Ada lebih banyak contoh AWS SDK yang tersedia di repo Contoh [SDK AWS Doc](https://github.com/awsdocs/aws-doc-sdk-examples). GitHub
Terjemahan disediakan oleh mesin penerjemah. Jika konten terjemahan yang diberikan bertentangan dengan versi bahasa Inggris aslinya, utamakan versi bahasa Inggris.
# Contoh kode untuk Amazon EMR menggunakan AWS SDKs
Contoh kode berikut menunjukkan cara menggunakan Amazon EMR dengan AWS perangkat pengembangan perangkat lunak (SDK).
*Tindakan* merupakan kutipan kode dari program yang lebih besar dan harus dijalankan dalam konteks. Sementara tindakan menunjukkan cara memanggil fungsi layanan individual, Anda dapat melihat tindakan dalam konteks dalam skenario terkait.
*Skenario* adalah contoh kode yang menunjukkan kepada Anda bagaimana menyelesaikan tugas tertentu dengan memanggil beberapa fungsi dalam layanan atau dikombinasikan dengan yang lain Layanan AWS.
**Sumber daya lainnya**
+ **[Panduan Manajemen EMR Amazon](https://docs.aws.amazon.com/emr/latest/ManagementGuide/emr-what-is-emr.html)** - Informasi lebih lanjut tentang Amazon EMR.
+ **[Referensi API Amazon EMR](https://docs.aws.amazon.com/emr/latest/APIReference/Welcome.html)** - Detail tentang semua tindakan EMR Amazon yang tersedia.
+ **[AWS Pusat Pengembang](https://aws.amazon.com/developer/code-examples/?awsf.sdk-code-examples-product=product%23emr)** - Contoh kode yang dapat Anda filter berdasarkan kategori atau pencarian teks lengkap.
+ **[AWS Contoh SDK](https://github.com/awsdocs/aws-doc-sdk-examples)** — GitHub repo dengan kode lengkap dalam bahasa pilihan. Termasuk instruksi untuk mengatur dan menjalankan kode.
**Contents**
+ [Hal-hal mendasar](emr_code_examples_basics.md)
+ [Tindakan](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)
+ [Skenario](emr_code_examples_scenarios.md)
+ [Buat cluster EMR Amazon berumur pendek dan jalankan langkah](emr_example_emr_Scenario_ShortLivedEmrCluster_section.md)
+ [Jalankan skrip shell untuk menginstal pustaka](emr_example_emr_Usage_InstallLibrariesWithSsm_section.md)
# Contoh dasar untuk Amazon EMR menggunakan AWS SDKs
Contoh kode berikut menunjukkan cara menggunakan dasar-dasar Amazon EMR dengan. AWS SDKs
**Contents**
+ [Tindakan](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)
# Tindakan untuk Amazon EMR menggunakan AWS SDKs
Contoh kode berikut menunjukkan cara melakukan tindakan EMR Amazon individual dengan. AWS SDKs Setiap contoh menyertakan tautan ke GitHub, di mana Anda dapat menemukan instruksi untuk mengatur dan menjalankan kode.
Kutipan ini memanggil Amazon EMR API dan merupakan kutipan kode dari program yang lebih besar yang harus dijalankan dalam konteks. Anda dapat melihat tindakan dalam konteks di[Skenario untuk Amazon EMR menggunakan AWS SDKs](emr_code_examples_scenarios.md).
Contoh berikut hanya mencakup tindakan yang paling umum digunakan. Untuk daftar lengkapnya, lihat Referensi [API EMR Amazon](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)
# Gunakan `AddJobFlowSteps` dengan AWS SDK
Contoh kode berikut menunjukkan cara menggunakan`AddJobFlowSteps`.
------
#### [ Python ]
**SDK untuk Python (Boto3)**
Ada lebih banyak tentang GitHub. Temukan contoh lengkapnya dan pelajari cara mengatur dan menjalankannya di [Repositori Contoh Kode AWS](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/python/example_code/emr#code-examples).
Tambahkan langkah Spark, yang dijalankan oleh cluster segera setelah ditambahkan.
```
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
```
Jalankan perintah Amazon EMR File System (EMRFS) sebagai langkah pekerjaan di cluster. Ini dapat digunakan untuk mengotomatiskan perintah EMRFS pada cluster alih-alih menjalankan perintah secara manual melalui koneksi 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()
```
+ Untuk detail API, lihat [AddJobFlowSteps](https://docs.aws.amazon.com/goto/boto3/elasticmapreduce-2009-03-31/AddJobFlowSteps)di *AWS SDK for Python (Boto3) Referensi* API.
------
#### [ SAP ABAP ]
**SDK for SAP ABAP**
Ada lebih banyak tentang GitHub. Temukan contoh lengkapnya dan pelajari cara mengatur dan menjalankannya di [Repositori Contoh Kode 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.
```
+ Untuk detail API, lihat [AddJobFlowSteps](https://docs.aws.amazon.com/sdk-for-sap-abap/v1/api/latest/index.html)di *AWS SDK untuk referensi SAP ABAP* API.
------
# Gunakan `DescribeCluster` dengan AWS SDK atau CLI
Contoh kode berikut menunjukkan cara menggunakan`DescribeCluster`.
------
#### [ CLI ]
**AWS CLI**
Perintah:
```
aws emr describe-cluster --cluster-id j-XXXXXXXX
```
Output:
```
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"
}
}
```
+ Untuk detail API, lihat [DescribeCluster](https://awscli.amazonaws.com/v2/documentation/api/latest/reference/emr/describe-cluster.html)di *Referensi AWS CLI Perintah*.
------
#### [ Python ]
**SDK untuk Python (Boto3)**
Ada lebih banyak tentang GitHub. Temukan contoh lengkapnya dan pelajari cara mengatur dan menjalankannya di [Repositori Contoh Kode 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
```
+ Untuk detail API, lihat [DescribeCluster](https://docs.aws.amazon.com/goto/boto3/elasticmapreduce-2009-03-31/DescribeCluster)di *AWS SDK for Python (Boto3) Referensi* API.
------
#### [ SAP ABAP ]
**SDK for SAP ABAP**
Ada lebih banyak tentang GitHub. Temukan contoh lengkapnya dan pelajari cara mengatur dan menjalankannya di [Repositori Contoh Kode 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.
```
+ Untuk detail API, lihat [DescribeCluster](https://docs.aws.amazon.com/sdk-for-sap-abap/v1/api/latest/index.html)di *AWS SDK untuk referensi SAP ABAP* API.
------
# Gunakan `DescribeStep` dengan AWS SDK atau CLI
Contoh kode berikut menunjukkan cara menggunakan`DescribeStep`.
------
#### [ CLI ]
**AWS CLI**
Perintah berikut menjelaskan langkah dengan ID langkah `s-3LZC0QUT43AM` dalam cluster dengan ID cluster`j-3SD91U2E1L2QX`:
```
aws emr describe-step --cluster-id j-3SD91U2E1L2QX --step-id s-3LZC0QUT43AM
```
Output:
```
{
"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"
}
}
```
+ Untuk detail API, lihat [DescribeStep](https://awscli.amazonaws.com/v2/documentation/api/latest/reference/emr/describe-step.html)di *Referensi AWS CLI Perintah*.
------
#### [ Python ]
**SDK untuk Python (Boto3)**
Ada lebih banyak tentang GitHub. Temukan contoh lengkapnya dan pelajari cara mengatur dan menjalankannya di [Repositori Contoh Kode 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
```
+ Untuk detail API, lihat [DescribeStep](https://docs.aws.amazon.com/goto/boto3/elasticmapreduce-2009-03-31/DescribeStep)di *AWS SDK for Python (Boto3) Referensi* API.
------
#### [ SAP ABAP ]
**SDK for SAP ABAP**
Ada lebih banyak tentang GitHub. Temukan contoh lengkapnya dan pelajari cara mengatur dan menjalankannya di [Repositori Contoh Kode 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.
```
+ Untuk detail API, lihat [DescribeStep](https://docs.aws.amazon.com/sdk-for-sap-abap/v1/api/latest/index.html)di *AWS SDK untuk referensi SAP ABAP* API.
------
# Gunakan `ListSteps` dengan AWS SDK atau CLI
Contoh kode berikut menunjukkan cara menggunakan`ListSteps`.
------
#### [ CLI ]
**AWS CLI**
Perintah berikut mencantumkan semua langkah dalam cluster dengan ID cluster`j-3SD91U2E1L2QX`:
```
aws emr list-steps --cluster-id j-3SD91U2E1L2QX
```
+ Untuk detail API, lihat [ListSteps](https://awscli.amazonaws.com/v2/documentation/api/latest/reference/emr/list-steps.html)di *Referensi AWS CLI Perintah*.
------
#### [ Python ]
**SDK untuk Python (Boto3)**
Ada lebih banyak tentang GitHub. Temukan contoh lengkapnya dan pelajari cara mengatur dan menjalankannya di [Repositori Contoh Kode 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
```
+ Untuk detail API, lihat [ListSteps](https://docs.aws.amazon.com/goto/boto3/elasticmapreduce-2009-03-31/ListSteps)di *AWS SDK for Python (Boto3) Referensi* API.
------
#### [ SAP ABAP ]
**SDK for SAP ABAP**
Ada lebih banyak tentang GitHub. Temukan contoh lengkapnya dan pelajari cara mengatur dan menjalankannya di [Repositori Contoh Kode 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.
```
+ Untuk detail API, lihat [ListSteps](https://docs.aws.amazon.com/sdk-for-sap-abap/v1/api/latest/index.html)di *AWS SDK untuk referensi SAP ABAP* API.
------
# Gunakan `RunJobFlow` dengan AWS SDK
Contoh kode berikut menunjukkan cara menggunakan`RunJobFlow`.
------
#### [ Python ]
**SDK untuk Python (Boto3)**
Ada lebih banyak tentang GitHub. Temukan contoh lengkapnya dan pelajari cara mengatur dan menjalankannya di [Repositori Contoh Kode 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
```
+ Untuk detail API, lihat [RunJobFlow](https://docs.aws.amazon.com/goto/boto3/elasticmapreduce-2009-03-31/RunJobFlow)di *AWS SDK for Python (Boto3) Referensi* API.
------
#### [ SAP ABAP ]
**SDK for SAP ABAP**
Ada lebih banyak tentang GitHub. Temukan contoh lengkapnya dan pelajari cara mengatur dan menjalankannya di [Repositori Contoh Kode 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.
```
+ Untuk detail API, lihat [RunJobFlow](https://docs.aws.amazon.com/sdk-for-sap-abap/v1/api/latest/index.html)di *AWS SDK untuk referensi SAP ABAP* API.
------
# Gunakan `TerminateJobFlows` dengan AWS SDK
Contoh kode berikut menunjukkan cara menggunakan`TerminateJobFlows`.
------
#### [ Python ]
**SDK untuk Python (Boto3)**
Ada lebih banyak tentang GitHub. Temukan contoh lengkapnya dan pelajari cara mengatur dan menjalankannya di [Repositori Contoh Kode 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
```
+ Untuk detail API, lihat [TerminateJobFlows](https://docs.aws.amazon.com/goto/boto3/elasticmapreduce-2009-03-31/TerminateJobFlows)di *AWS SDK for Python (Boto3) Referensi* API.
------
#### [ SAP ABAP ]
**SDK for SAP ABAP**
Ada lebih banyak tentang GitHub. Temukan contoh lengkapnya dan pelajari cara mengatur dan menjalankannya di [Repositori Contoh Kode 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.
```
+ Untuk detail API, lihat [TerminateJobFlows](https://docs.aws.amazon.com/sdk-for-sap-abap/v1/api/latest/index.html)di *AWS SDK untuk referensi SAP ABAP* API.
------
# Skenario untuk Amazon EMR menggunakan AWS SDKs
Contoh kode berikut menunjukkan kepada Anda bagaimana menerapkan skenario umum di Amazon EMR dengan. AWS SDKs Skenario ini menunjukkan kepada Anda cara menyelesaikan tugas tertentu dengan memanggil beberapa fungsi dalam Amazon EMR atau digabungkan dengan yang lain. Layanan AWS Setiap skenario menyertakan tautan ke kode sumber lengkap, di mana Anda dapat menemukan instruksi tentang cara mengatur dan menjalankan kode.
Skenario menargetkan tingkat pengalaman menengah untuk membantu Anda memahami tindakan layanan dalam konteks.
**Topics**
+ [Buat cluster EMR Amazon berumur pendek dan jalankan langkah](emr_example_emr_Scenario_ShortLivedEmrCluster_section.md)
+ [Jalankan skrip shell untuk menginstal pustaka](emr_example_emr_Usage_InstallLibrariesWithSsm_section.md)
# Buat klaster EMR Amazon berumur pendek dan jalankan langkah menggunakan SDK AWS
Contoh kode berikut menunjukkan cara membuat klaster EMR Amazon berumur pendek yang menjalankan langkah dan secara otomatis berakhir setelah langkah selesai.
------
#### [ Python ]
**SDK untuk Python (Boto3)**
Buat cluster EMR Amazon berumur pendek yang memperkirakan nilai pi menggunakan Apache Spark untuk memparalelkan sejumlah besar perhitungan. Pekerjaan tersebut menulis output ke log EMR Amazon dan ke bucket Amazon Simple Storage Service (Amazon S3). Cluster mengakhiri dirinya sendiri setelah menyelesaikan pekerjaan.
+ Buat bucket Amazon S3 dan unggah skrip pekerjaan.
+ Buat peran AWS Identity and Access Management (IAM).
+ Buat grup keamanan Amazon Elastic Compute Cloud (Amazon EC2).
+ Buat cluster berumur pendek dan jalankan satu langkah pekerjaan.
Contoh ini paling baik dilihat di GitHub. Untuk kode sumber lengkap dan instruksi tentang cara mengatur dan menjalankan, lihat contoh lengkapnya di [GitHub](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/python/example_code/emr).
**Layanan yang digunakan dalam contoh ini**
+ Amazon EMR
------
# Jalankan skrip shell untuk menginstal pustaka di instans EMR Amazon menggunakan SDK AWS
Contoh kode berikut menunjukkan cara menggunakan AWS Systems Manager skrip shell pada instans EMR Amazon yang menginstal pustaka tambahan. Dengan cara ini, Anda dapat mengotomatiskan manajemen instance alih-alih menjalankan perintah secara manual melalui koneksi SSH.
------
#### [ Python ]
**SDK untuk Python (Boto3)**
Ada lebih banyak tentang GitHub. Temukan contoh lengkapnya dan pelajari cara mengatur dan menjalankannya di [Repositori Contoh Kode AWS](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/python/example_code/emr#code-examples).
```
import argparse
import time
import boto3
def install_libraries_on_core_nodes(cluster_id, script_path, emr_client, ssm_client):
"""
Copies and runs a shell script on the core nodes in the cluster.
:param cluster_id: The ID of the cluster.
:param script_path: The path to the script, typically an Amazon S3 object URL.
:param emr_client: The Boto3 Amazon EMR client.
:param ssm_client: The Boto3 AWS Systems Manager client.
"""
core_nodes = emr_client.list_instances(
ClusterId=cluster_id, InstanceGroupTypes=["CORE"]
)["Instances"]
core_instance_ids = [node["Ec2InstanceId"] for node in core_nodes]
print(f"Found core instances: {core_instance_ids}.")
commands = [
# Copy the shell script from Amazon S3 to each node instance.
f"aws s3 cp {script_path} /home/hadoop",
# Run the shell script to install libraries on each node instance.
"bash /home/hadoop/install_libraries.sh",
]
for command in commands:
print(f"Sending '{command}' to core instances...")
command_id = ssm_client.send_command(
InstanceIds=core_instance_ids,
DocumentName="AWS-RunShellScript",
Parameters={"commands": [command]},
TimeoutSeconds=3600,
)["Command"]["CommandId"]
while True:
# Verify the previous step succeeded before running the next step.
cmd_result = ssm_client.list_commands(CommandId=command_id)["Commands"][0]
if cmd_result["StatusDetails"] == "Success":
print(f"Command succeeded.")
break
elif cmd_result["StatusDetails"] in ["Pending", "InProgress"]:
print(f"Command status is {cmd_result['StatusDetails']}, waiting...")
time.sleep(10)
else:
print(f"Command status is {cmd_result['StatusDetails']}, quitting.")
raise RuntimeError(
f"Command {command} failed to run. "
f"Details: {cmd_result['StatusDetails']}"
)
def main():
parser = argparse.ArgumentParser()
parser.add_argument("cluster_id", help="The ID of the cluster.")
parser.add_argument("script_path", help="The path to the script in Amazon S3.")
args = parser.parse_args()
emr_client = boto3.client("emr")
ssm_client = boto3.client("ssm")
install_libraries_on_core_nodes(
args.cluster_id, args.script_path, emr_client, ssm_client
)
if __name__ == "__main__":
main()
```
+ Untuk detail API, lihat [ListInstances](https://docs.aws.amazon.com/goto/boto3/elasticmapreduce-2009-03-31/ListInstances)di *AWS SDK for Python (Boto3) Referensi* API.
------