Concept: diagnose, optimize, and anticipate performance in analytic platforms such as databases through deep learning models.
The complexity of database and NoSQL systems can exceed even talented engineers’ abilities to mentally model and understand cause and effect across entire platforms. Reacting to performance incidents begins to involve trial and error, while predicting the impact of changes relies on intuitive estimates.
Consider a prestigious new client starting to use a cross-channel digital marketing platform. Their high volume campaigns analyze hundreds of millions of data points to segment, target, and reach consumers across display ads, email, and app notifications. This imposes new pressures on databases supporting the system. Late on a Friday before a holiday weekend, a critical query begins to perform slowly. A DBA analyzes the situation, creates new indices, and goes home. The situation should be solved. Instead, performance slows down for many clients across the whole platform. This is counter-intuitive; indices require some compute power to generate, but should not negatively affect the whole platform. After two days of analysis, a senior engineer discovers that implementing new indices caused the database to generate sub-optimal new query plans for many queries unrelated to the initial issue.
A solution will show how changes to an analytic platform will affect performance. If a solution can show cause and effect as changes are made, than it can also predict performance issues by extrapolating trends or performing sensitivity analyses to understand which patterns constrain performance.
Deep learning is a sub-set of machine learning that creates high level models and representations of complex structures. The techniques are often associated with image, text, and video processing. Deep learning techniques can symbolically represent the data models, queries, indices, constraints, views and other elements that form a database (as well as modelling storage and CPU performance, and other workloads like replication or mirroring for disaster recovery). Deep learning techniques typically involve training some form of neural network. Data is fed through the network, adjusting how many layers of cells each perform actions on the data, and then pass it along to adjacent cells until the output begins to match test data.
NoSQL systems and databases generate logs that can be used to constantly train the neural network. Higher level abstractions of cause and effect for a given database, such as SQL Server or MySQL, could also be analyzed through a cloud services across many instances and users (which would involve few privacy concerns since the system is only analyzing performance metadata).
Part of the process of generating product ideas should be to look at the state of the market, and study competitors. I have not found any credible solutions. Many products show what is happening within a database, but none show why and how a change has or will affect performance.
A product should perhaps first target Oracle Exadata because it is used for larger databases and the nature of big, monolithic devices makes testing and staging environments expensive. Sure, Exadata devices can be carved into multiple logical databases, and one of these could be used for testing, but in my experience this can introduce other issues (even when using “caging” to limit resources used by testing), contends for i/o accessing the data, and defeats the point of using a comparatively inexpensive model to predict performance.
The product’s value to a customer should roughly follow data volumes multiplied by query complexity times data model complexity. This is hard to translate to licensing terms. Pricing could simply follow data size.
What are your thoughts? Would you buy this product?
Jeremy Lehman is a principal with Third Derivative advising companies on product management and technical execution of analytic software. Previously, he led global product development and operations for Experian Marketing Services; led sales technology and CRM, in addition to investment banking and wealth management IT, at Barclays; and served at CTO for equities at Citigroup.