捆绑包配置示例
本文提供了捆绑包功能和常见捆绑包用例的示例配置。
提示
本文中的部分示例以及其他示例可以在捆绑包示例存储库中找到。
使用无服务器计算的作业
Databricks 资产捆绑包支持在无服务器计算上运行的作业。 若要对此进行配置,可以省略作业的 clusters
设置,也可以指定环境,如以下示例所示。
# A serverless job (no cluster definition)
resources:
jobs:
serverless_job_no_cluster:
name: serverless_job_no_cluster
email_notifications:
on_failure:
- someone@example.com
tasks:
- task_key: notebook_task
notebook_task:
notebook_path: ../src/notebook.ipynb
# A serverless job (environment spec)
resources:
jobs:
serverless_job_environment:
name: serverless_job_environment
tasks:
- task_key: task
spark_python_task:
python_file: ../src/main.py
# The key that references an environment spec in a job.
environment_key: default
# A list of task execution environment specifications that can be referenced by tasks of this job.
environments:
- environment_key: default
# Full documentation of this spec can be found at:
# https://docs.databricks.com/api/workspace/jobs/create#environments-spec
spec:
client: "1"
dependencies:
- cowsay
使用无服务器计算的管道
Databricks 资产捆绑包支持在无服务器计算上运行的管道。 若要对此进行配置,请将管道 serverless
设置设置为 true
。 以下示例配置定义了一个在无服务器计算上运行的管道,以及一个每小时触发管道刷新的作业。
# A pipeline that runs on serverless compute
resources:
pipelines:
my_pipeline:
name: my_pipeline
target: ${bundle.environment}
serverless: true
catalog: users
libraries:
- notebook:
path: ../src/my_pipeline.ipynb
configuration:
bundle.sourcePath: /Workspace/${workspace.file_path}/src
# This defines a job to refresh a pipeline that is triggered every hour
resources:
jobs:
my_job:
name: my_job
# Run this job once an hour.
trigger:
periodic:
interval: 1
unit: HOURS
email_notifications:
on_failure:
- someone@example.com
tasks:
- task_key: refresh_pipeline
pipeline_task:
pipeline_id: ${resources.pipelines.my_pipeline.id}
包含 SQL 笔记本的作业
以下示例配置定义了一个包含 SQL 笔记本的作业。
resources:
jobs:
job_with_sql_notebook:
name: Job to demonstrate using a SQL notebook with a SQL warehouse
tasks:
- task_key: notebook
notebook_task:
notebook_path: ./select.sql
warehouse_id: 799f096837fzzzz4
使用多个 wheel 文件的作业
以下示例配置定义了一个包含一个作业的捆绑包,该作业使用了多个 *.whl
文件。
# job.yml
resources:
jobs:
example_job:
name: "Example with multiple wheels"
tasks:
- task_key: task
spark_python_task:
python_file: ../src/call_wheel.py
libraries:
- whl: ../my_custom_wheel1/dist/*.whl
- whl: ../my_custom_wheel2/dist/*.whl
new_cluster:
node_type_id: i3.xlarge
num_workers: 0
spark_version: 14.3.x-scala2.12
spark_conf:
"spark.databricks.cluster.profile": "singleNode"
"spark.master": "local[*, 4]"
custom_tags:
"ResourceClass": "SingleNode"
# databricks.yml
bundle:
name: job_with_multiple_wheels
include:
- ./resources/job.yml
workspace:
host: https://myworkspace.cloud.databricks.com
artifacts:
my_custom_wheel1:
type: whl
build: poetry build
path: ./my_custom_wheel1
my_custom_wheel2:
type: whl
build: poetry build
path: ./my_custom_wheel2
targets:
dev:
default: true
mode: development
使用 requirements.txt 文件的作业
以下示例配置定义了一个使用 requirements.txt 文件的作业。
resources:
jobs:
job_with_requirements_txt:
name: Example job that uses a requirements.txt file
tasks:
- task_key: task
job_cluster_key: default
spark_python_task:
python_file: ../src/main.py
libraries:
- requirements: /Workspace/${workspace.file_path}/requirements.txt
将 JAR 文件上传到 Unity Catalog 的捆绑包
可以将 Unity Catalog 卷指定为项目路径,以便将 JAR 文件和 wheel 文件等所有项目上传到 Unity Catalog 卷。 以下示例捆绑包将 JAR 文件上传到 Unity Catalog。 有关 artifact_path
映射的信息,请参阅 artifact_path。
bundle:
name: jar-bundle
workspace:
host: https://myworkspace.cloud.databricks.com
artifact_path: /Volumes/main/default/my_volume
artifacts:
my_java_code:
path: ./sample-java
build: "javac PrintArgs.java && jar cvfm PrintArgs.jar META-INF/MANIFEST.MF PrintArgs.class"
files:
- source: ./sample-java/PrintArgs.jar
resources:
jobs:
jar_job:
name: "Spark Jar Job"
tasks:
- task_key: SparkJarTask
new_cluster:
num_workers: 1
spark_version: "14.3.x-scala2.12"
node_type_id: "i3.xlarge"
spark_jar_task:
main_class_name: PrintArgs
libraries:
- jar: ./sample-java/PrintArgs.jar