DataFrameReaderEx

Instance Constructors

new DataFrameReaderEx(reader: DataFrameReader)

reader
A DataFrame reader.

Value Members

final def !=(arg0: Any): Boolean

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final def ##(): Int

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def address(url: String, user: String = "", password: String = ""): DataFrameReader

Specifies the connection details of the cluster to read from.
Specifies the connection details of the cluster to read from.
Overrides the default cluster specified in the Spark configuration for the duration of this read operation.
url
A string of the form "IRIS://host:port/namespace" that specifies the cluster to read from.
user
The user account with which to make the connection to the cluster named in the "url" option above.
password
The password for the given user account.
returns
The same DataFrameReader on which this method was invoked.
def address(address: Address): DataFrameReader

Specifies the connection details of the cluster to read from.
Specifies the connection details of the cluster to read from.
Overrides the default cluster specified in the Spark configuration for the duration of this read operation.
address
The connection details of the cluster to read from.
returns
The same DataFrameReader on which this method was invoked.
final def asInstanceOf[T0]: T0

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final def eq(arg0: AnyRef): Boolean

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def equals(arg0: Any): Boolean

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def finalize(): Unit

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final def getClass(): Class[_]

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def iris(text: String, column: String, lo: Long, hi: Long, partitions: ℕ): DataFrame

Executes a query on the given cluster to compute a suitably partitioned DataFrame.
Executes a query on the given cluster to compute a suitably partitioned DataFrame.
This enables one to write, for example:
```
spark.read.iris("SELECT * FROM Owls","column",0,10000,2)
```
as a convenient shorthand for the more explicit:
```
spark.read
     .format("com.intersystems.spark")
     .option("query","SELECT * FROM Owls")
     .option("paftitionCol","column")
     .option("lowerBound",0)
     .option("upperBound",10000)
     .option("numPartitions",2)
     .load()
```
The following options affect how the operation is performed:
- url: A string of the form "IRIS://host:port/namespace" that specifies the cluster from which the data is to be read. If omitted, the default cluster specified via the "spark.iris.master.url" configuration setting is used instead.
- user: The account with which to make the connection to the cluster named in the "url" option above.
- password: The password for the given user account.
- fetchsize: The number of rows to fetch per server round trip.
Default = 1000.
text
The text of a query to be executed on the cluster or the name of an existing table in the cluster to load.
column
The name of the integral valued column in the result set with which to further partition the query.
lo
The lower bound of the partitioning column.
hi
The upper bound of the partitioning column.
partitions
The number of partitions per instance to create.
returns
The results of the query in the form of a suitably partitioned DataFrame.

Exceptions thrown
SQLException if a database access error occurs.
See also
JDBC to Other Databases for more on the semantics of the column, lo, hi, and partitions parameters.
def iris(text: String, mfpi: ℕ = 1): DataFrame

Executes a query on the given cluster to compute a suitably partitioned DataFrame.
Executes a query on the given cluster to compute a suitably partitioned DataFrame.
This enables one to write, for example:
```
spark.read.iris("SELECT * FROM table",2)
```
as a convenient shorthand for the more explicit:
```
spark.read
     .format("com.intersystems.spark")
     .option("query","SELECT * FROM table")
     .option("mfpi",2)
     .load()
```
The following options affect how the operation is performed:
- url: A string of the form "IRIS://host:port/namespace" that specifies the cluster from which the data is to be read. If omitted, the default cluster specified via the "spark.iris.master.url" configuration setting is used instead.
- user: The account with which to make the connection to the cluster named in the "url" option above.
- password: The password for the given user account.
- fetchsize: The number of rows to fetch per server round trip.
Default = 1000.
text
The text of a query to be executed on the cluster or the name of an existing table in the cluster to load.
mfpi
The maximum number of factors per distinct instance to include in the factorization implicitly performed by the server, or 0 if no limit is necessary.
returns
The results of the query in the form of a suitably partitioned DataFrame.

Exceptions thrown
SQLException if a database access error occurs.
final def isInstanceOf[T0]: Boolean

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final def notify(): Unit

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final def notifyAll(): Unit

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final def wait(): Unit

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Related Doc: package spark

implicit class DataFrameReaderEx extends AnyRef

Instance Constructors

new DataFrameReaderEx(reader: DataFrameReader)

Value Members

final def !=(arg0: Any): Boolean

final def ##(): Int

final def ==(arg0: Any): Boolean

def address(url: String, user: String = "", password: String = ""): DataFrameReader

def address(address: Address): DataFrameReader

final def asInstanceOf[T0]: T0

def clone(): AnyRef

final def eq(arg0: AnyRef): Boolean

def equals(arg0: Any): Boolean

def finalize(): Unit

final def getClass(): Class[_]

def hashCode(): Int

def iris(text: String, column: String, lo: Long, hi: Long, partitions: ℕ): DataFrame

def iris(text: String, mfpi: ℕ = 1): DataFrame

final def isInstanceOf[T0]: Boolean

final def ne(arg0: AnyRef): Boolean

final def notify(): Unit

final def notifyAll(): Unit

final def synchronized[T0](arg0: ⇒ T0): T0

def toString(): String

final def wait(): Unit

final def wait(arg0: Long, arg1: Int): Unit

final def wait(arg0: Long): Unit

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