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Quick Start GuideTraditional relational databases force an impossible choice: run analytical queries on transactional data (blocking writes with table locks), or replicate data to separate OLAP systems (data staleness and ETL complexity). Relational database MVCC helps but analytical queries still cause lock contention on write-heavy tables.
Aggregation queries on transactional data create read locks that block writes. Replicating to data warehouses introduces latency and synchronization complexity. Separate OLAP systems require ETL pipelines and duplicate storage infrastructure.
Concurrent Analytical Queries On Transactional Data
Applications execute transactional writes while analytical queries run concurrently on consistent snapshots without blocking operations.
Integration Pattern: Transactional applications write to Apache Ignite tables using standard ACID operations. Analytical queries use SQL aggregations (SUM, COUNT, GROUP BY, HAVING) to generate reports and dashboards directly from transactional tables.
Consistency Model: Snapshot isolation ensures analytical queries read consistent data at point-in-time without blocking writes. Queries see committed transactions only. No table locks or read blocking during aggregations.
Performance Characteristics: Memory-first storage delivers low-latency analytical query execution. Concurrent writes continue without degradation during query execution. Horizontal scalability handles both transactional and analytical workload growth.
When This Pattern Works
This architecture pattern is best for:
Example Use Cases:
Snapshot isolation enables analytical queries without acquiring read locks. Transactional writes proceed without blocking during query execution. Queries read consistent snapshots at point-in-time. Eliminates table-level locks required by traditional databases during aggregations.
Single platform supports both transactional and analytical workloads. No data replication to separate OLAP systems. Analytical queries access current data without ETL latency. Reduces infrastructure complexity and operational overhead.
Support for common SQL aggregations (SUM, COUNT, AVG, MIN, MAX, GROUP BY, HAVING). Familiar query syntax for reporting and business intelligence. SQL-based analytics without learning specialized query languages. Standard JDBC/ODBC connectivity for BI tools.
Analytical queries operate on live transactional data without replication delays. Memory-first architecture delivers low-latency query execution. Operational dashboards reflect current state immediately. Business decisions based on real-time data.
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