The world of real-time analytics has skyrocketed in the last few years, with millions of clients, customers, and analysts around the US engaging with this form of analytics. Many industries now employ real-time analytics to deliver rapid, insightful data experiences to customers and analysts alike. Yet, in order to provide these systems, businesses have to ingest, process, and output potentially hundreds of thousands of bytes of data every single hour.
Your data system may need to process data from IoT (Internet of Things) tools or user-engaged systems. From there, you must extract the most useful information or collate it into an appropriate format before then feeding it back into data analysis and visualization tools. Real-time analytics is a mammoth task, one that requires a huge amount of processing power and infrastructural support.
What’s more, there could potentially be a large number of analysts who are simultaneously working on this data, interacting with it, and changing it. In order to not create logic errors or data view errors, tools must provide a high degree of concurrency, letting everyone see the real-time updates to data as they come in from data ingestion and analysts’ edits.
In order to maintain a high degree of data integrity, your business must provide concurrency and permit multiple clients or users to interact at the same time. In this article, we’ll dive into methods you can use to overcome barriers to concurrency, helping your organization create a high-concurrency data system.
Let’s dive right in.
Why Do Data Systems Need Concurrency Control?
Data concurrency control minimizes the opportunity for errors and conflicts within data systems. Effective concurrency control ensures that data systems can run continuously without conflicts while maintaining the system’s ACID properties. In complex systems, concurrency control helps businesses to meet their performance requirements and keep data consistent across all users.
When your organization poorly executes concurrency, you may have an uncontrolled system where numerous changes start to clash with one another. Over time, poor concurrency can lead to a number of problems.
When a secondary change alters initial data without effective data integrity, then all processes or functions that rely on that initial value will start to receive an error. This problem is known as the lost update problem, which can cause huge knock-on problems and reduce the overall data integrity and functionality of your data system.
Equally, if there is a lack of concurrency within a system, transactions that were aborted could still have processing relying on them. If a value produced by a transaction disappears, it could trigger a dirty read that outputs incorrect results. Especially in complex data systems, concurrency control is absolutely vital when ensuring a high degree of data integrity.
How to Achieve a High Degree of Concurrency in Data Systems
Achieving high concurrency won’t happen without some major infrastructural and architectural changes to your system. However, by optimizing workloads, introducing new strategies, and affirming the available infrastructure your organization has, you can begin the long pathway toward optimization.
Here are some strategies to combat some of the most common concurrency problems you will encounter in large-scale data systems.
1. Cache Commonly Fetched Data
Cache and index functions are a fantastic way of reducing the strain on systems, which will help to free up resources for other functions. High concurrency requires a huge amount of resources, especially in systems that are scaled and have many users working at the same time.
An organization can use caching in several ways, such as SQL caching, partition caching, or caching front-end objects that are commonly interacted with. Caching prepared statements is an effective way of reducing the total resources used on average per user. While this may not provide a huge difference in large-scale systems, this is an effective approach for those who have not yet begun to optimize their front-end and back-end workloads.
2. Use Concurrency Scaling for Write Workloads
Depending on the data warehouse and architecture that you employ, your organization may be able to support concurrency scaling for write workloads. Concurrency scaling automatically scales query processing power when concurrent queries are executed in your organization. By elastically scaling, this tool can provide a stable performance for hundreds of simultaneous users.
Most of the time, businesses will have already optimized their workloads and overprovisioned to meet peak demand. However, these other strategies often lead to wasted resources at non-peak times and can frustrate the system. Using concurrency scaling, as offered by systems like Amazon Redshift, can overcome these difficult areas and provide improved results.
3. Ensure You Use Effective Data Warehouse
Cloud data warehouses have been a leading tool in the world of data management over the past few years. Especially as data warehouse expand their capabilities and offer flexible plans to businesses, they have rapidly become the go-to choice, now surpassing the use of on-premise data sites. If your business uses a cloud data warehouse as the central site of operation for your data system, you next need to ensure that it is as effective as possible.
Various cloud data warehouses offer similar services but very different experiences, with distinct capabilities, processes, and tools leading them to have alternative advantages. One of the core areas where cloud data warehouses differ is within scalability, with warehouses and query engines approaching data scalability and continuous ingestion in distinct ways.
If we explore the difference between Snowflake vs BigQuery, two leading cloud data databases, we instantly see that they approach scalability using disparate systems. Snowflake is extremely scalable, using auto-scaling horizontally to provide high concurrency, even during peak hours. Alternatively, BigQuery offers approaches that have alternating scalability capabilities and limit concurrency by user count.
Depending on your specific needs and scalability needs, the best choice to promote a high-concurrency data system will change. Be sure to understand the exact offerings of your cloud data warehouse before committing to a system, as they form the core of your data infrastructure.
Simultaneous access to data is one of the most important aspects of a data system that has many users working at once. Without a high degree of concurrency, an organization can accidentally cause major data errors that can quickly disable whole sectors of data analytics and presentation.
Yet, achieving concurrency is not a straightforward process. Scaling your data ingestion will rapidly reduce how effective your machines are, making it more complex to manage and maintain databases across several users. Even one-off data events can be impossible to manage for organizations that aren’t able to scale dynamically and horizontally to manage the additional strain.
By focusing on improvising concurrency across your organization, your baseline levels of data observability, integrity, and access will skyrocket. While not an easy thing to achieve, setting your sights on a high degree of data concurrency can radically shift how your organization operates its data system and empower your business.
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