# Amazon Neptune Construct Library
<!--BEGIN STABILITY BANNER-->---
> The APIs of higher level constructs in this module are experimental and under active development.
> They are subject to non-backward compatible changes or removal in any future version. These are
> not subject to the [Semantic Versioning](https://semver.org/) model and breaking changes will be
> announced in the release notes. This means that while you may use them, you may need to update
> your source code when upgrading to a newer version of this package.
---
<!--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-alpha` 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: To use the Neptune engine versions `1.2.0.0` or later, including the newly added `1.3` series, it's necessary to specify the appropriate `engineVersion` prop in `neptune.DatabaseCluster`. Additionally, for both 1.2 and 1.3 series, the corresponding `family` prop must be set to `ParameterGroupFamily.NEPTUNE_1_2` or `ParameterGroupFamily.NEPTUNE_1_3` respectively in `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
)
```
## Port
By default, Neptune uses port `8182`. You can override the default port by specifying the `port` property:
```python
cluster = neptune.DatabaseCluster(self, "Database",
vpc=vpc,
instance_type=neptune.InstanceType.R5_LARGE,
port=12345
)
```
## 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
## Copy tags to snapshot
By setting `copyTagsToSnapshot` to true, all tags of the cluster are copied to the snapshots when they are created.
```python
cluster = neptune.DatabaseCluster(self, "Database",
vpc=vpc,
instance_type=neptune.InstanceType.R5_LARGE,
copy_tags_to_snapshot=True
)
```
## Neptune Serverless
You can configure a Neptune Serverless cluster using the dedicated instance type along with the
`serverlessScalingConfiguration` property.
> Visit [Using Amazon Neptune Serverless](https://docs.aws.amazon.com/neptune/latest/userguide/neptune-serverless-using.html) for more details.
```python
cluster = neptune.DatabaseCluster(self, "ServerlessDatabase",
vpc=vpc,
instance_type=neptune.InstanceType.SERVERLESS,
serverless_scaling_configuration=neptune.ServerlessScalingConfiguration(
min_capacity=1,
max_capacity=5
)
)
```
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"description": "# Amazon Neptune Construct Library\n\n<!--BEGIN STABILITY BANNER-->---\n\n\n\n\n> The APIs of higher level constructs in this module are experimental and under active development.\n> They are subject to non-backward compatible changes or removal in any future version. These are\n> not subject to the [Semantic Versioning](https://semver.org/) model and breaking changes will be\n> announced in the release notes. This means that while you may use them, you may need to update\n> your source code when upgrading to a newer version of this package.\n\n---\n<!--END STABILITY BANNER-->\n\nAmazon 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\u2019s SPARQL, enabling you to build queries that efficiently navigate highly connected datasets.\n\nThe `@aws-cdk/aws-neptune-alpha` package contains primitives for setting up Neptune database clusters and instances.\n\n```python\nimport aws_cdk.aws_neptune_alpha as neptune\n```\n\n## Starting a Neptune Database\n\nTo set up a Neptune database, define a `DatabaseCluster`. You must always launch a database in a VPC.\n\n```python\ncluster = neptune.DatabaseCluster(self, \"Database\",\n vpc=vpc,\n instance_type=neptune.InstanceType.R5_LARGE\n)\n```\n\nBy default only writer instance is provisioned with this construct.\n\n## Connecting\n\nTo control who can access the cluster, use the `.connections` attribute. Neptune databases have a default port, so\nyou don't need to specify the port:\n\n```python\ncluster.connections.allow_default_port_from_any_ipv4(\"Open to the world\")\n```\n\nThe endpoints to access your database cluster will be available as the `.clusterEndpoint` and `.clusterReadEndpoint`\nattributes:\n\n```python\nwrite_address = cluster.cluster_endpoint.socket_address\n```\n\n## IAM Authentication\n\nYou can also authenticate to a database cluster using AWS Identity and Access Management (IAM) database authentication;\nSee [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\nversions and limitations.\n\nThe following example shows enabling IAM authentication for a database cluster and granting connection access to an IAM role.\n\n```python\ncluster = neptune.DatabaseCluster(self, \"Cluster\",\n vpc=vpc,\n instance_type=neptune.InstanceType.R5_LARGE,\n iam_authentication=True\n)\nrole = iam.Role(self, \"DBRole\", assumed_by=iam.AccountPrincipal(self.account))\n# Use one of the following statements to grant the role the necessary permissions\ncluster.grant_connect(role) # Grant the role neptune-db:* access to the DB\ncluster.grant(role, \"neptune-db:ReadDataViaQuery\", \"neptune-db:WriteDataViaQuery\")\n```\n\n## Customizing parameters\n\nNeptune allows configuring database behavior by supplying custom parameter groups. For more details, refer to the\nfollowing link: [https://docs.aws.amazon.com/neptune/latest/userguide/parameters.html](https://docs.aws.amazon.com/neptune/latest/userguide/parameters.html)\n\n```python\ncluster_params = neptune.ClusterParameterGroup(self, \"ClusterParams\",\n description=\"Cluster parameter group\",\n parameters={\n \"neptune_enable_audit_log\": \"1\"\n }\n)\n\ndb_params = neptune.ParameterGroup(self, \"DbParams\",\n description=\"Db parameter group\",\n parameters={\n \"neptune_query_timeout\": \"120000\"\n }\n)\n\ncluster = neptune.DatabaseCluster(self, \"Database\",\n vpc=vpc,\n instance_type=neptune.InstanceType.R5_LARGE,\n cluster_parameter_group=cluster_params,\n parameter_group=db_params\n)\n```\n\nNote: To use the Neptune engine versions `1.2.0.0` or later, including the newly added `1.3` series, it's necessary to specify the appropriate `engineVersion` prop in `neptune.DatabaseCluster`. Additionally, for both 1.2 and 1.3 series, the corresponding `family` prop must be set to `ParameterGroupFamily.NEPTUNE_1_2` or `ParameterGroupFamily.NEPTUNE_1_3` respectively in `neptune.ClusterParameterGroup` and `neptune.ParameterGroup`.\n\n## Adding replicas\n\n`DatabaseCluster` allows launching replicas along with the writer instance. This can be specified using the `instanceCount`\nattribute.\n\n```python\ncluster = neptune.DatabaseCluster(self, \"Database\",\n vpc=vpc,\n instance_type=neptune.InstanceType.R5_LARGE,\n instances=2\n)\n```\n\nAdditionally, it is also possible to add replicas using `DatabaseInstance` for an existing cluster.\n\n```python\nreplica1 = neptune.DatabaseInstance(self, \"Instance\",\n cluster=cluster,\n instance_type=neptune.InstanceType.R5_LARGE\n)\n```\n\n## Automatic minor version upgrades\n\nBy setting `autoMinorVersionUpgrade` to true, Neptune will automatically update\nthe engine of the entire cluster to the latest minor version after a stabilization\nwindow of 2 to 3 weeks.\n\n```python\nneptune.DatabaseCluster(self, \"Cluster\",\n vpc=vpc,\n instance_type=neptune.InstanceType.R5_LARGE,\n auto_minor_version_upgrade=True\n)\n```\n\n## Port\n\nBy default, Neptune uses port `8182`. You can override the default port by specifying the `port` property:\n\n```python\ncluster = neptune.DatabaseCluster(self, \"Database\",\n vpc=vpc,\n instance_type=neptune.InstanceType.R5_LARGE,\n port=12345\n)\n```\n\n## Logging\n\nNeptune supports various methods for monitoring performance and usage. One of those methods is logging\n\n1. 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`\n2. Neptune provides the ability to export those logs to CloudWatch Logs\n\n```python\n# Cluster parameter group with the neptune_enable_audit_log param set to 1\ncluster_parameter_group = neptune.ClusterParameterGroup(self, \"ClusterParams\",\n description=\"Cluster parameter group\",\n parameters={\n \"neptune_enable_audit_log\": \"1\"\n }\n)\n\ncluster = neptune.DatabaseCluster(self, \"Database\",\n vpc=vpc,\n instance_type=neptune.InstanceType.R5_LARGE,\n # Audit logs are enabled via the clusterParameterGroup\n cluster_parameter_group=cluster_parameter_group,\n # Optionally configuring audit logs to be exported to CloudWatch Logs\n cloudwatch_logs_exports=[neptune.LogType.AUDIT],\n # Optionally set a retention period on exported CloudWatch Logs\n cloudwatch_logs_retention=logs.RetentionDays.ONE_MONTH\n)\n```\n\nFor more information on monitoring, refer to https://docs.aws.amazon.com/neptune/latest/userguide/monitoring.html.\nFor more information on audit logs, refer to https://docs.aws.amazon.com/neptune/latest/userguide/auditing.html.\nFor more information on exporting logs to CloudWatch Logs, refer to https://docs.aws.amazon.com/neptune/latest/userguide/cloudwatch-logs.html.\n\n## Metrics\n\nBoth `DatabaseCluster` and `DatabaseInstance` provide a `metric()` method to help with cluster-level and instance-level monitoring.\n\n```python\n# cluster: neptune.DatabaseCluster\n# instance: neptune.DatabaseInstance\n\n\ncluster.metric(\"SparqlRequestsPerSec\") # cluster-level SparqlErrors metric\ninstance.metric(\"SparqlRequestsPerSec\")\n```\n\nFor more details on the available metrics, refer to https://docs.aws.amazon.com/neptune/latest/userguide/cw-metrics.html\n\n## Copy tags to snapshot\n\nBy setting `copyTagsToSnapshot` to true, all tags of the cluster are copied to the snapshots when they are created.\n\n```python\ncluster = neptune.DatabaseCluster(self, \"Database\",\n vpc=vpc,\n instance_type=neptune.InstanceType.R5_LARGE,\n copy_tags_to_snapshot=True\n)\n```\n\n## Neptune Serverless\n\nYou can configure a Neptune Serverless cluster using the dedicated instance type along with the\n`serverlessScalingConfiguration` property.\n\n> Visit [Using Amazon Neptune Serverless](https://docs.aws.amazon.com/neptune/latest/userguide/neptune-serverless-using.html) for more details.\n\n```python\ncluster = neptune.DatabaseCluster(self, \"ServerlessDatabase\",\n vpc=vpc,\n instance_type=neptune.InstanceType.SERVERLESS,\n serverless_scaling_configuration=neptune.ServerlessScalingConfiguration(\n min_capacity=1,\n max_capacity=5\n )\n)\n```\n",
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