case class DataSource(sparkSession: SparkSession, className: String, paths: Seq[String] = Nil, userSpecifiedSchema: Option[StructType] = None, partitionColumns: Seq[String] = Seq.empty, bucketSpec: Option[BucketSpec] = None, options: Map[String, String] = Map.empty, catalogTable: Option[CatalogTable] = None) extends Logging with Product with Serializable
The main class responsible for representing a pluggable Data Source in Spark SQL. In addition to acting as the canonical set of parameters that can describe a Data Source, this class is used to resolve a description to a concrete implementation that can be used in a query plan (either batch or streaming) or to write out data using an external library.
From an end user's perspective a DataSource description can be created explicitly using org.apache.spark.sql.DataFrameReader or CREATE TABLE USING DDL. Additionally, this class is used when resolving a description from a metastore to a concrete implementation.
Many of the arguments to this class are optional, though depending on the specific API being used these optional arguments might be filled in during resolution using either inference or external metadata. For example, when reading a partitioned table from a file system, partition columns will be inferred from the directory layout even if they are not specified.
- paths
A list of file system paths that hold data. These will be globbed before if the "globPaths" option is true, and will be qualified. This option only works when reading from a FileFormat.
- userSpecifiedSchema
An optional specification of the schema of the data. When present we skip attempting to infer the schema.
- partitionColumns
A list of column names that the relation is partitioned by. This list is generally empty during the read path, unless this DataSource is managed by Hive. In these cases, during
resolveRelation, we will callgetOrInferFileFormatSchemafor file based DataSources to infer the partitioning. In other cases, if this list is empty, then this table is unpartitioned.- bucketSpec
An optional specification for bucketing (hash-partitioning) of the data.
- catalogTable
Optional catalog table reference that can be used to push down operations over the datasource to the catalog service.
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Instance Constructors
- new DataSource(sparkSession: SparkSession, className: String, paths: Seq[String] = Nil, userSpecifiedSchema: Option[StructType] = None, partitionColumns: Seq[String] = Seq.empty, bucketSpec: Option[BucketSpec] = None, options: Map[String, String] = Map.empty, catalogTable: Option[CatalogTable] = None)
- paths
A list of file system paths that hold data. These will be globbed before if the "globPaths" option is true, and will be qualified. This option only works when reading from a FileFormat.
- userSpecifiedSchema
An optional specification of the schema of the data. When present we skip attempting to infer the schema.
- partitionColumns
A list of column names that the relation is partitioned by. This list is generally empty during the read path, unless this DataSource is managed by Hive. In these cases, during
resolveRelation, we will callgetOrInferFileFormatSchemafor file based DataSources to infer the partitioning. In other cases, if this list is empty, then this table is unpartitioned.- bucketSpec
An optional specification for bucketing (hash-partitioning) of the data.
- catalogTable
Optional catalog table reference that can be used to push down operations over the datasource to the catalog service.
Type Members
- case class SourceInfo(name: String, schema: StructType, partitionColumns: Seq[String]) extends Product with Serializable
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- val bucketSpec: Option[BucketSpec]
- val catalogTable: Option[CatalogTable]
- val className: String
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- def createSink(outputMode: OutputMode): Sink
Returns a sink that can be used to continually write data.
- def createSource(metadataPath: String): Source
Returns a source that can be used to continually read data.
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- def globPaths: Boolean
Whether or not paths should be globbed before being used to access files.
- def initializeLogIfNecessary(isInterpreter: Boolean, silent: Boolean): Boolean
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- val options: Map[String, String]
- val partitionColumns: Seq[String]
- val paths: Seq[String]
- def planForWriting(mode: SaveMode, data: LogicalPlan): LogicalPlan
Returns a logical plan to write the given LogicalPlan out to this DataSource.
- def productElementNames: Iterator[String]
- Definition Classes
- Product
- lazy val providingClass: Class[_]
- def resolveRelation(checkFilesExist: Boolean = true): BaseRelation
Create a resolved BaseRelation that can be used to read data from or write data into this DataSource
Create a resolved BaseRelation that can be used to read data from or write data into this DataSource
- checkFilesExist
Whether to confirm that the files exist when generating the non-streaming file based datasource. StructuredStreaming jobs already list file existence, and when generating incremental jobs, the batch is considered as a non-streaming file based data source. Since we know that files already exist, we don't need to check them again.
- lazy val sourceInfo: SourceInfo
- val sparkSession: SparkSession
- final def synchronized[T0](arg0: => T0): T0
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- val userSpecifiedSchema: Option[StructType]
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- def writeAndRead(mode: SaveMode, data: LogicalPlan, outputColumnNames: Seq[String], physicalPlan: SparkPlan, metrics: Map[String, SQLMetric]): BaseRelation
Writes the given LogicalPlan out to this DataSource and returns a BaseRelation for the following reading.
Writes the given LogicalPlan out to this DataSource and returns a BaseRelation for the following reading.
- mode
The save mode for this writing.
- data
The input query plan that produces the data to be written. Note that this plan is analyzed and optimized.
- outputColumnNames
The original output column names of the input query plan. The optimizer may not preserve the output column's names' case, so we need this parameter instead of
data.output.- physicalPlan
The physical plan of the input query plan. We should run the writing command with this physical plan instead of creating a new physical plan, so that the metrics can be correctly linked to the given physical plan and shown in the web UI.