public AvroParquetFileReader(LogFilePath logFilePath, CompressionCodec codec) throws IOException { Path path = new Path(logFilePath.getLogFilePath()); String topic = logFilePath.getTopic(); Schema schema = schemaRegistryClient.getSchema(topic); reader = AvroParquetReader.builder(path). build (); writer = new …

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Some Related articles (introduction): AvroParquetReader< GenericRecord > reader = new AvroParquetReader< GenericRecord > (testConf, file); GenericRecord nextRecord = reader. read(); assertNotNull(nextRecord); assertEquals(map, nextRecord. get(" mymap "));} @Test (expected = RuntimeException. class) public void testMapRequiredValueWithNull throws Exception You can also download parquet-tools jar and use it to see the content of a Parquet file, file metadata of the Parquet file, Parquet schema etc. As example to see the content of a Parquet file- $ hadoop jar /parquet-tools-1.10.0.jar cat /test/EmpRecord.parquet .

Avroparquetreader example

Lets say we want to check the status of 4th bit of PIND register. To write the java application is easy once you know how to do it. Instead of using the AvroParquetReader or the ParquetReader class that you find frequently when searching for a solution to read parquet files use the class ParquetFileReader instead. A big data architect provides a tutorial on working with Avro files when transferring data from an Oracle database to an S3 database using Apache Sqoop. ParquetIO.Read and ParquetIO.ReadFiles provide ParquetIO.Read.withAvroDataModel(GenericData) allowing implementations to set the data model associated with the AvroParquetReader. For more advanced use cases, like reading each file in a PCollection of FileIO.ReadableFile, use the ParquetIO.ReadFiles transform.

Writing is also trivial.

The following example provides reading the Parquet file data using Java. Using ReadParquet in Java. // Path to read entire Hive table ReadParquet reader = new

If I use aws sdk for this I can get inputstream like this: S3Object object = s3Client.getObject(new GetObjectRequest(bucketName, bucketKey)); InputStream inputStream = object.getObjectContent(); Read Write Parquet Files using Spark Problem: Using spark read and write Parquet Files , data schema available as Avro.(Solution: JavaSparkContext => SQLContext => DataFrame => Row => DataFrame => parquet 2018-10-17 · from fastparquet import ParquetFile from fastparquet import write pf = ParquetFile(test_file) df = pf.to_pandas() which gives you a Pandas DataFrame. Writing is also trivial.

I have 2 avro schemas: classA. ClassB. The fields of ClassB are a subset of ClassA. final Builder builder = AvroParquetReader.builder (files [0].getPath ()); final ParquetReader reader = builder.build (); //AvroParquetReader readerA = new AvroParquetReader (files [0].getPath ()); ClassB record = null; final

Avro Parquet.

Also, I’ve explained working with Avro partition and how it improves while reading Avro file. Using Partition we can achieve a significant performance on reading.
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// Path to read entire Hive table ReadParquet reader = new Prerequisites; Data Type Mapping; Creating the External Table; Example.

For example: I won’t say one is better and the other one is not as it totally depends where are they going to be used. Apache Avro is a remote procedure call and data serialization framework developed within… Drill supports files in the Avro format. Starting from Drill 1.18, the Avro format supports the Schema provisioning feature.. Preparing example data.
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object ParquetSample { def main(args: Array[String]) { val path = new Path("hdfs://hadoop-cluster/path-to-parquet-file") val reader = AvroParquetReader.builder[GenericRecord]().build(path) .asInstanceOf[ParquetReader[GenericRecord]] val iter = Iterator.continually(reader.read).takeWhile(_ != null) } }

In the sample above, for example, you could enable the fater coders as follows: $ mvn -q exec:java -Dexec.mainClass=example.SpecificMain \ -Dorg.apache.avro.specific.use_custom_coders=true Note that you do not have to recompile your Avro schema to have access to this feature. ParquetIO.Read and ParquetIO.ReadFiles provide ParquetIO.Read.withAvroDataModel(GenericData) allowing implementations to set the data model associated with the AvroParquetReader. For more advanced use cases, like reading each file in a PCollection of FileIO.ReadableFile, use the ParquetIO.ReadFiles transform.

I have 2 avro schemas: classA. ClassB. The fields of ClassB are a subset of ClassA. final Builder builder = AvroParquetReader.builder (files [0].getPath ()); final ParquetReader reader = builder.build (); //AvroParquetReader readerA = new AvroParquetReader (files [0].getPath ()); ClassB record = null; final

Avro is a row or record oriented May 22, 2018 Most examples I came up with did so in the context of Hadoop HDFS.

Using Partition we can achieve a significant performance on reading. References: Apache Avro Data Source Guide; Complete Scala example for Reference Example of reading writing Parquet in java without BigData tools. */ public class ParquetReaderWriterWithAvro {private static final Logger LOGGER = LoggerFactory. getLogger(ParquetReaderWriterWithAvro. class); private static final Schema SCHEMA; private static final String SCHEMA_LOCATION = " /org/maxkons/hadoop_snippets/parquet/avroToParquet.avsc "; In the above example, the fully qualified name for the schema is com.example.FullName.