DataFrameReader
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package sql
Loads CSV files and returns the result as a DataFrame.
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 set the following CSV-specific options to deal with CSV files:
sep (default ,): sets the single character as a separator for each
field and value.encoding (default UTF-8): decodes the CSV files by the given encoding
type.quote (default "): sets the single character used for escaping quoted values where
the separator can be part of the value. If you would like to turn off quotations, you need to
set not null but an empty string. This behaviour is different from
com.databricks.spark.csv.escape (default \): sets the single character used for escaping quotes inside
an already quoted value.comment (default empty string): sets the single character used for skipping lines
beginning with this character. By default, it is disabled.header (default false): uses the first line as names of columns.inferSchema (default false): infers the input schema automatically from data. It
requires one extra pass over the data.ignoreLeadingWhiteSpace (default false): a flag indicating whether or not leading
whitespaces from values being read should be skipped.ignoreTrailingWhiteSpace (default false): a flag indicating whether or not trailing
whitespaces from values being read should be skipped.nullValue (default empty string): sets the string representation of a null value. Since
2.0.1, this applies to all supported types including the string type.nanValue (default NaN): sets the string representation of a non-number" value.positiveInf (default Inf): sets the string representation of a positive infinity
value.negativeInf (default -Inf): sets the string representation of a negative infinity
value.dateFormat (default yyyy-MM-dd): sets the string that indicates a date format.
Custom date formats follow the formats at java.text.SimpleDateFormat. This applies to
date type.timestampFormat (default yyyy-MM-dd'T'HH:mm:ss.SSSXXX): sets the string that
indicates a timestamp format. Custom date formats follow the formats at
java.text.SimpleDateFormat. This applies to timestamp type.maxColumns (default 20480): defines a hard limit of how many columns
a record can have.maxCharsPerColumn (default -1): defines the maximum number of characters allowed
for any given value being read. By default, it is -1 meaning unlimited lengthmode (default PERMISSIVE): allows a mode for dealing with corrupt records
during parsing. It supports the following case-insensitive modes.PERMISSIVE : sets other fields to null when it meets a corrupted record, and puts
the malformed string into a field configured by columnNameOfCorruptRecord. To keep
corrupt records, an user can set a string type field named columnNameOfCorruptRecord
in an user-defined schema. If a schema does not have the field, it drops corrupt records
during parsing. When a length of parsed CSV tokens is shorter than an expected length
of a schema, it sets null for extra fields.DROPMALFORMED : ignores the whole corrupted records.FAILFAST : throws an exception when it meets corrupted records.columnNameOfCorruptRecord (default is the value specified in
spark.sql.columnNameOfCorruptRecord): allows renaming the new field having malformed string
created by PERMISSIVE mode. This overrides spark.sql.columnNameOfCorruptRecord.multiLine (default false): parse one record, which may span multiple lines.
2.0.0
Loads an Dataset[String] storing CSV rows and returns the result as a DataFrame.
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.
input Dataset with one CSV row per record
2.2.0
Loads a CSV file and returns the result as a DataFrame.
Loads a CSV file and returns the result as a DataFrame. See the documentation on the
other overloaded csv() method for more details.
2.0.0
Specifies the input data source format.
Specifies the input data source format.
1.4.0
Construct a DataFrame representing the database table accessible via JDBC URL
url named table using connection properties.
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.
JDBC database url of the form jdbc:subprotocol:subname
Name of the table in the external database.
Condition in the where clause for each partition.
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.
1.4.0
Construct a DataFrame representing the database table accessible via JDBC URL
url named table.
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.
JDBC database url of the form jdbc:subprotocol:subname.
Name of the table in the external database.
the name of a column of integral type that will be used for partitioning.
the minimum value of columnName used to decide partition stride.
the maximum value of columnName used to decide partition stride.
the number of partitions. This, along with lowerBound (inclusive),
upperBound (exclusive), form partition strides for generated WHERE
clause expressions used to split the column columnName evenly. When
the input is less than 1, the number is set to 1.
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.
1.4.0
Construct a DataFrame representing the database table accessible via JDBC URL
url named table and connection properties.
Construct a DataFrame representing the database table accessible via JDBC URL
url named table and connection properties.
1.4.0
Loads a Dataset[String] storing JSON objects (JSON Lines
text format or newline-delimited JSON) and returns the result as a DataFrame.
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.
input Dataset with one JSON object per record
2.2.0
Loads JSON files and returns the results as a DataFrame.
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 set the following JSON-specific options to deal with non-standard JSON files:
primitivesAsString (default false): infers all primitive values as a string typeprefersDecimal (default false): infers all floating-point values as a decimal
type. If the values do not fit in decimal, then it infers them as doubles.allowComments (default false): ignores Java/C++ style comment in JSON recordsallowUnquotedFieldNames (default false): allows unquoted JSON field namesallowSingleQuotes (default true): allows single quotes in addition to double quotesallowNumericLeadingZeros (default false): allows leading zeros in numbers
(e.g. 00012)allowBackslashEscapingAnyCharacter (default false): allows accepting quoting of all
character using backslash quoting mechanismmode (default PERMISSIVE): allows a mode for dealing with corrupt records
during parsing.PERMISSIVE : sets other fields to null when it meets a corrupted record, and puts
the malformed string into a field configured by columnNameOfCorruptRecord. To keep
corrupt records, an user can set a string type field named columnNameOfCorruptRecord
in an user-defined schema. If a schema does not have the field, it drops corrupt records
during parsing. When inferring a schema, it implicitly adds a columnNameOfCorruptRecord
field in an output schema.DROPMALFORMED : ignores the whole corrupted records.FAILFAST : throws an exception when it meets corrupted records.columnNameOfCorruptRecord (default is the value specified in
spark.sql.columnNameOfCorruptRecord): allows renaming the new field having malformed string
created by PERMISSIVE mode. This overrides spark.sql.columnNameOfCorruptRecord.dateFormat (default yyyy-MM-dd): sets the string that indicates a date format.
Custom date formats follow the formats at java.text.SimpleDateFormat. This applies to
date type.timestampFormat (default yyyy-MM-dd'T'HH:mm:ss.SSSXXX): sets the string that
indicates a timestamp format. Custom date formats follow the formats at
java.text.SimpleDateFormat. This applies to timestamp type.multiLine (default false): parse one record, which may span multiple lines,
per file2.0.0
Loads a JSON file and returns the results as a DataFrame.
Loads a JSON file and returns the results as a DataFrame.
See the documentation on the overloaded json() method with varargs for more details.
1.4.0
Loads input in as a DataFrame, for data sources that support multiple paths.
Loads input in as a DataFrame, for data sources that support multiple paths.
Only works if the source is a HadoopFsRelationProvider.
1.6.0
Loads input in as a DataFrame, for data sources that require a path (e.g.
Loads input in as a DataFrame, for data sources that require a path (e.g. data backed by
a local or distributed file system).
1.4.0
Loads input in as a DataFrame, for data sources that don't require a path (e.g.
Loads input in as a DataFrame, for data sources that don't require a path (e.g. external
key-value stores).
1.4.0
Adds an input option for the underlying data source.
Adds an input option for the underlying data source.
2.0.0
Adds an input option for the underlying data source.
Adds an input option for the underlying data source.
2.0.0
Adds an input option for the underlying data source.
Adds an input option for the underlying data source.
2.0.0
Adds an input option for the underlying data source.
Adds an input option for the underlying data source.
You can set the following option(s):
timeZone (default session local timezone): sets the string that indicates a timezone
to be used to parse timestamps in the JSON/CSV datasources or partition values.1.4.0
Adds input options for the underlying data source.
Adds input options for the underlying data source.
You can set the following option(s):
timeZone (default session local timezone): sets the string that indicates a timezone
to be used to parse timestamps in the JSON/CSV datasources or partition values.1.4.0
(Scala-specific) Adds input options for the underlying data source.
(Scala-specific) Adds input options for the underlying data source.
You can set the following option(s):
timeZone (default session local timezone): sets the string that indicates a timezone
to be used to parse timestamps in the JSON/CSV datasources or partition values.1.4.0
Loads ORC files and returns the result as a DataFrame.
Loads ORC files and returns the result as a DataFrame.
input paths
2.0.0
Currently, this method can only be used after enabling Hive support.
Loads an ORC file and returns the result as a DataFrame.
Loads an ORC file and returns the result as a DataFrame.
input path
1.5.0
Currently, this method can only be used after enabling Hive support.
Loads a Parquet file, returning the result as a DataFrame.
Loads a Parquet file, returning the result as a DataFrame.
You can set the following Parquet-specific option(s) for reading Parquet files:
mergeSchema (default is the value specified in spark.sql.parquet.mergeSchema): sets
whether we should merge schemas collected from all Parquet part-files. This will override
spark.sql.parquet.mergeSchema.
1.4.0
Loads a Parquet file, returning the result as a DataFrame.
Loads a Parquet file, returning the result as a DataFrame. See the documentation
on the other overloaded parquet() method for more details.
2.0.0
Specifies the input 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.
1.4.0
Returns the specified table as a DataFrame.
Returns the specified table as a DataFrame.
1.4.0
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.
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.
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")
input paths
1.6.0
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.
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.
2.0.0
Loads text files and returns a Dataset of String.
Loads text files and returns a Dataset of String. The underlying schema of the Dataset contains a single string column named "value".
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.
Each line in the text files is a new element in the resulting Dataset. For example:
// Scala: spark.read.textFile("/path/to/spark/README.md") // Java: spark.read().textFile("/path/to/spark/README.md")
input path
2.0.0
Loads text files and returns a Dataset of String.
Loads text files and returns a Dataset of String. See the documentation on the
other overloaded textFile() method for more details.
2.0.0
Loads an RDD[String] storing JSON objects (JSON Lines
text format or newline-delimited JSON) and returns the result as a DataFrame.
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.
input RDD with one JSON object per record
(Since version 2.2.0) Use json(Dataset[String]) instead.
1.4.0
Loads a JavaRDD[String] storing JSON objects (JSON
Lines text format or newline-delimited JSON) and returns the result as
a DataFrame.
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.
input RDD with one JSON object per record
(Since version 2.2.0) Use json(Dataset[String]) instead.
1.4.0
Interface used to load a Dataset from external storage systems (e.g. file systems, key-value stores, etc). Use
SparkSession.readto access this.1.4.0