SNOW-1888076: built-in function support wave1

CHANGELOG.md

@@ -41,8 +41,22 @@
   - `regr_sxy`
   - `regr_syy`
   - `try_to_binary`
+  - `base64`
+  - `base64_decode_string`
+  - `base64_encode`
+  - `editdistance`
+  - `hex`
+  - `hex_encode`
+  - `instr`
+  - `levenshtein`
+  - `log1p`
+  - `log2`
+  - `log10`
+  - `percentile_approx`
+  - `unbase64`
 - Added support for specifying a schema string (including implicit struct syntax) when calling `DataFrame.create_dataframe`.
 - Added support for `DataFrameWriter.insert_into/insertInto`. This method also supports local testing mode.
+- Added support for multiple columns in the functions `map_cat` and `map_concat`.
 
 #### Experimental Features
 

docs/source/snowpark/functions.rst

@@ -81,6 +81,9 @@ Functions
     atanh
     atan2
     avg
+    base64
+    base64_decode_string
+    base64_encode
     bit_length
     bitmap_bit_position
     bitmap_bucket_number
@@ -157,6 +160,7 @@ Functions
     desc_nulls_last
     div0
     divnull
+    editdistance
     endswith
     equal_nan
     equal_null
@@ -178,12 +182,15 @@ Functions
     grouping
     grouping_id
     hash
+    hex
+    hex_encode
     hour
     iff
     ifnull
     in_
     initcap
     insert
+    instr
     is_array
     is_binary
     is_boolean
@@ -211,12 +218,16 @@ Functions
     least
     left
     length
+    levenshtein
     listagg
     lit
     ln
     locate
     localtimestamp
     log
+    log1p
+    log2
+    log10
     lower
     lpad
     ltrim
@@ -257,6 +268,7 @@ Functions
     parse_json
     parse_xml
     percent_rank
+    percentile_approx
     percentile_cont
     position
     pow
@@ -350,6 +362,7 @@ Functions
     udaf
     udf
     udtf
+    unbase64
     uniform
     unix_timestamp
     upper

src/snowflake/snowpark/functions.py

@@ -160,6 +160,7 @@
 import functools
 import sys
 import typing
+from functools import reduce
 from random import randint
 from types import ModuleType
 from typing import Callable, Dict, List, Optional, Tuple, Union, overload
@@ -1345,6 +1346,9 @@ def approx_percentile(
     )
 
 
+percentile_approx = approx_percentile
+
+
 @publicapi
 def approx_percentile_accumulate(col: ColumnOrName, _emit_ast: bool = True) -> Column:
     """Returns the internal representation of the t-Digest state (as a JSON object) at the end of aggregation.
@@ -2956,6 +2960,63 @@ def log(
     return builtin("log", _ast=ast, _emit_ast=False)(b, arg)
 
 
+@publicapi
+def log1p(
+    x: Union[ColumnOrName, int, float],
+    _emit_ast: bool = True,
+) -> Column:
+    """
+    Returns the natural logarithm of (1 + x).
+
+    Example::
+
+        >>> df = session.create_dataframe([0, 1], schema=["a"])
+        >>> df.select(log1p(df["a"]).alias("log1p")).collect()
+        [Row(LOG1P=0.0), Row(LOG1P=0.6931471805599453)]
+    """
+    x = (
+        lit(x, _emit_ast=False)
+        if isinstance(x, (int, float))
+        else _to_col_if_str(x, "log")
+    )
+    one_plus_x = _to_col_if_str(x, "log1p") + lit(1, _emit_ast=_emit_ast)
+    return ln(one_plus_x, _emit_ast=_emit_ast)
+
+
+@publicapi
+def log10(
+    x: Union[ColumnOrName, int, float],
+    _emit_ast: bool = True,
+) -> Column:
+    """
+    Returns the base-10 logarithm of x.
+
+    Example::
+
+        >>> df = session.create_dataframe([1, 10], schema=["a"])
+        >>> df.select(log10(df["a"]).alias("log10")).collect()
+        [Row(LOG10=0.0), Row(LOG10=1.0)]
+    """
+    return _log10(x, _emit_ast=_emit_ast)
+
+
+@publicapi
+def log2(
+    x: Union[ColumnOrName, int, float],
+    _emit_ast: bool = True,
+) -> Column:
+    """
+    Returns the base-2 logarithm of x.
+
+    Example::
+
+        >>> df = session.create_dataframe([1, 2, 8], schema=["a"])
+        >>> df.select(log2(df["a"]).alias("log2")).collect()
+        [Row(LOG2=0.0), Row(LOG2=1.0), Row(LOG2=3.0)]
+    """
+    return _log2(x, _emit_ast=_emit_ast)
+
+
 # Create base 2 and base 10 wrappers for use with the Modin log2 and log10 functions
 def _log2(x: Union[ColumnOrName, int, float], _emit_ast: bool = True) -> Column:
     return log(2, x, _emit_ast=_emit_ast)
@@ -7112,12 +7173,15 @@ def array_unique_agg(col: ColumnOrName, _emit_ast: bool = True) -> Column:
 
 
 @publicapi
-def map_cat(col1: ColumnOrName, col2: ColumnOrName, _emit_ast: bool = True):
-    """Returns the concatenatation of two MAPs.
+def map_cat(
+    col1: ColumnOrName, col2: ColumnOrName, *cols: ColumnOrName, _emit_ast: bool = True
+):
+    """Returns the concatenatation of two or more MAPs.
 
     Args:
         col1: The source map
         col2: The map to be appended to col1
+        cols: More maps to be appended
 
     Example::
         >>> df = session.sql("select {'k1': 'v1'} :: MAP(STRING,STRING) as A, {'k2': 'v2'} :: MAP(STRING,STRING) as B")
@@ -7131,10 +7195,30 @@ def map_cat(col1: ColumnOrName, col2: ColumnOrName, _emit_ast: bool = True):
         |}                        |
         ---------------------------
         <BLANKLINE>
+        >>> df = session.sql("select {'k1': 'v1'} :: MAP(STRING,STRING) as A, {'k2': 'v2'} :: MAP(STRING,STRING) as B, {'k3': 'v3'} :: MAP(STRING,STRING) as C")
+        >>> df.select(map_cat("A", "B", "C")).show()
+        -------------------------------------------
+        |"MAP_CAT(MAP_CAT(""A"", ""B""), ""C"")"  |
+        -------------------------------------------
+        |{                                        |
+        |  "k1": "v1",                            |
+        |  "k2": "v2",                            |
+        |  "k3": "v3"                             |
+        |}                                        |
+        -------------------------------------------
+        <BLANKLINE>
     """
     m1 = _to_col_if_str(col1, "map_cat")
     m2 = _to_col_if_str(col2, "map_cat")
-    return builtin("map_cat", _emit_ast=_emit_ast)(m1, m2)
+
+    def map_cat_two_maps(first, second):
+        return builtin("map_cat", _emit_ast=_emit_ast)(first, second)
+
+    cols_to_concat = [m1, m2]
+    for c in cols:
+        cols_to_concat.append(_to_col_if_str(c, "map_cat"))
+
+    return reduce(map_cat_two_maps, cols_to_concat)
 
 
 @publicapi
@@ -11064,3 +11148,138 @@ def try_to_binary(
         if fmt
         else builtin("try_to_binary", _emit_ast=_emit_ast)(c)
     )
+
+
+@publicapi
+def base64_encode(
+    e: ColumnOrName,
+    max_line_length: Optional[int] = 0,
+    alphabet: Optional[str] = None,
+    _emit_ast: bool = True,
+) -> Column:
+    """
+    Encodes the input (string or binary) using Base64 encoding.
+
+    Example:
+        >>> df = session.create_dataframe(["Snowflake", "Data"], schema=["input"])
+        >>> df.select(base64_encode(col("input")).alias("encoded")).collect()
+        [Row(ENCODED='U25vd2ZsYWtl'), Row(ENCODED='RGF0YQ==')]
+    """
+    # Convert input to a column if it is not already one.
+    col_input = _to_col_if_str(e, "base64_encode")
+
+    # Prepare arguments for the function call.
+    args = [col_input]
+
+    if max_line_length:
+        args.append(lit(max_line_length))
+
+    if alphabet:
+        args.append(lit(alphabet))
+
+    # Call the built-in Base64 encode function.
+    return builtin("base64_encode", _emit_ast=_emit_ast)(*args)
+
+
+base64 = base64_encode
+
+
+@publicapi
+def base64_decode_string(
+    e: ColumnOrName, alphabet: Optional[str] = None, _emit_ast: bool = True
+) -> Column:
+    """
+    Decodes a Base64-encoded string to a string.
+
+    Example:
+        >>> df = session.create_dataframe(["U25vd2ZsYWtl", "SEVMTE8="], schema=["input"])
+        >>> df.select(base64_decode_string(col("input")).alias("decoded")).collect()
+        [Row(DECODED='Snowflake'), Row(DECODED='HELLO')]
+    """
+    # Convert input to a column if it is not already one.
+    col_input = _to_col_if_str(e, "base64_decode_string")
+
+    # Prepare arguments for the function call.
+    args = [col_input]
+
+    if alphabet:
+        args.append(lit(alphabet))
+
+    # Call the built-in Base64 encode function.
+    return builtin("base64_decode_string", _emit_ast=_emit_ast)(*args)
+
+
+unbase64 = base64_decode_string
+
+
+@publicapi
+def hex_encode(e: ColumnOrName, case: int = 1, _emit_ast: bool = True):
+    """
+    Encodes the input using hexadecimal (also ‘hex’ or ‘base16’) encoding.
+
+    Example:
+        >>> df = session.create_dataframe(["Snowflake", "Hello"], schema=["input"])
+        >>> df.select(hex_encode(col("input")).alias("hex_encoded")).collect()
+        [Row(HEX_ENCODED='536E6F77666C616B65'), Row(HEX_ENCODED='48656C6C6F')]
+    """
+    col_input = _to_col_if_str(e, "base64_decode_string")
+    return builtin("hex_encode", _emit_ast=_emit_ast)(col_input, lit(case))
+
+
+hex = hex_encode
+
+
+@publicapi
+def editdistance(
+    string_expr1: ColumnOrName,
+    string_expr2: ColumnOrName,
+    max_distance: Optional[int] = None,
+    _emit_ast: bool = True,
+) -> Column:
+    """Computes the Levenshtein distance between two input strings.
+
+    Optionally, a maximum distance can be specified. If the distance exceeds this value,
+    the computation halts and returns the maximum distance.
+
+    Example::
+
+        >>> df = session.create_dataframe(
+        ...     [["abc", "def"], ["abcdef", "abc"], ["snow", "flake"]],
+        ...     schema=["s1", "s2"]
+        ... )
+        >>> df.select(
+        ...     editdistance(col("s1"), col("s2")).alias("distance"),
+        ...     editdistance(col("s1"), col("s2"), 2).alias("max_2_distance")
+        ... ).collect()
+        [Row(DISTANCE=3, MAX_2_DISTANCE=2), Row(DISTANCE=3, MAX_2_DISTANCE=2), Row(DISTANCE=5, MAX_2_DISTANCE=2)]
+    """
+    s1 = _to_col_if_str(string_expr1, "editdistance")
+    s2 = _to_col_if_str(string_expr2, "editdistance")
+
+    args = [s1, s2]
+    if max_distance is not None:
+        max_dist = (
+            max_distance
+            if isinstance(max_distance, Column)
+            else lit(max_distance, _emit_ast=_emit_ast)
+        )
+        args.append(max_dist)
+
+    return builtin("editdistance", _emit_ast=False)(*args)
+
+
+levenshtein = editdistance
+
+
+@publicapi
+def instr(str: ColumnOrName, substr: str):
+    """
+    Locate the position of the first occurrence of substr column in the given string. Returns null if either of the arguments are null.
+
+    Example::
+        >>> df = session.create_dataframe([["hello world"], ["world hello"]], schema=["text"])
+        >>> df.select(instr(col("text"), "world").alias("position")).collect()
+        [Row(POSITION=7), Row(POSITION=1)]
+    """
+    s1 = _to_col_if_str(str, "instr")
+    return position(lit(substr), s1)