aws_emr
Amazon EMR is a web service that makes it easy to process large amounts of data efficiently. Amazon EMR uses Hadoop processing combined with several AWS products to do tasks such as web indexing, data mining, log file analysis, machine learning, scientific simulation, and data warehousing.
Summary
Functions
-
add_instance_fleet(Client, Input)
Adds an instance fleet to a running cluster.
- add_instance_fleet(Client, Input, Options)
-
add_instance_groups(Client, Input)
Adds one or more instance groups to a running cluster.
- add_instance_groups(Client, Input, Options)
-
add_job_flow_steps(Client, Input)
AddJobFlowSteps adds new steps to a running cluster.
- add_job_flow_steps(Client, Input, Options)
-
add_tags(Client, Input)
Adds tags to an Amazon EMR resource.
- add_tags(Client, Input, Options)
-
cancel_steps(Client, Input)
Cancels a pending step or steps in a running cluster.
- cancel_steps(Client, Input, Options)
-
create_security_configuration(Client, Input)
Creates a security configuration, which is stored in the service and can be specified when a cluster is created.
- create_security_configuration(Client, Input, Options)
-
delete_security_configuration(Client, Input)
Deletes a security configuration.
- delete_security_configuration(Client, Input, Options)
-
describe_cluster(Client, Input)
Provides cluster-level details including status, hardware and software configuration, VPC settings, and so on.
- describe_cluster(Client, Input, Options)
-
describe_job_flows(Client, Input)
This API is deprecated and will eventually be removed.
- describe_job_flows(Client, Input, Options)
-
describe_security_configuration(Client, Input)
Provides the details of a security configuration by returning the configuration JSON.
- describe_security_configuration(Client, Input, Options)
-
describe_step(Client, Input)
Provides more detail about the cluster step.
- describe_step(Client, Input, Options)
-
get_block_public_access_configuration(Client, Input)
Returns the Amazon EMR block public access configuration for your AWS account in the current Region.
- get_block_public_access_configuration(Client, Input, Options)
-
get_managed_scaling_policy(Client, Input)
Fetches the attached managed scaling policy for an Amazon EMR cluster.
- get_managed_scaling_policy(Client, Input, Options)
-
list_bootstrap_actions(Client, Input)
Provides information about the bootstrap actions associated with a cluster.
- list_bootstrap_actions(Client, Input, Options)
-
list_clusters(Client, Input)
Provides the status of all clusters visible to this AWS account.
- list_clusters(Client, Input, Options)
-
list_instance_fleets(Client, Input)
Lists all available details about the instance fleets in a cluster.
- list_instance_fleets(Client, Input, Options)
-
list_instance_groups(Client, Input)
Provides all available details about the instance groups in a cluster.
- list_instance_groups(Client, Input, Options)
-
list_instances(Client, Input)
Provides information for all active EC2 instances and EC2 instances terminated in the last 30 days, up to a maximum of 2,000.
- list_instances(Client, Input, Options)
-
list_security_configurations(Client, Input)
Lists all the security configurations visible to this account, providing their creation dates and times, and their names.
- list_security_configurations(Client, Input, Options)
-
list_steps(Client, Input)
Provides a list of steps for the cluster in reverse order unless you specify
stepIds
with the request of filter byStepStates
. - list_steps(Client, Input, Options)
-
modify_cluster(Client, Input)
Modifies the number of steps that can be executed concurrently for the cluster specified using ClusterID.
- modify_cluster(Client, Input, Options)
-
modify_instance_fleet(Client, Input)
Modifies the target On-Demand and target Spot capacities for the instance fleet with the specified InstanceFleetID within the cluster specified using ClusterID.
- modify_instance_fleet(Client, Input, Options)
-
modify_instance_groups(Client, Input)
ModifyInstanceGroups modifies the number of nodes and configuration settings of an instance group.
- modify_instance_groups(Client, Input, Options)
-
put_auto_scaling_policy(Client, Input)
Creates or updates an automatic scaling policy for a core instance group or task instance group in an Amazon EMR cluster.
- put_auto_scaling_policy(Client, Input, Options)
-
put_block_public_access_configuration(Client, Input)
Creates or updates an Amazon EMR block public access configuration for your AWS account in the current Region.
- put_block_public_access_configuration(Client, Input, Options)
-
put_managed_scaling_policy(Client, Input)
Creates or updates a managed scaling policy for an Amazon EMR cluster.
- put_managed_scaling_policy(Client, Input, Options)
-
remove_auto_scaling_policy(Client, Input)
Removes an automatic scaling policy from a specified instance group within an EMR cluster.
- remove_auto_scaling_policy(Client, Input, Options)
-
remove_managed_scaling_policy(Client, Input)
Removes a managed scaling policy from a specified EMR cluster.
- remove_managed_scaling_policy(Client, Input, Options)
-
remove_tags(Client, Input)
Removes tags from an Amazon EMR resource.
- remove_tags(Client, Input, Options)
-
run_job_flow(Client, Input)
RunJobFlow creates and starts running a new cluster (job flow).
- run_job_flow(Client, Input, Options)
-
set_termination_protection(Client, Input)
SetTerminationProtection locks a cluster (job flow) so the EC2 instances in the cluster cannot be terminated by user intervention, an API call, or in the event of a job-flow error.
- set_termination_protection(Client, Input, Options)
-
set_visible_to_all_users(Client, Input)
Sets the Cluster$VisibleToAllUsers value, which determines whether the cluster is visible to all IAM users of the AWS account associated with the cluster.
- set_visible_to_all_users(Client, Input, Options)
-
terminate_job_flows(Client, Input)
TerminateJobFlows shuts a list of clusters (job flows) down.
- terminate_job_flows(Client, Input, Options)
Functions
add_instance_fleet(Client, Input)
Adds an instance fleet to a running cluster.
add_instance_fleet(Client, Input, Options)
add_instance_groups(Client, Input)
Adds one or more instance groups to a running cluster.
add_instance_groups(Client, Input, Options)
add_job_flow_steps(Client, Input)
AddJobFlowSteps adds new steps to a running cluster. A maximum of 256 steps are allowed in each job flow.
If your cluster is long-running (such as a Hive data warehouse) or complex, you may require more than 256 steps to process your data. You can bypass the 256-step limitation in various ways, including using SSH to connect to the master node and submitting queries directly to the software running on the master node, such as Hive and Hadoop. For more information on how to do this, see Add More than 256 Steps to a Cluster in the Amazon EMR Management Guide.
A step specifies the location of a JAR file stored either on the master node of the cluster or in Amazon S3. Each step is performed by the main function of the main class of the JAR file. The main class can be specified either in the manifest of the JAR or by using the MainFunction parameter of the step.
Amazon EMR executes each step in the order listed. For a step to be considered complete, the main function must exit with a zero exit code and all Hadoop jobs started while the step was running must have completed and run successfully.
You can only add steps to a cluster that is in one of the following states: STARTING, BOOTSTRAPPING, RUNNING, or WAITING.add_job_flow_steps(Client, Input, Options)
add_tags(Client, Input)
Adds tags to an Amazon EMR resource. Tags make it easier to associate clusters in various ways, such as grouping clusters to track your Amazon EMR resource allocation costs. For more information, see Tag Clusters.
add_tags(Client, Input, Options)
cancel_steps(Client, Input)
Cancels a pending step or steps in a running cluster. Available only
in Amazon EMR versions 4.8.0 and later, excluding version 5.0.0. A maximum
of 256 steps are allowed in each CancelSteps request. CancelSteps is
idempotent but asynchronous; it does not guarantee a step will be
canceled, even if the request is successfully submitted. You can only
cancel steps that are in a PENDING
state.
cancel_steps(Client, Input, Options)
create_security_configuration(Client, Input)
Creates a security configuration, which is stored in the service and can be specified when a cluster is created.
create_security_configuration(Client, Input, Options)
delete_security_configuration(Client, Input)
Deletes a security configuration.
delete_security_configuration(Client, Input, Options)
describe_cluster(Client, Input)
Provides cluster-level details including status, hardware and software configuration, VPC settings, and so on.
describe_cluster(Client, Input, Options)
describe_job_flows(Client, Input)
This API is deprecated and will eventually be removed. We recommend you use ListClusters, DescribeCluster, ListSteps, ListInstanceGroups and ListBootstrapActions instead.
DescribeJobFlows returns a list of job flows that match all of the supplied parameters. The parameters can include a list of job flow IDs, job flow states, and restrictions on job flow creation date and time.
Regardless of supplied parameters, only job flows created within the last two months are returned.
If no parameters are supplied, then job flows matching either of the following criteria are returned:
Job flows created and completed in the last two weeks
Job flows created within the last two months that are in one of the following states:
RUNNING
,WAITING
,SHUTTING_DOWN
,STARTING
describe_job_flows(Client, Input, Options)
describe_security_configuration(Client, Input)
Provides the details of a security configuration by returning the configuration JSON.
describe_security_configuration(Client, Input, Options)
describe_step(Client, Input)
Provides more detail about the cluster step.
describe_step(Client, Input, Options)
get_block_public_access_configuration(Client, Input)
Returns the Amazon EMR block public access configuration for your AWS account in the current Region. For more information see Configure Block Public Access for Amazon EMR in the Amazon EMR Management Guide.
get_block_public_access_configuration(Client, Input, Options)
get_managed_scaling_policy(Client, Input)
Fetches the attached managed scaling policy for an Amazon EMR cluster.
get_managed_scaling_policy(Client, Input, Options)
list_bootstrap_actions(Client, Input)
Provides information about the bootstrap actions associated with a cluster.
list_bootstrap_actions(Client, Input, Options)
list_clusters(Client, Input)
Provides the status of all clusters visible to this AWS account. Allows you to filter the list of clusters based on certain criteria; for example, filtering by cluster creation date and time or by status. This call returns a maximum of 50 clusters per call, but returns a marker to track the paging of the cluster list across multiple ListClusters calls.
list_clusters(Client, Input, Options)
list_instance_fleets(Client, Input)
Lists all available details about the instance fleets in a cluster.
list_instance_fleets(Client, Input, Options)
list_instance_groups(Client, Input)
Provides all available details about the instance groups in a cluster.
list_instance_groups(Client, Input, Options)
list_instances(Client, Input)
Provides information for all active EC2 instances and EC2 instances terminated in the last 30 days, up to a maximum of 2,000. EC2 instances in any of the following states are considered active: AWAITING_FULFILLMENT, PROVISIONING, BOOTSTRAPPING, RUNNING.
list_instances(Client, Input, Options)
list_security_configurations(Client, Input)
Lists all the security configurations visible to this account, providing their creation dates and times, and their names. This call returns a maximum of 50 clusters per call, but returns a marker to track the paging of the cluster list across multiple ListSecurityConfigurations calls.
list_security_configurations(Client, Input, Options)
list_steps(Client, Input)
Provides a list of steps for the cluster in reverse order unless you
specify stepIds
with the request of filter by
StepStates
. You can specify a maximum of ten
stepIDs
.
list_steps(Client, Input, Options)
modify_cluster(Client, Input)
Modifies the number of steps that can be executed concurrently for the cluster specified using ClusterID.
modify_cluster(Client, Input, Options)
modify_instance_fleet(Client, Input)
Modifies the target On-Demand and target Spot capacities for the instance fleet with the specified InstanceFleetID within the cluster specified using ClusterID. The call either succeeds or fails atomically.
modify_instance_fleet(Client, Input, Options)
modify_instance_groups(Client, Input)
ModifyInstanceGroups modifies the number of nodes and configuration settings of an instance group. The input parameters include the new target instance count for the group and the instance group ID. The call will either succeed or fail atomically.
modify_instance_groups(Client, Input, Options)
put_auto_scaling_policy(Client, Input)
Creates or updates an automatic scaling policy for a core instance group or task instance group in an Amazon EMR cluster. The automatic scaling policy defines how an instance group dynamically adds and terminates EC2 instances in response to the value of a CloudWatch metric.
put_auto_scaling_policy(Client, Input, Options)
put_block_public_access_configuration(Client, Input)
Creates or updates an Amazon EMR block public access configuration for your AWS account in the current Region. For more information see Configure Block Public Access for Amazon EMR in the Amazon EMR Management Guide.
put_block_public_access_configuration(Client, Input, Options)
put_managed_scaling_policy(Client, Input)
Creates or updates a managed scaling policy for an Amazon EMR cluster. The managed scaling policy defines the limits for resources, such as EC2 instances that can be added or terminated from a cluster. The policy only applies to the core and task nodes. The master node cannot be scaled after initial configuration.
put_managed_scaling_policy(Client, Input, Options)
remove_auto_scaling_policy(Client, Input)
Removes an automatic scaling policy from a specified instance group within an EMR cluster.
remove_auto_scaling_policy(Client, Input, Options)
remove_managed_scaling_policy(Client, Input)
Removes a managed scaling policy from a specified EMR cluster.
remove_managed_scaling_policy(Client, Input, Options)
remove_tags(Client, Input)
Removes tags from an Amazon EMR resource. Tags make it easier to associate clusters in various ways, such as grouping clusters to track your Amazon EMR resource allocation costs. For more information, see Tag Clusters.
The following example removes the stack tag with value Prod from a cluster:remove_tags(Client, Input, Options)
run_job_flow(Client, Input)
RunJobFlow creates and starts running a new cluster (job flow). The
cluster runs the steps specified. After the steps complete, the cluster
stops and the HDFS partition is lost. To prevent loss of data, configure
the last step of the job flow to store results in Amazon S3. If the
JobFlowInstancesConfig KeepJobFlowAliveWhenNoSteps
parameter is set to TRUE
, the cluster transitions to the
WAITING state rather than shutting down after the steps have completed.
For additional protection, you can set the JobFlowInstancesConfig
TerminationProtected
parameter to TRUE
to lock
the cluster and prevent it from being terminated by API call, user
intervention, or in the event of a job flow error.
A maximum of 256 steps are allowed in each job flow.
If your cluster is long-running (such as a Hive data warehouse) or complex, you may require more than 256 steps to process your data. You can bypass the 256-step limitation in various ways, including using the SSH shell to connect to the master node and submitting queries directly to the software running on the master node, such as Hive and Hadoop. For more information on how to do this, see Add More than 256 Steps to a Cluster in the Amazon EMR Management Guide.
For long running clusters, we recommend that you periodically store your results.
run_job_flow(Client, Input, Options)
set_termination_protection(Client, Input)
SetTerminationProtection locks a cluster (job flow) so the EC2
instances in the cluster cannot be terminated by user intervention, an API
call, or in the event of a job-flow error. The cluster still terminates
upon successful completion of the job flow. Calling
SetTerminationProtection
on a cluster is similar to calling
the Amazon EC2 DisableAPITermination
API on all EC2 instances
in a cluster.
SetTerminationProtection
is used to prevent accidental
termination of a cluster and to ensure that in the event of an error, the
instances persist so that you can recover any data stored in their
ephemeral instance storage.
To terminate a cluster that has been locked by setting
SetTerminationProtection
to true
, you must first
unlock the job flow by a subsequent call to
SetTerminationProtection
in which you set the value to
false
.
set_termination_protection(Client, Input, Options)
set_visible_to_all_users(Client, Input)
Sets the Cluster$VisibleToAllUsers value, which determines
whether the cluster is visible to all IAM users of the AWS account
associated with the cluster. Only the IAM user who created the cluster or
the AWS account root user can call this action. The default value,
true
, indicates that all IAM users in the AWS account can
perform cluster actions if they have the proper IAM policy permissions. If
set to false
, only the IAM user that created the cluster can
perform actions. This action works on running clusters. You can override
the default true
setting when you create a cluster by using
the VisibleToAllUsers
parameter with RunJobFlow
.
set_visible_to_all_users(Client, Input, Options)
terminate_job_flows(Client, Input)
TerminateJobFlows shuts a list of clusters (job flows) down. When a job flow is shut down, any step not yet completed is canceled and the EC2 instances on which the cluster is running are stopped. Any log files not already saved are uploaded to Amazon S3 if a LogUri was specified when the cluster was created.
The maximum number of clusters allowed is 10. The call toTerminateJobFlows
is asynchronous. Depending on the
configuration of the cluster, it may take up to 1-5 minutes for the
cluster to completely terminate and release allocated resources, such as
Amazon EC2 instances.