BigQuery 读取存储 API，ARROW 格式为 NUMERIC 和 BIGNUMERIC 数据附加 0

Question

我尝试了 Google 文档中的代码以实现 Read Storage API 实现。 但是 Numeric 和 BigNumeric 列返回时附加了 0。

例如：我的表有数字数据 123，下面的代码返回如下： Schema<numeric_datatype: Decimal(38, 9, 128)> numeric_datatype 123000000000.000000000

使用的代码： https://cloud.google.com/bigquery/docs/reference/storage/libraries

请帮助理解和解决问题。

import java.io.IOException;
import java.util.ArrayList;
import java.util.List;
import org.apache.arrow.memory.BufferAllocator;
import org.apache.arrow.memory.RootAllocator;
import org.apache.arrow.util.Preconditions;
import org.apache.arrow.vector.VectorSchemaRoot;
import org.apache.arrow.vector.FieldVector;
import org.apache.arrow.vector.VectorLoader;
import org.apache.arrow.vector.ipc.ReadChannel;
import org.apache.arrow.vector.ipc.message.MessageSerializer;
import org.apache.arrow.vector.types.pojo.Field;
import org.apache.arrow.vector.types.pojo.Schema;
import org.apache.arrow.vector.util.ByteArrayReadableSeekableByteChannel;

import com.google.cloud.bigquery.storage.v1.ArrowRecordBatch;
import com.google.cloud.bigquery.storage.v1.ArrowSchema;
import com.google.cloud.bigquery.storage.v1.BigQueryReadClient;
import com.google.cloud.bigquery.storage.v1.CreateReadSessionRequest;
import com.google.cloud.bigquery.storage.v1.DataFormat;
import com.google.cloud.bigquery.storage.v1.ReadRowsRequest;
import com.google.cloud.bigquery.storage.v1.ReadRowsResponse;
import com.google.cloud.bigquery.storage.v1.ReadSession;
import com.google.cloud.bigquery.storage.v1.ReadSession.TableModifiers;
import com.google.cloud.bigquery.storage.v1.ReadSession.TableReadOptions;
import com.google.protobuf.Timestamp;

import com.google.api.gax.rpc.ServerStream;

public class StorageArrowExample {

    private static class SimpleRowReader implements AutoCloseable {

        BufferAllocator allocator = new RootAllocator(Long.MAX_VALUE);

        private final VectorSchemaRoot root;
        private final VectorLoader loader;

        public SimpleRowReader(ArrowSchema arrowSchema) throws IOException {
            Schema schema = MessageSerializer.deserializeSchema(new ReadChannel(
                    new ByteArrayReadableSeekableByteChannel(arrowSchema.getSerializedSchema().toByteArray())));
            System.out.println(schema);
            Preconditions.checkNotNull(schema);
            List<FieldVector> vectors = new ArrayList<>();
            for (Field field : schema.getFields()) {
                vectors.add(field.createVector(allocator));
            }
            root = new VectorSchemaRoot(vectors);
            root.syncSchema();
            loader = new VectorLoader(root);
        }

        public void processRows(ArrowRecordBatch batch) throws IOException {
            org.apache.arrow.vector.ipc.message.ArrowRecordBatch deserializedBatch = MessageSerializer
                    .deserializeRecordBatch(new ReadChannel(
                            new ByteArrayReadableSeekableByteChannel(batch.getSerializedRecordBatch().toByteArray())),
                            allocator);
            System.out.println(deserializedBatch);
            loader.load(deserializedBatch);
            // Release buffers from batch (they are still held in the vectors in root).
            deserializedBatch.close();
            String test = root.contentToTSVString();
            System.out.println(root.contentToTSVString());
            root.clear();
        }

        @Override
        public void close() throws Exception {
// TODO Auto-generated method stub
        }
    }

    public static void main(String... args) throws Exception {

        String projectId = "****";
        String table = "****";
        String dataset = "****";
        Integer snapshotMillis = null;
        if (args.length > 1) {
            snapshotMillis = Integer.parseInt(args[1]);
        }

        try (BigQueryReadClient client = BigQueryReadClient.create()) {
            String parent = String.format("projects/%s", projectId);
            String srcTable = String.format("projects/%s/datasets/%s/tables/%s", projectId, table, dataset);
            TableReadOptions options = TableReadOptions.newBuilder().addSelectedFields("numeric_datatype")
                    .addSelectedFields("bignumeric_datatype").clearArrowSerializationOptions().build();

            ReadSession.Builder sessionBuilder = ReadSession.newBuilder().setTable(srcTable)
                    .setDataFormat(DataFormat.ARROW).setReadOptions(options);

            // Optionally specify the snapshot time. When unspecified, snapshot time is
            // "now".
            if (snapshotMillis != null) {
                Timestamp t = Timestamp.newBuilder().setSeconds(snapshotMillis / 1000)
                        .setNanos((int) ((snapshotMillis % 1000) * 1000000)).build();
                TableModifiers modifiers = TableModifiers.newBuilder().setSnapshotTime(t).build();
                sessionBuilder.setTableModifiers(modifiers);
            }

            // Begin building the session creation request.
            CreateReadSessionRequest.Builder builder = CreateReadSessionRequest.newBuilder().setParent(parent)
                    .setReadSession(sessionBuilder).setMaxStreamCount(1);

            ReadSession session = client.createReadSession(builder.build());
            // Setup a simple reader and start a read session.
            try (SimpleRowReader reader = new SimpleRowReader(session.getArrowSchema())) {
                Preconditions.checkState(session.getStreamsCount() > 0);
                String streamName = session.getStreams(0).getName();

                ReadRowsRequest readRowsRequest = ReadRowsRequest.newBuilder().setReadStream(streamName).build();

                ServerStream<ReadRowsResponse> stream = client.readRowsCallable().call(readRowsRequest);
                for (ReadRowsResponse response : stream) {
                    Preconditions.checkState(response.hasArrowRecordBatch());
                    reader.processRows(response.getArrowRecordBatch());
                }
            }
        }
    }
}

Answer 1

使用您的代码我无法重现该问题。

创建测试表：

CREATE or REPLACE table abc.num
AS SELECT CAST(123 as numeric) as numeric_datatype, 
          CAST(123 as bignumeric) as bignumeric_datatype

然后运行您的代码（在粘贴的代码中生成表 URL 交换数据集和表）我得到：

ArrowRecordBatch [length=1, nodes=[ArrowFieldNode [length=1, nullCount=0], ArrowFieldNode [length=1, nullCount=0]], #buffers=4, buffersLayout=[ArrowBuffer [offset=0, size=0], ArrowBuffer [offset=0, size=16], ArrowBuffer [offset=16, size=0], ArrowBuffer [offset=16, size=32]], closed=false]
numeric_datatype    bignumeric_datatype
123.000000000   123.00000000000000000000000000000000000000

小数点后的额外数字是预期的，因为 numeric 的小数位数是 9，而 bignumeric 的小数位数是 38，这意味着完整值在逻辑上包括这些值。 contentToTSVString只是在从 DecimalVector/Decimal256Vector 返回的 BigDecimal 上调用 toString。 如果要删除小数位，可以调用 DecimalVector 的getObject并在打印值之前调用stripTrailingZeros 。

相关依赖：

   <dependency>
            <groupId>com.google.api.grpc</groupId>
            <artifactId>grpc-google-cloud-bigquerystorage-v1</artifactId>
            <version>2.12.0</version>
        </dependency>
        <dependency>
            <groupId>com.google.api.grpc</groupId>
            <artifactId>proto-google-cloud-bigquerystorage-v1</artifactId>
            <version>2.12.0</version>
        </dependency>
        <dependency>
            <groupId>org.apache.arrow</groupId>
            <artifactId>arrow-memory-netty</artifactId>
            <version>6.0.0</version>
        </dependency>
        <dependency>
            <groupId>com.google.cloud</groupId>
            <artifactId>google-cloud-bigquerystorage</artifactId>
            <version>2.12.0</version>
        </dependency>
        <dependency>
            <groupId>org.apache.arrow</groupId>
            <artifactId>arrow-vector</artifactId>
            <version>6.0.0</version>
        </dependency>
        <!-- https://mvnrepository.com/artifact/com.google.api/gax -->
        <dependency>
            <groupId>com.google.api</groupId>
            <artifactId>gax</artifactId>
            <version>2.12.2</version>
        </dependency>