Background and Architectural Context

HSQLDB in Enterprise Systems

HSQLDB is a lightweight relational database engine written in Java, often used as an embedded database for unit testing, small deployments, or in-memory analytics. In enterprise setups, it can serve as a persistent store in standalone mode or as a transient, high-speed store in memory mode. Its ease of embedding makes it popular in Java EE, Spring Boot, and OSGi-based systems, but this also means it is frequently run with defaults that are unsuitable for long-lived, concurrent workloads.

Storage Models

• Mem: Purely in-memory, fastest, data lost on shutdown unless scripted dumps are used.
• File: Persistent storage, writes to .script, .log, and .data files.
• Res: Read-only from a resource, useful for lookups.

Each mode has unique performance and reliability trade-offs that influence troubleshooting strategy.

Root Causes in Large Deployments

1) Transaction Mode Misconfiguration

HSQLDB defaults to READ_COMMITTED isolation, but many embedded setups unintentionally operate in AUTO_COMMIT mode. This can cause lock contention in batch inserts or unexpected visibility of uncommitted changes in concurrent threads.

2) Log and Script File Growth

In file mode, the .log file records all changes since the last checkpoint. Long-running systems without explicit CHECKPOINT commands can see massive growth, causing startup slowness and potential file corruption after crashes.

3) Memory Leaks in Long-Lived In-Memory Databases

When using mem mode with frequent schema changes (e.g., table drops and recreations in multi-tenant systems), unreferenced table structures can persist due to uncollected soft references, leading to gradual memory bloat.

4) Unreleased Table Locks

Batch operations interrupted by exceptions may leave table-level locks held until the session is closed. In pooled connections, this can block unrelated operations until the pooled session is reset.

5) Schema Evolution and Cached Plans

HSQLDB caches query execution plans. Altering a table (e.g., changing column types) without clearing these caches in the same session can cause runtime errors or incorrect query results.

Diagnostics and Observability

Inspecting Locks

Use HSQLDB's INFORMATION_SCHEMA.LOCKS table to identify blocking sessions and locked tables.

SELECT * FROM INFORMATION_SCHEMA.LOCKS;
-- Look for TABLE_NAME and SESSION_ID patterns indicating contention

Monitoring File Growth

Check .log and .script sizes in persistent mode. A rapidly growing .log file without corresponding checkpoints indicates misconfigured maintenance.

Heap Analysis

In memory mode, take periodic heap dumps and analyze for lingering Table or Session objects to detect uncollected schema remnants.

Query Plan Invalidation

Force query plan regeneration by closing and reopening connections after schema changes, or by executing SET DATABASE EVENT LOG SQL TRUE to monitor recompilations.

Step-by-Step Remediation

1) Configure Transactions Appropriately

Disable auto-commit for multi-statement transactions and ensure explicit commits.

conn.setAutoCommit(false);
try (PreparedStatement ps = conn.prepareStatement("INSERT INTO orders VALUES (?, ?)") ) {
  // batch insert
  ps.executeBatch();
  conn.commit();
} catch (SQLException e) {
  conn.rollback();
}

2) Schedule Regular Checkpoints

In persistent mode, schedule explicit checkpoints to trim logs and prevent startup delays.

CHECKPOINT DEFRAG;

3) Reset or Reinitialize In-Memory Databases Periodically

For long-running in-memory instances, periodically export and reload the schema to reclaim memory, or restart the instance during maintenance windows.

4) Implement Connection Pool Cleanup

Ensure that pooled connections are fully rolled back and closed after exceptions to release locks.

finally {
  if (!conn.getAutoCommit()) conn.rollback();
  conn.close();
}

5) Clear Cached Plans After Schema Changes

Disconnect and reconnect after DDL changes, or isolate DDL in dedicated sessions.

Common Pitfalls

• Using mem mode for production without persistence or replication strategy.
• Ignoring growth of .log file until startup times become unacceptable.
• Mixing DDL and DML in the same transaction in high-concurrency workloads, causing unnecessary locks.

Best Practices for Long-Term Stability

• Match transaction isolation to workload requirements; avoid auto-commit in multi-step operations.
• Automate checkpoints in persistent deployments.
• Establish heap monitoring for in-memory modes.
• Keep DDL operations off critical transaction paths.
• Document maintenance windows for database resets or reloads.

Conclusion

HSQLDB's simplicity and embeddability are strengths, but in enterprise contexts they can mask operational hazards. Understanding its transaction semantics, storage modes, and maintenance requirements is critical for avoiding elusive issues like lock contention, log bloat, and memory leaks. By adopting proactive diagnostics, disciplined transaction management, and scheduled maintenance, teams can operate HSQLDB reliably even in demanding, long-lived production environments.

FAQs

1. Why does my HSQLDB log file grow indefinitely?

Because changes are recorded until a checkpoint occurs. Without regular checkpoints, the log grows continuously, slowing restarts and risking corruption after crashes.

2. How can I detect table locks that block other sessions?

Query INFORMATION_SCHEMA.LOCKS to see which sessions hold locks. Long-lived locks often indicate uncommitted transactions or interrupted batch jobs.

3. Can I safely use mem mode for multi-hour jobs?

Yes, but you should monitor heap usage, avoid frequent schema changes, and plan for persistence if the JVM might restart unexpectedly.

4. Why do queries fail after I alter a table in the same session?

Cached execution plans may be invalid. Closing and reopening the connection forces plan regeneration to match the new schema.

5. How often should I run CHECKPOINT in persistent mode?

The frequency depends on write volume. For write-heavy systems, every few minutes or after large batch jobs is advisable to keep logs manageable.