Spark 3.0.0 ScalaDoc - org.apache.spark.sql.streaming.DataStreamReader

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def csv(path: String): DataFrame

Loads a CSV file stream 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:

maxFilesPerTrigger (default: no max limit): sets the maximum number of new files to be considered in every trigger.
sep (default ,): sets a 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 a 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 form com.databricks.spark.csv.
escape (default \): sets a single character used for escaping quotes inside an already quoted value.
charToEscapeQuoteEscaping (default escape or \0): sets a single character used for escaping the escape for the quote character. The default value is escape character when escape and quote characters are different, \0 otherwise.
comment (default empty string): sets a 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.
emptyValue (default empty string): sets the string representation of an empty value.
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 length
mode (default PERMISSIVE): allows a mode for dealing with corrupt records during parsing. It supports the following case-insensitive modes.
PERMISSIVE : when it meets a corrupted record, puts the malformed string into a field configured by columnNameOfCorruptRecord, and sets other fields to null. 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. A record with less/more tokens than schema is not a corrupted record to CSV. When it meets a record having fewer tokens than the length of the schema, sets null to extra fields. When the record has more tokens than the length of the schema, it drops extra tokens.
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.
locale (default is en-US): sets a locale as language tag in IETF BCP 47 format. For instance, this is used while parsing dates and timestamps.
lineSep (default covers all \r, \r\n and \n): defines the line separator that should be used for parsing. Maximum length is 1 character.

Since: 2.0.0

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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def format(source: String): DataStreamReader

Specifies the input data source format.

Since: 2.0.0

final def getClass(): Class[_]

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def hashCode(): Int

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def initializeLogIfNecessary(isInterpreter: Boolean, silent: Boolean = false): Boolean

Attributes: protected
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def initializeLogIfNecessary(isInterpreter: Boolean): Unit

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final def isInstanceOf[T0]: Boolean

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def isTraceEnabled(): Boolean

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def json(path: String): DataFrame

Loads a JSON file stream 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:

maxFilesPerTrigger (default: no max limit): sets the maximum number of new files to be considered in every trigger.
primitivesAsString (default false): infers all primitive values as a string type
prefersDecimal (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 records
allowUnquotedFieldNames (default false): allows unquoted JSON field names
allowSingleQuotes (default true): allows single quotes in addition to double quotes
allowNumericLeadingZeros (default false): allows leading zeros in numbers (e.g. 00012)
allowBackslashEscapingAnyCharacter (default false): allows accepting quoting of all character using backslash quoting mechanism
allowUnquotedControlChars (default false): allows JSON Strings to contain unquoted control characters (ASCII characters with value less than 32, including tab and line feed characters) or not.
mode (default PERMISSIVE): allows a mode for dealing with corrupt records during parsing.
PERMISSIVE : when it meets a corrupted record, puts the malformed string into a field configured by columnNameOfCorruptRecord, and sets other fields to null. 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 file
lineSep (default covers all \r, \r\n and \n): defines the line separator that should be used for parsing.
dropFieldIfAllNull (default false): whether to ignore column of all null values or empty array/struct during schema inference.
locale (default is en-US): sets a locale as language tag in IETF BCP 47 format. For instance, this is used while parsing dates and timestamps.

Since: 2.0.0

def load(path: String): DataFrame

Loads input in as a DataFrame, for data streams that read from some path.

Since: 2.0.0

def load(): DataFrame

Loads input data stream in as a DataFrame, for data streams that don't require a path (e.g.

Loads input data stream in as a DataFrame, for data streams that don't require a path (e.g. external key-value stores).

Since: 2.0.0

def log: Logger

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def logDebug(msg: ⇒ String, throwable: Throwable): Unit

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def logDebug(msg: ⇒ String): Unit

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def logError(msg: ⇒ String, throwable: Throwable): Unit

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def logError(msg: ⇒ String): Unit

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def logInfo(msg: ⇒ String, throwable: Throwable): Unit

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def logInfo(msg: ⇒ String): Unit

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def logName: String

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def logTrace(msg: ⇒ String, throwable: Throwable): Unit

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def logTrace(msg: ⇒ String): Unit

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def logWarning(msg: ⇒ String, throwable: Throwable): Unit

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def logWarning(msg: ⇒ String): Unit

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final def ne(arg0: AnyRef): Boolean

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final def notify(): Unit

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final def notifyAll(): Unit

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def option(key: String, value: Double): DataStreamReader

Adds an input option for the underlying data source.

Since: 2.0.0

def option(key: String, value: Long): DataStreamReader

Adds an input option for the underlying data source.

Since: 2.0.0

def option(key: String, value: Boolean): DataStreamReader

Adds an input option for the underlying data source.

Since: 2.0.0

def option(key: String, value: String): DataStreamReader

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.

Since: 2.0.0

def options(options: Map[String, String]): DataStreamReader

(Java-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 data sources or partition values.

Since: 2.0.0

def options(options: Map[String, String]): DataStreamReader

(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 data sources or partition values.

Since: 2.0.0

def orc(path: String): DataFrame

Loads a ORC file stream, returning the result as a DataFrame.

You can set the following ORC-specific option(s) for reading ORC files:

maxFilesPerTrigger (default: no max limit): sets the maximum number of new files to be considered in every trigger.

Since: 2.3.0

def parquet(path: String): DataFrame

Loads a Parquet file stream, returning the result as a DataFrame.

You can set the following Parquet-specific option(s) for reading Parquet files:

maxFilesPerTrigger (default: no max limit): sets the maximum number of new files to be considered in every trigger.
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.

Since: 2.0.0

def schema(schemaString: String): DataStreamReader

Specifies the schema by using the input DDL-formatted string.

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.

Since: 2.3.0

def schema(schema: StructType): DataStreamReader

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.

Since: 2.0.0

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

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def text(path: String): DataFrame

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.

By default, each line in the text files is a new row in the resulting DataFrame. For example:

// Scala:
spark.readStream.text("/path/to/directory/")

// Java:
spark.readStream().text("/path/to/directory/")

You can set the following text-specific options to deal with text files:

maxFilesPerTrigger (default: no max limit): sets the maximum number of new files to be considered in every trigger.
wholetext (default false): If true, read a file as a single row and not split by "\n".
lineSep (default covers all \r, \r\n and \n): defines the line separator that should be used for parsing.

Since: 2.0.0

def textFile(path: String): Dataset[String]

Loads text file(s) and returns a Dataset of String.

Loads text file(s) 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.

By default, each line in the text file is a new element in the resulting Dataset. For example:

// Scala:
spark.readStream.textFile("/path/to/spark/README.md")

// Java:
spark.readStream().textFile("/path/to/spark/README.md")

You can set the following text-specific options to deal with text files:

maxFilesPerTrigger (default: no max limit): sets the maximum number of new files to be considered in every trigger.
wholetext (default false): If true, read a file as a single row and not split by "\n".
lineSep (default covers all \r, \r\n and \n): defines the line separator that should be used for parsing.

path: input path

Since: 2.1.0

def toString(): String

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final def wait(): Unit

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DataStreamReader

final class DataStreamReader extends Logging

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DataStreamReader 

final class DataStreamReader extends Logging

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DataStreamReader