Template Spanner ke Vertex AI Vector Search

Template untuk Spanner ke file Vector Search Vertex AI di Cloud Storage membuat pipeline batch yang mengekspor data embedding vektor dari tabel Spanner ke Cloud Storage dalam format JSON. Gunakan parameter template untuk menentukan folder Cloud Storage yang akan dijadikan tujuan ekspor embedding vektor. Folder Cloud Storage berisi daftar file .json yang diekspor, yang mewakili embedding vektor dalam format yang didukung indeks Vector Search Vertex AI.

Untuk mengetahui informasi selengkapnya, lihat Format dan struktur data input.

Persyaratan pipeline

Database Spanner harus ada.
Bucket Cloud Storage untuk menghasilkan data harus ada.
Selain peran Identity and Access Management (IAM) yang diperlukan untuk menjalankan tugas Dataflow, Anda juga memerlukan peran IAM yang diperlukan untuk membaca data Spanner dan menulis ke bucket Cloud Storage.

Parameter template

Parameter	Deskripsi
`spannerProjectId`	Project ID instance Spanner.
`spannerInstanceId`	ID instance Spanner yang akan digunakan untuk mengekspor embedding vektor.
`spannerDatabaseId`	ID database Spanner untuk mengekspor embedding vektor.
`spannerTable`	Tabel Spanner untuk dibaca.
`spannerColumnsToExport`	Daftar kolom wajib yang dipisahkan koma untuk indeks Vertex AI Vector Search. Kolom `id` dan `embedding` diperlukan oleh Vector Search. Jika nama kolom Anda tidak cocok dengan struktur input indeks Vertex AI Vector Search, buat pemetaan kolom menggunakan alias. Jika nama kolom tidak cocok dengan format yang diharapkan oleh Vertex AI, gunakan notasi `from:to`. Misalnya, jika Anda memiliki kolom bernama `id` dan `my_embedding`, tentukan `id, my_embedding:embedding`.
`gcsOutputFolder`	Folder Cloud Storage tempat file output akan ditulis. Jalur harus diakhiri dengan garis miring. Contoh: `gs://your-bucket/folder1/`.
`gcsOutputFilePrefix`	Awalan nama file untuk menulis file output. Misalnya: `vector-embeddings`.
`spannerHost`	Opsional: Endpoint Spanner yang akan dipanggil di template. Nilai defaultnya adalah `https://batch-spanner.googleapis.com`.
`spannerVersionTime`	Opsional: Jika ditetapkan, menentukan waktu untuk mengambil versi database. Nilainya adalah string dalam format tanggal RFC-3339 dalam waktu Unix epoch. Misalnya: `1990-12-31T23:59:60Z`. Stempel waktu harus berada di masa lampau, dan kesempatan stempel waktu maksimum berlaku. Jika tidak disetel, batas kuat akan digunakan untuk membaca data terbaru. Nilai defaultnya adalah kosong.
`spannerDataBoostEnabled`	Opsional: Jika ditetapkan ke `true`, template akan menggunakan komputasi on-demand Spanner. Tugas ekspor berjalan pada resource komputasi independen yang tidak memengaruhi workload Spanner saat ini. Penggunaan opsi ini akan menimbulkan biaya tambahan di Spanner. Untuk informasi selengkapnya, lihat ringkasan Spanner Data Boost. Nilai defaultnya adalah `false`.
`spannerPriority`	Opsional: Prioritas permintaan untuk panggilan Spanner. Nilai yang diizinkan adalah `HIGH`, `MEDIUM`, dan `LOW`. Nilai defaultnya adalah `MEDIUM`.

Menjalankan template

Konsol

Buka halaman Create job from template Dataflow.

Buka Buat tugas dari template

Di kolom Job name, masukkan nama pekerjaan yang unik.
Opsional: Untuk Endpoint regional, pilih nilai dari menu drop-down. Region default-nya adalah us-central1.
Untuk daftar region tempat Anda dapat menjalankan tugas Dataflow, lihat Lokasi Dataflow.
Dari menu drop-down Dataflow template, pilih the Spanner to Vertex AI Vector Search files on Cloud Storage template.
Di kolom parameter yang disediakan, masukkan parameter value Anda.
Klik Run job.

gcloud

Di shell atau terminal Anda, jalankan template:

gcloud dataflow jobs run JOB_NAME \
    --gcs-location=gs://dataflow-templates-REGION_NAME/VERSION/Cloud_Spanner_vectors_to_Cloud_Storage \
    --project=PROJECT_ID \
    --region=REGION_NAME \
    --parameters \
       spannerProjectId=SPANNER_PROJECT_ID,\
       spannerInstanceId=SPANNER_INSTANCE_ID,\
       spannerDatabaseId=SPANNER_DATABASE_ID,\
       spannerTable=SPANNER_TABLE,\
       spannerColumnsToExport=SPANNER_COLUMNS_TO_EXPORT,\
       gcsOutputFolder=GCS_OUTPUT_FOLDER,\
       gcsOutputFilePrefix=GCS_OUTPUT_FILE_PREFIX,\

Ganti kode berikut:

JOB_NAME: nama pekerjaan unik pilihan Anda
VERSION: versi template yang ingin Anda gunakan
Anda dapat menggunakan nilai berikut:
- latest untuk menggunakan versi terbaru template, yang tersedia di folder induk tidak bertanggal di bucket— gs://dataflow-templates-REGION_NAME/latest/
- nama versi, seperti 2023-09-12-00_RC00, untuk menggunakan versi template tertentu, yang dapat ditemukan bertingkat di folder induk bertanggal masing-masing dalam bucket— gs://dataflow-templates-REGION_NAME/
Perhatian: Versi terbaru template mungkin diperbarui dengan perubahan yang dapat menyebabkan gangguan. Lingkungan produksi Anda harus menggunakan template yang disimpan di folder induk bertanggal terbaru agar perubahan yang dapat menyebabkan gangguan ini tidak memengaruhi alur kerja produksi Anda.
REGION_NAME: region tempat Anda ingin men-deploy tugas Dataflow, misalnya us-central1
SPANNER_PROJECT_ID: project ID Spanner
SPANNER_INSTANCE_ID: ID instance Spanner
SPANNER_DATABASE_ID: ID database Spanner
SPANNER_TABLE: tabel Spanner
SPANNER_COLUMNS_TO_EXPORT: kolom yang akan diekspor dari tabel Spanner
GCS_OUTPUT_FOLDER: folder Cloud Storage untuk menghasilkan file
GCS_OUTPUT_FILE_PREFIX: awalan file output di Cloud Storage

API

Untuk menjalankan template menggunakan REST API, kirim permintaan HTTP POST. Untuk informasi selengkapnya tentang API dan cakupan otorisasinya, lihat projects.templates.launch.

POST https://dataflow.googleapis.com/v1b3/projects/PROJECT_ID/locations/LOCATION/templates:launch?gcsPath=gs://dataflow-templates-LOCATION/VERSION/Cloud_Spanner_vectors_to_Cloud_Storage
{
   "jobName": "JOB_NAME",
   "parameters": {
     "spannerProjectId": "SPANNER_PROJECT_ID",
     "spannerInstanceId": "SPANNER_INSTANCE_ID",
     "spannerDatabaseId": "SPANNER_DATABASE_ID",
     "spannerTable": "SPANNER_TABLE",
     "spannerColumnsToExport": "SPANNER_COLUMNS_TO_EXPORT",
     "gcsOutputFolder": "GCS_OUTPUT_FOLDER",
     "gcsOutputFilePrefix": "GCS_OUTPUT_FILE_PREFIX",
   },
   "environment": { "maxWorkers": "10" }
}

Ganti kode berikut:

PROJECT_ID: ID project Google Cloud tempat Anda ingin menjalankan tugas Dataflow
JOB_NAME: nama pekerjaan unik pilihan Anda
VERSION: versi template yang ingin Anda gunakan
Anda dapat menggunakan nilai berikut:
- latest untuk menggunakan versi terbaru template, yang tersedia di folder induk tidak bertanggal di bucket— gs://dataflow-templates-REGION_NAME/latest/
- nama versi, seperti 2023-09-12-00_RC00, untuk menggunakan versi template tertentu, yang dapat ditemukan bertingkat di folder induk bertanggal masing-masing dalam bucket— gs://dataflow-templates-REGION_NAME/
Perhatian: Versi terbaru template mungkin diperbarui dengan perubahan yang dapat menyebabkan gangguan. Lingkungan produksi Anda harus menggunakan template yang disimpan di folder induk bertanggal terbaru agar perubahan yang dapat menyebabkan gangguan ini tidak memengaruhi alur kerja produksi Anda.
LOCATION: region tempat Anda ingin men-deploy tugas Dataflow, misalnya us-central1
SPANNER_PROJECT_ID: project ID Spanner
SPANNER_INSTANCE_ID: ID instance Spanner
SPANNER_DATABASE_ID: ID database Spanner
SPANNER_TABLE: tabel Spanner
SPANNER_COLUMNS_TO_EXPORT: kolom yang akan diekspor dari tabel Spanner
GCS_OUTPUT_FOLDER: folder Cloud Storage untuk menghasilkan file
GCS_OUTPUT_FILE_PREFIX: awalan file output di Cloud Storage

Kode sumber template

Java

/*
 * Copyright (C) 2023 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.templates;

import com.google.cloud.spanner.Options.RpcPriority;
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.templates.SpannerVectorEmbeddingExport.SpannerToVectorEmbeddingJsonOptions;
import com.google.cloud.teleport.templates.common.SpannerConverters;
import com.google.cloud.teleport.templates.common.SpannerConverters.CreateTransactionFnWithTimestamp;
import com.google.cloud.teleport.templates.common.SpannerConverters.VectorSearchStructValidator;
import org.apache.beam.sdk.Pipeline;
import org.apache.beam.sdk.io.FileSystems;
import org.apache.beam.sdk.io.TextIO;
import org.apache.beam.sdk.io.gcp.spanner.LocalSpannerIO;
import org.apache.beam.sdk.io.gcp.spanner.ReadOperation;
import org.apache.beam.sdk.io.gcp.spanner.SpannerConfig;
import org.apache.beam.sdk.io.gcp.spanner.Transaction;
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.ValueProvider;
import org.apache.beam.sdk.transforms.Create;
import org.apache.beam.sdk.transforms.MapElements;
import org.apache.beam.sdk.transforms.PTransform;
import org.apache.beam.sdk.transforms.ParDo;
import org.apache.beam.sdk.transforms.SerializableFunction;
import org.apache.beam.sdk.transforms.View;
import org.apache.beam.sdk.values.PBegin;
import org.apache.beam.sdk.values.PCollection;
import org.apache.beam.sdk.values.PCollectionView;
import org.apache.beam.sdk.values.TypeDescriptors;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;

/**
 * Dataflow template which export vector embeddings from Spanner to GCS in json format. It exports a
 * Spanner table using <a
 * href="https://cloud.google.com/spanner/docs/reads#read_data_in_parallel">Batch API</a>, which
 * creates multiple workers in parallel for better performance. The result is written to a JSON file
 * in Google Cloud Storage.
 *
 * <p>Check out <a
 * href="https://github.com/GoogleCloudPlatform/DataflowTemplates/blob/main/v1/README_Cloud_Spanner_to_Vector_Embedding.md">README</a>
 * for instructions on how to use or modify this template.
 */
@Template(
    name = "Cloud_Spanner_vectors_to_Cloud_Storage",
    category = TemplateCategory.BATCH,
    displayName = "Cloud Spanner vectors to Cloud Storage for Vertex Vector Search",
    optionsClass = SpannerToVectorEmbeddingJsonOptions.class,
    description = {
      "The Cloud Spanner to Vector Embeddings on Cloud Storage template is a batch pipeline that exports vector embeddings data from Cloud Spanner's table to Cloud Storage in JSON format. "
          + "Vector embeddings are exported to a Cloud Storage folder specified by the user in the template parameters."
          + " The Cloud Storage folder will contain the list of exported `.json` files representing vector embeddings in a format supported by Vertex AI Vector Search Index.\n",
      "Check <a href=\"https://cloud.google.com/vertex-ai/docs/vector-search/setup/format-structure#json\">Vector Search Format Structure</a> for additional details."
    },
    documentation =
        "https://cloud.google.com/dataflow/docs/guides/templates/provided/cloud-spanner-to-vertex-vector-search",
    contactInformation = "https://cloud.google.com/support",
    requirements = {
      "The Cloud Spanner database must exist.",
      "The output Cloud Storage bucket must exist.",
      "In addition to the Identity and Access Management (IAM) roles necessary to run Dataflow jobs, you must also have the <a href=\"https://cloud.google.com/spanner/docs/export#iam\">appropriate IAM roles</a> for reading your Cloud Spanner data and writing to your Cloud Storage bucket."
    })
@SuppressWarnings("unused")
public class SpannerVectorEmbeddingExport {

  private static final Logger LOG = LoggerFactory.getLogger(SpannerVectorEmbeddingExport.class);

  /** Custom PipelineOptions. */
  public interface SpannerToVectorEmbeddingJsonOptions extends PipelineOptions {
    @TemplateParameter.ProjectId(
        order = 10,
        description = "Cloud Spanner Project Id",
        helpText = "The project ID of the Cloud Spanner instance.")
    ValueProvider<String> getSpannerProjectId();

    void setSpannerProjectId(ValueProvider<String> value);

    @TemplateParameter.Text(
        order = 20,
        regexes = {"[a-z][a-z0-9\\-]*[a-z0-9]"},
        description = "Cloud Spanner instance ID",
        helpText =
            "The instance ID of the Cloud Spanner from which you want to export the vector embeddings.")
    ValueProvider<String> getSpannerInstanceId();

    void setSpannerInstanceId(ValueProvider<String> spannerInstanceId);

    @TemplateParameter.Text(
        order = 30,
        regexes = {"[a-z][a-z0-9_\\-]*[a-z0-9]"},
        description = "Cloud Spanner database ID",
        helpText =
            "The database ID of the Cloud Spanner from which you want to export the vector embeddings.")
    ValueProvider<String> getSpannerDatabaseId();

    void setSpannerDatabaseId(ValueProvider<String> spannerDatabaseId);

    @TemplateParameter.Text(
        order = 40,
        regexes = {"^.+$"},
        description = "Spanner Table",
        helpText = "Spanner Table to read from")
    ValueProvider<String> getSpannerTable();

    void setSpannerTable(ValueProvider<String> table);

    @TemplateParameter.Text(
        order = 50,
        description = "Columns to Export from Spanner Table",
        helpText =
            "Comma separated list of columns which are required for Vertex AI Vector Search Index."
                + " The `id` & `embedding` are required columns for Vertex Vector Search."
                + " If the column names don't precisely align with the Vertex AI Vector Search Index input structure,"
                + " you can establish column mappings using aliases. If you have the columns that don't match the"
                + " format expected by Vertex, you can use the notation `from:to`. For example, if the columns are"
                + " `id` and `my_embedding`, in which `id` matches what Vertex expects but the embedding column is named differently,"
                + " `id, my_embedding:embedding` should be specified.")
    ValueProvider<String> getSpannerColumnsToExport();

    void setSpannerColumnsToExport(ValueProvider<String> value);

    @TemplateParameter.GcsWriteFolder(
        order = 60,
        description = "Output files folder in Cloud Storage",
        helpText = "The Cloud Storage folder for writing output files. Must end with a slash.",
        example = "gs://your-bucket/folder1/")
    ValueProvider<String> getGcsOutputFolder();

    void setGcsOutputFolder(ValueProvider<String> value);

    @TemplateParameter.Text(
        order = 70,
        description = "Output files prefix in Cloud Storage",
        helpText = "The filename prefix for writing output files.",
        example = "vector-embeddings")
    ValueProvider<String> getGcsOutputFilePrefix();

    void setGcsOutputFilePrefix(ValueProvider<String> textWritePrefix);

    @TemplateParameter.Text(
        order = 80,
        optional = true,
        description = "Cloud Spanner Endpoint to call",
        helpText =
            "The Cloud Spanner endpoint to call in the template. The default is set to https://batch-spanner.googleapis.com.",
        example = "https://batch-spanner.googleapis.com")
    @Default.String("https://batch-spanner.googleapis.com")
    ValueProvider<String> getSpannerHost();

    void setSpannerHost(ValueProvider<String> value);

    @TemplateParameter.Text(
        order = 90,
        optional = true,
        regexes = {
          "^([0-9]{4})-([0-9]{2})-([0-9]{2})T([0-9]{2}):([0-9]{2}):(([0-9]{2})(\\.[0-9]+)?)Z$"
        },
        description = "Timestamp to read stale data from a version in the past.",
        helpText =
            "If set, specifies the time when the database version must be taken."
                + " String is in the RFC 3339 format in UTC time. "
                + " Timestamp must be in the past and maximum timestamp staleness applies; see "
                + "<a href=\"https://cloud.google.com/spanner/docs/timestamp-bounds#maximum_timestamp_staleness\">Maximum Timestamp Staleness</a>."
                + " If not set, strong bound is used to read the latest data; see "
                + "<a href=\"https://cloud.google.com/spanner/docs/timestamp-bounds#strong\">Timestamp Strong Bounds</a>.",
        example = "1990-12-31T23:59:60Z")
    @Default.String(value = "")
    ValueProvider<String> getSpannerVersionTime();

    void setSpannerVersionTime(ValueProvider<String> value);

    @TemplateParameter.Boolean(
        order = 100,
        optional = true,
        description = "Use independent compute resource (Spanner DataBoost).",
        helpText =
            "Use Spanner on-demand compute so the export job will run on independent compute"
                + " resources and have no impact to current Spanner workloads. This will incur"
                + " additional charges in Spanner."
                + " Refer <a href=\" https://cloud.google.com/spanner/docs/databoost/databoost-overview\">Data Boost Overview</a>.")
    @Default.Boolean(false)
    ValueProvider<Boolean> getSpannerDataBoostEnabled();

    void setSpannerDataBoostEnabled(ValueProvider<Boolean> value);

    @TemplateParameter.Enum(
        order = 110,
        enumOptions = {
          @TemplateEnumOption("LOW"),
          @TemplateEnumOption("MEDIUM"),
          @TemplateEnumOption("HIGH")
        },
        optional = true,
        description = "Priority for Spanner RPC invocations",
        helpText =
            "The request priority for Cloud Spanner calls. The value must be one of:"
                + " [HIGH,MEDIUM,LOW]. Defaults to: MEDIUM.")
    ValueProvider<RpcPriority> getSpannerPriority();

    void setSpannerPriority(ValueProvider<RpcPriority> value);
  }

  /**
   * Runs a pipeline which reads in vector embeddings records from Spanner, and writes the JSON to
   * TextIO sink.
   *
   * @param args arguments to the pipeline
   */
  public static void main(String[] args) {
    LOG.info("Starting pipeline setup");
    PipelineOptionsFactory.register(SpannerToVectorEmbeddingJsonOptions.class);

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

    FileSystems.setDefaultPipelineOptions(options);
    Pipeline pipeline = Pipeline.create(options);

    SpannerConfig spannerConfig =
        SpannerConfig.create()
            .withHost(options.getSpannerHost())
            .withProjectId(options.getSpannerProjectId())
            .withInstanceId(options.getSpannerInstanceId())
            .withDatabaseId(options.getSpannerDatabaseId())
            .withRpcPriority(options.getSpannerPriority())
            .withDataBoostEnabled(options.getSpannerDataBoostEnabled());

    ValueProvider<String> gcsOutputFilePrefix = options.getGcsOutputFilePrefix();

    // Concatenating cloud storage folder with file prefix to get complete path
    ValueProvider<String> gcsOutputFilePathWithPrefix =
        ValueProvider.NestedValueProvider.of(
            options.getGcsOutputFolder(),
            (SerializableFunction<String, String>)
                folder -> {
                  if (!folder.endsWith("/")) {
                    // Appending the slash if not provided by user
                    folder = folder + "/";
                  }
                  return folder + gcsOutputFilePrefix.get();
                });

    PTransform<PBegin, PCollection<ReadOperation>> spannerExport =
        SpannerConverters.ExportTransformFactory.create(
            options.getSpannerTable(),
            spannerConfig,
            gcsOutputFilePathWithPrefix,
            options.getSpannerVersionTime(),
            options.getSpannerColumnsToExport(),
            ValueProvider.StaticValueProvider.of(/* disable_schema_export= */ false));

    /* CreateTransaction and CreateTransactionFn classes in LocalSpannerIO
     * only take a timestamp object for exact staleness which works when
     * parameters are provided during template compile time. They do not work with
     * a Timestamp valueProvider which can take parameters at runtime. Hence a new
     * ParDo class CreateTransactionFnWithTimestamp had to be created for this
     * purpose.
     */
    PCollectionView<Transaction> tx =
        pipeline
            .apply("Setup for Transaction", Create.of(1))
            .apply(
                "Create transaction",
                ParDo.of(
                    new CreateTransactionFnWithTimestamp(
                        spannerConfig, options.getSpannerVersionTime())))
            .apply("As PCollectionView", View.asSingleton());

    PCollection<String> json =
        pipeline
            .apply("Create export", spannerExport)
            // We need to use LocalSpannerIO.readAll() instead of LocalSpannerIO.read()
            // because ValueProvider parameters such as table name required for
            // LocalSpannerIO.read() can be read only inside DoFn but LocalSpannerIO.read() is of
            // type PTransform<PBegin, Struct>, which prevents prepending it with DoFn that reads
            // these parameters at the pipeline execution time.
            .apply(
                "Read all records",
                LocalSpannerIO.readAll().withTransaction(tx).withSpannerConfig(spannerConfig))
            .apply(
                "Struct To JSON",
                MapElements.into(TypeDescriptors.strings())
                    .via(
                        struct ->
                            (new SpannerConverters.StructJSONPrinter(
                                    new VectorSearchStructValidator()))
                                .print(struct)));

    json.apply(
        "Write to storage", TextIO.write().to(gcsOutputFilePathWithPrefix).withSuffix(".json"));

    pipeline.run();
    LOG.info("Completed pipeline setup");
  }
}

Langkah selanjutnya

Pelajari Template Dataflow.
Lihat daftar template yang disediakan Google.