A scalable time series database built on Bigtable, Cassandra, and Elasticsearch for high-volume metrics.
Heroic is a scalable time series database designed for high-volume metrics storage and querying. It solves the problem of managing large-scale monitoring data by providing a distributed architecture that can leverage backends like Bigtable, Cassandra, and Elasticsearch. Originally built by Spotify, it handles infrastructure telemetry and observability workloads efficiently.
Engineers and DevOps teams managing large-scale monitoring systems, particularly those needing a scalable, self-hosted time series database alternative to commercial solutions.
Developers choose Heroic for its proven scalability at Spotify, modular backend support allowing integration with existing infrastructure, and its open-source nature enabling full control over deployment and customization.
The Heroic Time Series Database
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Heroic was developed and used by Spotify for their extensive infrastructure telemetry, demonstrating reliable handling of large-scale metrics ingestion and querying.
It supports multiple backends like Bigtable, Cassandra, and Elasticsearch, allowing integration with existing infrastructure and avoiding vendor lock-in for storage.
Integrates with Kafka for high-throughput metric consumption, enabling efficient real-time data processing as highlighted in the features.
The component-based design lets users customize metric, metadata, and suggestion backends independently, providing flexibility for specific use cases.
The project is no longer actively maintained, with no bug fixes, security patches, or new features being released, as stated in the deprecation notice.
Requires configuration of multiple components, Java 11, Maven, Gradle, and specific backend services, increasing operational complexity and setup time.
Being deprecated, it lacks the active community, documentation updates, and third-party integrations compared to alternatives like Prometheus or InfluxDB.