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venv/lib/python3.10/site-packages/pyarrow/include/parquet/arrow/reader.h

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+// 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.
+
+#pragma once
+
+#include <cstdint>
+// N.B. we don't include async_generator.h as it's relatively heavy
+#include <functional>
+#include <memory>
+#include <vector>
+
+#include "parquet/file_reader.h"
+#include "parquet/platform.h"
+#include "parquet/properties.h"
+
+namespace arrow {
+
+class ChunkedArray;
+class KeyValueMetadata;
+class RecordBatchReader;
+struct Scalar;
+class Schema;
+class Table;
+class RecordBatch;
+
+}  // namespace arrow
+
+namespace parquet {
+
+class FileMetaData;
+class SchemaDescriptor;
+
+namespace arrow {
+
+class ColumnChunkReader;
+class ColumnReader;
+struct SchemaManifest;
+class RowGroupReader;
+
+/// \brief Arrow read adapter class for deserializing Parquet files as Arrow row batches.
+///
+/// This interfaces caters for different use cases and thus provides different
+/// interfaces. In its most simplistic form, we cater for a user that wants to
+/// read the whole Parquet at once with the `FileReader::ReadTable` method.
+///
+/// More advanced users that also want to implement parallelism on top of each
+/// single Parquet files should do this on the RowGroup level. For this, they can
+/// call `FileReader::RowGroup(i)->ReadTable` to receive only the specified
+/// RowGroup as a table.
+///
+/// In the most advanced situation, where a consumer wants to independently read
+/// RowGroups in parallel and consume each column individually, they can call
+/// `FileReader::RowGroup(i)->Column(j)->Read` and receive an `arrow::Column`
+/// instance.
+///
+/// Finally, one can also get a stream of record batches using
+/// `FileReader::GetRecordBatchReader()`. This can internally decode columns
+/// in parallel if use_threads was enabled in the ArrowReaderProperties.
+///
+/// The parquet format supports an optional integer field_id which can be assigned
+/// to a field.  Arrow will convert these field IDs to a metadata key named
+/// PARQUET:field_id on the appropriate field.
+// TODO(wesm): nested data does not always make sense with this user
+// interface unless you are only reading a single leaf node from a branch of
+// a table. For example:
+//
+// repeated group data {
+//   optional group record {
+//     optional int32 val1;
+//     optional byte_array val2;
+//     optional bool val3;
+//   }
+//   optional int32 val4;
+// }
+//
+// In the Parquet file, there are 4 leaf nodes:
+//
+// * data.record.val1
+// * data.record.val2
+// * data.record.val3
+// * data.val4
+//
+// When materializing this data in an Arrow array, we would have:
+//
+// data: list<struct<
+//   record: struct<
+//    val1: int32,
+//    val2: string (= list<uint8>),
+//    val3: bool,
+//   >,
+//   val4: int32
+// >>
+//
+// However, in the Parquet format, each leaf node has its own repetition and
+// definition levels describing the structure of the intermediate nodes in
+// this array structure. Thus, we will need to scan the leaf data for a group
+// of leaf nodes part of the same type tree to create a single result Arrow
+// nested array structure.
+//
+// This is additionally complicated "chunky" repeated fields or very large byte
+// arrays
+class PARQUET_EXPORT FileReader {
+ public:
+  /// Factory function to create a FileReader from a ParquetFileReader and properties
+  static ::arrow::Status Make(::arrow::MemoryPool* pool,
+                              std::unique_ptr<ParquetFileReader> reader,
+                              const ArrowReaderProperties& properties,
+                              std::unique_ptr<FileReader>* out);
+
+  /// Factory function to create a FileReader from a ParquetFileReader
+  static ::arrow::Status Make(::arrow::MemoryPool* pool,
+                              std::unique_ptr<ParquetFileReader> reader,
+                              std::unique_ptr<FileReader>* out);
+
+  // Since the distribution of columns amongst a Parquet file's row groups may
+  // be uneven (the number of values in each column chunk can be different), we
+  // provide a column-oriented read interface. The ColumnReader hides the
+  // details of paging through the file's row groups and yielding
+  // fully-materialized arrow::Array instances
+  //
+  // Returns error status if the column of interest is not flat.
+  // The indicated column index is relative to the schema
+  virtual ::arrow::Status GetColumn(int i, std::unique_ptr<ColumnReader>* out) = 0;
+
+  /// \brief Return arrow schema for all the columns.
+  virtual ::arrow::Status GetSchema(std::shared_ptr<::arrow::Schema>* out) = 0;
+
+  /// \brief Read column as a whole into a chunked array.
+  ///
+  /// The index i refers the index of the top level schema field, which may
+  /// be nested or flat - e.g.
+  ///
+  /// 0 foo.bar
+  ///   foo.bar.baz
+  ///   foo.qux
+  /// 1 foo2
+  /// 2 foo3
+  ///
+  /// i=0 will read the entire foo struct, i=1 the foo2 primitive column etc
+  virtual ::arrow::Status ReadColumn(int i,
+                                     std::shared_ptr<::arrow::ChunkedArray>* out) = 0;
+
+  /// \brief Return a RecordBatchReader of all row groups and columns.
+  virtual ::arrow::Result<std::unique_ptr<::arrow::RecordBatchReader>>
+  GetRecordBatchReader() = 0;
+
+  /// \brief Return a RecordBatchReader of row groups selected from row_group_indices.
+  ///
+  /// Note that the ordering in row_group_indices matters. FileReaders must outlive
+  /// their RecordBatchReaders.
+  ///
+  /// \returns error Result if row_group_indices contains an invalid index
+  virtual ::arrow::Result<std::unique_ptr<::arrow::RecordBatchReader>>
+  GetRecordBatchReader(const std::vector<int>& row_group_indices) = 0;
+
+  /// \brief Return a RecordBatchReader of row groups selected from
+  /// row_group_indices, whose columns are selected by column_indices.
+  ///
+  /// Note that the ordering in row_group_indices and column_indices
+  /// matter. FileReaders must outlive their RecordBatchReaders.
+  ///
+  /// \returns error Result if either row_group_indices or column_indices
+  ///     contains an invalid index
+  virtual ::arrow::Result<std::unique_ptr<::arrow::RecordBatchReader>>
+  GetRecordBatchReader(const std::vector<int>& row_group_indices,
+                       const std::vector<int>& column_indices) = 0;
+
+  /// \brief Return a RecordBatchReader of row groups selected from
+  /// row_group_indices, whose columns are selected by column_indices.
+  ///
+  /// Note that the ordering in row_group_indices and column_indices
+  /// matter. FileReaders must outlive their RecordBatchReaders.
+  ///
+  /// \param row_group_indices which row groups to read (order determines read order).
+  /// \param column_indices which columns to read (order determines output schema).
+  /// \param[out] out record batch stream from parquet data.
+  ///
+  /// \returns error Status if either row_group_indices or column_indices
+  ///     contains an invalid index
+  /// \deprecated Deprecated in 21.0.0. Use arrow::Result version instead.
+  ARROW_DEPRECATED("Deprecated in 21.0.0. Use arrow::Result version instead.")
+  ::arrow::Status GetRecordBatchReader(const std::vector<int>& row_group_indices,
+                                       const std::vector<int>& column_indices,
+                                       std::shared_ptr<::arrow::RecordBatchReader>* out);
+
+  /// \deprecated Deprecated in 21.0.0. Use arrow::Result version instead.
+  ARROW_DEPRECATED("Deprecated in 21.0.0. Use arrow::Result version instead.")
+  ::arrow::Status GetRecordBatchReader(const std::vector<int>& row_group_indices,
+                                       std::shared_ptr<::arrow::RecordBatchReader>* out);
+
+  /// \deprecated Deprecated in 21.0.0. Use arrow::Result version instead.
+  ARROW_DEPRECATED("Deprecated in 21.0.0. Use arrow::Result version instead.")
+  ::arrow::Status GetRecordBatchReader(std::shared_ptr<::arrow::RecordBatchReader>* out);
+
+  /// \brief Return a generator of record batches.
+  ///
+  /// The FileReader must outlive the generator, so this requires that you pass in a
+  /// shared_ptr.
+  ///
+  /// \returns error Result if either row_group_indices or column_indices contains an
+  ///     invalid index
+  virtual ::arrow::Result<
+      std::function<::arrow::Future<std::shared_ptr<::arrow::RecordBatch>>()>>
+  GetRecordBatchGenerator(std::shared_ptr<FileReader> reader,
+                          const std::vector<int> row_group_indices,
+                          const std::vector<int> column_indices,
+                          ::arrow::internal::Executor* cpu_executor = NULLPTR,
+                          int64_t rows_to_readahead = 0) = 0;
+
+  /// Read all columns into a Table
+  virtual ::arrow::Status ReadTable(std::shared_ptr<::arrow::Table>* out) = 0;
+
+  /// \brief Read the given columns into a Table
+  ///
+  /// The indicated column indices are relative to the internal representation
+  /// of the parquet table. For instance :
+  /// 0 foo.bar
+  ///       foo.bar.baz           0
+  ///       foo.bar.baz2          1
+  ///   foo.qux                   2
+  /// 1 foo2                      3
+  /// 2 foo3                      4
+  ///
+  /// i=0 will read foo.bar.baz, i=1 will read only foo.bar.baz2 and so on.
+  /// Only leaf fields have indices; foo itself doesn't have an index.
+  /// To get the index for a particular leaf field, one can use
+  /// manifest().schema_fields to get the top level fields, and then walk the
+  /// tree to identify the relevant leaf fields and access its column_index.
+  /// To get the total number of leaf fields, use FileMetadata.num_columns().
+  virtual ::arrow::Status ReadTable(const std::vector<int>& column_indices,
+                                    std::shared_ptr<::arrow::Table>* out) = 0;
+
+  virtual ::arrow::Status ReadRowGroup(int i, const std::vector<int>& column_indices,
+                                       std::shared_ptr<::arrow::Table>* out) = 0;
+
+  virtual ::arrow::Status ReadRowGroup(int i, std::shared_ptr<::arrow::Table>* out) = 0;
+
+  virtual ::arrow::Status ReadRowGroups(const std::vector<int>& row_groups,
+                                        const std::vector<int>& column_indices,
+                                        std::shared_ptr<::arrow::Table>* out) = 0;
+
+  virtual ::arrow::Status ReadRowGroups(const std::vector<int>& row_groups,
+                                        std::shared_ptr<::arrow::Table>* out) = 0;
+
+  /// \brief Scan file contents with one thread, return number of rows
+  virtual ::arrow::Status ScanContents(std::vector<int> columns,
+                                       const int32_t column_batch_size,
+                                       int64_t* num_rows) = 0;
+
+  /// \brief Return a reader for the RowGroup, this object must not outlive the
+  ///   FileReader.
+  virtual std::shared_ptr<RowGroupReader> RowGroup(int row_group_index) = 0;
+
+  /// \brief The number of row groups in the file
+  virtual int num_row_groups() const = 0;
+
+  virtual ParquetFileReader* parquet_reader() const = 0;
+
+  /// Set whether to use multiple threads during reads of multiple columns.
+  /// By default only one thread is used.
+  virtual void set_use_threads(bool use_threads) = 0;
+
+  /// Set number of records to read per batch for the RecordBatchReader.
+  virtual void set_batch_size(int64_t batch_size) = 0;
+
+  virtual const ArrowReaderProperties& properties() const = 0;
+
+  virtual const SchemaManifest& manifest() const = 0;
+
+  virtual ~FileReader() = default;
+};
+
+class RowGroupReader {
+ public:
+  virtual ~RowGroupReader() = default;
+  virtual std::shared_ptr<ColumnChunkReader> Column(int column_index) = 0;
+  virtual ::arrow::Status ReadTable(const std::vector<int>& column_indices,
+                                    std::shared_ptr<::arrow::Table>* out) = 0;
+  virtual ::arrow::Status ReadTable(std::shared_ptr<::arrow::Table>* out) = 0;
+
+ private:
+  struct Iterator;
+};
+
+class ColumnChunkReader {
+ public:
+  virtual ~ColumnChunkReader() = default;
+  virtual ::arrow::Status Read(std::shared_ptr<::arrow::ChunkedArray>* out) = 0;
+};
+
+// At this point, the column reader is a stream iterator. It only knows how to
+// read the next batch of values for a particular column from the file until it
+// runs out.
+//
+// We also do not expose any internal Parquet details, such as row groups. This
+// might change in the future.
+class PARQUET_EXPORT ColumnReader {
+ public:
+  virtual ~ColumnReader() = default;
+
+  // Scan the next array of the indicated size. The actual size of the
+  // returned array may be less than the passed size depending how much data is
+  // available in the file.
+  //
+  // When all the data in the file has been exhausted, the result is set to
+  // nullptr.
+  //
+  // Returns Status::OK on a successful read, including if you have exhausted
+  // the data available in the file.
+  virtual ::arrow::Status NextBatch(int64_t batch_size,
+                                    std::shared_ptr<::arrow::ChunkedArray>* out) = 0;
+};
+
+/// \brief Experimental helper class for bindings (like Python) that struggle
+/// either with std::move or C++ exceptions
+class PARQUET_EXPORT FileReaderBuilder {
+ public:
+  FileReaderBuilder();
+
+  /// Create FileReaderBuilder from Arrow file and optional properties / metadata
+  ::arrow::Status Open(std::shared_ptr<::arrow::io::RandomAccessFile> file,
+                       const ReaderProperties& properties = default_reader_properties(),
+                       std::shared_ptr<FileMetaData> metadata = NULLPTR);
+
+  /// Create FileReaderBuilder from file path and optional properties / metadata
+  ::arrow::Status OpenFile(const std::string& path, bool memory_map = false,
+                           const ReaderProperties& props = default_reader_properties(),
+                           std::shared_ptr<FileMetaData> metadata = NULLPTR);
+
+  ParquetFileReader* raw_reader() { return raw_reader_.get(); }
+
+  /// Set Arrow MemoryPool for memory allocation
+  FileReaderBuilder* memory_pool(::arrow::MemoryPool* pool);
+  /// Set Arrow reader properties
+  FileReaderBuilder* properties(const ArrowReaderProperties& arg_properties);
+  /// Build FileReader instance
+  ::arrow::Status Build(std::unique_ptr<FileReader>* out);
+  ::arrow::Result<std::unique_ptr<FileReader>> Build();
+
+ private:
+  ::arrow::MemoryPool* pool_;
+  ArrowReaderProperties properties_;
+  std::unique_ptr<ParquetFileReader> raw_reader_;
+};
+
+/// \defgroup parquet-arrow-reader-factories Factory functions for Parquet Arrow readers
+///
+/// @{
+
+/// \brief Build FileReader from Arrow file and MemoryPool
+///
+/// Advanced settings are supported through the FileReaderBuilder class.
+PARQUET_EXPORT
+::arrow::Result<std::unique_ptr<FileReader>> OpenFile(
+    std::shared_ptr<::arrow::io::RandomAccessFile>, ::arrow::MemoryPool* allocator);
+
+/// @}
+
+PARQUET_EXPORT
+::arrow::Status StatisticsAsScalars(const Statistics& Statistics,
+                                    std::shared_ptr<::arrow::Scalar>* min,
+                                    std::shared_ptr<::arrow::Scalar>* max);
+
+namespace internal {
+
+PARQUET_EXPORT
+::arrow::Status FuzzReader(const uint8_t* data, int64_t size);
+
+}  // namespace internal
+}  // namespace arrow
+}  // namespace parquet

venv/lib/python3.10/site-packages/pyarrow/include/parquet/arrow/schema.h

@@ -0,0 +1,184 @@
+// 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.
+
+#pragma once
+
+#include <cassert>
+#include <memory>
+#include <unordered_map>
+#include <unordered_set>
+#include <vector>
+
+#include "arrow/result.h"
+#include "arrow/status.h"
+#include "arrow/type.h"
+#include "arrow/type_fwd.h"
+
+#include "parquet/level_conversion.h"
+#include "parquet/platform.h"
+#include "parquet/schema.h"
+
+namespace parquet {
+
+class ArrowReaderProperties;
+class ArrowWriterProperties;
+class WriterProperties;
+
+namespace arrow {
+
+/// \defgroup arrow-to-parquet-schema-conversion Functions to convert an Arrow
+/// schema into a Parquet schema.
+///
+/// @{
+
+PARQUET_EXPORT
+::arrow::Status FieldToNode(const std::shared_ptr<::arrow::Field>& field,
+                            const WriterProperties& properties,
+                            const ArrowWriterProperties& arrow_properties,
+                            schema::NodePtr* out);
+
+PARQUET_EXPORT
+::arrow::Status ToParquetSchema(const ::arrow::Schema* arrow_schema,
+                                const WriterProperties& properties,
+                                const ArrowWriterProperties& arrow_properties,
+                                std::shared_ptr<SchemaDescriptor>* out);
+
+PARQUET_EXPORT
+::arrow::Status ToParquetSchema(const ::arrow::Schema* arrow_schema,
+                                const WriterProperties& properties,
+                                std::shared_ptr<SchemaDescriptor>* out);
+
+/// @}
+
+/// \defgroup parquet-to-arrow-schema-conversion Functions to convert a Parquet
+/// schema into an Arrow schema.
+///
+/// @{
+
+PARQUET_EXPORT
+::arrow::Status FromParquetSchema(
+    const SchemaDescriptor* parquet_schema, const ArrowReaderProperties& properties,
+    const std::shared_ptr<const ::arrow::KeyValueMetadata>& key_value_metadata,
+    std::shared_ptr<::arrow::Schema>* out);
+
+PARQUET_EXPORT
+::arrow::Status FromParquetSchema(const SchemaDescriptor* parquet_schema,
+                                  const ArrowReaderProperties& properties,
+                                  std::shared_ptr<::arrow::Schema>* out);
+
+PARQUET_EXPORT
+::arrow::Status FromParquetSchema(const SchemaDescriptor* parquet_schema,
+                                  std::shared_ptr<::arrow::Schema>* out);
+
+/// @}
+
+/// \brief Bridge between an arrow::Field and parquet column indices.
+struct PARQUET_EXPORT SchemaField {
+  std::shared_ptr<::arrow::Field> field;
+  std::vector<SchemaField> children;
+
+  // Only set for leaf nodes
+  int column_index = -1;
+
+  parquet::internal::LevelInfo level_info;
+
+  bool is_leaf() const { return column_index != -1; }
+};
+
+/// \brief Bridge between a parquet Schema and an arrow Schema.
+///
+/// Expose parquet columns as a tree structure. Useful traverse and link
+/// between arrow's Schema and parquet's Schema.
+struct PARQUET_EXPORT SchemaManifest {
+  static ::arrow::Status Make(
+      const SchemaDescriptor* schema,
+      const std::shared_ptr<const ::arrow::KeyValueMetadata>& metadata,
+      const ArrowReaderProperties& properties, SchemaManifest* manifest);
+
+  const SchemaDescriptor* descr;
+  std::shared_ptr<::arrow::Schema> origin_schema;
+  std::shared_ptr<const ::arrow::KeyValueMetadata> schema_metadata;
+  std::vector<SchemaField> schema_fields;
+
+  std::unordered_map<int, const SchemaField*> column_index_to_field;
+  std::unordered_map<const SchemaField*, const SchemaField*> child_to_parent;
+
+  ::arrow::Status GetColumnField(int column_index, const SchemaField** out) const {
+    auto it = column_index_to_field.find(column_index);
+    if (it == column_index_to_field.end()) {
+      return ::arrow::Status::KeyError("Column index ", column_index,
+                                       " not found in schema manifest, may be malformed");
+    }
+    *out = it->second;
+    return ::arrow::Status::OK();
+  }
+
+  const SchemaField* GetParent(const SchemaField* field) const {
+    // Returns nullptr also if not found
+    auto it = child_to_parent.find(field);
+    if (it == child_to_parent.end()) {
+      return NULLPTR;
+    }
+    return it->second;
+  }
+
+  /// Coalesce a list of field indices (relative to the equivalent arrow::Schema) which
+  /// correspond to the column root (first node below the parquet schema's root group) of
+  /// each leaf referenced in column_indices.
+  ///
+  /// For example, for leaves `a.b.c`, `a.b.d.e`, and `i.j.k` (column_indices=[0,1,3])
+  /// the roots are `a` and `i` (return=[0,2]).
+  ///
+  /// root
+  /// -- a  <------
+  /// -- -- b  |  |
+  /// -- -- -- c  |
+  /// -- -- -- d  |
+  /// -- -- -- -- e
+  /// -- f
+  /// -- -- g
+  /// -- -- -- h
+  /// -- i  <---
+  /// -- -- j  |
+  /// -- -- -- k
+  ::arrow::Result<std::vector<int>> GetFieldIndices(
+      const std::vector<int>& column_indices) const {
+    const schema::GroupNode* group = descr->group_node();
+    std::unordered_set<int> already_added;
+
+    std::vector<int> out;
+    for (int column_idx : column_indices) {
+      if (column_idx < 0 || column_idx >= descr->num_columns()) {
+        return ::arrow::Status::IndexError("Column index ", column_idx, " is not valid");
+      }
+
+      auto field_node = descr->GetColumnRoot(column_idx);
+      auto field_idx = group->FieldIndex(*field_node);
+      if (field_idx == -1) {
+        return ::arrow::Status::IndexError("Column index ", column_idx, " is not valid");
+      }
+
+      if (already_added.insert(field_idx).second) {
+        out.push_back(field_idx);
+      }
+    }
+    return out;
+  }
+};
+
+}  // namespace arrow
+}  // namespace parquet

venv/lib/python3.10/site-packages/pyarrow/include/parquet/arrow/test_util.h

@@ -0,0 +1,487 @@
+// 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.
+
+#pragma once
+
+#include <limits>
+#include <memory>
+#include <random>
+#include <string>
+#include <utility>
+#include <vector>
+
+#include "arrow/array.h"
+#include "arrow/array/builder_binary.h"
+#include "arrow/array/builder_decimal.h"
+#include "arrow/array/builder_primitive.h"
+#include "arrow/testing/gtest_util.h"
+#include "arrow/testing/random.h"
+#include "arrow/type_fwd.h"
+#include "arrow/type_traits.h"
+#include "arrow/util/decimal.h"
+#include "arrow/util/float16.h"
+#include "parquet/column_reader.h"
+#include "parquet/test_util.h"
+
+namespace parquet {
+
+using internal::RecordReader;
+
+namespace arrow {
+
+using ::arrow::Array;
+using ::arrow::ChunkedArray;
+using ::arrow::Status;
+
+template <typename T, int32_t PRECISION, typename = ::arrow::enable_if_decimal<T>>
+struct DecimalWithPrecisionAndScale {
+  using type = T;
+  static_assert(PRECISION >= T::kMinPrecision && PRECISION <= T::kMaxPrecision,
+                "Invalid precision value");
+  static constexpr ::arrow::Type::type type_id = T::type_id;
+  static constexpr int32_t precision = PRECISION;
+  static constexpr int32_t scale = PRECISION - 1;
+};
+template <int32_t PRECISION>
+using Decimal32WithPrecisionAndScale =
+    DecimalWithPrecisionAndScale<::arrow::Decimal32Type, PRECISION>;
+template <int32_t PRECISION>
+using Decimal64WithPrecisionAndScale =
+    DecimalWithPrecisionAndScale<::arrow::Decimal64Type, PRECISION>;
+template <int32_t PRECISION>
+using Decimal128WithPrecisionAndScale =
+    DecimalWithPrecisionAndScale<::arrow::Decimal128Type, PRECISION>;
+template <int32_t PRECISION>
+using Decimal256WithPrecisionAndScale =
+    DecimalWithPrecisionAndScale<::arrow::Decimal256Type, PRECISION>;
+
+template <class ArrowType>
+::arrow::enable_if_floating_point<ArrowType, Status> NonNullArray(
+    size_t size, std::shared_ptr<Array>* out) {
+  using c_type = typename ArrowType::c_type;
+  std::vector<c_type> values;
+  if constexpr (::arrow::is_half_float_type<ArrowType>::value) {
+    values.resize(size);
+    test::random_float16_numbers(static_cast<int>(size), 0, ::arrow::util::Float16(0.0f),
+                                 ::arrow::util::Float16(1.0f), values.data());
+  } else {
+    ::arrow::random_real(size, 0, static_cast<c_type>(0), static_cast<c_type>(1),
+                         &values);
+  }
+  ::arrow::NumericBuilder<ArrowType> builder;
+  RETURN_NOT_OK(builder.AppendValues(values.data(), values.size()));
+  return builder.Finish(out);
+}
+
+template <class ArrowType>
+::arrow::enable_if_integer<ArrowType, Status> NonNullArray(size_t size,
+                                                           std::shared_ptr<Array>* out) {
+  std::vector<typename ArrowType::c_type> values;
+  ::arrow::randint(size, 0, 64, &values);
+
+  // Passing data type so this will work with TimestampType too
+  ::arrow::NumericBuilder<ArrowType> builder(std::make_shared<ArrowType>(),
+                                             ::arrow::default_memory_pool());
+  RETURN_NOT_OK(builder.AppendValues(values.data(), values.size()));
+  return builder.Finish(out);
+}
+
+template <class ArrowType>
+::arrow::enable_if_date<ArrowType, Status> NonNullArray(size_t size,
+                                                        std::shared_ptr<Array>* out) {
+  std::vector<typename ArrowType::c_type> values;
+  ::arrow::randint(size, 0, 24, &values);
+  for (size_t i = 0; i < size; i++) {
+    values[i] *= 86400000;
+  }
+
+  // Passing data type so this will work with TimestampType too
+  ::arrow::NumericBuilder<ArrowType> builder(std::make_shared<ArrowType>(),
+                                             ::arrow::default_memory_pool());
+  RETURN_NOT_OK(builder.AppendValues(values.data(), values.size()));
+  return builder.Finish(out);
+}
+
+template <class ArrowType>
+::arrow::enable_if_base_binary<ArrowType, Status> NonNullArray(
+    size_t size, std::shared_ptr<Array>* out) {
+  using BuilderType = typename ::arrow::TypeTraits<ArrowType>::BuilderType;
+  BuilderType builder;
+  for (size_t i = 0; i < size; i++) {
+    RETURN_NOT_OK(builder.Append("test-string"));
+  }
+  return builder.Finish(out);
+}
+
+template <typename ArrowType>
+::arrow::enable_if_fixed_size_binary<ArrowType, Status> NonNullArray(
+    size_t size, std::shared_ptr<Array>* out) {
+  using BuilderType = typename ::arrow::TypeTraits<ArrowType>::BuilderType;
+  // set byte_width to the length of "fixed": 5
+  // todo: find a way to generate test data with more diversity.
+  BuilderType builder(::arrow::fixed_size_binary(5));
+  for (size_t i = 0; i < size; i++) {
+    RETURN_NOT_OK(builder.Append("fixed"));
+  }
+  return builder.Finish(out);
+}
+
+template <int32_t byte_width>
+static void random_decimals(int64_t n, uint32_t seed, int32_t precision, uint8_t* out) {
+  auto gen = ::arrow::random::RandomArrayGenerator(seed);
+  std::shared_ptr<Array> decimals;
+  if constexpr (byte_width == 4) {
+    decimals = gen.Decimal32(::arrow::decimal32(precision, 0), n);
+  } else if constexpr (byte_width == 8) {
+    decimals = gen.Decimal64(::arrow::decimal64(precision, 0), n);
+  } else if constexpr (byte_width == 16) {
+    decimals = gen.Decimal128(::arrow::decimal128(precision, 0), n);
+  } else {
+    decimals = gen.Decimal256(::arrow::decimal256(precision, 0), n);
+  }
+  std::memcpy(out, decimals->data()->GetValues<uint8_t>(1, 0), byte_width * n);
+}
+
+template <typename ArrowType, int32_t precision = ArrowType::precision>
+::arrow::enable_if_t<std::is_same_v<ArrowType, DecimalWithPrecisionAndScale<
+                                                   typename ArrowType::type, precision>>,
+                     Status>
+NonNullArray(size_t size, std::shared_ptr<Array>* out) {
+  constexpr int32_t kDecimalPrecision = precision;
+  constexpr int32_t kDecimalScale = ArrowType::scale;
+
+  const auto type =
+      std::make_shared<typename ArrowType::type>(kDecimalPrecision, kDecimalScale);
+  const int32_t byte_width = type->byte_width();
+
+  constexpr int32_t seed = 0;
+
+  ARROW_ASSIGN_OR_RAISE(auto out_buf, ::arrow::AllocateBuffer(size * byte_width));
+  random_decimals<ArrowType::type::kByteWidth>(size, seed, kDecimalPrecision,
+                                               out_buf->mutable_data());
+
+  using Builder = typename ::arrow::TypeTraits<typename ArrowType::type>::BuilderType;
+  Builder builder(type);
+  RETURN_NOT_OK(builder.AppendValues(out_buf->data(), size));
+  return builder.Finish(out);
+}
+
+template <class ArrowType>
+::arrow::enable_if_boolean<ArrowType, Status> NonNullArray(size_t size,
+                                                           std::shared_ptr<Array>* out) {
+  std::vector<uint8_t> values;
+  ::arrow::randint(size, 0, 1, &values);
+  ::arrow::BooleanBuilder builder;
+  RETURN_NOT_OK(builder.AppendValues(values.data(), values.size()));
+  return builder.Finish(out);
+}
+
+// This helper function only supports (size/2) nulls.
+template <typename ArrowType>
+::arrow::enable_if_floating_point<ArrowType, Status> NullableArray(
+    size_t size, size_t num_nulls, uint32_t seed, std::shared_ptr<Array>* out) {
+  using c_type = typename ArrowType::c_type;
+  std::vector<c_type> values;
+  if constexpr (::arrow::is_half_float_type<ArrowType>::value) {
+    values.resize(size);
+    test::random_float16_numbers(static_cast<int>(size), 0, ::arrow::util::Float16(-1e4f),
+                                 ::arrow::util::Float16(1e4f), values.data());
+  } else {
+    ::arrow::random_real(size, seed, static_cast<c_type>(-1e10),
+                         static_cast<c_type>(1e10), &values);
+  }
+  std::vector<uint8_t> valid_bytes(size, 1);
+
+  for (size_t i = 0; i < num_nulls; i++) {
+    valid_bytes[i * 2] = 0;
+  }
+
+  ::arrow::NumericBuilder<ArrowType> builder;
+  if (values.size() > 0) {
+    RETURN_NOT_OK(builder.AppendValues(values.data(), values.size(), valid_bytes.data()));
+  }
+  return builder.Finish(out);
+}
+
+// This helper function only supports (size/2) nulls.
+template <typename ArrowType>
+::arrow::enable_if_integer<ArrowType, Status> NullableArray(size_t size, size_t num_nulls,
+                                                            uint32_t seed,
+                                                            std::shared_ptr<Array>* out) {
+  std::vector<typename ArrowType::c_type> values;
+
+  // Seed is random in Arrow right now
+  (void)seed;
+  ::arrow::randint(size, 0, 64, &values);
+  std::vector<uint8_t> valid_bytes(size, 1);
+
+  for (size_t i = 0; i < num_nulls; i++) {
+    valid_bytes[i * 2] = 0;
+  }
+
+  // Passing data type so this will work with TimestampType too
+  ::arrow::NumericBuilder<ArrowType> builder(std::make_shared<ArrowType>(),
+                                             ::arrow::default_memory_pool());
+  RETURN_NOT_OK(builder.AppendValues(values.data(), values.size(), valid_bytes.data()));
+  return builder.Finish(out);
+}
+
+template <typename ArrowType>
+::arrow::enable_if_date<ArrowType, Status> NullableArray(size_t size, size_t num_nulls,
+                                                         uint32_t seed,
+                                                         std::shared_ptr<Array>* out) {
+  std::vector<typename ArrowType::c_type> values;
+
+  // Seed is random in Arrow right now
+  (void)seed;
+  ::arrow::randint(size, 0, 24, &values);
+  for (size_t i = 0; i < size; i++) {
+    values[i] *= 86400000;
+  }
+  std::vector<uint8_t> valid_bytes(size, 1);
+
+  for (size_t i = 0; i < num_nulls; i++) {
+    valid_bytes[i * 2] = 0;
+  }
+
+  // Passing data type so this will work with TimestampType too
+  ::arrow::NumericBuilder<ArrowType> builder(std::make_shared<ArrowType>(),
+                                             ::arrow::default_memory_pool());
+  RETURN_NOT_OK(builder.AppendValues(values.data(), values.size(), valid_bytes.data()));
+  return builder.Finish(out);
+}
+
+// This helper function only supports (size/2) nulls yet.
+template <typename ArrowType>
+::arrow::enable_if_base_binary<ArrowType, Status> NullableArray(
+    size_t size, size_t num_nulls, uint32_t seed, std::shared_ptr<::arrow::Array>* out) {
+  std::vector<uint8_t> valid_bytes(size, 1);
+
+  for (size_t i = 0; i < num_nulls; i++) {
+    valid_bytes[i * 2] = 0;
+  }
+
+  using BuilderType = typename ::arrow::TypeTraits<ArrowType>::BuilderType;
+  BuilderType builder;
+
+  const int kBufferSize = 10;
+  uint8_t buffer[kBufferSize];
+  for (size_t i = 0; i < size; i++) {
+    if (!valid_bytes[i]) {
+      RETURN_NOT_OK(builder.AppendNull());
+    } else {
+      ::arrow::random_bytes(kBufferSize, seed + static_cast<uint32_t>(i), buffer);
+      if (ArrowType::is_utf8) {
+        // Trivially force data to be valid UTF8 by making it all ASCII
+        for (auto& byte : buffer) {
+          byte &= 0x7f;
+        }
+      }
+      RETURN_NOT_OK(builder.Append(buffer, kBufferSize));
+    }
+  }
+  return builder.Finish(out);
+}
+
+// This helper function only supports (size/2) nulls yet,
+// same as NullableArray<String|Binary>(..)
+template <typename ArrowType>
+::arrow::enable_if_fixed_size_binary<ArrowType, Status> NullableArray(
+    size_t size, size_t num_nulls, uint32_t seed, std::shared_ptr<::arrow::Array>* out) {
+  std::vector<uint8_t> valid_bytes(size, 1);
+
+  for (size_t i = 0; i < num_nulls; i++) {
+    valid_bytes[i * 2] = 0;
+  }
+
+  using BuilderType = typename ::arrow::TypeTraits<ArrowType>::BuilderType;
+  const int byte_width = 10;
+  BuilderType builder(::arrow::fixed_size_binary(byte_width));
+
+  const int kBufferSize = byte_width;
+  uint8_t buffer[kBufferSize];
+  for (size_t i = 0; i < size; i++) {
+    if (!valid_bytes[i]) {
+      RETURN_NOT_OK(builder.AppendNull());
+    } else {
+      ::arrow::random_bytes(kBufferSize, seed + static_cast<uint32_t>(i), buffer);
+      RETURN_NOT_OK(builder.Append(buffer));
+    }
+  }
+  return builder.Finish(out);
+}
+
+template <typename ArrowType, int32_t precision = ArrowType::precision>
+::arrow::enable_if_t<std::is_same_v<ArrowType, DecimalWithPrecisionAndScale<
+                                                   typename ArrowType::type, precision>>,
+                     Status>
+NullableArray(size_t size, size_t num_nulls, uint32_t seed,
+              std::shared_ptr<::arrow::Array>* out) {
+  std::vector<uint8_t> valid_bytes(size, '\1');
+
+  for (size_t i = 0; i < num_nulls; ++i) {
+    valid_bytes[i * 2] = '\0';
+  }
+
+  constexpr int32_t kDecimalPrecision = precision;
+  constexpr int32_t kDecimalScale = ArrowType::scale;
+
+  const auto type =
+      std::make_shared<typename ArrowType::type>(kDecimalPrecision, kDecimalScale);
+  const int32_t byte_width = type->byte_width();
+
+  ARROW_ASSIGN_OR_RAISE(auto out_buf, ::arrow::AllocateBuffer(size * byte_width));
+  random_decimals<ArrowType::type::kByteWidth>(size, seed, precision,
+                                               out_buf->mutable_data());
+
+  using Builder = typename ::arrow::TypeTraits<typename ArrowType::type>::BuilderType;
+  Builder builder(type);
+  RETURN_NOT_OK(builder.AppendValues(out_buf->data(), size, valid_bytes.data()));
+  return builder.Finish(out);
+}
+
+// This helper function only supports (size/2) nulls yet.
+template <class ArrowType>
+::arrow::enable_if_boolean<ArrowType, Status> NullableArray(size_t size, size_t num_nulls,
+                                                            uint32_t seed,
+                                                            std::shared_ptr<Array>* out) {
+  std::vector<uint8_t> values;
+
+  // Seed is random in Arrow right now
+  (void)seed;
+
+  ::arrow::randint(size, 0, 1, &values);
+  std::vector<uint8_t> valid_bytes(size, 1);
+
+  for (size_t i = 0; i < num_nulls; i++) {
+    valid_bytes[i * 2] = 0;
+  }
+
+  ::arrow::BooleanBuilder builder;
+  RETURN_NOT_OK(builder.AppendValues(values.data(), values.size(), valid_bytes.data()));
+  return builder.Finish(out);
+}
+
+/// Wrap an Array into a ListArray by splitting it up into size lists.
+///
+/// This helper function only supports (size/2) nulls.
+Status MakeListArray(const std::shared_ptr<Array>& values, int64_t size,
+                     int64_t null_count, const std::string& item_name,
+                     bool nullable_values, std::shared_ptr<::arrow::ListArray>* out) {
+  // We always include an empty list
+  int64_t non_null_entries = size - null_count - 1;
+  int64_t length_per_entry = values->length() / non_null_entries;
+
+  auto offsets = AllocateBuffer();
+  RETURN_NOT_OK(offsets->Resize((size + 1) * sizeof(int32_t)));
+  int32_t* offsets_ptr = reinterpret_cast<int32_t*>(offsets->mutable_data());
+
+  auto null_bitmap = AllocateBuffer();
+  int64_t bitmap_size = ::arrow::bit_util::BytesForBits(size);
+  RETURN_NOT_OK(null_bitmap->Resize(bitmap_size));
+  uint8_t* null_bitmap_ptr = null_bitmap->mutable_data();
+  memset(null_bitmap_ptr, 0, bitmap_size);
+
+  int32_t current_offset = 0;
+  for (int64_t i = 0; i < size; i++) {
+    offsets_ptr[i] = current_offset;
+    if (!(((i % 2) == 0) && ((i / 2) < null_count))) {
+      // Non-null list (list with index 1 is always empty).
+      ::arrow::bit_util::SetBit(null_bitmap_ptr, i);
+      if (i != 1) {
+        current_offset += static_cast<int32_t>(length_per_entry);
+      }
+    }
+  }
+  offsets_ptr[size] = static_cast<int32_t>(values->length());
+
+  auto value_field = ::arrow::field(item_name, values->type(), nullable_values);
+  *out = std::make_shared<::arrow::ListArray>(::arrow::list(value_field), size, offsets,
+                                              values, null_bitmap, null_count);
+
+  return Status::OK();
+}
+
+// Make an array containing only empty lists, with a null values array
+Status MakeEmptyListsArray(int64_t size, std::shared_ptr<Array>* out_array) {
+  // Allocate an offsets buffer containing only zeroes
+  const int64_t offsets_nbytes = (size + 1) * sizeof(int32_t);
+  ARROW_ASSIGN_OR_RAISE(auto offsets_buffer, ::arrow::AllocateBuffer(offsets_nbytes));
+  memset(offsets_buffer->mutable_data(), 0, offsets_nbytes);
+
+  auto value_field =
+      ::arrow::field("item", ::arrow::float64(), false /* nullable_values */);
+  auto list_type = ::arrow::list(value_field);
+
+  std::vector<std::shared_ptr<Buffer>> child_buffers = {nullptr /* null bitmap */,
+                                                        nullptr /* values */};
+  auto child_data =
+      ::arrow::ArrayData::Make(value_field->type(), 0, std::move(child_buffers));
+
+  std::vector<std::shared_ptr<Buffer>> buffers = {nullptr /* bitmap */,
+                                                  std::move(offsets_buffer)};
+  auto array_data = ::arrow::ArrayData::Make(list_type, size, std::move(buffers));
+  array_data->child_data.push_back(child_data);
+
+  *out_array = ::arrow::MakeArray(array_data);
+  return Status::OK();
+}
+
+std::shared_ptr<::arrow::Table> MakeSimpleTable(
+    const std::shared_ptr<ChunkedArray>& values, bool nullable) {
+  auto schema = ::arrow::schema({::arrow::field("col", values->type(), nullable)});
+  return ::arrow::Table::Make(schema, {values});
+}
+
+std::shared_ptr<::arrow::Table> MakeSimpleTable(const std::shared_ptr<Array>& values,
+                                                bool nullable) {
+  auto carr = std::make_shared<::arrow::ChunkedArray>(values);
+  return MakeSimpleTable(carr, nullable);
+}
+
+template <typename T>
+void ExpectArray(T* expected, Array* result) {
+  auto p_array = static_cast<::arrow::PrimitiveArray*>(result);
+  for (int i = 0; i < result->length(); i++) {
+    EXPECT_EQ(expected[i], reinterpret_cast<const T*>(p_array->values()->data())[i]);
+  }
+}
+
+template <typename ArrowType>
+void ExpectArrayT(void* expected, Array* result) {
+  ::arrow::PrimitiveArray* p_array = static_cast<::arrow::PrimitiveArray*>(result);
+  for (int64_t i = 0; i < result->length(); i++) {
+    EXPECT_EQ(reinterpret_cast<typename ArrowType::c_type*>(expected)[i],
+              reinterpret_cast<const typename ArrowType::c_type*>(
+                  p_array->values()->data())[i]);
+  }
+}
+
+template <>
+void ExpectArrayT<::arrow::BooleanType>(void* expected, Array* result) {
+  ::arrow::BooleanBuilder builder;
+  ARROW_EXPECT_OK(
+      builder.AppendValues(reinterpret_cast<uint8_t*>(expected), result->length()));
+
+  std::shared_ptr<Array> expected_array;
+  ARROW_EXPECT_OK(builder.Finish(&expected_array));
+  EXPECT_TRUE(result->Equals(*expected_array));
+}
+
+}  // namespace arrow
+
+}  // namespace parquet

venv/lib/python3.10/site-packages/pyarrow/include/parquet/arrow/writer.h

@@ -0,0 +1,176 @@
+// 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.
+
+#pragma once
+
+#include <cstdint>
+#include <memory>
+
+#include "parquet/platform.h"
+#include "parquet/properties.h"
+
+namespace arrow {
+
+class Array;
+class ChunkedArray;
+class RecordBatch;
+class Schema;
+class Table;
+
+}  // namespace arrow
+
+namespace parquet {
+
+class FileMetaData;
+class ParquetFileWriter;
+
+namespace arrow {
+
+/// \brief Iterative FileWriter class
+///
+/// For basic usage, can write a Table at a time, creating one or more row
+/// groups per write call.
+///
+/// For advanced usage, can write column-by-column: Start a new RowGroup or
+/// Chunk with NewRowGroup, then write column-by-column the whole column chunk.
+///
+/// If PARQUET:field_id is present as a metadata key on a field, and the corresponding
+/// value is a nonnegative integer, then it will be used as the field_id in the parquet
+/// file.
+class PARQUET_EXPORT FileWriter {
+ public:
+  static ::arrow::Status Make(MemoryPool* pool, std::unique_ptr<ParquetFileWriter> writer,
+                              std::shared_ptr<::arrow::Schema> schema,
+                              std::shared_ptr<ArrowWriterProperties> arrow_properties,
+                              std::unique_ptr<FileWriter>* out);
+
+  /// \brief Try to create an Arrow to Parquet file writer.
+  ///
+  /// \param schema schema of data that will be passed.
+  /// \param pool memory pool to use.
+  /// \param sink output stream to write Parquet data.
+  /// \param properties general Parquet writer properties.
+  /// \param arrow_properties Arrow-specific writer properties.
+  ///
+  /// \since 11.0.0
+  static ::arrow::Result<std::unique_ptr<FileWriter>> Open(
+      const ::arrow::Schema& schema, MemoryPool* pool,
+      std::shared_ptr<::arrow::io::OutputStream> sink,
+      std::shared_ptr<WriterProperties> properties = default_writer_properties(),
+      std::shared_ptr<ArrowWriterProperties> arrow_properties =
+          default_arrow_writer_properties());
+
+  /// Return the Arrow schema to be written to.
+  virtual std::shared_ptr<::arrow::Schema> schema() const = 0;
+
+  /// \brief Write a Table to Parquet.
+  ///
+  /// \param table Arrow table to write.
+  /// \param chunk_size maximum number of rows to write per row group.
+  virtual ::arrow::Status WriteTable(
+      const ::arrow::Table& table, int64_t chunk_size = DEFAULT_MAX_ROW_GROUP_LENGTH) = 0;
+
+  /// \brief Start a new row group.
+  ///
+  /// Returns an error if not all columns have been written.
+  virtual ::arrow::Status NewRowGroup() = 0;
+
+  /// \brief Write ColumnChunk in row group using an array.
+  virtual ::arrow::Status WriteColumnChunk(const ::arrow::Array& data) = 0;
+
+  /// \brief Write ColumnChunk in row group using slice of a ChunkedArray
+  virtual ::arrow::Status WriteColumnChunk(
+      const std::shared_ptr<::arrow::ChunkedArray>& data, int64_t offset,
+      int64_t size) = 0;
+
+  /// \brief Write ColumnChunk in a row group using a ChunkedArray
+  virtual ::arrow::Status WriteColumnChunk(
+      const std::shared_ptr<::arrow::ChunkedArray>& data) = 0;
+
+  /// \brief Start a new buffered row group.
+  ///
+  /// Returns an error if not all columns have been written.
+  virtual ::arrow::Status NewBufferedRowGroup() = 0;
+
+  /// \brief Write a RecordBatch into the buffered row group.
+  ///
+  /// Multiple RecordBatches can be written into the same row group
+  /// through this method.
+  ///
+  /// WriterProperties.max_row_group_length() is respected and a new
+  /// row group will be created if the current row group exceeds the
+  /// limit.
+  ///
+  /// Batches get flushed to the output stream once NewBufferedRowGroup()
+  /// or Close() is called.
+  ///
+  /// WARNING: If you are writing multiple files in parallel in the same
+  /// executor, deadlock may occur if ArrowWriterProperties::use_threads
+  /// is set to true to write columns in parallel. Please disable use_threads
+  /// option in this case.
+  virtual ::arrow::Status WriteRecordBatch(const ::arrow::RecordBatch& batch) = 0;
+
+  /// \brief Write the footer and close the file.
+  virtual ::arrow::Status Close() = 0;
+  virtual ~FileWriter();
+
+  virtual MemoryPool* memory_pool() const = 0;
+  /// \brief Add key-value metadata to the file.
+  /// \param[in] key_value_metadata the metadata to add.
+  /// \note This will overwrite any existing metadata with the same key.
+  /// \return Error if Close() has been called.
+  ///
+  /// WARNING: If `store_schema` is enabled, `ARROW:schema` would be stored
+  /// in the key-value metadata. Overwriting this key would result in
+  /// `store_schema` being unusable during read.
+  virtual ::arrow::Status AddKeyValueMetadata(
+      const std::shared_ptr<const ::arrow::KeyValueMetadata>& key_value_metadata) = 0;
+  /// \brief Return the file metadata, only available after calling Close().
+  virtual const std::shared_ptr<FileMetaData> metadata() const = 0;
+};
+
+/// \brief Write Parquet file metadata only to indicated Arrow OutputStream
+PARQUET_EXPORT
+::arrow::Status WriteFileMetaData(const FileMetaData& file_metadata,
+                                  ::arrow::io::OutputStream* sink);
+
+/// \brief Write metadata-only Parquet file to indicated Arrow OutputStream
+PARQUET_EXPORT
+::arrow::Status WriteMetaDataFile(const FileMetaData& file_metadata,
+                                  ::arrow::io::OutputStream* sink);
+
+/// \brief Write a Table to Parquet.
+///
+/// This writes one table in a single shot. To write a Parquet file with
+/// multiple tables iteratively, see parquet::arrow::FileWriter.
+///
+/// \param table Table to write.
+/// \param pool memory pool to use.
+/// \param sink output stream to write Parquet data.
+/// \param chunk_size maximum number of rows to write per row group.
+/// \param properties general Parquet writer properties.
+/// \param arrow_properties Arrow-specific writer properties.
+::arrow::Status PARQUET_EXPORT
+WriteTable(const ::arrow::Table& table, MemoryPool* pool,
+           std::shared_ptr<::arrow::io::OutputStream> sink,
+           int64_t chunk_size = DEFAULT_MAX_ROW_GROUP_LENGTH,
+           std::shared_ptr<WriterProperties> properties = default_writer_properties(),
+           std::shared_ptr<ArrowWriterProperties> arrow_properties =
+               default_arrow_writer_properties());
+
+}  // namespace arrow
+}  // namespace parquet