In concurrent database environments, lockups are inevitable. They are part of the normal mechanics of consistency control. The problem arises when what should be a temporary lock turns into a permanent standoff — the infamous deadlock — or a buildup of stuck sessions waiting for resource release.
When not handled carefully, locks and deadlocks can seriously degrade performance, cause timeouts, and even disrupt critical applications. In this article, we analyze how to accurately identify these events and, more importantly, how to prevent them.
Locks: what they are and why they happen
Every relational database must ensure transactional integrity. To achieve this, it uses locks — mechanisms that prevent two processes from simultaneously modifying the same data.
The main types of locks include:
- Row locks: common in UPDATE and DELETE operations.
- Page or block locks: affect a set of records.
- Table locks: less common, but can be caused by DDL operations or poorly optimized full scans.
- Metadata locks: occur during structural changes, statistics updates, or recompilations.
Locks are normal. What isn’t normal is holding them for too long or allowing them to escalate to the point of creating wait chains.
Deadlocks: when a lock turns into a standoff
A deadlock occurs when two or more sessions enter a dependency cycle, where each is waiting for a resource held by the other. None can proceed — the system detects the standoff and usually kills one of the involved sessions to free the resources.
Classic example:
- Session A locks row X.
- Session B locks row Y.
- Session A tries to access row Y.
- Session B tries to access row X.
- Neither can proceed.
These events are more common than you might think and, if they happen frequently, indicate flaws in the transactional design of the application or a lack of concurrency control.
How to detect locks and deadlocks
Accurate detection requires visibility on two levels: active sessions and the resource wait structure.
1. Queries for real-time detection
Oracle
sqlCopyEditSELECT blocking_session, sid, wait_class, event, seconds_in_wait
FROM v$session
WHERE blocking_session IS NOT NULL;
SQL Server
sqlCopyEditSELECT
blocking_session_id AS blocker,
session_id AS waiter,
wait_type, wait_time
FROM sys.dm_exec_requests
WHERE blocking_session_id <> 0;
PostgreSQL
sqlCopyEditSELECT
pid,
pg_blocking_pids(pid) AS blocked_by
FROM pg_stat_activity
WHERE wait_event_type = 'Lock';
These queries help identify who is blocking whom, for how long, and what type of wait is occurring.
2. Deadlocks detected through logs
Modern DBMSs automatically record deadlocks in their internal logs. The problem is that if you only see them after they happen, it means the monitoring is incomplete.
Specific observability tools can:
- Correlate latency spikes with lock events
- Alert about sessions waiting for more than X seconds
- Visually display the blocking chain (blocking tree)
- Capture deadlock events with context (query, users, involved resources)
Continuous visibility is the key difference between reactive diagnosis and strategic correction.
How to eliminate and avoid locks and deadlocks
Eliminating locks requires a combination of query optimization, transaction review, and, in some cases, fine-tuning of the database configuration.
1. Optimize the lock and deadlock retention time
Long transactions are the enemy of concurrency. Whenever possible:
- Commit frequently, especially in batch operations
- Avoid leaving transactions open in idle sessions
- Break large operations into smaller batches
2. Maintain a consistent order of resource access
Many deadlocks can be avoided simply by ensuring that all transactions access data in the same order. Example: always update Table A before Table B.
3. Use appropriate indexes
Reads or updates without an index force the database to lock more records (or pages) than necessary. A good index allows precise access and fewer locks.
4. Avoid automatic lock escalation
In some DBMSs, a large number of row locks can escalate to a table lock. Properly configuring thresholds (or avoiding unnecessary scans) reduces this risk.
5. Implement concurrency control in the application
Reviewing the use of ORM, connection pools, and retry practices helps avoid scenarios where multiple sessions compete for the same resources in vicious cycles.
When monitoring makes a difference
Monitoring locks in real time is the first step. But the key difference lies in:
- Detecting trends (e.g., sessions that frequently cause locks)
- Correlating latency spikes with lock wait times
- Visualizing the top N blocking sessions
- Alerting on configurable thresholds for wait times or blocking chains
Observability doesn’t solve the problem by itself — but it provides the necessary data to solve it accurately.
Conclusion
Locks are an inevitable part of database architecture. The problem is not their existence, but their duration and impact. Deadlocks, in turn, indicate that concurrency control has gotten out of hand.
Eliminating these situations requires transactional discipline, optimized queries, and active monitoring. Expecting the database to “fix itself” is a risky bet — and in critical environments, unsustainable.
Want to know what is locking your database right now? Look at the waiting sessions. Want to prevent it from happening again? Monitor lock patterns with depth and context. That’s where real performance begins.
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