Modello Streaming Data Generator a Pub/Sub, BigQuery e Cloud Storage

Il modello Generatore di dati in streaming viene utilizzato per generare un numero illimitato o fisso di record o messaggi sintetici in base allo schema fornito dall'utente alla frequenza specificata. Le destinazioni compatibili includono argomenti Pub/Sub, tabelle BigQuery e bucket Cloud Storage.

Di seguito è riportato un insieme di alcuni possibili casi d'uso:

  • Simula la pubblicazione di eventi in tempo reale su larga scala in un argomento Pub/Sub per misurare e determinare il numero e le dimensioni dei consumer necessari per elaborare gli eventi pubblicati.
  • Genera dati sintetici in una tabella BigQuery o in un bucket Cloud Storage per valutare i benchmark sul rendimento o come proof of concept.

Destinazioni e formati di codifica supportati

La tabella seguente descrive i canali di destinazione e i formati di codifica supportati da questo modello:
JSON Avro Parquet
Pub/Sub No
BigQuery No No
Cloud Storage

Requisiti della pipeline

  • L'account di servizio worker deve disporre del ruolo Dataflow Worker (roles/dataflow.worker) assegnato. Per maggiori informazioni, consulta Introduzione a IAM.
  • Crea un file schema contenente un modello JSON per i dati generati. Questo modello utilizza la libreria JSON Data Generator, quindi puoi fornire varie funzioni di generazione di dati fittizi per ogni campo dello schema. Per saperne di più, consulta la documentazione di json-data-generator.

    Ad esempio:

    {
      "id": {{integer(0,1000)}},
      "name": "{{uuid()}}",
      "isInStock": {{bool()}}
    }
    
  • Carica il file dello schema in un bucket Cloud Storage.
  • La destinazione di output deve esistere prima dell'esecuzione. La destinazione deve essere un argomento Pub/Sub, una tabella BigQuery o un bucket Cloud Storage, a seconda del tipo di destinazione.
  • Se la codifica di output è Avro o Parquet, crea un file dello schema Avro e memorizzalo in una posizione Cloud Storage.
  • Assegna all'account di servizio di lavoro un ruolo IAM aggiuntivo a seconda della destinazione desiderata.
    Destinazione Ruolo IAM necessario aggiuntivo A quale risorsa applicare
    Pub/Sub Pubblico Pub/Sub (roles/pubsub.publisher)
    (per ulteriori informazioni, consulta Controllo dell'accesso a Pub/Sub con IAM)
    Argomento Pub/Sub
    BigQuery BigQuery Data Editor (roles/bigquery.dataEditor)
    (per ulteriori informazioni, consulta Controllo dell'accesso a BigQuery con IAM)
    Set di dati BigQuery
    Cloud Storage Amministratore oggetti Cloud Storage (roles/storage.objectAdmin)
    (per ulteriori informazioni, consulta Controllo dell'accesso a Cloud Storage con IAM)
    Bucket Cloud Storage

Parametri del modello

Parametro Descrizione
schemaLocation Posizione del file dello schema. Ad esempio: gs://mybucket/filename.json.
qps Numero di messaggi da pubblicare al secondo. Ad esempio: 100.
sinkType (Facoltativo) Tipo di destinazione di output. I valori possibili sono PUBSUB, BIGQUERY, GCS. Il valore predefinito è PUBSUB.
outputType (Facoltativo) Tipo di codifica di output. I valori possibili sono JSON, AVRO, PARQUET. Il valore predefinito è JSON.
avroSchemaLocation (Facoltativo) Posizione del file dello schema AVRO. Obbligatorio quando outputType è AVRO o PARQUET. Ad esempio: gs://mybucket/filename.avsc.
topic (Facoltativo) Nome dell'argomento Pub/Sub a cui la pipeline deve pubblicare i dati. Obbligatorio quando sinkType è Pub/Sub. Ad esempio: projects/my-project-id/topics/my-topic-id.
outputTableSpec (Facoltativo) Nome della tabella BigQuery di output. Obbligatorio quando sinkType è BigQuery. Ad esempio: my-project-ID:my_dataset_name.my-table-name.
writeDisposition (Facoltativo) Istruzione di scrittura BigQuery. I valori possibili sono WRITE_APPEND, WRITE_EMPTY o WRITE_TRUNCATE. Il valore predefinito è WRITE_APPEND.
outputDeadletterTable (Facoltativo) Nome della tabella BigQuery di output per contenere i record con errori. Se non viene fornito, la pipeline crea la tabella durante l'esecuzione con il nome {output_table_name}_error_records. Ad esempio: my-project-ID:my_dataset_name.my-table-name.
outputDirectory (Facoltativo) Percorso della posizione Cloud Storage di output. Obbligatorio quando sinkType è Cloud Storage. Ad esempio: gs://mybucket/pathprefix/.
outputFilenamePrefix (Facoltativo) Il prefisso del nome dei file di output scritti in Cloud Storage. Il valore predefinito è output-.
windowDuration (Facoltativo) Intervallo della finestra in cui l'output viene scritto in Cloud Storage. Il valore predefinito è 1m (ovvero 1 minuto).
numShards (Facoltativo) Numero massimo di shard di output. Obbligatorio quando sinkType è Cloud Storage e deve essere impostato su un numero maggiore o uguale a 1.
messagesLimit (Facoltativo) Numero massimo di messaggi di output. Il valore predefinito è 0, che indica illimitato.
autoscalingAlgorithm (Facoltativo) Algoritmo utilizzato per la scalabilità automatica dei worker. I valori possibili sono THROUGHPUT_BASED per attivare la scalabilità automatica o NONE per disattivarla.
maxNumWorkers (Facoltativo) Numero massimo di macchine worker. Ad esempio: 10.

Esegui il modello

  1. Vai alla pagina Crea job da modello di Dataflow.
  2. Vai a Crea job da modello
  3. Nel campo Nome job, inserisci un nome univoco per il job.
  4. (Facoltativo) Per Endpoint a livello di regione, seleziona un valore dal menu a discesa. La regione predefinita è us-central1.

    Per un elenco delle regioni in cui puoi eseguire un job Dataflow, consulta Località di Dataflow.

  5. Nel menu a discesa Modello di flusso di dati, seleziona the Streaming Data Generator template.
  6. Nei campi dei parametri forniti, inserisci i valori dei parametri.
  7. Fai clic su Esegui job.

Nella shell o nel terminale, esegui il modello:

gcloud dataflow flex-template run JOB_NAME \
    --project=PROJECT_ID \
    --region=REGION_NAME \
    --template-file-gcs-location=gs://dataflow-templates-REGION_NAME/VERSION/flex/Streaming_Data_Generator \
    --parameters \
schemaLocation=SCHEMA_LOCATION,\
qps=QPS,\
topic=PUBSUB_TOPIC
  

Sostituisci quanto segue:

  • PROJECT_ID: l'ID del progetto Google Cloud in cui vuoi eseguire il job Dataflow
  • REGION_NAME: la regione in cui vuoi eseguire il deployment del job Dataflow, ad esempio us-central1
  • JOB_NAME: un nome di job univoco a tua scelta
  • VERSION: la versione del modello che vuoi utilizzare

    Puoi utilizzare i seguenti valori:

  • SCHEMA_LOCATION: il percorso del file dello schema in Cloud Storage. Ad esempio: gs://mybucket/filename.json.
  • QPS: il numero di messaggi da pubblicare al secondo
  • PUBSUB_TOPIC: l'argomento Pub/Sub di output. Ad esempio: projects/my-project-id/topics/my-topic-id.

Per eseguire il modello utilizzando l'API REST, invia una richiesta POST HTTP. Per ulteriori informazioni sull'API e sui relativi ambiti di autorizzazione, consulta projects.templates.launch.

POST https://dataflow.googleapis.com/v1b3/projects/PROJECT_ID/locations/LOCATION/flexTemplates:launch
{
   "launch_parameter": {
      "jobName": "JOB_NAME",
      "parameters": {
          "schemaLocation": "SCHEMA_LOCATION",
          "qps": "QPS",
          "topic": "PUBSUB_TOPIC"
      },
      "containerSpecGcsPath": "gs://dataflow-templates-LOCATION/VERSION/flex/Streaming_Data_Generator",
   }
}
  

Sostituisci quanto segue:

  • PROJECT_ID: l'ID del progetto Google Cloud in cui vuoi eseguire il job Dataflow
  • LOCATION: la regione in cui vuoi eseguire il deployment del job Dataflow, ad esempio us-central1
  • JOB_NAME: un nome di job univoco a tua scelta
  • VERSION: la versione del modello che vuoi utilizzare

    Puoi utilizzare i seguenti valori:

  • SCHEMA_LOCATION: il percorso del file dello schema in Cloud Storage. Ad esempio: gs://mybucket/filename.json.
  • QPS: il numero di messaggi da pubblicare al secondo
  • PUBSUB_TOPIC: l'argomento Pub/Sub di output. Ad esempio: projects/my-project-id/topics/my-topic-id.
Java
/*
 * Copyright (C) 2020 Google LLC
 *
 * Licensed under the Apache License, Version 2.0 (the "License"); you may not
 * use this file except in compliance with the License. You may obtain a copy of
 * the License at
 *
 *   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 "AS IS" BASIS, WITHOUT
 * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
 * License for the specific language governing permissions and limitations under
 * the License.
 */
package com.google.cloud.teleport.v2.templates;

import static org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.base.Preconditions.checkArgument;
import static org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.base.Preconditions.checkNotNull;

import com.github.vincentrussell.json.datagenerator.JsonDataGenerator;
import com.github.vincentrussell.json.datagenerator.JsonDataGeneratorException;
import com.github.vincentrussell.json.datagenerator.impl.JsonDataGeneratorImpl;
import com.google.cloud.teleport.metadata.Template;
import com.google.cloud.teleport.metadata.TemplateCategory;
import com.google.cloud.teleport.metadata.TemplateParameter;
import com.google.cloud.teleport.metadata.TemplateParameter.TemplateEnumOption;
import com.google.cloud.teleport.v2.common.UncaughtExceptionLogger;
import com.google.cloud.teleport.v2.templates.StreamingDataGenerator.StreamingDataGeneratorOptions;
import com.google.cloud.teleport.v2.transforms.StreamingDataGeneratorWriteToBigQuery;
import com.google.cloud.teleport.v2.transforms.StreamingDataGeneratorWriteToGcs;
import com.google.cloud.teleport.v2.transforms.StreamingDataGeneratorWriteToJdbc;
import com.google.cloud.teleport.v2.transforms.StreamingDataGeneratorWriteToKafka;
import com.google.cloud.teleport.v2.transforms.StreamingDataGeneratorWriteToPubSub;
import com.google.cloud.teleport.v2.transforms.StreamingDataGeneratorWriteToSpanner;
import com.google.cloud.teleport.v2.utils.DurationUtils;
import com.google.cloud.teleport.v2.utils.GCSUtils;
import com.google.cloud.teleport.v2.utils.MetadataValidator;
import java.io.ByteArrayOutputStream;
import java.io.IOException;
import javax.annotation.Nonnull;
import org.apache.beam.sdk.Pipeline;
import org.apache.beam.sdk.PipelineResult;
import org.apache.beam.sdk.io.FileSystems;
import org.apache.beam.sdk.io.GenerateSequence;
import org.apache.beam.sdk.options.Default;
import org.apache.beam.sdk.options.PipelineOptions;
import org.apache.beam.sdk.options.PipelineOptionsFactory;
import org.apache.beam.sdk.options.Validation.Required;
import org.apache.beam.sdk.transforms.DoFn;
import org.apache.beam.sdk.transforms.PTransform;
import org.apache.beam.sdk.transforms.ParDo;
import org.apache.beam.sdk.transforms.windowing.FixedWindows;
import org.apache.beam.sdk.transforms.windowing.Window;
import org.apache.beam.sdk.values.PCollection;
import org.apache.beam.sdk.values.PDone;
import org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.annotations.VisibleForTesting;
import org.joda.time.Duration;
import org.joda.time.Instant;

/**
 * The {@link StreamingDataGenerator} is a streaming pipeline which generates messages at a
 * specified rate to either Pub/Sub, BigQuery, GCS, JDBC, or Spanner. The messages are generated
 * according to a schema template which instructs the pipeline how to populate the messages with
 * fake data compliant to constraints.
 *
 * <p>The number of workers executing the pipeline must be large enough to support the supplied QPS.
 * Use a general rule of 2,500 QPS per core in the worker pool.
 *
 * <p>See <a href="https://github.com/vincentrussell/json-data-generator">json-data-generator</a>
 * for instructions on how to construct the schema file.
 *
 * <p>Check out <a
 * href="https://github.com/GoogleCloudPlatform/DataflowTemplates/blob/main/v2/streaming-data-generator/README_Streaming_Data_Generator.md">README</a>
 * for instructions on how to use or modify this template.
 */
@Template(
    name = "Streaming_Data_Generator",
    category = TemplateCategory.UTILITIES,
    displayName = "Streaming Data Generator",
    description =
        "A pipeline to publish messages at specified QPS.This template can be used to benchmark"
            + " performance of streaming pipelines.",
    optionsClass = StreamingDataGeneratorOptions.class,
    flexContainerName = "streaming-data-generator",
    documentation =
        "https://cloud.google.com/dataflow/docs/guides/templates/provided/streaming-data-generator",
    contactInformation = "https://cloud.google.com/support",
    streaming = true,
    supportsAtLeastOnce = true)
public class StreamingDataGenerator {

  /**
   * The {@link StreamingDataGeneratorOptions} class provides the custom execution options passed by
   * the executor at the command-line.
   */
  public interface StreamingDataGeneratorOptions extends PipelineOptions {
    @TemplateParameter.Long(
        order = 1,
        description = "Required output rate",
        helpText = "Indicates rate of messages per second to be published to Pub/Sub")
    @Required
    Long getQps();

    void setQps(Long value);

    @TemplateParameter.Enum(
        order = 2,
        enumOptions = {@TemplateEnumOption("GAME_EVENT")},
        optional = true,
        description = "Schema template to generate fake data",
        helpText = "Pre-existing schema template to use. The value must be one of: [GAME_EVENT]")
    SchemaTemplate getSchemaTemplate();

    void setSchemaTemplate(SchemaTemplate value);

    @TemplateParameter.GcsReadFile(
        order = 3,
        optional = true,
        description = "Location of Schema file to generate fake data",
        helpText = "Cloud Storage path of schema location.",
        example = "gs://<bucket-name>/prefix")
    String getSchemaLocation();

    void setSchemaLocation(String value);

    @TemplateParameter.PubsubTopic(
        order = 4,
        optional = true,
        description = "Output Pub/Sub topic",
        helpText = "The name of the topic to which the pipeline should publish data.",
        example = "projects/<project-id>/topics/<topic-name>")
    String getTopic();

    void setTopic(String value);

    @TemplateParameter.Long(
        order = 5,
        optional = true,
        description = "Maximum number of output Messages",
        helpText =
            "Indicates maximum number of output messages to be generated. 0 means unlimited.")
    @Default.Long(0L)
    Long getMessagesLimit();

    void setMessagesLimit(Long value);

    @TemplateParameter.Enum(
        order = 6,
        enumOptions = {
          @TemplateEnumOption("AVRO"),
          @TemplateEnumOption("JSON"),
          @TemplateEnumOption("PARQUET")
        },
        optional = true,
        description = "Output Encoding Type",
        helpText = "The message Output type. Default is JSON.")
    @Default.Enum("JSON")
    OutputType getOutputType();

    void setOutputType(OutputType value);

    @TemplateParameter.GcsReadFile(
        order = 7,
        optional = true,
        parentName = "outputType",
        parentTriggerValues = {"AVRO", "PARQUET"},
        description = "Location of Avro Schema file",
        helpText =
            "Cloud Storage path of Avro schema location. Mandatory when output type is AVRO or"
                + " PARQUET.",
        example = "gs://your-bucket/your-path/schema.avsc")
    String getAvroSchemaLocation();

    void setAvroSchemaLocation(String value);

    @TemplateParameter.Enum(
        order = 8,
        enumOptions = {
          @TemplateEnumOption("BIGQUERY"),
          @TemplateEnumOption("GCS"),
          @TemplateEnumOption("PUBSUB"),
          @TemplateEnumOption("JDBC"),
          @TemplateEnumOption("SPANNER"),
          @TemplateEnumOption("KAFKA")
        },
        optional = true,
        description = "Output Sink Type",
        helpText = "The message Sink type. Default is PUBSUB")
    @Default.Enum("PUBSUB")
    SinkType getSinkType();

    void setSinkType(SinkType value);

    @TemplateParameter.BigQueryTable(
        order = 9,
        optional = true,
        parentName = "sinkType",
        parentTriggerValues = {"BIGQUERY"},
        description = "Output BigQuery table",
        helpText = "Output BigQuery table. Mandatory when sinkType is BIGQUERY",
        example = "<project>:<dataset>.<table_name>")
    String getOutputTableSpec();

    void setOutputTableSpec(String value);

    @TemplateParameter.Enum(
        order = 10,
        enumOptions = {
          @TemplateEnumOption("WRITE_APPEND"),
          @TemplateEnumOption("WRITE_EMPTY"),
          @TemplateEnumOption("WRITE_TRUNCATE")
        },
        optional = true,
        parentName = "sinkType",
        parentTriggerValues = {"BIGQUERY"},
        description = "Write Disposition to use for BigQuery",
        helpText =
            "BigQuery WriteDisposition. For example, WRITE_APPEND, WRITE_EMPTY or WRITE_TRUNCATE.")
    @Default.String("WRITE_APPEND")
    String getWriteDisposition();

    void setWriteDisposition(String writeDisposition);

    @TemplateParameter.BigQueryTable(
        order = 11,
        optional = true,
        parentName = "sinkType",
        parentTriggerValues = {"BIGQUERY"},
        description = "The dead-letter table name to output failed messages to BigQuery",
        helpText =
            "Messages failed to reach the output table for all kind of reasons (e.g., mismatched"
                + " schema, malformed json) are written to this table. If it doesn't exist, it will"
                + " be created during pipeline execution.",
        example = "your-project-id:your-dataset.your-table-name")
    String getOutputDeadletterTable();

    void setOutputDeadletterTable(String outputDeadletterTable);

    @TemplateParameter.Duration(
        order = 12,
        optional = true,
        parentName = "sinkType",
        parentTriggerValues = {"GCS"},
        description = "Window duration",
        helpText =
            "The window duration/size in which data will be written to Cloud Storage. Allowed"
                + " formats are: Ns (for seconds, example: 5s), Nm (for minutes, example: 12m), Nh"
                + " (for hours, example: 2h).",
        example = "1m")
    @Default.String("1m")
    String getWindowDuration();

    void setWindowDuration(String windowDuration);

    @TemplateParameter.GcsWriteFolder(
        order = 13,
        optional = true,
        description = "Output file directory in Cloud Storage",
        helpText =
            "The path and filename prefix for writing output files. Must end with a slash. DateTime"
                + " formatting is used to parse directory path for date & time formatters.",
        example = "gs://your-bucket/your-path/")
    String getOutputDirectory();

    void setOutputDirectory(String outputDirectory);

    @TemplateParameter.Text(
        order = 14,
        optional = true,
        description = "Output filename prefix of the files to write",
        helpText = "The prefix to place on each windowed file.",
        example = "output-")
    @Default.String("output-")
    String getOutputFilenamePrefix();

    void setOutputFilenamePrefix(String outputFilenamePrefix);

    @TemplateParameter.Integer(
        order = 15,
        optional = true,
        description = "Maximum output shards",
        helpText =
            "The maximum number of output shards produced when writing. A higher number of shards"
                + " means higher throughput for writing to Cloud Storage, but potentially higher"
                + " data aggregation cost across shards when processing output Cloud Storage files."
                + " Default value is decided by Dataflow.")
    @Default.Integer(0)
    Integer getNumShards();

    void setNumShards(Integer numShards);

    @TemplateParameter.Text(
        order = 16,
        optional = true,
        regexes = {"^.+$"},
        description = "JDBC driver class name.",
        helpText = "JDBC driver class name to use.",
        example = "com.mysql.jdbc.Driver")
    String getDriverClassName();

    void setDriverClassName(String driverClassName);

    @TemplateParameter.Text(
        order = 17,
        optional = true,
        regexes = {
          "(^jdbc:[a-zA-Z0-9/:@.?_+!*=&-;]+$)|(^([A-Za-z0-9+/]{4}){1,}([A-Za-z0-9+/]{0,3})={0,3})"
        },
        description = "JDBC connection URL string.",
        helpText = "Url connection string to connect to the JDBC source.",
        example = "jdbc:mysql://some-host:3306/sampledb")
    String getConnectionUrl();

    void setConnectionUrl(String connectionUrl);

    @TemplateParameter.Text(
        order = 18,
        optional = true,
        regexes = {"^.+$"},
        description = "JDBC connection username.",
        helpText = "User name to be used for the JDBC connection.")
    String getUsername();

    void setUsername(String username);

    @TemplateParameter.Password(
        order = 19,
        optional = true,
        description = "JDBC connection password.",
        helpText = "Password to be used for the JDBC connection.")
    String getPassword();

    void setPassword(String password);

    @TemplateParameter.Text(
        order = 20,
        optional = true,
        regexes = {"^[a-zA-Z0-9_;!*&=@#-:\\/]+$"},
        description = "JDBC connection property string.",
        helpText =
            "Properties string to use for the JDBC connection. Format of the string must be"
                + " [propertyName=property;]*.",
        example = "unicode=true;characterEncoding=UTF-8")
    String getConnectionProperties();

    void setConnectionProperties(String connectionProperties);

    @TemplateParameter.Text(
        order = 21,
        optional = true,
        regexes = {"^.+$"},
        description = "Statement which will be executed against the database.",
        helpText =
            "SQL statement which will be executed to write to the database. The statement must"
                + " specify the column names of the table in any order. Only the values of the"
                + " specified column names will be read from the json and added to the statement.",
        example = "INSERT INTO tableName (column1, column2) VALUES (?,?)")
    String getStatement();

    void setStatement(String statement);

    @TemplateParameter.ProjectId(
        order = 22,
        optional = true,
        parentName = "sinkType",
        parentTriggerValues = {"SPANNER"},
        description = "GCP Project Id of where the Spanner table lives.",
        helpText = "GCP Project Id of where the Spanner table lives.")
    String getProjectId();

    void setProjectId(String projectId);

    @TemplateParameter.Text(
        order = 23,
        optional = true,
        parentName = "sinkType",
        parentTriggerValues = {"SPANNER"},
        regexes = {"^.+$"},
        description = "Cloud Spanner instance name.",
        helpText = "Cloud Spanner instance name.")
    String getSpannerInstanceName();

    void setSpannerInstanceName(String spannerInstanceName);

    @TemplateParameter.Text(
        order = 24,
        optional = true,
        parentName = "sinkType",
        parentTriggerValues = {"SPANNER"},
        regexes = {"^.+$"},
        description = "Cloud Spanner database name.",
        helpText = "Cloud Spanner database name.")
    String getSpannerDatabaseName();

    void setSpannerDatabaseName(String spannerDBName);

    @TemplateParameter.Text(
        order = 25,
        optional = true,
        parentName = "sinkType",
        parentTriggerValues = {"SPANNER"},
        regexes = {"^.+$"},
        description = "Cloud Spanner table name.",
        helpText = "Cloud Spanner table name.")
    String getSpannerTableName();

    void setSpannerTableName(String spannerTableName);

    @TemplateParameter.Long(
        order = 26,
        optional = true,
        parentName = "sinkType",
        parentTriggerValues = {"SPANNER"},
        description = "Max mutatated cells per batch.",
        helpText =
            "Specifies the cell mutation limit (maximum number of mutated cells per batch). Default value is 5000")
    Long getMaxNumMutations();

    void setMaxNumMutations(Long value);

    @TemplateParameter.Long(
        order = 27,
        optional = true,
        parentName = "sinkType",
        parentTriggerValues = {"SPANNER"},
        description = "Max rows per batch.",
        helpText =
            "Specifies the row mutation limit (maximum number of mutated rows per batch). Default value is 1000")
    Long getMaxNumRows();

    void setMaxNumRows(Long value);

    @TemplateParameter.Long(
        order = 28,
        optional = true,
        parentName = "sinkType",
        parentTriggerValues = {"SPANNER"},
        description = "Max batch size in bytes.",
        helpText =
            "Specifies the batch size limit (max number of bytes mutated per batch). Default value is 1MB")
    Long getBatchSizeBytes();

    void setBatchSizeBytes(Long value);

    @TemplateParameter.Long(
        order = 29,
        optional = true,
        parentName = "sinkType",
        parentTriggerValues = {"SPANNER"},
        description = "Commit deadline in seconds for write requests.",
        helpText = "Specifies the deadline in seconds for the Commit API call.")
    Long getCommitDeadlineSeconds();

    void setCommitDeadlineSeconds(Long value);

    @TemplateParameter.Text(
        order = 30,
        optional = true,
        parentName = "sinkType",
        parentTriggerValues = {"KAFKA"},
        regexes = {"[,:a-zA-Z0-9._-]+"},
        description = "Output Kafka Bootstrap Server",
        helpText = "Kafka Bootstrap Server ",
        example = "localhost:9092")
    String getBootstrapServer();

    void setBootstrapServer(String bootstrapServer);

    @TemplateParameter.Text(
        order = 31,
        optional = true,
        parentName = "sinkType",
        parentTriggerValues = {"KAFKA"},
        regexes = {"[a-zA-Z0-9._-]+"},
        description = "Kafka topic to write to",
        helpText = "Kafka topic to write to.",
        example = "topic")
    String getKafkaTopic();

    void setKafkaTopic(String outputTopic);
  }

  /** Allowed list of existing schema templates. */
  public enum SchemaTemplate {
    GAME_EVENT(
        "{\n"
            + "  \"eventId\": \"{{uuid()}}\",\n"
            + "  \"eventTimestamp\": {{timestamp()}},\n"
            + "  \"ipv4\": \"{{ipv4()}}\",\n"
            + "  \"ipv6\": \"{{ipv6()}}\",\n"
            + "  \"country\": \"{{country()}}\",\n"
            + "  \"username\": \"{{username()}}\",\n"
            + "  \"quest\": \"{{random(\"A Break In the Ice\", \"Ghosts of Perdition\", \"Survive"
            + " the Low Road\")}}\",\n"
            + "  \"score\": {{integer(100, 10000)}},\n"
            + "  \"completed\": {{bool()}}\n"
            + "}"),
    LOG_ENTRY(
        "{\n"
            + "  \"logName\": \"{{alpha(10,20)}}\",\n"
            + "  \"resource\": {\n"
            + "    \"type\": \"{{alpha(5,10)}}\"\n"
            + "  },\n"
            + "  \"timestamp\": {{timestamp()}},\n"
            + "  \"receiveTimestamp\": {{timestamp()}},\n"
            + "  \"severity\": \"{{random(\"DEFAULT\", \"DEBUG\", \"INFO\", \"NOTICE\","
            + " \"WARNING\", \"ERROR\", \"CRITICAL\", \"ERROR\")}}\",\n"
            + "  \"insertId\": \"{{uuid()}}\",\n"
            + "  \"trace\": \"{{uuid()}}\",\n"
            + "  \"spanId\": \"{{uuid()}}\",\n"
            + "  \"jsonPayload\": {\n"
            + "    \"bytes_sent\": {{integer(1000,20000)}},\n"
            + "    \"connection\": {\n"
            + "      \"dest_ip\": \"{{ipv4()}}\",\n"
            + "      \"dest_port\": {{integer(0,65000)}},\n"
            + "      \"protocol\": {{integer(0,6)}},\n"
            + "      \"src_ip\": \"{{ipv4()}}\",\n"
            + "      \"src_port\": {{integer(0,65000)}}\n"
            + "    },\n"
            + "    \"dest_instance\": {\n"
            + "      \"project_id\": \"{{concat(\"PROJECT\", integer(0,3))}}\",\n"
            + "      \"region\": \"{{country()}}\",\n"
            + "      \"vm_name\": \"{{username()}}\",\n"
            + "      \"zone\": \"{{state()}}\"\n"
            + "    },\n"
            + "    \"end_time\": {{timestamp()}},\n"
            + "    \"packets_sent\": {{integer(100,400)}},\n"
            + "    \"reporter\": \"{{random(\"SRC\", \"DEST\")}}\",\n"
            + "    \"rtt_msec\": {{integer(0,20)}},\n"
            + "    \"start_time\": {{timestamp()}}\n"
            + "  }\n"
            + "}");

    private final String schema;

    SchemaTemplate(String schema) {
      this.schema = schema;
    }

    public String getSchema() {
      return schema;
    }
  }

  /** Allowed list of message encoding types. */
  public enum OutputType {
    JSON(".json"),
    AVRO(".avro"),
    PARQUET(".parquet");

    private final String fileExtension;

    /** Sets file extension associated with output type. */
    OutputType(String fileExtension) {
      this.fileExtension = fileExtension;
    }

    /** Returns file extension associated with output type. */
    public String getFileExtension() {
      return fileExtension;
    }
  }

  /** Allowed list of sink types. */
  public enum SinkType {
    PUBSUB,
    BIGQUERY,
    GCS,
    JDBC,
    SPANNER,
    KAFKA
  }

  /**
   * The main entry-point for pipeline execution. This method will start the pipeline but will not
   * wait for it's execution to finish. If blocking execution is required, use the {@link
   * StreamingDataGenerator#run(StreamingDataGeneratorOptions)} method to start the pipeline and
   * invoke {@code result.waitUntilFinish()} on the {@link PipelineResult}.
   *
   * @param args command-line args passed by the executor.
   */
  public static void main(String[] args) {
    UncaughtExceptionLogger.register();

    StreamingDataGeneratorOptions options =
        PipelineOptionsFactory.fromArgs(args)
            .withValidation()
            .as(StreamingDataGeneratorOptions.class);

    run(options);
  }

  /**
   * Runs the pipeline to completion with the specified options. This method does not wait until the
   * pipeline is finished before returning. Invoke {@code result.waitUntilFinish()} on the result
   * object to block until the pipeline is finished running if blocking programmatic execution is
   * required.
   *
   * @param options the execution options.
   * @return the pipeline result.
   */
  public static PipelineResult run(@Nonnull StreamingDataGeneratorOptions options) {
    checkNotNull(options, "options argument to run method cannot be null.");
    MetadataValidator.validate(options);

    // FileSystems does not set the default configuration in workers till Pipeline.run
    // Explicitly registering standard file systems.
    FileSystems.setDefaultPipelineOptions(options);
    String schema = getSchema(options.getSchemaTemplate(), options.getSchemaLocation());

    // Create the pipeline
    Pipeline pipeline = Pipeline.create(options);

    /*
     * Steps:
     *  1) Trigger at the supplied QPS
     *  2) Generate messages containing fake data
     *  3) Write messages to appropriate Sink
     */
    PCollection<byte[]> generatedMessages =
        pipeline
            .apply("Trigger", createTrigger(options))
            .apply("Generate Fake Messages", ParDo.of(new MessageGeneratorFn(schema)));

    if (options.getSinkType().equals(SinkType.GCS)) {
      generatedMessages =
          generatedMessages.apply(
              options.getWindowDuration() + " Window",
              Window.into(
                  FixedWindows.of(DurationUtils.parseDuration(options.getWindowDuration()))));
    }

    generatedMessages.apply(
        "Write To " + options.getSinkType().name(), createSink(options, schema));

    return pipeline.run();
  }

  /**
   * Creates either Bounded or UnBounded Source based on messageLimit pipeline option.
   *
   * @param options the pipeline options.
   */
  private static GenerateSequence createTrigger(@Nonnull StreamingDataGeneratorOptions options) {
    checkNotNull(options, "options argument to createTrigger method cannot be null.");
    GenerateSequence generateSequence =
        GenerateSequence.from(0L)
            .withRate(options.getQps(), /* periodLength= */ Duration.standardSeconds(1L));

    return options.getMessagesLimit() > 0
        ? generateSequence.to(options.getMessagesLimit())
        : generateSequence;
  }

  /**
   * The {@link MessageGeneratorFn} class generates fake messages based on supplied schema
   *
   * <p>See <a href="https://github.com/vincentrussell/json-data-generator">json-data-generator</a>
   * for instructions on how to construct the schema file.
   */
  @VisibleForTesting
  static class MessageGeneratorFn extends DoFn<Long, byte[]> {

    // Not initialized inline or constructor because {@link JsonDataGenerator} is not serializable.
    private transient JsonDataGenerator dataGenerator;
    private final String schema;

    MessageGeneratorFn(String schema) {
      this.schema = schema;
    }

    @Setup
    public void setup() {
      dataGenerator = new JsonDataGeneratorImpl();
    }

    @ProcessElement
    public void processElement(
        @Element Long element,
        @Timestamp Instant timestamp,
        OutputReceiver<byte[]> receiver,
        ProcessContext context)
        throws IOException, JsonDataGeneratorException {

      byte[] payload;

      // Generate the fake JSON according to the schema.
      try (ByteArrayOutputStream byteArrayOutputStream = new ByteArrayOutputStream()) {
        dataGenerator.generateTestDataJson(schema, byteArrayOutputStream);
        payload = byteArrayOutputStream.toByteArray();
      }

      receiver.output(payload);
    }
  }

  /**
   * Creates appropriate sink based on sinkType pipeline option.
   *
   * @param options the pipeline options.
   */
  @VisibleForTesting
  static PTransform<PCollection<byte[]>, PDone> createSink(
      @Nonnull StreamingDataGeneratorOptions options, @Nonnull String schema) {
    checkNotNull(options, "options argument to createSink method cannot be null.");
    checkNotNull(schema, "schema argument to createSink method cannot be null.");

    switch (options.getSinkType()) {
      case PUBSUB:
        checkArgument(
            options.getTopic() != null,
            String.format(
                "Missing required value --topic for %s sink type", options.getSinkType().name()));
        return StreamingDataGeneratorWriteToPubSub.Writer.builder(options, schema).build();
      case BIGQUERY:
        checkArgument(
            options.getOutputTableSpec() != null,
            String.format(
                "Missing required value --outputTableSpec in format"
                    + " <project>:<dataset>.<table_name> for %s sink type",
                options.getSinkType().name()));
        return StreamingDataGeneratorWriteToBigQuery.builder(options).build();
      case GCS:
        checkArgument(
            options.getOutputDirectory() != null,
            String.format(
                "Missing required value --outputDirectory in format gs:// for %s sink type",
                options.getSinkType().name()));
        return StreamingDataGeneratorWriteToGcs.builder(options).build();
      case JDBC:
        checkArgument(
            options.getDriverClassName() != null,
            String.format(
                "Missing required value --driverClassName for %s sink type",
                options.getSinkType().name()));
        checkArgument(
            options.getConnectionUrl() != null,
            String.format(
                "Missing required value --connectionUrl for %s sink type",
                options.getSinkType().name()));
        checkArgument(
            options.getStatement() != null,
            String.format(
                "Missing required value --statement for %s sink type",
                options.getSinkType().name()));
        return StreamingDataGeneratorWriteToJdbc.builder(options).build();
      case SPANNER:
        checkArgument(
            options.getProjectId() != null,
            String.format(
                "Missing required value --projectId for %s sink type",
                options.getSinkType().name()));
        checkArgument(
            options.getSpannerInstanceName() != null,
            String.format(
                "Missing required value --spannerInstanceName for %s sink type",
                options.getSinkType().name()));
        checkArgument(
            options.getSpannerDatabaseName() != null,
            String.format(
                "Missing required value --spannerDatabaseName for %s sink type",
                options.getSinkType().name()));
        checkArgument(
            options.getSpannerTableName() != null,
            String.format(
                "Missing required value --spannerTableName for %s sink type",
                options.getSinkType().name()));
        return StreamingDataGeneratorWriteToSpanner.builder(options).build();
      case KAFKA:
        checkArgument(
            options.getBootstrapServer() != null,
            String.format(
                "Missing required value --bootstrapServer for %s sink type",
                options.getSinkType().name()));
        checkArgument(
            options.getKafkaTopic() != null,
            String.format(
                "Missing required value --kafkaTopic for %s sink type",
                options.getSinkType().name()));
        return StreamingDataGeneratorWriteToKafka.Writer.builder(options).build();
      default:
        throw new IllegalArgumentException("Unsupported Sink.");
    }
  }

  private static String getSchema(SchemaTemplate schemaTemplate, String schemaLocation) {
    checkArgument(
        schemaTemplate != null || schemaLocation != null,
        "Either schemaTemplate or schemaLocation argument of MessageGeneratorFn class must be"
            + " provided.");
    if (schemaLocation != null) {
      return GCSUtils.getGcsFileAsString(schemaLocation);
    } else {
      return schemaTemplate.getSchema();
    }
  }
}

Passaggi successivi