Imagine your online marketplace as a bustling city. Buyers and sellers mingle, deals are struck, and everything runs smoothly. But what if, hidden beneath the surface, lurks a struggling database unfit for the task? Transactions grind to a halt, products vanish into the digital ether, and frustrated users flee for the greener pastures of a competitor’s marketplace.
This, dear marketplace entrepreneur, is the nightmare scenario of choosing the wrong database solution. In the complex world of marketplace development, every decision (whether technology, features or site architecture) carries weight. Your database selection will have a major impact on risks, such as resource wastage or lack of scalability, as well as your business goals.
Here’s the thing though: there’s no one-size-fits-all answer. The ideal database for your marketplace depends on a variety of factors, kind of like picking the right equipment for a sports activity. You’ll agree, anyone wearing football boots to a tennis game will sabotage their chances of winning, even if they have the best racket!
Factors to consider when designing a database system for your marketplace
Type of transactions? If your marketplace deals in lots of small transactions, a trusty relational database like MySQL is your best friend. It keeps things organised and predictable, perfect for basic retail marketplaces.
Product Palooza or One-Size-Fits-All? The more diverse your product range, the more complex your category system becomes. This can strain a relational database. You should keep this caveat in mind when you design your marketplace category structure.
Support for navigation features? Usually the search engine functionality will determine the database type. That’s why high-volume specialised search engines like ElasticSearch utilise NoSQL distributed databases to deliver near instant search results.
Speed (Throughput vs Concurrency vs Latency): Understanding the difference between the various components of speed is essential to database design. The total response time (speed) it takes to execute user queries can be influenced by throughput (the number of queries that can be processed in a certain amount of time), concurrency (the number of queries that can be processed simultaneously), and latency, which measures the time it takes for user queries to reach the server and vice versa.
For instance, interactive gaming sites and auction platforms with bidding features demand low latency for real-time interactions, while streaming services prioritise concurrency and throughput to deliver smooth content. Both are useful for browsing and video chatting.
Note that these different speed requirements also affect the makeup of the rest of your tech stack. For example, technologies like Socket.io, which utilises Websocket protocols, work together with databases to provide low-latency solutions to real-time features such as instant messaging.
Can your database handle increased loads? Scaling is one of the most significant challenges in marketplace development, with databases filling a key role.
One of the first questions you should ask yourself is which parts of your marketplace are you scaling? Different components of your marketplace will experience growth at varying rates. Is it the number of users, products, or transactions that’s exploding? Identifying the scaling bottleneck is crucial for database optimisation.
Next on the scaling checklist is whether your database requires vertical or horizontal scaling? When your database starts to struggle, you have two primary options: vertical scaling (adding more CPU or RAM resources to an existing SQL database server) or horizontal scaling (adding more NoSQL databases to distribute the load). The best approach depends on your specific needs and budget.
Vertical or horizontal scaling is not an either or scenario. Let’s say your marketplace is experiencing rapid user growth. Initially, you might opt for vertical scaling by upgrading your server’s hardware. However, if user numbers continue to soar, horizontal scaling by distributing the load across multiple servers becomes necessary. This is known as database sharding, which divides your data into smaller, more manageable chunks.
Selling products with constantly evolving (dynamic) features and attributes? Rigid relational databases might struggle. Here’s where NoSQL databases like MongoDB come into their own. They excel at handling unstructured data such as multimedia content (digital photos, audio, and video files), email message fields, text files, IoT log files, scientific data (surveys, scans, seismic signals), products with different types and numbers of attributes (cotton shirts with pockets vs woolen shirts with zippers), and user-generated content (social media, product reviews, customer support).
But hold on a minute, despite all these use cases, isn’t MongoDB missing something? In a word, yes, ACID. This zesty acronym stands for Atomicity, Consistency, Isolation and Durability – basically, the holy grail of reliable data transactions. While relational databases (also called transactional systems) excel in this area, MongoDB takes a more relaxed approach. So, if your marketplace relies on super-secure transactions, MongoDB might not be the knight in shining armour you seek.
Two common database pitfalls that trip up marketplace startups
Over-engineering your database is like buying those ridiculously expensive hiking boots for a stroll in the park – overkill and a waste of resources. On the other hand, under-engineering is like tackling Mount Everest in flip-flops – your database simply won’t be able to handle the climb as your business grows.
The sweet spot is a database that’s robust enough for your current needs, with the flexibility to scale up when your marketplace takes off. Think of it as a sturdy pair of hiking boots that can handle a leisurely stroll or a challenging trek, depending on your adventure.
PRO TIP! It’s best to map out your tech stack after a detailed discovery phase. Learn more about discovery in marketplace development here 👉 Why marketplace startups should test problem-solution fit with a low-fidelity MVP
Service marketplaces have unique database requirements
Building a service marketplace can be notoriously difficult due to their unique requirements, databases included. Services often require more specialised databases compared to product-based marketplaces due to a number of differentiating factors.
Service-Specific Attributes
Complex Service Descriptions: Services often have more intricate details than products. For example, a property management platform, such Nestify, might require specifications on room size, property layouts, and photo uploads. This necessitates flexible data structures to accommodate the different data types.
Skill and Expertise: Service providers often have a wide range of skills and expertise. The database should efficiently store and categorise these details for accurate matching with customer needs.
Availability and Scheduling: Service providers have specific working hours and availability. The database should be able to manage complex scheduling, considering factors like appointment duration, lead time, and recurring services.
Real-Time Updates and Availability
Dynamic Pricing: Service prices can fluctuate based on demand, time of day, or other factors. The database should support real-time updates and calculations.
Service Provider Availability: Service providers’ availability can change frequently. The database should efficiently handle updates and reflect real-time availability to customers.
Customer Bookings: As bookings are made, the database must update service provider availability and customer schedules in real-time.
Reviews and Ratings
Detailed Reviews: Service reviews often include more textual content than product reviews, requiring efficient storage and retrieval.
Skill-Based Ratings: Customers may want to rate specific attributes of service providers, such as the order process or quality of communication, necessitating a flexible rating system.
Geographic and Location-Based Services
Geolocation Data: Accurate location data for both service providers and customers is essential for matching and distance-based calculations. Ride-sharing apps like Uber, for example, use geospatial data to direct drivers to riders’ precise locations.
Service Areas: Defining service areas and ensuring efficient matching within those areas requires robust geographic data handling.
Payment and Invoicing
Complex Pricing Models: Service marketplaces often have more varied pricing models (hourly rates, project-based, etc.) compared to product marketplaces.
Invoicing and Payments: Handling different payment methods, taxes, and invoicing requirements can be complex and requires a well-structured database.
Database Options for Service Marketplaces
To address these unique requirements, service marketplaces might utilise one or a combination of the following database types:
- NoSQL databases for handling unstructured service descriptions and flexible data structures.
- Graph databases like Neo4j prioritise the relationships between different sets of data, which makes it ideal for managing complex relationships between service providers, customers, and services.
- Geo-spatial databases are efficient at supporting location-based services like farming, mining, transport and construction, which often require the visualisation of building footprints, transportation routes, or other points of interest.
- Real-time databases for handling dynamic pricing, availability updates, and real-time bookings. Another option is to use an an in-memory database like Redis as a cache to take pressure off the primary database.
Consequences of choosing the wrong database for your marketplace startup
It’s one thing to warn someone of a danger, but sometimes a concrete example can get the message across even better. So here are a few scenarios where the marketplaces didn’t do their database homework properly.
Scenario 1: Over-reliance on Relational Databases for a High-Traffic Marketplace
A rapidly-growing fashion marketplace selected a traditional relational database management system (RDBMS) like MySQL.
Consequences: As the marketplace gained popularity, the RDBMS struggled to handle the increasing volume of product listings, user data, and real-time searches. This led to slow load times, frequent system crashes, and a degraded user experience. The company eventually had to invest significant time and resources in migrating to a more scalable NoSQL database.
Scenario 2: Using NoSQL for a Transaction-Heavy Marketplace
A high-volume retail marketplace for digital goods used a NoSQL database like MongoDB.
Consequences: While MongoDB excels at handling unstructured data, it lacks strong ACID compliance, which is crucial for financial transactions. The marketplace experienced data inconsistencies, lost transactions, and a damaged reputation due to unreliable order processing.
Scenario 3: Ignoring Data Complexity and Choosing a Simpler Database
A niche marketplace for collectible items with complex product attributes (e.g. condition, authenticity, provenance) implemented a basic key-value database.
Consequences: Standard key-value databases don’t support complex queries, which limits how much you can filter and sort data before accessing it. The marketplace therefore struggled to efficiently store and manage product information, leading to inaccurate search results, difficulty in filtering products, and a poor user experience. This hindered sales and customer satisfaction.
Scenario 4: Neglecting Scalability in the Database Design
A fast-growing online marketplace for electronics invested in a single, powerful database server.
Consequences: As the marketplace expanded, the database became a performance bottleneck. The website experienced slow load times, frequent outages, and difficulty in handling peak traffic. The company had to invest heavily in infrastructure upgrades and database optimisation to recover.
Examples of successful marketplaces that leveraged database design to boost growth
FanPass, a hugely-successful event ticketing marketplace, didn’t settle for a one-size-fits-all solution. They built a diverse, but highly targeted, database ecosystem that helped to boost sales to £50,000 a day. Different database solutions are used to support the unique requirements of key features:
- NoSQL databases (ElasticSearch & Algolia), which allow search results to be ordered according to weights assigned to selected criteria, have improved the speed and accuracy of their popular autocomplete search.
- Cache database Redis is used for blazing-fast access to data that rarely changes.
- MySQL provides reliable storage for frequently updated data.
- Amazon S3 buckets hold all those event posters and seating diagrams photos.
Airbnb manages a massive amount of user data, property listings, and booking information through a combination of relational and NoSQL databases, ensuring both data integrity and lightning-fast search functionality.
Etsy relies heavily on MySQL for its vast product listings and user information, while utilising NoSQL solutions (like Cassandra) to handle the ever-growing volume of user reviews and product interactions.
Important! The caveat with such diverse database systems is that you must be super-organised with how you channel different data-sets to the correct database.
Yes, we know that’s quite a lot of information to digest, just for databases! So let’s recap:
What are the pros and cons of different database models?
Relational Databases (SQL)
Think of a relational database as a giant spreadsheet with interconnected tables. Each table stores information about a specific thing (e.g. products, users, or orders).
- Traditional e-commerce stores
- Banking and financial systems
- Inventory management systems
Dynamic Databases (NoSQL)
Dynamic databases are more flexible and can handle unstructured or semi-structured data. They are designed to scale horizontally and handle large volumes of data.
- Content management
- Real-time data queries
- IoT devices
- Mobile applications
Structure
Based on a column-row table structure. Multiple tables can be joined to create relationships between datasets.
Can be document-oriented, key-value pairs, graph structures, or wide-column data stores.
Complexity
Data is organised in a predictable way, making it easy to manage and query. Can become complex for large datasets or intricate relationships.
Can be more complex to manage and query due to their flexible nature.
Flexibility
Requires defining data structure upfront, which can be inflexible for evolving data needs.
Can adapt to changing or unpredictable data structures without major overhauls.
Reliability
Strong support for ACID properties (Atomicity, Consistency, Isolation, Durability), ensuring data integrity.
May not guarantee the same level of data consistency as relational databases, but offers high availability and throughput, since data is distributed over multiple servers.
Performance
SQL databases work harder to maintain consistency which can slow down performance.
NoSQL databases are generally faster since they have less resource constraints than SQL databases.
Scalability
Vertically scalability has its limits. Tricky to scale horizontally due to the challenge of keeping ACID properties consistent over multiple servers.
Horizontal scaling has a greater overall capacity than vertical scaling. Excellent for handling massive amounts of data and high intensity traffic.
Don’t forget to plan and test your database system properly
A last reminder to not embark on your marketplace adventure without a proper discovery phase. It is critical that you map out your entire tech stack, to ensure that your database system (SQL, NoSQL, or hybrid) offers suitable support for your platform’s current and future needs.
The work doesn’t stop there though. Once you have your first database up and running, it’s time to test how well it supports your users’ needs and ultimately meets your business objectives.
Start by identifying appropriate metrics for your database use case. This should be followed by implementing a database monitoring tool such Grafana or Datadog. Now you can run performance experiments under different conditions and scenarios. This is particularly valuable for stress testing your database system before a growth phase, as well as flagging existing issues that affect business metrics such shopping cart abandonment.
After analysing the results of your monitoring or experiments, you can make an informed decision about which aspects of your database should be optimised and how to go about it. It could, for instance, mean deciding between scaling vertically (adding CPU power), scaling horizontally (adding additional database servers) or perhaps optmising another component of your infrastructure (query optimisation, data caching).
The bottom-line is that the usual BUILD-MEASURE-LEARN loop applies as much to your database performance as it does to the rest of your marketplace.