org.apache.spark.sql.streaming
DataStreamReader
Related Doc:
package streaming
Loads a CSV file stream and returns the result as a 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 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 form
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): defines whether or not leading whitespaces
from values being read should be skipped.ignoreTrailingWhiteSpace (default false): defines 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.SSSZZ): 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 1000000): defines the maximum number of characters allowed
for any given value being read.mode (default PERMISSIVE): allows a mode for dealing with corrupt records
during parsing.PERMISSIVE : sets other fields to null when it meets a corrupted record. When
a schema is set by user, it sets null for extra fields.DROPMALFORMED : ignores the whole corrupted records.FAILFAST : throws an exception when it meets corrupted records.2.0.0
Specifies the input data source format.
Specifies the input data source format.
2.0.0
Loads a JSON file stream (one object per line) and returns the result as a DataFrame.
Loads a JSON file stream (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.
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 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 new field configured by columnNameOfCorruptRecord. When
a schema is set by user, 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.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.SSSZZ): sets the string that
indicates a timestamp format. Custom date formats follow the formats at
java.text.SimpleDateFormat. This applies to timestamp type.2.0.0
Loads input in as a DataFrame, for data streams that read from some path.
Loads input in as a DataFrame, for data streams that read from some path.
2.0.0
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).
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.
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 input options for the underlying data source.
Adds input options for the underlying data source.
2.0.0
(Scala-specific) Adds input options for the underlying data source.
(Scala-specific) Adds input options for the underlying data source.
2.0.0
Loads a Parquet file stream, returning the result as a 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.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.
2.0.0
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.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.2.0.0
Interface used to load a streaming Dataset from external storage systems (e.g. file systems, key-value stores, etc). Use SparkSession.readStream to access this.
2.0.0