¶ TiDB
¶ Database Technologies
¶ Introduction to TiDB: A Modern Approach to Distributed, Real-Time Data
There are moments in the evolution of technology when a new idea doesn’t simply improve an existing system, but redefines how people think about a problem. TiDB is one of those ideas. It arrived at a time when organizations were increasingly struggling to balance two conflicting demands: the need for strong consistency and the need for horizontal scalability. Traditional relational databases offered reliability and rich querying but struggled to scale without expensive hardware or layers of caching. NoSQL systems scaled effortlessly but required teams to accept weaker consistency models or limited query expressiveness. TiDB stepped into this divide with the promise that you could have both—true horizontal scaling and full SQL capabilities—without forcing unpleasant trade-offs.
TiDB was created by PingCAP with a clear philosophy: make distributed databases feel natural. In other words, give engineers the elasticity and resilience they expect from a cloud-native environment while keeping the familiarity of the relational model they’ve used for decades. It’s an ambitious goal, but the system’s architecture makes it possible. At its core, TiDB is a distributed SQL database built on a combination of a stateless SQL layer, a distributed storage engine called TiKV, and a placement-drivers component that orchestrates data distribution. Together, they form a system that behaves like a powerful, always-on relational database capable of expanding seamlessly as workloads grow.
One of the most striking things about TiDB is how it treats complexity. Instead of exposing users to the delicate details of distributed systems, it hides them behind an interface that feels immediately comfortable. Applications connect to TiDB as if they were connecting to a traditional relational database. SQL queries work as expected, transaction semantics remain familiar, and developers don’t need to rewrite code to get better performance. Behind the scenes, TiDB quietly balances data across nodes, manages replication, and handles failover. It is like using a tool that carries the weight for you, letting you focus on building your application instead of worrying about performance bottlenecks.
This simplicity has made TiDB a popular choice for organizations transitioning from monolithic database systems to cloud architectures. As companies expand, they often encounter the limits of single-node databases. Writes become slow, queries overload the system, replication becomes fragile, and downtime becomes risky. Adding more CPUs or memory delays the problem temporarily, but doesn’t solve it. TiDB offers a different mindset. Instead of upgrading vertically, you scale horizontally by adding more nodes. The system treats these additions as new workers in a team; each one takes on part of the load, improving performance and balancing traffic. Because of this, organizations that once struggled with data growth now find themselves able to absorb it without anxiety.
Just as important as scalability is how TiDB approaches consistency. Many distributed systems sacrifice strict consistency in favor of speed, but TiDB uses a model inspired by Google Spanner and its Percolator transaction processing. This means distributed transactions remain strongly consistent without requiring exotic hardware or complex setup. For application developers, this is liberating. They can rely on correct ordering, safe writes, and predictable behavior without needing to implement workarounds or layers of validation on top. For industries such as finance, logistics, gaming, analytics, and large-scale e-commerce, this is more than a convenience—it’s a requirement.
TiDB also stands out for its hybrid design that supports both OLTP and OLAP workloads. In many organizations, online transactional processing systems and analytical systems operate as separate worlds. One handles real-time requests; the other crunches data for insights. This separation often leads to data duplication, synchronization delays, integration challenges, and rising maintenance costs. TiDB’s architecture, especially with the introduction of TiFlash, offers an elegant alternative: the ability to run analytical queries on real-time data without impacting transactional workloads. TiFlash uses a columnar storage engine specifically optimized for analytics, while TiKV continues handling transactional storage. Both remain consistent, giving teams a unified platform where operational and analytical intelligence can coexist.
This dual capability has made TiDB attractive to businesses that produce enormous streams of data while still needing to run complex queries across that data. Instead of building massive ETL pipelines or maintaining specialized systems for every requirement, they rely on TiDB as a single, cohesive engine. It is not simply about convenience—although simplifying architectures is valuable in itself—but about enabling faster decision-making. In competitive markets, insights lose value when delayed. TiDB’s real-time operational analytics allow teams to see patterns as they unfold, not days later.
Beyond its technical strengths, TiDB reflects the energy and openness of modern open-source ecosystems. From its early days, it has been community-driven. Developers from around the world contribute code, propose new ideas, refine features, and push the platform forward. This shared effort creates a technology that grows faster than closed systems typically can. It also means that users are not locked into a vendor-controlled vision; they can explore the ecosystem, extend it, and tailor it to their needs. This flexibility has made TiDB a favorite among teams who want to build long-term, future-proof data architectures.
From a practical standpoint, TiDB eases the burden of database operations. Automatic failover, automated scheduling of data, seamless scaling, and online schema changes mean that maintenance tasks become far less painful. Traditional databases require careful orchestration for schema migrations, and even then, downtime can be unavoidable. TiDB allows schema changes while the system remains fully operational, which is invaluable for businesses with global operations where downtime in any region can have immediate impact. The platform embodies the idea that reliability shouldn’t be a luxury—it should be a built-in expectation.
Another aspect that sets TiDB apart is its focus on observability. Distributed systems are inherently complex, and debugging performance or storage issues can feel overwhelming without the right tools. TiDB includes metrics, dashboards, logs, and tracing features that give administrators a clear view into what the system is doing at any moment. Instead of guessing where the bottleneck lies, teams can pinpoint it, address it, and move forward with confidence. This visibility allows organizations to maintain performance even as they scale their clusters significantly.
What is especially compelling about TiDB is the range of stories from real-world users. Companies running massive e-commerce operations use TiDB to handle millions of concurrent transactions. Logistics platforms rely on it to track shipments and optimize routes in real time. Financial platforms use its strong consistency to maintain accurate records across distributed infrastructures. Gaming companies leverage its scalability during peak usage, such as launches or seasonal spikes. And analytics-driven businesses use TiFlash to uncover insights without impacting production traffic. These examples demonstrate that TiDB is not just a theoretical technology, but a practical solution for some of the most demanding data environments.
Across this 100-article course, you will dive into the many layers of TiDB: the architecture, the storage engines, the SQL layer, the distributed transaction model, deployment options, monitoring, performance tuning, scaling strategies, best practices, and real-world use cases. You will explore how to design schemas optimized for distributed storage, how to run heavy analytical queries efficiently, how to scale a cluster without downtime, and how to ensure high availability across multiple data centers. You will also learn how TiDB integrates with popular tools, how to build applications that leverage its unique strengths, and how to think about distributed SQL from first principles.
This journey will not only teach you how TiDB works, but how to reason about data in a distributed world. Modern systems are no longer monoliths. Data lives across regions, availability zones, servers, and services. Understanding TiDB will strengthen your intuition for large-scale systems and equip you with the knowledge to build robust, scalable solutions that remain dependable as workloads grow. You’ll see how concepts like Raft replication, columnar storage, auto-sharding, distributed indexing, and real-time analytics come together in a coherent way that feels both innovative and practical.
As technology expands and data volumes accelerate, organizations increasingly seek platforms that allow them to scale without compromising reliability. TiDB stands firmly in this category. It represents a shift toward databases that adapt to the dynamic, elastic, globally distributed nature of modern systems. It gives teams the power to grow without fear of hitting a ceiling, to analyze without waiting for batch processes, and to maintain consistency without sacrificing speed.
The story of TiDB is ultimately a story about freedom—the freedom to scale, the freedom to innovate, the freedom to run demanding applications on infrastructure that won’t hold you back. It empowers companies to treat their data not as a constraint, but as a driving force for smarter decisions, better performance, and more resilient architectures.
As you continue through this course, keep that perspective in mind. TiDB is more than a tool. It is a new way of thinking about data systems, one that aligns with the scale, speed, and demands of the modern world. Through these articles, you will gain both the conceptual understanding and the practical skills to harness its full potential. And once you do, you will see why TiDB has become one of the most exciting technologies in the landscape of modern database engineering.
¶ TiDB
¶ Beginner Level (Chapters 1-25)
1. Introduction to TiDB: What It Is and Its Key Features
2. Understanding the TiDB Architecture: Components and Concepts
3. Getting Started with TiDB: Installation and Setup
4. TiDB Data Model: Key-Value Store, SQL Layer, and Distributed Processing
5. Creating and Managing Databases in TiDB
6. Basic CRUD Operations in TiDB: Create, Read, Update, Delete
7. Understanding TiDB’s SQL Compatibility: MySQL Compatibility
8. The TiDB Cluster: Overview of Nodes, Regions, and Shards
9. Using TiDB’s SQL Interface for Basic Queries
10. Using TiDB with the MySQL Command-Line Client
11. Simple SQL Queries: SELECT, WHERE, and ORDER BY
12. Handling Data Types in TiDB: INT, VARCHAR, DATE, and More
13. Working with Indexes in TiDB: Types and Benefits
14. Creating and Managing Tables in TiDB
15. Inserting and Modifying Data in TiDB Tables
16. Filtering Data with WHERE Clauses in TiDB
17. Sorting and Grouping Data in TiDB: ORDER BY and GROUP BY
18. Join Operations in TiDB: INNER, LEFT, RIGHT, and FULL Joins
19. Basic Aggregation in TiDB: COUNT, SUM, AVG, MIN, and MAX
20. Transactions in TiDB: COMMIT and ROLLBACK
21. Using TiDB for Data Backup and Recovery
22. Basic Security Configuration in TiDB
23. TiDB’s User and Role Management System
24. Monitoring TiDB: Key Metrics and Health Checks
25. Exploring TiDB Documentation and Community Resources
¶ Intermediate Level (Chapters 26-50)
26. Understanding TiDB’s Distributed Transactions
27. TiDB’s Multi-Version Concurrency Control (MVCC)
28. Optimizing Queries in TiDB: Indexing and Query Execution Plans
29. Advanced SQL Queries in TiDB: Subqueries and Nested Queries
30. Using TiDB’s Data Types Effectively: JSON, ENUM, and More
31. Handling Time-Series Data in TiDB
32. Partitioning Tables in TiDB for Better Performance
33. Managing Large Datasets in TiDB
34. Configuring TiDB for High Availability
35. Replication in TiDB: Concepts and Setup
36. Horizontal Scaling in TiDB: Adding and Removing Nodes
37. Integrating TiDB with External Data Sources
38. Using TiDB’s TiKV and TiFlash Storage Engines
39. Performance Tuning for TiDB: Query Optimization
40. Using TiDB with MySQL-Compatible Tools and Libraries
41. Configuring and Managing TiDB’s Cluster Topology
42. Using TiDB’s Backup and Restore Tools
43. Ensuring Data Consistency in TiDB’s Distributed Environment
44. Advanced Data Management Techniques in TiDB
45. Understanding TiDB’s Distributed SQL Processing
46. Working with TiDB’s Data Consistency Models
47. Integrating TiDB with Apache Kafka for Real-Time Streaming
48. Using TiDB for ETL and Data Pipeline Management
49. Configuring TiDB for Optimal Write Throughput
50. Monitoring and Troubleshooting Performance Issues in TiDB
¶ Advanced Level (Chapters 51-75)
51. TiDB Internals: How the Query Engine Works
52. Optimizing TiDB for Large-Scale Deployments
53. Understanding and Managing TiDB’s Distributed Key-Value Store (TiKV)
54. Advanced Query Optimization in TiDB
55. Implementing Multi-Region Deployments with TiDB
56. Sharding Data with TiDB for Horizontal Scaling
57. Using TiDB for Big Data Analytics
58. Deep Dive into TiDB’s TiFlash Storage Engine for OLAP
59. Managing Multi-Tenant Environments with TiDB
60. Advanced Backup and Disaster Recovery Strategies for TiDB
61. Integrating TiDB with Apache Spark for Distributed Data Processing
62. Using TiDB for Real-Time Data Processing and Analytics
63. Building and Managing Large TiDB Clusters
64. Understanding TiDB’s Raft Consensus Protocol
65. Cross-Region and Multi-Cloud Deployments with TiDB
66. Managing and Securing TiDB in Production
67. Customizing TiDB’s Query Optimizer
68. Using TiDB with Kubernetes for Containerized Deployments
69. Advanced Security Practices in TiDB: Encryption and Auditing
70. Integrating TiDB with Machine Learning and AI Workflows
71. Scaling TiDB for Real-Time and Batch Processing Workloads
72. Using TiDB with Data Lakes and Data Warehouses
73. TiDB and Cloud-Native Architectures
74. Customizing TiDB’s Write-Ahead Log (WAL) and Storage Systems
75. Setting Up Multi-Cluster Replication with TiDB
¶ Expert Level (Chapters 76-100)
76. Advanced Performance Tuning for TiDB Clusters
77. Handling Global Data Distribution with TiDB
78. Custom Data Distribution Strategies in TiDB
79. Building a High-Availability Architecture with TiDB
80. TiDB’s Distributed Transactions: Best Practices and Case Studies
81. Optimizing TiDB’s Storage and Network Performance
82. Handling Massive Data Growth in TiDB
83. Building Complex Data Pipelines with TiDB
84. Implementing Consistent Data Models in Distributed Systems
85. Designing a Fault-Tolerant TiDB Cluster for Maximum Uptime
86. TiDB’s Role in Real-Time Analytics at Scale
87. High Throughput and Low Latency Techniques in TiDB
88. Handling Mixed Workloads (OLTP & OLAP) in TiDB
89. Building Real-Time Dashboards and Analytics on TiDB
90. Integrating TiDB with Apache Flink for Stream Processing
91. Implementing Eventual Consistency in TiDB
92. Scaling TiDB for IoT and Edge Computing Applications
93. TiDB and the Cloud: Best Practices for Deployment
94. Advanced Troubleshooting Techniques in TiDB
95. Building Custom Applications on TiDB: Case Studies
96. Automating TiDB Operations with DevOps Tools
97. Data Governance and Compliance in TiDB
98. Exploring TiDB’s Future: Upcoming Features and Roadmap
99. Using TiDB for Blockchain and Decentralized Applications
100. Contributing to TiDB’s Open Source Ecosystem: Development and Best Practices