Introduction

ParquetSharp is a cross-platform .NET library for reading and writing Apache Parquet files.

ParquetSharp is implemented in C# as a PInvoke wrapper around Apache Parquet C++ to provide high performance and compatibility. Check out ParquetSharp.DataFrame if you need a convenient integration with the .NET DataFrames.

Supported platforms:

	Status
Release Nuget
Pre-Release Nuget
CI Build

Why use Parquet?

Apache Parquet is an open source, column-oriented data file format designed for efficient data storage and retrieval. It provides efficient data compression and encoding schemes with enhanced performance to handle complex data in bulk. Relative to CSV files, Parquet executes queries 34x faster while taking up 87% less space. Source

Quickstart

The following examples show how to write and then read a Parquet file with three columns representing a timeseries of object-value pairs. These use the low-level API, which is the recommended API for working with native .NET types and closely maps to the API of Apache Parquet C++. For reading and writing data in the Apache Arrow format, an Arrow based API is also provided.

How to write a Parquet File:

var timestamps = new DateTime[] { /* ... */ };
var objectIds = new int[] { /* ... */ };
var values = new float[] { /* ... */ };

var columns = new Column[]
{
    new Column<DateTime>("Timestamp"),
    new Column<int>("ObjectId"),
    new Column<float>("Value")
};

using var file = new ParquetFileWriter("float_timeseries.parquet", columns);
using var rowGroup = file.AppendRowGroup();

using (var timestampWriter = rowGroup.NextColumn().LogicalWriter<DateTime>())
{
    timestampWriter.WriteBatch(timestamps);
}
using (var objectIdWriter = rowGroup.NextColumn().LogicalWriter<int>())
{
    objectIdWriter.WriteBatch(objectIds);
}
using (var valueWriter = rowGroup.NextColumn().LogicalWriter<float>())
{
    valueWriter.WriteBatch(values);
}

file.Close();

How to read a Parquet file:

using var file = new ParquetFileReader("float_timeseries.parquet");

for (int rowGroup = 0; rowGroup < file.FileMetaData.NumRowGroups; ++rowGroup) {
    using var rowGroupReader = file.RowGroup(rowGroup);
    var groupNumRows = checked((int) rowGroupReader.MetaData.NumRows);

    var groupTimestamps = rowGroupReader.Column(0).LogicalReader<DateTime>().ReadAll(groupNumRows);
    var groupObjectIds = rowGroupReader.Column(1).LogicalReader<int>().ReadAll(groupNumRows);
    var groupValues = rowGroupReader.Column(2).LogicalReader<float>().ReadAll(groupNumRows);
}

file.Close();

Documentation

For more detailed information on how to use ParquetSharp, see the following documentation:

• Writing Parquet files
• Reading Parquet files
• Working with nested data
• Reading and writing Arrow data — how to read and write data using the Apache Arrow format
• Row-oriented API — a higher level API that abstracts away the column-oriented nature of Parquet files
• Custom types — how to customize the mapping between .NET and Parquet types, including using the DateOnly and TimeOnly types added in .NET 6.
• Encryption — using Parquet Modular Encryption to read and write encrypted data
• Writing TimeSpan data — interoperability with other libraries when writing TimeSpan data
• Use from PowerShell

Rationale

We desired a Parquet implementation with the following properties:

• Cross platform (originally Windows and Linux - but now also macOS).
• Callable from .NET Core.
• Good performance.
• Well maintained.
• Close to official Parquet reference implementations.

Not finding an existing solution meeting these requirements, we decided to implement a .NET wrapper around apache-parquet-cpp (now part of Apache Arrow) starting at version 1.4.0. The library tries to stick closely to the existing C++ API, although it does provide higher level APIs to facilitate its usage from .NET. The user should always be able to access the lower-level API.

Performance

The following benchmarks can be reproduced by running ParquetSharp.Benchmark.csproj. The relative performance of ParquetSharp 10.0.1 is compared to Parquet.NET 4.6.2, an alternative open-source .NET library that is fully managed. The Decimal tests focus purely on handling the C# decimal type, while the TimeSeries tests benchmark three columns of the types {int, DateTime, float}. Results are from a Ryzen 5900X on Linux 6.2.7 using the dotnet 6.0.14 runtime.

If performance is a concern for you, we recommend benchmarking your own workloads and testing different encodings and compression methods. For example, disabling dictionary encoding for floating point columns can often significantly improve performance.

Known Limitations

Because this library is a thin wrapper around the Parquet C++ library, misuse can cause native memory access violations.

Typically this can arise when attempting to access an instance whose owner has been disposed. Because some objects and properties are exposed by Parquet C++ via regular pointers (instead of consistently using std::shared_ptr), dereferencing these after the owner class instance has been destructed will lead to an invalid pointer access.

As only 64-bit runtimes are available, ParquetSharp cannot be referenced by a 32-bit project. For example, using the library from F# Interactive requires running fsiAnyCpu.exe rather than fsi.exe.

Building

Dev Container

ParquetSharp can be built and tested within a dev container. This is a probably the easiest way to get started, as all the C++ dependencies are prebuilt into the container image.

GitHub Codespaces

If you have a GitHub account, you can simply open ParquetSharp in a new GitHub Codespace by clicking on the green "Code" button at the top of this page.

Choose the "unspecified" CMake kit when prompted and let the C++ configuration run. Once done, you can build the C++ code via the "Build" button in the status bar at the bottom.

You can then build the C# code by right-clicking the ParquetSharp solution in the Solution Explorer on the left and choosing "Build". The Test Explorer will then get populated with all the C# tests too.

Visual Studio Code

If you want to work locally in Visual Studio Code, all you need is to have Docker and the Dev Containers extension installed.

Simply open up your copy of ParquetSharp in VS Code and click "Reopen in container" when prompted. Once the project has been opened, you can follow the GitHub Codespaces instructions above.

CLI

If the CLI is how you roll, then you can install the Dev Container CLI tool and issue the following command in the your copy of ParquetSharp to get up and running:

devcontainer up

Build the C++ code and run the C# tests with:

devcontainer exec ./build_unix.sh
devcontainer exec dotnet test csharp.test

Native

Building ParquetSharp natively requires the following dependencies:

• A modern C++ compiler toolchain
• .NET SDK 8.0
• Apache Arrow (15.0.2)

For building Arrow (including Parquet) and its dependencies, we recommend using Microsoft's vcpkg. The build scripts will use an existing vcpkg installation if either of the VCPKG_INSTALLATION_ROOT or VCPKG_ROOT environment variables are defined, otherwise vcpkg will be downloaded into the build directory.

Windows

Building ParquetSharp on Windows requires Visual Studio 2022 (17.0 or higher).

Open a Visual Studio Developer PowerShell and run the following commands to build the C++ code and run the C# tests:

build_windows.ps1
dotnet test csharp.test

cmake must be available in the PATH for the build script to succeed.

Unix

Build the C++ code and run the C# tests with:

./build_unix.sh
dotnet test csharp.test

Known Issues

An issue that may occur when building ParquetSharp locally using build_windows.ps1 is Visual Studio not being detected by CMake:

CMake Error at CMakeLists.txt:2 (project):   Generator

  Visual Studio 17 2022

could not find any instance of Visual Studio.

This is a known issue: (1) (2). It can be solved by ensuring that all required Visual Studio Build Tools are properly installed and that the relevant version of Visual Studio is available, and finally rebooting the machine. Another potential solution is to reinstall Visual Studio with the required build tools.

When building, you may come across the following problem with Microsoft.Cpp.Default.props:

error MSB4019: The imported project "C:\Program Files (x86)\Microsoft Visual Studio\2022\BuildTools\MSBuild\Microsoft\V
C\v170\Microsoft.Cpp.Default.props" was not found. Confirm that the expression in the Import declaration "C:\Program Fi
les (x86)\Microsoft Visual Studio\2022\BuildTools\MSBuild\Microsoft\VC\v170\\Microsoft.Cpp.Default.props" is correct, a
nd that the file exists on disk.

To resolve this, make sure that the "Desktop development with C++" option is selected when installing Visual Studio Build Tools. If installation is successful, the required directory and files should be present.

Another common issue is the following:

CMake Error at CMakeLists.txt:2 (project):
  The CMAKE_C_COMPILER:

    C:/Program Files/Microsoft Visual Studio/2022/Community/VC/Tools/MSVC/14.37.32822/bin/Hostx64/x64/cl.exe

  is not a full path to an existing compiler tool.

CMake Error at CMakeLists.txt:2 (project):
  The CMAKE_CXX_COMPILER:

    C:/Program Files/Microsoft Visual Studio/2022/Community/VC/Tools/MSVC/14.37.32822/bin/Hostx64/x64/cl.exe

  is not a full path to an existing compiler tool.

This is also related to installed Visual Studio modules. Make sure to install "C++/CLI support for build tools" from the list of optional components for Desktop development with C++ for the relevant version of Visual Studio.

For any other build issues, please open a new discussion.

Contributing

We welcome new contributors! We will happily receive PRs for bug fixes or small changes. If you're contemplating something larger please get in touch first by opening a GitHub Issue describing the problem and how you propose to solve it.

Security

Please see our security policy for details on reporting security vulnerabilities.

License

Copyright 2018-2023 G-Research

Licensed under the Apache License, Version 2.0 (the "License"); you may not use these files 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.