How can you prepare for future growth and changes in a scalable DBMS?

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A scalable database management system (DBMS) can handle increasing data volume, user demand, and performance requirements without compromising on reliability, availability, and security. However, scaling a DBMS is not a one-time task, but a continuous process that requires planning, monitoring, and adapting to future growth and changes. In this article, you will learn some practical tips on how to prepare for scaling your DBMS in a proactive and flexible way.

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Enoch Easudoss

Data Architect | DWH | DataLake | Python | Informatica | AWS - Azure - GCP

1 Assess your current and future needs

Before you decide on a scaling strategy, you need to understand your current and future needs in terms of data size, user traffic, query complexity, and service level agreements (SLAs). You can use various tools and metrics to measure and analyze these aspects, such as database size, throughput, latency, availability, and error rate. You also need to estimate how these factors will change over time, based on your business goals, user behavior, and market trends. This will help you identify the bottlenecks and gaps in your current DBMS and set realistic and achievable targets for scaling.

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Enoch Easudoss

Data Architect | DWH | DataLake | Python | Informatica | AWS - Azure - GCP
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In my experience, before we work on forecasts and estimations to assess future needs, it is imperative to look at the current usage and the ROI. Most often trimming down less used DB objects optimizes gains before investment. Having said that my order of priorities would be: 1. Can the system scale to meet the SLAs? 2. Can I partition DB objects with a lot of data? Or adding partitions would be helpful? 3. How spread is the user traffic? Peaking during specific days or specific hours in a day? 4. What are my fastest-growing DB objects?

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2 Choose a suitable scaling method

There are two main methods of scaling a DBMS: vertical and horizontal. Vertical scaling involves adding more resources to a single server, such as CPU, memory, disk space, or network bandwidth. Horizontal scaling involves adding more servers to a cluster, either by replicating or partitioning the data. Each method has its advantages and disadvantages, depending on your needs and constraints. For example, vertical scaling is simpler and cheaper to implement, but has a limit on how much you can upgrade a single server. Horizontal scaling is more complex and costly to implement, but offers higher scalability and availability. You need to weigh the pros and cons of each method and choose the one that best suits your DBMS.

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Enoch Easudoss

Data Architect | DWH | DataLake | Python | Informatica | AWS - Azure - GCP
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We are in an era of container orchestration, it is indeed very tempting to look at Kubernetes as a magic bullet. It could be a viable option for some industries and scenarios but most often not for ACID requirements. That leaves us with either vertical/horizontal scaling for DBMS or to say specifically for RDBMS. The bottom line is to look at the ROI for both options. Vertical scaling could be relatively cost-effective for temporary spikes (for example high sales during the holiday season) and Horizontal scaling is to be considered for scenarios where the future use and needs are consistently increasing (for example a company picking up more users/sales every week or month).

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3 Implement a scalable architecture

Once you have chosen a scaling method, you need to implement a scalable architecture that supports it. A scalable architecture is a design that allows you to add or remove resources without affecting the functionality and performance of your DBMS. To do this, you should consider using load balancers, caching, sharding, and replication. Load balancers can improve the performance and availability of your DBMS by preventing overloading and failures of individual servers. Caching reduces the load and latency of your DBMS by avoiding redundant or expensive queries. Sharding increases scalability and availability of your DBMS by distributing the data and the load across multiple servers. Finally, replication enhances the availability and reliability of your DBMS by providing backup and failover options in case of server failures or disasters.

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Enoch Easudoss

Data Architect | DWH | DataLake | Python | Informatica | AWS - Azure - GCP
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Most often there is very little scope for re-architecture or major changes to the DB design. However, having a transient layer to de-couple the statefulness of a CRUD operation is very helpful. Let me explain, Can the systems and tools accessing the DB implement re-try and wait operations independent of DB properties of timeout and write/read lock? This essentially is an investment but when there is very little leeway to modify DB design, this is an excellent approach to optimize scalability. There are of course caveats that this is not suitable for hierarchical or complex time-sensitive data operations, especially transactional systems. However, highly recommended for Analytical systems.

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4 Monitor and optimize your DBMS performance

Scaling your DBMS is not a one-off task, but a continuous process that requires monitoring and optimization. You need to keep track of the performance and health of your DBMS, using various tools and metrics, such as CPU utilization, memory usage, disk I/O, network latency, query response time, and error rate. You also need to identify and troubleshoot any issues or anomalies that may affect your DBMS performance, such as slow queries, deadlocks, resource contention, or data inconsistency. You can use various techniques to optimize your DBMS performance, such as indexing, query tuning, compression, or partitioning.

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Enoch Easudoss

Data Architect | DWH | DataLake | Python | Informatica | AWS - Azure - GCP
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Rome wasn't built in a day. An optimized and highly scalable Database is not possible without the following: 1. Robust monitoring system. 2. Periodic maintenance (deleting/archiving data, freeing up tablespaces/filegroup, reclaiming blocks, etc.) 3. Routine Health checks on the underlying infrastructure. 4. Patches and upgrades from the OEM installed in a timely fashion. Though there is some research in failure prediction using RNN models looks very promising; the key is to drill maintenance routines as a habit.

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5 Plan for future growth and changes

Scaling your DBMS is not only about meeting your current needs, but also about anticipating and preparing for your future needs. You need to have a clear vision and roadmap for your DBMS growth and evolution, based on your business goals, user feedback, and market trends. You also need to have a flexible and adaptable scaling strategy that can accommodate changing requirements and scenarios, such as spikes in user demand, new features or functionality, or migration to a different platform or technology. You can use various methods to plan for future growth and changes, such as scenario analysis, simulation, testing, or benchmarking.

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Enoch Easudoss

Data Architect | DWH | DataLake | Python | Informatica | AWS - Azure - GCP
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I don't know where I saw this quote but this is one of my favorite "Think Twice Do It Once". When it comes to scaling a DBMS the thinking or to say planning never really stops. That begs the question - how do I plan for future growth? My answer is to look at two things: 1. What are the current variables that I use for forecasting (users, concurrency, SLAs, uptime, DB object analysis, etc.)? 2. What is my projected ROI? Now, if the number of variables changes (say, for example, a new requirement from the market/feedback loop) then it impacts my ROI as well as our choice of scalability solution. This gives a good framework to work with for continuous forecasting and future planning.

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6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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How can you prepare for future growth and changes in a scalable DBMS?

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1 Assess your current and future needs

2 Choose a suitable scaling method

3 Implement a scalable architecture

4 Monitor and optimize your DBMS performance

5 Plan for future growth and changes

6 Here’s what else to consider

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