Integrating Database Management into DevOps Pt. 1 – Challenges

DevOps is transforming the way software development and It operations teams work together. However, integrating database management into the process presents its unique sets of challenges. In a series of articles, out of which this is the first one, we will try not only to bring these problems to light but to make an attempt to get closer to a solution.?

Integration of Database Tools in DevOps

DevOps is a methodology that was primarily sculpted for application development. Therefore, it comes as no surprise that originally, DevOps tools such as Jenkins, Docker, and Kubernetes were tailored for streamlining application development, not necessarily for dealing with database systems.?

This specialization led to quite a kerfuffle in weaving database management into DevOps. Traditional DevOps tools, great at handling code, stumble when faced with database-specific tasks like schema management and data migrations. For instance, applying CI/CD practices to databases is more complicated due to persistent data and rollback complexities.

Real-world scenarios highlight these challenges vividly. In a microservices-driven environment, for example, each service might have its own database schema, leading to synchronization headaches. Moreover, databases, with their specific configurations, often disrupt the DevOps goal of uniform environments across development, testing, and production.

Data Persistence

Unlike the relatively straightforward process of modifying application code, changes in databases enrich the lives of those who deal with them with data integrity concerns. Ensuring that data remains accurate and intact during and after transitions such as schema changes or environment transfers is an absolute necessity.?

Potential Issues

One major risk is the potential for data loss. This can occur during data export and import processes if the tools used are not able to handle the volume or complexity of the data. Data loss can also happen if there are errors in the way data is transferred between environments or during the schema migration process.

Another significant challenge is the possibility of data corruption. During the migration or transfer process, if data is not correctly handled or if there are compatibility issues between different database systems, it can lead to data being altered in unintended ways. This not only affects the immediate usability of the data but can also have long-term implications on data reliability.

Data inconsistency is another risk during environment transfers and schema changes. This often occurs when there are discrepancies in how data is replicated or synchronized across different environments or database instances. Inconsistent data can lead to faulty analytics, incorrect business decisions, and a lack of trust in the system's data reliability.

Size and Scalability

In the land of DevOp, both pipelines and applications have scalability as a core tenet in design. Quite arguably, DevOps pipelines are hard-engineered to scale quickly and deal with the ever-changing demands of modern application development. In such instances, this level of scalability is key to being able to effectively manage many instances and versions of applications.

However, it complicates the scenario when it comes to scaling of databases. Databases do not inherently scale anywhere near as smoothly as do applications. This discrepancy arises from multiple reasons that are inherent in database management and architecture. The challenges of database scalability in DevOps are further highlighted by a revealing statistic from 2019 (from a lack of more recent data). A significant 46% of DevOps teams on accelerated release schedules, with weekly or even daily releases, reported extreme difficulty in speeding up their database release cycles to match this pace.?

Potential Issues

First, large databases have performance problems often as they are growing. Larger data marts will likely slow down under the increased load meaning longer response times and less efficiency of data retrieval and processing. This can lead to a very problematic scenario in a DevOps environment where speed and efficiency really matter.

Another important issue is managing schema changes and data synchronization particularly if there are many instances of the application. Every instance of the application might access the database in a different manner and hence need unique schema adaptations or forms for the data. Coordinating these changes across various instances and making sure to maintain the same data integrity and consistency could be haphazard.

Add the high availability and disaster recovery needs for a large-scale database environment, and the picture grows complex. Keeping a database available and resilient is essential even in the face of system failures or other disruptions but it grows difficult as the plain size of the database keeps increasing.

Configuration Drifts and Errors?

The persistence of manual operations in database management often becomes a source of significant challenges, specifically leading to configuration drifts and errors. These issues can have a profound impact on the stability and reliability of database deployments, thereby affecting the overall efficiency of DevOps practices.

The Reliance on Manual Operations

Despite advances in automation, many aspects of database management still rely heavily on manual interventions. This reliance is partly due to the complex and sensitive nature of database configurations and the need for precision in handling data. Manual operations in a DevOps context, where changes are frequent and rapid, increase the likelihood of human error, leading to configuration drifts — unintentional changes or inconsistencies in the database environment.

Implications of Configuration Drifts and Errors

Configuration drifts and errors can have several uwanted effects on database systems:

• Stability Issues: Inconsistent configurations across different environments can lead to unexpected behavior and system instability. For instance, a database configuration that works in a development environment might not perform the same way in production, leading to system crashes or performance degradation.

• Security Vulnerabilities: Manual configurations are prone to oversights, which can inadvertently leave the database vulnerable to security threats. An incorrectly configured database may expose sensitive data or become an easy target for cyber attacks.

• Operational Inefficiency: Resolving configuration drifts often requires significant troubleshooting and rectification efforts, which can be time-consuming and divert resources from other critical tasks.

Real-World Examples of Configuration Drifts

Configuration drifts can arise from several scenarios:

• Out-of-Process Changes: When emergency fixes or changes are made directly in the production environment without being replicated in the development or testing environments, it can lead to disparities. For example, a hotfix applied to resolve an immediate issue in production might not be documented or applied in other environments, leading to drift.

• Team Discrepancies: In organizations where multiple teams handle different aspects of the database, inconsistencies can arise. One team might make changes that are not communicated or synchronized with the changes made by another team, resulting in configuration drift.

• Modified Production Hotfixes: Hotfixes applied in production environments are often done in a rush to fix immediate issues. These quick fixes, if not properly documented and applied across all environments, can lead to significant configuration drifts.

Data Bottlenecks

The major issue in data bottlenecks is the latency of data provisioning. Developers might need to have types of data in many cases for them to perform tests on new features, but getting such data could be a tedious and time-consuming process. The process usually involves a number of steps that goes from making the requests to the IT departments in order to justify them and waiting for a rather implementation that may take place days or even weeks. Formation of such a change in the database that does not fall precisely under the timeline framed for the application concerned further adds up to the inefficiency, hence leading to this delay.

Another very prominent issue arising relates to the problem of the persistence of data. While application code is maintained on a pipeline like this, databases don't fit into that model nearly as well in most companies. In general, continuous deployment is a problem with databases because changes in many cases involve refactoring of data onto a new structure which isn't an instant exercise without the right tools as well as processes.

Complexities Due to Tool Overload

The sheer number of tools available to aid in integrating a DevOps process can, paradoxically, create difficulties. With so many tools in use, ensuring they all work well together and are future-proof can lead to a lot of churn and inefficiency. The introduction of a new tool in one area can inadvertently break something in another, leading to further delays and complications.

The Human Factor

The most significant challenge in overcoming data bottlenecks in DevOps might very well be the human element. Teams composed of development and operations professionals often face difficulties in synchronizing application and database changes due to their differing approaches to development. Each company typically has its own process for merging application and database development, which is often complex and difficult to change.