A list of file system paths that hold data. These will be globbed before and qualified. This option only works when reading from a FileFormat.
An optional specification of the schema of the data. When present we skip attempting to infer the schema.
A list of column names that the relation is partitioned by. When this list is empty, the relation is unpartitioned.
An optional specification for bucketing (hash-partitioning) of the data.
An optional specification for bucketing (hash-partitioning) of the data.
Returns a sink that can be used to continually write data.
Returns a source that can be used to continually read data.
Returns true if there is a single path that has a metadata log indicating which files should be read.
A list of column names that the relation is partitioned by.
A list of column names that the relation is partitioned by. When this list is empty, the relation is unpartitioned.
A list of file system paths that hold data.
A list of file system paths that hold data. These will be globbed before and qualified. This option only works when reading from a FileFormat.
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
A flag to indicate whether to check the existence of path or not. This flag will be set to false when we create an empty table (the path of the table does not exist).
An optional specification of the schema of the data.
An optional specification of the schema of the data. When present we skip attempting to infer the schema.
Writes the give DataFrame out to this DataSource.
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.
A list of file system paths that hold data. These will be globbed before and qualified. This option only works when reading from a FileFormat.
An optional specification of the schema of the data. When present we skip attempting to infer the schema.
A list of column names that the relation is partitioned by. When this list is empty, the relation is unpartitioned.
An optional specification for bucketing (hash-partitioning) of the data.