DataFrameReader (Spark 3.4.2 JavaDoc)

Object
- org.apache.spark.sql.DataFrameReader

All Implemented Interfaces:

org.apache.spark.internal.Logging
```
public class DataFrameReader
extends Object
implements org.apache.spark.internal.Logging
```
Interface used to load a Dataset from external storage systems (e.g. file systems, key-value stores, etc). Use SparkSession.read to access this.

Since:

1.4.0

Nested Class Summary
- Nested classes/interfaces inherited from interface org.apache.spark.internal.Logging
  org.apache.spark.internal.Logging.SparkShellLoggingFilter

Method Summary

All Methods Instance Methods Concrete Methods Deprecated Methods
Modifier and Type	Method and Description
`Dataset<Row>`	`csv(Dataset<String> csvDataset)` Loads an `Dataset[String]` storing CSV rows and returns the result as a `DataFrame`.
`Dataset<Row>`	`csv(scala.collection.Seq<String> paths)` Loads CSV files and returns the result as a `DataFrame`.
`Dataset<Row>`	`csv(String... paths)` Loads CSV files and returns the result as a `DataFrame`.
`Dataset<Row>`	`csv(String path)` Loads a CSV file and returns the result as a `DataFrame`.
`DataFrameReader`	`format(String source)` Specifies the input data source format.
`Dataset<Row>`	`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.
`Dataset<Row>`	`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.
`Dataset<Row>`	`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.
`Dataset<Row>`	`json(Dataset<String> jsonDataset)` Loads a `Dataset[String]` storing JSON objects (JSON Lines text format or newline-delimited JSON) and returns the result as a `DataFrame`.
`Dataset<Row>`	`json(JavaRDD<String> jsonRDD)` Deprecated. Use json(Dataset[String]) instead. Since 2.2.0.
`Dataset<Row>`	`json(RDD<String> jsonRDD)` Deprecated. Use json(Dataset[String]) instead. Since 2.2.0.
`Dataset<Row>`	`json(scala.collection.Seq<String> paths)` Loads JSON files and returns the results as a `DataFrame`.
`Dataset<Row>`	`json(String... paths)` Loads JSON files and returns the results as a `DataFrame`.
`Dataset<Row>`	`json(String path)` Loads a JSON file and returns the results as a `DataFrame`.
`Dataset<Row>`	`load()` Loads input in as a `DataFrame`, for data sources that don't require a path (e.g.
`Dataset<Row>`	`load(scala.collection.Seq<String> paths)` Loads input in as a `DataFrame`, for data sources that support multiple paths.
`Dataset<Row>`	`load(String... paths)` Loads input in as a `DataFrame`, for data sources that support multiple paths.
`Dataset<Row>`	`load(String path)` Loads input in as a `DataFrame`, for data sources that require a path (e.g.
`DataFrameReader`	`option(String key, boolean value)` Adds an input option for the underlying data source.
`DataFrameReader`	`option(String key, double value)` Adds an input option for the underlying data source.
`DataFrameReader`	`option(String key, long value)` Adds an input option for the underlying data source.
`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.
`Dataset<Row>`	`orc(scala.collection.Seq<String> paths)` Loads ORC files and returns the result as a `DataFrame`.
`Dataset<Row>`	`orc(String... paths)` Loads ORC files and returns the result as a `DataFrame`.
`Dataset<Row>`	`orc(String path)` Loads an ORC file and returns the result as a `DataFrame`.
`Dataset<Row>`	`parquet(scala.collection.Seq<String> paths)` Loads a Parquet file, returning the result as a `DataFrame`.
`Dataset<Row>`	`parquet(String... paths)` Loads a Parquet file, returning the result as a `DataFrame`.
`Dataset<Row>`	`parquet(String path)` Loads a Parquet file, returning the result as a `DataFrame`.
`DataFrameReader`	`schema(String schemaString)` Specifies the schema by using the input DDL-formatted string.
`DataFrameReader`	`schema(StructType schema)` Specifies the input schema.
`Dataset<Row>`	`table(String tableName)` Returns the specified table/view as a `DataFrame`.
`Dataset<Row>`	`text(scala.collection.Seq<String> paths)` Loads text files and returns a `DataFrame` whose schema starts with a string column named "value", and followed by partitioned columns if there are any.
`Dataset<Row>`	`text(String... paths)` Loads text files and returns a `DataFrame` whose schema starts with a string column named "value", and followed by partitioned columns if there are any.
`Dataset<Row>`	`text(String path)` Loads text files and returns a `DataFrame` whose schema starts with a string column named "value", and followed by partitioned columns if there are any.
`Dataset<String>`	`textFile(scala.collection.Seq<String> paths)` Loads text files and returns a `Dataset` of String.
`Dataset<String>`	`textFile(String... paths)` Loads text files and returns a `Dataset` of String.
`Dataset<String>`	`textFile(String path)` Loads text files and returns a `Dataset` of String.

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

Methods inherited from interface org.apache.spark.internal.Logging
$init$, initializeForcefully, initializeLogIfNecessary, initializeLogIfNecessary, initializeLogIfNecessary$default$2, initLock, isTraceEnabled, log, logDebug, logDebug, logError, logError, logInfo, logInfo, logName, logTrace, logTrace, logWarning, logWarning, org$apache$spark$internal$Logging$$log__$eq, org$apache$spark$internal$Logging$$log_, uninitialize

- Method Detail
  - csv
```
public Dataset<Row> csv(String... paths)
```
    Loads CSV files and returns the result as a DataFrame.
    This function will go through the input once to determine the input schema if inferSchema is enabled. To avoid going through the entire data once, disable inferSchema option or specify the schema explicitly using schema.
    You can find the CSV-specific options for reading CSV files in Data Source Option in the version you use.
    
    Parameters:
    
    paths - (undocumented)
    
    Returns:
    
    (undocumented)
    
    Since:
    
    2.0.0
  - csv
```
public Dataset<Row> csv(String path)
```
    Loads a CSV file and returns the result as a DataFrame. See the documentation on the other overloaded csv() method for more details.
    
    Parameters:
    
    path - (undocumented)
    
    Returns:
    
    (undocumented)
    
    Since:
    
    2.0.0
  - csv
```
public Dataset<Row> csv(Dataset<String> csvDataset)
```
    Loads an Dataset[String] storing CSV rows and returns the result as a DataFrame.
    If the schema is not specified using schema function and inferSchema option is enabled, this function goes through the input once to determine the input schema.
    If the schema is not specified using schema function and inferSchema option is disabled, it determines the columns as string types and it reads only the first line to determine the names and the number of fields.
    If the enforceSchema is set to false, only the CSV header in the first line is checked to conform specified or inferred schema.
    
    Parameters:
    
    csvDataset - input Dataset with one CSV row per record
    
    Returns:
    
    (undocumented)
    
    Since:
    
    2.2.0
    
    Note:
    
    if header option is set to true when calling this API, all lines same with the header will be removed if exists.
  - csv
```
public Dataset<Row> csv(scala.collection.Seq<String> paths)
```
    Loads CSV files and returns the result as a DataFrame.
    This function will go through the input once to determine the input schema if inferSchema is enabled. To avoid going through the entire data once, disable inferSchema option or specify the schema explicitly using schema.
    You can find the CSV-specific options for reading CSV files in Data Source Option in the version you use.
    
    Parameters:
    
    paths - (undocumented)
    
    Returns:
    
    (undocumented)
    
    Since:
    
    2.0.0
  - format
```
public DataFrameReader format(String source)
```
    Specifies the input data source format.
    
    Parameters:
    
    source - (undocumented)
    
    Returns:
    
    (undocumented)
    
    Since:
    
    1.4.0
  - jdbc
```
public Dataset<Row> 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.
    You can find the JDBC-specific option and parameter documentation for reading tables via JDBC in Data Source Option in the version you use.
    
    Parameters:
    
    url - (undocumented)
    
    table - (undocumented)
    
    properties - (undocumented)
    
    Returns:
    
    (undocumented)
    
    Since:
    
    1.4.0
  - jdbc
```
public Dataset<Row> 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.
    You can find the JDBC-specific option and parameter documentation for reading tables via JDBC in Data Source Option in the version you use.
    
    Parameters:
    
    table - Name of the table in the external database.
    
    columnName - Alias of partitionColumn option. Refer to partitionColumn in Data Source Option in the version you use.
    
    connectionProperties - JDBC database connection arguments, a list of arbitrary string tag/value. Normally at least a "user" and "password" property should be included. "fetchsize" can be used to control the number of rows per fetch and "queryTimeout" can be used to wait for a Statement object to execute to the given number of seconds.
    
    url - (undocumented)
    
    lowerBound - (undocumented)
    
    upperBound - (undocumented)
    
    numPartitions - (undocumented)
    
    Returns:
    
    (undocumented)
    
    Since:
    
    1.4.0
  - jdbc
```
public Dataset<Row> 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.
    You can find the JDBC-specific option and parameter documentation for reading tables via JDBC in Data Source Option in the version you use.
    
    Parameters:
    
    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. "fetchsize" can be used to control the number of rows per fetch.
    
    url - (undocumented)
    
    Returns:
    
    (undocumented)
    
    Since:
    
    1.4.0
  - json
```
public Dataset<Row> json(String... paths)
```
    Loads JSON files and returns the results as a DataFrame.
    JSON Lines (newline-delimited JSON) is supported by default. For JSON (one record per file), set the multiLine option to true.
    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.
    You can find the JSON-specific options for reading JSON files in Data Source Option in the version you use.
    
    Parameters:
    
    paths - (undocumented)
    
    Returns:
    
    (undocumented)
    
    Since:
    
    2.0.0
  - json
```
public Dataset<Row> json(String path)
```
    Loads a JSON file and returns the results as a DataFrame.
    See the documentation on the overloaded json() method with varargs for more details.
    
    Parameters:
    
    path - (undocumented)
    
    Returns:
    
    (undocumented)
    
    Since:
    
    1.4.0
  - json
```
public Dataset<Row> json(scala.collection.Seq<String> paths)
```
    Loads JSON files and returns the results as a DataFrame.
    JSON Lines (newline-delimited JSON) is supported by default. For JSON (one record per file), set the multiLine option to true.
    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.
    You can find the JSON-specific options for reading JSON files in Data Source Option in the version you use.
    
    Parameters:
    
    paths - (undocumented)
    
    Returns:
    
    (undocumented)
    
    Since:
    
    2.0.0
  - json
```
public Dataset<Row> json(JavaRDD<String> jsonRDD)
```
    Deprecated. Use json(Dataset[String]) instead. Since 2.2.0.
    
    Loads a JavaRDD[String] storing JSON objects (JSON Lines text format or newline-delimited JSON) 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 Dataset<Row> json(RDD<String> jsonRDD)
```
    Deprecated. Use json(Dataset[String]) instead. Since 2.2.0.
    
    Loads an RDD[String] storing JSON objects (JSON Lines text format or newline-delimited JSON) 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 Dataset<Row> json(Dataset<String> jsonDataset)
```
    Loads a Dataset[String] storing JSON objects (JSON Lines text format or newline-delimited JSON) 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:
    
    jsonDataset - input Dataset with one JSON object per record
    
    Returns:
    
    (undocumented)
    
    Since:
    
    2.2.0
  - load
```
public Dataset<Row> load(String... paths)
```
    Loads input in as a DataFrame, for data sources that support multiple paths. Only works if the source is a HadoopFsRelationProvider.
    
    Parameters:
    
    paths - (undocumented)
    
    Returns:
    
    (undocumented)
    
    Since:
    
    1.6.0
  - load
```
public Dataset<Row> 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
  - load
```
public Dataset<Row> 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 Dataset<Row> load(scala.collection.Seq<String> paths)
```
    Loads input in as a DataFrame, for data sources that support multiple paths. Only works if the source is a HadoopFsRelationProvider.
    
    Parameters:
    
    paths - (undocumented)
    
    Returns:
    
    (undocumented)
    
    Since:
    
    1.6.0
  - option
```
public DataFrameReader option(String key,
                              String value)
```
    Adds an input option for the underlying data source.
    All options are maintained in a case-insensitive way in terms of key names. If a new option has the same key case-insensitively, it will override the existing option.
    
    Parameters:
    
    key - (undocumented)
    
    value - (undocumented)
    
    Returns:
    
    (undocumented)
    
    Since:
    
    1.4.0
  - option
```
public DataFrameReader option(String key,
                              boolean value)
```
    Adds an input option for the underlying data source.
    All options are maintained in a case-insensitive way in terms of key names. If a new option has the same key case-insensitively, it will override the existing option.
    
    Parameters:
    
    key - (undocumented)
    
    value - (undocumented)
    
    Returns:
    
    (undocumented)
    
    Since:
    
    2.0.0
  - option
```
public DataFrameReader option(String key,
                              long value)
```
    Adds an input option for the underlying data source.
    All options are maintained in a case-insensitive way in terms of key names. If a new option has the same key case-insensitively, it will override the existing option.
    
    Parameters:
    
    key - (undocumented)
    
    value - (undocumented)
    
    Returns:
    
    (undocumented)
    
    Since:
    
    2.0.0
  - option
```
public DataFrameReader option(String key,
                              double value)
```
    Adds an input option for the underlying data source.
    All options are maintained in a case-insensitive way in terms of key names. If a new option has the same key case-insensitively, it will override the existing option.
    
    Parameters:
    
    key - (undocumented)
    
    value - (undocumented)
    
    Returns:
    
    (undocumented)
    
    Since:
    
    2.0.0
  - options
```
public DataFrameReader options(scala.collection.Map<String,String> options)
```
    (Scala-specific) Adds input options for the underlying data source.
    All options are maintained in a case-insensitive way in terms of key names. If a new option has the same key case-insensitively, it will override the existing option.
    
    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.
    All options are maintained in a case-insensitive way in terms of key names. If a new option has the same key case-insensitively, it will override the existing option.
    
    Parameters:
    
    options - (undocumented)
    
    Returns:
    
    (undocumented)
    
    Since:
    
    1.4.0
  - orc
```
public Dataset<Row> orc(String... paths)
```
    Loads ORC files and returns the result as a DataFrame.
    ORC-specific option(s) for reading ORC files can be found in Data Source Option in the version you use.
    
    Parameters:
    
    paths - input paths
    
    Returns:
    
    (undocumented)
    
    Since:
    
    2.0.0
  - orc
```
public Dataset<Row> orc(String path)
```
    Loads an ORC file and returns the result as a DataFrame.
    
    Parameters:
    
    path - input path
    
    Returns:
    
    (undocumented)
    
    Since:
    
    1.5.0
  - orc
```
public Dataset<Row> orc(scala.collection.Seq<String> paths)
```
    Loads ORC files and returns the result as a DataFrame.
    ORC-specific option(s) for reading ORC files can be found in Data Source Option in the version you use.
    
    Parameters:
    
    paths - input paths
    
    Returns:
    
    (undocumented)
    
    Since:
    
    2.0.0
  - parquet
```
public Dataset<Row> parquet(String... paths)
```
    Loads a Parquet file, returning the result as a DataFrame.
    Parquet-specific option(s) for reading Parquet files can be found in Data Source Option in the version you use.
    
    Parameters:
    
    paths - (undocumented)
    
    Returns:
    
    (undocumented)
    
    Since:
    
    1.4.0
  - parquet
```
public Dataset<Row> parquet(String path)
```
    Loads a Parquet file, returning the result as a DataFrame. See the documentation on the other overloaded parquet() method for more details.
    
    Parameters:
    
    path - (undocumented)
    
    Returns:
    
    (undocumented)
    
    Since:
    
    2.0.0
  - parquet
```
public Dataset<Row> parquet(scala.collection.Seq<String> paths)
```
    Loads a Parquet file, returning the result as a DataFrame.
    Parquet-specific option(s) for reading Parquet files can be found in Data Source Option in the version you use.
    
    Parameters:
    
    paths - (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
  - schema
```
public DataFrameReader schema(String schemaString)
```
    Specifies the schema by using the input DDL-formatted string. 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.
```
   spark.read.schema("a INT, b STRING, c DOUBLE").csv("test.csv")
 
```
    Parameters:
    
    schemaString - (undocumented)
    
    Returns:
    
    (undocumented)
    
    Since:
    
    2.3.0
  - table
```
public Dataset<Row> table(String tableName)
```
    Returns the specified table/view as a DataFrame. If it's a table, it must support batch reading and the returned DataFrame is the batch scan query plan of this table. If it's a view, the returned DataFrame is simply the query plan of the view, which can either be a batch or streaming query plan.
    
    Parameters:
    
    tableName - is either a qualified or unqualified name that designates a table or view. If a database is specified, it identifies the table/view from the database. Otherwise, it first attempts to find a temporary view with the given name and then match the table/view from the current database. Note that, the global temporary view database is also valid here.
    
    Returns:
    
    (undocumented)
    
    Since:
    
    1.4.0
  - text
```
public Dataset<Row> text(String... paths)
```
    Loads text files and returns a DataFrame whose schema starts with a string column named "value", and followed by partitioned columns if there are any. The text files must be encoded as UTF-8.
    By default, each line in the text files is a new row in the resulting DataFrame. For example:
```
   // Scala:
   spark.read.text("/path/to/spark/README.md")

   // Java:
   spark.read().text("/path/to/spark/README.md")
 
```
    You can find the text-specific options for reading text files in Data Source Option in the version you use.
    Parameters:
    
    paths - input paths
    
    Returns:
    
    (undocumented)
    
    Since:
    
    1.6.0
  - text
```
public Dataset<Row> text(String path)
```
    Loads text files and returns a DataFrame whose schema starts with a string column named "value", and followed by partitioned columns if there are any. See the documentation on the other overloaded text() method for more details.
    
    Parameters:
    
    path - (undocumented)
    
    Returns:
    
    (undocumented)
    
    Since:
    
    2.0.0
  - text
```
public Dataset<Row> text(scala.collection.Seq<String> paths)
```
    Loads text files and returns a DataFrame whose schema starts with a string column named "value", and followed by partitioned columns if there are any. The text files must be encoded as UTF-8.
    By default, each line in the text files is a new row in the resulting DataFrame. For example:
```
   // Scala:
   spark.read.text("/path/to/spark/README.md")

   // Java:
   spark.read().text("/path/to/spark/README.md")
 
```
    You can find the text-specific options for reading text files in Data Source Option in the version you use.
    Parameters:
    
    paths - input paths
    
    Returns:
    
    (undocumented)
    
    Since:
    
    1.6.0
  - textFile
```
public Dataset<String> textFile(String... paths)
```
    Loads text files and returns a Dataset of String. The underlying schema of the Dataset contains a single string column named "value". The text files must be encoded as UTF-8.
    If the directory structure of the text files contains partitioning information, those are ignored in the resulting Dataset. To include partitioning information as columns, use text.
    By default, each line in the text files is a new row in the resulting DataFrame. For example:
```
   // Scala:
   spark.read.textFile("/path/to/spark/README.md")

   // Java:
   spark.read().textFile("/path/to/spark/README.md")
 
```
    You can set the text-specific options as specified in DataFrameReader.text.
    Parameters:
    
    paths - input path
    
    Returns:
    
    (undocumented)
    
    Since:
    
    2.0.0
  - textFile
```
public Dataset<String> textFile(String path)
```
    Loads text files and returns a Dataset of String. See the documentation on the other overloaded textFile() method for more details.
    
    Parameters:
    
    path - (undocumented)
    
    Returns:
    
    (undocumented)
    
    Since:
    
    2.0.0
  - textFile
```
public Dataset<String> textFile(scala.collection.Seq<String> paths)
```
    Loads text files and returns a Dataset of String. The underlying schema of the Dataset contains a single string column named "value". The text files must be encoded as UTF-8.
    If the directory structure of the text files contains partitioning information, those are ignored in the resulting Dataset. To include partitioning information as columns, use text.
    By default, each line in the text files is a new row in the resulting DataFrame. For example:
```
   // Scala:
   spark.read.textFile("/path/to/spark/README.md")

   // Java:
   spark.read().textFile("/path/to/spark/README.md")
 
```
    You can set the text-specific options as specified in DataFrameReader.text.
    Parameters:
    
    paths - input path
    
    Returns:
    
    (undocumented)
    
    Since:
    
    2.0.0

Class DataFrameReader

Nested Class Summary

Nested classes/interfaces inherited from interface org.apache.spark.internal.Logging

Method Summary

Methods inherited from class Object

Methods inherited from interface org.apache.spark.internal.Logging

Method Detail

csv

csv

csv

csv

format

jdbc

jdbc

jdbc

json

json

json

json

json

json

load

load

load

load

option

option

option

option

options

options

orc

orc

orc

parquet

parquet

parquet

schema

schema

table

text

text

text

textFile

textFile

textFile