New technology has allowed everyday businesses to generate large volumes of information. The demands placed on businesses by their customers (transactions), technology (application logs), analytics (business related), and IoT data (IoT devices), etc., as well as the fact that the amount of data being produced is increasing continuously create challenges for businesses as they use more data and grow their business by increasing their digital footprint through using more technology.
Many companies store all of their information in the same performance tier regardless of whether people have access to the data or not. This will typically lead to increased operational complexity, cost, and ultimately less overall productivity because the data cannot be accessed efficiently. By moving to a hot and/or cold data tiering strategy, you can optimize performance and cost efficiency.
By utilizing the appropriate level of planning and working with an experienced managed services provider for your MongoDB environment, you can design and organize your data effectively while maintaining a fast performance to your applications and having the ability to scale in the future.
To have a successful tiering strategy, it’s important to grasp how hot (interactive) data differ from cold (less used) data
Defining Hot Data
Hot data is described as being actively accessed, updated and/or queried by end users or different applications. Hot data is primarily used for real-time purposes, and thus requires high-performance infrastructure for rapid response time.
Common examples of hot data include:
- Transactions made by customers
- Current e-commerce orders
- Real-time analytics
- Frequently accessed user profiles
- Session-based application data
Due to the direct correlation between application delivery speed and the storage speed of Hot Data, it should remain in high-performance storage resources.
What cold data is
Cold data consists of data that has been around for a while, or infrequently accessed. However, it is still essential to keep that data because of regulatory mandates, compliance reviews, reporting or historical significance.
Examples of cold data would be as follows:
- archived log files
- historical all customer records
- old transaction records
- backup data set
- long-term analytical data set
Cold data is well suited on lower-performing storage devices because it does not require high-performance storage capabilities. Companies can save greatly on their total infrastructure costs by using less expensive tiers of storage for their cold data.
What is the Importance of Data Tiering in MongoDB
When the size of MongoDB increases, the expense of keeping all of the information in high performance storage results in inefficient use of space. Therefore, if companies use high performance storage for critical workloads, and archive databases will be kept in low performance storage, Data Tiering optimizes database storage and use.
The benefits of Smart (Data) Tiering are
1. Cost Reduction
With Low Tier (Cold Data) storage being used, companies will also be able to limit Cloud costs and will be able to optimize their storage.
2. Query Performance
Keeping the data that is accessed frequently (Hot Data) in performance optimized storage will improve speed and latency because of high performance storage.
3. Scalability
By utilizing a Storage Tiering strategy for their MongoDB Databases will allow organizations to scale up their databases without incurring additional Premium Infrastructure Costs.
4. Improved Resource Allocation
All Database resources, such as Memory, CPU and Storage will concentrate on Active workloads and not on storing Historical Data that is not Active.
Strategies for Smart Data Tiering in MongoDB
A successful storage tiering strategy involves a combination of Planning, Monitoring and Automation. Typically, Organizations that partner with a Mongodb consulting services vendor benefit from an optimized Architecture and proactive Management.
Understand your Data Access Patterns
In order to plan a successful Storage Tiering Strategy, the first step is to understand how the data is being used within the application.
Analyze the following:
- How Frequently will Queries be run?
- Age of the Data?
- What type of Reading/Writing Activities will occur?
- Storage Consumption?
- Reporting Requirements?
To be able to successfully identify Hot and Cold Data, you will need Monitoring tools to gather Performance Analytics.
Separate Collection of Historical Data from Active Data
One of the best ways to separate active and historical collections is to keep them available separately.
Examples would be separate collections or databases for:
- Active Orders
- Archived Orders
Doing this will keep operational queries much lighter, and reduce the indexing workload of any production systems.
Automate Your Archiving Process
Automation can greatly help reduce operational complexity. Organizations can create automated policies to assist with archiving records after they are older than some period of time.
Examples would be:
- Log records older than 90 days for example
- Inactive customer records archived
- Moving historical analytic records monthly
Automation will save time by eliminating any manual efforts and will also help ensure database performance is uniform across all systems.
Indexing Strategy Optimization
Indexes increase the speed of queries. However, creating indexes on cold data increases overall storage costs and maintenance effort.
Recommendations for optimizing the indexing strategy are:
- Only indexing data that is frequently queried
- Eliminating unused indexes from archives
- Using lightweight index types for reporting on historical data
Many professional MongoDB Managed Services providers will complete periodic index audits to help increase database efficiency.
Take Advantage of Cloud Storage Classes
With today’s cloud solutions, you have many choices (classifications) of storage options that perform at different levels of pricing.
You can store:
- Hot data with SSDs
- Warm data at a balanced level
- Cold data with archived storage
Having the ability to store data in these tiered modes will help businesses to maintain performance and manage their long-term overall expenses.
Monitor Database Performance Regularly
The data tiering process is ongoing and has to be updated on a regular basis as the usage of applications along with their workload, the way that the data is accessed has changed, and will continue to change, as companies grow.
Continuous monitoring allows a business to:
- Detect performance issues within the database
- Identify inefficient storage
- Review the policies related to archiving data
- Improve the efficiency of their queries
With the help from a managed service provider that is knowledgeable about MongoDB, companies will have access to continuous proactive monitoring along with performance tuning to keep their database running smoothly.
Issues with Data Tiering in MongoDB
While there are some positive things that can be gained from tiered storage, there are many other problems that companies may face when they implement tiered storage solutions.
Data Migration
Data that is large in size will need to be moved from one collection or tier of storage to another, which requires a great deal of planning to avoid downtime and poor consistency of data.
Query Optimization
Applications will need to ensure that archived data can be read efficiently when the need arises.
Compliance
Some industry sectors necessitate the long-term retention of data, which affects the construction of a storage architecture.
Infrastructure Management
The use of many tiers of storage will add to your company’s operational overhead if you have not automated your processes.
This is where partnering with a third-party MongoDB consultant can prove beneficial.
How Mydbops Can Help Organizations Reduce Their MongoDB Costs
Mydbops helps clients achieve a more efficient and cost-effective MongoDB architecture through deployment of and ongoing support for the most innovative optimization technologies while minimizing the complexity involved in managing the data layer of their environments.
Mydbops has years of experience supporting enterprise-level customers with MongoDB deployments by:
- Providing architecture designs for improved data tiering
- Optimizing indexing and querying enhancements
- Automating archival capabilities for data management
- Reducing infrastructure costs
- Enhancing the database’s scalability and reliability
- Continuously monitoring database performance
At Mydbops we provide an array of specialized MongoDB Managed Services tailored specifically to MongoDB. Each service has been designed to address ever-changing user needs and ensure that your systems maintain a high level of uptime and overall efficiency.
Final Note
Modern organizations face a challenge when it comes to the growing amount of data they generate. Without pre-planning storage and managing costs before implementing new applications, organizations will find that their end users’ productivity and/or total organization’s operational budget can be affected by high storage and/or application performance costs.
With the use of smart hot/cold data tier strategies in MongoDB, companies can save money by using less hardware yet still maintain high performance for their mission critical workloads. They can do this by separating out active/current data from historical data, and optimizing storage usage according to workload requirements.
Organizations looking to improve the performance, scalability, and cost efficiency of their MongoDB Databases can benefit greatly from working with experienced Mongodb consulting service providers like Mydbops. Through proactive optimization strategies and expert MongoDB Managed Services, businesses can build future-ready database environments that support long-term growth and operational success.
Visit Mydbops to learn more about advanced MongoDB solutions and contact the team today for expert database support and optimization services.
