[[["容易理解","easyToUnderstand","thumb-up"],["確實解決了我的問題","solvedMyProblem","thumb-up"],["其他","otherUp","thumb-up"]],[["缺少我需要的資訊","missingTheInformationINeed","thumb-down"],["過於複雜/步驟過多","tooComplicatedTooManySteps","thumb-down"],["過時","outOfDate","thumb-down"],["翻譯問題","translationIssue","thumb-down"],["示例/程式碼問題","samplesCodeIssue","thumb-down"],["其他","otherDown","thumb-down"]],["上次更新時間：2025-09-05 (世界標準時間)。"],[[["\u003cp\u003eThis guide provides various design patterns for performing high volume address validation using Google Cloud Platform, integrating with existing processes and pipelines.\u003c/p\u003e\n"],["\u003cp\u003eYou can leverage Cloud Run, Compute Engine, or Google Kubernetes Engine for one-time address validation tasks, uploading data to Cloud Storage for processing.\u003c/p\u003e\n"],["\u003cp\u003eFor recurring data pipelines, use Cloud Storage, Dataflow, and BigQuery to efficiently process and validate large address datasets regularly.\u003c/p\u003e\n"],["\u003cp\u003eTo implement a long-lasting recurring address validation process, use Memorystore for persistent storage, Cloud Scheduler for periodic triggers, and BigQuery for caching results.\u003c/p\u003e\n"],["\u003cp\u003eThis approach allows for periodic revalidation of existing addresses and validation of new ones, offering higher resiliency and the ability to process large datasets.\u003c/p\u003e\n"]]],["This content outlines how to perform high-volume address validation on Google Cloud Platform. It details running validation scripts from Cloud Run, Compute Engine, or Kubernetes Engine for one-time tasks. For data pipelines, it suggests using Cloud Storage for CSV files, Dataflow for processing, and BigQuery for caching. Recurring processes leverage Cloud Storage, Memorystore, and BigQuery, scheduled by Cloud Scheduler. It highlights an open-source Python library for implementation and provides resources for further learning.\n"],null,["Objective\n\nThe [High Volume Address Validation](https://developers.google.com/maps/architecture/high-volume-address-validation) tutorial guided you through different scenarios where high volume address validation can be used. In this tutorial, we will introduce you to different design patterns within Google Cloud Platform for running High Volume Address Validation.\n\nWe will start with an overview on running High Volume Address Validation in Google Cloud Platform with Cloud Run, Compute Engine or Google Kubernetes Engine for one time executions. We will then see how this capability can be included as part of a data pipeline.\n\nBy the end of this article you should have a good understanding of the different options for running Address Validation in high volume in your Google Cloud environment.\n| **Try now:** Visit cloud console and enable the [Address Validation API](https://console.cloud.google.com/marketplace/product/google/addressvalidation.googleapis.com?utm_source=architecture_high_volume).\n\nReference architecture on Google Cloud Platform\n\nThis section dives deeper into different design patterns for High Volume Address Validation using [Google Cloud Platform](https://cloud.google.com/). By running on Google Cloud Platform, you can integrate with your existing processes and data pipelines.\n\nRunning High Volume Address Validation one time on Google Cloud Platform\n\nShown below is a reference architecture of how to build an integration\non Google Cloud Platform which is more suitable for one off operations or testing.\n\nIn this case, we recommend uploading the CSV file to a [Cloud Storage](https://cloud.google.com/storage/docs/creating-buckets) bucket. The High Volume Address Validation script can then be run from a [Cloud Run](https://cloud.google.com/run) environment.\nHowever you can execute it any other runtime environment like [Compute Engine](https://cloud.google.com/compute) or [Google Kubernetes Engine](https://cloud.google.com/kubernetes-engine).\nThe output CSV can also be uploaded to the [Cloud Storage](https://cloud.google.com/storage/docs/creating-buckets) bucket.\n\nRunning as a Google Cloud Platform data pipeline\n\nThe deployment pattern shown in the previous section is great for quickly testing High Volume Address Validation for one time usage.\nHowever if you need to use it regularly as part of a data pipeline, then you can better leverage Google Cloud Platform native capabilities to make it more robust. Some of the changes which you can make include:\n\n- In this case, you can dump CSV files in [Cloud Storage](https://cloud.google.com/storage/docs/creating-buckets) buckets.\n- A [Dataflow](https://cloud.google.com/dataflow) job can pick up the addresses to be processed and then cache in [BigQuery](https://cloud.google.com/bigquery/docs/introduction).\n- The [Dataflow Python library](https://cloud.google.com/dataflow/docs/quickstarts/create-pipeline-python) can be be extended to have logic for High Volume Address Validation to validate the addresses from the Dataflow job.\n\nRunning the script from a data pipeline as a long lasting recurring process\n\nAnother common approach is to validate a batch of addresses as part of a streaming data pipeline as a recurring process.\nYou may also have the addresses in a bigquery datastore. In this approach we will see how to build out a recurring data pipeline (which needs to be triggered daily/weekly/monthly)\n\n- Upload the initial CSV file to a [Cloud Storage](https://cloud.google.com/storage/docs/creating-buckets) bucket.\n- Use [Memorystore](https://cloud.google.com/memorystore) as a persistent datastore to maintain intermediate state for the long running process.\n- Cache the final addresses in a [BigQuery](https://cloud.google.com/bigquery/docs/introduction) datastore.\n- Set up [Cloud Scheduler](https://cloud.google.com/scheduler) to run the script periodically.\n\nThis architecture has the following advantages:\n\n- Using [Cloud Scheduler](https://cloud.google.com/scheduler), address validation can be done periodically. You might want to revalidate the addresses on a monthly basis or validate any new addresses on a monthly/quarterly basis. This architecture helps solve that use case.\n- If customer data is in [BigQuery](https://cloud.google.com/bigquery/docs/introduction), then the validated addresses or the validation Flags can be cached directly there.\n Note: What can be cached and how is described in details in the [High Volume Address Validation article](https://developers.google.com/maps/architecture/high-volume-address-validation#caching_for_production_use)\n\n- Using [Memorystore](https://cloud.google.com/memorystore) provides higher resiliency and ability to process more addresses. This steps adds a statefulness to the whole processing pipeline which is needed for handling very large address datasets.\n Other database technologies like cloud SQL\\[https://cloud.google.com/sql\\] or any other [flavour of database](https://cloud.google.com/products/databases) which Google cloud Platform offers can be used here as well. However we believe memorystore perfectless balances the scaling and simplicity needs, thus should be the first choice.\n\nConclusion\n\nBy applying the patterns described here, you can use Address Validation API for different use cases and from different use cases on Google Cloud Platform.\n\nWe have written an open-source Python library to help you get started with the use cases described above. It can be invoked from a command line on your computer or it can be invoked from [Google Cloud Platform](https://cloud.google.com/) or other cloud providers.\n\nLearn more about how to use the library from this [article](https://developers.google.com/maps/architecture/high-volume-address-validation-library-oss).\n\nNext Steps\n\nDownload the [Improve checkout, delivery, and operations with reliable addresses](https://mapsplatform.withgoogle.com/address-validation-whitepaper/home.html?utm_source=architecture&utm_medium=website&utm_campaign=FY23-Q1-global-Maps-website-dl-GMP-AV-Whitepaper&utm_content=av_whitepaper)Whitepaper and view the [Improving checkout, delivery, and operations with Address Validation](https://mapsonair.withgoogle.com/events/improving-checkout-operations-with-address-validation-webinar?utm_source=architecture&utm_medium=website&utm_campaign=FY23-Q1-global-Maps-onlineevent-er-GMP-Improving-Ops-with-Address-Validation&utm_content=january_webinar)Webinar.\n\nSuggested further reading:\n\n- [Address Validation API Documentation](https://developers.google.com/maps/documentation/address-validation)\n- [Geocoding and Address Validation](https://developers.google.com/maps/architecture/geocoding-address-validation)\n- Explore the [Address Validation demo](https://developers.google.com/maps/documentation/address-validation/demo)\n\nContributors\n\nGoogle maintains this article. The following contributors originally wrote it. \n\nPrincipal authors:\n\n[Henrik Valve](https://www.linkedin.com/in/henrikvalve/) \\| Solutions Engineer \n\n[Thomas Anglaret](https://www.linkedin.com/in/thomas-anglaret/) \\| Solutions Engineer \n\n[Sarthak Ganguly](https://www.linkedin.com/in/sarthakganguly/) \\| Solutions Engineer"]]
