Daily Post July 23 2025
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OpenObserve
This is an observability platform designed to unify logs, metrics, and traces into a single, integrated solution for cloud-native environments. It is built specifically as a cost-efficient alternative to traditional observability stacks, which often involve separate tools for logging, metrics, and tracing. OpenObserve delivers a cloud-native, lightweight architecture that can scale to petabytes of data, offering high performance and low operational complexity.
Cloud-native environments are computing environments designed specifically to build, deploy, and run scalable, resilient, and manageable applications that fully leverage cloud computing capabilities. Think Containers, Microservices, Immutable Infrastructure, and DevOps and Continuous Delivery Practices etc...
Open Source
OpenObserve is genuinely open source and released under the AGPL-3.0 license. This ensures transparency and control for users, allowing them to inspect, modify, and contribute to the codebase. Its compatibility with open industry standards, including OpenTelemetry, shows its commitment to openness and interoperability, enabling users to avoid proprietary vendor ecosystems and maintain flexible observability solutions.
Self-Hosting and Deployment Options
The platform supports both self-hosted and cloud-based deployment. Organizations preferring control over their data, custom configurations, or adherence to strict compliance policies can self-host OpenObserve on their own infrastructure. The architecture is stateless, facilitating horizontal scaling without replication challenges, and is well-suited for enterprise-grade deployments. For users looking simplicity, a managed cloud version is available that enables fast onboarding with minimal setup time.
Compatibility with Cloud Platforms
It integrates with major cloud storage backends for long-term data retention. In single-node deployments, it supports local disk storage, while its high-availability (HA) mode leverages object storage solutions such as Amazon S3, Google Cloud Storage, Azure Blob Storage, MinIO, or any S3-compatible storage. This design allows organizations to utilize existing cloud infrastructure. Additionally, OpenObserve runs well within container orchestration systems like Kubernetes, supporting modern cloud-native deployment patterns.
How is it built?
It is a ground-up development written in Rust, designed specifically for efficient log search rather than being a general-purpose search engine like Elasticsearch. While OpenObserve offers an Elasticsearch-compatible _bulk API endpoint, enabling integration with popular log forwarders such as Fluentd, Fluent Bit, and Vector, it fundamentally differs in architecture and purpose. Elasticsearch, which relies on row-based indices and index mappings, OpenObserve uses columnar storage, partitioning, bloom filters, and inverted indexes to provide faster aggregation queries and substantially reduce storage costs. This design choice helps OpenObserve deliver around 140 times lower storage costs in some scenarios than Elasticsearch, making it a specialized, performance-optimized alternative rather than a derivative or fork of Elasticsearch.
Avoiding Vendor Lock-In
The open source foundation and adherence to open standards significantly mitigate vendor lock-in risks. Its ability to operate in multi-cloud and hybrid environments, use widely supported cloud storage formats like Apache Parquet for compressed data storage, and the provision of a standard Elasticsearch-compatible ingestion API enable organizations to avoid dependence on any single vendor's proprietary technology. This contrasts with commercial observability platforms, which often couple tightly to specific cloud vendors or proprietary APIs, resulting in increased costs and reduced flexibility over time.
Architecture
The platform is developed in Rust, a language known for safety and high performance, and employs the DataFusion query engine to deliver fast queries directly on compressed Parquet files. This approach is different with Elasticsearch’s JVM-based design and heavy indexing mechanisms. OpenObserve’s architecture is stateless, easy to scale horizontally, and features smart caching to maintain responsiveness even when handling massive telemetry datasets. These technology choices support both operational efficiency and cost reduction.
It is an tool that is worth looking especially if you want to explore things other than a Elastic based system https://openobserve.ai/