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- hashbakamp(arg_expression)
- DESCRIPTION:
Function finds the fallback AMP corresponding to the hash bucket number specified
in the expression and returns the AMP ID. If no hash bucket is specified, function
returns one less than the maximum number of fallback AMPs in the system.
PARAMETERS:
arg_expression:
Optional Argument.
Specifies an expression that evaluates to a valid hash bucket number.
Supported Types/Values:
* ColumnExpression, i.e., SQLAlchemy Column -
Format for the argument: '<dataframe>.<dataframe_column>.expression'.
* A Literal Value.
* SQLAlchemy TextClause
Specifies a TextClause object that is generated by passing a string
to 'text()' function from SQLAlchemy.
Following are the supported String Formats that can be passed to
text() function:
* Contiguous Map:
Format: MAP = Contiguousmap_name
For example,
text("MAP = " + Contiguousmap_name)
* ColumnExpression with Contiguous Map:
Format: ColumnExpression/literal value MAP = Contiguousmap_name
For example,
text(str(ColumnExpression) + " MAP = " + Contiguousmap_name)
* ColumnExpression with Sparse Map:
Format: ColumnExpression MAP = sparsemap_name COLOCATE USING = colocation_name
For example,
text(str(ColumnExpression) + " MAP = " + sparsemap_name + " COLOCATE USING = " + colocation_name)
where,
MAP - Specifies an object that specifies which AMPs store the rows of a table.
Contiguousmap_name - Specifies the name of the contiguous map, the map that
includes all AMPs within a specified range.
sparsemap_name - Specifies the name of the sparse map, the map that includes a
subset of AMPs from a contiguous map.
COLOCATE USING - Specifies a clause that forces tables that use the same sparse
map to be stored on the same subset of AMPs.
COLOCATE USING is required with a sparse map. It cannot be used
with a contiguous map.
colocation_name - Specifies the colocation name, usually databasename_tablename.
NOTES:
1. Function accepts positional arguments only.
2. If no argument is passed to hashbakamp(), function returns an INTEGER that is one less than the
maximum number of AMPs in the system.
3. If ColumnExpression is not specified:
a. For a contiguous map: The function returns an INTEGER representing the highest fallback
AMP number in the specified or default contiguous map. For a contiguous
map starting at AMP zero, adding one to the result gives the total number
of AMPs in the contiguous map.
b. For a sparse map: Expression must be specified for a sparse map.
4. If ColumnExpression is specified, the function returns the ID of the fallback AMP corresponding
to the hash bucket number specified in ColumnExpression, based on the specified or default map.
EXAMPLES:
# Load the data to run the example.
>>> load_example_data("dataframe", "admissions_train")
>>>
# Create a DataFrame on 'admissions_train' table.
>>> admissions_train = DataFrame("admissions_train")
>>> admissions_train
masters gpa stats programming admitted
id
22 yes 3.46 Novice Beginner 0
36 no 3.00 Advanced Novice 0
15 yes 4.00 Advanced Advanced 1
38 yes 2.65 Advanced Beginner 1
5 no 3.44 Novice Novice 0
17 no 3.83 Advanced Advanced 1
34 yes 3.85 Advanced Beginner 0
13 no 4.00 Advanced Novice 1
26 yes 3.57 Advanced Advanced 1
19 yes 1.98 Advanced Advanced 0
>>>
# Import func from sqlalchemy to execute hashbakamp() function.
>>> from sqlalchemy import func
# Example 1: Executing hashbakamp() function without argument to return fallback AMP number.
# Create a sqlalchemy Function object.
>>> hashbakamp_func_ = func.hashbakamp()
>>>
# Pass the Function object as input to DataFrame.assign().
>>> df = admissions_train.assign(hashbakamp_=hashbakamp_func_)
>>> print(df)
masters gpa stats programming admitted hashbakamp_
id
13 no 4.00 Advanced Novice 1 3
26 yes 3.57 Advanced Advanced 1 3
5 no 3.44 Novice Novice 0 3
19 yes 1.98 Advanced Advanced 0 3
15 yes 4.00 Advanced Advanced 1 3
40 yes 3.95 Novice Beginner 0 3
7 yes 2.33 Novice Novice 1 3
22 yes 3.46 Novice Beginner 0 3
36 no 3.00 Advanced Novice 0 3
38 yes 2.65 Advanced Beginner 1 3
>>>
# Example 2: Executing hashbakamp() function to return fallback AMP number corresponding
# to values in "id" column.
# Create a sqlalchemy Function object.
>>> hashbakamp_func_ = func.hashbakamp(admissions_train.id.expression)
>>>
# Pass the Function object as input to DataFrame.assign().
>>> df = admissions_train.assign(hashbakamp_id_=hashbakamp_func_)
>>> print(df)
masters gpa stats programming admitted hashbakamp_id_
id
5 no 3.44 Novice Novice 0 2
7 yes 2.33 Novice Novice 1 0
22 yes 3.46 Novice Beginner 0 3
19 yes 1.98 Advanced Advanced 0 0
15 yes 4.00 Advanced Advanced 1 1
17 no 3.83 Advanced Advanced 1 2
34 yes 3.85 Advanced Beginner 0 3
13 no 4.00 Advanced Novice 1 0
36 no 3.00 Advanced Novice 0 1
40 yes 3.95 Novice Beginner 0 3
>>>
# Example 3: Executing hashbakamp() function to return fallback AMP number using
# contiguous map.
# To use contiguous map, we must import 'text' from sqlalchemy.
>>> from sqlalchemy import text
# Create a sqlalchemy Function object.
>>> hashbakamp_func_ = func.hashbakamp(text("MAP = TD_Map1"))
>>>
# Pass the Function object as input to DataFrame.assign().
>>> df = admissions_train.assign(hashbakamp_contiguous_map_=hashbakamp_func_)
>>> print(df)
masters gpa stats programming admitted hashbakamp_contiguous_map_
id
5 no 3.44 Novice Novice 0 3
34 yes 3.85 Advanced Beginner 0 3
13 no 4.00 Advanced Novice 1 3
40 yes 3.95 Novice Beginner 0 3
22 yes 3.46 Novice Beginner 0 3
19 yes 1.98 Advanced Advanced 0 3
36 no 3.00 Advanced Novice 0 3
15 yes 4.00 Advanced Advanced 1 3
7 yes 2.33 Novice Novice 1 3
17 no 3.83 Advanced Advanced 1 3
>>>
# Example 4: Executing hashbakamp() function to return fallback AMP number using
# contiguous map and using values from "id" column.
# To use contiguous map, we must import 'text' from sqlalchemy.
>>> from sqlalchemy import text
# Create a sqlalchemy Function object.
>>> hashbakamp_func_ = func.hashbakamp(text(str(admissions_train.id.expression) + " MAP = TD_Map1"))
>>>
# Pass the Function object as input to DataFrame.assign().
>>> df = admissions_train.assign(hashbakamp_id_col_contiguous_map_=hashbakamp_func_)
>>> print(df)
masters gpa stats programming admitted hashbakamp_id_col_contiguous_map_
id
13 no 4.00 Advanced Novice 1 0
26 yes 3.57 Advanced Advanced 1 1
5 no 3.44 Novice Novice 0 2
19 yes 1.98 Advanced Advanced 0 0
15 yes 4.00 Advanced Advanced 1 1
40 yes 3.95 Novice Beginner 0 3
7 yes 2.33 Novice Novice 1 0
22 yes 3.46 Novice Beginner 0 3
36 no 3.00 Advanced Novice 0 1
38 yes 2.65 Advanced Beginner 1 1
>>>
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