'Read local Parquet file without Hadoop Path API

I'm trying to read a local Parquet file, however the only APIs I can find are tightly coupled with Hadoop, and require a Hadoop Path as input (even for pointing to a local file).

ParquetReader<GenericRecord> reader = AvroParquetReader.<GenericRecord>builder(file).build();
GenericRecord nextRecord = reader.read();

is the most popular answer in how to read a parquet file, in a standalone java code?, but requires a Hadoop Path and has now been deprecated for a mysterious InputFile instead. The only implementation of InputFile I can find is HadoopInputFile, so again no help.

In Avro this is a simple:

DatumReader<GenericRecord> datumReader = new GenericDatumReader<>();
this.dataFileReader = new DataFileReader<>(file, datumReader);

(where file is java.io.File). What's the Parquet equivalent?

I am asking for no Hadoop Path dependency in the answers, because Hadoop drags in bloat and jar hell, and it seems silly to require it for reading local files.

To further explain the backstory, I maintain a small IntelliJ plugin that allows users to drag-and-drop Avro files into a pane for viewing in a table. This plugin is currently 5MB. If I include Parquet and Hadoop dependencies, it bloats to over 50MB, and doesn't even work.

POST-ANSWER ADDENDUM

Now that I have it working (thanks to the accepted answer), here is my working solution that avoids all the annoying errors that can be dragged in by depending heavily on the Hadoop Path API:

javahadoopparquet


Solution 1:[1]

If the need for not using Hadoop is really unavoidable, you can try Spark and run it in a local version. A quick start guide can be find here: https://spark.apache.org/docs/latest/index.html. For downloading, you can download at this link: https://archive.apache.org/dist/spark/ (find a version you like, there is always a build without hadoop. Unfortunately, the size of compressed version is still around 10-15M). You will also able to find some Java example at examples/src/main.

After that, you can read the file in as a Spark Dataframe like this

import org.apache.spark.api.java.*;
import org.apache.spark.api.java.function.*; 

SparkSession spark = SparkSession.builder().appName("Reducing dependecy by adding more dependencies").master("local[*]").getOrCreate();
        DataFrame parquet = sqlContext.read().parquet("C:/files/myfile.csv.parquet");
    parquet.show(20);

This solution do satisfy the original conditions in the question. However, it doesn't devoid from the fact that it's like beating around the bush (but hell yeah it's funny). Still, it might helps to open a new possible way to tackle this.

Solution 2:[2]

You can use ParquetFileReader class for that

dependencies {
    compile group: 'org.apache.hadoop', name: 'hadoop-common', version: '3.2.0'
    compile group: 'org.apache.parquet', name: 'parquet-hadoop', version: '1.10.1'
}

You can specify your local directory path here

private static Path path = new Path("file:\\C:\\myfile.snappy.parquet");

ParquetFileReader r = new ParquetFileReader(conf, path, footer);

Solution 3:[3]

Here is a complete sample application, also using the LocalInputFile.java class that is part of the solution above, to read a parquet file with minimum dependencies:

https://github.com/GeoscienceAustralia/wit_tooling/tree/main/examples/java/parquet-reader

In contrast to the other example solutions, this project also avoids the Avro dependency.

Sources

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Source: Stack Overflow

Solution Source
Solution 1
Solution 2 Ben Watson
Solution 3

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