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Database management anno now. A marriage between humans and AI?

Popular cloud database providers (DBaaS and PaaS) have increasingly integrated services such as automatic indexing and query optimization into their offerings. In addition, more and more AI-driven tools are becoming available. Automatic indexing and query optimization using AI is absolutely possible and becoming more sophisticated, but without human oversight or any customization, we don’t really believe in that. Rather, as in so many fields, AI is a valuable, complementary tool. It can help database administrators identify, create and manage indexes more efficiently. In this blog, Edco Wallet, co-founder of OptimaData, shares with you some considerations.

Edco Wallet

Co-Founder & eigenaar
Edco Wallet - Co-Founder & eigenaar

Query optimization

Query optimization using AI focuses on improving the performance of database queries by automatically suggesting the most efficient execution plans. Some ways AI can play a role in query optimization:

  • Query plan analysis
  • Workload profiling
  • Cost modeling
  • Adaptive query optimization
  • Parallel query processing
  • Automatic statistics collection
  • Index recommendation

Index Advice

In this blog, I take a closer look at indexing. For all of the aforementioned applications, human expertise and monitoring are necessary to ensure that the automated optimizations remain effective in various situations, including index advice. That said, AI can be a valuable tool in automated indexing, for example, for the following:

1 Pattern recognition

Machine-learning algorithms can recognize patterns in queries and the use of data. Based on these patterns, they can make suggestions, such as which fields can be indexed to improve query performance.

2 Workload analysis

AI can analyze the workload of the database and it can identify which queries are executed the most. Based on this analysis, it can make suggestions for creating indexes that optimize the performance of frequently used queries.

3 Cost-benefit analysis

Automatic indexing with AI can consider the costs and benefits of creating indexes. It can determine which indexes are likely to provide the greatest performance gains compared to the overhead of updating and maintaining those indexes.

4 Adaptive adaptation

Some systems use adaptive indexing, where you can use AI to continuously monitor the database and have indexes dynamically adjusted based on changing workloads and usage patterns.

Limitations and considerations

These are all wonderful features, of course, but I also see some limitations:

1 Complexity of Workloads.

Automatic indexing with AI can be less effective in situations with complex queries or workloads. That’s when human expertise is really needed to determine the optimal indexes.

2 Specific requirements

Some databases have specific requirements or optimizations for automatic indexing that AI may not fully understand. Again, human intervention is needed.

3 Monitoring and evaluation

When deploying automatic indexing with AI, it is important to regularly monitor and evaluate the proposed indexes to ensure that they are still relevant and effective. In fact, you could say that AI calculates with what is available and provides the best direction from that. Human action by an expert involves looking not only at what is there now and what is available, but also at the desired situation and how best to work toward it. AI can help in that process, though, by providing quick insight into what options are available now based on the current set-up.

What are the risks of deploying AI in database management?

In addition to the limitations mentioned, there are some real risks.

1 Incorrect decisions.

AI models are only as good as the data they are trained with. If the training data is biased, incomplete or incorrect, AI models can make inaccurate recommendations or incorrect decisions. Now, there are many database environments that are difficult to predict.

We see more often in analyses that behavior from three months ago is now not really relevant because, for example, the code has changed or the end of the year is approaching and year-end figures need to be retrieved. Thus, every period has trends in the databases. So if AI acts on this too briefly, you run the risk of being behind the times.

2 Insufficient transparency

Some AI models, particularly deep neural networks, can be considered black boxes. This means that it can be difficult to understand the exact reasoning behind a decision. This lack of transparency can lead to hard-to-explain decisions and thus distrust.

3 Overfitting

Overfitting occurs when an AI model is overfitted with training data and therefore does not generalize well to new, previously unseen data. This can lead to poor performance on new queries or workloads. Or, in the long run, “index everything.”

4 Outdated models

If AI models are not regularly updated with new data, they can become outdated and less effective. Changing workloads and data patterns must be tracked and incorporated into model updates.

5 Inadequate security

Implementation of AI in databases poses new security risks. If security measures are insufficient, malicious parties may attempt to manipulate the model or gain unauthorized access to sensitive information.

6 Privacy issues

The use of AI in databases can lead to privacy issues, especially if the model is trained on sensitive information. It is important to ensure that personal information is adequately protected and that the model complies with applicable privacy laws and regulations.

7 Unexpected behavior patterns

AI models can behave differently than expected, especially in complex and dynamic environments. Unforeseen interactions between different parts of the system can lead to unexpected behavior of the database.

8 Dependence on data quality

AI models are highly dependent on the quality of the data it receives. If the data is of poor quality, it can lead to suboptimal performance and decision-making of the AI system.

9 Lack of human expertise

Although AI systems are advanced, I mentioned earlier that human expertise is still indispensable. Lack of involvement of database administrators and other experts can lead to misinterpretations of results and inadequate maintenance of the system.

10 Lack of compliancy

Failure to comply with laws and regulations regarding data management, privacy and security can lead to legal problems and financial penalties.

Balance is everything

To mitigate these risks, it is important to balance the benefits of AI with managing potential challenges. You do this through careful design, a smart data model, good monitoring, accurate evaluation and optimal collaboration between AI and domain experts.

Be realistic

In general, automatic indexing or query optimization using AI is a valuable addition to the tasks of database administrators, but it is important to have realistic expectations and evaluate the results regularly. It cannot completely replace what customization and human expertise can provide, especially in complex and unique situations.

Want to know more?

Want to learn more about the application of AI in database management or need additional expertise in optimizing and indexing your databases? Feel free to contact us without obligation, we’d love to help.