For similar capabilities to Amazon Timestream for LiveAnalytics, consider Amazon Timestream for InfluxDB. It offers simplified data ingestion and single-digit millisecond query response times for real-time analytics. Learn more [here](https://docs.aws.amazon.com/timestream/latest/developerguide/timestream-for-influxdb.html).

# Deployment models in Timestream for InfluxDB 3
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## Single-node deployment
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 In a single-node deployment (available for both Core and Enterprise versions), a single instance handles all database operations. This configuration features: 
+  One node performing multiple critical roles: 
  +  Writer role: Handles all write operations to the database. 
  +  Reader role: Processes read queries and data retrieval operations. 
  +  Compactor role: Optimizes storage by compacting data files (Enterprise version only). 
+  Core version: Single node operates with writer and reader roles only. 
+  Enterprise version: Single node operates with writer, reader, and compactor roles for complete functionality. 
+  Simplified management with all operations consolidated on a single instance. 
+  Lower cost compared to multi-node deployments. 
+  Suitable for development, testing, or smaller production workloads. 

 Single-node deployments are ideal for getting started with Timestream for InfluxDB 3 or for workloads with moderate performance requirements. 

## Multi-node deployment
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 Multi-node deployments (available only for Enterprise edition) distribute workloads across multiple instances for improved performance, scalability, and availability. Enterprise clusters support up to 15 nodes with the following configuration: 
+  Flexible node allocation based on workload requirements: 
  +  1-4 writer/reader nodes (ingestQueryInstances) that handle both write and read operations.
  +  0-13 reader-only nodes (queryOnlyInstances) dedicated to processing read queries.
  +  1 dedicated compactor node for optimizing storage (required for clusters with 3\+ nodes). 
+  Horizontal scaling capabilities to accommodate growing workloads. 
+  Enhanced fault tolerance with workload distribution across multiple nodes in different Availability Zones. 
+  Improved query performance. 
+  Better handling of concurrent operations. 

 Multi-node deployments are recommended for production environments with high throughput requirements, large data volumes, or the need for enhanced availability.