Understanding the Pervasive PSQL Architecture

Embedded vs Client-Server Mode

Pervasive PSQL supports both embedded and client-server modes. In embedded mode, the engine runs in the application process. In client-server, a dedicated database engine handles networked connections. Each mode influences resource contention and debugging strategies differently.

File-Based Storage System

Pervasive uses a Btrieve-based file storage engine. Each table is a physical file with internal page and index structures. Concurrency and file locking are tightly coupled with OS-level I/O and network protocols.

Common Troubleshooting Scenarios

1. Record Lock Contention in High-Concurrency Workloads

Applications with frequent updates to shared records (e.g., inventory counts or session tokens) often trigger lock contention. This appears as client-side freezing or timeouts.

Root Cause

Lock escalation due to default pessimistic concurrency and lack of fine-grained record design.

// Pseudo-code example of unsafe update pattern
BEGIN TRANSACTION
UPDATE Orders SET status = 'shipped' WHERE id = ?
COMMIT

Fix

Refactor to use record-level granularity, optimistic updates, or conflict resolution strategies outside the DB layer.

2. Slow Index Resolution on Large Datasets

Queries that used to perform well may suddenly degrade due to index fragmentation or implicit full-table scans.

Diagnostic

Use Pervasive Control Center or monitor BTI.EXE to view disk I/O spikes and slow operations.

Remediation

  • Rebuild indexes periodically using BUTIL -REBUILD
  • Enable high-availability snapshot backups to minimize disruption
BUTIL -REBUILD orders.mkd

3. ODBC Driver Misconfiguration

Incorrect DSN parameters or 32/64-bit driver mismatches lead to authentication failures, NULL result sets, or segmentation faults.

Fix

Ensure consistency between client bitness and driver version. Validate DSN with test queries using pvquery.

Advanced Diagnostics Techniques

Monitoring File Handles and Locks

Use OS tools like handle.exe (Windows) or lsof (Linux) to inspect open files and locks held by w3dbsmgr.exe.

handle.exe | findstr orders.mkd
lsof | grep orders.mkd

Transaction Isolation Debugging

Pervasive supports multiple isolation levels. Anomalies like phantom reads or non-repeatable reads arise if isolation mismatches occur between modules.

Fix

Standardize transaction levels across services. Avoid mixing dirty read logic (Read Uncommitted) with critical writes.

Architectural Anti-Patterns

1. Monolithic Schema Design

Tables used by multiple logical modules become hot spots. Lack of modular boundaries results in unintentional coupling and transaction lock chains.

2. Excessive Btrieve API Usage in Modern Workloads

Direct Btrieve calls, though performant, are inflexible and difficult to trace. They bypass standard SQL abstraction layers, making debugging harder.

3. Poor Error Logging

Silent failures or generic return codes like 94 (Permission Error) or 3012 (File in Use) can mask underlying issues. Without detailed context, resolution becomes guesswork.

Step-by-Step Remediation Plan

Step 1: Reproduce the Problem

Use test data snapshots and instrument logs to isolate behavior. Enable verbose logging in the PSQL engine configuration.

Step 2: Analyze Locking Patterns

Capture application logs during transaction activity. Correlate with DB file access timestamps and user sessions.

Step 3: Validate Table and Index Health

Run integrity checks and rebuild indexes for suspect tables. Look for stale statistics and misaligned page usage.

BUTIL -STAT orders.mkd
BUTIL -REBUILD orders.mkd

Step 4: Refactor Transactions

Where possible, shorten transaction scope. Avoid cross-table writes without explicit lock ordering.

Step 5: Upgrade PSQL Engine and ODBC Drivers

Ensure you are running the latest supported versions. Many known issues in legacy drivers have been patched in newer releases.

Best Practices for Stability and Performance

  • Segment tables logically and limit row width
  • Adopt scheduled index rebuilds and file integrity checks
  • Use application-level deadlock detection and retries
  • Monitor disk I/O and memory buffers regularly
  • Keep ODBC clients in sync with PSQL engine versions

Conclusion

Troubleshooting Pervasive PSQL in enterprise environments demands a nuanced understanding of its storage engine, transaction model, and driver behaviors. Generic solutions rarely suffice—every issue needs careful profiling, root cause analysis, and surgical architectural corrections. By combining disciplined schema design, proactive maintenance, and robust observability, tech leaders can ensure high availability and performance of PSQL-based systems even under growing workloads.

FAQs

1. How do I prevent frequent file locking issues?

Limit concurrent write access to the same table, and reduce long-lived transactions. Use row-level locks where possible.

2. Can Pervasive PSQL scale for high-volume OLTP?

Yes, with tuning. However, it requires optimized indexes, minimal lock contention, and efficient transaction boundaries.

3. What are the signs of index corruption?

Sudden performance drops, failed inserts, or incorrect result sets. Use BUTIL -STAT and rebuild affected files.

4. Is there a native performance monitoring tool?

Yes, Pervasive Control Center (PCC) offers real-time statistics and engine health. Log-based profiling supplements this.

5. How often should I rebuild indexes?

Depends on write frequency. For high-write tables, weekly or monthly rebuilds help maintain consistent performance.