This page describes how to export Cloud Spanner databases with the Google Cloud Console.
To export a Cloud Spanner database using the REST API or the
command-line tool, complete the steps in the
Before you begin section on this page, then see the
detailed instructions in Cloud Spanner to Cloud Storage Avro.
Before you begin
To export a Cloud Spanner database, first you need to enable the Cloud Spanner, Cloud Storage, Compute Engine, and Dataflow APIs:
You also need enough quota and the required IAM permissions.
The quota requirements for export jobs, by Google Cloud service, are as follows:
- Cloud Spanner: No additional compute capacity is required to export a database, though you might need to add more compute capacity so that your job finishes in a reasonable amount of time. See Optimizing jobs for more details.
- Cloud Storage: To export, you must create a bucket for your exported files if you do not already have one. You can do this in the Cloud Console, either through the Cloud Storage page or while creating your export through the Cloud Spanner page. You do not need to set a size for your bucket.
- Dataflow: Export jobs are subject to the same CPU, disk usage, and IP address Compute Engine quotas as other Dataflow jobs.
Compute Engine: Before running your export job, you must set up initial quotas for Compute Engine, which Dataflow uses. These quotas represent the maximum number of resources that you allow Dataflow to use for your job. Recommended starting values are:
- CPUs: 200
- In-use IP addresses: 200
- Standard persistent disk: 50 TB
Generally, you do not have to make any other adjustments. Dataflow provides autoscaling so that you only pay for the actual resources used during the export. If your job can make use of more resources, the Dataflow UI displays a warning icon. The job should finish even if there is a warning icon.
To export a database, you also need to have IAM roles with sufficient permissions to use all of the services involved in an export job. For information on granting roles and permissions, see Applying IAM roles.
To export a database, you need the following roles:
- At the Google Cloud project level:
- Cloud Spanner Viewer
- Dataflow Admin
- Storage Admin
- At the Cloud Spanner database or instance level, or at the
Google Cloud project level:
- Cloud Spanner Reader
- Cloud Spanner Database Admin (required only for import jobs)
Exporting a database
After you satisfy the quota and IAM requirements described above, you can export an existing Cloud Spanner database.
To export your Cloud Spanner database to a Cloud Storage bucket, follow these steps.
Go to the Cloud Spanner Instances page.
Click the name of the instance that contains your database.
Click the Import/Export menu item in the left pane and then click the Export button.
Under Choose where to store your export, click Browse.
If you do not already have a Cloud Storage bucket for your export:
- Click New bucket .
- Enter a name for your bucket. Bucket names must be unique across Cloud Storage.
- Select a default storage class and location, then click Create.
- Click your bucket to select it.
If you already have a bucket, either select the bucket from the initial list or click Search to filter the list, then click your bucket to select it.
Select the database that you want to export in the Choose a database to export drop-down menu.
(Optional) To export your database from an earlier point in time, check the box and enter a timestamp.
Select a region in the Choose a region for the export job drop-down menu.
(Optional) To encrypt the Dataflow pipeline state with a customer-managed encryption key:
- Click Show encryption options.
- Select Use a customer-managed encryption key (CMEK).
- Select your key from the drop-down list.
This option does not affect the destination Cloud Storage bucket-level encryption. To enable CMEK for your Cloud Storage bucket, refer to Using CMEK with Cloud Storage.
Select the checkbox under Confirm charges to acknowledge that there are charges in addition to those incurred by your existing Cloud Spanner instance.
The Cloud Console displays the Database Import/Export page, which now shows a line item for your export job in the import/export jobs list, including the job's elapsed time:
When the job finishes or terminates, the status is updated in the import/export list. If the job succeeded, the status Succeeded is displayed:
If the job failed, the status Succeeded is displayed:
To view the details of the Dataflow operation for your job, click on the job's name in the Dataflow job name column.
If your job fails, check the job's Dataflow logs for error details.
To avoid Cloud Storage charges for files your failed export job created, delete the folder and its files. See Viewing your export for information on how to find the folder.
Viewing your export in Cloud Storage
To view the folder that contains your exported database in the Cloud Console, navigate to the Cloud Storage browser and click on the bucket you previously selected:
The bucket now contains a folder with the exported database inside. The folder name begins with your instance's ID, database name, and the timestamp of your export job. The folder contains:
TableName-manifest.jsonfile for each table in the database you exported.
One or more
TableName.avro-#####-of-#####files. The first number in the extension
.avro-#####-of-#####represents the index of the Avro file, starting at zero, and the second represents the number of Avro files generated for each table.
Songs.avro-00001-of-00002is the second of two files that contain the data for the
Choosing a region for your export job
You might want to choose a different region based on whether your Cloud Spanner instance uses a regional or multi-region configuration. To avoid network egress charges, choose a region that overlaps with your Cloud Spanner instance location.
Regional instance configurations
If your Cloud Spanner instance configuration is regional, choose the same region for your export job to take advantage of free egress within the same region.
If the same region is not available, charges will apply. Refer to the Cloud Spanner network egress pricing to choose a region that will incur the lowest network egress charges.
Multi-region instance configurations
If your Cloud Spanner instance configuration is multi-region, choose one of the regions that make up the multi-region configuration to take advantage of free egress within the same region.
If an overlapping region is not available, egress charges will apply. Refer to the Cloud Spanner network egress pricing to choose a region that will incur the lowest network egress charges.
Viewing or troubleshooting jobs in the Dataflow UI
After you start an export job, you can view details of the job, including logs, in the Dataflow section of the Cloud Console.
Viewing Dataflow job details
To see details for any import/export jobs that you ran within the last week, including any jobs currently running:
- Navigate to the Database overview page for the database.
- Click the Import/Export left pane menu item. The database Import/Export page displays a list of recent jobs.
In the database Import/Export page, click the job name in the Dataflow job name column:
The Cloud Console displays details of the Dataflow job.
To view a job that you ran more than one week ago:
Go to the Dataflow jobs page in the Cloud Console.
Find your job in the list, then click its name.
The Cloud Console displays details of the Dataflow job.
Viewing Dataflow logs for your job
To view a Dataflow job's logs, navigate to the job's details page as described above, then click Logs to the right of the job's name.
If a job fails, look for errors in the logs. If there are errors, the error count displays next to Logs:
To view job errors:
Click on the error count next to Logs.
The Cloud Console displays the job's logs. You may need to scroll to see the errors.
Locate entries with the error icon .
Click on an individual log entry to expand its contents.
For more information about troubleshooting Dataflow jobs, see Troubleshooting your pipeline.
Troubleshooting failed export jobs
If you see the following errors in your job logs:
com.google.cloud.spanner.SpannerException: NOT_FOUND: Session not found --or-- com.google.cloud.spanner.SpannerException: DEADLINE_EXCEEDED: Deadline expired before operation could complete.
Check the 99% Read latency in the Monitoring tab of your Cloud Spanner database in the Cloud Console. If it is showing high (multiple second) values, then it indicates that the instance is overloaded, causing reads to timeout and fail.
One cause of high latency is that the Dataflow job is running using too many workers, putting too much load on the Cloud Spanner instance.To specify a limit on the number of Dataflow workers, instead of using the Import/Export tab in the instance details page of your Cloud Spanner database in the Cloud Console, you must start the export using the Dataflow Cloud Spanner to Cloud Storage Avro template and specify the maximum number of workers as described below:
If you are using the Dataflow console, the Max workers parameter is located in the Optional parameters section of the Create job from template page.
If you are using gcloud, specify the
max-workersargument. For example:
gcloud dataflow jobs run my-export-job \ --gcs-location='gs://dataflow-templates/latest/Cloud_Spanner_to_GCS_Avro' \ --region=us-central1 \ --parameters='instanceId=test-instance,databaseId=example-db,outputDir=gs://my-gcs-bucket' \ --max-workers=10
Optimizing slow running export jobs
If you have followed the suggestions in initial settings, you should generally not have to make any other adjustments. If your job is running slowly, there are a few other optimizations you can try:
Optimize the job and data location: Run your Dataflow job in the same region where your Cloud Spanner instance and Cloud Storage bucket are located.
Ensure sufficient Dataflow resources: If the relevant Compute Engine quotas limit your Dataflow job's resources, the job's Dataflow page in the Google Cloud Console displays a warning icon and log messages:
In this situation, increasing the quotas for CPUs, in-use IP addresses, and standard persistent disk might shorten the run time of the job, but you might incur more Compute Engine charges.
Check the Cloud Spanner CPU utilization: If you see that the CPU utilization for the instance is over 65%, you can increase the compute capacity in that instance. The capacity adds more Cloud Spanner resources and the job should speed up, but you incur more Cloud Spanner charges.
Factors affecting export job performance
Several factors influence the time it takes to complete an export job.
Cloud Spanner database size: Processing more data takes more time and resources.
Cloud Spanner database schema: The number of tables, the size of the rows, the number of secondary indexes and the number of foreign keys influence the time it takes to run an export job.
Data location: Data is transferred between Cloud Spanner and Cloud Storage using Dataflow. Ideally all three components are located in the same region. If the components are not in the same region, moving the data across regions slows the job down.
Number of Dataflow workers: Optimal Dataflow workers are necessary for good performance. By using autoscaling, Dataflow chooses the number of workers for the job depending on the amount of work that needs to be done. The number of workers will, however, be capped by the quotas for CPUs, in-use IP addresses, and standard persistent disk. The Dataflow UI displays a warning icon if it encounters quota caps. In this situation, progress is slower, but the job should still complete.
Existing load on Cloud Spanner: An export job typically adds a light load on a Cloud Spanner instance. If the instance already has a substantial existing load, then the job runs more slowly.
Amount of Cloud Spanner compute capacity: If the CPU utilization for the instance is over 65%, then the job runs more slowly.