java/source/dataset.rst

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.. to you under the Apache License, Version 2.0 (the
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..   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
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.. KIND, either express or implied.  See the License for the
.. specific language governing permissions and limitations
.. under the License.

.. _arrow-dataset:

=======
Dataset
=======

* `Arrow Java Dataset`_: Java implementation of Arrow Datasets library. Implement Dataset Java API by JNI to C++.

.. contents::

Constructing Datasets
=====================

We can construct a dataset with an auto-inferred schema.

.. testcode::

   import org.apache.arrow.dataset.file.FileFormat;
   import org.apache.arrow.dataset.file.FileSystemDatasetFactory;
   import org.apache.arrow.dataset.jni.NativeMemoryPool;
   import org.apache.arrow.dataset.scanner.ScanOptions;
   import org.apache.arrow.dataset.scanner.Scanner;
   import org.apache.arrow.dataset.source.Dataset;
   import org.apache.arrow.dataset.source.DatasetFactory;
   import org.apache.arrow.memory.BufferAllocator;
   import org.apache.arrow.memory.RootAllocator;
   import java.util.stream.StreamSupport;

   String uri = "file:" + System.getProperty("user.dir") + "/thirdpartydeps/parquetfiles/data1.parquet";
   ScanOptions options = new ScanOptions(/*batchSize*/ 32768);
   try (
       BufferAllocator allocator = new RootAllocator();
       DatasetFactory datasetFactory = new FileSystemDatasetFactory(allocator, NativeMemoryPool.getDefault(), FileFormat.PARQUET, uri);
       Dataset dataset = datasetFactory.finish();
       Scanner scanner = dataset.newScan(options)
   ) {
       System.out.println(StreamSupport.stream(scanner.scan().spliterator(), false).count());
   } catch (Exception e) {
       e.printStackTrace();
   }

.. testoutput::

   1

Let construct our dataset with predefined schema.

.. testcode::

   import org.apache.arrow.dataset.file.FileFormat;
   import org.apache.arrow.dataset.file.FileSystemDatasetFactory;
   import org.apache.arrow.dataset.jni.NativeMemoryPool;
   import org.apache.arrow.dataset.scanner.ScanOptions;
   import org.apache.arrow.dataset.scanner.Scanner;
   import org.apache.arrow.dataset.source.Dataset;
   import org.apache.arrow.dataset.source.DatasetFactory;
   import org.apache.arrow.memory.BufferAllocator;
   import org.apache.arrow.memory.RootAllocator;
   import java.util.stream.StreamSupport;

   String uri = "file:" + System.getProperty("user.dir") + "/thirdpartydeps/parquetfiles/data1.parquet";
   ScanOptions options = new ScanOptions(/*batchSize*/ 32768);
   try (
       BufferAllocator allocator = new RootAllocator();
       DatasetFactory datasetFactory = new FileSystemDatasetFactory(allocator, NativeMemoryPool.getDefault(), FileFormat.PARQUET, uri);
       Dataset dataset = datasetFactory.finish(datasetFactory.inspect());
       Scanner scanner = dataset.newScan(options)
   ) {
       System.out.println(StreamSupport.stream(scanner.scan().spliterator(), false).count());
   } catch (Exception e) {
       e.printStackTrace();
   }

.. testoutput::

   1

Getting the Schema
==================

During Dataset Construction
***************************

.. testcode::

   import org.apache.arrow.dataset.file.FileFormat;
   import org.apache.arrow.dataset.file.FileSystemDatasetFactory;
   import org.apache.arrow.dataset.jni.NativeMemoryPool;
   import org.apache.arrow.dataset.source.DatasetFactory;
   import org.apache.arrow.memory.BufferAllocator;
   import org.apache.arrow.memory.RootAllocator;
   import org.apache.arrow.vector.types.pojo.Schema;

   String uri = "file:" + System.getProperty("user.dir") + "/thirdpartydeps/parquetfiles/data1.parquet";
   try (
       BufferAllocator allocator = new RootAllocator();
       DatasetFactory datasetFactory = new FileSystemDatasetFactory(allocator, NativeMemoryPool.getDefault(), FileFormat.PARQUET, uri)
   ) {
       Schema schema = datasetFactory.inspect();

       System.out.println(schema);
   } catch (Exception e) {
       e.printStackTrace();
   }

.. testoutput::

   Schema<id: Int(32, true), name: Utf8>(metadata: {parquet.avro.schema={"type":"record","name":"User","namespace":"org.apache.arrow.dataset","fields":[{"name":"id","type":["int","null"]},{"name":"name","type":["string","null"]}]}, writer.model.name=avro})

From a Dataset
**************

.. testcode::

   import org.apache.arrow.dataset.file.FileFormat;
   import org.apache.arrow.dataset.file.FileSystemDatasetFactory;
   import org.apache.arrow.dataset.jni.NativeMemoryPool;
   import org.apache.arrow.dataset.scanner.ScanOptions;
   import org.apache.arrow.dataset.scanner.Scanner;
   import org.apache.arrow.dataset.source.Dataset;
   import org.apache.arrow.dataset.source.DatasetFactory;
   import org.apache.arrow.memory.BufferAllocator;
   import org.apache.arrow.memory.RootAllocator;
   import org.apache.arrow.vector.types.pojo.Schema;

   String uri = "file:" + System.getProperty("user.dir") + "/thirdpartydeps/parquetfiles/data1.parquet";
   ScanOptions options = new ScanOptions(/*batchSize*/ 32768);
   try (
       BufferAllocator allocator = new RootAllocator();
       DatasetFactory datasetFactory = new FileSystemDatasetFactory(allocator, NativeMemoryPool.getDefault(), FileFormat.PARQUET, uri);
       Dataset dataset = datasetFactory.finish();
       Scanner scanner = dataset.newScan(options)
   ) {
       Schema schema = scanner.schema();

       System.out.println(schema);
   } catch (Exception e) {
       e.printStackTrace();
   }

.. testoutput::

   Schema<id: Int(32, true), name: Utf8>(metadata: {parquet.avro.schema={"type":"record","name":"User","namespace":"org.apache.arrow.dataset","fields":[{"name":"id","type":["int","null"]},{"name":"name","type":["string","null"]}]}, writer.model.name=avro})

Query Parquet File
==================

Let query information for a parquet file.

Query Data Content For File
***************************

.. testcode::

   import org.apache.arrow.dataset.file.FileFormat;
   import org.apache.arrow.dataset.file.FileSystemDatasetFactory;
   import org.apache.arrow.dataset.jni.NativeMemoryPool;
   import org.apache.arrow.dataset.scanner.ScanOptions;
   import org.apache.arrow.dataset.scanner.Scanner;
   import org.apache.arrow.dataset.source.Dataset;
   import org.apache.arrow.dataset.source.DatasetFactory;
   import org.apache.arrow.memory.BufferAllocator;
   import org.apache.arrow.memory.RootAllocator;
   import org.apache.arrow.vector.VectorSchemaRoot;
   import org.apache.arrow.vector.ipc.ArrowReader;

   String uri = "file:" + System.getProperty("user.dir") + "/thirdpartydeps/parquetfiles/data1.parquet";
   ScanOptions options = new ScanOptions(/*batchSize*/ 32768);
   try (
       BufferAllocator allocator = new RootAllocator();
       DatasetFactory datasetFactory = new FileSystemDatasetFactory(allocator, NativeMemoryPool.getDefault(), FileFormat.PARQUET, uri);
       Dataset dataset = datasetFactory.finish();
       Scanner scanner = dataset.newScan(options);
       ArrowReader reader = scanner.scanBatches()
   ) {
       while (reader.loadNextBatch()) {
           try (VectorSchemaRoot root = reader.getVectorSchemaRoot()) {
               System.out.print(root.contentToTSVString());
           }
       }
   } catch (Exception e) {
       e.printStackTrace();
   }

.. testoutput::

   id    name
   1    David
   2    Gladis
   3    Juan

Let's try to read a Parquet file with gzip compression and 3 row groups:

.. code-block::

   $ parquet-tools meta data4_3rg_gzip.parquet

   file schema: schema
   age:         OPTIONAL INT64 R:0 D:1
   name:        OPTIONAL BINARY L:STRING R:0 D:1
   row group 1: RC:4 TS:182 OFFSET:4
   row group 2: RC:4 TS:190 OFFSET:420
   row group 3: RC:3 TS:179 OFFSET:838

.. testcode::

   import org.apache.arrow.dataset.file.FileFormat;
   import org.apache.arrow.dataset.file.FileSystemDatasetFactory;
   import org.apache.arrow.dataset.jni.NativeMemoryPool;
   import org.apache.arrow.dataset.scanner.ScanOptions;
   import org.apache.arrow.dataset.scanner.Scanner;
   import org.apache.arrow.dataset.source.Dataset;
   import org.apache.arrow.dataset.source.DatasetFactory;
   import org.apache.arrow.memory.BufferAllocator;
   import org.apache.arrow.memory.RootAllocator;
   import org.apache.arrow.vector.VectorSchemaRoot;
   import org.apache.arrow.vector.ipc.ArrowReader;

   String uri = "file:" + System.getProperty("user.dir") + "/thirdpartydeps/parquetfiles/data4_3rg_gzip.parquet";
   ScanOptions options = new ScanOptions(/*batchSize*/ 32768);
   try (
       BufferAllocator allocator = new RootAllocator();
       DatasetFactory datasetFactory = new FileSystemDatasetFactory(allocator, NativeMemoryPool.getDefault(), FileFormat.PARQUET, uri);
       Dataset dataset = datasetFactory.finish();
       Scanner scanner = dataset.newScan(options);
       ArrowReader reader = scanner.scanBatches()
   ) {
       int totalBatchSize = 0;
       int count = 1;
       while (reader.loadNextBatch()) {
           try (VectorSchemaRoot root = reader.getVectorSchemaRoot()) {
               totalBatchSize += root.getRowCount();
               System.out.println("Number of rows per batch["+ count++ +"]: " + root.getRowCount());
               System.out.print(root.contentToTSVString());
           }
       }
       System.out.println("Total batch size: " + totalBatchSize);
   } catch (Exception e) {
       e.printStackTrace();
   }

.. testoutput::

   Number of rows per batch[1]: 4
   age    name
   10    Jean
   10    Lu
   10    Kei
   10    Sophia
   Number of rows per batch[2]: 4
   age    name
   10    Mara
   20    Arit
   20    Neil
   20    Jason
   Number of rows per batch[3]: 3
   age    name
   20    John
   20    Peter
   20    Ismael
   Total batch size: 11

Query Data Content For Directory
********************************

Consider that we have these files: data1: 3 rows, data2: 3 rows and data3: 250 rows.

.. testcode::

   import org.apache.arrow.dataset.file.FileFormat;
   import org.apache.arrow.dataset.file.FileSystemDatasetFactory;
   import org.apache.arrow.dataset.jni.NativeMemoryPool;
   import org.apache.arrow.dataset.scanner.ScanOptions;
   import org.apache.arrow.dataset.scanner.Scanner;
   import org.apache.arrow.dataset.source.Dataset;
   import org.apache.arrow.dataset.source.DatasetFactory;
   import org.apache.arrow.memory.BufferAllocator;
   import org.apache.arrow.memory.RootAllocator;
   import org.apache.arrow.vector.VectorSchemaRoot;
   import org.apache.arrow.vector.ipc.ArrowReader;

   String uri = "file:" + System.getProperty("user.dir") + "/thirdpartydeps/parquetfiles/";
   ScanOptions options = new ScanOptions(/*batchSize*/ 100);
   try (
       BufferAllocator allocator = new RootAllocator();
       DatasetFactory datasetFactory = new FileSystemDatasetFactory(allocator, NativeMemoryPool.getDefault(), FileFormat.PARQUET, uri);
       Dataset dataset = datasetFactory.finish();
       Scanner scanner = dataset.newScan(options);
       ArrowReader reader = scanner.scanBatches()
   ) {
       int count = 1;
       while (reader.loadNextBatch()) {
           try (VectorSchemaRoot root = reader.getVectorSchemaRoot()) {
               System.out.println("Batch: " + count++ + ", RowCount: " + root.getRowCount());
           }
       }
   } catch (Exception e) {
       e.printStackTrace();
   }

.. testoutput::

   Batch: 1, RowCount: 3
   Batch: 2, RowCount: 3
   Batch: 3, RowCount: 100
   Batch: 4, RowCount: 100
   Batch: 5, RowCount: 50
   Batch: 6, RowCount: 4
   Batch: 7, RowCount: 4
   Batch: 8, RowCount: 3

Query Data Content with Projection
**********************************

In case we need to project only certain columns we could configure ScanOptions with projections needed.

.. testcode::

   import org.apache.arrow.dataset.file.FileFormat;
   import org.apache.arrow.dataset.file.FileSystemDatasetFactory;
   import org.apache.arrow.dataset.jni.NativeMemoryPool;
   import org.apache.arrow.dataset.scanner.ScanOptions;
   import org.apache.arrow.dataset.scanner.Scanner;
   import org.apache.arrow.dataset.source.Dataset;
   import org.apache.arrow.dataset.source.DatasetFactory;
   import org.apache.arrow.memory.BufferAllocator;
   import org.apache.arrow.memory.RootAllocator;
   import org.apache.arrow.vector.VectorSchemaRoot;
   import org.apache.arrow.vector.ipc.ArrowReader;

   String uri = "file:" + System.getProperty("user.dir") + "/thirdpartydeps/parquetfiles/data1.parquet";
   String[] projection = new String[] {"name"};
   ScanOptions options = new ScanOptions(/*batchSize*/ 32768, Optional.of(projection));
   try (
       BufferAllocator allocator = new RootAllocator();
       DatasetFactory datasetFactory = new FileSystemDatasetFactory(allocator, NativeMemoryPool.getDefault(), FileFormat.PARQUET, uri);
       Dataset dataset = datasetFactory.finish();
       Scanner scanner = dataset.newScan(options);
       ArrowReader reader = scanner.scanBatches()
   ) {
       while (reader.loadNextBatch()) {
           try (VectorSchemaRoot root = reader.getVectorSchemaRoot()) {
               System.out.print(root.contentToTSVString());
           }
       }
   } catch (Exception e) {
       e.printStackTrace();
   }

.. testoutput::

   name
   David
   Gladis
   Juan

Query Arrow Files
=================


Query Data Content For File
***************************

Let's read an Arrow file with 3 record batches, each with 3 rows.

.. testcode::

   import org.apache.arrow.dataset.file.FileFormat;
   import org.apache.arrow.dataset.file.FileSystemDatasetFactory;
   import org.apache.arrow.dataset.jni.NativeMemoryPool;
   import org.apache.arrow.dataset.scanner.ScanOptions;
   import org.apache.arrow.dataset.scanner.Scanner;
   import org.apache.arrow.dataset.source.Dataset;
   import org.apache.arrow.dataset.source.DatasetFactory;
   import org.apache.arrow.memory.BufferAllocator;
   import org.apache.arrow.memory.RootAllocator;
   import org.apache.arrow.vector.VectorSchemaRoot;
   import org.apache.arrow.vector.ipc.ArrowReader;

   import java.io.IOException;

   String uri = "file:" + System.getProperty("user.dir") + "/thirdpartydeps/arrowfiles/random_access.arrow";
   ScanOptions options = new ScanOptions(/*batchSize*/ 32768);
   try (
       BufferAllocator allocator = new RootAllocator();
       DatasetFactory datasetFactory = new FileSystemDatasetFactory(allocator, NativeMemoryPool.getDefault(), FileFormat.ARROW_IPC, uri);
       Dataset dataset = datasetFactory.finish();
       Scanner scanner = dataset.newScan(options);
       ArrowReader reader = scanner.scanBatches()
   ) {
       int count = 1;
       while (reader.loadNextBatch()) {
           try (VectorSchemaRoot root = reader.getVectorSchemaRoot()) {
               System.out.println("Number of rows per batch["+ count++ +"]: " + root.getRowCount());
           }
       }
   } catch (Exception e) {
       e.printStackTrace();
   }

.. testoutput::

   Number of rows per batch[1]: 3
   Number of rows per batch[2]: 3
   Number of rows per batch[3]: 3

Query ORC File
==============

Query Data Content For File
***************************

Let's read an ORC file with zlib compression 385 stripes, each with 5000 rows.

.. code-block::

   $ orc-metadata demo-11-zlib.orc | more

   { "name": "demo-11-zlib.orc",
     "type": "struct<_col0:int,_col1:string,_col2:string,_col3:string,_col4:int,_col5:string,_col6:int,_col7:int,_col8:int>",
     "stripe count": 385,
     "compression": "zlib", "compression block": 262144,
     "stripes": [
       { "stripe": 0, "rows": 5000,
         "offset": 3, "length": 1031,
         "index": 266, "data": 636, "footer": 129
       },
   ...

.. testcode::

   import org.apache.arrow.dataset.file.FileFormat;
   import org.apache.arrow.dataset.file.FileSystemDatasetFactory;
   import org.apache.arrow.dataset.jni.NativeMemoryPool;
   import org.apache.arrow.dataset.scanner.ScanOptions;
   import org.apache.arrow.dataset.scanner.Scanner;
   import org.apache.arrow.dataset.source.Dataset;
   import org.apache.arrow.dataset.source.DatasetFactory;
   import org.apache.arrow.memory.BufferAllocator;
   import org.apache.arrow.memory.RootAllocator;
   import org.apache.arrow.vector.VectorSchemaRoot;
   import org.apache.arrow.vector.ipc.ArrowReader;

   String uri = "file:" + System.getProperty("user.dir") + "/thirdpartydeps/orc/data1-zlib.orc";
   ScanOptions options = new ScanOptions(/*batchSize*/ 32768);
   try (
       BufferAllocator allocator = new RootAllocator();
       DatasetFactory datasetFactory = new FileSystemDatasetFactory(allocator, NativeMemoryPool.getDefault(), FileFormat.ORC, uri);
       Dataset dataset = datasetFactory.finish();
       Scanner scanner = dataset.newScan(options);
       ArrowReader reader = scanner.scanBatches()
   ) {
       int totalBatchSize = 0;
       while (reader.loadNextBatch()) {
           try (VectorSchemaRoot root = reader.getVectorSchemaRoot()) {
               totalBatchSize += root.getRowCount();
           }
       }
       System.out.println("Total batch size: " + totalBatchSize);
   } catch (Exception e) {
       e.printStackTrace();
   }

.. testoutput::

   Total batch size: 1920800

Query CSV File
==============

Query Data Content For File
***************************

Let's read a CSV file.

.. testcode::

   import org.apache.arrow.dataset.file.FileFormat;
   import org.apache.arrow.dataset.file.FileSystemDatasetFactory;
   import org.apache.arrow.dataset.jni.NativeMemoryPool;
   import org.apache.arrow.dataset.scanner.ScanOptions;
   import org.apache.arrow.dataset.scanner.Scanner;
   import org.apache.arrow.dataset.source.Dataset;
   import org.apache.arrow.dataset.source.DatasetFactory;
   import org.apache.arrow.memory.BufferAllocator;
   import org.apache.arrow.memory.RootAllocator;
   import org.apache.arrow.vector.VectorSchemaRoot;
   import org.apache.arrow.vector.ipc.ArrowReader;

   String uri = "file:" + System.getProperty("user.dir") + "/thirdpartydeps/csv/tech_acquisitions.csv";
   ScanOptions options = new ScanOptions(/*batchSize*/ 32768);
   try (
       BufferAllocator allocator = new RootAllocator();
       DatasetFactory datasetFactory = new FileSystemDatasetFactory(allocator, NativeMemoryPool.getDefault(), FileFormat.CSV, uri);
       Dataset dataset = datasetFactory.finish();
       Scanner scanner = dataset.newScan(options);
       ArrowReader reader = scanner.scanBatches()
   ) {
       int totalBatchSize = 0;
       while (reader.loadNextBatch()) {
           try (VectorSchemaRoot root = reader.getVectorSchemaRoot()) {
               totalBatchSize += root.getRowCount();
               System.out.print(root.contentToTSVString());
           }
       }
       System.out.println("Total batch size: " + totalBatchSize);
   } catch (Exception e) {
       e.printStackTrace();
   }

.. testoutput::

   Acquirer    Acquiree    Amount in billions (USD)    Date of acquisition
   NVIDIA    Mellanox    6.9    04/05/2020
   AMD    Xilinx    35.0    27/10/2020
   Salesforce    Slack    27.7    01/12/2020
   Total batch size: 3

.. _Arrow Java Dataset: https://arrow.apache.org/docs/dev/java/dataset.html