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Cloudera Data Engineering

CDE is the Cloudera Data Engineering Service, a containerized managed service for Cloudera Data Platform designed for Large Scale Batch Pipelines with Spark, Airflow and Iceberg. It allows you to submit batch jobs to auto-scaling virtual clusters. As a Cloud-Native service, CDE enables you to spend more time on your applications, and less time on infrastructure.

CDE allows you to create, manage, and schedule Apache Spark jobs without the overhead of creating and maintaining Spark clusters. With CDE, you define virtual clusters with a range of CPU and memory resources, and the cluster scales up and down as needed to run your Spark workloads, helping to control your cloud costs.

Cloudera Data Engineering (CDE) provides a command line interface (CLI) client. You can use the CLI to create and update jobs, view job details, manage job resources, run jobs, and more.

CDE Files Resources

resource in Cloudera Data Engineering (CDE) is a named collection of files used by a job. Resources can include application code, configuration files, custom Docker images, and Python virtual environment specifications.

Resources are associated with virtual clusters. A resource can be used by multiple jobs, and jobs can use multiple resources.The resource types supported by CDE are filespython-env, and custom-runtime-image.

In this article we will walk through some useful commands for operating with CDE Files Resources efficiently.

Requirements

The following are required in order to reproduce these commands in your CDE environment:

  1. A CDE Service on version 1.19.0 or above.
  2. A working installation of the CDE CLI. Please follow these instructions to install the CLI.

Steps

Clone this git repository in your local machine. Then run the following commands:

Create a Files Resource:

 

% cde resource create --name myScripts \
--type files

 

Uplaod multiple files to the same Files Resource:

 

% cde resource upload --name myScripts \
--local-path cde_jobs/spark_geospatial.py \
--local-path cde_jobs/utils.py
3.5KB/3.5KB 100% [==============================================] spark_geospatial.py
4.0KB/4.0KB 100% [==============================================] utils.py

 

Describe Files resource:

 

% cde resource describe --name myScripts
{
"name": "myScripts",
"type": "files",
"status": "ready",
"signature": "08531cbe538858eb20bda5ff1b7567ae4623d885",
"created": "2023-11-28T23:31:31Z",
"modified": "2023-11-28T23:33:32Z",
"retentionPolicy": "keep_indefinitely",
"files": [
{
"path": "spark_geospatial.py",
"signature": "ec91fb5bddfcd16a0bcbe344f229b5e326b759c5",
"sizeBytes": 3529,
"created": "2023-11-28T23:33:32Z",
"modified": "2023-11-28T23:33:32Z"
},
{
"path": "utils.py",
"signature": "aa5a8ea4b4f240183da8bd2d2b354eeaa58fd97a",
"sizeBytes": 3996,
"created": "2023-11-28T23:33:32Z",
"modified": "2023-11-28T23:33:32Z"
}
]
}

 

Create a Files Resource for data:

 

% cde resource create --name myData \
--type files

 

Upload an archive file to the resource:

 

% cde resource upload-archive --name myData \
--local-path data/ne_50m_admin_0_countries_lakes.zip
817.5KB/817.5KB 100% [==============================================] ne_50m_admin_0_countries_lakes.zip

 

Describe resource metadata. Notice that the archive has been unarchived for you:

 

% cde resource describe --name myData
{
"name": "myData",
"type": "files",
"status": "ready",
"signature": "d552dff8fb80a0c7067afa1c4227b29010cce67b",
"created": "2023-11-28T23:35:43Z",
"modified": "2023-11-28T23:36:56Z",
"retentionPolicy": "keep_indefinitely",
"files": [
{
"path": "ne_50m_admin_0_countries_lakes.cpg",
"signature": "663b90c899fa25a111067be0c22ffc64dcf581c2",
"sizeBytes": 5,
"created": "2023-11-28T23:36:55Z",
"modified": "2023-11-28T23:36:55Z"
},
{
"path": "ne_50m_admin_0_countries_lakes.dbf",
"signature": "eec48a122399782bbef02aa8108e99aeaf52e506",
"sizeBytes": 786828,
"created": "2023-11-28T23:36:56Z",
"modified": "2023-11-28T23:36:56Z"
},
{
"path": "ne_50m_admin_0_countries_lakes.prj",
"signature": "308d6355be935e0f7853161b1adda5bcd48188ff",
"sizeBytes": 143,
"created": "2023-11-28T23:36:56Z",
"modified": "2023-11-28T23:36:56Z"
},
{
"path": "ne_50m_admin_0_countries_lakes.README.html",
"signature": "4bec87fbbe5f4e0e18edb3d6a4f10e9e2a705581",
"sizeBytes": 38988,
"created": "2023-11-28T23:36:55Z",
"modified": "2023-11-28T23:36:55Z"
},
{
"path": "ne_50m_admin_0_countries_lakes.shp",
"signature": "57c38f48c5234db925a9fb1b31785250bd7c8d86",
"sizeBytes": 1652200,
"created": "2023-11-28T23:36:56Z",
"modified": "2023-11-28T23:36:56Z"
},
{
"path": "ne_50m_admin_0_countries_lakes.shx",
"signature": "983eba7b34cf94b0cfe8bda8b2b7d533bd233c49",
"sizeBytes": 2036,
"created": "2023-11-28T23:36:56Z",
"modified": "2023-11-28T23:36:56Z"
},
{
"path": "ne_50m_admin_0_countries_lakes.VERSION.txt",
"signature": "9e3d18e5216a7b4dfd402b00a25ee794842b481b",
"sizeBytes": 7,
"created": "2023-11-28T23:36:55Z",
"modified": "2023-11-28T23:36:55Z"
}
]
}

 

Create CDE Credentials for accessing your Docker repository:

 

cde credential create --name my-docker-creds \
--type docker-basic \
--docker-server hub.docker.com \
--docker-username pauldefusco

 

Create a CDE Custom Docker Runtime Resource:

 

cde resource create --name dex-spark-runtime-sedona-geospatial \
--image pauldefusco/dex-spark-runtime-3.2.3-7.2.15.8:1.20.0-b15-sedona-geospatial-003 \
--image-engine spark3 \
--type custom-runtime-image

 

Notice that the custom image has already been created. If you want to learn more about how to create Custom Docker Resources please visit this Cloudera Community Article

Finally, create a job leveraging all three resources above:

 

cde job create --name geospatialRdd \
--type spark \
--mount-1-prefix code/ --mount-1-resource myScripts \
--mount-2-prefix data/ --mount-2-resource myData \
--runtime-image-resource-name dex-spark-runtime-sedona-geospatial \
--packages org.apache.sedona:sedona-spark-shaded-3.0_2.12:1.5.0,org.datasyslab:geotools-wrapper:1.5.0-28.2 \
--application-file code/spark_geospatial.py \
--arg myArg \
--max-executors 4 \
--min-executors 2 \
--executor-cores 2

 

Notice the following:

  • When using multiple Files resources you should prefix each i.e. "data" and "code". The "data" prefix is used at line 82 ("/app/mount/data") in the "spark_geospatial.py" script in order to access the data from the resource. The "code" prefix is usded in the same CLI command in the application file argument.
  • If you are using any Spark packages you can set these directly at job creation.
  • You can pass one or multiple arguments to the Python script via the --arg argument. The arguments are referenced in the script with the "sys.argv" syntax e.g. line 60 in "geospatial_rdd.py".

Run the job:

 

cde job run --name geospatialRdd --executor-cores 4

 

Notice that at runtime, you can override spark configs that were set at job creation. For example, the "--executor-cores" was originally set to 2 and is now overridden to 4.

List job runs filtering by job name:

 

% cde run list --filter 'job[eq]geospatialRdd'
[
{
"id": 21815,
"job": "geospatialRdd",
"type": "spark",
"status": "failed",
"user": "pauldefusco",
"started": "2023-11-29T00:32:02Z",
"ended": "2023-11-29T00:32:36Z",
"mounts": [
{
"dirPrefix": "data/",
"resourceName": "myData"
},
{
"dirPrefix": "code/",
"resourceName": "myScripts"
}
],
"runtimeImageResourceName": "dex-spark-runtime-sedona-geospatial",
"spark": {
"sparkAppID": "spark-f542530da24f485da4993338dca81d3c",
"sparkAppURL": "https://58kqsms2.cde-g6hpr9f8.go01-dem.ylcu-atmi.cloudera.site/hs/history/spark-f542530da24f485da4993338dca81d3c/jobs/",
"spec": {
"file": "code/geospatial_rdd.py",
"args": [
"myArg"
],
"driverMemory": "1g",
"driverCores": 1,
"executorMemory": "1g",
"executorCores": 4,
"conf": {
"spark.dynamicAllocation.maxExecutors": "4",
"spark.dynamicAllocation.minExecutors": "2",
"spark.jars.packages": "org.apache.sedona:sedona-spark-shaded-3.0_2.12:1.5.0,org.datasyslab:geotools-wrapper:1.5.0-28.2"
}
}
},
"identity": {
"disableRoleProxy": true,
"role": "instance"
}
}
]

 

Open Spark UI for respective run:

 

% cde run ui --id 21815

 

You can delete single files from resources:

 

% cde resource delete-file --name myScripts --resource-path utils.py

 

You can delete the resource:

 

% cde resource delete --name myScripts

 

Summary and Next Steps

Cloudera Data Engineering (CDE) provides a command line interface (CLI) client. You can use the CLI to create and update jobs, view job details, manage job resources, run jobs, and so on.

In this article we have reviewed some advanced use cases for the CLI. If you are using the CDE CLI you might also find the following articles and demos interesting:

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