| # Licensed to the Apache Software Foundation (ASF) under one |
| # or more contributor license agreements. See the NOTICE file |
| # distributed with this work for additional information |
| # regarding copyright ownership. The ASF licenses this file |
| # to you under the Apache License, Version 2.0 (the |
| # "License"); you may not use this file except in compliance |
| # with the License. You may obtain a copy of the License at |
| # |
| # http://www.apache.org/licenses/LICENSE-2.0 |
| # |
| # Unless required by applicable law or agreed to in writing, |
| # software distributed under the License is distributed on an |
| # "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY |
| # KIND, either express or implied. See the License for the |
| # specific language governing permissions and limitations |
| # under the License. |
| """ |
| Functions to interact with Arrow memory allocated by Arrow Java. |
| |
| These functions convert the objects holding the metadata, the actual |
| data is not copied at all. |
| |
| This will only work with a JVM running in the same process such as provided |
| through jpype. Modules that talk to a remote JVM like py4j will not work as the |
| memory addresses reported by them are not reachable in the python process. |
| """ |
| |
| from __future__ import absolute_import |
| |
| |
| import pyarrow as pa |
| |
| |
| def jvm_buffer(arrowbuf): |
| """ |
| Construct an Arrow buffer from io.netty.buffer.ArrowBuf |
| |
| Parameters |
| ---------- |
| |
| arrowbuf: io.netty.buffer.ArrowBuf |
| Arrow Buffer representation on the JVM |
| |
| Returns |
| ------- |
| pyarrow.Buffer |
| Python Buffer that references the JVM memory |
| """ |
| address = arrowbuf.memoryAddress() |
| size = arrowbuf.capacity() |
| return pa.foreign_buffer(address, size, arrowbuf.asNettyBuffer()) |
| |
| |
| def _from_jvm_int_type(jvm_type): |
| """ |
| Convert a JVM int type to its Python equivalent. |
| |
| Parameters |
| ---------- |
| jvm_type: org.apache.arrow.vector.types.pojo.ArrowType$Int |
| |
| Returns |
| ------- |
| typ: pyarrow.DataType |
| """ |
| if jvm_type.isSigned: |
| if jvm_type.bitWidth == 8: |
| return pa.int8() |
| elif jvm_type.bitWidth == 16: |
| return pa.int16() |
| elif jvm_type.bitWidth == 32: |
| return pa.int32() |
| elif jvm_type.bitWidth == 64: |
| return pa.int64() |
| else: |
| if jvm_type.bitWidth == 8: |
| return pa.uint8() |
| elif jvm_type.bitWidth == 16: |
| return pa.uint16() |
| elif jvm_type.bitWidth == 32: |
| return pa.uint32() |
| elif jvm_type.bitWidth == 64: |
| return pa.uint64() |
| |
| |
| def _from_jvm_float_type(jvm_type): |
| """ |
| Convert a JVM float type to its Python equivalent. |
| |
| Parameters |
| ---------- |
| jvm_type: org.apache.arrow.vector.types.pojo.ArrowType$FloatingPoint |
| |
| Returns |
| ------- |
| typ: pyarrow.DataType |
| """ |
| precision = jvm_type.getPrecision().toString() |
| if precision == 'HALF': |
| return pa.float16() |
| elif precision == 'SINGLE': |
| return pa.float32() |
| elif precision == 'DOUBLE': |
| return pa.float64() |
| |
| |
| def _from_jvm_time_type(jvm_type): |
| """ |
| Convert a JVM time type to its Python equivalent. |
| |
| Parameters |
| ---------- |
| jvm_type: org.apache.arrow.vector.types.pojo.ArrowType$Time |
| |
| Returns |
| ------- |
| typ: pyarrow.DataType |
| """ |
| time_unit = jvm_type.getUnit().toString() |
| if time_unit == 'SECOND': |
| assert jvm_type.bitWidth == 32 |
| return pa.time32('s') |
| elif time_unit == 'MILLISECOND': |
| assert jvm_type.bitWidth == 32 |
| return pa.time32('ms') |
| elif time_unit == 'MICROSECOND': |
| assert jvm_type.bitWidth == 64 |
| return pa.time64('us') |
| elif time_unit == 'NANOSECOND': |
| assert jvm_type.bitWidth == 64 |
| return pa.time64('ns') |
| |
| |
| def _from_jvm_timestamp_type(jvm_type): |
| """ |
| Convert a JVM timestamp type to its Python equivalent. |
| |
| Parameters |
| ---------- |
| jvm_type: org.apache.arrow.vector.types.pojo.ArrowType$Timestamp |
| |
| Returns |
| ------- |
| typ: pyarrow.DataType |
| """ |
| time_unit = jvm_type.getUnit().toString() |
| timezone = jvm_type.getTimezone() |
| if time_unit == 'SECOND': |
| return pa.timestamp('s', tz=timezone) |
| elif time_unit == 'MILLISECOND': |
| return pa.timestamp('ms', tz=timezone) |
| elif time_unit == 'MICROSECOND': |
| return pa.timestamp('us', tz=timezone) |
| elif time_unit == 'NANOSECOND': |
| return pa.timestamp('ns', tz=timezone) |
| |
| |
| def _from_jvm_date_type(jvm_type): |
| """ |
| Convert a JVM date type to its Python equivalent |
| |
| Parameters |
| ---------- |
| jvm_type: org.apache.arrow.vector.types.pojo.ArrowType$Date |
| |
| Returns |
| ------- |
| typ: pyarrow.DataType |
| """ |
| day_unit = jvm_type.getUnit().toString() |
| if day_unit == 'DAY': |
| return pa.date32() |
| elif day_unit == 'MILLISECOND': |
| return pa.date64() |
| |
| |
| def field(jvm_field): |
| """ |
| Construct a Field from a org.apache.arrow.vector.types.pojo.Field |
| instance. |
| |
| Parameters |
| ---------- |
| jvm_field: org.apache.arrow.vector.types.pojo.Field |
| |
| Returns |
| ------- |
| pyarrow.Field |
| """ |
| name = jvm_field.getName() |
| jvm_type = jvm_field.getType() |
| |
| typ = None |
| if not jvm_type.isComplex(): |
| type_str = jvm_type.getTypeID().toString() |
| if type_str == 'Null': |
| typ = pa.null() |
| elif type_str == 'Int': |
| typ = _from_jvm_int_type(jvm_type) |
| elif type_str == 'FloatingPoint': |
| typ = _from_jvm_float_type(jvm_type) |
| elif type_str == 'Utf8': |
| typ = pa.string() |
| elif type_str == 'Binary': |
| typ = pa.binary() |
| elif type_str == 'FixedSizeBinary': |
| typ = pa.binary(jvm_type.getByteWidth()) |
| elif type_str == 'Bool': |
| typ = pa.bool_() |
| elif type_str == 'Time': |
| typ = _from_jvm_time_type(jvm_type) |
| elif type_str == 'Timestamp': |
| typ = _from_jvm_timestamp_type(jvm_type) |
| elif type_str == 'Date': |
| typ = _from_jvm_date_type(jvm_type) |
| elif type_str == 'Decimal': |
| typ = pa.decimal128(jvm_type.getPrecision(), jvm_type.getScale()) |
| else: |
| raise NotImplementedError( |
| "Unsupported JVM type: {}".format(type_str)) |
| else: |
| # TODO: The following JVM types are not implemented: |
| # Struct, List, FixedSizeList, Union, Dictionary |
| raise NotImplementedError( |
| "JVM field conversion only implemented for primitive types.") |
| |
| nullable = jvm_field.isNullable() |
| if jvm_field.getMetadata().isEmpty(): |
| metadata = None |
| else: |
| metadata = dict(jvm_field.getMetadata()) |
| return pa.field(name, typ, nullable, metadata) |
| |
| |
| def schema(jvm_schema): |
| """ |
| Construct a Schema from a org.apache.arrow.vector.types.pojo.Schema |
| instance. |
| |
| Parameters |
| ---------- |
| jvm_schema: org.apache.arrow.vector.types.pojo.Schema |
| |
| Returns |
| ------- |
| pyarrow.Schema |
| """ |
| fields = jvm_schema.getFields() |
| fields = [field(f) for f in fields] |
| metadata = jvm_schema.getCustomMetadata() |
| if metadata.isEmpty(): |
| meta = None |
| else: |
| meta = {k: metadata[k] for k in metadata.keySet()} |
| return pa.schema(fields, meta) |
| |
| |
| def array(jvm_array): |
| """ |
| Construct an (Python) Array from its JVM equivalent. |
| |
| Parameters |
| ---------- |
| jvm_array : org.apache.arrow.vector.ValueVector |
| |
| Returns |
| ------- |
| array : Array |
| """ |
| if jvm_array.getField().getType().isComplex(): |
| minor_type_str = jvm_array.getMinorType().toString() |
| raise NotImplementedError( |
| "Cannot convert JVM Arrow array of type {}," |
| " complex types not yet implemented.".format(minor_type_str)) |
| dtype = field(jvm_array.getField()).type |
| length = jvm_array.getValueCount() |
| buffers = [jvm_buffer(buf) |
| 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 |
| ) |
