![]() ![]() MessageType schema = readFooter.getFileMetaData(). The format is explicitly designed to separate the metadata from the data. ParquetMetadata readFooter = ParquetFileReader.readFooter(conf, path, ParquetMetadataConverter.NO_FILTER) The columns chunks should then be read sequentially. String PATH_SCHEMA = “s3://” + object.getBucketName() + “/” + object.getKey() Ĭonfiguration conf = new Configuration() Ĭonf.set(“fs.s3.awsAccessKeyId”, credentials.accessKeyId) Ĭonf.set(“fs.s3.awsSecretAccessKey”, cretKey) : .3Service.(Lorg/jets3t/service/security/AWSCredentials ) I am getting the below error when trying to use the above code To install Parquet support to your Rust project, add this to your “cargo.Package de.jofre.test import java.io.IOException import .Configuration import .Path import .page.PageReadStore import .data.Group import. import .converter.ParquetMetadataConverter import .ParquetFileReader import .metadata.ParquetMetadata import .ColumnIOFactory import .MessageColumnIO import .RecordReader import .MessageType import .Type public class Main Author padmalcom Posted on JCategories Computer Tags BigData, Java, Parquet For my first Rust program reading Parquet data I didn’t need to worry about column-first access. For the most part the two APIs are fairly similar. On the other hand Rust has a nice serialized record batch reader you can point at a Parquet file while C++ doesn’t make things quite so convenient. The Rust API currently doesn’t appear to do the same, though you can do this with the low-level Parquet API in Rust. The Rust and C++ APIs offer some different things at the moment, but this could change soon: C++ has a high-level way to read in an entire column from a single Parquet file, spanning all row groups. ![]() Working with schemas is similar to how it works with the Java and C++ Parquet and Arrow APIs. To work out what to do you’ll need to read the source code to “rs-arrow”, but even then it helps to know where to look.įor reading Parquet data as records there’s a high-level Arrow backed API, and there is also a low-level Parquet API. ![]() The documentation has some extremely basic example code which may not be enough to get you started, especially if you’re not super familiar with the Parquet and Arrow APIs for Java or C++. Reading a subset of columns from the file (a “schema projection”.) Doing this could vastly speed up reads.Īll these are possible and fairly easy with Rust if you can see it done.Getting parquet file metadata statistics like numbers of rows and row groups.Printing the schema including logical and physical column types, column numbers and names.In this post I’ll give example code to accomplish each of the following: While that code has too many Stata specific quirks to make it easy to present as example code, the main points shown here were what made it possible. The project was to allow the Stata statistical software to read from Parquet data. Fast-forward to this summer and it seems like the “rs-arrow” project is ready to learn and use! My first real development project required a few key features I’ll outline in the post. saveAsParquetFile(people. So, it wasn’t surprising that when I first checked out Rust support for Parquet and Arrow last winter it looked promising but seemed incomplete. Parquet is widely adopted by a number of major companies including tech giants such as Social media to Save the file as parquet file use the method. Much of the change directly addressed things I found painful in the build process and with the API. Perfect for a quick viewing of your parquet files, no need to fiddle with any programming libraries. It lets you read parquet files directly on your PC. In 2018 I made a post about working with Parquet data in C++ and within a few months parts of it were out of date. Parquet Viewer is a fast and easy parquet file reader. The Arrow and Parquet projects have undergone a lot of change over the last few years. The Rust Arrow library arrow-rs has recently become a first-class project outside the main Arrow project. In this article I’ll present some sample code to fill that gap. Effectively using Rust to access data in the Parquet format isn’t too dificult, but more detailed examples than those in the official documentation would really help get people started. ![]()
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