This Guidance uses an OpenSearch Service observability connector to automate the collection of Apache Spark logs and metrics, then transforms them through OpenSearch Ingestion pipelines, which are highly configurable and can evolve to your needs. OpenSearch Service provides a powerful search capability, and the built-in OpenSearch Dashboards improve observability, providing visuals that shorten time to insights and aid troubleshooting.

AWS Identity and Access Management (IAM) lets you control access to the pipeline as well as to the OpenSearch indexes. You can use IAM policies to make sure that metric and log collection processes happen within the security boundaries of their current Apache Spark application. You can use dedicated management roles, pipeline roles, and ingestion roles to enforce the least privilege principal. Additionally, this Guidance uses Amazon Virtual Private Cloud (Amazon VPC) for communication with OpenSearch to achieve proper network traffic isolation, and it uses AWS KMS to encrypt the data before it is stored in OpenSearch.

The OpenSearch Service observability connector collects and sends logs and metrics to OpenSearch Ingestion pipelines, which automatically scale in and out as new logs and metrics are produced. This limits the impact of unexpected activity spikes on OpenSearch Service cluster performance and stability. Additionally, the observability connector’s buffering and sampling feature helps you further reduce potential ingestion back pressure. To maintain high availability and increase reliability, you can enable multi–Availability Zone (AZ) deployments on the OpenSearch Service cluster, which will then distribute Ingestion OpenSearch Compute Units (Ingestion OCUs) across AZs.

OpenSearch Service lets you specify minimum and maximum Ingestion OCUs for your OpenSearch Ingestion pipeline, and it will automatically scale up and down based on the pipeline's processing requirements and the load generated by your client application. The observability connector integrates with the native Apache Spark low-level plugin interface to collect the data while limiting performance overhead on Apache Spark jobs. Additionally, the built-in custom Apache Spark metric and log collector implements API consumption best practices, such buffering and exponential backoff, to minimize the impact on the Apache Spark application.

The OpenSearch Service observability connector pre-aggregates certain metrics to reduce postprocessing and ingestion volumes, optimizing the volume of metrics and logs produced. This reduces the risk of Ingestion OCU overconsumption. The OpenSearch Ingestion pipeline then uses dynamic scaling to make sure that you don’t incur charges for periods of inactivity; instead, you only pay for the pipeline’s effective usage. You can also start and stop pipelines on demand. OpenSearch Domains can also use ultrawarm nodes to reduce the cost of infrequently accessed indexes. Additionally, this Guidance reduces the need for custom developed components, which can help further optimize the total cost of ownership.

OpenSearch Ingestion pipelines are natively serverless and provide elasticity for data ingestion and transformation, minimizing the environmental impact of backend services. This Guidance also supports both provisioned clusters and serverless collections, and you can use OpenSearch Serverless to further optimize sustainability. Additionally, you can use the insights provided by OpenSearch Dashboards to optimize your Apache Spark workloads and reduce your overall environmental impact.