CHALLENGER
Apache Pinot
Real-time OLAP for user-facing analytics · multi-component deployment · no native observability
GREPTIMEDB
GreptimeDB
Purpose-built for observability with native protocol support
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Apache Pinot comparison at a glance.
See what changes operationally at scale.
Apache Pinot is a real-time distributed OLAP datastore designed for low-latency analytics on large-scale data. Originally built at LinkedIn for user-facing dashboards, Pinot features columnar storage, pluggable indexing, and supports streaming and batch ingestion. Excels at business analytics. PromQL, OpenTelemetry, and log/trace ingestion are outside the core. PromQL support is available via a plugin (beta in v1.4).
Feature comparison
| Feature/Aspect | GreptimeDB | Apache Pinot |
|---|---|---|
| Data Model | Metrics, Logs & Traces in one database | Real-time OLAP Analytics Database |
| Value Model | Multi-Value (supports complex data structures) | Multi-Value (dimensions and metrics) |
| Multi-model Support | Metrics, Logs & Traces in one database | Analytics data only (requires separate systems for observability) |
| Query Languages | SQL & PromQL (dual interface) | SQL & PromQL (via plugin, experimental in v1.3.0+) |
| Ingestion Protocols | SQL gRPC InfluxDB Line Protocol Prometheus Remote Storage OpenTelemetry Loki Push API Elasticsearch Bulk API HTTP API | Kafka Pulsar Kinesis Batch (Hadoop, Spark, S3) REST API |
| Data Retention | Flexible TTL policies with tiered storage | Tiered storage (hot, warm, cold) |
| Continuous Aggregation | Built-in SQL aggregation, Pipeline ETL engine & Flow streaming computation | Real-time roll-ups and pre-aggregation at ingestion |
| Deployment Complexity | Single system deployment | Complex multi-component deployment (Controller, Broker, Server) |
| Use Cases | Unified observability, real-time analytics, IoT monitoring, edge computing | User-facing dashboards, business analytics, interactive reporting |
| Architecture | Cloud-native distributed with compute-storage disaggregation | Distributed OLAP with Controller, Broker, Server architecture |
| Storage Format | Apache Parquet (columnar, compressed) | Columnar with dictionary encoding, compression |
| Storage Scalability | Object storage integration with unlimited capacity | Deep storage with horizontal scaling |
| High Availability | Native clustering with automatic failover | Replication and Zookeeper-based coordination |
| License | Apache 2.0 | Apache 2.0 |
| Written Language | Rust (memory safety, performance) | Java (ecosystem compatibility) |
| Deployment Options | Single-node, cluster, Kubernetes-native, edge-to-cloud with unified API | Multi-component deployment (Controller, Broker, Server, Minion) |
| Operational Complexity | Single unified system with simplified Kubernetes operations | Complex multi-service orchestration |
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