8 Mins Read  November 19, 2019  sandeep paithankar

DevOps for Databases: Collaboration for Convenience or Practicality?

“Currently, DevOps is more like a philosophical movement, not yet a precise collection of practices, descriptive or prescriptive.”  – Gene Kim 

DevOps can rightly be termed as a messiah of the IT sector. It has revolutionized the market, changed the game sphere, and transcended traditionalistic ideas after its inception into the industry. Stats reveal that many businesses are keen on adopting DevOps into their models with a reported increase of 17% in 2018. 

DevOps, as the name suggests, is an amalgamation of the word development and operations. It is the perfect mix of necessitated collaboration and free communication between software development teams and information tech operators. DevOps believes in refining and enhancing the relationship between these significant groups to bring about increased efficiency and performance output. The foundation of DevOps lies in the fact that it aims for improved overall working including but not limited to software testing, releasing and development.

What Makes DevOps the ‘It’ Development Process Today?

Functionings of DevOps is modeled quite differently than the traditional development processes. In DevOps, the age-old traditional development process is customized and modified to a continuous ongoing process of product development and release. This cyclical process is better suited for repetitive short developmental cycles where the cycle recaps over coding, building, testing, packaging, releasing, configuring and monitoring. 

How Do Databases Fit into this Scheme? 

Usually, a big number of RDBMS database DevOps are built around maintaining the wholeness of information and data. Data segregation goes against the modules of RDBMS database DevOps. Several trade-offs have been incorporated analyzing how databases work around changes (schema). To work around the problem of data manipulation and change, many database DevOps prefer schemaless (changeless) datastores like MongoDatabase, which has made data trading increasingly seamless and efficient.

Database and DevOps: A Guideline

The essence of DevOps lies in closing the bridge between a business’s development and operational teams. But its functionality doesn’t just stop there, for DevOps to be fully effective, specialized emphasis must be given to evaluate every manually driven operation for its full automation. This is where databases step in. 

The DevOps method demands increased collaboration between the functioning teams. This leads to quite a closed knit working team overseeing various roles and responsibilities. The developer is responsible not only for application development but is also involved in the release process. The developer also partakes in monitoring the said application before and after its release in the market. In the same way, Quality Assurance is also responsible for product testing and its rightful working in the environment it is released. 

This paradigm shift in roles dictates every member of the functioning team to be highly versatile and flexible when overthrown with added responsibilities. There really is no running away. The developer has to accept the fact that her roles and responsibilities branch out not only to her entitled work but also to other operational sectors. 

Database collaboration in DevOps calls for a cause towards the greater good. The operational team has to collectively come together to be responsible for the application they bring out in the market. Therefore, the role of a Database Administrator in a DevOps environment is limited to not only the development team but needs to be a collaborative effort that spans the development and operation teams. The developer would bring about project initiation, changes needed in performance and output while the operation team would handle data security and efficient consistency. 

Relational databases function on a sturdier model than DevOps. DevOps thrives on increased flexibility and versatility. A successful DevOps functioning would be a continuous compromise between the developer and operational services for the desired result.

Roadblocks to a Successful DevOps Operation  

There are several challenges to this mode of operation. Let’s take a look at some of them below.

  • Deployment Automation: Deployments can be a recurring challenge to the DevOps process. To counterattack this issue, the environment of the application has to be fully automated and loaded with the required data.
  • Incompatibility: The second challenge would be an incompatibility between your relational databases and microservice architectures. Microservices structure or model an application as a close-knit collection of loosely jointed services. In a microservice architecture, services are finely grained with lightweight protocols. The reason behind this is to get rid of any dependency on other adjoining microservices. This would prevent the blackout of one functioning microservice to another functioning microservice. In simpler words, a microservice in its pure raw form would be nothing more than just a single table on a database chain/cluster.
  • Lack of Collaboration: As discussed above, a collaboration between the working members of the DevOps team will result in the evident success of the application. Increased communication and transparency would be the key elements to achieve this objective. Needless to say, a dysfunctional communication channel would ruin the entire purpose of the undertaking. In such a scenario, communication channels, also called ChatOps, can play the most vital part in bringing the functional team closer and achieving the said goal.

DevOps for Databases: The Need of the Hour? 

Developers generally face the problem of waiting around for any changes made in the database system to be completed (conducted by database administrators) before they could finish up their assigned projects. This waiting period can be harmful and can have a negative impact on the overall functioning and productivity of the entire team. 

Database DevOps brings about a quick solution to this problem. A systematic and proper approach towards the integration and collaboration of database change as a fully functioning part of the DevOps process can act as a catalyst in the delivery of the required result. The exclusion of database changes in the DevOps process could negate and prove to be a hindrance to your development process. DevOps methodology functions on shorter repetitions and quicker release responses. DevOps incorporation into your database would result in a faster and highly efficient development process. 

What Goes into Facilitating DevOps for Databases?

DevOps functions on and idolizes automation. The workings of manual operation are seemingly too much for the DevOps environment, so much so that it rejects any sort of manual functioning. The developer and the operations system work in a cyclical model of constantly breaking down and remodeling their desired environments. 

Moreover, the task of remodeling an environment is a fully automated procedure. A new build environment must be distributed within a quick span of time in such a case. This is a quick 360 turn from the database environment which is known for its reliance on data consistency and dependability.

Here are some of the key factors that need to be taken care of and employed to ensure the smooth functioning of a database DevOps process:

  1. Automated Deployment

Database software installation is the devil. Quite literally. No task can be as tedious and dreary as the task of installing a fresh database software. Configuration, customization, setting up the topologies are few examples of the series of things you need to figure out for a successful installation. 

Manual set up is time-consuming and doesn’t always guarantee success, it is quite prone to error. One wrong click and you may need to start it all over again. To top it off, maintenance of the system is another issue that you might face after the said installation. Therefore, deployment automation is the most vital element to configure a database system. 

Deployment automation can be successfully achieved by significant DevOps tools like Puppet, Chef, and Ansible. These tools are specialized for deploying packages and static (config) files. 

Development or remodeling of an environment encompasses much more than just software deployment. Environments like QA, development and disaster recovery have various other objectives. Full deployment of an environment comes with additional settings and data to be configured and managed. The needed data and settings can be taken from pre-existing backups. This would prove to be quite a hefty task for DevOps tools like Puppet or Chef considering the fact that those are meant only to help around in automated deployment. Ansible would be the DevOps tool needed to address this issue. Ansible is equipped with several dependencies, scripting and workflow tools to provide the needed help. 

  1. Monitoring of Performance

You will now get to know your system inside out after getting a full analysis of the application and its services. Monitor anything and everything within your system. Microservices would aid in this process by providing an easy separation on a per-service dashboard system. This separation would confine each problem on an individual basis. Once confined, the performance can now be restored to its original function. 

  1. Changes in Schema

As stated above, changes in schema can’t be avoided in DevOps. They go hand in hand in the DevOps system. When a new version of an application or microservice demands an extra field to be saved, the change can be predicted. But the real threat lies when the fields are completely erased or changed. This complete deletion leads to the overall change in the structure of the table. 

For example, in cases of object-relational mapping, repetitive schema changes are conducted whenever any alteration of an object occurs. Schema changes could also lead to the development of internal locks in the database system. This would fuel the database server to run out of its resources owing to additional piling up of queries. Thus, early detection of schema changes is highly important for your database system. 

  1. Version Upgrades

Frequent upgrades of your system is highly important to keep your database software up to date and leave it properly functioning. An application upgrade by the developer gets rid of all the pre-existing bugs and defects hindering the proper functioning of the system. 

For example, Apple’s highly anticipated iOS 13 release came with users and customers complaining of a drained battery cycle. The company later released iOS13.1 to address and fix the cause of the battery drainage. 

Thus, software up-gradation is one of the key elements to keep your operating system running seamlessly. 

There are a few key parts to remember about version upgrades. Regular version upgrades are a routine check for database administrators. However, in a DevOps system, it may prove to be as challenging as the schema changes. Minor upgrades are easier to conduct and operate. All one needs to do is install the newest version and voila! 

In cases of a major upgrade, one must be equipped to conduct several tasks or conversions, as needed. For example, in MySQL, it is preferred to transfer all your database data into the new version only after you are done upgrading.

  1. Focus on Distribution of Data

In DevOps, data distribution becomes highly complex with the introduction of functional sharding, horizontal sharding, and multi-data center. To break it down, microservices are responsible for the functional sharding of your data. 

Each microservice is so designed to run and operate in its own assigned schema and table. Until and unless all your microservices utilize the same database cluster for storage, data will be shared between the various data stores utilized. 

Microservices may also develop signs of uneven usage. Some microservices may only contain user data, others would be equipped to store data details as well as log-click-data on every user interaction allowing them to depict various growth patterns and queries. 

Once a microservice expands further than the storage capacity of the database cluster, a revised scaling approach needs to be taken. On further growth, it is implied that the microservice data has expanded itself to a group of data clusters instead of a single database cluster. Multiple data centers pose the risk of unavailability of data locality. It also threatens data security and data management.       

  1. Data Flow Management 

Microservices was designed to take away complex issues from your application but the irony is that it increases the complexity of your infrastructure at the same time. Data locality, data sharding, and change in the schema can increase the complexity of your system as stated earlier. 

This issue can be reversed or fixed by the help of a proxy system. With the help of a proxy, one can easily manage the flow of data and confine the database infrastructure from the application. Proxies can also prove highly beneficial to scaling issues and also help with maintenance on your database infrastructure. 

For instance, if the node you are working on proves to be incompatible, another node can replace it in the cluster. The proxy will automatically detect the unavailability of the node you were previously working on and route the data traffic to another available node. 

  1. Release Management

Changes in a developing environment are fairly easy to make but the problem arises when one faces executing these changes in the production stage. A staging environment thus comes to your rescue when faced with this predicament. 

The staging environment plays host to a staging database which is simply a copy of the production database. Database administrators are then allocated the task to review and modify the said changes and decide if they are ready to venture into production mode. Once the required changes are approved by the administrators, release management tools can deploy the said changes into the production stage.

Parting Words

Database modulations are a tricky thing boggling even the best of minds. It is, therefore, highly recommended to incorporate them with the collaboration of DevOps to improve efficiency and ensure quicker results. 

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