TiDB Cloud
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Scalable and Efficient Database Solution for Modern AI Workloads
What do you like best about the product?
I really like how well TiDB works with both organized data and vector embeddings all in one distributed database. This let me handle research metadata and embedding vectors together without needing multiple systems, which made the overall architecture of InsightForge AI much simpler. I also value its strong consistency and quick query responses, which made sure the Retrieval-Augmented Generation process was reliable and accurate. Plus, TiDB's scalability helped me manage growing research data without slowing things down. Since it works well with SQL, integrating it was easy, allowing me to create a solid, efficient, and scalable backend. The SQL compatibility helped me design schemas, query research metadata, and integrate TiDB smoothly with my Node.js backend. The distributed architecture ensured high availability and consistent performance as I added more embedding data and research documents. I also used TiDB to store and retrieve vector embeddings efficiently, which allowed for accurate semantic search in my pipeline. Its ability to handle both structured queries and similarity-based searches was key for producing reliable, citation-backed research reports.
What do you dislike about the product?
One area where TiDB could improve is how easy it is to work with vector embeddings and semantic search processes. Although TiDB can store and query vector data, getting good performance for large-scale embedding searches often needs extra query tuning and changes to the database structure. Having more built-in tools for vector indexing, optimizing similarity searches, and monitoring performance would make development easier. Also, better documentation and examples for setting up Retrieval-Augmented Generational pipelines would help developers use TiDB more effectively. Improving debugging and observability when handling mixed workloads that include both SQL and vector queries would also make the overall experience better and simplify integration.
What problems is the product solving and how is that benefiting you?
I use TiDB for scalable storage and quick data retrieval, storing both structured metadata and vector embeddings. Its distributed nature ensures fast, consistent, and scalable operations, allowing efficient querying and integrating semantic vector searches with SQL queries.
Effortlessly Scalable, Robust MySQL Alternative
What do you like best about the product?
I like TiDB's ability to combine scalability, strong consistency, and MySQL compatibility in a single distributed SQL database. The horizontal scalability is particularly impressive because we can scale out by adding nodes without downtime or major architectural changes, which is ideal for growing applications with unpredictable workloads. I really appreciate TiDB's operational simplicity for a distributed database. The scalability, performance, and operational simplicity are what make TiDB most valuable to us. The ability to scale horizontally allows us to handle growing traffic and data without redesigning our system. Performance remains stable even under high concurrency, which is critical for our production workloads. Operational features like automatic failover, replication, and data rebalancing reduce maintenance overhead and minimize downtime risk. Overall, TiDB helps us grow confidently while keeping infrastructure management efficient and predictable.
What do you dislike about the product?
While TiDB performs well overall, there are a few areas that could be improved. First, operating a distributed database can still be complex, especially for smaller teams without strong DevOps experience. Although TiDB simplifies many aspects, understanding cluster tuning and resource planning requires a learning curve. Second, certain advanced query optimizations may require manual tuning in very complex workloads. Performance is strong, but fine-tuning for edge cases can take time.
What problems is the product solving and how is that benefiting you?
We use TiDB to handle high-traffic transactional workloads and real-time analytics, allowing us to scale horizontally without redesign. It solves performance bottlenecks under high concurrency and eliminates single points of failure with built-in replication and automatic failover.
Effortless Integration of Structured and Vector Data
What do you like best about the product?
I like how well TiDB handles both structured data and vector search in one place. It makes it easy to store shelter details and vector embeddings in the same database, allowing me to do semantic matching and apply filters like capacity and distance in a single query. The integration of relational and vector data management in one system significantly simplifies development and maintenance. TiDB Serverless is also great for its scalability and ease of setup, enabling me to quickly build and test systems without handling infrastructure setup. Despite constant updates from AI agents, the performance remains steady, making it reliable for real-time situations like disaster response.
What do you dislike about the product?
One area where TiDB could improve is by offering more detailed documentation and examples focused on vector search scenarios. Although the core features worked well, I initially needed to experiment with query structures, similarity thresholds, and indexing approaches to get accurate shelter matching results. More hands-on guides or best practices for real-world AI applications would make it easier for people to adopt and use TiDB effectively. Also, having better built-in observability for vector queries, like clearer performance metrics or explanations of similarity scores, would help with tuning and troubleshooting. Since my project involved multiple AI agents and real-time updates, greater transparency into how vector queries perform would make optimization easier.
What problems is the product solving and how is that benefiting you?
I use TiDB to handle structured data and vector embeddings in one database, allowing me to perform semantic and structured searches efficiently, manage real-time updates, and ensure reliability and responsiveness for disaster response.
Scalable, Efficient, and Developer-Friendly
What do you like best about the product?
I use TiDB as the main database for my project, Zesty, because it's reliable, scalable, and fast. I like that it handles scalability well; as the workload increases with growing user data, TiDB doesn't slow down, which is crucial for a growing AI platform. It also makes management easier by automatically dealing with complex tasks like database sharding and distribution. I really enjoy that TiDB scales easily without needing to change how my application works and handles heavy traffic and real-time data efficiently. Its strong consistency and dependable performance are also highly appreciated. TiDB feels like using a regular SQL database but runs as a strong distributed system, allowing me to use standard MySQL-like queries, which makes development feel easier and more familiar. Additionally, TiDB handles both transactional and analytical tasks without needing separate systems, which suits my needs perfectly.
What do you dislike about the product?
At first, understanding the distributed architecture took some time. More real-world examples of SaaS and AI in the documentation would be helpful. Performance tuning feels a bit complicated for beginners. Making some small changes to the onboarding process would improve the experience. It was hard to grasp how performance tuning works in a distributed system that includes TiDB, TiKV, and PD. There were a lot of metrics and dashboards, which made things a bit confusing for someone just starting out. A straightforward, step-by-step guide on performance tuning for typical SaaS or AI workloads would make it much easier for new users to get started.
What problems is the product solving and how is that benefiting you?
I use TiDB for its scalability and reliability in managing Zesty's growing user data. It handles heavy traffic, maintains strong consistency, and simplifies database management, allowing me to focus on developing features. TiDB combines SQL familiarity with distributed power and handles both transactional and analytical tasks without separate systems.
TiDB: Seamless Scalability with Transactional Consistency
What do you like best about the product?
I like that TiDB combines strong transactional consistency with horizontal scalability while supporting analytical queries in the same system. This is especially valuable for Branchat where we manage complex, evolving conversation structures and need fast insights from historical data. TiDB's MySQL compatibility, reliability, and reduced operational overhead make it easy to adopt and maintain, allowing us to scale confidently without redesigning our architecture. The initial setup was straightforward and smooth. Its MySQL compatibility made it easy to integrate with our existing development workflow, and the documentation helped us to get up and running quickly.
What do you dislike about the product?
If there's one area for improvement, it would be simplifying advanced configuration and tuning for newer users, especially around performance optimization at scale. Some distributed system concepts have a learning curve.
What problems is the product solving and how is that benefiting you?
TiDB solves scalability, consistency, and data complexity for us. It handles structured conversations with strong transactional consistency and scalable analytical queries. TiDB's horizontal scalability and reduced need for separate OLAP systems allow us to grow smoothly as data increases.
Effortless Setup and High-Speed Performance
What do you like best about the product?
I have used TiDB for high-speed caching and really enjoy its RAG service using vector DB inference features. The high-speed inference and easy SQL capabilities are great. I particularly like TiDB's AI helper, which has been a huge help in writing proper schemas. I also enjoy the clustering aspect of TiDB. Everything worked well for me, and I found the initial setup to be very easy.
What do you dislike about the product?
Nothing
What problems is the product solving and how is that benefiting you?
I use TiDB for high speed caching and RAG with its vector DB inference features, benefiting from high speed inference and easy SQL.
Easy to integrate cloude DB
What do you like best about the product?
It is free and easy to implemntation and speedy sql database
What do you dislike about the product?
I haven't found anything significant that I would consider a real dislike. as i was use multiple features in it.
What problems is the product solving and how is that benefiting you?
I am a student searching for a platform that offers something initially free and reliable for the future.
Scalable solution for transactional database usage
What do you like best about the product?
* Mostly MySQL compatible, makes it easier to migrate from mysql
* Much more scalable than single box mysql to support our business need
* Pingcap as the vendor is very responsive in answering our questions and address our concerns and feature request.
* Much more scalable than single box mysql to support our business need
* Pingcap as the vendor is very responsive in answering our questions and address our concerns and feature request.
What do you dislike about the product?
* Some of the surrounding components may still need some work, including ticdc and log backup, though I've heard some improvements have been done in newer releases
* Not supporting shared lock, this makes some implementation on our side harder to work around the issue
* Not supporting shared lock, this makes some implementation on our side harder to work around the issue
What problems is the product solving and how is that benefiting you?
Scalable solution to support nearly unbounded growth.
Down time free maintenance
Down time free maintenance
Effortless Scalability with Hybrid Workloads
What do you like best about the product?
TiDB makes it easy to scale horizontally without complex sharding. It supports both transactional and analytical workloads (HTAP) out of the box, and its MySQL compatibility means minimal changes to existing apps. The Kubernetes operator and built-in observability tools make operations smooth and efficient.
What do you dislike about the product?
TiDB has a learning curve, especially around its distributed architecture and tuning performance for complex queries. TiFlash setup and resource planning can be tricky at scale. Also, some advanced MySQL features aren’t fully supported yet, which can limit compatibility in certain edge cases.
What problems is the product solving and how is that benefiting you?
TiDB solves the challenge of scaling relational databases without giving up SQL or consistency. It allows us to handle high traffic and large datasets by scaling out horizontally, which wasn’t possible with traditional databases. The HTAP architecture also lets us run real-time analytics on live data, reducing the need for separate systems and improving decision-making speed.
A High-Performance Distributed Database Compatible with MySQL
What do you like best about the product?
The most upside of TiDB in my point of view is it's compatibility with MySQL , most of the MySQL syntax supported in it. So it is easy to integrate it with the existing applications and tools and easy to handle by the DBAs.
It is easy to migrate the MySQL data to the TiDB database through the migration tool TiDB DM. This makes it very feasible to implement TiDB alongside existing MySQL infrastructure with minimal disruption.
TiDB's DM feature and TiCDC are widely used features and it is frequently used where real-time analytics is needed on live transactional data.
As we know TiDB providing customer support through the active community on github and slack also provides the great documentation to understand the concepts and usage of tools to make it easy for the users.
It is easy to migrate the MySQL data to the TiDB database through the migration tool TiDB DM. This makes it very feasible to implement TiDB alongside existing MySQL infrastructure with minimal disruption.
TiDB's DM feature and TiCDC are widely used features and it is frequently used where real-time analytics is needed on live transactional data.
As we know TiDB providing customer support through the active community on github and slack also provides the great documentation to understand the concepts and usage of tools to make it easy for the users.
What do you dislike about the product?
The main cons in TiDB is it's cost. In many cases, customers are cost-sensitive, and TiDB's distributed architecture while powerful may appear less appealing compared to more lightweight, single-node database solutions.
Connecting with the existing system is easy with TiDB but still the TiKV needs optimization in some situations.
Connecting with the existing system is easy with TiDB but still the TiKV needs optimization in some situations.
What problems is the product solving and how is that benefiting you?
TiDB is used to solve the slowness in the real time analytical query executions and also in the high availability like it can withstand in fail overs. We expected the solution like the database which can be the MySQL compatible because some of the other databases need the huge data transform when migration, but with TiDB we can use the same data in MySQL without change the application queries and without downtime we can migrate with TiDB using TiDB DM.
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