Monitoring table spaces

Table spaces, in the traditional sense, do not exist in SAP HANA in the same way they do in disk-based relational databases like Oracle or SQL Server. HANA is an in-memory database, and its primary storage is RAM. Data is persisted to disk in data volumes and log volumes for durability and recovery, but these are not "table spaces" that you create and manage for individual tables in the same way.

Instead of monitoring "table spaces," in SAP HANA, you monitor:

Memory Consumption: How much of the main memory (RAM) is used by different data components (tables, intermediate results, system overhead).
Disk Space for Data Volumes: The physical files on disk where the in-memory data is persisted (snapshot copies called savepoints).
Disk Space for Log Volumes: The physical files on disk where transaction logs (redo logs) are continuously written for recovery.
Disk Space for Backup Volumes: The directories/locations where database backups are stored.

Let's adjust the focus to "Monitoring Data Storage and Memory Usage in SAP HANA" to align with HANA's architecture, covering both in-memory and on-disk persistence.

Detailed Notes for Monitoring Data Storage and Memory Usage in SAP HANA

Monitoring storage and memory in SAP HANA is crucial for ensuring system stability, preventing performance degradation, and planning for capacity. It involves tracking memory utilization, disk space for data and log volumes, and understanding how data is stored.

I. Memory Consumption Monitoring (In-Memory)

This is the most critical aspect of HANA monitoring, as HANA's performance is directly tied to its ability to keep data in RAM.

Key Concepts:

Resident Memory: The amount of physical RAM currently being used by all HANA processes.
Allocated Memory: The total amount of memory that the HANA database has requested/allocated from the operating system. This is usually higher than resident memory.
Used Memory: The portion of allocated memory that is actively being used by HANA for data, code, intermediate results, etc.
Schema Memory: Memory consumed by data within specific schemas (user data, system views).
Table Memory: Memory consumed by individual tables, broken down by row store and column store.
System Memory: Memory used by HANA internal processes, caches, and temporary data.
Global Allocation Limit (GAL): A critical parameter that defines the maximum memory HANA can allocate from the OS. Exceeding this can lead to OOM (Out Of Memory) errors and system instability.

Monitoring Tools & Techniques:

SAP HANA Cockpit:
- Memory Usage Tile: Provides a high-level overview of used, allocated, and resident memory.
- Memory Usage Details: Drills down into memory consumption by various components (table load, schema, host).
- "Tables" App: Shows memory used by individual tables, their compression rates, and their storage type (row/column).
- "Top Consumers" App: Helps identify the largest memory consumers (tables, statements).
- "Workload Analysis" App: Can indirectly show memory impacts of queries.
SQL Commands (using hdbsql or SQL Console in Cockpit/Studio):
- SELECT * FROM M_HOST_RESOURCE_UTILIZATION;: General host resource usage.
- SELECT * FROM M_SERVICE_MEMORY;: Memory breakdown by service (Indexserver, Nameserver, etc.).
- SELECT * FROM M_EXPENSIVE_STATEMENTS;: Identify queries consuming high memory (and CPU).
- SELECT * FROM M_CS_TABLES WHERE TABLE_NAME='<YOUR_TABLE>';: Detailed info for column store tables (including memory usage, compression).
- SELECT TOP 10 TABLE_NAME, SCHEMA_NAME, MEMORY_SIZE_IN_MAIN AS "Memory (MB)" FROM M_CS_TABLES ORDER BY MEMORY_SIZE_IN_MAIN DESC;: Top 10 memory consuming column store tables.
- SELECT TOP 10 TABLE_NAME, SCHEMA_NAME, MEMORY_SIZE_IN_MAIN AS "Memory (MB)" FROM M_RS_TABLES ORDER BY MEMORY_SIZE_IN_MAIN DESC;: Top 10 memory consuming row store tables.
- SELECT * FROM SYS.M_HEAP_MEMORY;: Detailed breakdown of heap memory usage by components.
- SELECT * FROM M_MEMORY_USAGE_STATISTICS;: Provides historical memory usage statistics.
Alerts (HANA Cockpit, Solution Manager):
- Configure alerts for high memory utilization (e.g., above 80-90% of global_allocation_limit).
- Configure alerts for OOM dumps (hdb_oom_dump files).

II. Disk Space Monitoring (Persistence Layer)

HANA persists its in-memory data to disk for durability. Monitoring these disk volumes is crucial.

Key Concepts:

Data Volumes: Where the current consistent state of the in-memory database is written during savepoints. This is effectively the disk snapshot of your in-memory data.
Log Volumes: Where all committed transactions are continuously written before being backed up (redo logs). Critical for recovery and ensuring no data loss.
Trace/Dump Files: Directories for system traces, diagnostic files, and OOM dumps.
Backup Volumes: Dedicated locations for database data and log backups.

Monitoring Tools & Techniques:

SAP HANA Cockpit:
- Disk Usage Tile: Overview of data, log, and trace volume usage.
- Volume Usage Details: Drill down into specific file system usage.
- Backup Catalog: To monitor backup history and size.
OS-Level Tools:
- df -h (Linux/Unix): To check free disk space on all mounted file systems.
- du -sh <directory> (Linux/Unix): To check size of specific directories.
- Task Manager/File Explorer (Windows): For Windows-based HANA installations.
- Monitor I/O performance of disk subsystems.
SQL Commands:
- SELECT * FROM M_VOLUMES;: Shows all HANA volumes and their paths.
- SELECT * FROM SYS.M_BACKUP_CATALOG;: Provides information about completed data and log backups.
Alerts:
- Configure alerts for critical disk space thresholds (e.g., data or log volumes reaching 80-90% full).

III. Data Storage and Compression

HANA's column store uses various compression techniques to minimize memory footprint.

Key Concepts:

Main Store: The compressed, read-optimized part of the column store.
Delta Store: The uncompressed, write-optimized part of the column store, used for new inserts and updates.
Delta Merge: The process of moving data from the Delta Store to the Main Store, applying compression and optimizing data structures. This is a crucial background process for performance and memory optimization.
Compression Rate: The ratio of uncompressed size to compressed size. Higher is better.

Monitoring Tools & Techniques:

SAP HANA Cockpit:
- Tables App: Displays memory usage and compression rates for individual column store tables.
- Delta Merges Tile: Monitors the status and frequency of delta merges.
SQL Commands:
- SELECT SCHEMA_NAME, TABLE_NAME, ESTIMATED_UNCOMPRESSED_SIZE, MEMORY_SIZE_IN_MAIN, MEMORY_SIZE_IN_DELTA FROM M_CS_TABLES ORDER BY MEMORY_SIZE_IN_MAIN DESC;: Shows memory usage in main/delta store and estimated uncompressed size.
- SELECT SCHEMA_NAME, TABLE_NAME, REASON, STATUS FROM SYS.M_DELTA_MERGE_STATISTICS;: Monitor delta merge activity.
- CALL "SYS"."_SYS_STATISTICS"."RECALCULATE_OPTIMIZER_STATISTICS" (NULL, NULL, 'FORCE');: Manually trigger statistics recalculation, which can influence delta merge.

Important Configurations to Keep in Mind

These parameters are primarily found in global.ini and indexserver.ini. They are managed via SAP HANA Cockpit or SQL (ALTER SYSTEM ALTER CONFIGURATION).

Memory Management:
- global.ini -> [memorymanager] -> global_allocation_limit:
  - Purpose: The single most critical memory parameter. Defines the maximum total memory (in MB or as a percentage of physical RAM) that all SAP HANA processes on a host can allocate.
  - Recommendation: Typically set to 85% for productive systems, leaving 15% for the OS and other applications. Needs careful adjustment. Too low can cause OOMs, too high can lead to OS paging.
- indexserver.ini -> [memorymanager] -> table_load_threshold:
  - Purpose: Controls when tables are pre-loaded into memory at startup.
Persistence Layer (Data & Log Volumes):
- global.ini -> [persistence] -> basepath_datavolumes:
  - Purpose: Path to the data volumes. Critical for savepoints and recovery. Needs sufficient high-performance disk space.
- global.ini -> [persistence] -> basepath_logvolumes:
  - Purpose: Path to the log volumes. Critical for redo logs and ensuring data durability. Needs very high-performance, low-latency disk space (often SSD/NVMe).
- global.ini -> [persistence] -> log_mode:
  - Purpose: Controls how redo logs are handled. normal (recommended for production) ensures continuous log backups for point-in-time recovery. overwrite is only for development/test systems.
- global.ini -> [persistence] -> savepoint_interval_s:
  - Purpose: Frequency of automatic savepoints (default usually 300 seconds = 5 minutes). Too frequent can cause I/O spikes, too infrequent increases recovery time.
Column Store (Compression & Merge):
- indexserver.ini -> [columnstore] -> delta_merge_auto_interval:
  - Purpose: Defines the interval for automatic delta merge checks.
- indexserver.ini -> [columnstore] -> merge_compression_ratio_min_threshold:
  - Purpose: Controls when a merge is triggered based on potential compression gains.
- indexserver.ini -> [columnstore] -> optimize_compression:
  - Purpose: Enables/disables automatic compression optimization (e.g., after a delta merge). Recommended to keep enabled.
Trace & Dump Files:
- global.ini -> [trace] -> tracefile_maxsize:
  - Purpose: Maximum size of trace files before they are rotated.
- global.ini -> [trace] -> tracefile_maxbytes_disk:
  - Purpose: Max total disk space consumed by trace files. Important to prevent trace files from filling up the file system.

Best Practices for Monitoring and Management

Regular Monitoring: Establish daily/weekly routines for checking memory, disk, and delta merge status.
Alert Configuration: Set up proactive alerts for critical thresholds.
Capacity Planning: Regularly analyze historical memory and disk growth trends to forecast future requirements.
Housekeeping:
- Clean up old backups: Manage retention of data and log backups.
- Clean up old trace files and dumps: Use hdbcleandisk.py or manually delete old files.
- Delete old data: Implement data aging or archiving for less frequently accessed data.
Optimize Tables:
- Ensure column store tables are optimized for compression (e.g., by ensuring delta merges happen regularly).
- Review infrequently accessed row store tables; convert to column store if appropriate for better compression.
- Implement data aging for large tables.
Analyze OOM Dumps: If an OOM dump occurs, analyze the hdb_oom_dump file to understand what consumed the memory.

30 Interview Questions and Answers (One-Liner) for Monitoring HANA Storage & Memory

Q: What is the most critical resource to monitor in SAP HANA?
- A: Main memory (RAM).
Q: What is the purpose of "savepoints" in SAP HANA?
- A: To persist the current in-memory database state to disk for durability.
Q: What is the main difference between data volumes and log volumes in HANA?
- A: Data volumes store savepoint snapshots; log volumes store continuous transaction changes (redo logs).
Q: Which global.ini parameter limits the total memory HANA can use?
- A: global_allocation_limit.
Q: What happens if HANA exceeds its global_allocation_limit?
- A: It can lead to Out Of Memory (OOM) errors and system instability.
Q: What is the purpose of the Delta Store in a HANA column table?
- A: To store new inserts and updates in an uncompressed, write-optimized format.
Q: What is the "Delta Merge" process in HANA?
- A: Moving data from Delta Store to Main Store, applying compression.
Q: What is the recommended log_mode for a production HANA system?
- A: normal.
Q: Which tool is the primary web-based GUI for monitoring HANA memory and disk?
- A: SAP HANA Cockpit.
Q: How can you check free disk space at the OS level on a Linux HANA server?
- A: Using the df -h command.
Q: What is the significance of a high compression rate for a HANA table?
- A: It means the table consumes less memory.
Q: Which SQL view can you query to find top memory-consuming tables in HANA?
- A: M_CS_TABLES (for column store) or M_RS_TABLES (for row store).
Q: Where are HANA's active redo logs continuously written?
- A: Log volumes.
Q: What is the typical default percentage for global_allocation_limit in a production HANA system?
- A: 85%.
Q: What does M_HOST_RESOURCE_UTILIZATION SQL view provide?
- A: General host resource usage (CPU, Memory, Disk I/O).
Q: What are the two main types of data storage within an SAP HANA table?
- A: Row Store and Column Store.
Q: Which type of storage is optimized for analytical workloads and compression in HANA?
- A: Column Store.
Q: What happens to old hdb_oom_dump files?
- A: They accumulate and should be managed via hdbcleandisk.py or manually.
Q: What happens if log_mode is set to overwrite in a production HANA system?
- A: Point-in-time recovery is not possible.
Q: What is the purpose of basepath_logvolumes parameter in global.ini?
- A: Defines the file path for log volumes.
Q: What are "resident memory" and "allocated memory" in HANA?
- A: Resident is actual physical RAM used; allocated is requested/reserved memory.
Q: How can you check the overall memory usage by different HANA services (e.g., Indexserver)?
- A: Using M_SERVICE_MEMORY SQL view.
Q: What is the purpose of tracefile_maxbytes_disk?
- A: Limits total disk space used by trace files to prevent filling up the file system.
Q: What does a high log_fill_ratio indicate in HANA?
- A: The active log area is filling up, potentially due to failing or slow log backups.
Q: What is a key benefit of using column store for analytical reporting?
- A: Faster aggregations and better compression.
Q: Can you define "table spaces" for individual tables in HANA like in Oracle?
- A: No, HANA uses data volumes and log volumes, not traditional table spaces.
Q: What is the savepoint_interval_s parameter related to?
- A: The frequency of automatic savepoints.
Q: How does data aging contribute to HANA memory management?
- A: It moves less-frequently accessed data from hot memory to warmer/colder storage, freeing up main memory.
Q: What is a potential performance impact if delta merges are not happening frequently enough for a transactional column table?
- A: Increased memory usage (Delta Store grows) and slower read performance (data spread across Main and Delta).
Q: Which component within the Index Server is responsible for executing complex calculations and joins?
- A: Calculation Engine.

5 Scenario-Based Hard Questions and Answers for Monitoring HANA Storage & Memory

Scenario: Your SAP HANA production system, running with a global_allocation_limit of 85% of its 512GB RAM, has started showing "Out of Memory (OOM)" dumps (hdb_oom_dump files are generated). HANA Cockpit's memory usage shows "Used Memory" hovering around 90%, and "Resident Memory" is close to 512GB. Your M_CS_TABLES view shows a significant increase in memory for several large, new column store tables recently loaded.
- Q: What is the immediate cause of the OOM, and what specific actions would you take to address it in the short term and long term?
- A:
  - Immediate Cause: The SAP HANA processes are attempting to allocate more memory than the configured global_allocation_limit (85% of 512GB = ~435GB), or even hitting the physical RAM limit of the server, leading to OOM. The increase in new large tables confirms this.
  - Short-Term Actions:
    1. Identify Largest Consumers: Use M_CS_TABLES and HANA Cockpit's "Top Consumers" to pinpoint the exact tables or queries consuming the most memory.
    2. Unload Less Critical Tables: If possible and safe for operations, temporarily unload less critical tables from memory (UNLOAD <schema>.<table_name>) to free up space. This is a temporary measure.
    3. Increase global_allocation_limit (Cautiously): If there is still some free physical RAM on the server (check df -h or free -g), you might slightly increase global_allocation_limit (e.g., to 90%) as a very temporary measure to keep the system stable, but this only postpones the issue if data continues to grow. Monitor OS swap usage if you do this.
    4. Stop Non-Critical Processes/Jobs: Temporarily halt any non-essential data loading or large report executions.
  - Long-Term Actions:
    1. Add More RAM: The most direct solution if the data size consistently exceeds available memory. This requires hardware upgrade.
    2. Data Aging/Archiving: Implement data aging for the large, new tables if they contain historical data that can be moved to colder storage.
    3. Table Partitioning: Partition very large tables to distribute them across nodes in a scale-out system or manage memory more effectively.
    4. Optimize Compression: Review and optimize compression for the new large tables to ensure maximum memory efficiency. Run OPTIMIZE COMPRESSION if needed.
    5. Query Optimization: Work with application teams/developers to optimize queries that load massive amounts of data or generate large intermediate result sets.
Scenario: Your HANA system's data volume (basepath_datavolumes) is rapidly filling up, and you're getting alerts that it's at 95% utilization. Daily full data backups are running successfully to a separate backup volume. Memory consumption in HANA is stable and well within limits.
- Q: What is the most likely cause of the data volume filling up despite stable memory and successful backups, and how would you resolve this?
- A:
  - Most Likely Cause: Excessive accumulation of old trace files, diagnostic files, and old savepoint files that are not being properly cleaned up by HANA's internal processes or manual housekeeping. While the in-memory data size is stable, the on-disk persistence layer might be growing due to these accumulated files.
  - Resolution:
    1. Check Trace/Dump Directories: Use du -sh <trace_dir> and du -sh <dump_dir> (e.g., /usr/sap/<SID>/HDB<InstNo>/<hostname>/trace and hdb_oom_dump) to identify large files.
    2. Run hdbcleandisk.py: Use the SAP-provided Python script hdbcleandisk.py to automatically clean up old trace files, logs, and OOM dumps based on retention policies.
    3. Review global.ini Trace Parameters: Check tracefile_maxsize and tracefile_maxbytes_disk in global.ini and adjust if they are too high, allowing excessive trace file growth.
    4. Check Old Savepoint Snapshots: Sometimes, old savepoint files or temporary files associated with specific operations might not be cleaned up. Investigate these.
    5. Expand File System: If all housekeeping is optimized and the current data volume size is genuinely insufficient for future growth, the ultimate solution is to expand the file system where the data volumes reside (requires OS/storage administrator involvement).
Scenario: You notice that new data inserted into a large column store table in HANA is not getting compressed effectively. MEMORY_SIZE_IN_DELTA for this table in M_CS_TABLES is very high, while MEMORY_SIZE_IN_MAIN is relatively low, and M_DELTA_MERGE_STATISTICS shows "SUCCESS" status but "REASON" as "NOT_MERGED" with "MERGE_TYPE" as "OPTIMIZE_COMPRESSION."
- Q: What is the underlying issue, and what immediate action can you take?
- A:
  - Underlying Issue: The Delta Merge process, specifically the "optimize compression" merge, is not being triggered or executed effectively for this table, causing new data to accumulate in the uncompressed Delta Store. This prevents data from being moved to the highly compressed Main Store, leading to higher memory consumption. "NOT_MERGED" with "OPTIMIZE_COMPRESSION" indicates that the merge condition for compression optimization is not met or is being suppressed.
  - Immediate Action:
    1. Manually Trigger Merge: For the specific table, manually trigger a delta merge with the OPTIMIZE_COMPRESSION option: ALTER TABLE <schema_name>.<table_name> MERGE DELTA OPTIMIZE COMPRESSION;.
    2. Review indexserver.ini Parameters: Check indexserver.ini -> [columnstore] for parameters like merge_compression_ratio_min_threshold and optimize_compression. Ensure optimize_compression is enabled and the threshold is appropriate.
    3. Check Delta Merge Statistics/Errors: Investigate M_DELTA_MERGE_STATISTICS more deeply for any errors or reasons for not merging. Also check indexserver trace files for clues.
    4. Rebuild Statistics: Sometimes, outdated optimizer statistics on the table can affect merge decisions. Recalculate statistics: CALL "SYS"."_SYS_STATISTICS"."RECALCULATE_OPTIMIZER_STATISTICS" (NULL, '<schema_name>', '<table_name>');
Scenario: Your SAP HANA system has a total of 1TB of physical RAM. Your global_allocation_limit is set to 85% (850GB). You observe via HANA Cockpit that "Allocated Memory" is consistently around 700GB, but "Resident Memory" is only 400GB. The OS free -g command shows that the remaining 600GB of physical RAM is mostly free and not used by other processes.
- Q: What does this discrepancy between Allocated and Resident Memory indicate, and is it a performance concern?
- A:
  - Indication: This indicates that SAP HANA has allocated (reserved) a large amount of virtual memory from the operating system, but it is currently only resident (actively using/holding in physical RAM) a smaller portion of that allocated memory. The remaining allocated memory is not currently backed by physical pages in RAM, and might be in swap space (if it was ever used) or simply reserved. The OS has plenty of free RAM, so it's not a direct OS-level memory bottleneck.
  - Performance Concern: Generally, this is not a significant performance concern for HANA itself. It's a normal behavior for large applications like HANA to allocate more virtual memory than they are actively using as resident memory. The key is that Resident Memory is well within the physical RAM limit, and Used Memory (within HANA's allocated space) is also reasonable.
  - Potential Exception/Why monitor: While not a critical alert, consistently low resident memory compared to allocated could indicate:
    - Over-allocation: global_allocation_limit might be set too high if the actual data never reaches that size. While not harmful if OS has free RAM, it's inefficient.
    - Inefficient Data Loading/Management: Perhaps data is being loaded but then quickly freed up, leading to high allocations that aren't sustained.
    - OS Paging (if high Allocated, but low Resident and low physical free): If the OS showed low free RAM and high swap usage, then the discrepancy would indicate that HANA's allocated but non-resident pages are being swapped out by the OS, which would be a major performance concern. However, in this scenario, with 600GB free, that's not the case.
Scenario: Your SAP HANA production system is logging frequent "Disk Full" errors specifically for the log_volumes directory, causing transactions to halt and the system to go into a critical state. Your log backups are configured to run automatically every 15 minutes to Backint, and the Backint server has plenty of space.
- Q: What are the two most probable root causes for the log volumes filling up despite log backups, and what immediate and long-term actions would you take?
- A:
  - Most Probable Root Causes:
    1. Log Backups are Failing: Despite being configured, the actual log backups to Backint are failing (e.g., due to Backint configuration errors, network issues, or authentication problems), preventing the active log segments from being freed and reused.
    2. Long-Running Uncommitted Transactions: One or more very long-running or "stuck" uncommitted transactions are preventing HANA from freeing up log segments, even if successful log backups are technically occurring. These transactions hold open log segments, preventing their deletion.
  - Immediate Actions:
    1. Check Log Backup History/Status: Immediately verify the status of recent log backups in HANA Cockpit or M_BACKUP_CATALOG. Confirm they are indeed failing.
    2. Check Backint Configuration/Logs: If using Backint, review its specific parameter file (log_backup_backint_parameter_file) and check the Backint vendor's log files for errors.
    3. Check for Long Transactions: Use M_TRANSACTIONS to identify any transactions with very long START_TIME or high STATEMENT_MEMORY_SIZE. Attempt to identify the source (user, application, job) and potentially terminate (cautiously, if safe) the blocking transaction.
    4. Manually Trigger Log Backup (if Backint issues are temporary): If the Backint issue is intermittent, try a manual log backup via SQL: BACKUP LOG ALL USING BACKINT ('<external_backup_id>');
  - Long-Term Actions:
    1. Resolve Backint Failures: Fix the underlying Backint configuration, network, or permission issues to ensure continuous log backups.
    2. Monitor Transactions: Implement proactive monitoring for long-running transactions and processes.
    3. Optimize Long-Running Processes: Work with application teams to optimize any long-running transactions that are holding onto log segments for excessive periods.
    4. Increase Log Volume Size (Last Resort): Only consider increasing the physical size of the log volumes if you have absolutely confirmed that all other causes are resolved and your transaction volume legitimately requires a larger active log area. This is a workaround, not a solution to failing backups or bad transactions.

Rakshit Ranjan Singh

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