Common errors in a database: causes, errors, and solutions

Informatec Digital » Databases » Common errors in a database: causes, errors, and solutions

The goal is for you to leave with a clear roadmap: why these issues occur, how to detect them, and what actions to take. You'll find detailed guidelines on hardware and software errors in SQL Server (mirroring) , performance-crippling patterns, common query writing mistakes, classic modeling/development pitfalls, and an ITIL/ITSM approach to documenting and resolving recurring incidents with a well-structured Keyed Error Database (KEDB).

Hardware errors: signs, causes, and reaction times

Physical failures usually become apparent quickly because other system components alert the database engine. When this happens, the server receives an immediate hardware error report , although sometimes there are delays due to network or I/O timers that postpone the notification.

Common causes include a broken connection or damaged cable , a failing network card, changes to the router or firewall, endpoint reconfiguration, loss of the drive hosting the transaction log, or process/OS errors. These problems, if they affect the log disk or the network, can cause anything from disconnections to serious disruptions in database replication or mirroring.

Be aware that some network components and certain I/O subsystems apply their own internal timeouts . These timeouts are independent of the database and can delay detection, increasing the interval between the actual fault and when the engine becomes aware of it.

To better understand what's happening "on the wire," it's helpful to ask the network team what messages are arriving at the port during typical events such as DNS outages, disconnected cables, firewall port blocking , crashes of the application listening on the port, server name changes, or a reboot. This inventory of symptoms speeds up diagnosis when the service suddenly stops.

Software bugs and timeouts: when to fix them and how to avoid false positives

Software failures don't communicate on their own: the server could remain idle indefinitely without a monitoring mechanism. Therefore, in scenarios like database mirroring, instances are pinged periodically, and if no signal is received within the agreed timeframe, a problem is considered to have occurred.

Among the conditions that trigger these waiting times are network errors (TCP timeouts, corrupt, lost or incorrectly ordered packets) , an unresponsive operating system/server/database, Windows-level expirations, and a lack of resources: disk or CPU saturation, 100% transaction log, insufficient memory or threads.

If you find yourself in this situation, you can choose to increase the timeout, reduce the load, or upgrade the hardware to handle the demand. Setting the timeout too low causes false positives; setting it too high delays the response to actual failures.

The ping/timeout mechanism in SQL Server mirroring

To keep each connection alive, each instance sends pings at a fixed interval. If a ping is received within the timeout window (plus the sending time) , communication is assumed to be active and the timer is reset. If no ping arrives within that interval, the timeout is considered to have expired and the connection is closed, with the event handled according to the role and operating mode.

Even if the other server is working correctly, a timeout is considered a failure . If the configured value is too short for the normal latency of the environment, "phantom" errors will appear. Therefore, it is recommended not to go below 10 seconds.

In high-performance mode, the timeout is always 10 seconds ; this is usually sufficient to avoid false positives. In high-security mode, the default value is also 10 seconds, but it is configurable; adjust it to 10 seconds or more in this mode if the network is slow.

If you need to change it, remember that this modification is specific to high-security sessions . You can view and modify it from the engine administration or using T-SQL, depending on your version and policies.

How the server responds when there is an error

In the event of any type of error, the instance acts according to its role (primary/witness/secondary), operating mode, and connection status . If a partner is lost, the behavior differs depending on whether we are in high-performance or high-security mode with a witness, so it is crucial to document the operating mode of each session to anticipate downtime and switchovers.

Performance-killing patterns in SQL (and how to fix them)

There are four very common "sins" that unnecessarily worsen latency. They're easy to spot, and avoiding them will save CPU, I/O, and database access from day one.

Queries within loops: executing a query per iteration (the classic N+1) multiplies trips and latency. Retrieve the data all at once with a union or an IN, or use batch queries. Process the logic in your code using structures already loaded into memory.

Loading too much data—retrieving columns and rows you won't use—is like using a sledgehammer to crack a nut. Filter the database, select only what's necessary , use pagination if appropriate, and avoid SELECT * unless you have a clear reason.

Functions in the WHERE clause: Applying LOWER(), DATE(), or others to columns often prevents the use of indexes. It's better to compare without transforming the column: preprocess the data first or transform the literal. For example, filter by date range using date/time columns without wrapping them in functions.

Missing indexes: Forgetting indexes on columns that filter or join is like asking for a full scan. Periodically review where your application filters/joins and create the appropriate indexes (composite indexes when necessary). Balance: Too many indexes penalize writes.

Typical errors when writing SQL: syntax, order, and ambiguities

Most mistakes made by beginners (and even some more experienced users) are related to syntax and SQL injection . The database doesn't understand what you're asking it to and complains. An editor with text highlighting helps, but knowing the common pitfalls speeds up the process.

Misspelled words: Mistakes with FROM, WHERE, or table/column names are common. Messages usually indicate where the parser is failing . Use an editor with highlighting and autocomplete; if a keyword isn't highlighted, be suspicious.

Parentheses and quotation marks: Missing parentheses or quotation marks creates a difficult-to-see problem. Remember operator precedence (AND/OR) and group text with parentheses. In text literals, escape internal quotation marks or alternate between single and double quotation marks to avoid breaking the string (e.g., O'Reilly).

Invalid order in SELECT: the correct order is SELECT → FROM → WHERE → GROUP BY → HAVING → ORDER BY . Changing the order of ORDER BY or HAVING will cause errors. Memorize this or keep a cheat sheet handy.

Omit table aliases: In a self-join or when two tables have columns with the same name, you'll get an "ambiguous column" error. Use short, clear aliases and reference columns with `alias.column`. This also makes the SQL more readable.

Case-sensitive or special names: If you insist on using case-sensitive or space-sensitive names, you'll have to quote them with double quotes depending on the search engine. It's best to avoid these names; otherwise, be consistent when citing them.

Common mistakes in database development

Beyond writing queries, there are design decisions that make a difference in the medium and long term. Here are five common development mistakes, along with what to do instead to protect integrity and maintainability.

Overusing stored procedures: They're useful, but with modern ORMs and access layers, you no longer need to put all your logic in them. Stored procedures have maintenance and versioning costs ; create stored procedures for data access when justified, not for application business logic.

Avoid using primary keys: delegating uniqueness to views, stored procedures, or the application multiplies complexity and errors. Define real primary keys in all tables and use unique keys where appropriate; this will prevent post-deduplication and fragile queries.

Hard delete instead of soft delete: Physically deleting data complicates audits and recovery from "oops, I messed it up." For many use cases, add an active/inactive (soft delete) flag and exclude it from your queries. Leave physical deletion for controlled cleanups.

Known Error Database (KEDB): What it is and why it's a good idea for you

In operations, not everything can be resolved instantly. Temporary solutions (workarounds) are unavoidable due to limited resources, complexity, or the need for business continuity . The KEDB is the repository where you document every known error, its cause (if any), and its temporary or permanent solution.

It is part of the ITIL framework and intersects with Problem Management and Knowledge Management. When a recurring incident arises, the team consults the KEDB, applies the proven solution , and reduces downtime instead of starting from scratch.

Benefits for users: faster resolution, fewer interruptions, and more predictable results . For IT: efficiency (no need to reinvent the wheel), knowledge retention despite turnover, and data for continuous improvement. For stakeholders: transparency, informed capacity/risk decisions, and cost savings.

How to implement an effective KEDB step by step

1) Define scope and objectives: decide which failures are included (software, hardware, network, or specific areas), how they are classified, and what goals you are pursuing ( reduce MTTR, improve satisfaction , etc.). Prioritize critical systems and services and align the scope with business objectives and SLAs.

Guiding questions: Does it cover everything or do we start with the most impactful issues? Are we primarily looking to shorten resolution times or also to detect patterns for prevention?

2) Collect and document: Work with Incident/Problem Management and teams to capture recurring errors and effective workarounds that are not yet documented. Use a simple template with fields such as description, root cause (if known), temporary/permanent solution, impact, dates, and notes.

Tips: clear language, actionable steps, helpful classifications, linking related incidents , and updating entries when there are new developments.

3) Choose the tool: You need good search, categorization/tagging, links between items, scalability, reporting, and the ability to integrate with your ITSM platform. Prioritize a user-friendly interface to encourage adoption; consider AI-powered search and customizable workflows.

4) Train the teams: teach them how to document effectively, search efficiently, and maintain quality. Include practical exercises with real-world cases , quick guides, videos, mentoring, and regular refresher courses. Encourage feedback to improve the process.

5) Maintain and improve: Define responsibilities, KPIs, and a review cycle (monthly for critical issues, quarterly for everything else). Establish peer review of new entries, continuous documentation after each issue, and a feedback channel from users and support.

6) Promote its use: run an internal campaign, recognize those who contribute the most, and foster collaboration between departments (network, software, hardware). Integrate the KEDB into the service culture so that it becomes the first place people look.

KEDB vs. Knowledge Base (KDB): Key Differences

KEDB focuses on known failures and their solutions (temporary or permanent), closely aligned with Problem and Incident Management. It is typically integrated with ITSM and its primary audience is the technical team that resolves incidents.

The KEDB encompasses much more: best practices, procedures, configuration data, help articles, and more. Its maintenance is more extensive, with revisions to procedures and best practices in addition to technical articles. In short: the KEDB is the specialized subset of "when this fails, do this other thing."

As you can see, the stability and performance of a database depend as much on understanding physical and logical failures (and their detection times) as on writing good SQL, modeling intelligently, and organizing operational knowledge. If you correct the four performance patterns, avoid typical syntax errors, design with strong primary keys and sensible normalization, and also set up a live database management system (KEDB), you'll have a faster, more predictable, and easier-to-operate platform, even when things go wrong.