In Amazon RDS, 'freeable memory' refers to RAM that is currently allocated but can be freed by the system when needed. This includes memory used for caching (like MySQL's buffer pool) and temporary data structures. The sawtooth pattern you observe typically indicates periodic cache expiration and reallocation.

MySQL's InnoDB engine heavily utilizes freeable memory for:

• Buffer pool (caching tables and indexes)
• Query cache (deprecated in MySQL 8.0+)
• Sort buffers and temporary tables

# Sample MySQL command to check buffer pool usage
SHOW ENGINE INNODB STATUS\G

# Key metrics to monitor:
# - Innodb_buffer_pool_bytes_data
# - Innodb_buffer_pool_bytes_dirty

The cyclical pattern occurs because:

1. MySQL fills memory with cached data
2. Old/unused cache gets evicted when new data needs space
3. The process repeats as workload demands change

For better performance monitoring:

# CloudWatch metrics to track
aws cloudwatch get-metric-statistics \
  --namespace AWS/RDS \
  --metric-name FreeableMemory \
  --dimensions Name=DBInstanceIdentifier,Value=your-db-instance \
  --statistics Average \
  --period 60 \
  --start-time 2023-01-01T00:00:00 \
  --end-time 2023-01-02T00:00:00

Adjust these RDS parameters based on your freeable memory patterns:

# Recommended parameters in DB parameter group
innodb_buffer_pool_size = {total_memory * 0.75}
innodb_buffer_pool_instances = 8  # For larger instances
innodb_old_blocks_time = 1000     # Milliseconds before LRU eviction

Investigate if you see:

• Consistently low freeable memory (indicates undersizing)
• Frequent swapping (Check the SwapUsage metric)
• Abrupt drops without corresponding workload changes

Freeable memory in Amazon RDS refers to RAM that's currently allocated but could be reclaimed by the operating system if needed. This memory typically includes:

• MySQL's buffer cache (innodb_buffer_pool)
• Query cache (when enabled)
• Filesystem cache
• Temporary table memory

The characteristic sawtooth pattern occurs because MySQL's memory management works in cycles:

// Simplified representation of memory cycle
1. Memory allocated for active queries
2. Data cached for performance
3. Cache expiration/invalidation
4. Memory marked as freeable
5. Repeat

When investigating freeable memory alongside other RDS metrics:

For MySQL 5.7+ on RDS, consider these parameter group settings:

# In your DB parameter group:
innodb_buffer_pool_size = {DBInstanceClassMemory*3/4}
innodb_buffer_pool_instances = 8  # For larger instances
query_cache_size = 0  # Generally recommended to disable
innodb_old_blocks_time = 1000  # Millisecs before LRU eviction

Use these queries to identify memory consumers:

-- Top memory-using connections
SELECT id, user, host, db, command, time, state, 
       mem_used FROM sys.session 
ORDER BY mem_used DESC LIMIT 10;

-- Buffer pool utilization
SELECT (PagesData*PageSize)/1024/1024 AS MB_data,
       (PagesDirty*PageSize)/1024/1024 AS MB_dirty
FROM information_schema.INNODB_BUFFER_POOL_STATS;

Create a Lambda function to dynamically adjust memory settings:

import boto3

def lambda_handler(event, context):
    rds = boto3.client('rds')
    response = rds.describe_db_instances(DBInstanceIdentifier='your-db-id')
    
    # Get current metrics from CloudWatch
    cw = boto3.client('cloudwatch')
    metrics = cw.get_metric_statistics(
        Namespace='AWS/RDS',
        MetricName='FreeableMemory',
        Dimensions=[{'Name':'DBInstanceIdentifier', 'Value':'your-db-id'}],
        StartTime=datetime.utcnow() - timedelta(minutes=30),
        EndTime=datetime.utcnow(),
        Period=300,
        Statistics=['Average'])
    
    avg_memory = metrics['Datapoints'][0]['Average']
    # Add your adjustment logic here