To identify tables that should be pinned in the flash cache, you typically need to consider the access patterns and importance of specific tables in your database workload. While there isn’t a generic script that suits all databases due to differences in schema and usage patterns, here’s a general approach you can follow:

1. Identify Frequently Accessed Tables: Use database monitoring tools or query logs to identify tables that are frequently accessed. Tables that are heavily read or written to are good candidates for being pinned in the flash cache to improve performance.
2. Review Performance Metrics: Look at database performance metrics to identify tables that contribute significantly to I/O load or latency. High I/O tables can benefit from being cached in flash memory.
3. Consult Database Administrator: If possible, consult with your DBA or database administrator. They can provide insights based on query performance tuning efforts and historical data.
4. Consider Table Size and Usage: Tables that are large and accessed frequently may benefit the most from being pinned in the flash cache. Conversely, smaller tables or tables with infrequent access may not need to be prioritized for flash caching.
5. Scripting Approach: Depending on your database system (e.g., Oracle, MySQL, SQL Server), you may need to write a custom script or query to identify tables based on specific criteria such as size, access frequency, or performance impact.

For example, in Oracle Database, you might use queries against the DBA_TABLES or USER_TABLES views combined with performance data from AWR (Automatic Workload Repository) or ADDM (Automatic Database Diagnostic Monitor) reports to identify candidate tables.

How identify the tables for hybrid column compression in oracle exadata?

For Identifying tables for Hybrid Columnar Compression (HCC) in Oracle Exadata involves analyzing the workload and usage patterns of your database tables.

Here’s a basic script that can help you identify candidate tables for HCC based on certain criteria like size, read vs write operations, and the frequency of full table scans.

Example:
SET LINESIZE 200
SET PAGESIZE 1000

— Identify large tables with a significant amount of read operations and full table scans
SELECT
owner,
table_name,
num_rows,
blocks,
avg_row_len,
(SELECT value FROM v$sysstat WHERE name = ‘physical reads’) AS physical_reads,
(SELECT value FROM v$sysstat WHERE name = ‘physical writes’) AS physical_writes,
(SELECT SUM(decode(name, ‘table scans (long tables)’, value, 0))
FROM v$sysstat) AS full_table_