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About Apache Pinot
Apache Pinot is a real-time distributed OLAP datastore designed for ultra-low latency analytics at scale. Originally built at LinkedIn, Pinot specializes in user-facing analytical applications that require sub-second response times on large datasets. It features columnar storage, pluggable indexing technologies, and supports both real-time streaming and batch ingestion. Pinot excels at slice-and-dice operations, drill-downs, and complex analytical queries on high-cardinality, multi-dimensional data.
GreptimeDB vs. Apache Pinot
| Feature/Aspect | GreptimeDB | Apache Pinot |
|---|---|---|
| Data Model | Unified Observability Database | Real-time OLAP Analytics Database |
| Primary Focus | Time-series data with observability (metrics, logs, traces) | User-facing real-time analytics and reporting |
| Multi-model Support | Metrics, Logs & Traces in one database | Analytics data only (requires separate systems for observability) |
| Query Use Cases | Time-series queries, monitoring, alerting, observability | OLAP queries, slice-and-dice, drill-down, pivot operations |
| Ingestion Protocols | SQL gRPC InfluxDB Line Protocol Prometheus Remote Storage OpenTelemetry HTTP API | Kafka Pulsar Kinesis Batch (Hadoop, Spark, S3) REST API |
| Query Languages | SQL & PromQL (dual interface) | SQL & PromQL (via plugin, experimental in v1.3.0+) |
| Query Performance | Sub-second response optimized for time-series patterns | Sub-second response optimized for OLAP workloads |
| Indexing Strategy | Time-series optimized (inverted, full-text, vector search) | OLAP optimized (StarTree, Bloom filter, range, text, JSON, geospatial) |
| Storage Format | Apache Parquet (time-series optimized) | Columnar with dictionary encoding, compression |
| Real-time Processing | Native time-series streaming with Pipeline engine | Real-time OLAP with lambda architecture |
| Data Retention | Flexible TTL policies for observability data | Tiered storage (hot, warm, cold) |
| Scalability Model | Compute-storage separation for time-series workloads | Horizontal scaling for high-concurrency analytics |
| Concurrency | Optimized for monitoring and alerting queries | Hundreds of thousands of concurrent analytical queries |
| Use Cases | Infrastructure monitoring, application observability, IoT data analysis, real-time alerting | User-facing dashboards, business analytics, interactive reporting |
| Architecture | Cloud-native distributed with observability focus | Distributed OLAP with Controller, Broker, Server architecture |
| Data Compression | Time-series aware compression | Multiple compression schemes (Run Length, LZ4, Snappy) |
| Schema Management | Schema-on-write with time-series semantics | Schema-on-write with OLAP optimizations |
| Deployment Complexity | Single unified system with simplified Kubernetes operations | Complex multi-component deployment for analytics |
| License | Apache 2.0 | Apache 2.0 |
| Written Language | Rust (memory safety, performance) | Java (ecosystem compatibility) |
| Ecosystem Integration | Deep observability ecosystem (Grafana, Prometheus, OpenTelemetry) | Analytics ecosystem (Superset, Tableau, custom dashboards) |
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