path: root/python-aws-cdk-aws-neptune-alpha.spec

%global _empty_manifest_terminate_build 0
Name:		python-aws-cdk.aws-neptune-alpha
Version:	2.81.0a0
Release:	1
Summary:	The CDK Construct Library for AWS::Neptune
License:	Apache-2.0
URL:		https://github.com/aws/aws-cdk
Source0:	https://mirrors.nju.edu.cn/pypi/web/packages/d8/ca/5fe659954dce34f9527a2cac3eda29761e417eb00e0b937c8f5b1cb2b07e/aws-cdk.aws-neptune-alpha-2.81.0a0.tar.gz
BuildArch:	noarch

Requires:	python3-aws-cdk-lib
Requires:	python3-constructs
Requires:	python3-jsii
Requires:	python3-publication
Requires:	python3-typeguard

%description
<!--END STABILITY BANNER-->
Amazon Neptune is a fast, reliable, fully managed graph database service that makes it easy to build and run applications that work with highly connected datasets. The core of Neptune is a purpose-built, high-performance graph database engine. This engine is optimized for storing billions of relationships and querying the graph with milliseconds latency. Neptune supports the popular graph query languages Apache TinkerPop Gremlin and W3C’s SPARQL, enabling you to build queries that efficiently navigate highly connected datasets.
The `@aws-cdk/aws-neptune` package contains primitives for setting up Neptune database clusters and instances.
```python
import aws_cdk.aws_neptune_alpha as neptune
```
## Starting a Neptune Database
To set up a Neptune database, define a `DatabaseCluster`. You must always launch a database in a VPC.
```python
cluster = neptune.DatabaseCluster(self, "Database",
    vpc=vpc,
    instance_type=neptune.InstanceType.R5_LARGE
)
```
By default only writer instance is provisioned with this construct.
## Connecting
To control who can access the cluster, use the `.connections` attribute. Neptune databases have a default port, so
you don't need to specify the port:
```python
cluster.connections.allow_default_port_from_any_ipv4("Open to the world")
```
The endpoints to access your database cluster will be available as the `.clusterEndpoint` and `.clusterReadEndpoint`
attributes:
```python
write_address = cluster.cluster_endpoint.socket_address
```
## IAM Authentication
You can also authenticate to a database cluster using AWS Identity and Access Management (IAM) database authentication;
See [https://docs.aws.amazon.com/neptune/latest/userguide/iam-auth.html](https://docs.aws.amazon.com/neptune/latest/userguide/iam-auth.html) for more information and a list of supported
versions and limitations.
The following example shows enabling IAM authentication for a database cluster and granting connection access to an IAM role.
```python
cluster = neptune.DatabaseCluster(self, "Cluster",
    vpc=vpc,
    instance_type=neptune.InstanceType.R5_LARGE,
    iam_authentication=True
)
role = iam.Role(self, "DBRole", assumed_by=iam.AccountPrincipal(self.account))
# Use one of the following statements to grant the role the necessary permissions
cluster.grant_connect(role) # Grant the role neptune-db:* access to the DB
cluster.grant(role, "neptune-db:ReadDataViaQuery", "neptune-db:WriteDataViaQuery")
```
## Customizing parameters
Neptune allows configuring database behavior by supplying custom parameter groups.  For more details, refer to the
following link: [https://docs.aws.amazon.com/neptune/latest/userguide/parameters.html](https://docs.aws.amazon.com/neptune/latest/userguide/parameters.html)
```python
cluster_params = neptune.ClusterParameterGroup(self, "ClusterParams",
    description="Cluster parameter group",
    parameters={
        "neptune_enable_audit_log": "1"
    }
)
db_params = neptune.ParameterGroup(self, "DbParams",
    description="Db parameter group",
    parameters={
        "neptune_query_timeout": "120000"
    }
)
cluster = neptune.DatabaseCluster(self, "Database",
    vpc=vpc,
    instance_type=neptune.InstanceType.R5_LARGE,
    cluster_parameter_group=cluster_params,
    parameter_group=db_params
)
```
Note: if you want to use Neptune engine `1.2.0.0` or later, you need to specify the corresponding `engineVersion` prop to `neptune.DatabaseCluster` and `family` prop of `ParameterGroupFamily.NEPTUNE_1_2` to `neptune.ClusterParameterGroup` and `neptune.ParameterGroup`.
## Adding replicas
`DatabaseCluster` allows launching replicas along with the writer instance. This can be specified using the `instanceCount`
attribute.
```python
cluster = neptune.DatabaseCluster(self, "Database",
    vpc=vpc,
    instance_type=neptune.InstanceType.R5_LARGE,
    instances=2
)
```
Additionally, it is also possible to add replicas using `DatabaseInstance` for an existing cluster.
```python
replica1 = neptune.DatabaseInstance(self, "Instance",
    cluster=cluster,
    instance_type=neptune.InstanceType.R5_LARGE
)
```
## Automatic minor version upgrades
By setting `autoMinorVersionUpgrade` to true, Neptune will automatically update
the engine of the entire cluster to the latest minor version after a stabilization
window of 2 to 3 weeks.
```python
neptune.DatabaseCluster(self, "Cluster",
    vpc=vpc,
    instance_type=neptune.InstanceType.R5_LARGE,
    auto_minor_version_upgrade=True
)
```
## Logging
Neptune supports various methods for monitoring performance and usage. One of those methods is logging
1. Neptune provides logs e.g. audit logs which can be viewed or downloaded via the AWS Console. Audit logs can be enabled using the `neptune_enable_audit_log` parameter in `ClusterParameterGroup` or `ParameterGroup`
2. Neptune provides the ability to export those logs to CloudWatch Logs
```python
# Cluster parameter group with the neptune_enable_audit_log param set to 1
cluster_parameter_group = neptune.ClusterParameterGroup(self, "ClusterParams",
    description="Cluster parameter group",
    parameters={
        "neptune_enable_audit_log": "1"
    }
)
cluster = neptune.DatabaseCluster(self, "Database",
    vpc=vpc,
    instance_type=neptune.InstanceType.R5_LARGE,
    # Audit logs are enabled via the clusterParameterGroup
    cluster_parameter_group=cluster_parameter_group,
    # Optionally configuring audit logs to be exported to CloudWatch Logs
    cloudwatch_logs_exports=[neptune.LogType.AUDIT],
    # Optionally set a retention period on exported CloudWatch Logs
    cloudwatch_logs_retention=logs.RetentionDays.ONE_MONTH
)
```
For more information on monitoring, refer to https://docs.aws.amazon.com/neptune/latest/userguide/monitoring.html.
For more information on audit logs, refer to https://docs.aws.amazon.com/neptune/latest/userguide/auditing.html.
For more information on exporting logs to CloudWatch Logs, refer to https://docs.aws.amazon.com/neptune/latest/userguide/cloudwatch-logs.html.
## Metrics
Both `DatabaseCluster` and `DatabaseInstance` provide a `metric()` method to help with cluster-level and instance-level monitoring.
```python
# cluster: neptune.DatabaseCluster
# instance: neptune.DatabaseInstance
cluster.metric("SparqlRequestsPerSec") # cluster-level SparqlErrors metric
instance.metric("SparqlRequestsPerSec")
```
For more details on the available metrics, refer to https://docs.aws.amazon.com/neptune/latest/userguide/cw-metrics.html

%package -n python3-aws-cdk.aws-neptune-alpha
Summary:	The CDK Construct Library for AWS::Neptune
Provides:	python-aws-cdk.aws-neptune-alpha
BuildRequires:	python3-devel
BuildRequires:	python3-setuptools
BuildRequires:	python3-pip
%description -n python3-aws-cdk.aws-neptune-alpha
<!--END STABILITY BANNER-->
Amazon Neptune is a fast, reliable, fully managed graph database service that makes it easy to build and run applications that work with highly connected datasets. The core of Neptune is a purpose-built, high-performance graph database engine. This engine is optimized for storing billions of relationships and querying the graph with milliseconds latency. Neptune supports the popular graph query languages Apache TinkerPop Gremlin and W3C’s SPARQL, enabling you to build queries that efficiently navigate highly connected datasets.
The `@aws-cdk/aws-neptune` package contains primitives for setting up Neptune database clusters and instances.
```python
import aws_cdk.aws_neptune_alpha as neptune
```
## Starting a Neptune Database
To set up a Neptune database, define a `DatabaseCluster`. You must always launch a database in a VPC.
```python
cluster = neptune.DatabaseCluster(self, "Database",
    vpc=vpc,
    instance_type=neptune.InstanceType.R5_LARGE
)
```
By default only writer instance is provisioned with this construct.
## Connecting
To control who can access the cluster, use the `.connections` attribute. Neptune databases have a default port, so
you don't need to specify the port:
```python
cluster.connections.allow_default_port_from_any_ipv4("Open to the world")
```
The endpoints to access your database cluster will be available as the `.clusterEndpoint` and `.clusterReadEndpoint`
attributes:
```python
write_address = cluster.cluster_endpoint.socket_address
```
## IAM Authentication
You can also authenticate to a database cluster using AWS Identity and Access Management (IAM) database authentication;
See [https://docs.aws.amazon.com/neptune/latest/userguide/iam-auth.html](https://docs.aws.amazon.com/neptune/latest/userguide/iam-auth.html) for more information and a list of supported
versions and limitations.
The following example shows enabling IAM authentication for a database cluster and granting connection access to an IAM role.
```python
cluster = neptune.DatabaseCluster(self, "Cluster",
    vpc=vpc,
    instance_type=neptune.InstanceType.R5_LARGE,
    iam_authentication=True
)
role = iam.Role(self, "DBRole", assumed_by=iam.AccountPrincipal(self.account))
# Use one of the following statements to grant the role the necessary permissions
cluster.grant_connect(role) # Grant the role neptune-db:* access to the DB
cluster.grant(role, "neptune-db:ReadDataViaQuery", "neptune-db:WriteDataViaQuery")
```
## Customizing parameters
Neptune allows configuring database behavior by supplying custom parameter groups.  For more details, refer to the
following link: [https://docs.aws.amazon.com/neptune/latest/userguide/parameters.html](https://docs.aws.amazon.com/neptune/latest/userguide/parameters.html)
```python
cluster_params = neptune.ClusterParameterGroup(self, "ClusterParams",
    description="Cluster parameter group",
    parameters={
        "neptune_enable_audit_log": "1"
    }
)
db_params = neptune.ParameterGroup(self, "DbParams",
    description="Db parameter group",
    parameters={
        "neptune_query_timeout": "120000"
    }
)
cluster = neptune.DatabaseCluster(self, "Database",
    vpc=vpc,
    instance_type=neptune.InstanceType.R5_LARGE,
    cluster_parameter_group=cluster_params,
    parameter_group=db_params
)
```
Note: if you want to use Neptune engine `1.2.0.0` or later, you need to specify the corresponding `engineVersion` prop to `neptune.DatabaseCluster` and `family` prop of `ParameterGroupFamily.NEPTUNE_1_2` to `neptune.ClusterParameterGroup` and `neptune.ParameterGroup`.
## Adding replicas
`DatabaseCluster` allows launching replicas along with the writer instance. This can be specified using the `instanceCount`
attribute.
```python
cluster = neptune.DatabaseCluster(self, "Database",
    vpc=vpc,
    instance_type=neptune.InstanceType.R5_LARGE,
    instances=2
)
```
Additionally, it is also possible to add replicas using `DatabaseInstance` for an existing cluster.
```python
replica1 = neptune.DatabaseInstance(self, "Instance",
    cluster=cluster,
    instance_type=neptune.InstanceType.R5_LARGE
)
```
## Automatic minor version upgrades
By setting `autoMinorVersionUpgrade` to true, Neptune will automatically update
the engine of the entire cluster to the latest minor version after a stabilization
window of 2 to 3 weeks.
```python
neptune.DatabaseCluster(self, "Cluster",
    vpc=vpc,
    instance_type=neptune.InstanceType.R5_LARGE,
    auto_minor_version_upgrade=True
)
```
## Logging
Neptune supports various methods for monitoring performance and usage. One of those methods is logging
1. Neptune provides logs e.g. audit logs which can be viewed or downloaded via the AWS Console. Audit logs can be enabled using the `neptune_enable_audit_log` parameter in `ClusterParameterGroup` or `ParameterGroup`
2. Neptune provides the ability to export those logs to CloudWatch Logs
```python
# Cluster parameter group with the neptune_enable_audit_log param set to 1
cluster_parameter_group = neptune.ClusterParameterGroup(self, "ClusterParams",
    description="Cluster parameter group",
    parameters={
        "neptune_enable_audit_log": "1"
    }
)
cluster = neptune.DatabaseCluster(self, "Database",
    vpc=vpc,
    instance_type=neptune.InstanceType.R5_LARGE,
    # Audit logs are enabled via the clusterParameterGroup
    cluster_parameter_group=cluster_parameter_group,
    # Optionally configuring audit logs to be exported to CloudWatch Logs
    cloudwatch_logs_exports=[neptune.LogType.AUDIT],
    # Optionally set a retention period on exported CloudWatch Logs
    cloudwatch_logs_retention=logs.RetentionDays.ONE_MONTH
)
```
For more information on monitoring, refer to https://docs.aws.amazon.com/neptune/latest/userguide/monitoring.html.
For more information on audit logs, refer to https://docs.aws.amazon.com/neptune/latest/userguide/auditing.html.
For more information on exporting logs to CloudWatch Logs, refer to https://docs.aws.amazon.com/neptune/latest/userguide/cloudwatch-logs.html.
## Metrics
Both `DatabaseCluster` and `DatabaseInstance` provide a `metric()` method to help with cluster-level and instance-level monitoring.
```python
# cluster: neptune.DatabaseCluster
# instance: neptune.DatabaseInstance
cluster.metric("SparqlRequestsPerSec") # cluster-level SparqlErrors metric
instance.metric("SparqlRequestsPerSec")
```
For more details on the available metrics, refer to https://docs.aws.amazon.com/neptune/latest/userguide/cw-metrics.html

%package help
Summary:	Development documents and examples for aws-cdk.aws-neptune-alpha
Provides:	python3-aws-cdk.aws-neptune-alpha-doc
%description help
<!--END STABILITY BANNER-->
Amazon Neptune is a fast, reliable, fully managed graph database service that makes it easy to build and run applications that work with highly connected datasets. The core of Neptune is a purpose-built, high-performance graph database engine. This engine is optimized for storing billions of relationships and querying the graph with milliseconds latency. Neptune supports the popular graph query languages Apache TinkerPop Gremlin and W3C’s SPARQL, enabling you to build queries that efficiently navigate highly connected datasets.
The `@aws-cdk/aws-neptune` package contains primitives for setting up Neptune database clusters and instances.
```python
import aws_cdk.aws_neptune_alpha as neptune
```
## Starting a Neptune Database
To set up a Neptune database, define a `DatabaseCluster`. You must always launch a database in a VPC.
```python
cluster = neptune.DatabaseCluster(self, "Database",
    vpc=vpc,
    instance_type=neptune.InstanceType.R5_LARGE
)
```
By default only writer instance is provisioned with this construct.
## Connecting
To control who can access the cluster, use the `.connections` attribute. Neptune databases have a default port, so
you don't need to specify the port:
```python
cluster.connections.allow_default_port_from_any_ipv4("Open to the world")
```
The endpoints to access your database cluster will be available as the `.clusterEndpoint` and `.clusterReadEndpoint`
attributes:
```python
write_address = cluster.cluster_endpoint.socket_address
```
## IAM Authentication
You can also authenticate to a database cluster using AWS Identity and Access Management (IAM) database authentication;
See [https://docs.aws.amazon.com/neptune/latest/userguide/iam-auth.html](https://docs.aws.amazon.com/neptune/latest/userguide/iam-auth.html) for more information and a list of supported
versions and limitations.
The following example shows enabling IAM authentication for a database cluster and granting connection access to an IAM role.
```python
cluster = neptune.DatabaseCluster(self, "Cluster",
    vpc=vpc,
    instance_type=neptune.InstanceType.R5_LARGE,
    iam_authentication=True
)
role = iam.Role(self, "DBRole", assumed_by=iam.AccountPrincipal(self.account))
# Use one of the following statements to grant the role the necessary permissions
cluster.grant_connect(role) # Grant the role neptune-db:* access to the DB
cluster.grant(role, "neptune-db:ReadDataViaQuery", "neptune-db:WriteDataViaQuery")
```
## Customizing parameters
Neptune allows configuring database behavior by supplying custom parameter groups.  For more details, refer to the
following link: [https://docs.aws.amazon.com/neptune/latest/userguide/parameters.html](https://docs.aws.amazon.com/neptune/latest/userguide/parameters.html)
```python
cluster_params = neptune.ClusterParameterGroup(self, "ClusterParams",
    description="Cluster parameter group",
    parameters={
        "neptune_enable_audit_log": "1"
    }
)
db_params = neptune.ParameterGroup(self, "DbParams",
    description="Db parameter group",
    parameters={
        "neptune_query_timeout": "120000"
    }
)
cluster = neptune.DatabaseCluster(self, "Database",
    vpc=vpc,
    instance_type=neptune.InstanceType.R5_LARGE,
    cluster_parameter_group=cluster_params,
    parameter_group=db_params
)
```
Note: if you want to use Neptune engine `1.2.0.0` or later, you need to specify the corresponding `engineVersion` prop to `neptune.DatabaseCluster` and `family` prop of `ParameterGroupFamily.NEPTUNE_1_2` to `neptune.ClusterParameterGroup` and `neptune.ParameterGroup`.
## Adding replicas
`DatabaseCluster` allows launching replicas along with the writer instance. This can be specified using the `instanceCount`
attribute.
```python
cluster = neptune.DatabaseCluster(self, "Database",
    vpc=vpc,
    instance_type=neptune.InstanceType.R5_LARGE,
    instances=2
)
```
Additionally, it is also possible to add replicas using `DatabaseInstance` for an existing cluster.
```python
replica1 = neptune.DatabaseInstance(self, "Instance",
    cluster=cluster,
    instance_type=neptune.InstanceType.R5_LARGE
)
```
## Automatic minor version upgrades
By setting `autoMinorVersionUpgrade` to true, Neptune will automatically update
the engine of the entire cluster to the latest minor version after a stabilization
window of 2 to 3 weeks.
```python
neptune.DatabaseCluster(self, "Cluster",
    vpc=vpc,
    instance_type=neptune.InstanceType.R5_LARGE,
    auto_minor_version_upgrade=True
)
```
## Logging
Neptune supports various methods for monitoring performance and usage. One of those methods is logging
1. Neptune provides logs e.g. audit logs which can be viewed or downloaded via the AWS Console. Audit logs can be enabled using the `neptune_enable_audit_log` parameter in `ClusterParameterGroup` or `ParameterGroup`
2. Neptune provides the ability to export those logs to CloudWatch Logs
```python
# Cluster parameter group with the neptune_enable_audit_log param set to 1
cluster_parameter_group = neptune.ClusterParameterGroup(self, "ClusterParams",
    description="Cluster parameter group",
    parameters={
        "neptune_enable_audit_log": "1"
    }
)
cluster = neptune.DatabaseCluster(self, "Database",
    vpc=vpc,
    instance_type=neptune.InstanceType.R5_LARGE,
    # Audit logs are enabled via the clusterParameterGroup
    cluster_parameter_group=cluster_parameter_group,
    # Optionally configuring audit logs to be exported to CloudWatch Logs
    cloudwatch_logs_exports=[neptune.LogType.AUDIT],
    # Optionally set a retention period on exported CloudWatch Logs
    cloudwatch_logs_retention=logs.RetentionDays.ONE_MONTH
)
```
For more information on monitoring, refer to https://docs.aws.amazon.com/neptune/latest/userguide/monitoring.html.
For more information on audit logs, refer to https://docs.aws.amazon.com/neptune/latest/userguide/auditing.html.
For more information on exporting logs to CloudWatch Logs, refer to https://docs.aws.amazon.com/neptune/latest/userguide/cloudwatch-logs.html.
## Metrics
Both `DatabaseCluster` and `DatabaseInstance` provide a `metric()` method to help with cluster-level and instance-level monitoring.
```python
# cluster: neptune.DatabaseCluster
# instance: neptune.DatabaseInstance
cluster.metric("SparqlRequestsPerSec") # cluster-level SparqlErrors metric
instance.metric("SparqlRequestsPerSec")
```
For more details on the available metrics, refer to https://docs.aws.amazon.com/neptune/latest/userguide/cw-metrics.html

%prep
%autosetup -n aws-cdk.aws-neptune-alpha-2.81.0a0

%build
%py3_build

%install
%py3_install
install -d -m755 %{buildroot}/%{_pkgdocdir}
if [ -d doc ]; then cp -arf doc %{buildroot}/%{_pkgdocdir}; fi
if [ -d docs ]; then cp -arf docs %{buildroot}/%{_pkgdocdir}; fi
if [ -d example ]; then cp -arf example %{buildroot}/%{_pkgdocdir}; fi
if [ -d examples ]; then cp -arf examples %{buildroot}/%{_pkgdocdir}; fi
pushd %{buildroot}
if [ -d usr/lib ]; then
	find usr/lib -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/lib64 ]; then
	find usr/lib64 -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/bin ]; then
	find usr/bin -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/sbin ]; then
	find usr/sbin -type f -printf "/%h/%f\n" >> filelist.lst
fi
touch doclist.lst
if [ -d usr/share/man ]; then
	find usr/share/man -type f -printf "/%h/%f.gz\n" >> doclist.lst
fi
popd
mv %{buildroot}/filelist.lst .
mv %{buildroot}/doclist.lst .

%files -n python3-aws-cdk.aws-neptune-alpha -f filelist.lst
%dir %{python3_sitelib}/*

%files help -f doclist.lst
%{_docdir}/*

%changelog
* Mon May 29 2023 Python_Bot <Python_Bot@openeuler.org> - 2.81.0a0-1
- Package Spec generated