Database with Memory-First Storage

Database-First Platform

Apache Ignite is a database-first platform with memory-first storage profile. Multi-tier storage enables flexible data placement across memory (aimem), persistent memory-first (aipersist), and disk (rocksdb) storage engines. Full SQL support with ACID transactions for both transactional and analytical workloads.

Memory-first architecture delivers low-latency operations while horizontal scalability handles growing data volumes. Concurrent transactional writes and analytical queries operate on same data without separate systems. Not positioned as full HTAP but supports operational analytics patterns.

Architecture Pattern

Concurrent Transactional And Analytical Workloads

Single platform supports both transactional writes and analytical queries without separate systems for OLTP and OLAP.

Integration Pattern: Applications execute transactional operations (INSERT, UPDATE, DELETE) using standard SQL with ACID guarantees. Analytical queries (aggregations, joins, GROUP BY) run concurrently on same data. Snapshot isolation prevents analytical queries from blocking transactional writes.

Storage Configuration: Configure storage engine per table: aimem for hot transactional data, aipersist for durable memory-first operations, rocksdb for historical data. Multi-tier strategy places frequently accessed data in memory with cold data on disk. Single SQL interface across all storage tiers.

Performance Characteristics: Memory-first storage delivers low-latency transactional operations. Analytical queries leverage snapshot isolation for concurrent execution. Horizontal scalability handles growing data volumes across cluster. Not positioned as full HTAP but supports operational analytics patterns.

When This Pattern Works

This architecture pattern is best for:

Applications requiring both transactional operations and operational analytics
Systems needing low-latency data access with horizontal scalability
Workloads with mixed hot and cold data requiring flexible storage placement
Platforms consolidating transactional and analytical systems

Example Use Cases:

E-Commerce Platform: Transactional order processing with concurrent analytics for inventory management and sales reporting
Financial Services: Account transactions with real-time fraud detection analytics and compliance reporting
IoT Analytics: High-volume sensor data ingestion with operational dashboards and time-series aggregations

Key Benefits

Flexible Storage Placement

Multi-tier storage enables per-table storage engine configuration. Hot transactional data in memory (aimem) for low latency. Durable data with memory-first access (aipersist) for balanced performance. Historical data on disk (rocksdb) for cost-effective storage. Single SQL interface across all tiers.

Concurrent Workloads

Snapshot isolation enables analytical queries without blocking transactional writes. Single platform eliminates ETL between transactional and analytical systems. Operational analytics on live data without replication delays. Not full HTAP but supports operational reporting patterns.

Horizontal Scalability

Distributed architecture scales transactional and analytical workloads across cluster nodes. Add nodes to increase capacity for both write throughput and query performance. Partition-aware routing distributes load evenly. Memory-first storage maintains low latency at scale.

Full SQL Support

Standard SQL for transactional operations (INSERT, UPDATE, DELETE) and analytical queries (aggregations, joins, GROUP BY). ACID transactions with snapshot isolation. DDL for schema management. JDBC/ODBC connectivity for standard tooling.

Storage Engine Comparison

aimem (Pure Memory)

Pure in-memory storage with no persistence. Delivers lowest latency for hot data. Suitable for transactional workloads requiring microsecond-to-millisecond operations. Data lost on cluster restart. Best for caching and session management patterns.

aipersist (Memory-First Persistent)

Memory-first storage with automatic persistence to disk. Delivers low-latency operations with durability guarantees. No warm-up required after restart. Automatic page management between memory and disk. Best for transactional workloads requiring both performance and durability.

rocksdb (Disk-Based)

Disk-based storage using RocksDB engine. Optimized for large datasets exceeding memory capacity. Cost-effective storage for historical data. Higher latency than memory-first engines but better than remote disk access. Best for analytical workloads on cold data.

Mixed Strategy

Configure different storage engines per table within same cluster. Hot transactional tables use aimem or aipersist for low latency. Historical tables use rocksdb for cost-effective storage. Single SQL interface queries across all storage tiers. Optimize cost and performance through strategic placement.