const {adapt, managedwriter} = require('@google-cloud/bigquery-storage');const {WriterClient, JSONWriter} = managedwriter;async function appendJSONRowsDefaultStream() {        const projectId = 'my_project';  const datasetId = 'my_dataset';  const tableId = 'my_table';  const destinationTable = `projects/${projectId}/datasets/${datasetId}/tables/${tableId}`;  const writeClient = newWriterClient({projectId});  try {    const writeStream = await writeClient.getWriteStream({      streamId: `${destinationTable}/streams/_default`,      view: 'FULL'    });    const protoDescriptor = adapt.convertStorageSchemaToProto2Descriptor(      writeStream.tableSchema,      'root'    );    const connection = await writeClient.createStreamConnection({      streamId: managedwriter.DefaultStream,      destinationTable,    });    const streamId = connection.getStreamId();    const writer = newJSONWriter({      streamId,      connection,      protoDescriptor,    });    let rows = [];    const pendingWrites = [];    // Row 1    let row = {      row_num: 1,      customer_name: 'Octavia',    };    rows.push(row);    // Row 2    row = {      row_num: 2,      customer_name: 'Turing',    };    rows.push(row);    // Send batch.    let pw = writer.appendRows(rows);    pendingWrites.push(pw);    rows = [];    // Row 3    row = {      row_num: 3,      customer_name: 'Bell',    };    rows.push(row);    // Send batch.    pw = writer.appendRows(rows);    pendingWrites.push(pw);    const results = await Promise.all(      pendingWrites.map(pw => pw.getResult())    );    console.log('Write results:', results);  } catch (err) {    console.log(err);  } finally {    writeClient.close();  }}

npm install @google-cloud/bigquery-storage

// The read stream contains blocks of Avro-encoded bytes. We use the// 'avsc' library to decode these blocks. Install avsc with the following// command: npm install avscconst avro = require('avsc');// See reference documentation at// https://cloud.google.com/bigquery/docs/reference/storageconst {BigQueryReadClient} = require('@google-cloud/bigquery-storage');const client = newBigQueryReadClient();async function bigqueryStorageQuickstart() {  // Get current project ID. The read session is created in this project.  // This project can be different from that which contains the table.  const myProjectId = await client.getProjectId();  // This example reads baby name data from the public datasets.  const projectId = 'bigquery-public-data';  const datasetId = 'usa_names';  const tableId = 'usa_1910_current';  const tableReference = `projects/${projectId}/datasets/${datasetId}/tables/${tableId}`;  const parent = `projects/${myProjectId}`;  /* We limit the output columns to a subset of those allowed in the table,   * and set a simple filter to only report names from the state of   * Washington (WA).   */  const readOptions = {    selectedFields: ['name', 'number', 'state'],    rowRestriction: 'state = "WA"',  };  let tableModifiers = null;  const snapshotSeconds = 0;  // Set a snapshot time if it's been specified.  if (snapshotSeconds > 0) {    tableModifiers = {snapshotTime: {seconds: snapshotSeconds}};  }  // API request.  const request = {    parent,    readSession: {      table: tableReference,      // This API can also deliver data serialized in Apache Arrow format.      // This example leverages Apache Avro.      dataFormat: 'AVRO',      readOptions,      tableModifiers,    },  };  const [session] = await client.createReadSession(request);  const schema =JSON.parse(session.avroSchema.schema);  const avroType = avro.Type.forSchema(schema);  /* The offset requested must be less than the last   * row read from ReadRows. Requesting a larger offset is   * undefined.   */  let offset = 0;  const readRowsRequest = {    // Required stream name and optional offset. Offset requested must be less than    // the last row read from readRows(). Requesting a larger offset is undefined.    readStream: session.streams[0].name,    offset,  };  const names = new Set();  const states = [];  /* We'll use only a single stream for reading data from the table. Because   * of dynamic sharding, this will yield all the rows in the table. However,   * if you wanted to fan out multiple readers you could do so by having a   * reader process each individual stream.   */  client    .readRows(readRowsRequest)    .on('error', console.error)    .on('data', data => {      offset = data.avroRows.serializedBinaryRows.offset;      try {        // Decode all rows in buffer        let pos;        do {          const decodedData = avroType.decode(            data.avroRows.serializedBinaryRows,            pos          );          if (decodedData.value) {            names.add(decodedData.value.name);            if (!states.includes(decodedData.value.state)) {              states.push(decodedData.value.state);            }          }          pos = decodedData.offset;        } while (pos > 0);      } catch (error) {        console.log(error);      }    })    .on('end', () => {      console.log(`Got ${names.size} unique names in states: ${states}`);      console.log(`Last offset: ${offset}`);    });}