolVersion = a
case add: AddFile =>
activeFiles(add.pathAsUri) = add.copy(dataChange = false)
// Remove the tombstone to make sure we only output one `FileAction`.
tombstones.remove(add.pathAsUri)
case remove: RemoveFile =>
activeFiles.remove(remove.pathAsUri)
tombstones(remove.pathAsUri) = remove.copy(dataChange = false)
case ci: CommitInfo => // do nothing
case null => // Some crazy future feature. Ignore
}
}重点就在case add: AddFile和 case remove: RemoveFile处理以及checkpoint方法，能够很好的合并文件状态。
再调用collect方法,返回DeltaScan，之后获取文件路径作为要处理的文件路径。
把TahoeLogFileIndex传入HadoopFsRelation得到最后的BaseRelation 返回
注意：spark读取delta格式整个流程和spark读取其他数据格式流程一致，主要区别在于读取数据之前，会把文件状态在内存中进行一次合并，这样只需要读取文件状态为Addfile的就行了
以上是“spark delta如何读数据”这篇文章的所有内容，感谢各位的阅读！相信大家都有了一定的了解，希望分享的内容对大家有所帮助，如果还想学习更多知识，欢迎关注创新互联行业资讯频道！