If Database sharding sounds a bit complicated, it implies partitioning an on-prem server into multiple smaller servers, known as shards, each of which can carry different records. The concept is simplistic and enables scalability in distributed computing, but there are many factors to consider to derive the maximum benefit from it. Here’s a look at what database sharding means, its pros and cons, and the best practices to make it work.
The proliferation of smart devices worldwide, be it at homes or organizations, and the growing use of data has created a perfect data storm that organizations have had to contend with over the past decade. The arrival of cloud services and intense competition in the sector enabled organizations to find an alternative to expensive on-premise storage options. However, as it turns out, for specific use cases, on-premise solutions are becoming more affordable than the cloud itself.
The need to store, secure, structure, and analyze data is one of the top priorities for IT strategists, considering how vital historical data is for strategic decision-making and measuring performance. Whether hosted on-premise or in the cloud, data needs to be structured, organized and backed up to reduce inefficiencies, prepare for outages, and save costs. Database sharding has often been used to meet these needs. Here, we look at what database sharding means, how it can be optimally leveraged, and its alternatives.
See More: Why Enterprises Should Move on from Legacy Database Infrastructure
What Is Database Sharding?
Adi Gelvan, the CEO of Speedb, an Israeli startup providing drop-in data engines for NoSQL, says database sharding is a relatively simple way to store larger data sets and handle the increased load by separating the database into smaller parts. As data grows both in volume and importance, sharding helps businesses address the data growth challenge by splitting the dataset into logical pieces and running multiple datasets simultaneously.Â
The sharding process reduces the drag on system performance and improves or returns a database to a reasonable performance by absorbing large amounts of freshly-generated data. MongoDB says that sharding allows organizations to scaleOpens a new window databases “to handle the increased load to a nearly unlimited degree by providing increased read/write throughput, storage capacity, and high availability.†So, even if one shard becomes unavailable, the entire database remains functional as each shard is a replica set.
Another advantage of sharding is that, according to DigitalOcean, it facilitates horizontal scaling, where more machines are added to an existing stack to spread out the load and allow for more traffic and faster processing. It is a relatively cost-effective process compared to vertical scaling, where an existing server is expanded by adding more RAM or CPU to handle larger amounts of data.
When Sharding is the Problem, not the Answer
While sharding helps ease the load on a database and ensures a backup is in place, Gelvan says that sharding can only be a short-term option for scaling databases as sharding often takes on a life of its own, making it hard to manage the far larger number of data sets that the process creates.
“Sharding requires adding a new layer of code on top of the data engine (aka key-value storage engine), the software component used by databases to sort and index data. As the business continues to shard, maintaining the growing number of datasets becomes a significant challenge for developers who spend more and more time partitioning the data and distributing it among shards.
“The multiplicative nature of sharding leads to increased complexity and management overhead on top of regular maintenance. By making data engine maintenance a daily task, developers are being distracted from focusing on higher-value work. This creates a widespread problem of reduced efficiency that may impact profits, productivity, and the business’s ability to remain competitive in the marketplace,†he says.
According to MongoDB, as sharding requires additional machines and computing power over a single database server, each additional shard comes with higher costs, and the overall cost of the distributed database system becomes significant. Also, operating many shards introduces additional latency as the router must query each shard and merge the result (if the data required for the query is horizontally partitioned across multiple shards.)Â
Mike Dorfman, the CTO of Speedb, says that the need for developers to dedicate their time to sharding becomes greater often with NoSQL databases using key:value store technology. In the latter case, the underlying data engine used to sort and index the data for storage was often not designed for modern data architectures that scale significantly. “Scalability struggles also occur when applications with a massive number of very small files (vs a smaller number of massive sized files) scale, as increasingly happens with metadata whose storage can quickly dwarf the storage required for data volumes themselves,†he adds.
See More: Why the Future of Database Management Lies In Open Source
How to Do Sharding Right?
Gelvan says that database sharding isn’t the only solution to managing heavy workloads. “The goal is to shard when you want, not when you must,†he says. For example, sharding can be effective in replacing expensive servers with cheaper, smaller ones, creating specific isolated datasets, or replication. However, recent innovations have lessened or even eliminated the need for sharding.Â
Organizations can use newer approaches to database sharding to address concerns like the need to maintain individual shards at all times, high operating costs, and reduced efficiency. For instance, Speedb offers a data engine that can scale a single dataset into PBs without extra complexity or maintenance. This makes dataset maintenance a simple task rather than an all-consuming challenge that requires the involvement of the entire dev team.Â
“Based on a new data engine architecture, Speedb will simply continue to scale without any hiccups or breakdowns – even if the scale of the dataset grows beyond what was once considered too large,†says Gelvan.
Sharding can be a prudent option for small and medium businesses that do not need vast amounts of data to store or analyze to operate. They can make their servers more efficient by adding new shards, thereby increasing query response times, mitigating the impact of an outage, and using each shard for a different purpose as shards aren’t necessarily replicated.Â
Three Alternatives for Database Sharding
If database sharding doesn’t suit your organization’s needs, you can adopt various other approaches to scale your servers to accommodate larger datasets and perform more queries. Listed below are three alternatives you can choose:
Data Partitioning in relational DBs
If you’re using a relational database (that stores and helps access data points related to one another), you can set up partitioned tables to create multiple storage pools, each hosting different data sets that can be individually managed, updated and analyzed. This helps scale the server instance to handle large workloads without building and maintaining separate shards. The trade-off here is that multiple storage pools could create latency if the amount of data becomes too large.
Database clustering and load balancing
Another approach to reduce the load on individual servers and improve efficiency is the practice of database clustering. This involves connecting a single database with multiple servers and instances to ensure high availability and increase query response times. Since it enables data synchronization between different instances, users will have no difficulty in accessing the database if one of the instances suffers an outage. The use of load balancing also helps pre-allocate workloads for each machine connected to the database. This ensures that individual machines are not overburdened with increased traffic flows, and all the machines equally share the load.
Vertical cloud scaling
Vertical cloud scaling is a popular approach adopted by organizations worldwide to scale their servers. The approach involves adding high-performance CPUs, HDDs, and other components to existing cloud servers to enhance memory, processing power, networking, and other technical capabilities. This approach also accommodates replacing the entire existing server rack with a more powerful version to fulfill emerging needs. This is a high-cost approach but offers much-improved performance, reduced latency, and long-term cost efficiency.
Do you think database sharding is the best solution to help servers handle higher workloads? Let us know on LinkedInOpens a new window , TwitterOpens a new window , or FacebookOpens a new window . We’d love to hear from you!