google-native.dataflow/v1b3.Job

The client's unique identifier of the job, re-used across retried attempts. If this field is set, the service will ensure its uniqueness. The request to create a job will fail if the service has knowledge of a previously submitted job with the same client's ID and job name. The caller may use this field to ensure idempotence of job creation across retried attempts to create a job. By default, the field is empty and, in that case, the service ignores it.

The timestamp when the job was initially created. Immutable and set by the Cloud Dataflow service.

If this is specified, the job's initial state is populated from the given snapshot.

The current state of the job. Jobs are created in the JOB_STATE_STOPPED state unless otherwise specified. A job in the JOB_STATE_RUNNING state may asynchronously enter a terminal state. After a job has reached a terminal state, no further state updates may be made. This field may be mutated by the Cloud Dataflow service; callers cannot mutate it.

The timestamp associated with the current state.

The environment for the job.

Deprecated.

Deprecated:

Deprecated.

The unique ID of this job. This field is set by the Cloud Dataflow service when the Job is created, and is immutable for the life of the job.

This field is populated by the Dataflow service to support filtering jobs by the metadata values provided here. Populated for ListJobs and all GetJob views SUMMARY and higher.

User-defined labels for this job. The labels map can contain no more than 64 entries. Entries of the labels map are UTF8 strings that comply with the following restrictions: * Keys must conform to regexp: \p{Ll}\p{Lo}{0,62} * Values must conform to regexp: [\p{Ll}\p{Lo}\p{N}_-]{0,63} * Both keys and values are additionally constrained to be <= 128 bytes in size.

The [regional endpoint] (https://cloud.google.com/dataflow/docs/concepts/regional-endpoints) that contains this job.

The user-specified Cloud Dataflow job name. Only one Job with a given name can exist in a project within one region at any given time. Jobs in different regions can have the same name. If a caller attempts to create a Job with the same name as an already-existing Job, the attempt returns the existing Job. The name must match the regular expression [a-z]([-a-z0-9]{0,1022}[a-z0-9])?

Preliminary field: The format of this data may change at any time. A description of the user pipeline and stages through which it is executed. Created by Cloud Dataflow service. Only retrieved with JOB_VIEW_DESCRIPTION or JOB_VIEW_ALL.

The ID of the Cloud Platform project that the job belongs to.

If this job is an update of an existing job, this field is the job ID of the job it replaced. When sending a CreateJobRequest, you can update a job by specifying it here. The job named here is stopped, and its intermediate state is transferred to this job.

If another job is an update of this job (and thus, this job is in JOB_STATE_UPDATED), this field contains the ID of that job.

The job's requested state. UpdateJob may be used to switch between the JOB_STATE_STOPPED and JOB_STATE_RUNNING states, by setting requested_state. UpdateJob may also be used to directly set a job's requested state to JOB_STATE_CANCELLED or JOB_STATE_DONE, irrevocably terminating the job if it has not already reached a terminal state.

This field may ONLY be modified at runtime using the projects.jobs.update method to adjust job behavior. This field has no effect when specified at job creation.

Reserved for future use. This field is set only in responses from the server; it is ignored if it is set in any requests.

This field may be mutated by the Cloud Dataflow service; callers cannot mutate it.

The timestamp when the job was started (transitioned to JOB_STATE_PENDING). Flexible resource scheduling jobs are started with some delay after job creation, so start_time is unset before start and is updated when the job is started by the Cloud Dataflow service. For other jobs, start_time always equals to create_time and is immutable and set by the Cloud Dataflow service.

Exactly one of step or steps_location should be specified. The top-level steps that constitute the entire job. Only retrieved with JOB_VIEW_ALL.

The Cloud Storage location where the steps are stored.

A set of files the system should be aware of that are used for temporary storage. These temporary files will be removed on job completion. No duplicates are allowed. No file patterns are supported. The supported files are: Google Cloud Storage: storage.googleapis.com/{bucket}/{object} bucket.storage.googleapis.com/{object}

The map of transform name prefixes of the job to be replaced to the corresponding name prefixes of the new job.

The type of Cloud Dataflow job.

The level of information requested in response.

The client's unique identifier of the job, re-used across retried attempts. If this field is set, the service will ensure its uniqueness. The request to create a job will fail if the service has knowledge of a previously submitted job with the same client's ID and job name. The caller may use this field to ensure idempotence of job creation across retried attempts to create a job. By default, the field is empty and, in that case, the service ignores it.

The timestamp when the job was initially created. Immutable and set by the Cloud Dataflow service.

If this is specified, the job's initial state is populated from the given snapshot.

The current state of the job. Jobs are created in the JOB_STATE_STOPPED state unless otherwise specified. A job in the JOB_STATE_RUNNING state may asynchronously enter a terminal state. After a job has reached a terminal state, no further state updates may be made. This field may be mutated by the Cloud Dataflow service; callers cannot mutate it.

The timestamp associated with the current state.

The environment for the job.

Deprecated.

Deprecated:

Deprecated.

The unique ID of this job. This field is set by the Cloud Dataflow service when the Job is created, and is immutable for the life of the job.

This field is populated by the Dataflow service to support filtering jobs by the metadata values provided here. Populated for ListJobs and all GetJob views SUMMARY and higher.

User-defined labels for this job. The labels map can contain no more than 64 entries. Entries of the labels map are UTF8 strings that comply with the following restrictions: * Keys must conform to regexp: \p{Ll}\p{Lo}{0,62} * Values must conform to regexp: [\p{Ll}\p{Lo}\p{N}_-]{0,63} * Both keys and values are additionally constrained to be <= 128 bytes in size.

The [regional endpoint] (https://cloud.google.com/dataflow/docs/concepts/regional-endpoints) that contains this job.

The user-specified Cloud Dataflow job name. Only one Job with a given name can exist in a project within one region at any given time. Jobs in different regions can have the same name. If a caller attempts to create a Job with the same name as an already-existing Job, the attempt returns the existing Job. The name must match the regular expression [a-z]([-a-z0-9]{0,1022}[a-z0-9])?

Preliminary field: The format of this data may change at any time. A description of the user pipeline and stages through which it is executed. Created by Cloud Dataflow service. Only retrieved with JOB_VIEW_DESCRIPTION or JOB_VIEW_ALL.

The ID of the Cloud Platform project that the job belongs to.

If this job is an update of an existing job, this field is the job ID of the job it replaced. When sending a CreateJobRequest, you can update a job by specifying it here. The job named here is stopped, and its intermediate state is transferred to this job.

If another job is an update of this job (and thus, this job is in JOB_STATE_UPDATED), this field contains the ID of that job.

The job's requested state. UpdateJob may be used to switch between the JOB_STATE_STOPPED and JOB_STATE_RUNNING states, by setting requested_state. UpdateJob may also be used to directly set a job's requested state to JOB_STATE_CANCELLED or JOB_STATE_DONE, irrevocably terminating the job if it has not already reached a terminal state.

This field may ONLY be modified at runtime using the projects.jobs.update method to adjust job behavior. This field has no effect when specified at job creation.

Reserved for future use. This field is set only in responses from the server; it is ignored if it is set in any requests.

This field may be mutated by the Cloud Dataflow service; callers cannot mutate it.

The timestamp when the job was started (transitioned to JOB_STATE_PENDING). Flexible resource scheduling jobs are started with some delay after job creation, so start_time is unset before start and is updated when the job is started by the Cloud Dataflow service. For other jobs, start_time always equals to create_time and is immutable and set by the Cloud Dataflow service.

Exactly one of step or steps_location should be specified. The top-level steps that constitute the entire job. Only retrieved with JOB_VIEW_ALL.

The Cloud Storage location where the steps are stored.

A set of files the system should be aware of that are used for temporary storage. These temporary files will be removed on job completion. No duplicates are allowed. No file patterns are supported. The supported files are: Google Cloud Storage: storage.googleapis.com/{bucket}/{object} bucket.storage.googleapis.com/{object}

The map of transform name prefixes of the job to be replaced to the corresponding name prefixes of the new job.

The type of Cloud Dataflow job.

The level of information requested in response.

The client's unique identifier of the job, re-used across retried attempts. If this field is set, the service will ensure its uniqueness. The request to create a job will fail if the service has knowledge of a previously submitted job with the same client's ID and job name. The caller may use this field to ensure idempotence of job creation across retried attempts to create a job. By default, the field is empty and, in that case, the service ignores it.

The timestamp when the job was initially created. Immutable and set by the Cloud Dataflow service.

If this is specified, the job's initial state is populated from the given snapshot.

The current state of the job. Jobs are created in the JOB_STATE_STOPPED state unless otherwise specified. A job in the JOB_STATE_RUNNING state may asynchronously enter a terminal state. After a job has reached a terminal state, no further state updates may be made. This field may be mutated by the Cloud Dataflow service; callers cannot mutate it.

The timestamp associated with the current state.

The environment for the job.

Deprecated.

Deprecated:

Deprecated.

The unique ID of this job. This field is set by the Cloud Dataflow service when the Job is created, and is immutable for the life of the job.

This field is populated by the Dataflow service to support filtering jobs by the metadata values provided here. Populated for ListJobs and all GetJob views SUMMARY and higher.

User-defined labels for this job. The labels map can contain no more than 64 entries. Entries of the labels map are UTF8 strings that comply with the following restrictions: * Keys must conform to regexp: \p{Ll}\p{Lo}{0,62} * Values must conform to regexp: [\p{Ll}\p{Lo}\p{N}_-]{0,63} * Both keys and values are additionally constrained to be <= 128 bytes in size.

The [regional endpoint] (https://cloud.google.com/dataflow/docs/concepts/regional-endpoints) that contains this job.

The user-specified Cloud Dataflow job name. Only one Job with a given name can exist in a project within one region at any given time. Jobs in different regions can have the same name. If a caller attempts to create a Job with the same name as an already-existing Job, the attempt returns the existing Job. The name must match the regular expression [a-z]([-a-z0-9]{0,1022}[a-z0-9])?

Preliminary field: The format of this data may change at any time. A description of the user pipeline and stages through which it is executed. Created by Cloud Dataflow service. Only retrieved with JOB_VIEW_DESCRIPTION or JOB_VIEW_ALL.

The ID of the Cloud Platform project that the job belongs to.

If this job is an update of an existing job, this field is the job ID of the job it replaced. When sending a CreateJobRequest, you can update a job by specifying it here. The job named here is stopped, and its intermediate state is transferred to this job.

If another job is an update of this job (and thus, this job is in JOB_STATE_UPDATED), this field contains the ID of that job.

The job's requested state. UpdateJob may be used to switch between the JOB_STATE_STOPPED and JOB_STATE_RUNNING states, by setting requested_state. UpdateJob may also be used to directly set a job's requested state to JOB_STATE_CANCELLED or JOB_STATE_DONE, irrevocably terminating the job if it has not already reached a terminal state.

This field may ONLY be modified at runtime using the projects.jobs.update method to adjust job behavior. This field has no effect when specified at job creation.

Reserved for future use. This field is set only in responses from the server; it is ignored if it is set in any requests.

This field may be mutated by the Cloud Dataflow service; callers cannot mutate it.

The timestamp when the job was started (transitioned to JOB_STATE_PENDING). Flexible resource scheduling jobs are started with some delay after job creation, so start_time is unset before start and is updated when the job is started by the Cloud Dataflow service. For other jobs, start_time always equals to create_time and is immutable and set by the Cloud Dataflow service.

Exactly one of step or steps_location should be specified. The top-level steps that constitute the entire job. Only retrieved with JOB_VIEW_ALL.

The Cloud Storage location where the steps are stored.

A set of files the system should be aware of that are used for temporary storage. These temporary files will be removed on job completion. No duplicates are allowed. No file patterns are supported. The supported files are: Google Cloud Storage: storage.googleapis.com/{bucket}/{object} bucket.storage.googleapis.com/{object}

The map of transform name prefixes of the job to be replaced to the corresponding name prefixes of the new job.

The type of Cloud Dataflow job.

The level of information requested in response.

The client's unique identifier of the job, re-used across retried attempts. If this field is set, the service will ensure its uniqueness. The request to create a job will fail if the service has knowledge of a previously submitted job with the same client's ID and job name. The caller may use this field to ensure idempotence of job creation across retried attempts to create a job. By default, the field is empty and, in that case, the service ignores it.

The timestamp when the job was initially created. Immutable and set by the Cloud Dataflow service.

If this is specified, the job's initial state is populated from the given snapshot.

The current state of the job. Jobs are created in the JOB_STATE_STOPPED state unless otherwise specified. A job in the JOB_STATE_RUNNING state may asynchronously enter a terminal state. After a job has reached a terminal state, no further state updates may be made. This field may be mutated by the Cloud Dataflow service; callers cannot mutate it.

The timestamp associated with the current state.

The environment for the job.

Deprecated.

Deprecated:

Deprecated.

The unique ID of this job. This field is set by the Cloud Dataflow service when the Job is created, and is immutable for the life of the job.

This field is populated by the Dataflow service to support filtering jobs by the metadata values provided here. Populated for ListJobs and all GetJob views SUMMARY and higher.

User-defined labels for this job. The labels map can contain no more than 64 entries. Entries of the labels map are UTF8 strings that comply with the following restrictions: * Keys must conform to regexp: \p{Ll}\p{Lo}{0,62} * Values must conform to regexp: [\p{Ll}\p{Lo}\p{N}_-]{0,63} * Both keys and values are additionally constrained to be <= 128 bytes in size.

The [regional endpoint] (https://cloud.google.com/dataflow/docs/concepts/regional-endpoints) that contains this job.

The user-specified Cloud Dataflow job name. Only one Job with a given name can exist in a project within one region at any given time. Jobs in different regions can have the same name. If a caller attempts to create a Job with the same name as an already-existing Job, the attempt returns the existing Job. The name must match the regular expression [a-z]([-a-z0-9]{0,1022}[a-z0-9])?

Preliminary field: The format of this data may change at any time. A description of the user pipeline and stages through which it is executed. Created by Cloud Dataflow service. Only retrieved with JOB_VIEW_DESCRIPTION or JOB_VIEW_ALL.

The ID of the Cloud Platform project that the job belongs to.

If this job is an update of an existing job, this field is the job ID of the job it replaced. When sending a CreateJobRequest, you can update a job by specifying it here. The job named here is stopped, and its intermediate state is transferred to this job.

If another job is an update of this job (and thus, this job is in JOB_STATE_UPDATED), this field contains the ID of that job.

The job's requested state. UpdateJob may be used to switch between the JOB_STATE_STOPPED and JOB_STATE_RUNNING states, by setting requested_state. UpdateJob may also be used to directly set a job's requested state to JOB_STATE_CANCELLED or JOB_STATE_DONE, irrevocably terminating the job if it has not already reached a terminal state.

This field may ONLY be modified at runtime using the projects.jobs.update method to adjust job behavior. This field has no effect when specified at job creation.

Reserved for future use. This field is set only in responses from the server; it is ignored if it is set in any requests.

This field may be mutated by the Cloud Dataflow service; callers cannot mutate it.

The timestamp when the job was started (transitioned to JOB_STATE_PENDING). Flexible resource scheduling jobs are started with some delay after job creation, so start_time is unset before start and is updated when the job is started by the Cloud Dataflow service. For other jobs, start_time always equals to create_time and is immutable and set by the Cloud Dataflow service.

Exactly one of step or steps_location should be specified. The top-level steps that constitute the entire job. Only retrieved with JOB_VIEW_ALL.

The Cloud Storage location where the steps are stored.

A set of files the system should be aware of that are used for temporary storage. These temporary files will be removed on job completion. No duplicates are allowed. No file patterns are supported. The supported files are: Google Cloud Storage: storage.googleapis.com/{bucket}/{object} bucket.storage.googleapis.com/{object}

The map of transform name prefixes of the job to be replaced to the corresponding name prefixes of the new job.

The type of Cloud Dataflow job.

The level of information requested in response.

The client's unique identifier of the job, re-used across retried attempts. If this field is set, the service will ensure its uniqueness. The request to create a job will fail if the service has knowledge of a previously submitted job with the same client's ID and job name. The caller may use this field to ensure idempotence of job creation across retried attempts to create a job. By default, the field is empty and, in that case, the service ignores it.

The timestamp when the job was initially created. Immutable and set by the Cloud Dataflow service.

If this is specified, the job's initial state is populated from the given snapshot.

The current state of the job. Jobs are created in the JOB_STATE_STOPPED state unless otherwise specified. A job in the JOB_STATE_RUNNING state may asynchronously enter a terminal state. After a job has reached a terminal state, no further state updates may be made. This field may be mutated by the Cloud Dataflow service; callers cannot mutate it.

The timestamp associated with the current state.

The environment for the job.

Deprecated.

Deprecated:

Deprecated.

The unique ID of this job. This field is set by the Cloud Dataflow service when the Job is created, and is immutable for the life of the job.

This field is populated by the Dataflow service to support filtering jobs by the metadata values provided here. Populated for ListJobs and all GetJob views SUMMARY and higher.

User-defined labels for this job. The labels map can contain no more than 64 entries. Entries of the labels map are UTF8 strings that comply with the following restrictions: * Keys must conform to regexp: \p{Ll}\p{Lo}{0,62} * Values must conform to regexp: [\p{Ll}\p{Lo}\p{N}_-]{0,63} * Both keys and values are additionally constrained to be <= 128 bytes in size.

The [regional endpoint] (https://cloud.google.com/dataflow/docs/concepts/regional-endpoints) that contains this job.

The user-specified Cloud Dataflow job name. Only one Job with a given name can exist in a project within one region at any given time. Jobs in different regions can have the same name. If a caller attempts to create a Job with the same name as an already-existing Job, the attempt returns the existing Job. The name must match the regular expression [a-z]([-a-z0-9]{0,1022}[a-z0-9])?

Preliminary field: The format of this data may change at any time. A description of the user pipeline and stages through which it is executed. Created by Cloud Dataflow service. Only retrieved with JOB_VIEW_DESCRIPTION or JOB_VIEW_ALL.

The ID of the Cloud Platform project that the job belongs to.

If this job is an update of an existing job, this field is the job ID of the job it replaced. When sending a CreateJobRequest, you can update a job by specifying it here. The job named here is stopped, and its intermediate state is transferred to this job.

If another job is an update of this job (and thus, this job is in JOB_STATE_UPDATED), this field contains the ID of that job.

The job's requested state. UpdateJob may be used to switch between the JOB_STATE_STOPPED and JOB_STATE_RUNNING states, by setting requested_state. UpdateJob may also be used to directly set a job's requested state to JOB_STATE_CANCELLED or JOB_STATE_DONE, irrevocably terminating the job if it has not already reached a terminal state.

This field may ONLY be modified at runtime using the projects.jobs.update method to adjust job behavior. This field has no effect when specified at job creation.

Reserved for future use. This field is set only in responses from the server; it is ignored if it is set in any requests.

This field may be mutated by the Cloud Dataflow service; callers cannot mutate it.

The timestamp when the job was started (transitioned to JOB_STATE_PENDING). Flexible resource scheduling jobs are started with some delay after job creation, so start_time is unset before start and is updated when the job is started by the Cloud Dataflow service. For other jobs, start_time always equals to create_time and is immutable and set by the Cloud Dataflow service.

Exactly one of step or steps_location should be specified. The top-level steps that constitute the entire job. Only retrieved with JOB_VIEW_ALL.

The Cloud Storage location where the steps are stored.

A set of files the system should be aware of that are used for temporary storage. These temporary files will be removed on job completion. No duplicates are allowed. No file patterns are supported. The supported files are: Google Cloud Storage: storage.googleapis.com/{bucket}/{object} bucket.storage.googleapis.com/{object}

The map of transform name prefixes of the job to be replaced to the corresponding name prefixes of the new job.

The type of Cloud Dataflow job.

The level of information requested in response.

The client's unique identifier of the job, re-used across retried attempts. If this field is set, the service will ensure its uniqueness. The request to create a job will fail if the service has knowledge of a previously submitted job with the same client's ID and job name. The caller may use this field to ensure idempotence of job creation across retried attempts to create a job. By default, the field is empty and, in that case, the service ignores it.

The timestamp when the job was initially created. Immutable and set by the Cloud Dataflow service.

If this is specified, the job's initial state is populated from the given snapshot.

The current state of the job. Jobs are created in the JOB_STATE_STOPPED state unless otherwise specified. A job in the JOB_STATE_RUNNING state may asynchronously enter a terminal state. After a job has reached a terminal state, no further state updates may be made. This field may be mutated by the Cloud Dataflow service; callers cannot mutate it.

The timestamp associated with the current state.

The environment for the job.

Deprecated.

Deprecated:

Deprecated.

The unique ID of this job. This field is set by the Cloud Dataflow service when the Job is created, and is immutable for the life of the job.

This field is populated by the Dataflow service to support filtering jobs by the metadata values provided here. Populated for ListJobs and all GetJob views SUMMARY and higher.

User-defined labels for this job. The labels map can contain no more than 64 entries. Entries of the labels map are UTF8 strings that comply with the following restrictions: * Keys must conform to regexp: \p{Ll}\p{Lo}{0,62} * Values must conform to regexp: [\p{Ll}\p{Lo}\p{N}_-]{0,63} * Both keys and values are additionally constrained to be <= 128 bytes in size.

The [regional endpoint] (https://cloud.google.com/dataflow/docs/concepts/regional-endpoints) that contains this job.

The user-specified Cloud Dataflow job name. Only one Job with a given name can exist in a project within one region at any given time. Jobs in different regions can have the same name. If a caller attempts to create a Job with the same name as an already-existing Job, the attempt returns the existing Job. The name must match the regular expression [a-z]([-a-z0-9]{0,1022}[a-z0-9])?

Preliminary field: The format of this data may change at any time. A description of the user pipeline and stages through which it is executed. Created by Cloud Dataflow service. Only retrieved with JOB_VIEW_DESCRIPTION or JOB_VIEW_ALL.

The ID of the Cloud Platform project that the job belongs to.

If this job is an update of an existing job, this field is the job ID of the job it replaced. When sending a CreateJobRequest, you can update a job by specifying it here. The job named here is stopped, and its intermediate state is transferred to this job.

If another job is an update of this job (and thus, this job is in JOB_STATE_UPDATED), this field contains the ID of that job.

The job's requested state. UpdateJob may be used to switch between the JOB_STATE_STOPPED and JOB_STATE_RUNNING states, by setting requested_state. UpdateJob may also be used to directly set a job's requested state to JOB_STATE_CANCELLED or JOB_STATE_DONE, irrevocably terminating the job if it has not already reached a terminal state.

This field may ONLY be modified at runtime using the projects.jobs.update method to adjust job behavior. This field has no effect when specified at job creation.

Reserved for future use. This field is set only in responses from the server; it is ignored if it is set in any requests.

This field may be mutated by the Cloud Dataflow service; callers cannot mutate it.

The timestamp when the job was started (transitioned to JOB_STATE_PENDING). Flexible resource scheduling jobs are started with some delay after job creation, so start_time is unset before start and is updated when the job is started by the Cloud Dataflow service. For other jobs, start_time always equals to create_time and is immutable and set by the Cloud Dataflow service.

Exactly one of step or steps_location should be specified. The top-level steps that constitute the entire job. Only retrieved with JOB_VIEW_ALL.

The Cloud Storage location where the steps are stored.

A set of files the system should be aware of that are used for temporary storage. These temporary files will be removed on job completion. No duplicates are allowed. No file patterns are supported. The supported files are: Google Cloud Storage: storage.googleapis.com/{bucket}/{object} bucket.storage.googleapis.com/{object}

The map of transform name prefixes of the job to be replaced to the corresponding name prefixes of the new job.

The type of Cloud Dataflow job.

The level of information requested in response.

The provider-assigned unique ID for this managed resource.

The provider-assigned unique ID for this managed resource.

The provider-assigned unique ID for this managed resource.

The provider-assigned unique ID for this managed resource.

The provider-assigned unique ID for this managed resource.

The provider-assigned unique ID for this managed resource.

The algorithm to use for autoscaling.

The maximum number of workers to cap scaling at.

The algorithm to use for autoscaling.

The maximum number of workers to cap scaling at.

The algorithm to use for autoscaling.

The maximum number of workers to cap scaling at.

The algorithm to use for autoscaling.

The maximum number of workers to cap scaling at.

The algorithm to use for autoscaling.

The maximum number of workers to cap scaling at.

The algorithm to use for autoscaling.

The maximum number of workers to cap scaling at.

The algorithm to use for autoscaling.

The maximum number of workers to cap scaling at.

The algorithm to use for autoscaling.

The maximum number of workers to cap scaling at.

The algorithm to use for autoscaling.

The maximum number of workers to cap scaling at.

The algorithm to use for autoscaling.

The maximum number of workers to cap scaling at.

The algorithm to use for autoscaling.

The maximum number of workers to cap scaling at.

The algorithm to use for autoscaling.

The maximum number of workers to cap scaling at.

Dataset accessed in the connection.

Project accessed in the connection.

Query used to access data in the connection.

Table accessed in the connection.

Dataset accessed in the connection.

Project accessed in the connection.

Query used to access data in the connection.

Table accessed in the connection.

Dataset accessed in the connection.

Project accessed in the connection.

Query used to access data in the connection.

Table accessed in the connection.

Dataset accessed in the connection.

Project accessed in the connection.

Query used to access data in the connection.

Table accessed in the connection.

Dataset accessed in the connection.

Project accessed in the connection.

Query used to access data in the connection.

Table accessed in the connection.

Dataset accessed in the connection.

Project accessed in the connection.

Query used to access data in the connection.

Table accessed in the connection.

Dataset accessed in the connection.

Project accessed in the connection.

Query used to access data in the connection.

Table accessed in the connection.

Dataset accessed in the connection.

Project accessed in the connection.

Query used to access data in the connection.

Table accessed in the connection.

Dataset accessed in the connection.

Project accessed in the connection.

Query used to access data in the connection.

Table accessed in the connection.

Dataset accessed in the connection.

Project accessed in the connection.

Query used to access data in the connection.

Table accessed in the connection.

Dataset accessed in the connection.

Project accessed in the connection.

Query used to access data in the connection.

Table accessed in the connection.

Dataset accessed in the connection.

Project accessed in the connection.

Query used to access data in the connection.

Table accessed in the connection.

InstanceId accessed in the connection.

ProjectId accessed in the connection.

TableId accessed in the connection.

InstanceId accessed in the connection.

ProjectId accessed in the connection.

TableId accessed in the connection.

InstanceId accessed in the connection.

ProjectId accessed in the connection.

TableId accessed in the connection.

InstanceId accessed in the connection.

ProjectId accessed in the connection.

TableId accessed in the connection.

InstanceId accessed in the connection.

ProjectId accessed in the connection.

TableId accessed in the connection.

InstanceId accessed in the connection.

ProjectId accessed in the connection.

TableId accessed in the connection.

InstanceId accessed in the connection.

ProjectId accessed in the connection.

TableId accessed in the connection.

InstanceId accessed in the connection.

ProjectId accessed in the connection.

TableId accessed in the connection.

InstanceId accessed in the connection.

ProjectId accessed in the connection.

TableId accessed in the connection.

InstanceId accessed in the connection.

ProjectId accessed in the connection.

TableId accessed in the connection.

InstanceId accessed in the connection.

ProjectId accessed in the connection.

TableId accessed in the connection.

InstanceId accessed in the connection.

ProjectId accessed in the connection.

TableId accessed in the connection.

Dataflow service generated name for this source.

User name for the original user transform or collection with which this source is most closely associated.

Human-readable name for this transform; may be user or system generated.

Dataflow service generated name for this source.

User name for the original user transform or collection with which this source is most closely associated.

Human-readable name for this transform; may be user or system generated.

Dataflow service generated name for this source.

User name for the original user transform or collection with which this source is most closely associated.

Human-readable name for this transform; may be user or system generated.

Dataflow service generated name for this source.

User name for the original user transform or collection with which this source is most closely associated.

Human-readable name for this transform; may be user or system generated.

Dataflow service generated name for this source.

User name for the original user transform or collection with which this source is most closely associated.

Human-readable name for this transform; may be user or system generated.

Dataflow service generated name for this source.

User name for the original user transform or collection with which this source is most closely associated.

Human-readable name for this transform; may be user or system generated.

Dataflow service generated name for this source.

User name for the original user transform or collection with which this source is most closely associated.

Human-readable name for this transform; may be user or system generated.

Dataflow service generated name for this source.

User name for the original user transform or collection with which this source is most closely associated.

Human-readable name for this transform; may be user or system generated.

Dataflow service generated name for this source.

User name for the original user transform or collection with which this source is most closely associated.

Human-readable name for this transform; may be user or system generated.

Dataflow service generated name for this source.

User name for the original user transform or collection with which this source is most closely associated.

Human-readable name for this transform; may be user or system generated.

Dataflow service generated name for this source.

User name for the original user transform or collection with which this source is most closely associated.

Human-readable name for this transform; may be user or system generated.

Dataflow service generated name for this source.

User name for the original user transform or collection with which this source is most closely associated.

Human-readable name for this transform; may be user or system generated.

Dataflow service generated name for this source.

User name for the original user transform with which this transform is most closely associated.

Human-readable name for this transform; may be user or system generated.

Dataflow service generated name for this source.

User name for the original user transform with which this transform is most closely associated.

Human-readable name for this transform; may be user or system generated.

Dataflow service generated name for this source.

User name for the original user transform with which this transform is most closely associated.

Human-readable name for this transform; may be user or system generated.

Dataflow service generated name for this source.

User name for the original user transform with which this transform is most closely associated.

Human-readable name for this transform; may be user or system generated.

Dataflow service generated name for this source.

User name for the original user transform with which this transform is most closely associated.

Human-readable name for this transform; may be user or system generated.

Dataflow service generated name for this source.

User name for the original user transform with which this transform is most closely associated.

Human-readable name for this transform; may be user or system generated.

Dataflow service generated name for this source.

User name for the original user transform with which this transform is most closely associated.

Human-readable name for this transform; may be user or system generated.

Dataflow service generated name for this source.

User name for the original user transform with which this transform is most closely associated.

Human-readable name for this transform; may be user or system generated.

Dataflow service generated name for this source.

User name for the original user transform with which this transform is most closely associated.

Human-readable name for this transform; may be user or system generated.

Dataflow service generated name for this source.

User name for the original user transform with which this transform is most closely associated.

Human-readable name for this transform; may be user or system generated.

Dataflow service generated name for this source.

User name for the original user transform with which this transform is most closely associated.

Human-readable name for this transform; may be user or system generated.

Dataflow service generated name for this source.

User name for the original user transform with which this transform is most closely associated.

Human-readable name for this transform; may be user or system generated.

Namespace used in the connection.

ProjectId accessed in the connection.

Namespace used in the connection.

ProjectId accessed in the connection.

Namespace used in the connection.

ProjectId accessed in the connection.

Namespace used in the connection.

ProjectId accessed in the connection.

Namespace used in the connection.

ProjectId accessed in the connection.

Namespace used in the connection.

ProjectId accessed in the connection.

Namespace used in the connection.

ProjectId accessed in the connection.

Namespace used in the connection.

ProjectId accessed in the connection.

Namespace used in the connection.

ProjectId accessed in the connection.

Namespace used in the connection.

ProjectId accessed in the connection.

Namespace used in the connection.

ProjectId accessed in the connection.

Namespace used in the connection.

ProjectId accessed in the connection.

When true, enables the logging of the literal hot key to the user's Cloud Logging.

When true, enables the logging of the literal hot key to the user's Cloud Logging.

When true, enables the logging of the literal hot key to the user's Cloud Logging.

When true, enables the logging of the literal hot key to the user's Cloud Logging.

When true, enables the logging of the literal hot key to the user's Cloud Logging.

When true, enables the logging of the literal hot key to the user's Cloud Logging.

When true, enables the logging of the literal hot key to the user's Cloud Logging.

When true, enables the logging of the literal hot key to the user's Cloud Logging.

When true, enables the logging of the literal hot key to the user's Cloud Logging.

When true, enables the logging of the literal hot key to the user's Cloud Logging.

When true, enables the logging of the literal hot key to the user's Cloud Logging.

When true, enables the logging of the literal hot key to the user's Cloud Logging.

Disk storage type, as defined by Google Compute Engine. This must be a disk type appropriate to the project and zone in which the workers will run. If unknown or unspecified, the service will attempt to choose a reasonable default. For example, the standard persistent disk type is a resource name typically ending in "pd-standard". If SSD persistent disks are available, the resource name typically ends with "pd-ssd". The actual valid values are defined the Google Compute Engine API, not by the Cloud Dataflow API; consult the Google Compute Engine documentation for more information about determining the set of available disk types for a particular project and zone. Google Compute Engine Disk types are local to a particular project in a particular zone, and so the resource name will typically look something like this: compute.googleapis.com/projects/project-id/zones/zone/diskTypes/pd-standard

Directory in a VM where disk is mounted.

Size of disk in GB. If zero or unspecified, the service will attempt to choose a reasonable default.

Disk storage type, as defined by Google Compute Engine. This must be a disk type appropriate to the project and zone in which the workers will run. If unknown or unspecified, the service will attempt to choose a reasonable default. For example, the standard persistent disk type is a resource name typically ending in "pd-standard". If SSD persistent disks are available, the resource name typically ends with "pd-ssd". The actual valid values are defined the Google Compute Engine API, not by the Cloud Dataflow API; consult the Google Compute Engine documentation for more information about determining the set of available disk types for a particular project and zone. Google Compute Engine Disk types are local to a particular project in a particular zone, and so the resource name will typically look something like this: compute.googleapis.com/projects/project-id/zones/zone/diskTypes/pd-standard

Directory in a VM where disk is mounted.

Size of disk in GB. If zero or unspecified, the service will attempt to choose a reasonable default.

Disk storage type, as defined by Google Compute Engine. This must be a disk type appropriate to the project and zone in which the workers will run. If unknown or unspecified, the service will attempt to choose a reasonable default. For example, the standard persistent disk type is a resource name typically ending in "pd-standard". If SSD persistent disks are available, the resource name typically ends with "pd-ssd". The actual valid values are defined the Google Compute Engine API, not by the Cloud Dataflow API; consult the Google Compute Engine documentation for more information about determining the set of available disk types for a particular project and zone. Google Compute Engine Disk types are local to a particular project in a particular zone, and so the resource name will typically look something like this: compute.googleapis.com/projects/project-id/zones/zone/diskTypes/pd-standard

Directory in a VM where disk is mounted.

Size of disk in GB. If zero or unspecified, the service will attempt to choose a reasonable default.

Disk storage type, as defined by Google Compute Engine. This must be a disk type appropriate to the project and zone in which the workers will run. If unknown or unspecified, the service will attempt to choose a reasonable default. For example, the standard persistent disk type is a resource name typically ending in "pd-standard". If SSD persistent disks are available, the resource name typically ends with "pd-ssd". The actual valid values are defined the Google Compute Engine API, not by the Cloud Dataflow API; consult the Google Compute Engine documentation for more information about determining the set of available disk types for a particular project and zone. Google Compute Engine Disk types are local to a particular project in a particular zone, and so the resource name will typically look something like this: compute.googleapis.com/projects/project-id/zones/zone/diskTypes/pd-standard

Directory in a VM where disk is mounted.

Size of disk in GB. If zero or unspecified, the service will attempt to choose a reasonable default.

Disk storage type, as defined by Google Compute Engine. This must be a disk type appropriate to the project and zone in which the workers will run. If unknown or unspecified, the service will attempt to choose a reasonable default. For example, the standard persistent disk type is a resource name typically ending in "pd-standard". If SSD persistent disks are available, the resource name typically ends with "pd-ssd". The actual valid values are defined the Google Compute Engine API, not by the Cloud Dataflow API; consult the Google Compute Engine documentation for more information about determining the set of available disk types for a particular project and zone. Google Compute Engine Disk types are local to a particular project in a particular zone, and so the resource name will typically look something like this: compute.googleapis.com/projects/project-id/zones/zone/diskTypes/pd-standard

Directory in a VM where disk is mounted.

Size of disk in GB. If zero or unspecified, the service will attempt to choose a reasonable default.

Disk storage type, as defined by Google Compute Engine. This must be a disk type appropriate to the project and zone in which the workers will run. If unknown or unspecified, the service will attempt to choose a reasonable default. For example, the standard persistent disk type is a resource name typically ending in "pd-standard". If SSD persistent disks are available, the resource name typically ends with "pd-ssd". The actual valid values are defined the Google Compute Engine API, not by the Cloud Dataflow API; consult the Google Compute Engine documentation for more information about determining the set of available disk types for a particular project and zone. Google Compute Engine Disk types are local to a particular project in a particular zone, and so the resource name will typically look something like this: compute.googleapis.com/projects/project-id/zones/zone/diskTypes/pd-standard

Directory in a VM where disk is mounted.

Size of disk in GB. If zero or unspecified, the service will attempt to choose a reasonable default.

Disk storage type, as defined by Google Compute Engine. This must be a disk type appropriate to the project and zone in which the workers will run. If unknown or unspecified, the service will attempt to choose a reasonable default. For example, the standard persistent disk type is a resource name typically ending in "pd-standard". If SSD persistent disks are available, the resource name typically ends with "pd-ssd". The actual valid values are defined the Google Compute Engine API, not by the Cloud Dataflow API; consult the Google Compute Engine documentation for more information about determining the set of available disk types for a particular project and zone. Google Compute Engine Disk types are local to a particular project in a particular zone, and so the resource name will typically look something like this: compute.googleapis.com/projects/project-id/zones/zone/diskTypes/pd-standard

Directory in a VM where disk is mounted.

Size of disk in GB. If zero or unspecified, the service will attempt to choose a reasonable default.

Disk storage type, as defined by Google Compute Engine. This must be a disk type appropriate to the project and zone in which the workers will run. If unknown or unspecified, the service will attempt to choose a reasonable default. For example, the standard persistent disk type is a resource name typically ending in "pd-standard". If SSD persistent disks are available, the resource name typically ends with "pd-ssd". The actual valid values are defined the Google Compute Engine API, not by the Cloud Dataflow API; consult the Google Compute Engine documentation for more information about determining the set of available disk types for a particular project and zone. Google Compute Engine Disk types are local to a particular project in a particular zone, and so the resource name will typically look something like this: compute.googleapis.com/projects/project-id/zones/zone/diskTypes/pd-standard

Directory in a VM where disk is mounted.

Size of disk in GB. If zero or unspecified, the service will attempt to choose a reasonable default.

Disk storage type, as defined by Google Compute Engine. This must be a disk type appropriate to the project and zone in which the workers will run. If unknown or unspecified, the service will attempt to choose a reasonable default. For example, the standard persistent disk type is a resource name typically ending in "pd-standard". If SSD persistent disks are available, the resource name typically ends with "pd-ssd". The actual valid values are defined the Google Compute Engine API, not by the Cloud Dataflow API; consult the Google Compute Engine documentation for more information about determining the set of available disk types for a particular project and zone. Google Compute Engine Disk types are local to a particular project in a particular zone, and so the resource name will typically look something like this: compute.googleapis.com/projects/project-id/zones/zone/diskTypes/pd-standard

Directory in a VM where disk is mounted.

Size of disk in GB. If zero or unspecified, the service will attempt to choose a reasonable default.

Disk storage type, as defined by Google Compute Engine. This must be a disk type appropriate to the project and zone in which the workers will run. If unknown or unspecified, the service will attempt to choose a reasonable default. For example, the standard persistent disk type is a resource name typically ending in "pd-standard". If SSD persistent disks are available, the resource name typically ends with "pd-ssd". The actual valid values are defined the Google Compute Engine API, not by the Cloud Dataflow API; consult the Google Compute Engine documentation for more information about determining the set of available disk types for a particular project and zone. Google Compute Engine Disk types are local to a particular project in a particular zone, and so the resource name will typically look something like this: compute.googleapis.com/projects/project-id/zones/zone/diskTypes/pd-standard

Directory in a VM where disk is mounted.

Size of disk in GB. If zero or unspecified, the service will attempt to choose a reasonable default.

Disk storage type, as defined by Google Compute Engine. This must be a disk type appropriate to the project and zone in which the workers will run. If unknown or unspecified, the service will attempt to choose a reasonable default. For example, the standard persistent disk type is a resource name typically ending in "pd-standard". If SSD persistent disks are available, the resource name typically ends with "pd-ssd". The actual valid values are defined the Google Compute Engine API, not by the Cloud Dataflow API; consult the Google Compute Engine documentation for more information about determining the set of available disk types for a particular project and zone. Google Compute Engine Disk types are local to a particular project in a particular zone, and so the resource name will typically look something like this: compute.googleapis.com/projects/project-id/zones/zone/diskTypes/pd-standard

Directory in a VM where disk is mounted.

Size of disk in GB. If zero or unspecified, the service will attempt to choose a reasonable default.

Disk storage type, as defined by Google Compute Engine. This must be a disk type appropriate to the project and zone in which the workers will run. If unknown or unspecified, the service will attempt to choose a reasonable default. For example, the standard persistent disk type is a resource name typically ending in "pd-standard". If SSD persistent disks are available, the resource name typically ends with "pd-ssd". The actual valid values are defined the Google Compute Engine API, not by the Cloud Dataflow API; consult the Google Compute Engine documentation for more information about determining the set of available disk types for a particular project and zone. Google Compute Engine Disk types are local to a particular project in a particular zone, and so the resource name will typically look something like this: compute.googleapis.com/projects/project-id/zones/zone/diskTypes/pd-standard

Directory in a VM where disk is mounted.

Size of disk in GB. If zero or unspecified, the service will attempt to choose a reasonable default.

Contains value if the data is of a boolean type.

Contains value if the data is of duration type.

Contains value if the data is of float type.

Contains value if the data is of int64 type.

Contains value if the data is of java class type.

The key identifying the display data. This is intended to be used as a label for the display data when viewed in a dax monitoring system.

An optional label to display in a dax UI for the element.

The namespace for the key. This is usually a class name or programming language namespace (i.e. python module) which defines the display data. This allows a dax monitoring system to specially handle the data and perform custom rendering.

A possible additional shorter value to display. For example a java_class_name_value of com.mypackage.MyDoFn will be stored with MyDoFn as the short_str_value and com.mypackage.MyDoFn as the java_class_name value. short_str_value can be displayed and java_class_name_value will be displayed as a tooltip.

Contains value if the data is of string type.

Contains value if the data is of timestamp type.

An optional full URL.

Contains value if the data is of a boolean type.

Contains value if the data is of duration type.

Contains value if the data is of float type.

Contains value if the data is of int64 type.

Contains value if the data is of java class type.

The key identifying the display data. This is intended to be used as a label for the display data when viewed in a dax monitoring system.

An optional label to display in a dax UI for the element.

The namespace for the key. This is usually a class name or programming language namespace (i.e. python module) which defines the display data. This allows a dax monitoring system to specially handle the data and perform custom rendering.

A possible additional shorter value to display. For example a java_class_name_value of com.mypackage.MyDoFn will be stored with MyDoFn as the short_str_value and com.mypackage.MyDoFn as the java_class_name value. short_str_value can be displayed and java_class_name_value will be displayed as a tooltip.

Contains value if the data is of string type.

Contains value if the data is of timestamp type.

An optional full URL.

Contains value if the data is of a boolean type.

Contains value if the data is of duration type.

Contains value if the data is of float type.

Contains value if the data is of int64 type.

Contains value if the data is of java class type.

The key identifying the display data. This is intended to be used as a label for the display data when viewed in a dax monitoring system.

An optional label to display in a dax UI for the element.

The namespace for the key. This is usually a class name or programming language namespace (i.e. python module) which defines the display data. This allows a dax monitoring system to specially handle the data and perform custom rendering.

A possible additional shorter value to display. For example a java_class_name_value of com.mypackage.MyDoFn will be stored with MyDoFn as the short_str_value and com.mypackage.MyDoFn as the java_class_name value. short_str_value can be displayed and java_class_name_value will be displayed as a tooltip.

Contains value if the data is of string type.

Contains value if the data is of timestamp type.

An optional full URL.

Contains value if the data is of a boolean type.

Contains value if the data is of duration type.

Contains value if the data is of float type.

Contains value if the data is of int64 type.

Contains value if the data is of java class type.

The key identifying the display data. This is intended to be used as a label for the display data when viewed in a dax monitoring system.

An optional label to display in a dax UI for the element.

The namespace for the key. This is usually a class name or programming language namespace (i.e. python module) which defines the display data. This allows a dax monitoring system to specially handle the data and perform custom rendering.

A possible additional shorter value to display. For example a java_class_name_value of com.mypackage.MyDoFn will be stored with MyDoFn as the short_str_value and com.mypackage.MyDoFn as the java_class_name value. short_str_value can be displayed and java_class_name_value will be displayed as a tooltip.

Contains value if the data is of string type.

Contains value if the data is of timestamp type.

An optional full URL.

Contains value if the data is of a boolean type.

Contains value if the data is of duration type.

Contains value if the data is of float type.

Contains value if the data is of int64 type.

Contains value if the data is of java class type.

The key identifying the display data. This is intended to be used as a label for the display data when viewed in a dax monitoring system.

An optional label to display in a dax UI for the element.

The namespace for the key. This is usually a class name or programming language namespace (i.e. python module) which defines the display data. This allows a dax monitoring system to specially handle the data and perform custom rendering.

A possible additional shorter value to display. For example a java_class_name_value of com.mypackage.MyDoFn will be stored with MyDoFn as the short_str_value and com.mypackage.MyDoFn as the java_class_name value. short_str_value can be displayed and java_class_name_value will be displayed as a tooltip.

Contains value if the data is of string type.

Contains value if the data is of timestamp type.

An optional full URL.

Contains value if the data is of a boolean type.

Contains value if the data is of duration type.

Contains value if the data is of float type.

Contains value if the data is of int64 type.

Contains value if the data is of java class type.

The key identifying the display data. This is intended to be used as a label for the display data when viewed in a dax monitoring system.

An optional label to display in a dax UI for the element.

The namespace for the key. This is usually a class name or programming language namespace (i.e. python module) which defines the display data. This allows a dax monitoring system to specially handle the data and perform custom rendering.

A possible additional shorter value to display. For example a java_class_name_value of com.mypackage.MyDoFn will be stored with MyDoFn as the short_str_value and com.mypackage.MyDoFn as the java_class_name value. short_str_value can be displayed and java_class_name_value will be displayed as a tooltip.

Contains value if the data is of string type.

Contains value if the data is of timestamp type.

An optional full URL.

Contains value if the data is of a boolean type.

Contains value if the data is of duration type.

Contains value if the data is of float type.

Contains value if the data is of int64 type.

Contains value if the data is of java class type.

The key identifying the display data. This is intended to be used as a label for the display data when viewed in a dax monitoring system.

An optional label to display in a dax UI for the element.

The namespace for the key. This is usually a class name or programming language namespace (i.e. python module) which defines the display data. This allows a dax monitoring system to specially handle the data and perform custom rendering.

A possible additional shorter value to display. For example a java_class_name_value of com.mypackage.MyDoFn will be stored with MyDoFn as the short_str_value and com.mypackage.MyDoFn as the java_class_name value. short_str_value can be displayed and java_class_name_value will be displayed as a tooltip.

Contains value if the data is of string type.

Contains value if the data is of timestamp type.

An optional full URL.

Contains value if the data is of a boolean type.

Contains value if the data is of duration type.

Contains value if the data is of float type.

Contains value if the data is of int64 type.

Contains value if the data is of java class type.

The key identifying the display data. This is intended to be used as a label for the display data when viewed in a dax monitoring system.

An optional label to display in a dax UI for the element.

The namespace for the key. This is usually a class name or programming language namespace (i.e. python module) which defines the display data. This allows a dax monitoring system to specially handle the data and perform custom rendering.

A possible additional shorter value to display. For example a java_class_name_value of com.mypackage.MyDoFn will be stored with MyDoFn as the short_str_value and com.mypackage.MyDoFn as the java_class_name value. short_str_value can be displayed and java_class_name_value will be displayed as a tooltip.

Contains value if the data is of string type.

Contains value if the data is of timestamp type.

An optional full URL.

Contains value if the data is of a boolean type.

Contains value if the data is of duration type.

Contains value if the data is of float type.

Contains value if the data is of int64 type.

Contains value if the data is of java class type.

The key identifying the display data. This is intended to be used as a label for the display data when viewed in a dax monitoring system.

An optional label to display in a dax UI for the element.

The namespace for the key. This is usually a class name or programming language namespace (i.e. python module) which defines the display data. This allows a dax monitoring system to specially handle the data and perform custom rendering.

A possible additional shorter value to display. For example a java_class_name_value of com.mypackage.MyDoFn will be stored with MyDoFn as the short_str_value and com.mypackage.MyDoFn as the java_class_name value. short_str_value can be displayed and java_class_name_value will be displayed as a tooltip.

Contains value if the data is of string type.

Contains value if the data is of timestamp type.

An optional full URL.

Contains value if the data is of a boolean type.

Contains value if the data is of duration type.

Contains value if the data is of float type.

Contains value if the data is of int64 type.

Contains value if the data is of java class type.

The key identifying the display data. This is intended to be used as a label for the display data when viewed in a dax monitoring system.

An optional label to display in a dax UI for the element.

The namespace for the key. This is usually a class name or programming language namespace (i.e. python module) which defines the display data. This allows a dax monitoring system to specially handle the data and perform custom rendering.

A possible additional shorter value to display. For example a java_class_name_value of com.mypackage.MyDoFn will be stored with MyDoFn as the short_str_value and com.mypackage.MyDoFn as the java_class_name value. short_str_value can be displayed and java_class_name_value will be displayed as a tooltip.

Contains value if the data is of string type.

Contains value if the data is of timestamp type.

An optional full URL.

Contains value if the data is of a boolean type.

Contains value if the data is of duration type.

Contains value if the data is of float type.

Contains value if the data is of int64 type.

Contains value if the data is of java class type.

The key identifying the display data. This is intended to be used as a label for the display data when viewed in a dax monitoring system.

An optional label to display in a dax UI for the element.

The namespace for the key. This is usually a class name or programming language namespace (i.e. python module) which defines the display data. This allows a dax monitoring system to specially handle the data and perform custom rendering.

A possible additional shorter value to display. For example a java_class_name_value of com.mypackage.MyDoFn will be stored with MyDoFn as the short_str_value and com.mypackage.MyDoFn as the java_class_name value. short_str_value can be displayed and java_class_name_value will be displayed as a tooltip.

Contains value if the data is of string type.

Contains value if the data is of timestamp type.

An optional full URL.

Contains value if the data is of a boolean type.

Contains value if the data is of duration type.

Contains value if the data is of float type.

Contains value if the data is of int64 type.

Contains value if the data is of java class type.

The key identifying the display data. This is intended to be used as a label for the display data when viewed in a dax monitoring system.

An optional label to display in a dax UI for the element.

The namespace for the key. This is usually a class name or programming language namespace (i.e. python module) which defines the display data. This allows a dax monitoring system to specially handle the data and perform custom rendering.

A possible additional shorter value to display. For example a java_class_name_value of com.mypackage.MyDoFn will be stored with MyDoFn as the short_str_value and com.mypackage.MyDoFn as the java_class_name value. short_str_value can be displayed and java_class_name_value will be displayed as a tooltip.

Contains value if the data is of string type.

Contains value if the data is of timestamp type.

An optional full URL.

The type of cluster manager API to use. If unknown or unspecified, the service will attempt to choose a reasonable default. This should be in the form of the API service name, e.g. "compute.googleapis.com".

The dataset for the current project where various workflow related tables are stored. The supported resource type is: Google BigQuery: bigquery.googleapis.com/{dataset}

Any debugging options to be supplied to the job.

The list of experiments to enable. This field should be used for SDK related experiments and not for service related experiments. The proper field for service related experiments is service_options.

Which Flexible Resource Scheduling mode to run in.

Experimental settings.

The Cloud Dataflow SDK pipeline options specified by the user. These options are passed through the service and are used to recreate the SDK pipeline options on the worker in a language agnostic and platform independent way.

Identity to run virtual machines as. Defaults to the default account.

If set, contains the Cloud KMS key identifier used to encrypt data at rest, AKA a Customer Managed Encryption Key (CMEK). Format: projects/PROJECT_ID/locations/LOCATION/keyRings/KEY_RING/cryptoKeys/KEY

The list of service options to enable. This field should be used for service related experiments only. These experiments, when graduating to GA, should be replaced by dedicated fields or become default (i.e. always on).

The prefix of the resources the system should use for temporary storage. The system will append the suffix "/temp-{JOBNAME} to this resource prefix, where {JOBNAME} is the value of the job_name field. The resulting bucket and object prefix is used as the prefix of the resources used to store temporary data needed during the job execution. NOTE: This will override the value in taskrunner_settings. The supported resource type is: Google Cloud Storage: storage.googleapis.com/{bucket}/{object} bucket.storage.googleapis.com/{object}

A description of the process that generated the request.

A structure describing which components and their versions of the service are required in order to run the job.

The worker pools. At least one "harness" worker pool must be specified in order for the job to have workers.

The Compute Engine region (https://cloud.google.com/compute/docs/regions-zones/regions-zones) in which worker processing should occur, e.g. "us-west1". Mutually exclusive with worker_zone. If neither worker_region nor worker_zone is specified, default to the control plane's region.

The Compute Engine zone (https://cloud.google.com/compute/docs/regions-zones/regions-zones) in which worker processing should occur, e.g. "us-west1-a". Mutually exclusive with worker_region. If neither worker_region nor worker_zone is specified, a zone in the control plane's region is chosen based on available capacity.

The type of cluster manager API to use. If unknown or unspecified, the service will attempt to choose a reasonable default. This should be in the form of the API service name, e.g. "compute.googleapis.com".

The dataset for the current project where various workflow related tables are stored. The supported resource type is: Google BigQuery: bigquery.googleapis.com/{dataset}

Any debugging options to be supplied to the job.

The list of experiments to enable. This field should be used for SDK related experiments and not for service related experiments. The proper field for service related experiments is service_options.

Which Flexible Resource Scheduling mode to run in.

Experimental settings.

The Cloud Dataflow SDK pipeline options specified by the user. These options are passed through the service and are used to recreate the SDK pipeline options on the worker in a language agnostic and platform independent way.

Identity to run virtual machines as. Defaults to the default account.

If set, contains the Cloud KMS key identifier used to encrypt data at rest, AKA a Customer Managed Encryption Key (CMEK). Format: projects/PROJECT_ID/locations/LOCATION/keyRings/KEY_RING/cryptoKeys/KEY

The list of service options to enable. This field should be used for service related experiments only. These experiments, when graduating to GA, should be replaced by dedicated fields or become default (i.e. always on).

The prefix of the resources the system should use for temporary storage. The system will append the suffix "/temp-{JOBNAME} to this resource prefix, where {JOBNAME} is the value of the job_name field. The resulting bucket and object prefix is used as the prefix of the resources used to store temporary data needed during the job execution. NOTE: This will override the value in taskrunner_settings. The supported resource type is: Google Cloud Storage: storage.googleapis.com/{bucket}/{object} bucket.storage.googleapis.com/{object}

A description of the process that generated the request.

A structure describing which components and their versions of the service are required in order to run the job.

The worker pools. At least one "harness" worker pool must be specified in order for the job to have workers.

The Compute Engine region (https://cloud.google.com/compute/docs/regions-zones/regions-zones) in which worker processing should occur, e.g. "us-west1". Mutually exclusive with worker_zone. If neither worker_region nor worker_zone is specified, default to the control plane's region.

The Compute Engine zone (https://cloud.google.com/compute/docs/regions-zones/regions-zones) in which worker processing should occur, e.g. "us-west1-a". Mutually exclusive with worker_region. If neither worker_region nor worker_zone is specified, a zone in the control plane's region is chosen based on available capacity.

The type of cluster manager API to use. If unknown or unspecified, the service will attempt to choose a reasonable default. This should be in the form of the API service name, e.g. "compute.googleapis.com".

The dataset for the current project where various workflow related tables are stored. The supported resource type is: Google BigQuery: bigquery.googleapis.com/{dataset}

Any debugging options to be supplied to the job.

The list of experiments to enable. This field should be used for SDK related experiments and not for service related experiments. The proper field for service related experiments is service_options.

Which Flexible Resource Scheduling mode to run in.

Experimental settings.

The Cloud Dataflow SDK pipeline options specified by the user. These options are passed through the service and are used to recreate the SDK pipeline options on the worker in a language agnostic and platform independent way.

Identity to run virtual machines as. Defaults to the default account.

If set, contains the Cloud KMS key identifier used to encrypt data at rest, AKA a Customer Managed Encryption Key (CMEK). Format: projects/PROJECT_ID/locations/LOCATION/keyRings/KEY_RING/cryptoKeys/KEY

The list of service options to enable. This field should be used for service related experiments only. These experiments, when graduating to GA, should be replaced by dedicated fields or become default (i.e. always on).

The prefix of the resources the system should use for temporary storage. The system will append the suffix "/temp-{JOBNAME} to this resource prefix, where {JOBNAME} is the value of the job_name field. The resulting bucket and object prefix is used as the prefix of the resources used to store temporary data needed during the job execution. NOTE: This will override the value in taskrunner_settings. The supported resource type is: Google Cloud Storage: storage.googleapis.com/{bucket}/{object} bucket.storage.googleapis.com/{object}

A description of the process that generated the request.

A structure describing which components and their versions of the service are required in order to run the job.

The worker pools. At least one "harness" worker pool must be specified in order for the job to have workers.

The Compute Engine region (https://cloud.google.com/compute/docs/regions-zones/regions-zones) in which worker processing should occur, e.g. "us-west1". Mutually exclusive with worker_zone. If neither worker_region nor worker_zone is specified, default to the control plane's region.

The Compute Engine zone (https://cloud.google.com/compute/docs/regions-zones/regions-zones) in which worker processing should occur, e.g. "us-west1-a". Mutually exclusive with worker_region. If neither worker_region nor worker_zone is specified, a zone in the control plane's region is chosen based on available capacity.

The type of cluster manager API to use. If unknown or unspecified, the service will attempt to choose a reasonable default. This should be in the form of the API service name, e.g. "compute.googleapis.com".

The dataset for the current project where various workflow related tables are stored. The supported resource type is: Google BigQuery: bigquery.googleapis.com/{dataset}

Any debugging options to be supplied to the job.

The list of experiments to enable. This field should be used for SDK related experiments and not for service related experiments. The proper field for service related experiments is service_options.

Which Flexible Resource Scheduling mode to run in.

Experimental settings.

The Cloud Dataflow SDK pipeline options specified by the user. These options are passed through the service and are used to recreate the SDK pipeline options on the worker in a language agnostic and platform independent way.

Identity to run virtual machines as. Defaults to the default account.

If set, contains the Cloud KMS key identifier used to encrypt data at rest, AKA a Customer Managed Encryption Key (CMEK). Format: projects/PROJECT_ID/locations/LOCATION/keyRings/KEY_RING/cryptoKeys/KEY

The list of service options to enable. This field should be used for service related experiments only. These experiments, when graduating to GA, should be replaced by dedicated fields or become default (i.e. always on).

The prefix of the resources the system should use for temporary storage. The system will append the suffix "/temp-{JOBNAME} to this resource prefix, where {JOBNAME} is the value of the job_name field. The resulting bucket and object prefix is used as the prefix of the resources used to store temporary data needed during the job execution. NOTE: This will override the value in taskrunner_settings. The supported resource type is: Google Cloud Storage: storage.googleapis.com/{bucket}/{object} bucket.storage.googleapis.com/{object}

A description of the process that generated the request.

A structure describing which components and their versions of the service are required in order to run the job.

The worker pools. At least one "harness" worker pool must be specified in order for the job to have workers.

The Compute Engine region (https://cloud.google.com/compute/docs/regions-zones/regions-zones) in which worker processing should occur, e.g. "us-west1". Mutually exclusive with worker_zone. If neither worker_region nor worker_zone is specified, default to the control plane's region.

The Compute Engine zone (https://cloud.google.com/compute/docs/regions-zones/regions-zones) in which worker processing should occur, e.g. "us-west1-a". Mutually exclusive with worker_region. If neither worker_region nor worker_zone is specified, a zone in the control plane's region is chosen based on available capacity.

The type of cluster manager API to use. If unknown or unspecified, the service will attempt to choose a reasonable default. This should be in the form of the API service name, e.g. "compute.googleapis.com".

The dataset for the current project where various workflow related tables are stored. The supported resource type is: Google BigQuery: bigquery.googleapis.com/{dataset}

Any debugging options to be supplied to the job.

The list of experiments to enable. This field should be used for SDK related experiments and not for service related experiments. The proper field for service related experiments is service_options.

Which Flexible Resource Scheduling mode to run in.

Experimental settings.

The Cloud Dataflow SDK pipeline options specified by the user. These options are passed through the service and are used to recreate the SDK pipeline options on the worker in a language agnostic and platform independent way.

Identity to run virtual machines as. Defaults to the default account.

If set, contains the Cloud KMS key identifier used to encrypt data at rest, AKA a Customer Managed Encryption Key (CMEK). Format: projects/PROJECT_ID/locations/LOCATION/keyRings/KEY_RING/cryptoKeys/KEY

The list of service options to enable. This field should be used for service related experiments only. These experiments, when graduating to GA, should be replaced by dedicated fields or become default (i.e. always on).

The prefix of the resources the system should use for temporary storage. The system will append the suffix "/temp-{JOBNAME} to this resource prefix, where {JOBNAME} is the value of the job_name field. The resulting bucket and object prefix is used as the prefix of the resources used to store temporary data needed during the job execution. NOTE: This will override the value in taskrunner_settings. The supported resource type is: Google Cloud Storage: storage.googleapis.com/{bucket}/{object} bucket.storage.googleapis.com/{object}

A description of the process that generated the request.

A structure describing which components and their versions of the service are required in order to run the job.

The worker pools. At least one "harness" worker pool must be specified in order for the job to have workers.

The Compute Engine region (https://cloud.google.com/compute/docs/regions-zones/regions-zones) in which worker processing should occur, e.g. "us-west1". Mutually exclusive with worker_zone. If neither worker_region nor worker_zone is specified, default to the control plane's region.

The Compute Engine zone (https://cloud.google.com/compute/docs/regions-zones/regions-zones) in which worker processing should occur, e.g. "us-west1-a". Mutually exclusive with worker_region. If neither worker_region nor worker_zone is specified, a zone in the control plane's region is chosen based on available capacity.

The type of cluster manager API to use. If unknown or unspecified, the service will attempt to choose a reasonable default. This should be in the form of the API service name, e.g. "compute.googleapis.com".

The dataset for the current project where various workflow related tables are stored. The supported resource type is: Google BigQuery: bigquery.googleapis.com/{dataset}

Any debugging options to be supplied to the job.

The list of experiments to enable. This field should be used for SDK related experiments and not for service related experiments. The proper field for service related experiments is service_options.

Which Flexible Resource Scheduling mode to run in.

Experimental settings.

The Cloud Dataflow SDK pipeline options specified by the user. These options are passed through the service and are used to recreate the SDK pipeline options on the worker in a language agnostic and platform independent way.

Identity to run virtual machines as. Defaults to the default account.

If set, contains the Cloud KMS key identifier used to encrypt data at rest, AKA a Customer Managed Encryption Key (CMEK). Format: projects/PROJECT_ID/locations/LOCATION/keyRings/KEY_RING/cryptoKeys/KEY

The list of service options to enable. This field should be used for service related experiments only. These experiments, when graduating to GA, should be replaced by dedicated fields or become default (i.e. always on).

The prefix of the resources the system should use for temporary storage. The system will append the suffix "/temp-{JOBNAME} to this resource prefix, where {JOBNAME} is the value of the job_name field. The resulting bucket and object prefix is used as the prefix of the resources used to store temporary data needed during the job execution. NOTE: This will override the value in taskrunner_settings. The supported resource type is: Google Cloud Storage: storage.googleapis.com/{bucket}/{object} bucket.storage.googleapis.com/{object}

A description of the process that generated the request.

A structure describing which components and their versions of the service are required in order to run the job.

The worker pools. At least one "harness" worker pool must be specified in order for the job to have workers.

The Compute Engine region (https://cloud.google.com/compute/docs/regions-zones/regions-zones) in which worker processing should occur, e.g. "us-west1". Mutually exclusive with worker_zone. If neither worker_region nor worker_zone is specified, default to the control plane's region.

The Compute Engine zone (https://cloud.google.com/compute/docs/regions-zones/regions-zones) in which worker processing should occur, e.g. "us-west1-a". Mutually exclusive with worker_region. If neither worker_region nor worker_zone is specified, a zone in the control plane's region is chosen based on available capacity.

The type of cluster manager API to use. If unknown or unspecified, the service will attempt to choose a reasonable default. This should be in the form of the API service name, e.g. "compute.googleapis.com".

The dataset for the current project where various workflow related tables are stored. The supported resource type is: Google BigQuery: bigquery.googleapis.com/{dataset}

Any debugging options to be supplied to the job.

The list of experiments to enable. This field should be used for SDK related experiments and not for service related experiments. The proper field for service related experiments is service_options.

Which Flexible Resource Scheduling mode to run in.

Experimental settings.

The Cloud Dataflow SDK pipeline options specified by the user. These options are passed through the service and are used to recreate the SDK pipeline options on the worker in a language agnostic and platform independent way.

Identity to run virtual machines as. Defaults to the default account.

If set, contains the Cloud KMS key identifier used to encrypt data at rest, AKA a Customer Managed Encryption Key (CMEK). Format: projects/PROJECT_ID/locations/LOCATION/keyRings/KEY_RING/cryptoKeys/KEY

The list of service options to enable. This field should be used for service related experiments only. These experiments, when graduating to GA, should be replaced by dedicated fields or become default (i.e. always on).

The shuffle mode used for the job.

The prefix of the resources the system should use for temporary storage. The system will append the suffix "/temp-{JOBNAME} to this resource prefix, where {JOBNAME} is the value of the job_name field. The resulting bucket and object prefix is used as the prefix of the resources used to store temporary data needed during the job execution. NOTE: This will override the value in taskrunner_settings. The supported resource type is: Google Cloud Storage: storage.googleapis.com/{bucket}/{object} bucket.storage.googleapis.com/{object}

A description of the process that generated the request.

A structure describing which components and their versions of the service are required in order to run the job.

The worker pools. At least one "harness" worker pool must be specified in order for the job to have workers.

The Compute Engine region (https://cloud.google.com/compute/docs/regions-zones/regions-zones) in which worker processing should occur, e.g. "us-west1". Mutually exclusive with worker_zone. If neither worker_region nor worker_zone is specified, default to the control plane's region.

The Compute Engine zone (https://cloud.google.com/compute/docs/regions-zones/regions-zones) in which worker processing should occur, e.g. "us-west1-a". Mutually exclusive with worker_region. If neither worker_region nor worker_zone is specified, a zone in the control plane's region is chosen based on available capacity.

The type of cluster manager API to use. If unknown or unspecified, the service will attempt to choose a reasonable default. This should be in the form of the API service name, e.g. "compute.googleapis.com".

The dataset for the current project where various workflow related tables are stored. The supported resource type is: Google BigQuery: bigquery.googleapis.com/{dataset}

Any debugging options to be supplied to the job.

The list of experiments to enable. This field should be used for SDK related experiments and not for service related experiments. The proper field for service related experiments is service_options.

Which Flexible Resource Scheduling mode to run in.

Experimental settings.

The Cloud Dataflow SDK pipeline options specified by the user. These options are passed through the service and are used to recreate the SDK pipeline options on the worker in a language agnostic and platform independent way.

Identity to run virtual machines as. Defaults to the default account.

If set, contains the Cloud KMS key identifier used to encrypt data at rest, AKA a Customer Managed Encryption Key (CMEK). Format: projects/PROJECT_ID/locations/LOCATION/keyRings/KEY_RING/cryptoKeys/KEY

The list of service options to enable. This field should be used for service related experiments only. These experiments, when graduating to GA, should be replaced by dedicated fields or become default (i.e. always on).

The shuffle mode used for the job.

The prefix of the resources the system should use for temporary storage. The system will append the suffix "/temp-{JOBNAME} to this resource prefix, where {JOBNAME} is the value of the job_name field. The resulting bucket and object prefix is used as the prefix of the resources used to store temporary data needed during the job execution. NOTE: This will override the value in taskrunner_settings. The supported resource type is: Google Cloud Storage: storage.googleapis.com/{bucket}/{object} bucket.storage.googleapis.com/{object}

A description of the process that generated the request.

A structure describing which components and their versions of the service are required in order to run the job.

The worker pools. At least one "harness" worker pool must be specified in order for the job to have workers.

The Compute Engine region (https://cloud.google.com/compute/docs/regions-zones/regions-zones) in which worker processing should occur, e.g. "us-west1". Mutually exclusive with worker_zone. If neither worker_region nor worker_zone is specified, default to the control plane's region.

The Compute Engine zone (https://cloud.google.com/compute/docs/regions-zones/regions-zones) in which worker processing should occur, e.g. "us-west1-a". Mutually exclusive with worker_region. If neither worker_region nor worker_zone is specified, a zone in the control plane's region is chosen based on available capacity.

The type of cluster manager API to use. If unknown or unspecified, the service will attempt to choose a reasonable default. This should be in the form of the API service name, e.g. "compute.googleapis.com".

The dataset for the current project where various workflow related tables are stored. The supported resource type is: Google BigQuery: bigquery.googleapis.com/{dataset}

Any debugging options to be supplied to the job.

The list of experiments to enable. This field should be used for SDK related experiments and not for service related experiments. The proper field for service related experiments is service_options.

Which Flexible Resource Scheduling mode to run in.

Experimental settings.

The Cloud Dataflow SDK pipeline options specified by the user. These options are passed through the service and are used to recreate the SDK pipeline options on the worker in a language agnostic and platform independent way.

Identity to run virtual machines as. Defaults to the default account.

If set, contains the Cloud KMS key identifier used to encrypt data at rest, AKA a Customer Managed Encryption Key (CMEK). Format: projects/PROJECT_ID/locations/LOCATION/keyRings/KEY_RING/cryptoKeys/KEY

The list of service options to enable. This field should be used for service related experiments only. These experiments, when graduating to GA, should be replaced by dedicated fields or become default (i.e. always on).

The shuffle mode used for the job.

The prefix of the resources the system should use for temporary storage. The system will append the suffix "/temp-{JOBNAME} to this resource prefix, where {JOBNAME} is the value of the job_name field. The resulting bucket and object prefix is used as the prefix of the resources used to store temporary data needed during the job execution. NOTE: This will override the value in taskrunner_settings. The supported resource type is: Google Cloud Storage: storage.googleapis.com/{bucket}/{object} bucket.storage.googleapis.com/{object}

A description of the process that generated the request.

A structure describing which components and their versions of the service are required in order to run the job.

The worker pools. At least one "harness" worker pool must be specified in order for the job to have workers.

The Compute Engine region (https://cloud.google.com/compute/docs/regions-zones/regions-zones) in which worker processing should occur, e.g. "us-west1". Mutually exclusive with worker_zone. If neither worker_region nor worker_zone is specified, default to the control plane's region.

The Compute Engine zone (https://cloud.google.com/compute/docs/regions-zones/regions-zones) in which worker processing should occur, e.g. "us-west1-a". Mutually exclusive with worker_region. If neither worker_region nor worker_zone is specified, a zone in the control plane's region is chosen based on available capacity.

The type of cluster manager API to use. If unknown or unspecified, the service will attempt to choose a reasonable default. This should be in the form of the API service name, e.g. "compute.googleapis.com".

The dataset for the current project where various workflow related tables are stored. The supported resource type is: Google BigQuery: bigquery.googleapis.com/{dataset}

Any debugging options to be supplied to the job.

The list of experiments to enable. This field should be used for SDK related experiments and not for service related experiments. The proper field for service related experiments is service_options.

Which Flexible Resource Scheduling mode to run in.

Experimental settings.

The Cloud Dataflow SDK pipeline options specified by the user. These options are passed through the service and are used to recreate the SDK pipeline options on the worker in a language agnostic and platform independent way.

Identity to run virtual machines as. Defaults to the default account.

If set, contains the Cloud KMS key identifier used to encrypt data at rest, AKA a Customer Managed Encryption Key (CMEK). Format: projects/PROJECT_ID/locations/LOCATION/keyRings/KEY_RING/cryptoKeys/KEY

The list of service options to enable. This field should be used for service related experiments only. These experiments, when graduating to GA, should be replaced by dedicated fields or become default (i.e. always on).

The shuffle mode used for the job.

The prefix of the resources the system should use for temporary storage. The system will append the suffix "/temp-{JOBNAME} to this resource prefix, where {JOBNAME} is the value of the job_name field. The resulting bucket and object prefix is used as the prefix of the resources used to store temporary data needed during the job execution. NOTE: This will override the value in taskrunner_settings. The supported resource type is: Google Cloud Storage: storage.googleapis.com/{bucket}/{object} bucket.storage.googleapis.com/{object}

A description of the process that generated the request.

A structure describing which components and their versions of the service are required in order to run the job.

The worker pools. At least one "harness" worker pool must be specified in order for the job to have workers.

The Compute Engine region (https://cloud.google.com/compute/docs/regions-zones/regions-zones) in which worker processing should occur, e.g. "us-west1". Mutually exclusive with worker_zone. If neither worker_region nor worker_zone is specified, default to the control plane's region.

The Compute Engine zone (https://cloud.google.com/compute/docs/regions-zones/regions-zones) in which worker processing should occur, e.g. "us-west1-a". Mutually exclusive with worker_region. If neither worker_region nor worker_zone is specified, a zone in the control plane's region is chosen based on available capacity.

The type of cluster manager API to use. If unknown or unspecified, the service will attempt to choose a reasonable default. This should be in the form of the API service name, e.g. "compute.googleapis.com".

The dataset for the current project where various workflow related tables are stored. The supported resource type is: Google BigQuery: bigquery.googleapis.com/{dataset}

Any debugging options to be supplied to the job.

The list of experiments to enable. This field should be used for SDK related experiments and not for service related experiments. The proper field for service related experiments is service_options.

Which Flexible Resource Scheduling mode to run in.

Experimental settings.

The Cloud Dataflow SDK pipeline options specified by the user. These options are passed through the service and are used to recreate the SDK pipeline options on the worker in a language agnostic and platform independent way.

Identity to run virtual machines as. Defaults to the default account.

If set, contains the Cloud KMS key identifier used to encrypt data at rest, AKA a Customer Managed Encryption Key (CMEK). Format: projects/PROJECT_ID/locations/LOCATION/keyRings/KEY_RING/cryptoKeys/KEY

The list of service options to enable. This field should be used for service related experiments only. These experiments, when graduating to GA, should be replaced by dedicated fields or become default (i.e. always on).

The shuffle mode used for the job.

The prefix of the resources the system should use for temporary storage. The system will append the suffix "/temp-{JOBNAME} to this resource prefix, where {JOBNAME} is the value of the job_name field. The resulting bucket and object prefix is used as the prefix of the resources used to store temporary data needed during the job execution. NOTE: This will override the value in taskrunner_settings. The supported resource type is: Google Cloud Storage: storage.googleapis.com/{bucket}/{object} bucket.storage.googleapis.com/{object}

A description of the process that generated the request.

A structure describing which components and their versions of the service are required in order to run the job.

The worker pools. At least one "harness" worker pool must be specified in order for the job to have workers.

The Compute Engine region (https://cloud.google.com/compute/docs/regions-zones/regions-zones) in which worker processing should occur, e.g. "us-west1". Mutually exclusive with worker_zone. If neither worker_region nor worker_zone is specified, default to the control plane's region.

The Compute Engine zone (https://cloud.google.com/compute/docs/regions-zones/regions-zones) in which worker processing should occur, e.g. "us-west1-a". Mutually exclusive with worker_region. If neither worker_region nor worker_zone is specified, a zone in the control plane's region is chosen based on available capacity.

The type of cluster manager API to use. If unknown or unspecified, the service will attempt to choose a reasonable default. This should be in the form of the API service name, e.g. "compute.googleapis.com".

The dataset for the current project where various workflow related tables are stored. The supported resource type is: Google BigQuery: bigquery.googleapis.com/{dataset}

Any debugging options to be supplied to the job.

The list of experiments to enable. This field should be used for SDK related experiments and not for service related experiments. The proper field for service related experiments is service_options.

Which Flexible Resource Scheduling mode to run in.

Experimental settings.

The Cloud Dataflow SDK pipeline options specified by the user. These options are passed through the service and are used to recreate the SDK pipeline options on the worker in a language agnostic and platform independent way.

Identity to run virtual machines as. Defaults to the default account.

If set, contains the Cloud KMS key identifier used to encrypt data at rest, AKA a Customer Managed Encryption Key (CMEK). Format: projects/PROJECT_ID/locations/LOCATION/keyRings/KEY_RING/cryptoKeys/KEY

The list of service options to enable. This field should be used for service related experiments only. These experiments, when graduating to GA, should be replaced by dedicated fields or become default (i.e. always on).

The shuffle mode used for the job.

The prefix of the resources the system should use for temporary storage. The system will append the suffix "/temp-{JOBNAME} to this resource prefix, where {JOBNAME} is the value of the job_name field. The resulting bucket and object prefix is used as the prefix of the resources used to store temporary data needed during the job execution. NOTE: This will override the value in taskrunner_settings. The supported resource type is: Google Cloud Storage: storage.googleapis.com/{bucket}/{object} bucket.storage.googleapis.com/{object}

A description of the process that generated the request.

A structure describing which components and their versions of the service are required in order to run the job.

The worker pools. At least one "harness" worker pool must be specified in order for the job to have workers.

The Compute Engine region (https://cloud.google.com/compute/docs/regions-zones/regions-zones) in which worker processing should occur, e.g. "us-west1". Mutually exclusive with worker_zone. If neither worker_region nor worker_zone is specified, default to the control plane's region.

The Compute Engine zone (https://cloud.google.com/compute/docs/regions-zones/regions-zones) in which worker processing should occur, e.g. "us-west1-a". Mutually exclusive with worker_region. If neither worker_region nor worker_zone is specified, a zone in the control plane's region is chosen based on available capacity.

The time at which the stage transitioned to this state.

The name of the execution stage.

Executions stage states allow the same set of values as JobState.

The time at which the stage transitioned to this state.

The name of the execution stage.

Executions stage states allow the same set of values as JobState.

The time at which the stage transitioned to this state.

The name of the execution stage.

Executions stage states allow the same set of values as JobState.

The time at which the stage transitioned to this state.

The name of the execution stage.

Executions stage states allow the same set of values as JobState.

The time at which the stage transitioned to this state.

The name of the execution stage.

Executions stage states allow the same set of values as JobState.

The time at which the stage transitioned to this state.

The name of the execution stage.

Executions stage states allow the same set of values as JobState.