Weitere AWS SDK-Beispiele sind im GitHub Repo [AWS Doc SDK Examples](https://github.com/awsdocs/aws-doc-sdk-examples) verfügbar.
Die vorliegende Übersetzung wurde maschinell erstellt. Im Falle eines Konflikts oder eines Widerspruchs zwischen dieser übersetzten Fassung und der englischen Fassung (einschließlich infolge von Verzögerungen bei der Übersetzung) ist die englische Fassung maßgeblich.
# Aurora-Beispiele unter Verwendung von SDK für Python (Boto3)
Die folgenden Codebeispiele zeigen Ihnen, wie Sie AWS SDK für Python (Boto3) mit Aurora Aktionen ausführen und allgemeine Szenarien implementieren.
Bei *Grundlagen* handelt es sich um Codebeispiele, die Ihnen zeigen, wie Sie die wesentlichen Vorgänge innerhalb eines Services ausführen.
*Aktionen* sind Codeauszüge aus größeren Programmen und müssen im Kontext ausgeführt werden. Während Aktionen Ihnen zeigen, wie Sie einzelne Service-Funktionen aufrufen, können Sie Aktionen im Kontext der zugehörigen Szenarien anzeigen.
*Szenarien* sind Codebeispiele, die Ihnen zeigen, wie Sie bestimmte Aufgaben ausführen, indem Sie mehrere Funktionen innerhalb eines Service aufrufen oder mit anderen AWS-Services kombinieren.
Jedes Beispiel enthält einen Link zum vollständigen Quellcode, wo Sie Anweisungen zum Einrichten und Ausführen des Codes im Kodex finden.
**Topics**
+ [Erste Schritte](#get_started)
+ [Grundlagen](#basics)
+ [Aktionen](#actions)
+ [Szenarien](#scenarios)
## Erste Schritte
### Hello Aurora
Das folgenden Codebeispiel veranschaulicht die ersten Schritte mit Aurora.
**SDK für Python (Boto3)**
Es gibt noch mehr dazu GitHub. Hier finden Sie das vollständige Beispiel und erfahren, wie Sie das [AWS -Code-Beispiel-](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/python/example_code/aurora#code-examples) einrichten und ausführen.
```
import boto3
# Create an RDS client
rds = boto3.client("rds")
# Create a paginator for the describe_db_clusters operation
paginator = rds.get_paginator("describe_db_clusters")
# Use the paginator to get a list of DB clusters
response_iterator = paginator.paginate(
PaginationConfig={
"PageSize": 50, # Adjust PageSize as needed
"StartingToken": None,
}
)
# Iterate through the pages of the response
clusters_found = False
for page in response_iterator:
if "DBClusters" in page and page["DBClusters"]:
clusters_found = True
print("Here are your RDS Aurora clusters:")
for cluster in page["DBClusters"]:
print(
f"Cluster ID: {cluster['DBClusterIdentifier']}, Engine: {cluster['Engine']}"
)
if not clusters_found:
print("No clusters found!")
```
+ Einzelheiten zur API finden Sie unter [DBClustersDescribe](https://docs.aws.amazon.com/goto/boto3/rds-2014-10-31/DescribeDBClusters) in *AWS SDK for Python (Boto3) API-Referenz*.
## Grundlagen
### Kennenlernen der Grundlagen
Wie das aussehen kann, sehen Sie am nachfolgenden Beispielcode:
+ Erstellen Sie eine benutzerdefinierte Cluster-Parametergruppe von Aurora DB und legen Sie Parameterwerte fest.
+ Erstellen Sie einen DB-Cluster, der die Parametergruppe verwendet.
+ Erstellen Sie eine DB-Instance, die eine Datenbank enthält.
+ Erstellen Sie einen Snapshot des DB-Clusters und bereinigen Sie dann die Ressourcen.
**SDK für Python (Boto3)**
Es gibt noch mehr dazu. GitHub Hier finden Sie das vollständige Beispiel und erfahren, wie Sie das [AWS -Code-Beispiel-](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/python/example_code/aurora#code-examples) einrichten und ausführen.
Führen Sie ein interaktives Szenario an einem Prompt aus.
```
class AuroraClusterScenario:
"""Runs a scenario that shows how to get started using Aurora DB clusters."""
def __init__(self, aurora_wrapper):
"""
:param aurora_wrapper: An object that wraps Aurora DB cluster actions.
"""
self.aurora_wrapper = aurora_wrapper
def create_parameter_group(self, db_engine, parameter_group_name):
"""
Shows how to get available engine versions for a specified database engine and
create a DB cluster parameter group that is compatible with a selected engine family.
:param db_engine: The database engine to use as a basis.
:param parameter_group_name: The name given to the newly created parameter group.
:return: The newly created parameter group.
"""
print(
f"Checking for an existing DB cluster parameter group named {parameter_group_name}."
)
parameter_group = self.aurora_wrapper.get_parameter_group(parameter_group_name)
if parameter_group is None:
print(f"Getting available database engine versions for {db_engine}.")
engine_versions = self.aurora_wrapper.get_engine_versions(db_engine)
families = list({ver["DBParameterGroupFamily"] for ver in engine_versions})
family_index = q.choose("Which family do you want to use? ", families)
print(f"Creating a DB cluster parameter group.")
self.aurora_wrapper.create_parameter_group(
parameter_group_name, families[family_index], "Example parameter group."
)
parameter_group = self.aurora_wrapper.get_parameter_group(
parameter_group_name
)
print(f"Parameter group {parameter_group['DBClusterParameterGroupName']}:")
pp(parameter_group)
print("-" * 88)
return parameter_group
def set_user_parameters(self, parameter_group_name):
"""
Shows how to get the parameters contained in a custom parameter group and
update some of the parameter values in the group.
:param parameter_group_name: The name of the parameter group to query and modify.
"""
print("Let's set some parameter values in your parameter group.")
auto_inc_parameters = self.aurora_wrapper.get_parameters(
parameter_group_name, name_prefix="auto_increment"
)
update_params = []
for auto_inc in auto_inc_parameters:
if auto_inc["IsModifiable"] and auto_inc["DataType"] == "integer":
print(f"The {auto_inc['ParameterName']} parameter is described as:")
print(f"\t{auto_inc['Description']}")
param_range = auto_inc["AllowedValues"].split("-")
auto_inc["ParameterValue"] = str(
q.ask(
f"Enter a value between {param_range[0]} and {param_range[1]}: ",
q.is_int,
q.in_range(int(param_range[0]), int(param_range[1])),
)
)
update_params.append(auto_inc)
self.aurora_wrapper.update_parameters(parameter_group_name, update_params)
print(
"You can get a list of parameters you've set by specifying a source of 'user'."
)
user_parameters = self.aurora_wrapper.get_parameters(
parameter_group_name, source="user"
)
pp(user_parameters)
print("-" * 88)
def create_cluster(self, cluster_name, db_engine, db_name, parameter_group):
"""
Shows how to create an Aurora DB cluster that contains a database of a specified
type. The database is also configured to use a custom DB cluster parameter group.
:param cluster_name: The name given to the newly created DB cluster.
:param db_engine: The engine of the created database.
:param db_name: The name given to the created database.
:param parameter_group: The parameter group that is associated with the DB cluster.
:return: The newly created DB cluster.
"""
print("Checking for an existing DB cluster.")
cluster = self.aurora_wrapper.get_db_cluster(cluster_name)
if cluster is None:
admin_username = q.ask(
"Enter an administrator user name for the database: ", q.non_empty
)
admin_password = q.ask(
"Enter a password for the administrator (at least 8 characters): ",
q.non_empty,
)
engine_versions = self.aurora_wrapper.get_engine_versions(
db_engine, parameter_group["DBParameterGroupFamily"]
)
engine_choices = [
ver["EngineVersionDescription"] for ver in engine_versions
]
print("The available engines for your parameter group are:")
engine_index = q.choose("Which engine do you want to use? ", engine_choices)
print(
f"Creating DB cluster {cluster_name} and database {db_name}.\n"
f"The DB cluster is configured to use\n"
f"your custom parameter group {parameter_group['DBClusterParameterGroupName']}\n"
f"and selected engine {engine_choices[engine_index]}.\n"
f"This typically takes several minutes."
)
cluster = self.aurora_wrapper.create_db_cluster(
cluster_name,
parameter_group["DBClusterParameterGroupName"],
db_name,
db_engine,
engine_versions[engine_index]["EngineVersion"],
admin_username,
admin_password,
)
while cluster.get("Status") != "available":
wait(30)
cluster = self.aurora_wrapper.get_db_cluster(cluster_name)
print("Cluster created and available.\n")
print("Cluster data:")
pp(cluster)
print("-" * 88)
return cluster
def create_instance(self, cluster):
"""
Shows how to create a DB instance in an existing Aurora DB cluster. A new DB cluster
contains no DB instances, so you must add one. The first DB instance that is added
to a DB cluster defaults to a read-write DB instance.
:param cluster: The DB cluster where the DB instance is added.
:return: The newly created DB instance.
"""
print("Checking for an existing database instance.")
cluster_name = cluster["DBClusterIdentifier"]
db_inst = self.aurora_wrapper.get_db_instance(cluster_name)
if db_inst is None:
print("Let's create a database instance in your DB cluster.")
print("First, choose a DB instance type:")
inst_opts = self.aurora_wrapper.get_orderable_instances(
cluster["Engine"], cluster["EngineVersion"]
)
inst_choices = list(
{
opt["DBInstanceClass"] + ", storage type: " + opt["StorageType"]
for opt in inst_opts
}
)
inst_index = q.choose(
"Which DB instance class do you want to use? ", inst_choices
)
print(
f"Creating a database instance. This typically takes several minutes."
)
db_inst = self.aurora_wrapper.create_instance_in_cluster(
cluster_name,
cluster_name,
cluster["Engine"],
inst_opts[inst_index]["DBInstanceClass"],
)
while db_inst.get("DBInstanceStatus") != "available":
wait(30)
db_inst = self.aurora_wrapper.get_db_instance(cluster_name)
print("Instance data:")
pp(db_inst)
print("-" * 88)
return db_inst
@staticmethod
def display_connection(cluster):
"""
Displays connection information about an Aurora DB cluster and tips on how to
connect to it.
:param cluster: The DB cluster to display.
"""
print(
"You can now connect to your database using your favorite MySql client.\n"
"One way to connect is by using the 'mysql' shell on an Amazon EC2 instance\n"
"that is running in the same VPC as your database cluster. Pass the endpoint,\n"
"port, and administrator user name to 'mysql' and enter your password\n"
"when prompted:\n"
)
print(
f"\n\tmysql -h {cluster['Endpoint']} -P {cluster['Port']} -u {cluster['MasterUsername']} -p\n"
)
print(
"For more information, see the User Guide for Aurora:\n"
"\thttps://docs.aws.amazon.com/AmazonRDS/latest/AuroraUserGuide/CHAP_GettingStartedAurora.CreatingConnecting.Aurora.html#CHAP_GettingStartedAurora.Aurora.Connect"
)
print("-" * 88)
def create_snapshot(self, cluster_name):
"""
Shows how to create a DB cluster snapshot and wait until it's available.
:param cluster_name: The name of a DB cluster to snapshot.
"""
if q.ask(
"Do you want to create a snapshot of your DB cluster (y/n)? ", q.is_yesno
):
snapshot_id = f"{cluster_name}-{uuid.uuid4()}"
print(
f"Creating a snapshot named {snapshot_id}. This typically takes a few minutes."
)
snapshot = self.aurora_wrapper.create_cluster_snapshot(
snapshot_id, cluster_name
)
while snapshot.get("Status") != "available":
wait(30)
snapshot = self.aurora_wrapper.get_cluster_snapshot(snapshot_id)
pp(snapshot)
print("-" * 88)
def cleanup(self, db_inst, cluster, parameter_group):
"""
Shows how to clean up a DB instance, DB cluster, and DB cluster parameter group.
Before the DB cluster parameter group can be deleted, all associated DB instances and
DB clusters must first be deleted.
:param db_inst: The DB instance to delete.
:param cluster: The DB cluster to delete.
:param parameter_group: The DB cluster parameter group to delete.
"""
cluster_name = cluster["DBClusterIdentifier"]
parameter_group_name = parameter_group["DBClusterParameterGroupName"]
if q.ask(
"\nDo you want to delete the database instance, DB cluster, and parameter "
"group (y/n)? ",
q.is_yesno,
):
print(f"Deleting database instance {db_inst['DBInstanceIdentifier']}.")
self.aurora_wrapper.delete_db_instance(db_inst["DBInstanceIdentifier"])
print(f"Deleting database cluster {cluster_name}.")
self.aurora_wrapper.delete_db_cluster(cluster_name)
print(
"Waiting for the DB instance and DB cluster to delete.\n"
"This typically takes several minutes."
)
while db_inst is not None or cluster is not None:
wait(30)
if db_inst is not None:
db_inst = self.aurora_wrapper.get_db_instance(
db_inst["DBInstanceIdentifier"]
)
if cluster is not None:
cluster = self.aurora_wrapper.get_db_cluster(
cluster["DBClusterIdentifier"]
)
print(f"Deleting parameter group {parameter_group_name}.")
self.aurora_wrapper.delete_parameter_group(parameter_group_name)
def run_scenario(self, db_engine, parameter_group_name, cluster_name, db_name):
print("-" * 88)
print(
"Welcome to the Amazon Relational Database Service (Amazon RDS) get started\n"
"with Aurora DB clusters demo."
)
print("-" * 88)
parameter_group = self.create_parameter_group(db_engine, parameter_group_name)
self.set_user_parameters(parameter_group_name)
cluster = self.create_cluster(cluster_name, db_engine, db_name, parameter_group)
wait(5)
db_inst = self.create_instance(cluster)
self.display_connection(cluster)
self.create_snapshot(cluster_name)
self.cleanup(db_inst, cluster, parameter_group)
print("\nThanks for watching!")
print("-" * 88)
if __name__ == "__main__":
logging.basicConfig(level=logging.INFO, format="%(levelname)s: %(message)s")
try:
scenario = AuroraClusterScenario(AuroraWrapper.from_client())
scenario.run_scenario(
"aurora-mysql",
"doc-example-cluster-parameter-group",
"doc-example-aurora",
"docexampledb",
)
except Exception:
logging.exception("Something went wrong with the demo.")
```
Definieren Sie Funktionen, die vom Szenario aufgerufen werden, um Aurora-Aktionen zu verwalten.
```
class AuroraWrapper:
"""Encapsulates Aurora DB cluster actions."""
def __init__(self, rds_client):
"""
:param rds_client: A Boto3 Amazon Relational Database Service (Amazon RDS) client.
"""
self.rds_client = rds_client
@classmethod
def from_client(cls):
"""
Instantiates this class from a Boto3 client.
"""
rds_client = boto3.client("rds")
return cls(rds_client)
def get_parameter_group(self, parameter_group_name):
"""
Gets a DB cluster parameter group.
:param parameter_group_name: The name of the parameter group to retrieve.
:return: The requested parameter group.
"""
try:
response = self.rds_client.describe_db_cluster_parameter_groups(
DBClusterParameterGroupName=parameter_group_name
)
parameter_group = response["DBClusterParameterGroups"][0]
except ClientError as err:
if err.response["Error"]["Code"] == "DBParameterGroupNotFound":
logger.info("Parameter group %s does not exist.", parameter_group_name)
else:
logger.error(
"Couldn't get parameter group %s. Here's why: %s: %s",
parameter_group_name,
err.response["Error"]["Code"],
err.response["Error"]["Message"],
)
raise
else:
return parameter_group
def create_parameter_group(
self, parameter_group_name, parameter_group_family, description
):
"""
Creates a DB cluster parameter group that is based on the specified parameter group
family.
:param parameter_group_name: The name of the newly created parameter group.
:param parameter_group_family: The family that is used as the basis of the new
parameter group.
:param description: A description given to the parameter group.
:return: Data about the newly created parameter group.
"""
try:
response = self.rds_client.create_db_cluster_parameter_group(
DBClusterParameterGroupName=parameter_group_name,
DBParameterGroupFamily=parameter_group_family,
Description=description,
)
except ClientError as err:
logger.error(
"Couldn't create parameter group %s. Here's why: %s: %s",
parameter_group_name,
err.response["Error"]["Code"],
err.response["Error"]["Message"],
)
raise
else:
return response
def delete_parameter_group(self, parameter_group_name):
"""
Deletes a DB cluster parameter group.
:param parameter_group_name: The name of the parameter group to delete.
:return: Data about the parameter group.
"""
try:
response = self.rds_client.delete_db_cluster_parameter_group(
DBClusterParameterGroupName=parameter_group_name
)
except ClientError as err:
logger.error(
"Couldn't delete parameter group %s. Here's why: %s: %s",
parameter_group_name,
err.response["Error"]["Code"],
err.response["Error"]["Message"],
)
raise
else:
return response
def get_parameters(self, parameter_group_name, name_prefix="", source=None):
"""
Gets the parameters that are contained in a DB cluster parameter group.
:param parameter_group_name: The name of the parameter group to query.
:param name_prefix: When specified, the retrieved list of parameters is filtered
to contain only parameters that start with this prefix.
:param source: When specified, only parameters from this source are retrieved.
For example, a source of 'user' retrieves only parameters that
were set by a user.
:return: The list of requested parameters.
"""
try:
kwargs = {"DBClusterParameterGroupName": parameter_group_name}
if source is not None:
kwargs["Source"] = source
parameters = []
paginator = self.rds_client.get_paginator("describe_db_cluster_parameters")
for page in paginator.paginate(**kwargs):
parameters += [
p
for p in page["Parameters"]
if p["ParameterName"].startswith(name_prefix)
]
except ClientError as err:
logger.error(
"Couldn't get parameters for %s. Here's why: %s: %s",
parameter_group_name,
err.response["Error"]["Code"],
err.response["Error"]["Message"],
)
raise
else:
return parameters
def update_parameters(self, parameter_group_name, update_parameters):
"""
Updates parameters in a custom DB cluster parameter group.
:param parameter_group_name: The name of the parameter group to update.
:param update_parameters: The parameters to update in the group.
:return: Data about the modified parameter group.
"""
try:
response = self.rds_client.modify_db_cluster_parameter_group(
DBClusterParameterGroupName=parameter_group_name,
Parameters=update_parameters,
)
except ClientError as err:
logger.error(
"Couldn't update parameters in %s. Here's why: %s: %s",
parameter_group_name,
err.response["Error"]["Code"],
err.response["Error"]["Message"],
)
raise
else:
return response
def get_db_cluster(self, cluster_name):
"""
Gets data about an Aurora DB cluster.
:param cluster_name: The name of the DB cluster to retrieve.
:return: The retrieved DB cluster.
"""
try:
response = self.rds_client.describe_db_clusters(
DBClusterIdentifier=cluster_name
)
cluster = response["DBClusters"][0]
except ClientError as err:
if err.response["Error"]["Code"] == "DBClusterNotFoundFault":
logger.info("Cluster %s does not exist.", cluster_name)
else:
logger.error(
"Couldn't verify the existence of DB cluster %s. Here's why: %s: %s",
cluster_name,
err.response["Error"]["Code"],
err.response["Error"]["Message"],
)
raise
else:
return cluster
def create_db_cluster(
self,
cluster_name,
parameter_group_name,
db_name,
db_engine,
db_engine_version,
admin_name,
admin_password,
):
"""
Creates a DB cluster that is configured to use the specified parameter group.
The newly created DB cluster contains a database that uses the specified engine and
engine version.
:param cluster_name: The name of the DB cluster to create.
:param parameter_group_name: The name of the parameter group to associate with
the DB cluster.
:param db_name: The name of the database to create.
:param db_engine: The database engine of the database that is created, such as MySql.
:param db_engine_version: The version of the database engine.
:param admin_name: The user name of the database administrator.
:param admin_password: The password of the database administrator.
:return: The newly created DB cluster.
"""
try:
response = self.rds_client.create_db_cluster(
DatabaseName=db_name,
DBClusterIdentifier=cluster_name,
DBClusterParameterGroupName=parameter_group_name,
Engine=db_engine,
EngineVersion=db_engine_version,
MasterUsername=admin_name,
MasterUserPassword=admin_password,
)
cluster = response["DBCluster"]
except ClientError as err:
logger.error(
"Couldn't create database %s. Here's why: %s: %s",
db_name,
err.response["Error"]["Code"],
err.response["Error"]["Message"],
)
raise
else:
return cluster
def delete_db_cluster(self, cluster_name):
"""
Deletes a DB cluster.
:param cluster_name: The name of the DB cluster to delete.
"""
try:
self.rds_client.delete_db_cluster(
DBClusterIdentifier=cluster_name, SkipFinalSnapshot=True
)
logger.info("Deleted DB cluster %s.", cluster_name)
except ClientError:
logger.exception("Couldn't delete DB cluster %s.", cluster_name)
raise
def create_cluster_snapshot(self, snapshot_id, cluster_id):
"""
Creates a snapshot of a DB cluster.
:param snapshot_id: The ID to give the created snapshot.
:param cluster_id: The DB cluster to snapshot.
:return: Data about the newly created snapshot.
"""
try:
response = self.rds_client.create_db_cluster_snapshot(
DBClusterSnapshotIdentifier=snapshot_id, DBClusterIdentifier=cluster_id
)
snapshot = response["DBClusterSnapshot"]
except ClientError as err:
logger.error(
"Couldn't create snapshot of %s. Here's why: %s: %s",
cluster_id,
err.response["Error"]["Code"],
err.response["Error"]["Message"],
)
raise
else:
return snapshot
def get_cluster_snapshot(self, snapshot_id):
"""
Gets a DB cluster snapshot.
:param snapshot_id: The ID of the snapshot to retrieve.
:return: The retrieved snapshot.
"""
try:
response = self.rds_client.describe_db_cluster_snapshots(
DBClusterSnapshotIdentifier=snapshot_id
)
snapshot = response["DBClusterSnapshots"][0]
except ClientError as err:
logger.error(
"Couldn't get DB cluster snapshot %s. Here's why: %s: %s",
snapshot_id,
err.response["Error"]["Code"],
err.response["Error"]["Message"],
)
raise
else:
return snapshot
def create_instance_in_cluster(
self, instance_id, cluster_id, db_engine, instance_class
):
"""
Creates a database instance in an existing DB cluster. The first database that is
created defaults to a read-write DB instance.
:param instance_id: The ID to give the newly created DB instance.
:param cluster_id: The ID of the DB cluster where the DB instance is created.
:param db_engine: The database engine of a database to create in the DB instance.
This must be compatible with the configured parameter group
of the DB cluster.
:param instance_class: The DB instance class for the newly created DB instance.
:return: Data about the newly created DB instance.
"""
try:
response = self.rds_client.create_db_instance(
DBInstanceIdentifier=instance_id,
DBClusterIdentifier=cluster_id,
Engine=db_engine,
DBInstanceClass=instance_class,
)
db_inst = response["DBInstance"]
except ClientError as err:
logger.error(
"Couldn't create DB instance %s. Here's why: %s: %s",
instance_id,
err.response["Error"]["Code"],
err.response["Error"]["Message"],
)
raise
else:
return db_inst
def get_engine_versions(self, engine, parameter_group_family=None):
"""
Gets database engine versions that are available for the specified engine
and parameter group family.
:param engine: The database engine to look up.
:param parameter_group_family: When specified, restricts the returned list of
engine versions to those that are compatible with
this parameter group family.
:return: The list of database engine versions.
"""
try:
kwargs = {"Engine": engine}
if parameter_group_family is not None:
kwargs["DBParameterGroupFamily"] = parameter_group_family
response = self.rds_client.describe_db_engine_versions(**kwargs)
versions = response["DBEngineVersions"]
except ClientError as err:
logger.error(
"Couldn't get engine versions for %s. Here's why: %s: %s",
engine,
err.response["Error"]["Code"],
err.response["Error"]["Message"],
)
raise
else:
return versions
def get_orderable_instances(self, db_engine, db_engine_version):
"""
Gets DB instance options that can be used to create DB instances that are
compatible with a set of specifications.
:param db_engine: The database engine that must be supported by the DB instance.
:param db_engine_version: The engine version that must be supported by the DB instance.
:return: The list of DB instance options that can be used to create a compatible DB instance.
"""
try:
inst_opts = []
paginator = self.rds_client.get_paginator(
"describe_orderable_db_instance_options"
)
for page in paginator.paginate(
Engine=db_engine, EngineVersion=db_engine_version
):
inst_opts += page["OrderableDBInstanceOptions"]
except ClientError as err:
logger.error(
"Couldn't get orderable DB instances. Here's why: %s: %s",
err.response["Error"]["Code"],
err.response["Error"]["Message"],
)
raise
else:
return inst_opts
def get_db_instance(self, instance_id):
"""
Gets data about a DB instance.
:param instance_id: The ID of the DB instance to retrieve.
:return: The retrieved DB instance.
"""
try:
response = self.rds_client.describe_db_instances(
DBInstanceIdentifier=instance_id
)
db_inst = response["DBInstances"][0]
except ClientError as err:
if err.response["Error"]["Code"] == "DBInstanceNotFound":
logger.info("Instance %s does not exist.", instance_id)
else:
logger.error(
"Couldn't get DB instance %s. Here's why: %s: %s",
instance_id,
err.response["Error"]["Code"],
err.response["Error"]["Message"],
)
raise
else:
return db_inst
def delete_db_instance(self, instance_id):
"""
Deletes a DB instance.
:param instance_id: The ID of the DB instance to delete.
:return: Data about the deleted DB instance.
"""
try:
response = self.rds_client.delete_db_instance(
DBInstanceIdentifier=instance_id,
SkipFinalSnapshot=True,
DeleteAutomatedBackups=True,
)
db_inst = response["DBInstance"]
except ClientError as err:
logger.error(
"Couldn't delete DB instance %s. Here's why: %s: %s",
instance_id,
err.response["Error"]["Code"],
err.response["Error"]["Message"],
)
raise
else:
return db_inst
```
+ Weitere API-Informationen finden Sie in den folgenden Themen der *API-Referenz zum AWS SDK für Python (Boto3)*.
+ [CreateDBCluster](https://docs.aws.amazon.com/goto/boto3/rds-2014-10-31/CreateDBCluster)
+ [CreateDBClusterParameterGroup](https://docs.aws.amazon.com/goto/boto3/rds-2014-10-31/CreateDBClusterParameterGroup)
+ [DBClusterSchnappschuss erstellen](https://docs.aws.amazon.com/goto/boto3/rds-2014-10-31/CreateDBClusterSnapshot)
+ [CreateDBInstance](https://docs.aws.amazon.com/goto/boto3/rds-2014-10-31/CreateDBInstance)
+ [LöschenDBCluster](https://docs.aws.amazon.com/goto/boto3/rds-2014-10-31/DeleteDBCluster)
+ [LöschenDBClusterParameterGroup](https://docs.aws.amazon.com/goto/boto3/rds-2014-10-31/DeleteDBClusterParameterGroup)
+ [LöschenDBInstance](https://docs.aws.amazon.com/goto/boto3/rds-2014-10-31/DeleteDBInstance)
+ [Beschreiben DBCluster ParameterGroups](https://docs.aws.amazon.com/goto/boto3/rds-2014-10-31/DescribeDBClusterParameterGroups)
+ [Beschreiben Sie die DBCluster Parameter](https://docs.aws.amazon.com/goto/boto3/rds-2014-10-31/DescribeDBClusterParameters)
+ [Beschreiben Sie DBCluster Schnappschüsse](https://docs.aws.amazon.com/goto/boto3/rds-2014-10-31/DescribeDBClusterSnapshots)
+ [Beschreiben DBClusters](https://docs.aws.amazon.com/goto/boto3/rds-2014-10-31/DescribeDBClusters)
+ [DBEngineVersionen beschreiben](https://docs.aws.amazon.com/goto/boto3/rds-2014-10-31/DescribeDBEngineVersions)
+ [Beschreiben DBInstances](https://docs.aws.amazon.com/goto/boto3/rds-2014-10-31/DescribeDBInstances)
+ [DescribeOrderableDBInstanceOptionen](https://docs.aws.amazon.com/goto/boto3/rds-2014-10-31/DescribeOrderableDBInstanceOptions)
+ [Modifizieren SieDBClusterParameterGroup](https://docs.aws.amazon.com/goto/boto3/rds-2014-10-31/ModifyDBClusterParameterGroup)
## Aktionen
### `CreateDBCluster`
Das folgende Codebeispiel zeigt die Verwendung`CreateDBCluster`.
**SDK für Python (Boto3)**
Es gibt noch mehr dazu GitHub. Hier finden Sie das vollständige Beispiel und erfahren, wie Sie das [AWS -Code-Beispiel-](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/python/example_code/aurora#code-examples) einrichten und ausführen.
```
class AuroraWrapper:
"""Encapsulates Aurora DB cluster actions."""
def __init__(self, rds_client):
"""
:param rds_client: A Boto3 Amazon Relational Database Service (Amazon RDS) client.
"""
self.rds_client = rds_client
@classmethod
def from_client(cls):
"""
Instantiates this class from a Boto3 client.
"""
rds_client = boto3.client("rds")
return cls(rds_client)
def create_db_cluster(
self,
cluster_name,
parameter_group_name,
db_name,
db_engine,
db_engine_version,
admin_name,
admin_password,
):
"""
Creates a DB cluster that is configured to use the specified parameter group.
The newly created DB cluster contains a database that uses the specified engine and
engine version.
:param cluster_name: The name of the DB cluster to create.
:param parameter_group_name: The name of the parameter group to associate with
the DB cluster.
:param db_name: The name of the database to create.
:param db_engine: The database engine of the database that is created, such as MySql.
:param db_engine_version: The version of the database engine.
:param admin_name: The user name of the database administrator.
:param admin_password: The password of the database administrator.
:return: The newly created DB cluster.
"""
try:
response = self.rds_client.create_db_cluster(
DatabaseName=db_name,
DBClusterIdentifier=cluster_name,
DBClusterParameterGroupName=parameter_group_name,
Engine=db_engine,
EngineVersion=db_engine_version,
MasterUsername=admin_name,
MasterUserPassword=admin_password,
)
cluster = response["DBCluster"]
except ClientError as err:
logger.error(
"Couldn't create database %s. Here's why: %s: %s",
db_name,
err.response["Error"]["Code"],
err.response["Error"]["Message"],
)
raise
else:
return cluster
```
+ API-Einzelheiten finden Sie unter [Create DBCluster](https://docs.aws.amazon.com/goto/boto3/rds-2014-10-31/CreateDBCluster) in *AWS SDK for Python (Boto3) API-Referenz*.
### `CreateDBClusterParameterGroup`
Das folgende Codebeispiel zeigt, wie Sie es verwenden. `CreateDBClusterParameterGroup`
**SDK für Python (Boto3)**
Es gibt noch mehr dazu GitHub. Hier finden Sie das vollständige Beispiel und erfahren, wie Sie das [AWS -Code-Beispiel-](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/python/example_code/aurora#code-examples) einrichten und ausführen.
```
class AuroraWrapper:
"""Encapsulates Aurora DB cluster actions."""
def __init__(self, rds_client):
"""
:param rds_client: A Boto3 Amazon Relational Database Service (Amazon RDS) client.
"""
self.rds_client = rds_client
@classmethod
def from_client(cls):
"""
Instantiates this class from a Boto3 client.
"""
rds_client = boto3.client("rds")
return cls(rds_client)
def create_parameter_group(
self, parameter_group_name, parameter_group_family, description
):
"""
Creates a DB cluster parameter group that is based on the specified parameter group
family.
:param parameter_group_name: The name of the newly created parameter group.
:param parameter_group_family: The family that is used as the basis of the new
parameter group.
:param description: A description given to the parameter group.
:return: Data about the newly created parameter group.
"""
try:
response = self.rds_client.create_db_cluster_parameter_group(
DBClusterParameterGroupName=parameter_group_name,
DBParameterGroupFamily=parameter_group_family,
Description=description,
)
except ClientError as err:
logger.error(
"Couldn't create parameter group %s. Here's why: %s: %s",
parameter_group_name,
err.response["Error"]["Code"],
err.response["Error"]["Message"],
)
raise
else:
return response
```
+ API-Einzelheiten finden Sie unter [Create DBCluster ParameterGroup](https://docs.aws.amazon.com/goto/boto3/rds-2014-10-31/CreateDBClusterParameterGroup) in *AWS SDK for Python (Boto3) API-Referenz*.
### `CreateDBClusterSnapshot`
Das folgende Codebeispiel zeigt die Verwendung. `CreateDBClusterSnapshot`
**SDK für Python (Boto3)**
Es gibt noch mehr dazu GitHub. Hier finden Sie das vollständige Beispiel und erfahren, wie Sie das [AWS -Code-Beispiel-](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/python/example_code/aurora#code-examples) einrichten und ausführen.
```
class AuroraWrapper:
"""Encapsulates Aurora DB cluster actions."""
def __init__(self, rds_client):
"""
:param rds_client: A Boto3 Amazon Relational Database Service (Amazon RDS) client.
"""
self.rds_client = rds_client
@classmethod
def from_client(cls):
"""
Instantiates this class from a Boto3 client.
"""
rds_client = boto3.client("rds")
return cls(rds_client)
def create_cluster_snapshot(self, snapshot_id, cluster_id):
"""
Creates a snapshot of a DB cluster.
:param snapshot_id: The ID to give the created snapshot.
:param cluster_id: The DB cluster to snapshot.
:return: Data about the newly created snapshot.
"""
try:
response = self.rds_client.create_db_cluster_snapshot(
DBClusterSnapshotIdentifier=snapshot_id, DBClusterIdentifier=cluster_id
)
snapshot = response["DBClusterSnapshot"]
except ClientError as err:
logger.error(
"Couldn't create snapshot of %s. Here's why: %s: %s",
cluster_id,
err.response["Error"]["Code"],
err.response["Error"]["Message"],
)
raise
else:
return snapshot
```
+ API-Einzelheiten finden Sie unter [Create DBCluster Snapshot](https://docs.aws.amazon.com/goto/boto3/rds-2014-10-31/CreateDBClusterSnapshot) in *AWS SDK for Python (Boto3) API-Referenz*.
### `CreateDBInstance`
Das folgende Codebeispiel zeigt die Verwendung. `CreateDBInstance`
**SDK für Python (Boto3)**
Es gibt noch mehr dazu GitHub. Hier finden Sie das vollständige Beispiel und erfahren, wie Sie das [AWS -Code-Beispiel-](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/python/example_code/aurora#code-examples) einrichten und ausführen.
```
class AuroraWrapper:
"""Encapsulates Aurora DB cluster actions."""
def __init__(self, rds_client):
"""
:param rds_client: A Boto3 Amazon Relational Database Service (Amazon RDS) client.
"""
self.rds_client = rds_client
@classmethod
def from_client(cls):
"""
Instantiates this class from a Boto3 client.
"""
rds_client = boto3.client("rds")
return cls(rds_client)
def create_instance_in_cluster(
self, instance_id, cluster_id, db_engine, instance_class
):
"""
Creates a database instance in an existing DB cluster. The first database that is
created defaults to a read-write DB instance.
:param instance_id: The ID to give the newly created DB instance.
:param cluster_id: The ID of the DB cluster where the DB instance is created.
:param db_engine: The database engine of a database to create in the DB instance.
This must be compatible with the configured parameter group
of the DB cluster.
:param instance_class: The DB instance class for the newly created DB instance.
:return: Data about the newly created DB instance.
"""
try:
response = self.rds_client.create_db_instance(
DBInstanceIdentifier=instance_id,
DBClusterIdentifier=cluster_id,
Engine=db_engine,
DBInstanceClass=instance_class,
)
db_inst = response["DBInstance"]
except ClientError as err:
logger.error(
"Couldn't create DB instance %s. Here's why: %s: %s",
instance_id,
err.response["Error"]["Code"],
err.response["Error"]["Message"],
)
raise
else:
return db_inst
```
+ API-Einzelheiten finden Sie unter [Create DBInstance](https://docs.aws.amazon.com/goto/boto3/rds-2014-10-31/CreateDBInstance) in *AWS SDK for Python (Boto3) API-Referenz*.
### `DeleteDBCluster`
Das folgende Codebeispiel zeigt, wie Sie es verwenden. `DeleteDBCluster`
**SDK für Python (Boto3)**
Es gibt noch mehr dazu GitHub. Hier finden Sie das vollständige Beispiel und erfahren, wie Sie das [AWS -Code-Beispiel-](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/python/example_code/aurora#code-examples) einrichten und ausführen.
```
class AuroraWrapper:
"""Encapsulates Aurora DB cluster actions."""
def __init__(self, rds_client):
"""
:param rds_client: A Boto3 Amazon Relational Database Service (Amazon RDS) client.
"""
self.rds_client = rds_client
@classmethod
def from_client(cls):
"""
Instantiates this class from a Boto3 client.
"""
rds_client = boto3.client("rds")
return cls(rds_client)
def delete_db_cluster(self, cluster_name):
"""
Deletes a DB cluster.
:param cluster_name: The name of the DB cluster to delete.
"""
try:
self.rds_client.delete_db_cluster(
DBClusterIdentifier=cluster_name, SkipFinalSnapshot=True
)
logger.info("Deleted DB cluster %s.", cluster_name)
except ClientError:
logger.exception("Couldn't delete DB cluster %s.", cluster_name)
raise
```
+ API-Einzelheiten finden Sie unter [Delete DBCluster](https://docs.aws.amazon.com/goto/boto3/rds-2014-10-31/DeleteDBCluster) in *AWS SDK for Python (Boto3) API-Referenz*.
### `DeleteDBClusterParameterGroup`
Das folgende Codebeispiel zeigt, wie Sie es verwenden. `DeleteDBClusterParameterGroup`
**SDK für Python (Boto3)**
Es gibt noch mehr dazu GitHub. Hier finden Sie das vollständige Beispiel und erfahren, wie Sie das [AWS -Code-Beispiel-](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/python/example_code/aurora#code-examples) einrichten und ausführen.
```
class AuroraWrapper:
"""Encapsulates Aurora DB cluster actions."""
def __init__(self, rds_client):
"""
:param rds_client: A Boto3 Amazon Relational Database Service (Amazon RDS) client.
"""
self.rds_client = rds_client
@classmethod
def from_client(cls):
"""
Instantiates this class from a Boto3 client.
"""
rds_client = boto3.client("rds")
return cls(rds_client)
def delete_parameter_group(self, parameter_group_name):
"""
Deletes a DB cluster parameter group.
:param parameter_group_name: The name of the parameter group to delete.
:return: Data about the parameter group.
"""
try:
response = self.rds_client.delete_db_cluster_parameter_group(
DBClusterParameterGroupName=parameter_group_name
)
except ClientError as err:
logger.error(
"Couldn't delete parameter group %s. Here's why: %s: %s",
parameter_group_name,
err.response["Error"]["Code"],
err.response["Error"]["Message"],
)
raise
else:
return response
```
+ API-Einzelheiten finden Sie unter [Delete DBCluster ParameterGroup](https://docs.aws.amazon.com/goto/boto3/rds-2014-10-31/DeleteDBClusterParameterGroup) in *AWS SDK for Python (Boto3) API-Referenz*.
### `DeleteDBInstance`
Das folgende Codebeispiel zeigt die Verwendung. `DeleteDBInstance`
**SDK für Python (Boto3)**
Es gibt noch mehr dazu GitHub. Hier finden Sie das vollständige Beispiel und erfahren, wie Sie das [AWS -Code-Beispiel-](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/python/example_code/aurora#code-examples) einrichten und ausführen.
```
class AuroraWrapper:
"""Encapsulates Aurora DB cluster actions."""
def __init__(self, rds_client):
"""
:param rds_client: A Boto3 Amazon Relational Database Service (Amazon RDS) client.
"""
self.rds_client = rds_client
@classmethod
def from_client(cls):
"""
Instantiates this class from a Boto3 client.
"""
rds_client = boto3.client("rds")
return cls(rds_client)
def delete_db_instance(self, instance_id):
"""
Deletes a DB instance.
:param instance_id: The ID of the DB instance to delete.
:return: Data about the deleted DB instance.
"""
try:
response = self.rds_client.delete_db_instance(
DBInstanceIdentifier=instance_id,
SkipFinalSnapshot=True,
DeleteAutomatedBackups=True,
)
db_inst = response["DBInstance"]
except ClientError as err:
logger.error(
"Couldn't delete DB instance %s. Here's why: %s: %s",
instance_id,
err.response["Error"]["Code"],
err.response["Error"]["Message"],
)
raise
else:
return db_inst
```
+ API-Einzelheiten finden Sie unter [Delete DBInstance](https://docs.aws.amazon.com/goto/boto3/rds-2014-10-31/DeleteDBInstance) in *AWS SDK for Python (Boto3) API-Referenz*.
### `DescribeDBClusterParameterGroups`
Das folgende Codebeispiel zeigt, wie Sie es verwenden. `DescribeDBClusterParameterGroups`
**SDK für Python (Boto3)**
Es gibt noch mehr dazu GitHub. Hier finden Sie das vollständige Beispiel und erfahren, wie Sie das [AWS -Code-Beispiel-](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/python/example_code/aurora#code-examples) einrichten und ausführen.
```
class AuroraWrapper:
"""Encapsulates Aurora DB cluster actions."""
def __init__(self, rds_client):
"""
:param rds_client: A Boto3 Amazon Relational Database Service (Amazon RDS) client.
"""
self.rds_client = rds_client
@classmethod
def from_client(cls):
"""
Instantiates this class from a Boto3 client.
"""
rds_client = boto3.client("rds")
return cls(rds_client)
def get_parameter_group(self, parameter_group_name):
"""
Gets a DB cluster parameter group.
:param parameter_group_name: The name of the parameter group to retrieve.
:return: The requested parameter group.
"""
try:
response = self.rds_client.describe_db_cluster_parameter_groups(
DBClusterParameterGroupName=parameter_group_name
)
parameter_group = response["DBClusterParameterGroups"][0]
except ClientError as err:
if err.response["Error"]["Code"] == "DBParameterGroupNotFound":
logger.info("Parameter group %s does not exist.", parameter_group_name)
else:
logger.error(
"Couldn't get parameter group %s. Here's why: %s: %s",
parameter_group_name,
err.response["Error"]["Code"],
err.response["Error"]["Message"],
)
raise
else:
return parameter_group
```
+ Einzelheiten zur API finden Sie unter [DBClusterParameterGroupsDescribe](https://docs.aws.amazon.com/goto/boto3/rds-2014-10-31/DescribeDBClusterParameterGroups) in *AWS SDK for Python (Boto3) API-Referenz*.
### `DescribeDBClusterParameters`
Das folgende Codebeispiel zeigt die Verwendung. `DescribeDBClusterParameters`
**SDK für Python (Boto3)**
Es gibt noch mehr dazu GitHub. Hier finden Sie das vollständige Beispiel und erfahren, wie Sie das [AWS -Code-Beispiel-](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/python/example_code/aurora#code-examples) einrichten und ausführen.
```
class AuroraWrapper:
"""Encapsulates Aurora DB cluster actions."""
def __init__(self, rds_client):
"""
:param rds_client: A Boto3 Amazon Relational Database Service (Amazon RDS) client.
"""
self.rds_client = rds_client
@classmethod
def from_client(cls):
"""
Instantiates this class from a Boto3 client.
"""
rds_client = boto3.client("rds")
return cls(rds_client)
def get_parameters(self, parameter_group_name, name_prefix="", source=None):
"""
Gets the parameters that are contained in a DB cluster parameter group.
:param parameter_group_name: The name of the parameter group to query.
:param name_prefix: When specified, the retrieved list of parameters is filtered
to contain only parameters that start with this prefix.
:param source: When specified, only parameters from this source are retrieved.
For example, a source of 'user' retrieves only parameters that
were set by a user.
:return: The list of requested parameters.
"""
try:
kwargs = {"DBClusterParameterGroupName": parameter_group_name}
if source is not None:
kwargs["Source"] = source
parameters = []
paginator = self.rds_client.get_paginator("describe_db_cluster_parameters")
for page in paginator.paginate(**kwargs):
parameters += [
p
for p in page["Parameters"]
if p["ParameterName"].startswith(name_prefix)
]
except ClientError as err:
logger.error(
"Couldn't get parameters for %s. Here's why: %s: %s",
parameter_group_name,
err.response["Error"]["Code"],
err.response["Error"]["Message"],
)
raise
else:
return parameters
```
+ Einzelheiten zur API finden Sie unter [DBClusterDescribe Parameters](https://docs.aws.amazon.com/goto/boto3/rds-2014-10-31/DescribeDBClusterParameters) in *AWS SDK for Python (Boto3) API-Referenz*.
### `DescribeDBClusterSnapshots`
Das folgende Codebeispiel zeigt die Verwendung. `DescribeDBClusterSnapshots`
**SDK für Python (Boto3)**
Es gibt noch mehr dazu GitHub. Hier finden Sie das vollständige Beispiel und erfahren, wie Sie das [AWS -Code-Beispiel-](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/python/example_code/aurora#code-examples) einrichten und ausführen.
```
class AuroraWrapper:
"""Encapsulates Aurora DB cluster actions."""
def __init__(self, rds_client):
"""
:param rds_client: A Boto3 Amazon Relational Database Service (Amazon RDS) client.
"""
self.rds_client = rds_client
@classmethod
def from_client(cls):
"""
Instantiates this class from a Boto3 client.
"""
rds_client = boto3.client("rds")
return cls(rds_client)
def get_cluster_snapshot(self, snapshot_id):
"""
Gets a DB cluster snapshot.
:param snapshot_id: The ID of the snapshot to retrieve.
:return: The retrieved snapshot.
"""
try:
response = self.rds_client.describe_db_cluster_snapshots(
DBClusterSnapshotIdentifier=snapshot_id
)
snapshot = response["DBClusterSnapshots"][0]
except ClientError as err:
logger.error(
"Couldn't get DB cluster snapshot %s. Here's why: %s: %s",
snapshot_id,
err.response["Error"]["Code"],
err.response["Error"]["Message"],
)
raise
else:
return snapshot
```
+ Einzelheiten zur API finden Sie unter [Describe DBCluster Snapshots](https://docs.aws.amazon.com/goto/boto3/rds-2014-10-31/DescribeDBClusterSnapshots) in *AWS SDK for Python (Boto3)* API Reference.
### `DescribeDBClusters`
Das folgende Codebeispiel zeigt die Verwendung. `DescribeDBClusters`
**SDK für Python (Boto3)**
Es gibt noch mehr dazu GitHub. Hier finden Sie das vollständige Beispiel und erfahren, wie Sie das [AWS -Code-Beispiel-](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/python/example_code/aurora#code-examples) einrichten und ausführen.
```
class AuroraWrapper:
"""Encapsulates Aurora DB cluster actions."""
def __init__(self, rds_client):
"""
:param rds_client: A Boto3 Amazon Relational Database Service (Amazon RDS) client.
"""
self.rds_client = rds_client
@classmethod
def from_client(cls):
"""
Instantiates this class from a Boto3 client.
"""
rds_client = boto3.client("rds")
return cls(rds_client)
def get_db_cluster(self, cluster_name):
"""
Gets data about an Aurora DB cluster.
:param cluster_name: The name of the DB cluster to retrieve.
:return: The retrieved DB cluster.
"""
try:
response = self.rds_client.describe_db_clusters(
DBClusterIdentifier=cluster_name
)
cluster = response["DBClusters"][0]
except ClientError as err:
if err.response["Error"]["Code"] == "DBClusterNotFoundFault":
logger.info("Cluster %s does not exist.", cluster_name)
else:
logger.error(
"Couldn't verify the existence of DB cluster %s. Here's why: %s: %s",
cluster_name,
err.response["Error"]["Code"],
err.response["Error"]["Message"],
)
raise
else:
return cluster
```
+ Einzelheiten zur API finden Sie unter [DBClustersDescribe](https://docs.aws.amazon.com/goto/boto3/rds-2014-10-31/DescribeDBClusters) in *AWS SDK for Python (Boto3) API-Referenz*.
### `DescribeDBEngineVersions`
Das folgende Codebeispiel zeigt, wie Sie es verwenden. `DescribeDBEngineVersions`
**SDK für Python (Boto3)**
Es gibt noch mehr dazu GitHub. Hier finden Sie das vollständige Beispiel und erfahren, wie Sie das [AWS -Code-Beispiel-](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/python/example_code/aurora#code-examples) einrichten und ausführen.
```
class AuroraWrapper:
"""Encapsulates Aurora DB cluster actions."""
def __init__(self, rds_client):
"""
:param rds_client: A Boto3 Amazon Relational Database Service (Amazon RDS) client.
"""
self.rds_client = rds_client
@classmethod
def from_client(cls):
"""
Instantiates this class from a Boto3 client.
"""
rds_client = boto3.client("rds")
return cls(rds_client)
def get_engine_versions(self, engine, parameter_group_family=None):
"""
Gets database engine versions that are available for the specified engine
and parameter group family.
:param engine: The database engine to look up.
:param parameter_group_family: When specified, restricts the returned list of
engine versions to those that are compatible with
this parameter group family.
:return: The list of database engine versions.
"""
try:
kwargs = {"Engine": engine}
if parameter_group_family is not None:
kwargs["DBParameterGroupFamily"] = parameter_group_family
response = self.rds_client.describe_db_engine_versions(**kwargs)
versions = response["DBEngineVersions"]
except ClientError as err:
logger.error(
"Couldn't get engine versions for %s. Here's why: %s: %s",
engine,
err.response["Error"]["Code"],
err.response["Error"]["Message"],
)
raise
else:
return versions
```
+ Einzelheiten zur API finden Sie unter [DBEngineDescribe Versions](https://docs.aws.amazon.com/goto/boto3/rds-2014-10-31/DescribeDBEngineVersions) in *AWS SDK for Python (Boto3) API-Referenz*.
### `DescribeDBInstances`
Das folgende Codebeispiel zeigt die Verwendung. `DescribeDBInstances`
**SDK für Python (Boto3)**
Es gibt noch mehr dazu GitHub. Hier finden Sie das vollständige Beispiel und erfahren, wie Sie das [AWS -Code-Beispiel-](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/python/example_code/aurora#code-examples) einrichten und ausführen.
```
class AuroraWrapper:
"""Encapsulates Aurora DB cluster actions."""
def __init__(self, rds_client):
"""
:param rds_client: A Boto3 Amazon Relational Database Service (Amazon RDS) client.
"""
self.rds_client = rds_client
@classmethod
def from_client(cls):
"""
Instantiates this class from a Boto3 client.
"""
rds_client = boto3.client("rds")
return cls(rds_client)
def get_db_instance(self, instance_id):
"""
Gets data about a DB instance.
:param instance_id: The ID of the DB instance to retrieve.
:return: The retrieved DB instance.
"""
try:
response = self.rds_client.describe_db_instances(
DBInstanceIdentifier=instance_id
)
db_inst = response["DBInstances"][0]
except ClientError as err:
if err.response["Error"]["Code"] == "DBInstanceNotFound":
logger.info("Instance %s does not exist.", instance_id)
else:
logger.error(
"Couldn't get DB instance %s. Here's why: %s: %s",
instance_id,
err.response["Error"]["Code"],
err.response["Error"]["Message"],
)
raise
else:
return db_inst
```
+ Einzelheiten zur API finden Sie unter [DBInstancesDescribe](https://docs.aws.amazon.com/goto/boto3/rds-2014-10-31/DescribeDBInstances) in *AWS SDK for Python (Boto3) API-Referenz*.
### `DescribeOrderableDBInstanceOptions`
Das folgende Codebeispiel zeigt, wie Sie es verwenden. `DescribeOrderableDBInstanceOptions`
**SDK für Python (Boto3)**
Es gibt noch mehr dazu GitHub. Hier finden Sie das vollständige Beispiel und erfahren, wie Sie das [AWS -Code-Beispiel-](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/python/example_code/aurora#code-examples) einrichten und ausführen.
```
class AuroraWrapper:
"""Encapsulates Aurora DB cluster actions."""
def __init__(self, rds_client):
"""
:param rds_client: A Boto3 Amazon Relational Database Service (Amazon RDS) client.
"""
self.rds_client = rds_client
@classmethod
def from_client(cls):
"""
Instantiates this class from a Boto3 client.
"""
rds_client = boto3.client("rds")
return cls(rds_client)
def get_orderable_instances(self, db_engine, db_engine_version):
"""
Gets DB instance options that can be used to create DB instances that are
compatible with a set of specifications.
:param db_engine: The database engine that must be supported by the DB instance.
:param db_engine_version: The engine version that must be supported by the DB instance.
:return: The list of DB instance options that can be used to create a compatible DB instance.
"""
try:
inst_opts = []
paginator = self.rds_client.get_paginator(
"describe_orderable_db_instance_options"
)
for page in paginator.paginate(
Engine=db_engine, EngineVersion=db_engine_version
):
inst_opts += page["OrderableDBInstanceOptions"]
except ClientError as err:
logger.error(
"Couldn't get orderable DB instances. Here's why: %s: %s",
err.response["Error"]["Code"],
err.response["Error"]["Message"],
)
raise
else:
return inst_opts
```
+ Einzelheiten zur API finden Sie unter [DescribeOrderableDBInstanceOptionen](https://docs.aws.amazon.com/goto/boto3/rds-2014-10-31/DescribeOrderableDBInstanceOptions) in der *AWS API-Referenz zum SDK for Python (Boto3)*.
### `ModifyDBClusterParameterGroup`
Das folgende Codebeispiel zeigt die Verwendung. `ModifyDBClusterParameterGroup`
**SDK für Python (Boto3)**
Es gibt noch mehr dazu GitHub. Hier finden Sie das vollständige Beispiel und erfahren, wie Sie das [AWS -Code-Beispiel-](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/python/example_code/aurora#code-examples) einrichten und ausführen.
```
class AuroraWrapper:
"""Encapsulates Aurora DB cluster actions."""
def __init__(self, rds_client):
"""
:param rds_client: A Boto3 Amazon Relational Database Service (Amazon RDS) client.
"""
self.rds_client = rds_client
@classmethod
def from_client(cls):
"""
Instantiates this class from a Boto3 client.
"""
rds_client = boto3.client("rds")
return cls(rds_client)
def update_parameters(self, parameter_group_name, update_parameters):
"""
Updates parameters in a custom DB cluster parameter group.
:param parameter_group_name: The name of the parameter group to update.
:param update_parameters: The parameters to update in the group.
:return: Data about the modified parameter group.
"""
try:
response = self.rds_client.modify_db_cluster_parameter_group(
DBClusterParameterGroupName=parameter_group_name,
Parameters=update_parameters,
)
except ClientError as err:
logger.error(
"Couldn't update parameters in %s. Here's why: %s: %s",
parameter_group_name,
err.response["Error"]["Code"],
err.response["Error"]["Message"],
)
raise
else:
return response
```
+ Einzelheiten zur API finden Sie unter [Modify DBCluster ParameterGroup](https://docs.aws.amazon.com/goto/boto3/rds-2014-10-31/ModifyDBClusterParameterGroup) in *AWS SDK for Python (Boto3) API-Referenz*.
## Szenarien
### Leihbibliothek-REST-API erstellen
Im folgenden Codebeispiel wird veranschaulicht, wie man eine Leihbibliothek erstellt, in der Kunden Bücher mithilfe einer REST-API ausleihen und zurückgeben können, die von einer Amazon-Aurora-Datenbank unterstützt wird.
**SDK für Python (Boto3)**
Zeigt, wie die AWS SDK für Python (Boto3) mit der Amazon Relational Database Service (Amazon RDS) API und AWS Chalice verwendet wird, um eine REST-API zu erstellen, die von einer Amazon Aurora Aurora-Datenbank unterstützt wird. Der Webservice ist vollständig Serverless und stellt eine einfache Leihbibliothek dar, in der die Kunden Bücher ausleihen und zurückgeben können. Lernen Sie Folgendes:
+ Erstellen und verwalten Sie einen Serverless-Aurora-Datenbank-Cluster.
+ Wird AWS Secrets Manager zur Verwaltung von Datenbankanmeldedaten verwendet.
+ Implementieren Sie einen Datenspeicher-Layer, der Amazon RDS verwendet, um Daten in die und aus der Datenbank zu verschieben.
+ Verwenden Sie AWS Chalice, um eine serverlose REST-API für Amazon API Gateway bereitzustellen und. AWS Lambda
+ Verwenden Sie das Anforderungspaket, um Anfragen an den Webservice zu senden.
Den vollständigen Quellcode und Anweisungen zur Einrichtung und Ausführung finden Sie im vollständigen Beispiel unter. [GitHub](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/python/cross_service/aurora_rest_lending_library)
**In diesem Beispiel verwendete Dienste**
+ API Gateway
+ Aurora
+ Lambda
+ Secrets Manager
### Erstellen eines Trackers für Aurora-Serverless-Arbeitsaufgaben
Das folgende Codebeispiel zeigt, wie eine Webanwendung erstellt wird, die Arbeitselemente in einer Datenbank von Amazon Aurora Serverless verfolgt und mithilfe von Amazon Simple Email Service (Amazon SES) Berichte sendet.
**SDK für Python (Boto3)**
Zeigt, wie Sie mithilfe von Amazon Simple Email Service (Amazon SES) einen REST-Service erstellen, der Arbeitselemente in einer Amazon Aurora Aurora-Serverless-Datenbank nachverfolgt und Berichte per E-Mail versendet. AWS SDK für Python (Boto3) In diesem Beispiel wird das Flask-Web-Framework für das HTTP-Routing verwendet und in eine React-Webseite integriert, um eine voll funktionsfähige Webanwendung zu präsentieren.
+ Erstellen Sie einen Flask-REST-Service, der sich in integrieren lässt. AWS-Services
+ Lesen, schreiben und aktualisieren Sie Arbeitsaufgaben, die in einer Aurora-Serverless-Datenbank gespeichert sind.
+ Erstellen Sie ein AWS Secrets Manager Geheimnis, das Datenbankanmeldedaten enthält, und verwenden Sie es, um Aufrufe an die Datenbank zu authentifizieren.
+ Verwenden Sie Amazon SES, um E-Mail-Berichte über Arbeitsaufgaben zu senden.
Den vollständigen Quellcode und Anweisungen zur Einrichtung und Ausführung finden Sie im vollständigen Beispiel unter [GitHub](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/python/cross_service/aurora_item_tracker).
**In diesem Beispiel verwendete Dienste**
+ Aurora
+ Amazon RDS
+ Amazon RDS Data Service
+ Amazon SES