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The traditional solution that stores all the data in one concentrated node has hardly satisfied the requirement of massive data scenario in three aspects, performance, availability and operation cost.

In performance, the relational database mostly uses B+ tree index. When the data amount exceeds the threshold, deeper index will increase the disk IO access number, and thereby, weaken the performance of query. In the same time, high concurrency requests also make the centralized database to be the greatest limitation of the system.

In availability, capacity can be expanded at a relatively low cost and any extent with stateless service, which can make all the pressure, at last, fall on the database. But the single data node or simple primary-replica structure has been harder and harder to take these pressures. Therefore, database availability has become the key to the whole system.

From the aspect of operation costs, when the data in a database instance has reached above the threshold, DBA’s operation pressure will also increase. The time cost of data backup and data recovery will be more uncontrollable with increasing amount of data. Generally, it is a relatively reasonable range for the data in single database case to be within 1TB.

Under the circumstance that traditional relational databases cannot satisfy the requirement of the Internet, there are more and more attempts to store the data in native distributed NoSQL. But its incompatibility with SQL and imperfection in ecosystem block it from defeating the relational database in the competition, so the relational database still holds an unshakable position.

Sharding refers to splitting the data in one database and storing them in multiple tables and databases according to some certain standard, so that the performance and availability can be improved. Both methods can effectively avoid the query limitation caused by data exceeding affordable threshold. What’s more, database sharding can also effectively disperse TPS. Table sharding, though cannot ease the database pressure, can provide possibilities to transfer distributed transactions to local transactions, since cross-database upgrades are once involved, distributed transactions can turn pretty tricky sometimes. The use of multiple primary-replica sharding method can effectively avoid the data concentrating on one node and increase the architecture availability.

Splitting data through database sharding and table sharding is an effective method to deal with high TPS and mass amount data system, because it can keep the data amount lower than the threshold and evacuate the traffic. Sharding method can be divided into vertical sharding and horizontal sharding.

Vertical Sharding

According to business sharding method, it is called vertical sharding, or longitudinal sharding, the core concept of which is to specialize databases for different uses. Before sharding, a database consists of many tables corresponding to different businesses. But after sharding, tables are categorized into different databases according to business, and the pressure is also separated into different databases. The diagram below has presented the solution to assign user tables and order tables to different databases by vertical sharding according to business need.

Vertical Sharding

Vertical sharding requires to adjust the architecture and design from time to time. Generally speaking, it is not soon enough to deal with fast changing needs from Internet business and not able to really solve the single-node problem. it can ease problems brought by the high data amount and concurrency amount, but cannot solve them completely. After vertical sharding, if the data amount in the table still exceeds the single node threshold, it should be further processed by horizontal sharding.

Horizontal Sharding

Horizontal sharding is also called transverse sharding. Compared with the categorization method according to business logic of vertical sharding, horizontal sharding categorizes data to multiple databases or tables according to some certain rules through certain fields, with each sharding containing only part of the data. For example, according to primary key sharding, even primary keys are put into the 0 database (or table) and odd primary keys are put into the 1 database (or table), which is illustrated as the following diagram.

Horizontal Sharding

Theoretically, horizontal sharding has overcome the limitation of data processing volume in single machine and can be extended relatively freely, so it can be taken as a standard solution to database sharding and table sharding.


Although data sharding solves problems regarding performance, availability, and backup recovery of single points, the distributed architecture has introduced new problems while gaining benefits.

One of the major challenges is that application development engineers and database administrators become extremely overwhelmed with all these operations after such a scattered way of data sharding. They need to know from which specific sub-table can they fetch the data needed.

Another challenge is that SQL that works correctly in one single-node database does not necessarily work correctly in a sharded database. For example, table splitting results in table name changes, or incorrect handling of operations such as paging, sorting, and aggregate grouping.

Cross-library transactions are also tricky for a distributed database cluster. Reasonable use of table splitting can minimize the use of local transactions while reducing the amount of data in a single table, and appropriate use of different tables in the same database can effectively avoid the trouble caused by distributed transactions. In scenarios where cross-library transactions cannot be avoided, some businesses might still be in the need to maintain transaction consistency. The XA-based distributed transactions are not used by Internet giants on a large scale because their performance cannot meet the needs in scenarios with high concurrency, and most of them use flexible transactions with ultimate consistency instead of strong consistent transactions.


The main design goal of the data sharding modular of Apache ShardingSphere is to try to reduce the influence of sharding, in order to let users use horizontal sharding database group like one database.

Application Scenarios

Mass data high concurrency in OLTP scenarios

Most relational databases use B+ tree indexes, but when the amount of data exceeds the threshold, the increase in index depth will also increase the number of I/O in accessing the disk, which will lower the query performance. Data sharding through ShardingSphere enables data stored in a single database to be dispersed into multiple databases or tables according to a business dimension, which improves performance. The ShardingSphere-JDBC access port can meet the performance requirements of high concurrency in OLTP scenarios.

Mass data real-time analysis in OLAP scenarios

In traditional database architecture, if users want to analyze data, they need to use ETL tools first, synchronize the data to the data platform, and then perform data analysis. However, ETL tools will greatly reduce the effectiveness of data analysis. ShardingSphere-Proxy provides support for static entry and heterogeneous languages, independent of application deployment, which is suitable for real-time analysis in OLAP scenarios.