Buffer Pool Analysis by File (vbhfile.sql)

Use at Your Own Risk

This script is for educational purposes only. Always test in development environments before production use. See our disclaimer for full terms.

What This Script Does

This script provides a simple analysis of Oracle buffer cache distribution across datafiles. It shows how many buffers each datafile has in the cache and identifies duplicate blocks (extra blocks), which can indicate hot data or caching patterns useful for I/O performance analysis.

Script

rem  vbhfile.sql
rem
ttitle 'Buffer Pool by File'
rem
col dbafil format 999 heading 'FILE'
col buffer_count format b99999 heading 'BUFFERS'
col distinct_block format b99999 heading 'BLOCKS'
col extra_block format b99999 heading 'EXTRA|BLOCKS'
rem
break on report
compute sum of buffer_count distinct_block extra_block on report
rem
select dbafil,
       count(*) buffer_count,
       count( distinct dbablk ) distinct_block,
       count(*) - count( distinct dbablk ) extra_block
  from x$bh
 group by dbafil;

Usage

-- Run the script in SQL*Plus or SQLcl
@vbhfile.sql

-- No parameters required
-- Shows current buffer cache distribution

Required Privileges

• SELECT on X$BH (internal buffer header table)
• Typically requires DBA role due to X$ table access

Sample Output

Buffer Pool by File

FILE  BUFFERS  BLOCKS EXTRA
                      BLOCKS
---- -------- ------- ------
   1    12456   12234    222
   2     8901    8789    112
   3     5678    5623     55
   4     3456    3445     11
   5     2345    2342      3
   6     1234    1233      1
---- -------- ------- ------
sum   34070   33666    404

Key Output Columns

• FILE: Datafile number (dbafil)
• BUFFERS: Total number of buffers from this file in cache
• BLOCKS: Number of distinct blocks cached (unique blocks)
• EXTRA BLOCKS: Duplicate blocks in cache (BUFFERS - BLOCKS)

Understanding the Metrics

Buffer Distribution Analysis

• High BUFFERS: Files with heavy read activity
• BLOCKS vs BUFFERS: Shows block reuse patterns
• EXTRA BLOCKS: Multiple copies of same block in cache

File Activity Indicators

• Large buffer counts: Most actively accessed datafiles
• High extra block ratios: Frequently re-read blocks (hot data)
• Zero extra blocks: Sequential access patterns or cold data

I/O Pattern Recognition

• Even distribution: Balanced workload across files
• Concentrated activity: Hot spots in specific files
• Low buffer counts: Infrequently accessed or large scan operations

Performance Analysis

Hot File Identification

1. Highest buffer counts: Primary candidates for I/O optimization
2. High extra block ratios: Indicate data re-access patterns
3. Uneven distribution: May suggest storage layout issues

Buffer Cache Efficiency

• Extra blocks > 5%: Normal for OLTP workloads
• Extra blocks > 20%: Very hot data, consider caching strategies
• Zero extra blocks: Sequential processing or insufficient cache

Common Use Cases

I/O Performance Analysis

-- Identify files consuming most cache space
@vbhfile.sql
-- Focus on files with highest buffer counts

Storage Layout Review

-- Analyze file access patterns
@vbhfile.sql
-- Consider file placement and distribution

Buffer Cache Sizing

-- Understand cache utilization
@vbhfile.sql
-- Help determine optimal buffer cache size

Troubleshooting Insights

High Buffer Usage on Specific Files

1. Check for hot tables:

   -- Find objects in hot files
   SELECT owner, segment_name, segment_type
   FROM dba_extents
   WHERE file_id = &hot_file_number;

2. Analyze access patterns:

   • Frequent full table scans
   • Hot index blocks
   • Temporary sort operations

Uneven Buffer Distribution

1. Storage considerations:

   • Move hot objects to separate files
   • Use multiple datafiles per tablespace
   • Consider ASM for automatic distribution

2. Application optimization:

   • Review SQL execution plans
   • Consider partitioning strategies
   • Optimize indexing

Low Buffer Cache Utilization

1. Possible causes:

   • Large sequential scans bypassing cache
   • Insufficient buffer cache size
   • Data access patterns not suited for caching

2. Optimization strategies:

   • Increase buffer cache size
   • Tune SQL to reduce full scans
   • Consider table clustering

Advanced Analysis

Map Files to Tablespaces

-- Identify which tablespaces are hot
SELECT df.file_id, df.tablespace_name, df.file_name,
       bf.buffer_count, bf.extra_block
FROM dba_data_files df,
     (SELECT dbafil,
             COUNT(*) buffer_count,
             COUNT(*) - COUNT(DISTINCT dbablk) extra_block
      FROM x$bh
      GROUP BY dbafil) bf
WHERE df.file_id = bf.dbafil
ORDER BY bf.buffer_count DESC;

Compare with File I/O Statistics

-- Correlate buffer usage with I/O activity
SELECT f.file#, f.name, fs.phyblkrd, fs.phyblkwrt,
       bf.buffer_count, bf.extra_block
FROM v$datafile f, v$filestat fs,
     (SELECT dbafil,
             COUNT(*) buffer_count,
             COUNT(*) - COUNT(DISTINCT dbablk) extra_block
      FROM x$bh
      GROUP BY dbafil) bf
WHERE f.file# = fs.file#
AND f.file# = bf.dbafil
ORDER BY bf.buffer_count DESC;

Historical Trend Analysis

-- Run periodically to track changes
-- Save results for trending analysis
SELECT SYSDATE snapshot_time, dbafil,
       COUNT(*) buffer_count,
       COUNT(DISTINCT dbablk) distinct_blocks
FROM x$bh
GROUP BY dbafil;

Related Scripts

• Buffer Pool Summary by File (vbhstat.sql) - Enhanced buffer analysis with dirty blocks
• Buffer Pool Statistics (gvbp.sql) - Overall buffer cache performance
• Datafile I/O Statistics (gvio.md) - File-level I/O metrics
• Hot Blocks Analysis (hotblocks.md) - Specific hot block identification