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Serverless event processing

This reference architecture describes an event-driven data processing pipeline that does not require management of underlying infrastructure. This document is intended for developers and cloud architects who want to implement a serverless event processing system using Google-managed services.

Architecture

The following diagram shows an event-driven processing pipeline:

Serverless event processing architecture

This architecture includes the following components:

In this reference architecture, we use the creation of objects in a Cloud Storage bucket as an example of an event source, but it can be any event type and provider that Eventarc supports.

The example event processing workload that you deploy on Cloud Run emits the events it receives in its log. You can refactor it to implement your event processing business logic. For example, you can process the event, and save the results in a Cloud Storage bucket or in a Firestore database.

Use cases

With this reference architecture, you can realize use cases such as:

  • Data analysis. You can implement data processing pipelines to gather actionable information from data that users or other systems produce. For example, you can implement a document understanding process using Gemini and Cloud Run.
  • Process orchestration. You can orchestrate serverless, event-based processes in response to certain events. For example, you can implement a distributed, event-based configuration process to perform configuration actions on resources in your environment in response to events, such as bootstrapping a Compute Engine instance as soon as it’s online and ready.

Deploy the reference architecture

To deploy this reference architecture in a Google Cloud project, you need to authenticate with an account that has the owner role on that project.

In order to deploy this reference architecture, do the following from Cloud Shell:

  1. Clone this repository.
  2. Change the working directory to the directory where you cloned this repository.

  3. Change the working directory to the projects/serverless-event-processing/terraform directory:

    cd projects/serverless-event-processing/terraform

  4. Initialize the Terraform environment:

    terraform init

  5. Run the provisioning process with Terraform:

    terraform apply

    Confirm and authorize Terraform when asked.

    When you run the provisioning process for the first time, Terraform asks for inputs, such as the Google Cloud project ID where you want to provision resources in. Terraform then stores the inputs you provided in a variables file that it automatically generates from a template.

    Also, during this first run of the provisioning process, Terraform stores the backend locally because there's no remote backend available yet.

  6. Re-run the provisioning process to migrate the local Terraform backend to a remote backend on Cloud Storage:

    terraform init -migrate-state

    If Terraform asks for confirmation to migrate the backend, answer affirmatively.

Create example events and outputs

As an example, you can create objects in the Cloud Storage bucket we use as an event source. In order to deploy this reference architecture, do the following from Cloud Shell:

  1. Get the name of the source Cloud Storage bucket:

    SOURCE_CLOUD_STORAGE_BUCKET_NAME="$(terraform output -raw source_cloud_storage_bucket_name)"

  2. Create an example file:

    echo "Hello World" > random.txt

  3. Upload the example file in the Cloud Storage bucket:

    gsutil cp random.txt "gs://${SOURCE_CLOUD_STORAGE_BUCKET_NAME}/random.txt"

  4. View the log entries related to the event processing service:

    gcloud logging read "resource.type=cloud_run_revision \
  AND resource.labels.service_name=$(terraform output -raw event_processor_cloud_run_service_name) \
  AND jsonPayload.event.bucket=${SOURCE_CLOUD_STORAGE_BUCKET_NAME}"

Clean up

In order to delete the resources that this reference architecture provisions, do the following:

  1. Delete the remote backend configuration:

    rm backend.tf

  2. Initialize a local backend with and migrate the state stored in the remote backend to the local backend:

    terraform init -migrate-state

    If Terraform asks for confirmation to migrate the backend, answer affirmatively.

    Migrating back from a remote backend to a local backend is necessary because the resource deletion process deletes the remote backend storage.

  3. Delete resources:

    terraform destroy

    If Terraform asks for confirmation to migrate the backend, answer affirmatively.

Troubleshooting

  • When running terraform apply, you may get an error about the Eventarc Service Agent not being ready, similar to the following:

    Error: Error creating Trigger: googleapi: Error 400: Invalid resource state for "":
Permission denied while using the Eventarc Service Agent. If you recently
started to use Eventarc, it may take a few minutes before all necessary
permissions are propagated to the Service Agent. Otherwise, verify that it
has Eventarc Service Agent role.

    If that happens, try running terraform apply again.

  • For more information about troubleshooting Eventarc for Cloud Run, see Troubleshoot Eventarc for Cloud Run.

Design alternatives

The following sections present potential design alternatives for this architecture.

Results data storage

This reference architecture stores event processing results in a Cloud Storage bucket and in a Firestore database, depending on the type of data that you need to store. For example, you can use Cloud Storage to store objects, and Firestore to store structured data. If you don’t need to use both Cloud Storage and Firestore, you can use only one of the two, depending on your requirements.

Besides Cloud Storage and Firestore, you can also use other data stores, such as Cloud SQL and Cloud Spanner to store processing results. For more information about storing data when your event processing workloads run on Cloud Run, see Cloud Run storage options.

Minimize the number of Cloud Storage buckets

This reference architecture supports using Cloud Storage as an event source. For example, you can configure the reference architecture to automatically process objects in a Cloud Storage bucket as they are created or updated. This reference architecture also uses Cloud Storage to store event processing results. If you’re using Cloud Storage as an event source, and you want to keep the number of buckets to a minimum, you can use the same storage bucket both as an event source, and to store processing results.

If you use the same Cloud Storage bucket both as an event source, and to store processing results, you might encounter an issue that causes the event processing pipeline to be stuck in an endless loop. For example:

  1. You create an object in the Cloud Storage bucket.
  2. Eventarc sends an event about the newly created object.
  3. The pipeline receives the event, processes the object, and saves the result in the same bucket.
  4. Eventarc sends an event about the newly created object.
  5. The pipeline receives the event, processes the object, and saves the result in the same bucket.

And so on.

In order to avoid this endless loop, we recommend that you configure a path pattern filter to apply a trigger to objects to avoid triggering the pipeline on changes to objects that you use to store processing results.

Event sources

Eventarc is an abstraction layer on top of Cloud Pub/Sub. For application integration use cases, Eventarc aims to provide a more streamlined developer experience compared to Pub/Sub and is the recommended product. However, it’s possible to trigger a Cloud Run service directly through Cloud Pub/Sub, and it makes sense for some use cases, such as when you are using Pub/Sub already or if Eventarc doesn't support the events you need to send. If an event source is not compatible with Eventarc, but is compatible with Cloud Pub/Sub, you can still integrate with that source by configuring an Eventarc trigger filter in addition to publishing messages directly to Cloud Pub/Sub. For more information, see Eventarc event routing.

Cross-project event processing

This reference architecture assumes that Eventarc routes events to target destinations in the same Google Cloud project. For more information about routing events across projects, see Route events across Google Cloud projects.

Design considerations

This section describes design factors, best practices, and design recommendations that you should consider when you use this reference architecture to develop a topology that meets your specific requirements for security, reliability, operational efficiency, cost, and performance.

Security, privacy, and compliance

To help you secure your environment, consider the following:

Reliability

To understand the reliability properties of this reference architecture, consider the following:

To increase the reliability of a object processing pipeline based on this reference architecture, consider the following:

Finally, to understand what happens in the unlikely event of a zonal or regional outage, see Architecting disaster recovery for cloud infrastructure outages.

Cost optimization

For more information about optimizing the costs of your environment, see:

Operational efficiency

Cloud Run supports scaling the number of instances to handle all incoming requests or events. To accommodate unpredictable traffic patterns, you can also configure the minimum and maximum number of instances.

Cloud Run lets you configure resource allocation and limits. For example, you can configure CPU and memory limits, and CPU allocation for your Cloud Run jobs. If resources backing your Cloud Run jobs have capacity constraints, you can limit how many requests Cloud Run can serve concurrently per instance, instead of serving as many requests as possible considering CPU limits and allocation.

Cloud Storage provides tools to manage your object storage at scale. For example, you can produce Storage Insights inventory reports and further analyze these reports with BigQuery, or your tool of choice.

To monitor your environment, Cloud Storage and Cloud Run seamlessly integrate with Cloud Operations Suite. For example, you can monitor:

Cloud Storage and Cloud Run both integrate with Cloud Audit Logs to give you a detailed report about API operations performed on your environment.

Performance optimization

To help you process a large amount of objects, you can group the events to process according to a key, and deploy several instances of your Cloud Run services, each processing a group. This technique is sometimes called sharding. To help you evenly distribute work across Cloud Run service instances as they process event groups, we recommend that you define keys that uniformly distribute events across groups.

Migration considerations

This section provides information about migrating similar reference architectures hosted in other environments to this reference architecture.

Migrate from AWS to Google Cloud

A serverless event processing reference architecture on AWS similar to this Google Cloud reference architecture might use the following AWS products:

  • Amazon Simple Storage Service (Amazon S3): a scalable object storage service.
  • Amazon Simple Queue Service (Amazon SQS): an asynchronous message queueing service.
  • AWS Lambda: a compute service that runs code in response to events and automatically manages the compute resources.
  • Amazon DynamoDB: a scalable NoSQL database service.

Amazon SQS listens to Amazon S3 create object events. AWS Lambda is then configured to read messages from the Amazon SQS queue, process the messages and insert records into DynamoDB.

For more information about this architecture, see Process files uploaded to S3 with SQS, Lambda and DynamoDB.

To migrate this reference architecture from AWS to Google Cloud, we recommend that you:

  1. Design a migration strategy that takes into account the whole architecture.
  2. Refine the migration strategy to consider each component of the architecture.

Design the overall migration strategy

To minimize disruptions during the migration, we recommend that you design a gradual migration strategy that takes into account:

  • Validation and verification, and eventual rollback tasks for each step.
  • A deduplication to avoid processing the same event twice, while the migration is underway.

For more information about how to design your migration to Google Cloud, see Migrate to Google Cloud: Get started.

You can approach this migration from different angles. Depending on your requirements, there are several migration strategies that you can consider. Each strategy puts emphasis on different aspects of the migration, depending on what you migrate first:

  • Source data, such as data that you store in the Amazon S3 buckets that trigger the data processing.
  • Result data, such as the data processing results that you store in Amazon DynamoDB.
  • Data processing, such as the AWS Lambda workload.

All these strategies have the same starting point (reference architecture on AWS), and the same ending point (reference architecture on Google Cloud), but provide different benefits. Choosing one over the other depends on your requirements. When you’re considering which migration strategy to choose, we recommend that you also take into account a case-by-case approach where you pick different strategies for different workloads to migrate, according to your requirements for each workload.

Start with source data

When you start with source data, you focus on the migration by migrating data from Amazon S3 to Cloud Storage first.

By start with migrating source data first, you:

  • Benefit from using all the Google Cloud products that you need to migrate the reference architecture to Google Cloud, such as Cloud Storage, Eventarc, and Cloud Run, from the early phases of the migration.
  • Need to refactor the event-processing workload in one migration step. For example, you refactor the workload to get inputs from Cloud Storage and Eventarc instead of Amazon S3 and Amazon SQS, and store results in Cloud Storage and Firestore instead of Amazon DynamoDB. The effort to spend for this refactoring might be significant because you refactor both inputs and outputs of the workload in the same migration step, along with other modifications that you need to migrate the workload from AWS Lambda to Cloud Run. Although it’s technically possible to refactor inputs and outputs in different migration steps, this would greatly increase cross-cloud data transfer costs due to having source data and results data in two different environments.

While the migration is underway, your source Amazon S3 and Cloud Storage buckets store the same data, and migration tooling propagates modifications. After migrating data from Amazon S3 to Cloud Storage, you focus on migrating other components of the reference architecture, such as the event propagation mechanism, the event-processing workload, and the event-processing results data storage.

To design the rollback steps when starting with migrating source data first, you can start by rolling back to using Amazon S3 to get source data, instead of Cloud Storage. When you design rollback steps, we recommend that you consider how the scale of your environment might impact the rollback. For example, you might need to expend significant effort to roll back a change that involves thousands of Amazon S3 buckets, or objects.

Start with result data

When you start with result data, you focus on the migration by migrating event-processing results from Amazon DynamoDB to Firestore and Cloud Storage first.

By starting with migrating result data, you:

  • Benefit from having event-processing results in Google Cloud from the early phases of the migration. This allows you to refactor the consumers of those results to get data from Firestore and Cloud Storage, without affecting the rest of the event-processing stack.

  • Simplify the refactoring of the event-processing workload by splitting the refactoring of the event-processing workload in two steps:

    1. Refactor the AWS Lambda workload to store the results both in Amazon DynamoDB, Firestore, and Cloud Storage.
    2. Once the migration proceeds, refactor the event-processing workload to store the results in Firestore and Cloud Storage only, along with other modifications that you need to migrate the event processing workload from AWS Lambda to Cloud Run.

While the migration is underway, your Amazon DynamoDB, Firestore databases, and Cloud Storage buckets store the same data, and migration tooling propagates all the modifications that the workload to migrate or other workloads might apply to source data. After migrating data from Amazon DynamoDB to Firestore and Cloud Storage, you focus on migrating other components of the reference architecture, such as the event-processing source data storage, the event propagation mechanism, and the event-processing workload.

To design the rollback steps when starting with migrating result data first, you can roll back the changes that you implemented to refactor the consumers of those results to get data from Firestore and Cloud Storage instead of Amazon DynamoDB.

Start with data processing

When you start with data processing, you focus on migrating the event-processing workload from AWS Lambda to Cloud Run first.

By starting with migrating the event-processing workload, you:

  • Simplify the refactoring of the event-processing workload by splitting the refactoring of the event-processing workload in two steps:

    1. Refactor the AWS Lambda workload to migrate the event-processing workload to Cloud Run, but keep using Amazon S3 and Amazon SQS as sources, and Amazon DynamoDB to store results.
    2. As the migration proceeds, refactor the event-processing workload to get source data from Cloud Storage and Eventarc, and Amazon S3 and Amazon SQS, and store the results in both Amazon DynamoDB, Firestore and Cloud Storage.
    3. Once you approach the cutover from your AWS environment, refactor the event-processing workload to get source data from Cloud Storage and Eventarc only, and store the results in Firestore and Cloud Storage only.

  • Fully decouple refactoring the producers of source data and refactoring the consumers of event-processing results from the migration of the event-processing workload.

To refactor the event-processing workload, you might need to develop a lightweight data translation layer that receives events from Amazon SQS or EventBridge and forwards them to Cloud Run:

  • If you’re receiving events from Amazon SQS, you might need to implement event translation from Amazon SQS to emit CloudEvents, and event forwarding to Cloud Run.
  • If you’re receiving events from EventBridge, you can directly translate events to CloudEvents, and forward them to Cloud Run.

While the migration is underway, the event-processing workload stores results in Amazon DynamoDB, Firestore, and Cloud Storage. This may ease your migration because you would need to take into account migrating historical data only.

To design the rollback steps when starting with data processing, you can start by rolling back the changes that you implemented to refactor the event-processing workload to store results in AWS and Google Cloud.

Migrate each component of the architecture to Google Cloud

After you design the migration strategy, you focus on designing the details about migrating each component of the architecture to Google Cloud. For more information about migrating individual components from AWS to Google Cloud, see:

Migrate from Amazon DynamoDB to Firestore

To migrate from Amazon DynamoDB to Firestore, you can design and implement an automated process as follows:

  1. Export data from Amazon DynamoDB to Amazon S3.
  2. Migrate the exported data from Amazon S3 to Cloud Storage.
  3. Implement a Dataflow batch job to load results from Cloud Storage to Firestore.

When you design this process, we recommend that you design it as a gradual process that takes into account:

  • Validation and verification, and eventual rollback tasks for each step.
  • A deduplication to avoid processing the same event twice, while the migration is underway.

For more information about how to design your migration to Google Cloud, see Migrate to Google Cloud: Get started.