fugue_spark#
fugue_spark.dataframe#
- class fugue_spark.dataframe.SparkDataFrame(df=None, schema=None)[source]#
Bases:
DataFrame
DataFrame that wraps Spark DataFrame. Please also read the DataFrame Tutorial to understand this Fugue concept
- Parameters
df (Any) –
pyspark.sql.DataFrame
schema (Any) – Schema like object or
pyspark.sql.types.StructType
, defaults to None.
Note
You should use
fugue_spark.execution_engine.SparkExecutionEngine.to_df()
instead of construction it by yourself.If
schema
is set, then there will be type cast on the Spark DataFrame if the schema is different.
- property alias: str#
- alter_columns(columns)[source]#
Change column types
- Parameters
columns (Any) – Schema like object, all columns should be contained by the dataframe schema
- Returns
a new dataframe with altered columns, the order of the original schema will not change
- Return type
- as_array(columns=None, type_safe=False)[source]#
Convert to 2-dimensional native python array
- Parameters
columns (Optional[List[str]]) – columns to extract, defaults to None
type_safe (bool) – whether to ensure output conforms with its schema, defaults to False
- Returns
2-dimensional native python array
- Return type
List[Any]
Note
If
type_safe
is False, then the returned values are ‘raw’ values.
- as_array_iterable(columns=None, type_safe=False)[source]#
Convert to iterable of native python arrays
- Parameters
columns (Optional[List[str]]) – columns to extract, defaults to None
type_safe (bool) – whether to ensure output conforms with its schema, defaults to False
- Returns
iterable of native python arrays
- Return type
Iterable[Any]
Note
If
type_safe
is False, then the returned values are ‘raw’ values.
- as_local_bounded()[source]#
Convert this dataframe to a
LocalBoundedDataFrame
- Return type
- property empty: bool#
Whether this dataframe is empty
- head(n, columns=None)[source]#
Get first n rows of the dataframe as a new local bounded dataframe
- Parameters
n (int) – number of rows
columns (Optional[List[str]]) – selected columns, defaults to None (all columns)
- Returns
a local bounded dataframe
- Return type
- property is_bounded: bool#
Whether this dataframe is bounded
- property is_local: bool#
Whether this dataframe is a local Dataset
- property native: DataFrame#
The wrapped Spark DataFrame
- Return type
- native_as_df()[source]#
The dataframe form of the native object this Dataset class wraps. Dataframe form means the object contains schema information. For example the native an ArrayDataFrame is a python array, it doesn’t contain schema information, and its
native_as_df
should be either a pandas dataframe or an arrow dataframe.- Return type
DataFrame
- property num_partitions: int#
Number of physical partitions of this dataframe. Please read the Partition Tutorial
- peek_array()[source]#
Peek the first row of the dataframe as array
- Raises
FugueDatasetEmptyError – if it is empty
- Return type
List[Any]
fugue_spark.execution_engine#
- class fugue_spark.execution_engine.SparkExecutionEngine(spark_session=None, conf=None)[source]#
Bases:
ExecutionEngine
The execution engine based on
SparkSession
.Please read the ExecutionEngine Tutorial to understand this important Fugue concept
- Parameters
spark_session (Optional[SparkSession]) – Spark session, defaults to None to get the Spark session by
getOrCreate()
conf (Any) – Parameters like object defaults to None, read the Fugue Configuration Tutorial to learn Fugue specific options
- broadcast(df)[source]#
Broadcast the dataframe to all workers for a distributed computing framework
- Parameters
df (DataFrame) – the input dataframe
- Returns
the broadcasted dataframe
- Return type
- dropna(df, how='any', thresh=None, subset=None)[source]#
Drop NA recods from dataframe
- Parameters
df (DataFrame) – DataFrame
how (str) – ‘any’ or ‘all’. ‘any’ drops rows that contain any nulls. ‘all’ drops rows that contain all nulls.
thresh (Optional[int]) – int, drops rows that have less than thresh non-null values
subset (Optional[List[str]]) – list of columns to operate on
- Returns
DataFrame with NA records dropped
- Return type
- fillna(df, value, subset=None)[source]#
Fill
NULL
,NAN
,NAT
values in a dataframe- Parameters
df (DataFrame) – DataFrame
value (Any) – if scalar, fills all columns with same value. if dictionary, fills NA using the keys as column names and the values as the replacement values.
subset (Optional[List[str]]) – list of columns to operate on. ignored if value is a dictionary
- Returns
DataFrame with NA records filled
- Return type
- property fs: FileSystem#
File system of this engine instance
- get_current_parallelism()[source]#
Get the current number of parallelism of this engine
- Return type
int
- intersect(df1, df2, distinct=True)[source]#
Intersect
df1
anddf2
- Parameters
- Returns
the unioned dataframe
- Return type
Note
Currently, the schema of
df1
anddf2
must be identical, or an exception will be thrown.
- property is_distributed: bool#
Whether this engine is a distributed engine
- property is_spark_connect: bool#
- join(df1, df2, how, on=None)[source]#
Join two dataframes
- Parameters
df1 (DataFrame) – the first dataframe
df2 (DataFrame) – the second dataframe
how (str) – can accept
semi
,left_semi
,anti
,left_anti
,inner
,left_outer
,right_outer
,full_outer
,cross
on (Optional[List[str]]) – it can always be inferred, but if you provide, it will be validated against the inferred keys.
- Returns
the joined dataframe
- Return type
Note
Please read
get_join_schemas()
- load_df(path, format_hint=None, columns=None, **kwargs)[source]#
Load dataframe from persistent storage
- Parameters
path (Union[str, List[str]]) – the path to the dataframe
format_hint (Optional[Any]) – can accept
parquet
,csv
,json
, defaults to None, meaning to infercolumns (Optional[Any]) – list of columns or a Schema like object, defaults to None
kwargs (Any) – parameters to pass to the underlying framework
- Returns
an engine compatible dataframe
- Return type
For more details and examples, read Zip & Comap.
- property log: Logger#
Logger of this engine instance
- persist(df, lazy=False, **kwargs)[source]#
Force materializing and caching the dataframe
- Parameters
df (DataFrame) – the input dataframe
lazy (bool) –
True
: first usage of the output will trigger persisting to happen;False
(eager): persist is forced to happend immediately. Default toFalse
kwargs (Any) – parameter to pass to the underlying persist implementation
- Returns
the persisted dataframe
- Return type
Note
persist
can only guarantee the persisted dataframe will be computed for only once. However this doesn’t mean the backend really breaks up the execution dependency at the persisting point. Commonly, it doesn’t cause any issue, but if your execution graph is long, it may cause expected problems for example, stack overflow.
- register(df, name)[source]#
Register a virtual subclass of an ABC.
Returns the subclass, to allow usage as a class decorator.
- Parameters
df (DataFrame) –
name (str) –
- Return type
- repartition(df, partition_spec)[source]#
Partition the input dataframe using
partition_spec
.- Parameters
df (DataFrame) – input dataframe
partition_spec (PartitionSpec) – how you want to partition the dataframe
- Returns
repartitioned dataframe
- Return type
Note
Before implementing please read the Partition Tutorial
- sample(df, n=None, frac=None, replace=False, seed=None)[source]#
Sample dataframe by number of rows or by fraction
- Parameters
df (DataFrame) – DataFrame
n (Optional[int]) – number of rows to sample, one and only one of
n
andfrac
must be setfrac (Optional[float]) – fraction [0,1] to sample, one and only one of
n
andfrac
must be setreplace (bool) – whether replacement is allowed. With replacement, there may be duplicated rows in the result, defaults to False
seed (Optional[int]) – seed for randomness, defaults to None
- Returns
sampled dataframe
- Return type
- save_df(df, path, format_hint=None, mode='overwrite', partition_spec=None, force_single=False, **kwargs)[source]#
Save dataframe to a persistent storage
- Parameters
df (DataFrame) – input dataframe
path (str) – output path
format_hint (Optional[Any]) – can accept
parquet
,csv
,json
, defaults to None, meaning to infermode (str) – can accept
overwrite
,append
,error
, defaults to “overwrite”partition_spec (Optional[PartitionSpec]) – how to partition the dataframe before saving, defaults to empty
force_single (bool) – force the output as a single file, defaults to False
kwargs (Any) – parameters to pass to the underlying framework
- Return type
None
For more details and examples, read Load & Save.
- property spark_session: SparkSession#
- Returns
The wrapped spark session
- Return type
- subtract(df1, df2, distinct=True)[source]#
df1 - df2
- Parameters
- Returns
the unioned dataframe
- Return type
Note
Currently, the schema of
df1
anddf2
must be identical, or an exception will be thrown.
- take(df, n, presort, na_position='last', partition_spec=None)[source]#
Get the first n rows of a DataFrame per partition. If a presort is defined, use the presort before applying take. presort overrides partition_spec.presort. The Fugue implementation of the presort follows Pandas convention of specifying NULLs first or NULLs last. This is different from the Spark and SQL convention of NULLs as the smallest value.
- Parameters
df (DataFrame) – DataFrame
n (int) – number of rows to return
presort (str) – presort expression similar to partition presort
na_position (str) – position of null values during the presort. can accept
first
orlast
partition_spec (Optional[PartitionSpec]) – PartitionSpec to apply the take operation
- Returns
n rows of DataFrame per partition
- Return type
- to_df(df, schema=None)[source]#
Convert a data structure to
SparkDataFrame
- Parameters
data –
DataFrame
,pyspark.sql.DataFrame
,pyspark.RDD
, pandas DataFrame or list or iterable of arraysschema (Optional[Any]) – Schema like object or
pyspark.sql.types.StructType
defaults to None.df (Any) –
- Returns
engine compatible dataframe
- Return type
Note
if the input is already
SparkDataFrame
, it should return itselfFor
RDD
, list or iterable of arrays,schema
must be specifiedWhen
schema
is not None, a potential type cast may happen to ensure the dataframe’s schema.all other methods in the engine can take arbitrary dataframes and call this method to convert before doing anything
- class fugue_spark.execution_engine.SparkMapEngine(execution_engine)[source]#
Bases:
MapEngine
- Parameters
execution_engine (ExecutionEngine) –
- property is_distributed: bool#
Whether this engine is a distributed engine
- property is_spark_connect: bool#
Whether the spark session is created by spark connect
- map_dataframe(df, map_func, output_schema, partition_spec, on_init=None, map_func_format_hint=None)[source]#
Apply a function to each partition after you partition the dataframe in a specified way.
- Parameters
df (DataFrame) – input dataframe
map_func (Callable[[PartitionCursor, LocalDataFrame], LocalDataFrame]) – the function to apply on every logical partition
output_schema (Any) – Schema like object that can’t be None. Please also understand why we need this
partition_spec (PartitionSpec) – partition specification
on_init (Optional[Callable[[int, DataFrame], Any]]) – callback function when the physical partition is initializaing, defaults to None
map_func_format_hint (Optional[str]) – the preferred data format for
map_func
, it can bepandas
, pyarrow, etc, defaults to None. Certain engines can provide the most efficient map operations based on the hint.
- Returns
the dataframe after the map operation
- Return type
Note
Before implementing, you must read this to understand what map is used for and how it should work.
- class fugue_spark.execution_engine.SparkSQLEngine(execution_engine)[source]#
Bases:
SQLEngine
Spark SQL execution implementation.
- Parameters
execution_engine (ExecutionEngine) – it must be
SparkExecutionEngine
- Raises
ValueError – if the engine is not
SparkExecutionEngine
- property dialect: Optional[str]#
- property execution_engine_constraint: Type[ExecutionEngine]#
This defines the required ExecutionEngine type of this facet
- Returns
a subtype of
ExecutionEngine
- property is_distributed: bool#
Whether this engine is a distributed engine
- select(dfs, statement)[source]#
Execute select statement on the sql engine.
- Parameters
dfs (DataFrames) – a collection of dataframes that must have keys
statement (StructuredRawSQL) – the
SELECT
statement using thedfs
keys as tables.
- Returns
result of the
SELECT
statement- Return type
Examples
dfs = DataFrames(a=df1, b=df2) sql_engine.select( dfs, [(False, "SELECT * FROM "), (True,"a"), (False," UNION SELECT * FROM "), (True,"b")])
Note
There can be tables that is not in
dfs
. For example you want to select from hive without input DataFrames:>>> sql_engine.select(DataFrames(), "SELECT * FROM hive.a.table")
fugue_spark.ibis_engine#
- class fugue_spark.ibis_engine.SparkIbisEngine(execution_engine)[source]#
Bases:
IbisEngine
- Parameters
execution_engine (ExecutionEngine) –
- select(dfs, ibis_func)[source]#
Execute the ibis select expression.
- Parameters
dfs (DataFrames) – a collection of dataframes that must have keys
ibis_func (Callable[[BaseBackend], TableExpr]) – the ibis compute function
- Returns
result of the ibis function
- Return type
Note
This interface is experimental, so it is subjected to change.