D'autres exemples de AWS SDK sont disponibles dans le référentiel [AWS Doc SDK Examples](https://github.com/awsdocs/aws-doc-sdk-examples) GitHub .
Les traductions sont fournies par des outils de traduction automatique. En cas de conflit entre le contenu d'une traduction et celui de la version originale en anglais, la version anglaise prévaudra.
# Exemples de base pour l'utilisation d'Amazon EMR AWS SDKs
Les exemples de code suivants montrent comment utiliser les bases d'Amazon EMR avec. AWS SDKs
**Contents**
+ [Actions](emr_code_examples_actions.md)
+ [`AddJobFlowSteps`](emr_example_emr_AddJobFlowSteps_section.md)
+ [`DescribeCluster`](emr_example_emr_DescribeCluster_section.md)
+ [`DescribeStep`](emr_example_emr_DescribeStep_section.md)
+ [`ListSteps`](emr_example_emr_ListSteps_section.md)
+ [`RunJobFlow`](emr_example_emr_RunJobFlow_section.md)
+ [`TerminateJobFlows`](emr_example_emr_TerminateJobFlows_section.md)
# Actions pour Amazon EMR utilisant AWS SDKs
Les exemples de code suivants montrent comment effectuer des actions Amazon EMR individuelles avec. AWS SDKs Chaque exemple inclut un lien vers GitHub, où vous pouvez trouver des instructions pour configurer et exécuter le code.
Ces extraits appellent l’API Amazon EMR et sont des extraits de code de programmes plus volumineux qui doivent être exécutés en contexte. Vous pouvez voir les actions dans leur contexte dans [Scénarios d'utilisation d'Amazon EMR AWS SDKs](emr_code_examples_scenarios.md).
Les exemples suivants incluent uniquement les actions les plus couramment utilisées. Pour obtenir la liste complète, consultez la [Référence des API Amazon EMR](https://docs.aws.amazon.com/emr/latest/APIReference/Welcome.html).
**Topics**
+ [`AddJobFlowSteps`](emr_example_emr_AddJobFlowSteps_section.md)
+ [`DescribeCluster`](emr_example_emr_DescribeCluster_section.md)
+ [`DescribeStep`](emr_example_emr_DescribeStep_section.md)
+ [`ListSteps`](emr_example_emr_ListSteps_section.md)
+ [`RunJobFlow`](emr_example_emr_RunJobFlow_section.md)
+ [`TerminateJobFlows`](emr_example_emr_TerminateJobFlows_section.md)
# Utilisation `AddJobFlowSteps` avec un AWS SDK
Les exemples de code suivants illustrent comment utiliser `AddJobFlowSteps`.
------
#### [ Python ]
**Kit SDK for Python (Boto3)**
Il y en a plus à ce sujet GitHub. Trouvez l’exemple complet et découvrez comment le configurer et l’exécuter dans le [référentiel d’exemples de code AWS](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/python/example_code/emr#code-examples).
Ajoutez une étape Spark, qui est exécutée par le cluster dès son ajout.
```
def add_step(cluster_id, name, script_uri, script_args, emr_client):
"""
Adds a job step to the specified cluster. This example adds a Spark
step, which is run by the cluster as soon as it is added.
:param cluster_id: The ID of the cluster.
:param name: The name of the step.
:param script_uri: The URI where the Python script is stored.
:param script_args: Arguments to pass to the Python script.
:param emr_client: The Boto3 EMR client object.
:return: The ID of the newly added step.
"""
try:
response = emr_client.add_job_flow_steps(
JobFlowId=cluster_id,
Steps=[
{
"Name": name,
"ActionOnFailure": "CONTINUE",
"HadoopJarStep": {
"Jar": "command-runner.jar",
"Args": [
"spark-submit",
"--deploy-mode",
"cluster",
script_uri,
*script_args,
],
},
}
],
)
step_id = response["StepIds"][0]
logger.info("Started step with ID %s", step_id)
except ClientError:
logger.exception("Couldn't start step %s with URI %s.", name, script_uri)
raise
else:
return step_id
```
Exécutez une commande Amazon EMR File System (EMRFS) en tant qu’étape de travail sur un cluster. Cela peut être utilisé pour automatiser les commandes EMRFS sur un cluster au lieu d’exécuter des commandes manuellement via une connexion SSH.
```
import boto3
from botocore.exceptions import ClientError
def add_emrfs_step(command, bucket_url, cluster_id, emr_client):
"""
Add an EMRFS command as a job flow step to an existing cluster.
:param command: The EMRFS command to run.
:param bucket_url: The URL of a bucket that contains tracking metadata.
:param cluster_id: The ID of the cluster to update.
:param emr_client: The Boto3 Amazon EMR client object.
:return: The ID of the added job flow step. Status can be tracked by calling
the emr_client.describe_step() function.
"""
job_flow_step = {
"Name": "Example EMRFS Command Step",
"ActionOnFailure": "CONTINUE",
"HadoopJarStep": {
"Jar": "command-runner.jar",
"Args": ["/usr/bin/emrfs", command, bucket_url],
},
}
try:
response = emr_client.add_job_flow_steps(
JobFlowId=cluster_id, Steps=[job_flow_step]
)
step_id = response["StepIds"][0]
print(f"Added step {step_id} to cluster {cluster_id}.")
except ClientError:
print(f"Couldn't add a step to cluster {cluster_id}.")
raise
else:
return step_id
def usage_demo():
emr_client = boto3.client("emr")
# Assumes the first waiting cluster has EMRFS enabled and has created metadata
# with the default name of 'EmrFSMetadata'.
cluster = emr_client.list_clusters(ClusterStates=["WAITING"])["Clusters"][0]
add_emrfs_step(
"sync", "s3://elasticmapreduce/samples/cloudfront", cluster["Id"], emr_client
)
if __name__ == "__main__":
usage_demo()
```
+ Pour plus de détails sur l'API, consultez [AddJobFlowSteps](https://docs.aws.amazon.com/goto/boto3/elasticmapreduce-2009-03-31/AddJobFlowSteps)le *AWS manuel de référence de l'API SDK for Python (Boto3*).
------
#### [ SAP ABAP ]
**Kit SDK pour SAP ABAP**
Il y en a plus à ce sujet GitHub. Trouvez l’exemple complet et découvrez comment le configurer et l’exécuter dans le [référentiel d’exemples de code AWS](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/sap-abap/services/emr#code-examples).
```
TRY.
" Build args list for Spark submit
DATA lt_args TYPE /aws1/cl_emrxmlstringlist_w=>tt_xmlstringlist.
APPEND NEW /aws1/cl_emrxmlstringlist_w( 'spark-submit' ) TO lt_args.
APPEND NEW /aws1/cl_emrxmlstringlist_w( '--deploy-mode' ) TO lt_args.
APPEND NEW /aws1/cl_emrxmlstringlist_w( 'cluster' ) TO lt_args.
APPEND NEW /aws1/cl_emrxmlstringlist_w( iv_script_uri ) TO lt_args.
APPEND LINES OF it_script_args TO lt_args.
" Create step configuration
DATA(lo_hadoop_jar_step) = NEW /aws1/cl_emrhadoopjarstepcfg(
iv_jar = 'command-runner.jar'
it_args = lt_args
).
DATA(lo_step_config) = NEW /aws1/cl_emrstepconfig(
iv_name = iv_name
iv_actiononfailure = 'CONTINUE'
io_hadoopjarstep = lo_hadoop_jar_step
).
DATA lt_steps TYPE /aws1/cl_emrstepconfig=>tt_stepconfiglist.
APPEND lo_step_config TO lt_steps.
DATA(lo_result) = lo_emr->addjobflowsteps(
iv_jobflowid = iv_cluster_id
it_steps = lt_steps
).
" Get first step ID
DATA(lt_step_ids) = lo_result->get_stepids( ).
READ TABLE lt_step_ids INDEX 1 INTO DATA(lo_step_id_obj).
IF sy-subrc = 0.
ov_step_id = lo_step_id_obj->get_value( ).
MESSAGE |Step added with ID { ov_step_id }| TYPE 'I'.
ENDIF.
CATCH /aws1/cx_emrinternalservererr INTO DATA(lo_internal_error).
DATA(lv_error) = lo_internal_error->if_message~get_text( ).
MESSAGE lv_error TYPE 'E'.
ENDTRY.
```
+ Pour plus de détails sur l'API, reportez-vous [AddJobFlowSteps](https://docs.aws.amazon.com/sdk-for-sap-abap/v1/api/latest/index.html)à la section de référence du *AWS SDK pour l'API SAP ABAP*.
------
# Utilisation `DescribeCluster` avec un AWS SDK ou une CLI
Les exemples de code suivants illustrent comment utiliser `DescribeCluster`.
------
#### [ CLI ]
**AWS CLI**
Commande :
```
aws emr describe-cluster --cluster-id j-XXXXXXXX
```
Sortie :
```
For release-label based uniform instance groups cluster:
{
"Cluster": {
"Status": {
"Timeline": {
"ReadyDateTime": 1436475075.199,
"CreationDateTime": 1436474656.563,
},
"State": "WAITING",
"StateChangeReason": {
"Message": "Waiting for steps to run"
}
},
"Ec2InstanceAttributes": {
"ServiceAccessSecurityGroup": "sg-xxxxxxxx",
"EmrManagedMasterSecurityGroup": "sg-xxxxxxxx",
"IamInstanceProfile": "EMR_EC2_DefaultRole",
"Ec2KeyName": "myKey",
"Ec2AvailabilityZone": "us-east-1c",
"EmrManagedSlaveSecurityGroup": "sg-yyyyyyyyy"
},
"Name": "My Cluster",
"ServiceRole": "EMR_DefaultRole",
"Tags": [],
"TerminationProtected": true,
"UnhealthyNodeReplacement": true,
"ReleaseLabel": "emr-4.0.0",
"NormalizedInstanceHours": 96,
"InstanceGroups": [
{
"RequestedInstanceCount": 2,
"Status": {
"Timeline": {
"ReadyDateTime": 1436475074.245,
"CreationDateTime": 1436474656.564,
"EndDateTime": 1436638158.387
},
"State": "RUNNING",
"StateChangeReason": {
"Message": "",
}
},
"Name": "CORE",
"InstanceGroupType": "CORE",
"Id": "ig-YYYYYYY",
"Configurations": [],
"InstanceType": "m3.large",
"Market": "ON_DEMAND",
"RunningInstanceCount": 2
},
{
"RequestedInstanceCount": 1,
"Status": {
"Timeline": {
"ReadyDateTime": 1436475074.245,
"CreationDateTime": 1436474656.564,
"EndDateTime": 1436638158.387
},
"State": "RUNNING",
"StateChangeReason": {
"Message": "",
}
},
"Name": "MASTER",
"InstanceGroupType": "MASTER",
"Id": "ig-XXXXXXXXX",
"Configurations": [],
"InstanceType": "m3.large",
"Market": "ON_DEMAND",
"RunningInstanceCount": 1
}
],
"Applications": [
{
"Name": "Hadoop"
}
],
"VisibleToAllUsers": true,
"BootstrapActions": [],
"MasterPublicDnsName": "ec2-54-147-144-78.compute-1.amazonaws.com",
"AutoTerminate": false,
"Id": "j-XXXXXXXX",
"Configurations": [
{
"Properties": {
"fs.s3.consistent.retryPeriodSeconds": "20",
"fs.s3.enableServerSideEncryption": "true",
"fs.s3.consistent": "false",
"fs.s3.consistent.retryCount": "2"
},
"Classification": "emrfs-site"
}
]
}
}
For release-label based instance fleet cluster:
{
"Cluster": {
"Status": {
"Timeline": {
"ReadyDateTime": 1487897289.705,
"CreationDateTime": 1487896933.942
},
"State": "WAITING",
"StateChangeReason": {
"Message": "Waiting for steps to run"
}
},
"Ec2InstanceAttributes": {
"EmrManagedMasterSecurityGroup": "sg-xxxxx",
"RequestedEc2AvailabilityZones": [],
"RequestedEc2SubnetIds": [],
"IamInstanceProfile": "EMR_EC2_DefaultRole",
"Ec2AvailabilityZone": "us-east-1a",
"EmrManagedSlaveSecurityGroup": "sg-xxxxx"
},
"Name": "My Cluster",
"ServiceRole": "EMR_DefaultRole",
"Tags": [],
"TerminationProtected": false,
"UnhealthyNodeReplacement": false,
"ReleaseLabel": "emr-5.2.0",
"NormalizedInstanceHours": 472,
"InstanceCollectionType": "INSTANCE_FLEET",
"InstanceFleets": [
{
"Status": {
"Timeline": {
"ReadyDateTime": 1487897212.74,
"CreationDateTime": 1487896933.948
},
"State": "RUNNING",
"StateChangeReason": {
"Message": ""
}
},
"ProvisionedSpotCapacity": 1,
"Name": "MASTER",
"InstanceFleetType": "MASTER",
"LaunchSpecifications": {
"SpotSpecification": {
"TimeoutDurationMinutes": 60,
"TimeoutAction": "TERMINATE_CLUSTER"
}
},
"TargetSpotCapacity": 1,
"ProvisionedOnDemandCapacity": 0,
"InstanceTypeSpecifications": [
{
"BidPrice": "0.5",
"InstanceType": "m3.xlarge",
"WeightedCapacity": 1
}
],
"Id": "if-xxxxxxx",
"TargetOnDemandCapacity": 0
}
],
"Applications": [
{
"Version": "2.7.3",
"Name": "Hadoop"
}
],
"ScaleDownBehavior": "TERMINATE_AT_INSTANCE_HOUR",
"VisibleToAllUsers": true,
"BootstrapActions": [],
"MasterPublicDnsName": "ec2-xxx-xx-xxx-xx.compute-1.amazonaws.com",
"AutoTerminate": false,
"Id": "j-xxxxx",
"Configurations": []
}
}
For ami based uniform instance group cluster:
{
"Cluster": {
"Status": {
"Timeline": {
"ReadyDateTime": 1399400564.432,
"CreationDateTime": 1399400268.62
},
"State": "WAITING",
"StateChangeReason": {
"Message": "Waiting for steps to run"
}
},
"Ec2InstanceAttributes": {
"IamInstanceProfile": "EMR_EC2_DefaultRole",
"Ec2AvailabilityZone": "us-east-1c"
},
"Name": "My Cluster",
"Tags": [],
"TerminationProtected": true,
"UnhealthyNodeReplacement": true,
"RunningAmiVersion": "2.5.4",
"InstanceGroups": [
{
"RequestedInstanceCount": 1,
"Status": {
"Timeline": {
"ReadyDateTime": 1399400558.848,
"CreationDateTime": 1399400268.621
},
"State": "RUNNING",
"StateChangeReason": {
"Message": ""
}
},
"Name": "Master instance group",
"InstanceGroupType": "MASTER",
"InstanceType": "m1.small",
"Id": "ig-ABCD",
"Market": "ON_DEMAND",
"RunningInstanceCount": 1
},
{
"RequestedInstanceCount": 2,
"Status": {
"Timeline": {
"ReadyDateTime": 1399400564.439,
"CreationDateTime": 1399400268.621
},
"State": "RUNNING",
"StateChangeReason": {
"Message": ""
}
},
"Name": "Core instance group",
"InstanceGroupType": "CORE",
"InstanceType": "m1.small",
"Id": "ig-DEF",
"Market": "ON_DEMAND",
"RunningInstanceCount": 2
}
],
"Applications": [
{
"Version": "1.0.3",
"Name": "hadoop"
}
],
"BootstrapActions": [],
"VisibleToAllUsers": false,
"RequestedAmiVersion": "2.4.2",
"LogUri": "s3://myLogUri/",
"AutoTerminate": false,
"Id": "j-XXXXXXXX"
}
}
```
+ Pour plus de détails sur l'API, reportez-vous [DescribeCluster](https://awscli.amazonaws.com/v2/documentation/api/latest/reference/emr/describe-cluster.html)à la section *Référence des AWS CLI commandes*.
------
#### [ Python ]
**Kit SDK for Python (Boto3)**
Il y en a plus à ce sujet GitHub. Trouvez l’exemple complet et découvrez comment le configurer et l’exécuter dans le [référentiel d’exemples de code AWS](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/python/example_code/emr#code-examples).
```
def describe_cluster(cluster_id, emr_client):
"""
Gets detailed information about a cluster.
:param cluster_id: The ID of the cluster to describe.
:param emr_client: The Boto3 EMR client object.
:return: The retrieved cluster information.
"""
try:
response = emr_client.describe_cluster(ClusterId=cluster_id)
cluster = response["Cluster"]
logger.info("Got data for cluster %s.", cluster["Name"])
except ClientError:
logger.exception("Couldn't get data for cluster %s.", cluster_id)
raise
else:
return cluster
```
+ Pour plus de détails sur l'API, consultez [DescribeCluster](https://docs.aws.amazon.com/goto/boto3/elasticmapreduce-2009-03-31/DescribeCluster)le *AWS manuel de référence de l'API SDK for Python (Boto3*).
------
#### [ SAP ABAP ]
**Kit SDK pour SAP ABAP**
Il y en a plus à ce sujet GitHub. Trouvez l’exemple complet et découvrez comment le configurer et l’exécuter dans le [référentiel d’exemples de code AWS](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/sap-abap/services/emr#code-examples).
```
TRY.
oo_result = lo_emr->describecluster(
iv_clusterid = iv_cluster_id
).
DATA(lo_cluster) = oo_result->get_cluster( ).
DATA(lv_cluster_name) = lo_cluster->get_name( ).
MESSAGE |Retrieved cluster information for { lv_cluster_name }| TYPE 'I'.
CATCH /aws1/cx_emrinternalserverex INTO DATA(lo_internal_error).
DATA(lv_error) = lo_internal_error->if_message~get_text( ).
MESSAGE lv_error TYPE 'E'.
CATCH /aws1/cx_emrinvalidrequestex INTO DATA(lo_invalid_error).
lv_error = lo_invalid_error->if_message~get_text( ).
MESSAGE lv_error TYPE 'E'.
ENDTRY.
```
+ Pour plus de détails sur l'API, reportez-vous [DescribeCluster](https://docs.aws.amazon.com/sdk-for-sap-abap/v1/api/latest/index.html)à la section de référence du *AWS SDK pour l'API SAP ABAP*.
------
# Utilisation `DescribeStep` avec un AWS SDK ou une CLI
Les exemples de code suivants illustrent comment utiliser `DescribeStep`.
------
#### [ CLI ]
**AWS CLI**
La commande suivante décrit une étape avec l’ID d’étape `s-3LZC0QUT43AM` dans un cluster avec l’ID de cluster `j-3SD91U2E1L2QX` :
```
aws emr describe-step --cluster-id j-3SD91U2E1L2QX --step-id s-3LZC0QUT43AM
```
Sortie :
```
{
"Step": {
"Status": {
"Timeline": {
"EndDateTime": 1433200470.481,
"CreationDateTime": 1433199926.597,
"StartDateTime": 1433200404.959
},
"State": "COMPLETED",
"StateChangeReason": {}
},
"Config": {
"Args": [
"s3://us-west-2.elasticmapreduce/libs/hive/hive-script",
"--base-path",
"s3://us-west-2.elasticmapreduce/libs/hive/",
"--install-hive",
"--hive-versions",
"0.13.1"
],
"Jar": "s3://us-west-2.elasticmapreduce/libs/script-runner/script-runner.jar",
"Properties": {}
},
"Id": "s-3LZC0QUT43AM",
"ActionOnFailure": "TERMINATE_CLUSTER",
"Name": "Setup hive"
}
}
```
+ Pour plus de détails sur l'API, reportez-vous [DescribeStep](https://awscli.amazonaws.com/v2/documentation/api/latest/reference/emr/describe-step.html)à la section *Référence des AWS CLI commandes*.
------
#### [ Python ]
**Kit SDK for Python (Boto3)**
Il y en a plus à ce sujet GitHub. Trouvez l’exemple complet et découvrez comment le configurer et l’exécuter dans le [référentiel d’exemples de code AWS](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/python/example_code/emr#code-examples).
```
def describe_step(cluster_id, step_id, emr_client):
"""
Gets detailed information about the specified step, including the current state of
the step.
:param cluster_id: The ID of the cluster.
:param step_id: The ID of the step.
:param emr_client: The Boto3 EMR client object.
:return: The retrieved information about the specified step.
"""
try:
response = emr_client.describe_step(ClusterId=cluster_id, StepId=step_id)
step = response["Step"]
logger.info("Got data for step %s.", step_id)
except ClientError:
logger.exception("Couldn't get data for step %s.", step_id)
raise
else:
return step
```
+ Pour plus de détails sur l'API, consultez [DescribeStep](https://docs.aws.amazon.com/goto/boto3/elasticmapreduce-2009-03-31/DescribeStep)le *AWS manuel de référence de l'API SDK for Python (Boto3*).
------
#### [ SAP ABAP ]
**Kit SDK pour SAP ABAP**
Il y en a plus à ce sujet GitHub. Trouvez l’exemple complet et découvrez comment le configurer et l’exécuter dans le [référentiel d’exemples de code AWS](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/sap-abap/services/emr#code-examples).
```
TRY.
oo_result = lo_emr->describestep(
iv_clusterid = iv_cluster_id
iv_stepid = iv_step_id
).
DATA(lo_step) = oo_result->get_step( ).
DATA(lv_step_name) = lo_step->get_name( ).
MESSAGE |Retrieved step information for { lv_step_name }| TYPE 'I'.
CATCH /aws1/cx_emrinternalserverex INTO DATA(lo_internal_error).
DATA(lv_error) = lo_internal_error->if_message~get_text( ).
MESSAGE lv_error TYPE 'E'.
CATCH /aws1/cx_emrinvalidrequestex INTO DATA(lo_invalid_error).
lv_error = lo_invalid_error->if_message~get_text( ).
MESSAGE lv_error TYPE 'E'.
ENDTRY.
```
+ Pour plus de détails sur l'API, reportez-vous [DescribeStep](https://docs.aws.amazon.com/sdk-for-sap-abap/v1/api/latest/index.html)à la section de référence du *AWS SDK pour l'API SAP ABAP*.
------
# Utilisation `ListSteps` avec un AWS SDK ou une CLI
Les exemples de code suivants illustrent comment utiliser `ListSteps`.
------
#### [ CLI ]
**AWS CLI**
La commande suivante répertorie toutes les étapes du cluster doté de l’ID de cluster `j-3SD91U2E1L2QX` :
```
aws emr list-steps --cluster-id j-3SD91U2E1L2QX
```
+ Pour plus de détails sur l'API, reportez-vous [ListSteps](https://awscli.amazonaws.com/v2/documentation/api/latest/reference/emr/list-steps.html)à la section *Référence des AWS CLI commandes*.
------
#### [ Python ]
**Kit SDK for Python (Boto3)**
Il y en a plus à ce sujet GitHub. Trouvez l’exemple complet et découvrez comment le configurer et l’exécuter dans le [référentiel d’exemples de code AWS](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/python/example_code/emr#code-examples).
```
def list_steps(cluster_id, emr_client):
"""
Gets a list of steps for the specified cluster. In this example, all steps are
returned, including completed and failed steps.
:param cluster_id: The ID of the cluster.
:param emr_client: The Boto3 EMR client object.
:return: The list of steps for the specified cluster.
"""
try:
response = emr_client.list_steps(ClusterId=cluster_id)
steps = response["Steps"]
logger.info("Got %s steps for cluster %s.", len(steps), cluster_id)
except ClientError:
logger.exception("Couldn't get steps for cluster %s.", cluster_id)
raise
else:
return steps
```
+ Pour plus de détails sur l'API, consultez [ListSteps](https://docs.aws.amazon.com/goto/boto3/elasticmapreduce-2009-03-31/ListSteps)le *AWS manuel de référence de l'API SDK for Python (Boto3*).
------
#### [ SAP ABAP ]
**Kit SDK pour SAP ABAP**
Il y en a plus à ce sujet GitHub. Trouvez l’exemple complet et découvrez comment le configurer et l’exécuter dans le [référentiel d’exemples de code AWS](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/sap-abap/services/emr#code-examples).
```
TRY.
oo_result = lo_emr->liststeps(
iv_clusterid = iv_cluster_id
).
DATA(lt_steps) = oo_result->get_steps( ).
DATA(lv_step_count) = lines( lt_steps ).
MESSAGE |Retrieved { lv_step_count } steps for cluster| TYPE 'I'.
CATCH /aws1/cx_emrinternalserverex INTO DATA(lo_internal_error).
DATA(lv_error) = lo_internal_error->if_message~get_text( ).
MESSAGE lv_error TYPE 'E'.
CATCH /aws1/cx_emrinvalidrequestex INTO DATA(lo_invalid_error).
lv_error = lo_invalid_error->if_message~get_text( ).
MESSAGE lv_error TYPE 'E'.
ENDTRY.
```
+ Pour plus de détails sur l'API, reportez-vous [ListSteps](https://docs.aws.amazon.com/sdk-for-sap-abap/v1/api/latest/index.html)à la section de référence du *AWS SDK pour l'API SAP ABAP*.
------
# Utilisation `RunJobFlow` avec un AWS SDK
Les exemples de code suivants illustrent comment utiliser `RunJobFlow`.
------
#### [ Python ]
**Kit SDK for Python (Boto3)**
Il y en a plus à ce sujet GitHub. Trouvez l’exemple complet et découvrez comment le configurer et l’exécuter dans le [référentiel d’exemples de code AWS](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/python/example_code/emr#code-examples).
```
def run_job_flow(
name,
log_uri,
keep_alive,
applications,
job_flow_role,
service_role,
security_groups,
steps,
emr_client,
):
"""
Runs a job flow with the specified steps. A job flow creates a cluster of
instances and adds steps to be run on the cluster. Steps added to the cluster
are run as soon as the cluster is ready.
This example uses the 'emr-5.30.1' release. A list of recent releases can be
found here:
https://docs.aws.amazon.com/emr/latest/ReleaseGuide/emr-release-components.html.
:param name: The name of the cluster.
:param log_uri: The URI where logs are stored. This can be an Amazon S3 bucket URL,
such as 's3://my-log-bucket'.
:param keep_alive: When True, the cluster is put into a Waiting state after all
steps are run. When False, the cluster terminates itself when
the step queue is empty.
:param applications: The applications to install on each instance in the cluster,
such as Hive or Spark.
:param job_flow_role: The IAM role assumed by the cluster.
:param service_role: The IAM role assumed by the service.
:param security_groups: The security groups to assign to the cluster instances.
Amazon EMR adds all needed rules to these groups, so
they can be empty if you require only the default rules.
:param steps: The job flow steps to add to the cluster. These are run in order
when the cluster is ready.
:param emr_client: The Boto3 EMR client object.
:return: The ID of the newly created cluster.
"""
try:
response = emr_client.run_job_flow(
Name=name,
LogUri=log_uri,
ReleaseLabel="emr-5.30.1",
Instances={
"MasterInstanceType": "m5.xlarge",
"SlaveInstanceType": "m5.xlarge",
"InstanceCount": 3,
"KeepJobFlowAliveWhenNoSteps": keep_alive,
"EmrManagedMasterSecurityGroup": security_groups["manager"].id,
"EmrManagedSlaveSecurityGroup": security_groups["worker"].id,
},
Steps=[
{
"Name": step["name"],
"ActionOnFailure": "CONTINUE",
"HadoopJarStep": {
"Jar": "command-runner.jar",
"Args": [
"spark-submit",
"--deploy-mode",
"cluster",
step["script_uri"],
*step["script_args"],
],
},
}
for step in steps
],
Applications=[{"Name": app} for app in applications],
JobFlowRole=job_flow_role.name,
ServiceRole=service_role.name,
EbsRootVolumeSize=10,
VisibleToAllUsers=True,
)
cluster_id = response["JobFlowId"]
logger.info("Created cluster %s.", cluster_id)
except ClientError:
logger.exception("Couldn't create cluster.")
raise
else:
return cluster_id
```
+ Pour plus de détails sur l'API, consultez [RunJobFlow](https://docs.aws.amazon.com/goto/boto3/elasticmapreduce-2009-03-31/RunJobFlow)le *AWS manuel de référence de l'API SDK for Python (Boto3*).
------
#### [ SAP ABAP ]
**Kit SDK pour SAP ABAP**
Il y en a plus à ce sujet GitHub. Trouvez l’exemple complet et découvrez comment le configurer et l’exécuter dans le [référentiel d’exemples de code AWS](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/sap-abap/services/emr#code-examples).
```
TRY.
" Create instances configuration
DATA(lo_instances) = NEW /aws1/cl_emrjobflowinstsconfig(
iv_masterinstancetype = 'm5.xlarge'
iv_slaveinstancetype = 'm5.xlarge'
iv_instancecount = 3
iv_keepjobflowalivewhennos00 = iv_keep_alive
iv_emrmanagedmastersecgroup = iv_primary_sec_grp
iv_emrmanagedslavesecgroup = iv_secondary_sec_grp
).
DATA(lo_result) = lo_emr->runjobflow(
iv_name = iv_name
iv_loguri = iv_log_uri
iv_releaselabel = 'emr-5.30.1'
io_instances = lo_instances
it_steps = it_steps
it_applications = it_applications
iv_jobflowrole = iv_job_flow_role
iv_servicerole = iv_service_role
iv_ebsrootvolumesize = 10
iv_visibletoallusers = abap_true
).
ov_cluster_id = lo_result->get_jobflowid( ).
MESSAGE 'EMR cluster created successfully.' TYPE 'I'.
CATCH /aws1/cx_emrinternalservererr INTO DATA(lo_internal_error).
DATA(lv_error) = lo_internal_error->if_message~get_text( ).
MESSAGE lv_error TYPE 'E'.
CATCH /aws1/cx_emrclientexc INTO DATA(lo_client_error).
lv_error = lo_client_error->if_message~get_text( ).
MESSAGE lv_error TYPE 'E'.
ENDTRY.
```
+ Pour plus de détails sur l'API, reportez-vous [RunJobFlow](https://docs.aws.amazon.com/sdk-for-sap-abap/v1/api/latest/index.html)à la section de référence du *AWS SDK pour l'API SAP ABAP*.
------
# Utilisation `TerminateJobFlows` avec un AWS SDK
Les exemples de code suivants illustrent comment utiliser `TerminateJobFlows`.
------
#### [ Python ]
**Kit SDK for Python (Boto3)**
Il y en a plus à ce sujet GitHub. Trouvez l’exemple complet et découvrez comment le configurer et l’exécuter dans le [référentiel d’exemples de code AWS](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/python/example_code/emr#code-examples).
```
def terminate_cluster(cluster_id, emr_client):
"""
Terminates a cluster. This terminates all instances in the cluster and cannot
be undone. Any data not saved elsewhere, such as in an Amazon S3 bucket, is lost.
:param cluster_id: The ID of the cluster to terminate.
:param emr_client: The Boto3 EMR client object.
"""
try:
emr_client.terminate_job_flows(JobFlowIds=[cluster_id])
logger.info("Terminated cluster %s.", cluster_id)
except ClientError:
logger.exception("Couldn't terminate cluster %s.", cluster_id)
raise
```
+ Pour plus de détails sur l'API, consultez [TerminateJobFlows](https://docs.aws.amazon.com/goto/boto3/elasticmapreduce-2009-03-31/TerminateJobFlows)le *AWS manuel de référence de l'API SDK for Python (Boto3*).
------
#### [ SAP ABAP ]
**Kit SDK pour SAP ABAP**
Il y en a plus à ce sujet GitHub. Trouvez l’exemple complet et découvrez comment le configurer et l’exécuter dans le [référentiel d’exemples de code AWS](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/sap-abap/services/emr#code-examples).
```
TRY.
DATA lt_cluster_ids TYPE /aws1/cl_emrxmlstringlist_w=>tt_xmlstringlist.
APPEND NEW /aws1/cl_emrxmlstringlist_w( iv_cluster_id ) TO lt_cluster_ids.
lo_emr->terminatejobflows(
it_jobflowids = lt_cluster_ids
).
MESSAGE 'EMR cluster terminated successfully.' TYPE 'I'.
CATCH /aws1/cx_emrinternalservererr INTO DATA(lo_internal_error).
DATA(lv_error) = lo_internal_error->if_message~get_text( ).
MESSAGE lv_error TYPE 'E'.
ENDTRY.
```
+ Pour plus de détails sur l'API, reportez-vous [TerminateJobFlows](https://docs.aws.amazon.com/sdk-for-sap-abap/v1/api/latest/index.html)à la section de référence du *AWS SDK pour l'API SAP ABAP*.
------