Hi everyone. Welcome to the 6th chapter in our Tencent cloud practitioner course, introduction to TencentDB products. At the end of this chapter, you will have a better understanding of the database types and the features and functions of TencentDB products. The advantages and common use cases of TencentDB products, and the TencentDB billing modes. In this chapter will cover five sections. Basic database knowledge. TencentDB for relational databases. Tencent DB for non-relational databases. TencentDB services. And TencentDB billing modes. This video will cover the first section, basic database knowledge. Subsequent videos will cover the remaining four sections. Okay, let's get started with section 1, basic database knowledge. In this video will cover the different data types, database ranking, database trends and TencentDB products. Databases can be divided by data organization method into relational databases and non-relational databases. Databases can also be divided by architecture into centralized databases and distributed databases. Relational SQL stores data in relational tables, while non-relational NoSQL stores data in data sets such as, key values, JASON docs and hash tables. In terms of schema, relational SQL uses structured and predefined tables. While non-relational NoSQL uses dynamically adjusted and non-structured schema. Regarding scalability, relational SQL supports scaling up and provides higher processing capabilities. While non-relational NoSQL supports scaling out and allows the addition of more distributed notes. For data query, relational SQL supports standard query language. While non-relational NoSQL supports non-standard unstructured query language. The key feature of relational SQL is that it's ACID compliant. And the key feature of non-relational no SQL is that it's CAP and BASE compliant. The pros of relational SQL are that it's structured, transactional and easy to maintain. While the pros of non-relational NoSQL are that it supports high scalability, flexible adjustment and big data analytics. The cons of relational SQL are that it has low scalability, poor performance in high concurrency scenarios and no support for big data analytics. While the cons of non-relational NoSQL are that it has weak support for transaction processing and a lack of standardization. The top five mainstream relational databases in order are, ORACLE, SQL Server, MySQL, PostgresSQL and IBM Db2. Now let's take a look at the different types of mainstream NoSQL databases. Key value data bases, document databases, column databases and graph databases. A key value database uses hash tables to store keys and corresponding pointers that point to specific data. It's mainstream products are Redis and Memcashed. And its use cases include content cashing and online shopping carts. A document database stores data as documents in schema-free structures. It's mainstream products are MongoDB and CouchDB. And its use cases include application log systems and websites or blog platforms. A column database stores data by column with keys paired with pointers that point to multiple columns. It's mainstream products are HBase and Cassandra. And its use cases include distributed file systems and big data analytics. A graph database stores data by using graphs, with nodes storing entities and edges storing relationships between entities. It's mainstream products are, Neo4J, InfoGrid, and Infinite Graph. And its use cases include social networks and recommendation engines. This diagram demonstrates a centralized database structure. You have your access gateway that goes to your master library and monitoring agents which goes to your data storage and its configuration clusters, decision scheduling clusters and big data monitoring and analysis systems. All such interactions are conducted through the Tencent cloud management center. This diagram illustrates a distributed data structure. You have your application that goes through the console of public components and access gateway clusters. You have your availability zones and scheduling methods and your storage mechanisms such as HDFS. Here is the ranking of databases by popularity that is updated monthly. You can see that the database types mentioned previously, such as, Oracle, SQL Server, MySQL, PostgresSQL and IBM Db2, all rank at the top. Now let's move on to database trends. One prominent database trend is that databases are becoming integrated. Software and hardware are becoming integrated to form all-in-one database. NoSQL and SQL have been combined to form what's called NewSQL. NewSQL combines the advantages of NoSQL, which is its high scalability. And of SQL which is its relational models and ACID compliance, kept separate under older systems. OLAP and OLTP have been combined under new all-in one systems such as HTAP. Database management systems and AI are becoming increasingly integrated as well. Several breakthroughs have also occurred recently. Massive data processing has been enabled by big data analytics. While high performance has been enabled by clusters, distributed architectures and low-latency. Stability has been strengthened through data replication and disaster recovery. While the architecture has been enhanced by optimized kernels and cloud-native databases, which are built specifically for the cloud. Regarding relational databases, Tencent cloud supports MySQL, MariaDB, SQL Server, PostgreSQL, TDSQL and CynosDB. TDSQL and CynosDB are examples of the NewSQL paradigm. TDSQL features sharing, while CynosDB features distributed storage. Regarding NoSQL databases, Tencent cloud supports, Redis, Memcashed, MongoDB, CTSDB, HBase and TcaplusDB. Redis and Memcashed are used for cashing, while MongoDB is used for document storage. CTSDB is a time series database that can be used for logs and IOT. While HBase is a large NoSQL database that is suitable for big data analysis. And TcaplusDB is a distributed database that is suitable for gaming. Tencent cloud provides database services such as, DTS and TData. DTS is suitable for data transfers between different types of databases and data migration and hybrid cloud scenarios. While TData is suitable for private cloud use cases.
