BigQuery Storage API Client Libraries

This page shows how to get started with the Cloud Client Libraries for the BigQuery Storage API. Read more about the client libraries for Cloud APIs, including the older Google APIs Client Libraries, in Client Libraries Explained.

Installing the client library

Go

For more information, see Setting Up a Go Development Environment.

go get -u cloud.google.com/go/bigquery

Java

For more information, see Setting Up a Java Development Environment.

If you are using Maven, add the following to your pom.xml file. For more information about BOMs, see The Google Cloud Platform Libraries BOM.

<dependencyManagement>
  <dependencies>
    <dependency>
      <groupId>com.google.cloud</groupId>
      <artifactId>libraries-bom</artifactId>
      <version>20.1.0</version>
      <type>pom</type>
      <scope>import</scope>
    </dependency>
  </dependencies>
</dependencyManagement>

<dependencies>
  <dependency>
    <groupId>com.google.cloud</groupId>
    <artifactId>google-cloud-bigquerystorage</artifactId>
  </dependency>

If you are using Gradle, add the following to your dependencies:

implementation platform('com.google.cloud:libraries-bom:20.1.0')

compile 'com.google.cloud:google-cloud-bigquerystorage'

If you are using sbt, add the following to your dependencies:

libraryDependencies += "com.google.cloud" % "google-cloud-bigquerystorage" % "1.19.0"

If you're using IntelliJ or Eclipse, you can add client libraries to your project using the following IDE plugins:

The plugins provide additional functionality, such as key management for service accounts. Refer to each plugin's documentation for details.

Python

For more information, see Setting Up a Python Development Environment.

pip install --upgrade google-cloud-bigquery-storage

Setting up authentication

To run the client library, you must first set up authentication by creating a service account and setting an environment variable. Complete the following steps to set up authentication. For other ways to authenticate, see the GCP authentication documentation.

Cloud Console

Create a service account:

  1. In the Cloud Console, go to the Create service account page.

    Go to Create service account
  2. Select a project.

  3. In the Service account name field, enter a name. The Cloud Console fills in the Service account ID field based on this name.

    In the Service account description field, enter a description. For example, Service account for quickstart.

  4. Click Create.

  5. Click the Select a role field.

    Under Quick access, click Basic, then click Owner.

  6. Click Continue.

  7. Click Done to finish creating the service account.

    Do not close your browser window. You will use it in the next step.

Create a service account key:

  1. In the Cloud Console, click the email address for the service account that you created.
  2. Click Keys.
  3. Click Add key, then click Create new key.
  4. Click Create. A JSON key file is downloaded to your computer.
  5. Click Close.

Command line

You can run the following commands using the Cloud SDK on your local machine, or in Cloud Shell.

  1. Create the service account. Replace NAME with a name for the service account. 

    gcloud iam service-accounts create NAME

  2. Grant permissions to the service account. Replace PROJECT_ID with your project ID. 

    gcloud projects add-iam-policy-binding PROJECT_ID --member="serviceAccount:NAME@PROJECT_ID.iam.gserviceaccount.com" --role="roles/owner"

  3. Generate the key file. Replace FILE_NAME with a name for the key file. 

    gcloud iam service-accounts keys create FILE_NAME.json --iam-account=NAME@PROJECT_ID.iam.gserviceaccount.com

Provide authentication credentials to your application code by setting the environment variable GOOGLE_APPLICATION_CREDENTIALS. Replace [PATH] with the file path of the JSON file that contains your service account key. This variable only applies to your current shell session, so if you open a new session, set the variable again.

Linux or macOS

export GOOGLE_APPLICATION_CREDENTIALS="[PATH]"

For example:

export GOOGLE_APPLICATION_CREDENTIALS="/home/user/Downloads/my-key.json"

Windows

With PowerShell:

$env:GOOGLE_APPLICATION_CREDENTIALS="[PATH]"

For example:

$env:GOOGLE_APPLICATION_CREDENTIALS="C:\Users\username\Downloads\my-key.json"

With command prompt:

set GOOGLE_APPLICATION_CREDENTIALS=[PATH]

Using the client library

The following example shows basic interactions with the BigQuery Storage API.

Go

To use this sample, prepare your machine for Go development, and complete the BigQuery Storage API quickstart. For more information, see the BigQuery Storage API Go API reference documentation.

bigquery/bigquery_storage_quickstart/main.go 

// The bigquery_storage_quickstart application demonstrates usage of the
// BigQuery Storage read API.  It demonstrates API features such as column
// projection (limiting the output to a subset of a table's columns),
// column filtering (using simple predicates to filter records on the server
// side), establishing the snapshot time (reading data from the table at a
// specific point in time), and decoding Avro row blocks using the third party
// "github.com/linkedin/goavro" library.
package main

import (
	"context"
	"flag"
	"fmt"
	"io"
	"log"
	"sort"
	"sync"
	"time"

	bqStorage "cloud.google.com/go/bigquery/storage/apiv1"
	"github.com/golang/protobuf/ptypes"
	gax "github.com/googleapis/gax-go/v2"
	goavro "github.com/linkedin/goavro/v2"
	bqStoragepb "google.golang.org/genproto/googleapis/cloud/bigquery/storage/v1"
	"google.golang.org/grpc"
)

// rpcOpts is used to configure the underlying gRPC client to accept large
// messages.  The BigQuery Storage API may send message blocks up to 10MB
// in size.
var rpcOpts = gax.WithGRPCOptions(
	grpc.MaxCallRecvMsgSize(1024 * 1024 * 11),
)

// Command-line flags.
var (
	projectID = flag.String("project_id", "",
		"Cloud Project ID, used for session creation.")
	snapshotMillis = flag.Int64("snapshot_millis", 0,
		"Snapshot time to use for reads, represented in epoch milliseconds format.  Default behavior reads current data.")
)

func main() {
	flag.Parse()
	ctx := context.Background()
	bqReadClient, err := bqStorage.NewBigQueryReadClient(ctx)
	if err != nil {
		log.Fatalf("NewBigQueryStorageClient: %v", err)
	}
	defer bqReadClient.Close()

	// Verify we've been provided a parent project which will contain the read session.  The
	// session may exist in a different project than the table being read.
	if *projectID == "" {
		log.Fatalf("No parent project ID specified, please supply using the --project_id flag.")
	}

	// This example uses baby name data from the public datasets.
	srcProjectID := "bigquery-public-data"
	srcDatasetID := "usa_names"
	srcTableID := "usa_1910_current"
	readTable := fmt.Sprintf("projects/%s/datasets/%s/tables/%s",
		srcProjectID,
		srcDatasetID,
		srcTableID,
	)

	// 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).
	tableReadOptions := &bqStoragepb.ReadSession_TableReadOptions{
		SelectedFields: []string{"name", "number", "state"},
		RowRestriction: `state = "WA"`,
	}

	createReadSessionRequest := &bqStoragepb.CreateReadSessionRequest{
		Parent: fmt.Sprintf("projects/%s", *projectID),
		ReadSession: &bqStoragepb.ReadSession{
			Table: readTable,
			// This API can also deliver data serialized in Apache Arrow format.
			// This example leverages Apache Avro.
			DataFormat:  bqStoragepb.DataFormat_AVRO,
			ReadOptions: tableReadOptions,
		},
		MaxStreamCount: 1,
	}

	// Set a snapshot time if it's been specified.
	if *snapshotMillis > 0 {
		ts, err := ptypes.TimestampProto(time.Unix(0, *snapshotMillis*1000))
		if err != nil {
			log.Fatalf("Invalid snapshot millis (%d): %v", *snapshotMillis, err)
		}
		createReadSessionRequest.ReadSession.TableModifiers = &bqStoragepb.ReadSession_TableModifiers{
			SnapshotTime: ts,
		}
	}

	// Create the session from the request.
	session, err := bqReadClient.CreateReadSession(ctx, createReadSessionRequest, rpcOpts)
	if err != nil {
		log.Fatalf("CreateReadSession: %v", err)
	}

	if len(session.GetStreams()) == 0 {
		log.Fatalf("no streams in session.  if this was a small query result, consider writing to output to a named table.")
	}

	// 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
	// increasing the MaxStreamCount.
	readStream := session.GetStreams()[0].Name

	ch := make(chan *bqStoragepb.AvroRows)

	// Use a waitgroup to coordinate the reading and decoding goroutines.
	var wg sync.WaitGroup

	// Start the reading in one goroutine.
	wg.Add(1)
	go func() {
		defer wg.Done()
		if err := processStream(ctx, bqReadClient, readStream, ch); err != nil {
			log.Fatalf("processStream failure: %v", err)
		}
		close(ch)
	}()

	// Start Avro processing and decoding in another goroutine.
	wg.Add(1)
	go func() {
		defer wg.Done()
		err := processAvro(ctx, session.GetAvroSchema().GetSchema(), ch)
		if err != nil {
			log.Fatalf("Error processing avro: %v", err)
		}
	}()

	// Wait until both the reading and decoding goroutines complete.
	wg.Wait()

}

// printDatum prints the decoded row datum.
func printDatum(d interface{}) {
	m, ok := d.(map[string]interface{})
	if !ok {
		log.Printf("failed type assertion: %v", d)
	}
	// Go's map implementation returns keys in a random ordering, so we sort
	// the keys before accessing.
	keys := make([]string, len(m))
	i := 0
	for k := range m {
		keys[i] = k
		i++
	}
	sort.Strings(keys)
	for _, key := range keys {
		fmt.Printf("%s: %-20v ", key, valueFromTypeMap(m[key]))
	}
	fmt.Println()
}

// valueFromTypeMap returns the first value/key in the type map.  This function
// is only suitable for simple schemas, as complex typing such as arrays and
// records necessitate a more robust implementation.  See the goavro library
// and the Avro specification for more information.
func valueFromTypeMap(field interface{}) interface{} {
	m, ok := field.(map[string]interface{})
	if !ok {
		return nil
	}
	for _, v := range m {
		// Return the first key encountered.
		return v
	}
	return nil
}

// processStream reads rows from a single storage Stream, and sends the Avro
// data blocks to a channel. This function will retry on transient stream
// failures and bookmark progress to avoid re-reading data that's already been
// successfully transmitted.
func processStream(ctx context.Context, client *bqStorage.BigQueryReadClient, st string, ch chan<- *bqStoragepb.AvroRows) error {
	var offset int64

	// Streams may be long-running.  Rather than using a global retry for the
	// stream, implement a retry that resets once progress is made.
	retryLimit := 3

	for {
		retries := 0
		// Send the initiating request to start streaming row blocks.
		rowStream, err := client.ReadRows(ctx, &bqStoragepb.ReadRowsRequest{
			ReadStream: st,
			Offset:     offset,
		}, rpcOpts)
		if err != nil {
			return fmt.Errorf("Couldn't invoke ReadRows: %v", err)
		}

		// Process the streamed responses.
		for {
			r, err := rowStream.Recv()
			if err == io.EOF {
				return nil
			}
			if err != nil {
				retries++
				if retries >= retryLimit {
					return fmt.Errorf("processStream retries exhausted: %v", err)
				}
				// break the inner loop, and try to recover by starting a new streaming
				// ReadRows call at the last known good offset.
				break
			}

			rc := r.GetRowCount()
			if rc > 0 {
				// Bookmark our progress in case of retries and send the rowblock on the channel.
				offset = offset + rc
				// We're making progress, reset retries.
				retries = 0
				ch <- r.GetAvroRows()
			}
		}
	}
}

// processAvro receives row blocks from a channel, and uses the provided Avro
// schema to decode the blocks into individual row messages for printing.  Will
// continue to run until the channel is closed or the provided context is
// cancelled.
func processAvro(ctx context.Context, schema string, ch <-chan *bqStoragepb.AvroRows) error {
	// Establish a decoder that can process blocks of messages using the
	// reference schema. All blocks share the same schema, so the decoder
	// can be long-lived.
	codec, err := goavro.NewCodec(schema)
	if err != nil {
		return fmt.Errorf("couldn't create codec: %v", err)
	}

	for {
		select {
		case <-ctx.Done():
			// Context was cancelled.  Stop.
			return nil
		case rows, ok := <-ch:
			if !ok {
				// Channel closed, no further avro messages.  Stop.
				return nil
			}
			undecoded := rows.GetSerializedBinaryRows()
			for len(undecoded) > 0 {
				datum, remainingBytes, err := codec.NativeFromBinary(undecoded)

				if err != nil {
					if err == io.EOF {
						break
					}
					return fmt.Errorf("decoding error with %d bytes remaining: %v", len(undecoded), err)
				}
				printDatum(datum)
				undecoded = remainingBytes
			}
		}
	}
}

Java

Before trying this sample, follow the Java setup instructions in Setting Up a Java Development Environment. For more information, see the BigQuery Storage API Java API reference documentation.

samples/snippets/src/main/java/com/example/bigquerystorage/StorageArrowSample.java 

import com.google.api.gax.rpc.ServerStream;
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.common.base.Preconditions;
import com.google.protobuf.Timestamp;
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.vector.FieldVector;
import org.apache.arrow.vector.VectorLoader;
import org.apache.arrow.vector.VectorSchemaRoot;
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;

public class StorageArrowSample {

  /*
   * SimpleRowReader handles deserialization of the Apache Arrow-encoded row batches transmitted
   * from the storage API using a generic datum decoder.
   */
  private static class SimpleRowReader implements AutoCloseable {

    BufferAllocator allocator = new RootAllocator(Long.MAX_VALUE);

    // Decoder object will be reused to avoid re-allocation and too much garbage collection.
    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())));
      Preconditions.checkNotNull(schema);
      List<FieldVector> vectors = new ArrayList<>();
      for (Field field : schema.getFields()) {
        vectors.add(field.createVector(allocator));
      }
      root = new VectorSchemaRoot(vectors);
      loader = new VectorLoader(root);
    }

    /**
     * Sample method for processing Arrow data which only validates decoding.
     *
     * @param batch object returned from the ReadRowsResponse.
     */
    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);

      loader.load(deserializedBatch);
      // Release buffers from batch (they are still held in the vectors in root).
      deserializedBatch.close();
      System.out.println(root.contentToTSVString());
      // Release buffers from vectors in root.
      root.clear();
    }

    @Override
    public void close() {
      root.close();
      allocator.close();
    }
  }

  public static void main(String... args) throws Exception {
    // Sets your Google Cloud Platform project ID.
    // String projectId = "YOUR_PROJECT_ID";
    String projectId = args[0];
    Integer snapshotMillis = null;
    if (args.length > 1) {
      snapshotMillis = Integer.parseInt(args[1]);
    }

    try (BigQueryReadClient client = BigQueryReadClient.create()) {
      String parent = String.format("projects/%s", projectId);

      // This example uses baby name data from the public datasets.
      String srcTable =
          String.format(
              "projects/%s/datasets/%s/tables/%s",
              "bigquery-public-data", "usa_names", "usa_1910_current");

      // We specify the columns to be projected by adding them to the selected fields,
      // and set a simple filter to restrict which rows are transmitted.
      TableReadOptions options =
          TableReadOptions.newBuilder()
              .addSelectedFields("name")
              .addSelectedFields("number")
              .addSelectedFields("state")
              .setRowRestriction("state = \"WA\"")
              .build();

      // Start specifying the read session we want created.
      ReadSession.Builder sessionBuilder =
          ReadSession.newBuilder()
              .setTable(srcTable)
              // This API can also deliver data serialized in Apache Avro format.
              // This example leverages Apache Arrow.
              .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())) {

        // Assert that there are streams available in the session.  An empty table may not have
        // data available.  If no sessions are available for an anonymous (cached) table, consider
        // writing results of a query to a named table rather than consuming cached results
        // directly.
        Preconditions.checkState(session.getStreamsCount() > 0);

        // Use the first stream to perform reading.
        String streamName = session.getStreams(0).getName();

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

        // Process each block of rows as they arrive and decode using our simple row reader.
        ServerStream<ReadRowsResponse> stream = client.readRowsCallable().call(readRowsRequest);
        for (ReadRowsResponse response : stream) {
          Preconditions.checkState(response.hasArrowRecordBatch());
          reader.processRows(response.getArrowRecordBatch());
        }
      }
    }
  }
}

Python

Before trying this sample, follow the Python setup instructions in Setting Up a Python Development Environment. For more information, see the BigQuery Storage API Python API reference documentation.

samples/quickstart/quickstart.py 
from google.cloud.bigquery_storage import BigQueryReadClient
from google.cloud.bigquery_storage import types

# TODO(developer): Set the project_id variable.
# project_id = 'your-project-id'
#
# The read session is created in this project. This project can be
# different from that which contains the table.

client = BigQueryReadClient()

# This example reads baby name data from the public datasets.
table = "projects/{}/datasets/{}/tables/{}".format(
    "bigquery-public-data", "usa_names", "usa_1910_current"
)

requested_session = types.ReadSession()
requested_session.table = table
# This API can also deliver data serialized in Apache Arrow format.
# This example leverages Apache Avro.
requested_session.data_format = types.DataFormat.AVRO

# 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).
requested_session.read_options.selected_fields = ["name", "number", "state"]
requested_session.read_options.row_restriction = 'state = "WA"'

# Set a snapshot time if it's been specified.
if snapshot_millis > 0:
    snapshot_time = types.Timestamp()
    snapshot_time.FromMilliseconds(snapshot_millis)
    requested_session.table_modifiers.snapshot_time = snapshot_time

parent = "projects/{}".format(project_id)
session = client.create_read_session(
    parent=parent,
    read_session=requested_session,
    # We'll use only a single stream for reading data from the table. However,
    # if you wanted to fan out multiple readers you could do so by having a
    # reader process each individual stream.
    max_stream_count=1,
)
reader = client.read_rows(session.streams[0].name)

# The read stream contains blocks of Avro-encoded bytes. The rows() method
# uses the fastavro library to parse these blocks as an iterable of Python
# dictionaries. Install fastavro with the following command:
#
# pip install google-cloud-bigquery-storage[fastavro]
rows = reader.rows(session)

# Do any local processing by iterating over the rows. The
# google-cloud-bigquery-storage client reconnects to the API after any
# transient network errors or timeouts.
names = set()
states = set()

for row in rows:
    names.add(row["name"])
    states.add(row["state"])

print("Got {} unique names in states: {}".format(len(names), states))

Additional resources

What's next?

For users of the pandas and the pandas-gbq integration to BigQuery, see the tutorial Downloading BigQuery data to pandas using the BigQuery Storage API for more information about leveraging the storage API.