You can think of the relation between the Deployment spec and the template as follows:
- The template specifies which Flink job is executed and how to execute it, including its configuration.
- The Deployment spec defines how Job instances are managed over time, for instance how to execute upgrades or which Savepoint to restore from.
At the end of this page, you will find a full example that creates a deployment for a running Flink example job. The following sections will break down each part of the template in more detail.
Metadata and Spec¶
The template has two parts,
spec. The metadata section currently only accepts optional annotations. The spec specifies which artifact to execute and how to execute it.
kind: Deployment spec: template: metadata: ... spec: ...
The resource snippets in the following sections are not valid on their own and should be pasted in under the spec section of the template
The artifact section of the template specifies which artifact to execute. Currently, there is a single kind of supported artifact, jar.
JAR artifacts must package regular Flink jobs that are executed via the main method of their entry class.
artifact: kind: jar jarUri: https://artifacts.da-platform.com/flink-job.jar entryClass: com.daplatform.myjob.EntryPoint # Optional, if no Manifest entry mainArgs: --arg1=1 --arg2=2 # Optional
Application Manager needs to be able to access the artifact and will submit it to the created job cluster for execution. Application Manager will download the JAR artifact, and then upload it to the Flink cluster. The container or system running Application Manager needs to have access to the JAR location.
Check out the Advanced Configuration page if your artifact storage uses TLS and serves a certificate signed by a non-public CA.
Application Manager does not store the JAR artifacts permanently. If you want to be able to go back to earlier versions of your Flink job, ensure that you are versioning the JARs properly, and that they are stored for the time you want to be able to go back.
Custom Docker Images¶
By default, containers created for a Deployment will use the configured default Flink Docker images. You can overwrite this on a per Deployment basis as part of the artifact.
artifact: kind: jar jarUri: https://artifacts.daplatform.com/flink-job.jar flinkVersion: 1.5 flinkImageRegistry: registry.platform.data-artisans.net flinkImageRepository: v1.0/flink flinkImageTag: 1.5.0-dap1-scala_2.11
Each flink* attribute is optional. If not provided, it will fall back to the configured default of your Application Manager installation.
You can specify the Flink image by digest if you prefix the
flinkImageTag attribute with
@, for instance
Parallelism, Number of TaskManagers, and Slots¶
You can specify the parallelism of your jobs via the
parallelism key. By default, there will be as many TaskManager instances created as the specified parallelism. You can overwrite this behaviour via the
parallelism: 1 numberOfTaskManagers: 1 # Optional, defaults to parallelism
Each TaskManager will have Flink’s default setting for number of task slots (currently a single slot per TaskManager). Combining the configuration of parallelism and numberOfTaskManagers with the
taskmanager.numberOfTaskSlots option of Flink gives you full flexibility in how to execute your jobs.
In the following snippet, we specify two TaskManager instances with 4 slots each and a parallelism of 8 for our jobs.
parallelism: 8 numberOfTaskManagers: 2 flinkConfiguration: taskmanager.numberOfTaskSlots: 4
For more details on Flink task slots, consult the offical Flink documentation.
You can configure requested compute resources for CPU and memory via the
resources: jobmanager: cpu: 0.5 memory: 500M taskmanager: cpu: 1 memory: 2G
taskmanager configure the respective Flink components. By default, the above values are used as defaults. You can overwrite each value selectively.
Note that resources are configured per component instance, e.g. if you use 10 TaskManager instances, Application Manager will in total try to acquire 10 times the configured taskmanager resources.
You can specify CPU as a decimal number, e.g. 1, 1.0, or 0.5.
You can specify memory as a decimal number with an optional memory unit as indicated by the suffix (ignoring case):
G, e.g. 1G or 1.5G
M, e.g. 1000M or 1500M
If no unit is specified, the provided number is interpreted as bytes. Each memory unit is interpreted as a power of ten, e.g. 1K equals 1000 bytes.
The CPU resources you configure for your deployments are counted against your licensed resource quota (CPU Quota).
JVM Heap Size
Application Manager will respect the container memory cut-offs specified in
flinkConfiguration similar to Apache Flink’s behaviour.
By default, the maximum of the following two values is subtracted from the requested memory and set as the JVM heap size:
- Cutoff Fraction: 25% of the configured memory
- Minimum cutoff: 600 MiB
For the memory request of 2G, this means that 600 MiB will be cutoff from the configured heap (as 25% of 2G is less than 600MiB).
You can use
containerized.heap-cutoff-min (default: 600) and
containerized.heap-cutoff-ratio (default: 0.25) in
flinkConfiguration to adjust these values. For more details, consult the offical Flink documentation on Configuration.
You can customize the default log4j logger configuration by setting log levels for your desired loggers.
logging: log4jLoggers: "": INFO # Root log level com.company: DEBUG # Log level of com.company
The default log level for
It is possible to specify Kubernetes-specific options for a Deployment. These options will be forwarded to the pods created by Application Manager.
Please check out the Configure Kubernetes for more details.
kind: Deployment apiVersion: v1 metadata: name: TopSpeedWindowing Example labels: env: testing spec: state: running deploymentTargetId: 57b4c290-73ad-11e7-8cf7-a6006ad3dba0 startFromSavepoint: kind: latest upgradeStrategy: kind: stateless template: spec: artifact: kind: jar jarUri: http://repo1.maven.org/maven2/org/apache/flink/flink-examples-streaming_2.11/1.4.0/flink-examples-streaming_2.11-1.4.0-TopSpeedWindowing.jar mainArgs: --windowSize 10 --windowUnit minutes entryClass: org.apache.flink.streaming.examples.windowing.TopSpeedWindowing parallelism: 1 numberOfTaskManagers: 2 resources: taskManager: memory: 1.5g flinkConfiguration: taskmanager.numberOfTaskSlots: 1 state.savepoints.dir: s3://flink/savepoints logging: log4jLoggers: org.apache.flink.streaming.examples: DEBUG
You can copy-paste this Deployment resource into a file called deployment and post it via curl:
$ curl -H 'Content-Type: application/yaml' -H 'Accept: application/yaml' -d @deployment https://appmanager:8080
Note that you have to adjust the
state.savepoints.dir entry of the
flinkConfiguration map in order to make savepoint-specific features work, e.g., suspending a deployment or triggering a savepoint.