{"id":26348,"date":"2026-10-08T09:52:52","date_gmt":"2026-10-08T16:52:52","guid":{"rendered":"https:\/\/www.pingcap.com\/?post_type=article&p=26348"},"modified":"2026-10-08T09:53:04","modified_gmt":"2026-10-08T16:53:04","slug":"unlocking-htap-real-time-analytics-with-tidb","status":"publish","type":"article","link":"https:\/\/www.pingcap.com\/ko\/article\/unlocking-htap-real-time-analytics-with-tidb\/","title":{"rendered":"Real-Time Analytics With TiDB: HTAP Use Cases and the Business Case for Going Unified"},"content":{"rendered":"

A dashboard that’s accurate as of last night’s batch job is a dashboard that’s already wrong by the time a fraud analyst, a store manager, or a plant supervisor looks at it. Real-time analytics with \ud2f0DB<\/a> means the query runs against the same data that was just written, not a delayed copy. If you’re still deciding whether an HTAP database is the right category at all, the mechanism is covered in depth on What Is an HTAP Database?<\/a>; this piece assumes that part and focuses on what changes in production once you adopt it.<\/p>\n\n\n\n

<\/span>How TiDB Keeps Analytics and Transactions in Sync<\/strong><\/span><\/h2>\n\n\n\n

TiDB runs OLTP writes against TiKV, a row-based store, while TiFlash, a columnar store, replicates that data in real time through the Multi-Raft Learner protocol. There’s no ETL job in between: TiFlash is a learner in the same Raft group as TiKV, so an analytical query sees committed transactions without a separate pipeline moving them. The sibling page above walks through this mechanism with a code example and a benchmark; here, the short version is enough to explain why every use case below can query live data.<\/p>\n\n\n\n

<\/span>Challenges with Separate Transactional and Analytical Systems<\/strong><\/span><\/h2>\n\n\n\n

Traditionally, organizations have been forced to maintain separate systems for transactional (OLTP) and analytical (OLAP) tasks. OLTP systems are optimized for fast, reliable processing of business transactions, ensuring data integrity and supporting high concurrency. They are the realm of relational databases like MySQL and PostgreSQL. On the other hand, OLAP systems focus on complex queries and analytics, used for business intelligence and reporting, often exploiting techniques like large-scale data scans, aggregation, and real-time analytics. Examples include data warehouses and big data technologies like Apache Hadoop.<\/p>\n\n\n\n

This separation poses significant challenges:<\/p>\n\n\n\n

    \n
  1. Data Latency: The ETL process that synchronizes data between OLTP and OLAP systems introduces latency. This delay prevents businesses from accessing the most recent data for crucial decisions.<\/li>\n\n\n\n
  2. Complexity and Cost: Maintaining two separate systems involves managing different infrastructures, databases, and toolsets.<\/li>\n\n\n\n
  3. Inconsistencies: Data moves between systems at intervals, increasing the risk of inconsistencies.<\/li>\n\n\n\n
  4. Scalability Issues: Scaling transactional and analytical systems independently can be challenging, since the two systems often respond differently to load.<\/li>\n<\/ol>\n\n\n\n

    <\/span>Benefits of Unified HTAP Architecture<\/strong><\/span><\/h2>\n\n\n\n

    Hybrid Transactional and Analytical Processing (HTAP) provides a solution to the challenges above by enabling unified architectures where both transactional and analytical queries can be processed on the same data store:<\/p>\n\n\n\n

      \n
    1. Real-time Analytics: HTAP systems eliminate the ETL process, so businesses can react to current trends and events as they happen.<\/li>\n\n\n\n
    2. Simplified Architecture: There’s no need to manage separate systems for OLTP and OLAP.<\/li>\n\n\n\n
    3. Consistency: Running both workloads on the same data set ensures data consistency.<\/li>\n\n\n\n
    4. Resource Efficiency: There’s no need to provision and manage separate resources for each workload.<\/li>\n<\/ol>\n\n\n\n
      <\/th>Strengths<\/th>Weaknesses<\/th><\/tr><\/thead>
      OLTP<\/td>High concurrency, data integrity, fast transactional processing<\/td>Not built for complex analytical queries; typically pairs with a separate warehouse<\/td><\/tr>
      OLAP<\/td>Optimized for large-scale queries, aggregates, reporting<\/td>Can’t handle high-frequency transactional updates; relies on scheduled ETL<\/td><\/tr>
      HTAP<\/td>Combines both, real-time analytics on live transactional data, simplified infrastructure<\/td>Harder to build: needs careful isolation and optimization to balance both workloads<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n

      <\/span>Real-Time Transaction Processing at Peak Load<\/strong><\/span><\/h2>\n\n\n\n

      Real-time fraud checks, live risk scoring, and anomaly detection all depend on one thing first: the database has to keep up during the exact moments when transaction volume spikes and stale data is least acceptable. MNC Bank<\/a> migrated its Motion Apps mobile banking database to TiDB specifically to handle month-end and payout-day traffic spikes, moving from 1 QPS in normal operation with severe bottlenecks at peak, to 10x throughput, 50% lower latency, and 85% faster backups. That headroom is the prerequisite for running fraud and risk queries against live data without the analytical load competing with transaction processing for resources.<\/p>\n\n\n\n

      <\/span>Real-Time Customer and Inventory Analytics<\/strong><\/span><\/h2>\n\n\n\n

      Rakuten<\/a> rebuilt its loyalty program on TiDB to close the gap between a customer’s transaction and the system’s ability to act on it, moving off an architecture where transactional and analytical data lived apart and the sync between them was the bottleneck. For retail and loyalty programs specifically, that means a purchase can inform the next recommendation or reward calculation immediately instead of after the next batch cycle, and inventory counts reflect what was actually sold a moment ago rather than what a nightly job last recorded.<\/p>\n\n\n\n

      <\/span>The Cost and Complexity Case for Going Unified<\/strong><\/span><\/h2>\n\n\n\n

      Running separate OLTP and OLAP systems means provisioning, patching, and monitoring two infrastructures instead of one, plus the ETL or CDC pipeline connecting them, which is itself a system that can fail, drift out of schema sync, or lag under load. Consolidating onto TiDB’s HTAP architecture removes that middle layer entirely: one cluster, one set of credentials and access policies, one monitoring surface, and no pipeline job to debug at 2 a.m. when the nightly analytics run doesn’t finish in time. The trade-off is real, since a unified system asks one engine to serve two access patterns well, but for workloads where the pipeline was the operational pain point rather than a genuine scaling boundary, removing it is the more direct fix.<\/p>\n\n\n\n

      <\/span>\uc790\uc8fc \ubb3b\ub294 \uc9c8\ubb38<\/strong><\/span><\/h2>\n\n\n\n

      What industries benefit most from real-time HTAP analytics?<\/strong><\/p>\n\n\n\n