Work with Dataflow data pipelines (original) (raw)

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Work with Dataflow data pipelines

Overview

You can use Dataflow data pipelines for the following tasks:

For API documentation, see the Data Pipelines reference.

Features

Limitations

Types of data pipelines

Dataflow has two data pipeline types, streaming and batch. Both types of pipeline run jobs that are defined in Dataflowtemplates.

Streaming data pipeline

A streaming data pipeline runs a Dataflow streaming job immediately after it's created.

Batch data pipeline

A batch data pipeline runs a Dataflow batch job on a user-defined schedule. The batch pipeline input filename can be parameterized to allow for incremental batch pipeline processing.

Incremental batch pipelines

You can use datetime placeholders to specify an incremental input file format for a batch pipeline.

At each scheduled batch pipeline execution time, the placeholder portion of the input path is evaluated to the current (ortime-shifted) datetime. Date values are evaluated using the current date in the time zone of the scheduled job. If the evaluated path matches the path of an input file, the file is picked up for processing by the batch pipeline at the scheduled time.

Use time shift parameters

You can use + or - minute or hour time shift parameters. To support matching an input path with an evaluated datetime that is shifted before or after the current datetime of the pipeline schedule, enclose these parameters in curly braces. Use the format {[+|-][0-9]+[m|h]}. The batch pipeline continues to repeat at its scheduled time, but the input path is evaluated with the specified time offset.

Data pipeline roles

For Dataflow data pipeline operations to succeed, you need the necessary IAM roles, as follows:

  1. You need the appropriate role to perform operations:
  2. The service account used by Cloud Scheduler needs to have theroles/iam.serviceAccountUser role, whether the service account is user-specified or the default Compute Engine service account. For more information, see Data pipeline roles.
  3. You need to be able to act as the service account used by Cloud Scheduler and Dataflow by being granted theroles/iam.serviceAccountUserrole on that account. If you don't select a service account for Cloud Scheduler and Dataflow, thedefault Compute Engine service accountis used.

Create a data pipeline

You can create a Dataflow data pipeline in two ways:

  1. Import a job, or
  2. Create a data pipeline

The data pipelines setup page: When you first access the Dataflow pipelines feature in the Google Cloud console, a setup page opens. Enable the listed APIs to create data pipelines.

Import a job

You can import a Dataflow batch or streaming job that is based on aclassic or flex templateand make it a data pipeline.

  1. In the Google Cloud console, go to the Dataflow Jobspage.
    Go to Jobs
  2. Select a completed job, then on the Job Detailspage, select +Import as a pipeline.
  3. On the Create pipeline from template page, the parameters are populated with the options of the imported job.
  4. For a batch job, in the Schedule your pipeline section, provide a recurrence schedule. Providing an email account address for the Cloud Scheduler, which is used to schedule batch runs, is optional. If it's not specified, thedefault Compute Engine service account is used.

Create a data pipeline

  1. In the Google Cloud console, go to the DataflowData pipelines page.
    Go to Data pipelines
  2. Select +Create data pipeline.
  3. On the Create pipeline from template page, provide a pipeline name, and fill in the other template selection and parameter fields.
  4. For a batch job, in the Schedule your pipeline section, provide a recurrence schedule. Providing an email account address for the Cloud Scheduler, which is used to schedule batch runs, is optional. If a value is not specified, thedefault Compute Engine service account is used.

Create a batch data pipeline

To create this sample batch data pipeline, you must have access to the following resources in your project:

This example pipeline uses theCloud Storage Text to BigQuerybatch pipeline template. This template reads files in CSV format from Cloud Storage, runs a transform, then inserts values into a BigQuery table with three columns.

  1. Create the following files on your local drive:
    1. A bq_three_column_table.json file that contains the following schema of the destination BigQuery table.
    {  
  "BigQuery Schema": [  
    {  
      "name": "col1",  
      "type": "STRING"  
    },  
    {  
      "name": "col2",  
      "type": "STRING"  
    },  
    {  
      "name": "col3",  
      "type": "INT64"  
    }  
  ]  
}
    1. A split_csv_3cols.js JavaScript file, which implements a simple transformation on the input data before insertion into BigQuery.
    function transform(line) {  
    var values = line.split(',');  
    var obj = new Object();  
    obj.col1 = values[0];  
    obj.col2 = values[1];  
    obj.col3 = values[2];  
    var jsonString = JSON.stringify(obj);  
    return jsonString;  
}
    1. A file01.csv CSV file with several records that are inserted into the BigQuery table.
    b8e5087a,74,27531  
7a52c051,4a,25846  
672de80f,cd,76981  
111b92bf,2e,104653  
ff658424,f0,149364  
e6c17c75,84,38840  
833f5a69,8f,76892  
d8c833ff,7d,201386  
7d3da7fb,d5,81919  
3836d29b,70,181524  
ca66e6e5,d7,172076  
c8475eb6,03,247282  
558294df,f3,155392  
737b82a8,c7,235523  
82c8f5dc,35,468039  
57ab17f9,5e,480350  
cbcdaf84,bd,354127  
52b55391,eb,423078  
825b8863,62,88160  
26f16d4f,fd,397783
  2. Use the gcloud storage cp command to copy the files to folders in a Cloud Storage bucket in your project, as follows:
    1. Copy bq_three_column_table.json and split_csv_3cols.js togs://BUCKET_ID/text_to_bigquery/
      gcloud storage cp bq_three_column_table.json gs://BUCKET_ID/text_to_bigquery/
      gcloud storage cp split_csv_3cols.js gs://BUCKET_ID/text_to_bigquery/
    2. Copy file01.csv to gs://BUCKET_ID/inputs/
    gcloud storage cp file01.csv gs://BUCKET_ID/inputs/
  3. In the Google Cloud console, go to the Cloud Storage Bucketspage.
    Go to Buckets
  4. To create a tmp folder in your Cloud Storage bucket, select your folder name to open the Bucket details page, then click Create folder.
    Create folder button on the Bucket details page.
  5. In the Google Cloud console, go to the DataflowData pipelines page.
    Go to Data pipelines
  6. Select Create data pipeline. Enter or select the following items on the Create pipeline from template page:
    1. For Pipeline name, enter text_to_bq_batch_data_pipeline.
    2. For Regional endpoint, select a Compute Engine region. The source and destination regions must match. Therefore, your Cloud Storage bucket and BigQuery table must be in the same region.
    3. For Dataflow template, in Process Data in Bulk (batch), selectText Files on Cloud Storage to BigQuery.
    4. For Schedule your pipeline, select a schedule, such as Hourly at minute 25, in your timezone. You can edit the schedule after you submit the pipeline. Providing an email account address for the Cloud Scheduler, which is used to schedule batch runs, is optional. If it's not specified, thedefault Compute Engine service accountis used.
    5. In Required parameters, enter the following:
      1. For JavaScript UDF path in Cloud Storage:
        gs://BUCKET_ID/text_to_bigquery/split_csv_3cols.js
      2. For JSON path:
        BUCKET_ID/text_to_bigquery/bq_three_column_table.json
      3. For JavaScript UDF name: transform
      4. For BigQuery output table:
        PROJECT_ID:DATASET_ID.three_column_table
      5. For Cloud Storage input path:
        BUCKET_ID/inputs/file01.csv
      6. For Temporary BigQuery directory:
        BUCKET_ID/tmp
      7. For Temporary location:
        BUCKET_ID/tmp
    6. Click Create pipeline.
  7. Confirm pipeline and template information and view current and previous history from the Pipeline details page.
    Pipeline details page.

You can edit the data pipeline schedule from the Pipeline info panel on thePipeline details page.

Edit button next to the pipeline schedule.

You can also run a batch pipeline on demand using the Run button in the Dataflow Pipelines console.

Create a sample streaming data pipeline

You can create a sample streaming data pipeline by following thesample batch pipeline instructions, with the following differences:

Troubleshooting

This section shows you how to resolve issues with Dataflow data pipelines.

Data pipeline job fails to launch

When you use data pipelines to create a recurring job schedule, your Dataflow job might not launch, and a 503 status error appears in the Cloud Scheduler log files.

This issue occurs when Dataflow is temporarily unable to run the job.

To work around this issue, configure Cloud Scheduler to retry the job. Because the issue is temporary, when the job is retried, it might succeed. For more information about setting retry values in Cloud Scheduler, seeCreate a job.

Investigate pipeline objectives violations

The following sections describe how to investigate pipelines that don't meet performance objectives.

Recurring batch pipelines

For an initial analysis of the health of your pipeline, on the Pipeline info page in the Google Cloud console, use theIndividual job status and Thread time per step graphs. These graphs are located in the pipeline status panel.

Example investigation:

  1. You have a recurring batch pipeline that runs every hour at 3 minutes past the hour. Each job normally runs for approximately 9 minutes. You have an objective for all jobs to complete in less than 10 minutes.
  2. The job status graph shows that a job ran for more than 10 minutes.
  3. In the Update/Execution history table, find the job that ran during the hour of interest. Click through to the Dataflow job details page. On that page, find the longer running stage, and then look in the logs for possible errors to determine the cause of the delay.

Streaming pipelines

For an initial analysis of the health of your pipeline, on the Pipeline Details page, in the Pipeline info tab, use the data freshness graph. This graph is located in the pipeline status panel.

Example investigation:

  1. You have a streaming pipeline that normally produces an output with adata freshnessof 20 seconds.
  2. You set an objective of having a 30-second data freshness guarantee. When you review the data freshness graph, you notice that between 9 and 10 AM, data freshness jumped to almost 40 seconds.
    Data freshness graph showing an increase in the data freshness number of minutes.
  3. Switch to the Pipeline metrics tab, then view the CPU Utilization and Memory Utilization graphs for further analysis.

Error: Pipeline ID already exists within the project

If you try to create a new pipeline with a name that already exists in your project, you receive this error message: Pipeline Id already exist within the project. To avoid this issue, always choose unique names for your pipelines.

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Last updated 2025-06-12 UTC.