DataFrameReader (Spark 1.4.0 JavaDoc)

Object
- org.apache.spark.sql.DataFrameReader

```
public class DataFrameReader
extends Object
```
:: Experimental :: Interface used to load a DataFrame from external storage systems (e.g. file systems, key-value stores, etc). Use SQLContext.read to access this.

Since:

1.4.0

Method Summary

Methods
Modifier and Type	Method and Description
`DataFrameReader`	`format(String source)` Specifies the input data source format.
`DataFrame`	`jdbc(String url, String table, java.util.Properties properties)` Construct a `DataFrame` representing the database table accessible via JDBC URL url named table and connection properties.
`DataFrame`	`jdbc(String url, String table, String[] predicates, java.util.Properties connectionProperties)` Construct a `DataFrame` representing the database table accessible via JDBC URL url named table using connection properties.
`DataFrame`	`jdbc(String url, String table, String columnName, long lowerBound, long upperBound, int numPartitions, java.util.Properties connectionProperties)` Construct a `DataFrame` representing the database table accessible via JDBC URL url named table.
`DataFrame`	`json(JavaRDD<String> jsonRDD)` Loads an `JavaRDD[String]` storing JSON objects (one object per record) and returns the result as a `DataFrame`.
`DataFrame`	`json(RDD<String> jsonRDD)` Loads an `RDD[String]` storing JSON objects (one object per record) and returns the result as a `DataFrame`.
`DataFrame`	`json(String path)` Loads a JSON file (one object per line) and returns the result as a `DataFrame`.
`DataFrame`	`load()` Loads input in as a `DataFrame`, for data sources that don't require a path (e.g.
`DataFrame`	`load(String path)` Loads input in as a `DataFrame`, for data sources that require a path (e.g.
`DataFrameReader`	`option(String key, String value)` Adds an input option for the underlying data source.
`DataFrameReader`	`options(scala.collection.Map<String,String> options)` (Scala-specific) Adds input options for the underlying data source.
`DataFrameReader`	`options(java.util.Map<String,String> options)` Adds input options for the underlying data source.
`DataFrame`	`parquet(scala.collection.Seq<String> paths)` Loads a Parquet file, returning the result as a `DataFrame`.
`DataFrame`	`parquet(String... paths)` Loads a Parquet file, returning the result as a `DataFrame`.
`DataFrameReader`	`schema(StructType schema)` Specifies the input schema.
`DataFrame`	`table(String tableName)` Returns the specified table as a `DataFrame`.

Methods inherited from class Object
equals, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

- Method Detail
  - parquet
```
public DataFrame parquet(String... paths)
```
    Loads a Parquet file, returning the result as a DataFrame. This function returns an empty DataFrame if no paths are passed in.
    
    Parameters:
    paths - (undocumented)
    
    Returns:
    (undocumented)
    Since:
    
    1.4.0
  - format
```
public DataFrameReader format(String source)
```
    Specifies the input data source format.
    
    Parameters:
    source - (undocumented)
    
    Returns:
    (undocumented)
    Since:
    
    1.4.0
  - schema
```
public DataFrameReader schema(StructType schema)
```
    Specifies the input schema. Some data sources (e.g. JSON) can infer the input schema automatically from data. By specifying the schema here, the underlying data source can skip the schema inference step, and thus speed up data loading.
    
    Parameters:
    schema - (undocumented)
    
    Returns:
    (undocumented)
    Since:
    
    1.4.0
  - option
```
public DataFrameReader option(String key,
                     String value)
```
    Adds an input option for the underlying data source.
    
    Parameters:
    key - (undocumented)
    value - (undocumented)
    
    Returns:
    (undocumented)
    Since:
    
    1.4.0
  - options
```
public DataFrameReader options(scala.collection.Map<String,String> options)
```
    (Scala-specific) Adds input options for the underlying data source.
    
    Parameters:
    options - (undocumented)
    
    Returns:
    (undocumented)
    Since:
    
    1.4.0
  - options
```
public DataFrameReader options(java.util.Map<String,String> options)
```
    Adds input options for the underlying data source.
    
    Parameters:
    options - (undocumented)
    
    Returns:
    (undocumented)
    Since:
    
    1.4.0
  - load
```
public DataFrame load(String path)
```
    Loads input in as a DataFrame, for data sources that require a path (e.g. data backed by a local or distributed file system).
    
    Parameters:
    path - (undocumented)
    
    Returns:
    (undocumented)
    Since:
    
    1.4.0
  - load
```
public DataFrame load()
```
    Loads input in as a DataFrame, for data sources that don't require a path (e.g. external key-value stores).
    
    Returns:
    (undocumented)
    Since:
    
    1.4.0
  - jdbc
```
public DataFrame jdbc(String url,
             String table,
             java.util.Properties properties)
```
    Construct a DataFrame representing the database table accessible via JDBC URL url named table and connection properties.
    
    Parameters:
    url - (undocumented)
    table - (undocumented)
    properties - (undocumented)
    
    Returns:
    (undocumented)
    Since:
    
    1.4.0
  - jdbc
```
public DataFrame jdbc(String url,
             String table,
             String columnName,
             long lowerBound,
             long upperBound,
             int numPartitions,
             java.util.Properties connectionProperties)
```
    Construct a DataFrame representing the database table accessible via JDBC URL url named table. Partitions of the table will be retrieved in parallel based on the parameters passed to this function.
    Don't create too many partitions in parallel on a large cluster; otherwise Spark might crash your external database systems.
    
    Parameters:
    url - JDBC database url of the form jdbc:subprotocol:subname
    table - Name of the table in the external database.
    columnName - the name of a column of integral type that will be used for partitioning.
    lowerBound - the minimum value of columnName used to decide partition stride
    upperBound - the maximum value of columnName used to decide partition stride
    numPartitions - the number of partitions. the range minValue-maxValue will be split evenly into this many partitions
    connectionProperties - JDBC database connection arguments, a list of arbitrary string tag/value. Normally at least a "user" and "password" property should be included.
    
    Returns:
    (undocumented)
    Since:
    
    1.4.0
  - jdbc
```
public DataFrame jdbc(String url,
             String table,
             String[] predicates,
             java.util.Properties connectionProperties)
```
    Construct a DataFrame representing the database table accessible via JDBC URL url named table using connection properties. The predicates parameter gives a list expressions suitable for inclusion in WHERE clauses; each one defines one partition of the DataFrame.
    Don't create too many partitions in parallel on a large cluster; otherwise Spark might crash your external database systems.
    
    Parameters:
    url - JDBC database url of the form jdbc:subprotocol:subname
    table - Name of the table in the external database.
    predicates - Condition in the where clause for each partition.
    connectionProperties - JDBC database connection arguments, a list of arbitrary string tag/value. Normally at least a "user" and "password" property should be included.
    
    Returns:
    (undocumented)
    Since:
    
    1.4.0
  - json
```
public DataFrame json(String path)
```
    Loads a JSON file (one object per line) and returns the result as a DataFrame.
    This function goes through the input once to determine the input schema. If you know the schema in advance, use the version that specifies the schema to avoid the extra scan.
    
    Parameters:
    path - input path
    
    Returns:
    (undocumented)
    Since:
    
    1.4.0
  - json
```
public DataFrame json(JavaRDD<String> jsonRDD)
```
    Loads an JavaRDD[String] storing JSON objects (one object per record) and returns the result as a DataFrame.
    Unless the schema is specified using schema function, this function goes through the input once to determine the input schema.
    
    Parameters:
    jsonRDD - input RDD with one JSON object per record
    
    Returns:
    (undocumented)
    Since:
    
    1.4.0
  - json
```
public DataFrame json(RDD<String> jsonRDD)
```
    Loads an RDD[String] storing JSON objects (one object per record) and returns the result as a DataFrame.
    Unless the schema is specified using schema function, this function goes through the input once to determine the input schema.
    
    Parameters:
    jsonRDD - input RDD with one JSON object per record
    
    Returns:
    (undocumented)
    Since:
    
    1.4.0
  - parquet
```
public DataFrame parquet(scala.collection.Seq<String> paths)
```
    Loads a Parquet file, returning the result as a DataFrame. This function returns an empty DataFrame if no paths are passed in.
    
    Parameters:
    paths - (undocumented)
    
    Returns:
    (undocumented)
    Since:
    
    1.4.0
  - table
```
public DataFrame table(String tableName)
```
    Returns the specified table as a DataFrame.
    
    Parameters:
    tableName - (undocumented)
    
    Returns:
    (undocumented)
    Since:
    
    1.4.0

Class DataFrameReader

Method Summary

Methods inherited from class Object

Method Detail

parquet

format

schema

option

options

options

load

load

jdbc

jdbc

jdbc

json

json

json

parquet

table