Airflow Summit 2020 - Migrating airflow based spark jobs to kubernetes - the native way

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Roi Teveth (Data Engineer) and Itai Yaffe (Tech Lead, Big Data group) @ Nielsen: At Nielsen Identity Engine, we use Spark to process 10’s of TBs of data. Our ETLs, orchestrated by Airflow, spin-up AWS EMR clusters with thousands of nodes per day. In this talk, we’ll guide you through migrating Spark workloads to Kubernetes with minimal changes to Airflow DAGs, using the open-sourced GCP Spark-on-K8s operator and the native integration we recently contributed to the Airflow project.

Data & Analytics

@ItaiYaffe, @RTeveth
$30,000/month Savings Open your eyes Robust

@ItaiYaffe, @RTeveth
ETLs with
Airflow &
01. = 02.
03. -on-
Airflow Integration
& Contribution04.

@ItaiYaffe, @RTeveth
~30TB ingested/day
S3
~3000 nodes/day
10’s of TB
ingested/day
druid$100K’s/month

@ItaiYaffe, @RTeveth
Scalability
Cost Efficiency
Fault-tolerance

@ItaiYaffe, @RTeveth
~20 automatic DAG
deployments/day
~1000 DAG
Runs/day
~2 years in
production
Met all
requirements &
more
~40 users across 4
groups
6 contributions
to open-source

@ItaiYaffe, @RTeveth
EMR is an AWS
managed service to
run Hadoop & Spark
clusters
Allows you to reduce
costs by using Spot
instances
Charges management
cost for each
instance in a cluster

@ItaiYaffe, @RTeveth
●
●
○
■
○ …
■
■
emr_create_job_flow_operator emr_add_steps_operator emr_step_sensor
Creates new emr cluster Adds Spark step to the cluster Checks if the step succeeded

@ItaiYaffe, @RTeveth
$$$ Visibility Robustness

@ItaiYaffe, @RTeveth
ClusterControl plane Worker nodes
(EC2 in our case)
Pods
group of one or more
containers (such as
Docker containers)

@ItaiYaffe, @RTeveth
ClusterControl plane

@ItaiYaffe, @RTeveth
./bin/spark-submit
--master
k8s://https://<k8s-apiserver-host>:<k8s-apiserver-port>
--deploy-mode cluster
--name spark-pi
--class org.apache.spark.examples.SparkPi
--conf spark.executor.instances =3
--conf spark.kubernetes.container.image =<spark-image>

local:///path/to/examples.jar
Kubernetes control plane
Kubernetes cluster
SparkPi
driver
Executor
1
Executor
2
Executor
3

@ItaiYaffe, @RTeveth
https://github.com/GoogleCloudPlatform/spark-on-k8s-operator
●
●
●

@ItaiYaffe, @RTeveth
apiVersion:
"sparkoperator.k8s.io/v1beta2"
kind: SparkApplication
metadata:
name: "spark-pi”
namespace: default
spec:
...
driver:
...
executor:
...
Spark-pi.yaml
Kubernetes control plane
Kubernetes cluster
SparkPi
driver
Executor
1
Executor
2
Executor
3
kubectl
Spark-
on-K8s
operator

@ItaiYaffe, @RTeveth
…
https://github.com/apache/airflow/pull/7163

@ItaiYaffe, @RTeveth
KubernetesHook
SparkKubernetes
operator
SparkKubernetes
sensor

$@ItaiYaffe, @RTeveth ● ● ● apiVersion: "sparkoperator.k8s.io/v1beta2" kind: SparkApplication metadata: name: "spark-pi-{{ ds }}-{{ task_instance.try_number }}" namespace: default spec: ... driver: ... executor: ... SparkKubernetes operator$

@ItaiYaffe, @RTeveth
●
○ on_success_callback
on_failure_callback default_args
●
○ 😉

@ItaiYaffe, @RTeveth
Reduce costs Open your eyes Robust
●
●

Airflow Summit 2020 - Migrating airflow based spark jobs to kubernetes - the native way

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Airflow Summit 2020 - Migrating airflow based spark jobs to kubernetes - the native way

2. @ItaiYaffe, @RTeveth

3. @ItaiYaffe, @RTeveth $30,000/month Savings Open your eyes Robust

5. @ItaiYaffe, @RTeveth ● ● ● ● ● ●

6. @ItaiYaffe, @RTeveth ETLs with Airflow & 01. = 02. 03. -on- Airflow Integration & Contribution04.

7. @ItaiYaffe, @RTeveth ETLs with Airflow & 01. = 02. 03. -on- Airflow Integration & Contribution04.

8. @ItaiYaffe, @RTeveth ● ● ● ● ●

9. @ItaiYaffe, @RTeveth ~30TB ingested/day S3 ~3000 nodes/day 10’s of TB ingested/day druid$100K’s/month

10. @ItaiYaffe, @RTeveth Scalability Cost Efficiency Fault-tolerance

11. @ItaiYaffe, @RTeveth ● ● ● ○ ○ ○

12. @ItaiYaffe, @RTeveth ~20 automatic DAG deployments/day ~1000 DAG Runs/day ~2 years in production Met all requirements & more ~40 users across 4 groups 6 contributions to open-source

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19. @ItaiYaffe, @RTeveth EMR is an AWS managed service to run Hadoop & Spark clusters Allows you to reduce costs by using Spot instances Charges management cost for each instance in a cluster

20. @ItaiYaffe, @RTeveth ● ● ○ ■ ○ … ■ ■ emr_create_job_flow_operator emr_add_steps_operator emr_step_sensor Creates new emr cluster Adds Spark step to the cluster Checks if the step succeeded

21. @ItaiYaffe, @RTeveth ● ● ○ ■ ○ … ■ ■ emr_create_job_flow_operator emr_add_steps_operator emr_step_sensor Creates new emr cluster Adds Spark step to the cluster Checks if the step succeeded

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25. @ItaiYaffe, @RTeveth ClusterControl plane Worker nodes (EC2 in our case) Pods group of one or more containers (such as Docker containers)

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27. @ItaiYaffe, @RTeveth ●

28. @ItaiYaffe, @RTeveth ● ● ○ ○ ○ ■

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42. @ItaiYaffe, @RTeveth https://github.com/GoogleCloudPlatform/spark-on-k8s-operator ● ● ●

43. @ItaiYaffe, @RTeveth apiVersion: "sparkoperator.k8s.io/v1beta2" kind: SparkApplication metadata: name: "spark-pi” namespace: default spec: ... driver: ... executor: ... Spark-pi.yaml Kubernetes control plane Kubernetes cluster SparkPi driver Executor 1 Executor 2 Executor 3 kubectl Spark- on-K8s operator

44. @ItaiYaffe, @RTeveth VS

45. @ItaiYaffe, @RTeveth

46. @ItaiYaffe, @RTeveth …

47. @ItaiYaffe, @RTeveth … https://github.com/apache/airflow/pull/7163

48. @ItaiYaffe, @RTeveth ł

49. @ItaiYaffe, @RTeveth KubernetesHook SparkKubernetes operator SparkKubernetes sensor

50. @ItaiYaffe, @RTeveth ● ○ ● ○

51. @ItaiYaffe, @RTeveth ● ● ● apiVersion: "sparkoperator.k8s.io/v1beta2" kind: SparkApplication metadata: name: "spark-pi-{{ ds }}-{{ task_instance.try_number }}" namespace: default spec: ... driver: ... executor: ... SparkKubernetes operator

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