Music In this unit, we are going to talk about two real world use cases.

Let's first look at company A.

Company A is one of the most famous social media companies in the world.

Company A has more than 1.2 million QPS and more than 9 billion accounts.

HBase was used to support more than 100 business use cases.

Company A's HBase clusters have 9,000 VMs, 50 production clusters, and 6 PBs of data.

One of the most typical HBase use cases is to record the administration history.

The administration history records user rule violations, the subsequent actions taken against the user and the timelines for each action.

The support for transaction is necessary to prevent repetitive punishments and guarantee the accuracy of the publicly available transparent report.

The first generation of solution was to use HBase, as well as many in-house systems, such as index service and transaction service, which provides the functionalities unavailable in HBase.

However, this leads to high costs, including high maintenance cost and high infra cost for complicated systems.

Moreover, not all of the missing functionalities can be addressed with a workaround, such as cross table strong consistency transaction.

In addition to that, it is very hard to upgrade HBase to the latest versions.

In the second generation solutions, Company A uses TiDB to replace HBase.

TiDB natively supports index management and transaction control.

After switching to TiDB, Company A is thrilled to find out that there is 3 to 5 times 99-percentile query latency reduction.

TiDB also provides standard SQL interface with native support of cross-table strong consistency transactions.

The operation tasks have also been greatly simplified.

Let's now look at company B.

Company B is a mobile gaming and internet service provider, which offers gaming services to multiple regions around the world.

Company B has more than 50 million users worldwide.

In Company B, various independent databases serve specific business modules respectively, for example, audience, rewards and user tags management.

Those data are used for gaming campaigns and user conversions, which is of great importance for Company B.

Currently, analytics across multiple databases is necessary to effectively manage advertising and conversions.

In the first generation of the solution, multiple Amazon Aurora RDS instances are used for conducting transactional queries: one Amazon Aurora instance for each business module.

There is also a single Amazon Redshift database used to perform analytical queries.

As the data volume increases, when the data volume of a single table increases to 100 million rows, maintenance becomes difficult.

There are three main challenges.

The first one is that, additional ETL jobs are needed, and Amazon Redshift is not effective in handling data updates in real-time.

Due to the increase in data volume, the solution was unable to meet the real-time advertising requirements.

Secondly, updating and maintaining Aurora RDS is hard.

The ops team could only do maintenance in the middle of the night.

Thirdly, Online Data Definition Language (DDL) operations with Aurora are inefficient.

Online DDL with Aurora usually took hours and sometimes failed due to lack of resources.

After switching to TiDB Cloud, one TiDB cluster is enough to replace multiple Amazon Aurora clusters, as well as the Amazon Redshift cluster.

The throughput is three times more than that of Aurora, with query and write capabilities both improved.

TiDB Cloud also supports horizontal scalability, strong consistency, and high availability.

There is no data latency issue with the gaming platform.

At the same time, TiDB's built-in columnar storage, TiFlash, improves analytics performance without changing the application.

The operation task has been significantly simplified, and there is no application changes That marks the end of the Introduction to TiDB course.

Thanks for staying with me.

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