add arrays contains function

examples/get_started/common_sql_functions.py

@@ -9,7 +9,7 @@ def num_chars_udf(file):
     return ([],)
 
 
-dc = DataChain.from_storage("gs://datachain-demo/dogs-and-cats/")
+dc = DataChain.from_storage("gs://datachain-demo/dogs-and-cats/", anon=True)
 dc.map(num_chars_udf, params=["file"], output={"num_chars": list[str]}).select(
     "file.path", "num_chars"
 ).show(5)
@@ -32,6 +32,12 @@ def num_chars_udf(file):
     .show(5)
 )
 
+parts = string.split(path.name(C("file.path")), ".")
+chain = dc.mutate(
+    isdog=array.contains(parts, "dog"),
+    iscat=array.contains(parts, "cat"),
+)
+chain.select("file.path", "isdog", "iscat").show(5)
 
 chain = dc.mutate(
     a=array.length(string.split("file.path", "/")),
@@ -79,6 +85,15 @@ def num_chars_udf(file):
 3  dogs-and-cats/cat.10.json   cat.10  json
 4  dogs-and-cats/cat.100.jpg  cat.100   jpg
 
+[Limited by 5 rows]
+                        file isdog iscat
+                        path
+0    dogs-and-cats/cat.1.jpg     0     1
+1   dogs-and-cats/cat.1.json     0     1
+2   dogs-and-cats/cat.10.jpg     0     1
+3  dogs-and-cats/cat.10.json     0     1
+4  dogs-and-cats/cat.100.jpg     0     1
+
 [Limited by 5 rows]
 Processed: 400 rows [00:00, 16496.93 rows/s]
    a  b  greatest  least

src/datachain/func/__init__.py

@@ -15,7 +15,7 @@
     row_number,
     sum,
 )
-from .array import cosine_distance, euclidean_distance, length, sip_hash_64
+from .array import contains, cosine_distance, euclidean_distance, length, sip_hash_64
 from .conditional import case, greatest, ifelse, isnone, least
 from .numeric import bit_and, bit_hamming_distance, bit_or, bit_xor, int_hash_64
 from .random import rand
@@ -34,6 +34,7 @@
     "case",
     "collect",
     "concat",
+    "contains",
     "cosine_distance",
     "count",
     "dense_rank",

src/datachain/func/array.py

@@ -1,5 +1,5 @@
 from collections.abc import Sequence
-from typing import Union
+from typing import Any, Union
 
 from datachain.sql.functions import array
 
@@ -140,6 +140,44 @@ def length(arg: Union[str, Sequence, Func]) -> Func:
     return Func("length", inner=array.length, cols=cols, args=args, result_type=int)
 
 
+def contains(arr: Union[str, Sequence, Func], elem: Any) -> Func:
+    """
+    Checks whether the `arr` array has the `elem` element.
+
+    Args:
+        arr (str | Sequence | Func): Array to check for the element.
+            If a string is provided, it is assumed to be the name of the array column.
+            If a sequence is provided, it is assumed to be an array of values.
+            If a Func is provided, it is assumed to be a function returning an array.
+        elem (Any): Element to check for in the array.
+
+    Returns:
+        Func: A Func object that represents the contains function. Result of the
+            function will be 1 if the element is present in the array, and 0 otherwise.
+
+    Example:
+        ```py
+        dc.mutate(
+            contains1=func.array.contains("signal.values", 3),
+            contains2=func.array.contains([1, 2, 3, 4, 5], 7),
+        )
+        ```
+    """
+
+    def inner(arg):
+        is_json = type(elem) in [list, dict]
+        return array.contains(arg, elem, is_json)
+
+    if isinstance(arr, (str, Func)):
+        cols = [arr]
+        args = None
+    else:
+        cols = None
+        args = [arr]
+
+    return Func("contains", inner=inner, cols=cols, args=args, result_type=int)
+
+
 def sip_hash_64(arg: Union[str, Sequence]) -> Func:
     """
     Computes the SipHash-64 hash of the array.

src/datachain/sql/functions/array.py

@@ -1,6 +1,6 @@
 from sqlalchemy.sql.functions import GenericFunction
 
-from datachain.sql.types import Float, Int64
+from datachain.sql.types import Boolean, Float, Int64
 from datachain.sql.utils import compiler_not_implemented
 
 
@@ -37,6 +37,17 @@ class length(GenericFunction):  # noqa: N801
     inherit_cache = True
 
 
+class contains(GenericFunction):  # noqa: N801
+    """
+    Checks if element is in the array.
+    """
+
+    type = Boolean()
+    package = "array"
+    name = "contains"
+    inherit_cache = True
+
+
 class sip_hash_64(GenericFunction):  # noqa: N801
     """
     Computes the SipHash-64 hash of the array.
@@ -51,4 +62,5 @@ class sip_hash_64(GenericFunction):  # noqa: N801
 compiler_not_implemented(cosine_distance)
 compiler_not_implemented(euclidean_distance)
 compiler_not_implemented(length)
+compiler_not_implemented(contains)
 compiler_not_implemented(sip_hash_64)

src/datachain/sql/sqlite/base.py

@@ -87,6 +87,7 @@ def setup():
     compiles(sql_path.file_stem, "sqlite")(compile_path_file_stem)
     compiles(sql_path.file_ext, "sqlite")(compile_path_file_ext)
     compiles(array.length, "sqlite")(compile_array_length)
+    compiles(array.contains, "sqlite")(compile_array_contains)
     compiles(string.length, "sqlite")(compile_string_length)
     compiles(string.split, "sqlite")(compile_string_split)
     compiles(string.regexp_replace, "sqlite")(compile_string_regexp_replace)
@@ -269,13 +270,16 @@ def create_string_functions(conn):
 
     _registered_function_creators["string_functions"] = create_string_functions
 
-    has_json_extension = functions_exist(["json_array_length"])
+    has_json_extension = functions_exist(["json_array_length", "json_array_contains"])
     if not has_json_extension:
 
         def create_json_functions(conn):
             conn.create_function(
                 "json_array_length", 1, py_json_array_length, deterministic=True
             )
+            conn.create_function(
+                "json_array_contains", 3, py_json_array_contains, deterministic=True
+            )
 
         _registered_function_creators["json_functions"] = create_json_functions
 
@@ -428,10 +432,22 @@ def py_json_array_length(arr):
     return len(orjson.loads(arr))
 
 
+def py_json_array_contains(arr, value, is_json):
+    if is_json:
+        value = orjson.loads(value)
+    return value in orjson.loads(arr)
+
+
 def compile_array_length(element, compiler, **kwargs):
     return compiler.process(func.json_array_length(*element.clauses.clauses), **kwargs)
 
 
+def compile_array_contains(element, compiler, **kwargs):
+    return compiler.process(
+        func.json_array_contains(*element.clauses.clauses), **kwargs
+    )
+
+
 def compile_string_length(element, compiler, **kwargs):
     return compiler.process(func.length(*element.clauses.clauses), **kwargs)
 

src/datachain/sql/sqlite/types.py

@@ -31,6 +31,10 @@
     return orjson.dumps(arr).decode("utf-8")
 
 
+def adapt_dict(dct):
+    return orjson.dumps(dct).decode("utf-8")
+
+
 def convert_array(arr):
     return orjson.loads(arr)
 
@@ -52,6 +56,7 @@
 
 def register_type_converters():
     sqlite3.register_adapter(list, adapt_array)
+    sqlite3.register_adapter(dict, adapt_dict)
     sqlite3.register_converter("ARRAY", convert_array)
     if numpy_imported:
         sqlite3.register_adapter(np.ndarray, adapt_np_array)

tests/unit/sql/test_array.py

@@ -65,6 +65,24 @@ def test_length(warehouse):
     assert result == ((4, 5, 2),)
 
 
+def test_contains(warehouse):
+    query = select(
+        func.contains(["abc", "def", "g", "hi"], "abc").label("contains1"),
+        func.contains(["abc", "def", "g", "hi"], "cdf").label("contains2"),
+        func.contains([3.0, 5.0, 1.0, 6.0, 1.0], 1.0).label("contains3"),
+        func.contains([[1, None, 3], [4, 5, 6]], [1, None, 3]).label("contains4"),
+        # Not supported yet by CH, need to add it later + some Pydantic model as
+        # an input:
+        # func.contains(
+        #     [{"c": 1, "a": True}, {"b": False}], {"a": True, "c": 1}
+        # ).label("contains5"),
+        func.contains([1, None, 3], None).label("contains6"),
+        func.contains([1, True, 3], True).label("contains7"),
+    )
+    result = tuple(warehouse.db.execute(query))
+    assert result == ((1, 0, 1, 1, 1, 1),)
+
+
 def test_length_on_split(warehouse):
     query = select(
         func.array.length(func.string.split(func.literal("abc/def/g/hi"), "/")),

tests/unit/test_func.py

@@ -11,6 +11,7 @@
     isnone,
     literal,
 )
+from datachain.func.array import contains
 from datachain.func.random import rand
 from datachain.func.string import length as strlen
 from datachain.lib.signal_schema import SignalSchema
@@ -797,3 +798,27 @@ def test_isnone_with_ifelse_mutate(col):
     res = dc.mutate(test=ifelse(isnone(col), "NONE", "NOT_NONE"))
     assert list(res.order_by("num").collect("test")) == ["NOT_NONE"] * 3 + ["NONE"] * 2
     assert res.schema["test"] is str
+
+
+def test_array_contains():
+    dc = DataChain.from_values(
+        arr=[list(range(1, i)) * i for i in range(2, 7)],
+        val=list(range(2, 7)),
+    )
+
+    assert list(dc.mutate(res=contains("arr", 3)).order_by("val").collect("res")) == [
+        0,
+        0,
+        1,
+        1,
+        1,
+    ]
+    assert list(
+        dc.mutate(res=contains(C("arr"), 3)).order_by("val").collect("res")
+    ) == [0, 0, 1, 1, 1]
+    assert list(
+        dc.mutate(res=contains(C("arr"), 10)).order_by("val").collect("res")
+    ) == [0, 0, 0, 0, 0]
+    assert list(
+        dc.mutate(res=contains(C("arr"), None)).order_by("val").collect("res")
+    ) == [0, 0, 0, 0, 0]