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# Serverless Observability: where SLOs meet transforms

Transitioning to serverless often breaks traditional monitoring. Stop relying on outdated aggregations. Learn how Elasticsearch Transforms power dynamic SLOs to protect your error budgets proactively.

- **Speakers:** Diana Todea
- **Event:** WeAreDevelopers LIVE
- **Published:** February 12, 2024
- **Duration:** 57:28
- **URL:** https://www.wearedevelopers.com/videos/854-serverless-observability-where-slos-meet-transforms

## Summary

Transitioning environments to serverless architectures introduces unique scaling challenges, particularly around processing large datasets efficiently. To address this within Elasticsearch, migrating from standard rollup aggregations to Transforms allows teams to generate summarized, entity-centric indices that scale dynamically. This foundational shift powers more reliable observability frameworks, ensuring continuous performance tracking against Service Level Objectives (SLOs) without overburdening the system infrastructure.

In practice, building a resilient monitoring stack requires closely aligning Service Level Indicators (SLIs), Service Level Agreements (SLAs), and SLOs. By utilizing indicator types like APM availability, APM latency, custom KQL, and time-slice metrics, engineers can build precise, user-centric performance evaluations. Elastic's Transform service handles the heavy lifting by computing aggregated states in the background, feeding into burn rate alerts that detect error budget consumption before users notice critical degradation. Integrating these targets with external DevOps tooling ensures that real-time observability triggers rapid, contextual incident response.

Successfully integrating SLOs extends well beyond technical implementation; it demands robust cross-team collaboration. SREs, product managers, and customer success teams must align on user expectations to set realistic, actionable targets—avoiding the trap of overly ambitious percentiles during initial rollouts. Teams should adopt basic, foundational monitors first, iterating on their monitoring strategy via user feedback and historical performance data. Ultimately, maintaining a healthy transform pipeline translates directly to healthy SLOs, proving that scalable architecture combined with proactive communication forms the bedrock of modern reliability.

**Keywords:** serverless observability, elasticsearch transforms, service level objectives, error budget exhaustion, burn rate alerting, service level indicators, service level agreements, site reliability engineering, APM latency metrics, custom KQL aggregations, entity-centric indices, incident management collaboration, cross-team SLO alignment, serverless migration scaling, infrastructure alerting integration

## Chapters

1. **Migrating to serverless observability and data transforms** (00:02) — How large dataset environments scale data manipulation by converting Elasticsearch indices into summarized indices.
1. **Defining service level indicators, objectives, and agreements** (02:45) — Core equations and characteristics that separate actionable service level objectives from poorly defined metrics.
1. **Types of service level indicators in Elasticsearch** (05:49) — How to select and configure different indicator types for tracking logs, latency, and transactions.
1. **Transform architecture and summarizing index data** (09:43) — The mechanics of persistent tasks that convert source indices into summarized datasets for optimized queries.
1. **Creating data transforms and health alerts visually** (12:12) — Steps to configure persistent transform rules and set up health alerting via the stack management interface.
1. **Configuring burn rate alerts for error budgets** (15:54) — Calculating the rate of error budget consumption over multiple time windows to prevent alert fatigue.
1. **Defining service level objectives and dashboards in Kibana** (18:25) — A practical walkthrough of creating a service level objective and visualizing it on an aggregated dashboard.
1. **Managing transforms and objectives via developer tools APIs** (21:57) — Utilizing Elasticsearch APIs for deploying, configuring, and troubleshooting transforms during incidents.
1. **Integrating service level objectives into incident management** (24:27) — How site reliability engineers coordinate with product teams and customer support to align user expectations.
1. **Managing objective variants across serverless environments** (32:30) — Strategies for iterating and adjusting basic cluster indicators based on continuous system feedback.
1. **Setting realistic measurement targets and monitoring tools** (34:54) — Why organizations should avoid overly ambitious percentiles at launch and build custom observation tools.
1. **Tracking improvement impacts and cold start indicators** (37:45) — Correlating internal monitoring feedback with external agreements to validate objective performance against new deployments.
1. **Aligning cross-team goals and prioritizing backlog improvements** (41:05) — Fostering communication across engineering departments to prioritize reliability issues ahead of scheduled system upgrades.
1. **Incorporating user feedback and third-party dependency data** (45:36) — Synchronizing feature prioritization with objective stability and managing expectations for external tooling.
1. **Balancing feature releases with reliable service targets** (49:50) — Distributing responsibilities between software developers and site reliability teams while keeping runbook documentation updated.
1. **Evaluating transform scalability and small product adoption** (53:35) — Measuring the complexity and architectural impact before integrating data transforms within modest infrastructure setups.

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- [Real-world challenges in adopting serverless architectures](https://www.wearedevelopers.com/videos/56-end-the-monolith-lessons-learned-adopting-serverless) (from "End the Monolith! Lessons learned adopting Serverless")
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