Oracle DBA

In the rapidly evolving data landscape where enterprises demand real-time analytics and operational agility, Oracle 19c elevates the In-Memory Column Store (IMCS) with a powerful capability: automatic memory management. This enhancement streamlines the way memory is allocated for in-memory objects, ensuring performance tuning becomes more adaptive, resilient, and aligned with dynamic workloads. It brings a transformative layer of intelligence that empowers DBAs to focus on strategic outcomes rather than manual optimization tasks.

Oracle’s In-Memory Column Store accelerates analytic queries by storing data in a compressed, columnar format in-memory. With automatic memory management, the database now monitors workload patterns, adjusts IMCS usage, and redistributes space to where it delivers the highest impact — all without manual intervention. This leads to improved resource utilization, proactive optimization, and a more autonomous performance posture.

Key Highlight of Automatic Memory Management
The IMCS can automatically shrink, expand, or repopulate in-memory segments depending on workload demands. When combined with the larger Automatic Shared Memory Management (ASMM) or Automatic Memory Management (AMM) framework, the IM column store becomes more adaptive, reducing the risk of memory pressure while preserving mission-critical analytical speed.

Configuring the In-Memory Column Store with Automatic Memory Management
You begin by enabling the In-Memory Column Store with a size allocation. From there, Oracle autonomously governs fine-grained adjustments.

ALTER SYSTEM SET INMEMORY_SIZE = 2G SCOPE=SPFILE;

After restarting the database, the IMCS becomes available. Objects can now be populated automatically based on heat map activity, segment priorities, and workload patterns.

Populating a Table into IMCS with Priority

ALTER TABLE sales INMEMORY PRIORITY HIGH;

This instructs Oracle that the sales table is strategically important. With automatic memory management active, Oracle will ensure this table retains priority during memory rebalancing operations.

Monitoring Automatic IMCS Adjustments

SELECT segment_name, populate_status, inmemory_size, bytes_not_populated
FROM   v$im_segments;

As workload evolves, Oracle may resize segments, evict cold objects, or repopulate hot tables. The DBA gains real-time observability via views such as V$INMEMORY_AREA, V$IM_SEGMENTS, and V$IM_COLUMN_LEVEL.

Example Scenario: Dynamic Adjustments Under Load
Suppose your database hosts an order-processing application alongside a BI dashboard. During business hours, transactional tables such as orders and order_items receive heightened activity. Oracle detects these tables as hot segments and prioritizes them for in-memory residency.

Later in the evening, analytical workloads take precedence, hitting large fact tables like sales_facts. The IMCS automatically reallocates memory — gracefully evicting less active transactional segments and populating analytical segments for efficient reporting. This seamless orchestration happens without user-triggered operations, ensuring optimal performance across use cases.

Heat Map–Driven Optimization
With Automatic Data Optimization (ADO) and Heat Map, the IMCS observes access patterns and dynamically adapts. Tables and columns frequently queried in filters or aggregations automatically gain in-memory residency, delivering faster response times and better CPU efficiency.

Query Example Showing Performance Benefit

SELECT product_id, SUM(amount_sold)
FROM   sales
GROUP BY product_id;

When the sales table is in-memory, this aggregation benefits from vector processing, columnar scanning, and dictionary encoding — yielding substantial speedups compared to traditional buffer cache access.

Best Practices for Leveraging Automatic IMCS Management
Designate critical tables with INMEMORY PRIORITY to ensure they remain first-class citizens during memory pressure. Monitor IMCS health using dynamic performance views, and let the automatic framework respond to shifting workloads. Keep IMCS sizing aligned with business-critical SLAs and growth trajectories to maximize ROI from this adaptive technology.