Google Cloud BigQuery

The BigQuery connector provides Apache Pekko Stream sources and sinks to connect to Google Cloud BigQuery. BigQuery is a serverless data warehouse for storing and analyzing massive datasets. This connector is primarily intended for streaming data into and out of BigQuery tables and running SQL queries, although it also provides basic support for managing datasets and tables and flexible access to the BigQuery REST API.

Project Info: Apache Pekko Connectors Google Cloud BigQuery
Artifact	org.apache.pekko pekko-connectors-google-cloud-bigquery 1.0.2 Snapshots are available
JDK versions	OpenJDK 8 OpenJDK 11 OpenJDK 17
Scala versions	2.13.14, 2.12.20, 3.3.3
JPMS module name	pekko.stream.connectors.google.cloud.bigquery
License	Apache-2.0
API documentation	API (Scaladoc)
Forums	Github Discussions
Release notes	GitHub releases
Issues	Github issues
Sources	https://github.com/apache/pekko-connectors

Artifacts

sbt

val PekkoVersion = "1.0.3"
val PekkoHttpVersion = "1.0.1"
libraryDependencies ++= Seq(
  "org.apache.pekko" %% "pekko-connectors-google-cloud-bigquery" % "1.0.2",
  "org.apache.pekko" %% "pekko-stream" % PekkoVersion,
  "org.apache.pekko" %% "pekko-http" % PekkoHttpVersion,
  "org.apache.pekko" %% "pekko-http-spray-json" % PekkoHttpVersion
)

Maven

<properties>
  <pekko.version>1.0.3</pekko.version>
  <pekko.http.version>1.0.1</pekko.http.version>
  <scala.binary.version>2.13</scala.binary.version>
</properties>
<dependencies>
  <dependency>
    <groupId>org.apache.pekko</groupId>
    <artifactId>pekko-connectors-google-cloud-bigquery_${scala.binary.version}</artifactId>
    <version>1.0.2</version>
  </dependency>
  <dependency>
    <groupId>org.apache.pekko</groupId>
    <artifactId>pekko-stream_${scala.binary.version}</artifactId>
    <version>${pekko.version}</version>
  </dependency>
  <dependency>
    <groupId>org.apache.pekko</groupId>
    <artifactId>pekko-http_${scala.binary.version}</artifactId>
    <version>${pekko.http.version}</version>
  </dependency>
  <dependency>
    <groupId>org.apache.pekko</groupId>
    <artifactId>pekko-http-spray-json_${scala.binary.version}</artifactId>
    <version>${pekko.http.version}</version>
  </dependency>
</dependencies>

Gradle

def versions = [
  PekkoVersion: "1.0.3",
  PekkoHttpVersion: "1.0.1",
  ScalaBinary: "2.13"
]
dependencies {
  implementation "org.apache.pekko:pekko-connectors-google-cloud-bigquery_${versions.ScalaBinary}:1.0.2"
  implementation "org.apache.pekko:pekko-stream_${versions.ScalaBinary}:${versions.PekkoVersion}"
  implementation "org.apache.pekko:pekko-http_${versions.ScalaBinary}:${versions.PekkoHttpVersion}"
  implementation "org.apache.pekko:pekko-http-spray-json_${versions.ScalaBinary}:${versions.PekkoHttpVersion}"
}

To use the Jackson JSON library for marshalling you must also add the Apache Pekko HTTP module for Jackson support.

sbt

val PekkoHttpVersion = "1.0.1"
libraryDependencies += "org.apache.pekko" %% "pekko-http-jackson" % PekkoHttpVersion

Maven

<properties>
  <pekko.http.version>1.0.1</pekko.http.version>
  <scala.binary.version>2.13</scala.binary.version>
</properties>
<dependencies>
  <dependency>
    <groupId>org.apache.pekko</groupId>
    <artifactId>pekko-http-jackson_${scala.binary.version}</artifactId>
    <version>${pekko.http.version}</version>
  </dependency>
</dependencies>

Gradle

def versions = [
  PekkoHttpVersion: "1.0.1",
  ScalaBinary: "2.13"
]
dependencies {
  implementation "org.apache.pekko:pekko-http-jackson_${versions.ScalaBinary}:${versions.PekkoHttpVersion}"
}

The table below shows direct dependencies of this module and the second tab shows all libraries that it depends on transitively.

Configuration

The BigQuery connector shares its basic configuration with all the Google connectors in Apache Pekko Connectors. Additional BigQuery-specific configuration settings can be found in its reference.conf.

Imports

All of the examples below assume the following imports are in scope.

Scala

copysourceimport org.apache.pekko
import pekko.http.scaladsl.marshallers.sprayjson.SprayJsonSupport._
import pekko.stream.connectors.google.{ GoogleAttributes, GoogleSettings }
import pekko.stream.connectors.googlecloud.bigquery.InsertAllRetryPolicy
import pekko.stream.connectors.googlecloud.bigquery.model.{
  Dataset,
  Job,
  JobReference,
  JobState,
  QueryResponse,
  Table,
  TableDataListResponse,
  TableListResponse
}
import pekko.stream.connectors.googlecloud.bigquery.scaladsl.schema.BigQuerySchemas._
import pekko.stream.connectors.googlecloud.bigquery.scaladsl.schema.TableSchemaWriter
import pekko.stream.connectors.googlecloud.bigquery.scaladsl.spray.BigQueryRootJsonFormat
import pekko.stream.connectors.googlecloud.bigquery.scaladsl.spray.BigQueryJsonProtocol._
import pekko.stream.connectors.googlecloud.bigquery.scaladsl.BigQuery
import pekko.stream.scaladsl.{ Flow, Sink, Source }
import pekko.{ Done, NotUsed }

import scala.annotation.nowarn
import scala.collection.immutable.Seq
import scala.concurrent.Future

Java

copysource
import org.apache.pekko.Done;
import org.apache.pekko.NotUsed;
import org.apache.pekko.http.javadsl.marshallers.jackson.Jackson;
import org.apache.pekko.http.javadsl.marshalling.Marshaller;
import org.apache.pekko.http.javadsl.model.HttpEntity;
import org.apache.pekko.http.javadsl.model.RequestEntity;
import org.apache.pekko.http.javadsl.unmarshalling.Unmarshaller;
import org.apache.pekko.stream.connectors.google.GoogleAttributes;
import org.apache.pekko.stream.connectors.google.GoogleSettings;
import org.apache.pekko.stream.connectors.googlecloud.bigquery.InsertAllRetryPolicy;
import org.apache.pekko.stream.connectors.googlecloud.bigquery.javadsl.BigQuery;
import org.apache.pekko.stream.connectors.googlecloud.bigquery.javadsl.jackson.BigQueryMarshallers;
import org.apache.pekko.stream.connectors.googlecloud.bigquery.model.Dataset;
import org.apache.pekko.stream.connectors.googlecloud.bigquery.model.Job;
import org.apache.pekko.stream.connectors.googlecloud.bigquery.model.JobReference;
import org.apache.pekko.stream.connectors.googlecloud.bigquery.model.JobState;
import org.apache.pekko.stream.connectors.googlecloud.bigquery.model.QueryResponse;
import org.apache.pekko.stream.connectors.googlecloud.bigquery.model.Table;
import org.apache.pekko.stream.connectors.googlecloud.bigquery.model.TableDataInsertAllRequest;
import org.apache.pekko.stream.connectors.googlecloud.bigquery.model.TableDataListResponse;
import org.apache.pekko.stream.connectors.googlecloud.bigquery.model.TableFieldSchema;
import org.apache.pekko.stream.connectors.googlecloud.bigquery.model.TableFieldSchemaMode;
import org.apache.pekko.stream.connectors.googlecloud.bigquery.model.TableFieldSchemaType;
import org.apache.pekko.stream.connectors.googlecloud.bigquery.model.TableListResponse;
import org.apache.pekko.stream.connectors.googlecloud.bigquery.model.TableSchema;
import org.apache.pekko.stream.javadsl.Flow;
import org.apache.pekko.stream.javadsl.Sink;
import org.apache.pekko.stream.javadsl.Source;
import com.fasterxml.jackson.annotation.JsonCreator;
import com.fasterxml.jackson.annotation.JsonProperty;
import com.fasterxml.jackson.databind.JsonNode;
import com.fasterxml.jackson.databind.ObjectMapper;
import com.fasterxml.jackson.databind.ObjectReader;

import java.io.IOException;
import java.util.ArrayList;
import java.util.Collections;
import java.util.List;
import java.util.Optional;
import java.util.OptionalInt;
import java.util.OptionalLong;
import java.util.concurrent.CompletableFuture;
import java.util.concurrent.CompletionStage;
import java.util.function.Function;
import java.util.stream.Collectors;

Setup data classes

As a working example throughout this documentation, we will use the Person case class to model the data in our BigQuery tables.

Scala

copysourcecase class Person(name: String, age: Int, addresses: Seq[Address], isHakker: Boolean)
case class Address(street: String, city: String, postalCode: Option[Int])
implicit val addressFormat: BigQueryRootJsonFormat[Address] = bigQueryJsonFormat3(Address.apply)
implicit val personFormat: BigQueryRootJsonFormat[Person] = bigQueryJsonFormat4(Person.apply)

Java

copysourceObjectMapper objectMapper = new ObjectMapper();

public class Person {
  private String name;
  private Integer age;
  private List<Address> addresses;
  private Boolean isHakker;

  @JsonCreator
  public Person(@JsonProperty("f") JsonNode fields) throws IOException {
    name = fields.get(0).get("v").textValue();
    age = Integer.parseInt(fields.get(1).get("v").textValue());
    addresses = new ArrayList<>();
    ObjectReader addressReader = objectMapper.readerFor(Address.class);
    for (JsonNode node : fields.get(2).get("v")) {
      Address address = addressReader.readValue(node.get("v"));
      addresses.add(address);
    }
    isHakker = Boolean.parseBoolean(fields.get(3).get("v").textValue());
  }

  public String getName() {
    return name;
  }

  public Integer getAge() {
    return age;
  }

  public List<Address> getAddresses() {
    return addresses;
  }

  public Boolean getIsHakker() {
    return isHakker;
  }
}

public class Address {
  private String street;
  private String city;
  private Integer postalCode;

  @JsonCreator
  public Address(@JsonProperty("f") JsonNode fields) {
    street = fields.get(0).get("v").textValue();
    city = fields.get(1).get("v").textValue();
    postalCode =
        Optional.of(fields.get(2).get("v").textValue()).map(Integer::parseInt).orElse(null);
  }

  public String getStreet() {
    return street;
  }

  public String getCity() {
    return city;
  }

  public Integer getPostalCode() {
    return postalCode;
  }
}

public class NameAddressesPair {
  private String name;
  private List<Address> addresses;

  @JsonCreator
  public NameAddressesPair(@JsonProperty("f") JsonNode fields) throws IOException {
    name = fields.get(0).get("v").textValue();
    addresses = new ArrayList<>();
    ObjectReader addressReader = objectMapper.readerFor(Address.class);
    for (JsonNode node : fields.get(1).get("v")) {
      Address address = addressReader.readValue(node.get("v"));
      addresses.add(address);
    }
  }
}

To enable automatic support for (un)marshalling User and Address as BigQuery table rows and query results we create implicit BigQueryRootJsonFormat[T] instances. The bigQueryJsonFormatN methods are imported from BigQueryJsonProtocol, analogous to Spray’s DefaultJsonProtocol. To enable support for (un)marshalling User and Address as BigQuery table rows and query results we use Jackson’s @JsonCreator and @JsonProperty annotations. Note that a custom @JsonCreator constructor is necessary due to BigQuery’s unusual encoding of rows as “a series of JSON f,v objects for indicating fields and values” (reference documentation). In addition, we also define NameAddressesPair to model the result of the query in the next section.

Run a query

You can run a SQL query and stream the unmarshalled results with the BigQuery.query<Out>BigQuery.query[Out] BigQuery.<Out>queryBigQuery.<Out>query method. The output type Out can be a tuple or any user-defined class for which an implicit BigQueryRootJsonFormat[Out] is available. Note that the order and presence of fields in Out must strictly match your SQL query. To create the unmarshaller, use the BigQueryMarshallers.<Out>queryResponseUnmarshaller method.

Scala

copysourceval sqlQuery = s"SELECT name, addresses FROM $datasetId.$tableId WHERE age >= 100"
val centenarians: Source[(String, Seq[Address]), Future[QueryResponse[(String, Seq[Address])]]] =
  BigQuery.query[(String, Seq[Address])](sqlQuery, useLegacySql = false)

Java

copysourceString sqlQuery =
    String.format("SELECT name, addresses FROM %s.%s WHERE age >= 100", datasetId, tableId);
Unmarshaller<HttpEntity, QueryResponse<NameAddressesPair>> queryResponseUnmarshaller =
    BigQueryMarshallers.queryResponseUnmarshaller(NameAddressesPair.class);
Source<NameAddressesPair, CompletionStage<QueryResponse<NameAddressesPair>>> centenarians =
    BigQuery.query(sqlQuery, false, false, queryResponseUnmarshaller);

Notice that the source materializes a Future[QueryResponse[(String, Seq[Address])]] CompletionStage<QueryResponse<NameAddressesTuple>> which can be used to retrieve metadata related to the query. For example, you can use a dry run to estimate the number of bytes that will be read by a query.

Scala

copysourceval centenariansDryRun = BigQuery.query[(String, Seq[Address])](sqlQuery, dryRun = true, useLegacySql = false)
val bytesProcessed: Future[Long] = centenariansDryRun.to(Sink.ignore).run().map(_.totalBytesProcessed.get)

Java

copysourceSource<NameAddressesPair, CompletionStage<QueryResponse<NameAddressesPair>>>
    centenariansDryRun = BigQuery.query(sqlQuery, false, false, queryResponseUnmarshaller);
CompletionStage<Long> bytesProcessed =
    centenariansDryRun
        .to(Sink.ignore())
        .run(system)
        .thenApply(r -> r.getTotalBytesProcessed().getAsLong());

Finally, you can also stream all of the rows in a table without the expense of running a query with the BigQuery.tableData<Out>BigQuery.tableData[Out] BigQuery.<Out>listTableDataBigQuery.<Out>listTableData method.

Scala

copysourceval everyone: Source[Person, Future[TableDataListResponse[Person]]] =
  BigQuery.tableData[Person](datasetId, tableId)

Java

copysourceUnmarshaller<HttpEntity, TableDataListResponse<Person>> tableDataListUnmarshaller =
    BigQueryMarshallers.tableDataListResponseUnmarshaller(Person.class);
Source<Person, CompletionStage<TableDataListResponse<Person>>> everyone =
    BigQuery.listTableData(
        datasetId,
        tableId,
        OptionalLong.empty(),
        OptionalInt.empty(),
        Collections.emptyList(),
        tableDataListUnmarshaller);

Load data into BigQuery

The BigQuery connector enables loading data into tables via real-time streaming inserts or batch loading. For an overview of these strategies see the BigQuery documentation.

The BigQuery.insertAll<In>BigQuery.insertAll[In] BigQuery.<In>insertAllBigQuery.<In>insertAll method creates a sink that accepts batches of Seq[In] List<In> (for example created via the batch operator) and streams them directly into a table. To enable/disable BigQuery’s best-effort deduplication feature use the appropriate InsertAllRetryPolicyInsertAllRetryPolicy.

Scala

copysourceval peopleInsertSink: Sink[Seq[Person], NotUsed] =
  BigQuery.insertAll[Person](datasetId, tableId, InsertAllRetryPolicy.WithDeduplication)

Java

copysourceMarshaller<TableDataInsertAllRequest<Person>, RequestEntity> tableDataInsertAllMarshaller =
    BigQueryMarshallers.tableDataInsertAllRequestMarshaller();
Sink<List<Person>, NotUsed> peopleInsertSink =
    BigQuery.insertAll(
        datasetId,
        tableId,
        InsertAllRetryPolicy.withDeduplication(),
        Optional.empty(),
        tableDataInsertAllMarshaller);

As a cost-saving alternative to streaming inserts, you can also add data to a table via asynchronous load jobs. The BigQuery.insertAllAsync<In>BigQuery.insertAllAsync[In] BigQuery.<In>insertAllAsyncBigQuery.<In>insertAllAsync method creates a flow that starts a series of batch load jobs. By default, a new load job is created every minute to attempt to emulate near-real-time streaming inserts, although there is no guarantee when the job will actually run. The frequency with which new load jobs are created is controlled by the pekko.connectors.google.bigquery.load-job-per-table-quota configuration setting.

Scala

copysourceval peopleLoadFlow: Flow[Person, Job, NotUsed] = BigQuery.insertAllAsync[Person](datasetId, tableId)

Java

copysourceFlow<Person, Job, NotUsed> peopleLoadFlow =
    BigQuery.insertAllAsync(datasetId, tableId, Jackson.marshaller());

To check the status of the load jobs use the BigQuery.jobBigQuery.job BigQuery.getJobBigQuery.getJob method.

Scala

copysourcedef checkIfJobsDone(jobReferences: Seq[JobReference]): Future[Boolean] = {
  for {
    jobs <- Future.sequence(jobReferences.map(ref => BigQuery.job(ref.jobId.get)))
  } yield jobs.forall(job => job.status.exists(_.state == JobState.Done))
}

val isDone: Future[Boolean] = for {
  jobs <- Source(people).via(peopleLoadFlow).runWith(Sink.seq)
  jobReferences = jobs.flatMap(job => job.jobReference)
  isDone <- checkIfJobsDone(jobReferences)
} yield isDone

Java

copysourceFunction<List<JobReference>, CompletionStage<Boolean>> checkIfJobsDone =
    jobReferences -> {
      GoogleSettings settings = GoogleSettings.create(system);
      CompletionStage<Boolean> allAreDone = CompletableFuture.completedFuture(true);
      for (JobReference jobReference : jobReferences) {
        CompletionStage<Job> job =
            BigQuery.getJob(jobReference.getJobId().get(), Optional.empty(), settings, system);
        CompletionStage<Boolean> jobIsDone =
            job.thenApply(
                j ->
                    j.getStatus().map(s -> s.getState().equals(JobState.done())).orElse(false));
        allAreDone = allAreDone.thenCombine(jobIsDone, (a, b) -> a & b);
      }
      return allAreDone;
    };

CompletionStage<List<Job>> jobs =
    Source.from(people).via(peopleLoadFlow).runWith(Sink.<Job>seq(), system);
CompletionStage<List<JobReference>> jobReferences =
    jobs.thenApply(
        js -> js.stream().map(j -> j.getJobReference().get()).collect(Collectors.toList()));
CompletionStage<Boolean> isDone = jobReferences.thenCompose(checkIfJobsDone);

Managing datasets and tables

The BigQuery connector provides methods for basic management of datasets and tables.

Scala

copysourceval allDatasets: Source[Dataset, NotUsed] = BigQuery.datasets
val existingDataset: Future[Dataset] = BigQuery.dataset(datasetId)
val newDataset: Future[Dataset] = BigQuery.createDataset("newDatasetId")
val datasetDeleted: Future[Done] = BigQuery.deleteDataset(datasetId)
val allTablesInDataset: Source[Table, Future[TableListResponse]] = BigQuery.tables(datasetId)
val existingTable: Future[Table] = BigQuery.table(datasetId, tableId)
val tableDeleted: Future[Done] = BigQuery.deleteTable(datasetId, tableId)

Java

copysourceGoogleSettings settings = GoogleSettings.create(system);
Source<Dataset, NotUsed> allDatasets =
    BigQuery.listDatasets(OptionalInt.empty(), Optional.empty(), Collections.emptyMap());
CompletionStage<Dataset> existingDataset = BigQuery.getDataset(datasetId, settings, system);
CompletionStage<Dataset> newDataset = BigQuery.createDataset("newDatasetId", settings, system);
CompletionStage<Done> datasetDeleted =
    BigQuery.deleteDataset(datasetId, false, settings, system);
Source<Table, CompletionStage<TableListResponse>> allTablesInDataset =
    BigQuery.listTables(datasetId, OptionalInt.empty());
CompletionStage<Table> existingTable = BigQuery.getTable(datasetId, tableId, settings, system);
CompletionStage<Done> tableDeleted = BigQuery.deleteTable(datasetId, tableId, settings, system);

Creating a table requires a little more work to specify the schema. To enable automatic schema generation, you can bring implicit TableSchemaWriter[T] instances for your classes into scope via the bigQuerySchemaN methods in BigQuerySchemas.

Scala

copysourceimplicit val addressSchema: TableSchemaWriter[Address] = bigQuerySchema3(Address.apply)
implicit val personSchema: TableSchemaWriter[Person] = bigQuerySchema4(Person.apply)
val newTable: Future[Table] = BigQuery.createTable[Person](datasetId, "newTableId")

Java

copysourceTableSchema personSchema =
    TableSchema.create(
        TableFieldSchema.create("name", TableFieldSchemaType.string(), Optional.empty()),
        TableFieldSchema.create("age", TableFieldSchemaType.integer(), Optional.empty()),
        TableFieldSchema.create(
            "addresses",
            TableFieldSchemaType.record(),
            Optional.of(TableFieldSchemaMode.repeated()),
            TableFieldSchema.create("street", TableFieldSchemaType.string(), Optional.empty()),
            TableFieldSchema.create("city", TableFieldSchemaType.string(), Optional.empty()),
            TableFieldSchema.create(
                "postalCode",
                TableFieldSchemaType.integer(),
                Optional.of(TableFieldSchemaMode.nullable()))),
        TableFieldSchema.create("isHakker", TableFieldSchemaType.bool(), Optional.empty()));
CompletionStage<Table> newTable =
    BigQuery.createTable(datasetId, "newTableId", personSchema, settings, system);

Apply custom settings to a part of the stream

In certain situations it may be desirable to modify the GoogleSettingsGoogleSettings applied to a part of the stream, for example to change the project ID or use different RetrySettingsRetrySettings.

Scala

copysourceval defaultSettings: GoogleSettings = GoogleSettings()
val customSettings = defaultSettings.copy(projectId = "myOtherProject")
BigQuery.query[(String, Seq[Address])](sqlQuery).withAttributes(GoogleAttributes.settings(customSettings))

Java

copysourceGoogleSettings defaultSettings = GoogleSettings.create(system);
GoogleSettings customSettings = defaultSettings.withProjectId("myOtherProjectId");
BigQuery.query(sqlQuery, false, false, queryResponseUnmarshaller)
    .withAttributes(GoogleAttributes.settings(customSettings));

Make raw API requests

If you would like to interact with the BigQuery REST API beyond what the BigQuery connector supports, you can make authenticated raw requests via the BigQuery.singleRequestBigQuery.singleRequest and BigQuery.paginatedRequest<Out>BigQuery.paginatedRequest[Out] BigQuery.<Out>paginatedRequestBigQuery.<Out>paginatedRequest methods.