@aws-cdk/aws-glue
Advanced tools
All classes with the
Cfnprefix in this module (CFN Resources) are always stable and safe to use.
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 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 This means that while you may use them, you may need to update your source code when upgrading to a newer version of this package.
This module is part of the AWS Cloud Development Kit project.
A Job encapsulates a script that connects to data sources, processes them, and then writes output to a data target.
There are 3 types of jobs supported by AWS Glue: Spark ETL, Spark Streaming, and Python Shell jobs.
The glue.JobExecutable allows you to specify the type of job, the language to use and the code assets required by the job.
glue.Code allows you to refer to the different code assets required by the job, either from an existing S3 location or from a local file path.
These jobs run in an Apache Spark environment managed by AWS Glue.
An ETL job processes data in batches using Apache Spark.
new glue.Job(stack, 'ScalaSparkEtlJob', {
executable: glue.JobExecutable.scalaEtl({
glueVersion: glue.GlueVersion.V2_0,
script: glue.Code.fromBucket(bucket, 'src/com/example/HelloWorld.scala'),
className: 'com.example.HelloWorld',
extraJars: [glue.Code.fromBucket(bucket, 'jars/HelloWorld.jar')],
}),
description: 'an example Scala ETL job',
});
A Streaming job is similar to an ETL job, except that it performs ETL on data streams. It uses the Apache Spark Structured Streaming framework. Some Spark job features are not available to streaming ETL jobs.
new glue.Job(stack, 'PythonSparkStreamingJob', {
executable: glue.JobExecutable.pythonStreaming({
glueVersion: glue.GlueVersion.V2_0,
pythonVersion: glue.PythonVersion.THREE,
script: glue.Code.fromAsset(path.join(__dirname, 'job-script/hello_world.py')),
}),
description: 'an example Python Streaming job',
});
A Python shell job runs Python scripts as a shell and supports a Python version that depends on the AWS Glue version you are using. This can be used to schedule and run tasks that don't require an Apache Spark environment.
new glue.Job(stack, 'PythonShellJob', {
executable: glue.JobExecutable.pythonShell({
glueVersion: glue.GlueVersion.V1_0,
pythonVersion: PythonVersion.THREE,
script: glue.Code.fromBucket(bucket, 'script.py'),
}),
description: 'an example Python Shell job',
});
See documentation for more information on adding jobs in Glue.
A Connection allows Glue jobs, crawlers and development endpoints to access certain types of data stores. For example, to create a network connection to connect to a data source within a VPC:
new glue.Connection(stack, 'MyConnection', {
connectionType: glue.ConnectionTypes.NETWORK,
// The security groups granting AWS Glue inbound access to the data source within the VPC
securityGroups: [securityGroup],
// The VPC subnet which contains the data source
subnet,
});
If you need to use a connection type that doesn't exist as a static member on ConnectionType, you can instantiate a ConnectionType object, e.g: new glue.ConnectionType('NEW_TYPE').
See Adding a Connection to Your Data Store and Connection Structure documentation for more information on the supported data stores and their configurations.
A SecurityConfiguration is a set of security properties that can be used by AWS Glue to encrypt data at rest.
new glue.SecurityConfiguration(stack, 'MySecurityConfiguration', {
securityConfigurationName: 'name',
cloudWatchEncryption: {
mode: glue.CloudWatchEncryptionMode.KMS,
},
jobBookmarksEncryption: {
mode: glue.JobBookmarksEncryptionMode.CLIENT_SIDE_KMS,
},
s3Encryption: {
mode: glue.S3EncryptionMode.KMS,
},
});
By default, a shared KMS key is created for use with the encryption configurations that require one. You can also supply your own key for each encryption config, for example, for CloudWatch encryption:
new glue.SecurityConfiguration(stack, 'MySecurityConfiguration', {
securityConfigurationName: 'name',
cloudWatchEncryption: {
mode: glue.CloudWatchEncryptionMode.KMS,
kmsKey: key,
},
});
See documentation for more info for Glue encrypting data written by Crawlers, Jobs, and Development Endpoints.
A Database is a logical grouping of Tables in the Glue Catalog.
new glue.Database(stack, 'MyDatabase', {
databaseName: 'my_database'
});
A Glue table describes a table of data in S3: its structure (column names and types), location of data (S3 objects with a common prefix in a S3 bucket), and format for the files (Json, Avro, Parquet, etc.):
new glue.Table(stack, 'MyTable', {
database: myDatabase,
tableName: 'my_table',
columns: [{
name: 'col1',
type: glue.Schema.STRING,
}, {
name: 'col2',
type: glue.Schema.array(Schema.STRING),
comment: 'col2 is an array of strings' // comment is optional
}],
dataFormat: glue.DataFormat.JSON
});
By default, a S3 bucket will be created to store the table's data but you can manually pass the bucket and s3Prefix:
new glue.Table(stack, 'MyTable', {
bucket: myBucket,
s3Prefix: 'my-table/'
...
});
By default, an S3 bucket will be created to store the table's data and stored in the bucket root. You can also manually pass the bucket and s3Prefix:
To improve query performance, a table can specify partitionKeys on which data is stored and queried separately. For example, you might partition a table by year and month to optimize queries based on a time window:
new glue.Table(stack, 'MyTable', {
database: myDatabase,
tableName: 'my_table',
columns: [{
name: 'col1',
type: glue.Schema.STRING
}],
partitionKeys: [{
name: 'year',
type: glue.Schema.SMALL_INT
}, {
name: 'month',
type: glue.Schema.SMALL_INT
}],
dataFormat: glue.DataFormat.JSON
});
You can enable encryption on a Table's data:
Unencrypted - files are not encrypted. The default encryption setting.SSE-S3) with an Amazon S3-managed key.new glue.Table(stack, 'MyTable', {
encryption: glue.TableEncryption.S3_MANAGED
...
});
SSE-KMS) with an AWS KMS Key managed by the account owner.// KMS key is created automatically
new glue.Table(stack, 'MyTable', {
encryption: glue.TableEncryption.KMS
...
});
// with an explicit KMS key
new glue.Table(stack, 'MyTable', {
encryption: glue.TableEncryption.KMS,
encryptionKey: new kms.Key(stack, 'MyKey')
...
});
SSE-KMS), like Kms, except with an AWS KMS Key managed by the AWS Key Management Service.new glue.Table(stack, 'MyTable', {
encryption: glue.TableEncryption.KMS_MANAGED
...
});
CSE-KMS) with an AWS KMS Key managed by the account owner.// KMS key is created automatically
new glue.Table(stack, 'MyTable', {
encryption: glue.TableEncryption.CLIENT_SIDE_KMS
...
});
// with an explicit KMS key
new glue.Table(stack, 'MyTable', {
encryption: glue.TableEncryption.CLIENT_SIDE_KMS,
encryptionKey: new kms.Key(stack, 'MyKey')
...
});
Note: you cannot provide a Bucket when creating the Table if you wish to use server-side encryption (KMS, KMS_MANAGED or S3_MANAGED).
A table's schema is a collection of columns, each of which have a name and a type. Types are recursive structures, consisting of primitive and complex types:
new glue.Table(stack, 'MyTable', {
columns: [{
name: 'primitive_column',
type: glue.Schema.STRING
}, {
name: 'array_column',
type: glue.Schema.array(glue.Schema.INTEGER),
comment: 'array<integer>'
}, {
name: 'map_column',
type: glue.Schema.map(
glue.Schema.STRING,
glue.Schema.TIMESTAMP),
comment: 'map<string,string>'
}, {
name: 'struct_column',
type: glue.Schema.struct([{
name: 'nested_column',
type: glue.Schema.DATE,
comment: 'nested comment'
}]),
comment: "struct<nested_column:date COMMENT 'nested comment'>"
}],
...
| Name | Type | Comments |
|---|---|---|
| FLOAT | Constant | A 32-bit single-precision floating point number |
| INTEGER | Constant | A 32-bit signed value in two's complement format, with a minimum value of -2^31 and a maximum value of 2^31-1 |
| DOUBLE | Constant | A 64-bit double-precision floating point number |
| BIG_INT | Constant | A 64-bit signed INTEGER in two’s complement format, with a minimum value of -2^63 and a maximum value of 2^63 -1 |
| SMALL_INT | Constant | A 16-bit signed INTEGER in two’s complement format, with a minimum value of -2^15 and a maximum value of 2^15-1 |
| TINY_INT | Constant | A 8-bit signed INTEGER in two’s complement format, with a minimum value of -2^7 and a maximum value of 2^7-1 |
| Name | Type | Comments |
|---|---|---|
| DATE | Constant | A date in UNIX format, such as YYYY-MM-DD. |
| TIMESTAMP | Constant | Date and time instant in the UNiX format, such as yyyy-mm-dd hh:mm:ss[.f...]. For example, TIMESTAMP '2008-09-15 03:04:05.324'. This format uses the session time zone. |
| Name | Type | Comments |
|---|---|---|
| STRING | Constant | A string literal enclosed in single or double quotes |
| decimal(precision: number, scale?: number) | Function | precision is the total number of digits. scale (optional) is the number of digits in fractional part with a default of 0. For example, use these type definitions: decimal(11,5), decimal(15) |
| char(length: number) | Function | Fixed length character data, with a specified length between 1 and 255, such as char(10) |
| varchar(length: number) | Function | Variable length character data, with a specified length between 1 and 65535, such as varchar(10) |
| Name | Type | Comments |
|---|---|---|
| BOOLEAN | Constant | Values are true and false |
| BINARY | Constant | Value is in binary |
| Name | Type | Comments |
|---|---|---|
| array(itemType: Type) | Function | An array of some other type |
| map(keyType: Type, valueType: Type) | Function | A map of some primitive key type to any value type |
| struct(collumns: Column[]) | Function | Nested structure containing individually named and typed collumns |
FAQs
The CDK Construct Library for AWS::Glue
We found that @aws-cdk/aws-glue demonstrated a not healthy version release cadence and project activity because the last version was released a year ago. It has 4 open source maintainers collaborating on the project.
Did you know?
Socket for GitHub automatically highlights issues in each pull request and monitors the health of all your open source dependencies. Discover the contents of your packages and block harmful activity before you install or update your dependencies.
Security News
Critics call the Node.js EOL CVE a misuse of the system, sparking debate over CVE standards and the growing noise in vulnerability databases.
Security News
cURL and Go security teams are publicly rejecting CVSS as flawed for assessing vulnerabilities and are calling for more accurate, context-aware approaches.
Security News
Bun 1.2 enhances its JavaScript runtime with 90% Node.js compatibility, built-in S3 and Postgres support, HTML Imports, and faster, cloud-first performance.