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ARROW-3583: [Python/Java] Create RecordBatch from VectorSchemaRoot #2809

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ARROW-3583: [Python/Java] Create RecordBatch from VectorSchemaRoot #2809

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25 changes: 25 additions & 0 deletions python/pyarrow/jvm.py
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Original file line number Diff line number Diff line change
Expand Up @@ -276,3 +276,28 @@ def array(jvm_array):
for buf in list(jvm_array.getBuffers(False))]
null_count = jvm_array.getNullCount()
return pa.Array.from_buffers(dtype, length, buffers, null_count)


def record_batch(jvm_vector_schema_root):
"""
Construct a (Python) RecordBatch from a JVM VectorSchemaRoot

Parameters
----------
jvm_vector_schema_root : org.apache.arrow.vector.VectorSchemaRoot

Returns
-------
record_batch: pyarrow.RecordBatch
"""
pa_schema = schema(jvm_vector_schema_root.getSchema())

arrays = []
for name in pa_schema.names:
arrays.append(array(jvm_vector_schema_root.getVector(name)))

return pa.RecordBatch.from_arrays(
arrays,
pa_schema.names,
metadata=pa_schema.metadata
)
165 changes: 156 additions & 9 deletions python/pyarrow/tests/test_jvm.py
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Original file line number Diff line number Diff line change
Expand Up @@ -93,7 +93,7 @@ def _jvm_schema(jvm_spec, metadata=None):
# om = jpype.JClass('com.fasterxml.jackson.databind.ObjectMapper')()
# field = … # Code to instantiate the field
# jvm_spec = om.writeValueAsString(field)
@pytest.mark.parametrize('typ,jvm_spec', [
@pytest.mark.parametrize('pa_type,jvm_spec', [
(pa.null(), '{"name":"null"}'),
(pa.bool_(), '{"name":"bool"}'),
(pa.int8(), '{"name":"int","bitWidth":8,"isSigned":true}'),
Expand Down Expand Up @@ -142,7 +142,7 @@ def _jvm_schema(jvm_spec, metadata=None):
# pa.dictionary(pa.int32(), pa.array(['a', 'b', 'c'])),
])
@pytest.mark.parametrize('nullable', [True, False])
def test_jvm_types(root_allocator, typ, jvm_spec, nullable):
def test_jvm_types(root_allocator, pa_type, jvm_spec, nullable):
spec = {
'name': 'field_name',
'nullable': nullable,
Expand All @@ -152,7 +152,7 @@ def test_jvm_types(root_allocator, typ, jvm_spec, nullable):
}
jvm_field = _jvm_field(json.dumps(spec))
result = pa_jvm.field(jvm_field)
expected_field = pa.field('field_name', typ, nullable=nullable)
expected_field = pa.field('field_name', pa_type, nullable=nullable)
assert result == expected_field

jvm_schema = _jvm_schema(json.dumps(spec))
Expand All @@ -168,7 +168,7 @@ def test_jvm_types(root_allocator, typ, jvm_spec, nullable):
# These test parameters mostly use an integer range as an input as this is
# often the only type that is understood by both Python and Java
# implementations of Arrow.
@pytest.mark.parametrize('typ,data,jvm_type', [
@pytest.mark.parametrize('pa_type,py_data,jvm_type', [
(pa.bool_(), [True, False, True, True], 'BitVector'),
(pa.uint8(), list(range(128)), 'UInt1Vector'),
(pa.uint16(), list(range(128)), 'UInt2Vector'),
Expand All @@ -189,21 +189,168 @@ def test_jvm_types(root_allocator, typ, jvm_spec, nullable):
(pa.date64(), list(range(128)), 'DateMilliVector'),
# TODO(ARROW-2606): pa.decimal128(19, 4)
])
def test_jvm_array(root_allocator, typ, data, jvm_type):
def test_jvm_array(root_allocator, pa_type, py_data, jvm_type):
# Create vector
cls = "org.apache.arrow.vector.{}".format(jvm_type)
jvm_vector = jpype.JClass(cls)("vector", root_allocator)
jvm_vector.allocateNew(len(data))
for i, val in enumerate(data):
jvm_vector.allocateNew(len(py_data))
for i, val in enumerate(py_data):
jvm_vector.setSafe(i, val)
jvm_vector.setValueCount(len(data))
jvm_vector.setValueCount(len(py_data))

py_array = pa.array(data, type=typ)
py_array = pa.array(py_data, type=pa_type)
jvm_array = pa_jvm.array(jvm_vector)

assert py_array.equals(jvm_array)


# These test parameters mostly use an integer range as an input as this is
# often the only type that is understood by both Python and Java
# implementations of Arrow.
@pytest.mark.parametrize('pa_type,py_data,jvm_type,jvm_spec', [
# TODO: null
(pa.bool_(), [True, False, True, True], 'BitVector', '{"name":"bool"}'),
(
pa.uint8(),
list(range(128)),
'UInt1Vector',
'{"name":"int","bitWidth":8,"isSigned":false}'
),
(
pa.uint16(),
list(range(128)),
'UInt2Vector',
'{"name":"int","bitWidth":16,"isSigned":false}'
),
(
pa.uint32(),
list(range(128)),
'UInt4Vector',
'{"name":"int","bitWidth":32,"isSigned":false}'
),
(
pa.uint64(),
list(range(128)),
'UInt8Vector',
'{"name":"int","bitWidth":64,"isSigned":false}'
),
(
pa.int8(),
list(range(128)),
'TinyIntVector',
'{"name":"int","bitWidth":8,"isSigned":true}'
),
(
pa.int16(),
list(range(128)),
'SmallIntVector',
'{"name":"int","bitWidth":16,"isSigned":true}'
),
(
pa.int32(),
list(range(128)),
'IntVector',
'{"name":"int","bitWidth":32,"isSigned":true}'
),
(
pa.int64(),
list(range(128)),
'BigIntVector',
'{"name":"int","bitWidth":64,"isSigned":true}'
),
# TODO: float16
(
pa.float32(),
list(range(128)),
'Float4Vector',
'{"name":"floatingpoint","precision":"SINGLE"}'
),
(
pa.float64(),
list(range(128)),
'Float8Vector',
'{"name":"floatingpoint","precision":"DOUBLE"}'
),
(
pa.timestamp('s'),
list(range(128)),
'TimeStampSecVector',
'{"name":"timestamp","unit":"SECOND","timezone":null}'
),
(
pa.timestamp('ms'),
list(range(128)),
'TimeStampMilliVector',
'{"name":"timestamp","unit":"MILLISECOND","timezone":null}'
),
(
pa.timestamp('us'),
list(range(128)),
'TimeStampMicroVector',
'{"name":"timestamp","unit":"MICROSECOND","timezone":null}'
),
(
pa.timestamp('ns'),
list(range(128)),
'TimeStampNanoVector',
'{"name":"timestamp","unit":"NANOSECOND","timezone":null}'
),
# TODO(ARROW-2605): These types miss a conversion from pure Python objects
# * pa.time32('s')
# * pa.time32('ms')
# * pa.time64('us')
# * pa.time64('ns')
(
pa.date32(),
list(range(128)),
'DateDayVector',
'{"name":"date","unit":"DAY"}'
),
(
pa.date64(),
list(range(128)),
'DateMilliVector',
'{"name":"date","unit":"MILLISECOND"}'
),
# TODO(ARROW-2606): pa.decimal128(19, 4)
])
def test_jvm_record_batch(root_allocator, pa_type, py_data, jvm_type,
jvm_spec):
# Create vector
cls = "org.apache.arrow.vector.{}".format(jvm_type)
jvm_vector = jpype.JClass(cls)("vector", root_allocator)
jvm_vector.allocateNew(len(py_data))
for i, val in enumerate(py_data):
jvm_vector.setSafe(i, val)
jvm_vector.setValueCount(len(py_data))

# Create field
spec = {
'name': 'field_name',
'nullable': False,
'type': json.loads(jvm_spec),
# TODO: This needs to be set for complex types
'children': []
}
jvm_field = _jvm_field(json.dumps(spec))

# Create VectorSchemaRoot
jvm_fields = jpype.JClass('java.util.ArrayList')()
jvm_fields.add(jvm_field)
jvm_vectors = jpype.JClass('java.util.ArrayList')()
jvm_vectors.add(jvm_vector)
jvm_vsr = jpype.JClass('org.apache.arrow.vector.VectorSchemaRoot')
jvm_vsr = jvm_vsr(jvm_fields, jvm_vectors, len(py_data))

py_record_batch = pa.RecordBatch.from_arrays(
[pa.array(py_data, type=pa_type)],
['col']
)
jvm_record_batch = pa_jvm.record_batch(jvm_vsr)

assert py_record_batch.equals(jvm_record_batch)


def _string_to_varchar_holder(ra, string):
nvch_cls = "org.apache.arrow.vector.holders.NullableVarCharHolder"
holder = jpype.JClass(nvch_cls)()
Expand Down