This is putting a lot of pressure on the existing databases. Each shard contains a subset of the. Its Horizontal partitioning (often called sharding). Partitioning is commonly used in distributed databases and data warehouses, and is often implemented using techniques such as range partitioning, hash partitioning, or list partitioning. 1. In the case of MySQL, this means that each node is its own MySQL RDBMS, with its own set of data partitions. Sharding is a type of database partitioning that separates large databases into smaller, faster, and more manageable pieces called shards. Using some kind of third party library that encapsulates the partitioning of the data (like hibernate shards) Implementing it ourselves inside our application. Sharding is a way to split data in a distributed database system. For me this was one of the most confusing aspects of learning this stuff because they are often used interchangeably and there is a certain amount of overlap between the terms. Sharding, or database partitioning, is usually done to allow parallel processing of chunks of data. 1 do sharding by yourself. Sharding involves saving the partitioned data onto other computers and storage facilities. Horizontal scaling, also known as scale-out, refers to adding machines to share the data set and load. PostgreSQL allows you to declare that a table is divided into partitions. A primary key can be used as a sharding key. These shards are not only smaller, but also faster and hence easily manageable. Sharding is closely related to partitioning, and the terms are often used interchangeably. Sharding is to split a single table in multiple machine. By dividing a large table into smaller, individual tables, queries that access only a fraction of the data can run faster and use less CPU because there is less data to scan. Sharding can be performed and managed using (1) the elastic database tools libraries or (2) self. Introduction Modern innovations thrive on strategic data management. Sample code: Cloud Service Fundamentals in Windows Azure. I have a database in dedicated server. Sharding is commonly employed to improve scalability, distribute workload, and enhance performance for large-scale. It allows you to define a combination of sharded tables and unsharded tables. This is known as data sharding and it can be achieved through different strategies, each with its own tradeoffs. Your database is now causing the rest of your application to slow down. Sharding Key: A sharding key is a column of the database to be sharded. Choose a scheme that matches the data characteristics and query patterns, and avoid schemes that cause. These queries run in serial, not parallel execution. Database sharding is a technique for horizontally partitioning a large database into smaller and. Defining Database Sharding and Partitioning. Horizontal sharding. Take the example of Pizza (yes!!! your favorite food). Sharding is also a 1% feature. If Database sharding sounds a bit complicated, it implies partitioning an on-prem server into multiple smaller servers, known as shards, each of which can carry different records. In case of sharding the data might be nicely distributed and hence the queries. Cassandra is NOT a column oriented database. Relational schemas; Database partitioningSharding is a data tier architecture in which data is horizontally partitioned across independent databases. ReplicationThe distinction of horizontal vs vertical comes from the traditional tabular view of a database. Central to this strategy is database partitioning — serving as the backbone of today’s distributed database systems. Database sharding is also referred to as horizontal partitioning. Update 3: Building Scalable Databases: Pros and Cons of Various Database Sharding Schemes by Dare Obasanjo. If you work on an application that deals with time series data, specifically append-mostly time series data, you'll likely find this post about using Postgres range partitioning and Citus sharding together to scale time series workloads to be useful additional reading. A shard is an individual partition that exists on separate database server instance to spread load. Data sharding and partitioning are techniques to distribute and store data across multiple servers or nodes, improving performance, scalability, and availability. Sharding, or horizontal partitioning, is used to disperse the data among the data nodes located on commodity servers for effective management of big data on the cloud. Sharding is a database partitioning technique that involves breaking up a large database into smaller, more manageable parts called shards. 5. Sharding is similar to horizontal partitioning of data, but makes sure that that each partition is actually having a separate CPU and Memory allocated to it, as well as it can live as a separate. There are 5 types of distributed joins, as explained here, ordered from most preferred to least: This is the example you mentioned with the Countries table. pre-split the shard key range to ensure initial even distribution. Range-based sharding involves dividing data into contiguous ranges determined by the shard key values. Database sharding overcomes this limitation by splitting data into smaller chunks, called shards, and storing them across several database servers. We want to keep all data of a user on the same shard. database partitioning Splitting large databases into separate entities for faster retrieval. Database sharding is a technique for horizontal scaling of databases, where the data is split across multiple database instances, or shards, to improve performance and reduce the impact of large amounts of data on a single database. Difference between sharding and partitioning. Database partitioning vs. The following are the supportable features in Oracle Sharding. Partitioning assumes the partitions are on the same server. drop the original sharded collection. Sharding. However, instead of simply. When you partition a database, you provide the database system. William McKnight, in Information Management, 2014. Sharding is a strategy for scaling out your database by storing partitions of your data across multiple servers instead of putting everything on a single giant one. When you shard a database, you create. Database sharding is a type of database partitioning that separates large databases into smaller, faster, more easily managed parts called data shards. Each shard is held on a separate database server instance, spreading the load and reducing the response time. Each partition has its own name. In this technique, each shard is. It is essential to choose a sharding key that balances the load and distributes the data. A partitioning type is the method used by MariaDB to decide how rows are distributed over existing partitions. The first shard contains the following rows: store_ID. The main difference is that sharding implies the data is spread across multiple computers while partitioning is about grouping subsets of data within a single database instance. Database partitioning is the backbone of modern system design, which helps to improve scalability, manageability, and availability. Sharding, on the other hand, is a technique that involves distributing data across multiple nodes in a cluster based on a specific criterion, such as a shard key. Sharding is a way to split data in a distributed database system. e. The reasoning being is because partitioning is just a linear reduction in the amount of data, whereas B-Tree indexes results in a logarithmic reduction in the amount of data to search - which is a much smaller reduction comparatively. In this. MongoDB uses the shard key associated to the collection to partition the data into chunks owned by a specific shard. In this case, the records for stores with store IDs under 2000 are placed in one shard. The disadvantage is ultimately you are limited by what a single server can do. Data Partitioning; Database Sharding; Let us first discuss indexing followed by indexing and partitioning/ sharding. It is the mechanism to partition a table across one or more foreign servers. Database sharding is a useful database architecture pattern to use when the data stored in a database grows to an extent that it starts impacting the performance of the application. If this becomes an issue, you can easily migrate to sharding the data across multiple tables while not having to change the application because all the logic on how to retrieve and update the data is contained. However, system-managed sharding does not give the user any control on assignment of data to shards. A horizontal partition of data in a database is called a shard or database shard . sharding# Database partitioning deals with a single database instance, whereas sharding splits partitions (shards) across multiple database instances for scalability and availability. Geo. However, it does have a drawback with aggregating data across the multiple databases. 1. Note that the hashing algorithm is very different: PostgreSQL. A distributed SQL database needs to automatically partition the data in a table and distribute it across nodes. In this article, we’ll cover the basics of database sharding, its best use cases, and the different ways you can implement it. The meda data of each table (including schema, tags, etc. Partitioning a table using the SQL Server Management Studio Partitioning wizard. Figure 1 shows a stateless service with five instances distributed across a cluster using. Sharding, also known as horizontal partitioning, is a popular scale-out approach for relational databases. Replication may help with horizontal scaling of reads if you are OK to read data that potentially isn't the latest. However, a sharding key cannot be a. Each partition in our store is contained in a single shard, and each shard is replicated to a set of nodes. horizontal partitioning or sharding. Sharding is replicating [copying] the schema, and then dividing the data based on a shard key onto a separate database server instance, to spread the load. Sharding is the equivalent of “horizontal partitioning. A simple hashing function can be the modulus of the key and the number of shards. In general, it is best to prototype in InnoDB, grow the dataset until. 5. Database systems with large data sets or high throughput applications can challenge the capacity of a single server. Horizontal partitioning in blockchain sharding helps in converting the larger database into smaller and more efficient versions of the original while retaining the basic features. Almost all real-world systems consist of a database server that receives a lot of read requests and a non-negligible amount of write requests. One way to better distribute writes across a partition key space in DynamoDB is to expand the space. Within YugabyteDB partitioning is a user-defined, SQL-level concept, thus requiring an explicit definition through SQL. The schema of the table is replicated in every shard, and a unique portion of the whole table lives in. ; Each shard, on the other. For Cassandra, you can read it here and for MongoDB here (Btw if you don. Mỗi partitions có cùng schema và cột, nhưng cũng có các hàng hoàn toàn khác nhau. Later in the example, we will use a collection of books. DS has gained popularity over the past several years owing to the. Sharding is needed if a data set is too large to be stored in a single DB. 2 and earlier, if you must change a shard key after sharding a collection and cannot upgrade, the best option is to: dump all data from MongoDB into an external format. sharding allows for horizontal scaling of data writes by partitioning data across. 1. The shard catalog uses materialized views to automatically replicate changes to duplicated tables in all shards. However, horizontal partitioning is not the only option for achieving scalability. Introduction¶ This document discusses how sharding works in CouchDB along with how to safely add, move, remove, and create placement rules for shards and shard replicas. sharding in PostgreSQL. Like partitioning, sharding is also a method to divide off a database to be saved separately. The distribution used in system-managed sharding is intended to eliminate hot spots and provide uniform performance across shards. . After a failure is detected, it’s. In this model, documents with "close" shard key values are likely to be in the. Partitioning groups data. The following topics describe the sharding methods supported by Oracle Sharding: System-managed sharding is a sharding method which does not require the user to specify mapping of data to shards. Database partitioning is normally done for manageability, performance or availability [1] reasons, or for load balancing. These end customers are often referred to as "tenants". Sharding and Partitioning. The technique of partitioning a database over numerous computers is known as “database sharding,” and it is done with the goal of making an application more scalable. This makes it possible to scale the storage capacity of. Hyperscale computing is a computing architecture that can scale up or down quickly to meet increased demand on the system. The partitioning algorithm evenly and randomly. These partitions can then be stored, accessed, and managed. Each partition of data is called a shard. Sharding is a database server partitioning technique that can be used to distribute data across different servers in order to improve performance and scalability. Likewise, the data held in each is unique and independent of the data held in other. This key is responsible for partitioning the data. Probably write:read ratio is 7:3. For example, a database of university students may be sharded based on the first letter of. Database sharding is a powerful tool for optimizing the performance and scalability of a database. Data is automatically distributed across shards using partitioning by consistent hash. This enables them to execute a greater number of transactions per second. It helps in managing more transactions per. Database Sharding takes more work, but has the advantage. Even if you have not worked directly with this yet, this is a very important topic. A sharding key is an attribute or column that determines how the data is distributed among the shards. Partitioning by the hash of keys (timestamp in this case) Cassandra and MongoDB use MD5 as the Hash function for Sharding. The balancer migrates data between shards. In addition to vnode sharding, TDengine partitions the time-series data by time range. System-managed sharding uses partitioning by consistent hash to randomly distribute data across shards. Sharding is a database architecture pattern related to horizontal partitioning the practice of separating one table’s rows into multiple different tables, known as partitions. Database sharding allows you to distribute a single data set across multiple databases. Horizontally partitioning (sharding) data based on a partition key . # Example of. It seemed right to share a perspective on the question of "partitioning vs. We will also contrast it with Database partitioning that is often confused with sharding. It shouldn't be based on data that might change. Sharding vs. These queries run in serial, not parallel execution. In most distributed databases, the terms partitioning and sharding are used as synonyms. In this article we will talk about what database sharding is and how it works. Another advantage of sharding is being able to use the computational. Each partition has the. Assume we use 200 shards, we can find the shardID by userID % 200 . Neo4j sharding contains all of the fabric graphs (instances or databases) that are managed by a coordinating fabric database. SQL Server 2008 introduced a table partitioning wizard in SQL Server Management Studio. Sharding is possible with both SQL and NoSQL databases. Choose a scheme that matches the data characteristics and query patterns, and avoid schemes that cause. To choose the best method, you need to consider factors such as the size and growth rate of your data. For syntax and sample queries for horizontally partitioned data, see Querying horizontally partitioned data)Each partition holds a specific amount of data and is also called a shard. For example, a table of customers can be. High Availability: If an outage happens in sharded architecture, then only some specific shards will be. Database Sharding vs Database Partition The terms "sharding" and "partitioning" get thrown around a lot when talking about databases. All documents are assigned to a partition, and many documents are typically. This is a topic near and dear to me and I’m excited to think about it some this month. Sharding is a database partitioning technique where a large database is divided horizontally into smaller and more manageable parts called shards or partitions. Sharding is not implemented in MySQL, but can be done on top of MySQL. Although sharding and partitioning both break up a large database into smaller databases, there is a difference between the two methods. Horizontal Partitioning/Sharding. Overall, a database is sharded. Each partition has the same schema and columns, but also entirely different rows. Horizontal partitioning or sharding. Modern innovations thrive on strategic data management. If Database sharding sounds a bit complicated, it implies partitioning an on-prem server into multiple smaller servers, known as shards, each of which can carry different records. Now each partition sits on an entirely different physical machine, and under the control of a separate database instance with the same database schema. Let me elaborate. » Superior run-time performance using intelligent, data-dependent routing. Here, this partition is split to 3 tablets, in 3 ranges of yb_hash_code (): hash_split: [0x0000, 0x5555) goes from 0 to 21844, hash_split: [0x5555, 0xAAAA) from 21845 to 43689 and hash_split: [0xAAAA, 0xFFFF] from 43690 to 65535. The table that is divided is referred to as a partitioned table. In this strategy, we split the table data horizontally based on the range of values defined by the partition key. partitioning. Data in each shard does not have to share resources such as CPU or memory, and can be read or written in parallel. Sharding is a partitioning pattern for the NoSQL age. Sharding. Sharding is a type of partitioning, such as. . I am new to the database system design. Sharding which is also known as data partitioning works on…Database sharding is a horizontal scaling solution to manage load by managing reads and writes to the database. 1. How to use range partitioning & Citus sharding together for time series. With sharding or partitioning, you are not restricted to storing data on the memory of a single computer. Database sharding and partitioning are techniques used to manage large volumes of data, improving performance and scalability. SaaS architects must identify the mix of data partitioning strategies that will align the scale, isolation, performance, and compliance needs of your SaaS environment. The concept is simplistic and enables scalability in distributed computing, but there are many factors to consider to derive the maximum benefit from it. The partitioner determines how data is distributed across the nodes in a Cassandra cluster. Within a partitioned database, documents are formed into logical partitions by use of a partition key. Data sharding is the breakdown of data spread across multiple computers, either as horizontal or vertical partitioning. Sharding is a scale-out technique in which database tables are partitioned and each partition is hosted on its own RDBMS server. It can be either a single indexed column or multiple columns denoted by a value that determines the data division between the shards. Then I would try the regular partitioning via hash on vehicleNo first while enforcing the user_id key within the procedure. Oracle Sharding is implemented based on the Oracle Database partitioning feature. Sharding is actually a type of database partitioning, more specifically, Horizontal Partitioning. Sharding is a database architecture pattern related to horizontal partitioning — the practice of separating one table’s rows into multiple different tables, known as partitions. It is a partitioned row store. In the simplest sense, sharding your database involves breaking up your big database into many, much smaller databases that share nothing and can be spread. Horizontal Partitioning - Sharding (Topology 2): Data is partitioned horizontally to distribute rows across a scaled out data tier. Automatic failure detection and shard failover: Shard Manager can automatically detect server failures and network partition. I know that it is really hard to provide generic answer and things depend on factors like. Database sharding involves partitioning data across multiple servers, so each server contains a subset of the data. 1. Data partitioning or sharding is a technique of dividing data into independent components. Each shard contains a subset of the data, allowing for better performance and scalability. Products like elastics database queries and elastic database jobs have been created to fill this gap. Sharding With Azure Database for PostgreSQL Hyperscale. This initial. SHARDED means data is horizontally partitioned across the databases. Horizontal Partitioning and Sharding Horizontal partitioning separates rows by key fields; for example, all Arizona records are maintained in one index and New Mexico records in another, etc. To introduce horizontal scaling, the database is split into horizontal partitions, now called. Platform. If the partitioning mechanism that Azure Cosmos DB provides is not sufficient, you may need to shard the data at the application level. Excellent. Defining your partition key (also called a 'shard key' or 'distribution key') Sharding at the core is splitting your data up to where it resides in smaller chunks, spread across distinct separate buckets. Praveen M Dhulavvagol 1, Prasad M R 2, Niranjan C Ku ndur 3, Jagadisha N 4, S G Totad 5. Shard Manager supports spreading shard replicas across configurable fault domains, for instance, data center buildings for regional applications and regions for global applications. Database Sharding is the process where a huge Database is partitioned horizontally. There are many ways to split a dataset into shards. Sharding is a database architecture pattern related to horizontal partitioning, which is the practice of separating one table's rows into multiple different tables, known as partitions or shards. The biggest problem to solve when deciding the partitioning. Each physical database in such a configuration is called a shard. Sharding is usually a case of horizontal partitioning. Sharding on the other hand, and the load balancing of shards, is a storage level concept that is performed automatically by YugabyteDB based on your replication factor. When data is written to the table, a partitioning function will be used by MySQL to decide which partition to. 4. . To handle the high data volumes of time series data that cause the database to slow down over time, you can use sharding and partitioning together, splitting your data in 2 dimensions. Sharding can be used in system design interviews to help demonstrate a candidate’s understanding of scalability. This article series introduces and explains the concepts of data partitioning and sharding. 1 Answer. Sharding enables you to spread the load over more computers; reducing contention, and improving performance. Sharding is employed to distribute the database load across multiple servers, allowing for improved. For me this was one of the most confusing aspects of learning this stuff because they are often used interchangeably and there is a certain amount of overlap between the terms. Data partitioning, also known as data sharding or data segmentation, is the process of dividing a large dataset into smaller, more manageable subsets called partitions or shards. This article series introduces and explains the concepts of data partitioning and sharding. It’s a partitioning pattern that places each partition in potentially separate servers—potentially all over the world. A hashing function hashes the sharding key value, and the output maps data to a particular shard. In MongoDB 4. A primary key can be used as a sharding key. Sharding is a way to split data in a distributed database system. Study with Quizlet and memorize flashcards containing terms like Data partitioning (also known as sharding) is a technique to break up a big database (DB) into many smaller parts. Distributed. Each partition has the same schema and columns, but also entirely different rows. These smaller parts are called data shards. In the next step, you’ll create a new database, enable sharding for the database, and begin partitioning data in a collection. In this tutorial, we’ll discuss two methods for splitting databases into parts to manage them efficiently: sharding and partitioning. database-design. As I mentioned earlier in this guide, “sharding” is the process of distributing rows from one or more tables across multiple database instances on different servers. This key is an attribute of. The simplest way to implement sharding is to create a collection for each shard. To find the. For example, high query rates can exhaust the CPU. Partitioning could be a different database inside MySQL on the same server, or different tables, or even by column value in a singular table. Here, each partition is known as a shard and holds a specific subset of the data, such as all the orders for a specific set of customers. The core flow of data sharding is shown in the figure below: The main process is as follows: Obtain the SQL and parameters input by the user by parsing the database protocol package or JDBC driver;. What is sharding? Sharding is a type of database partitioning that separates large databases into smaller, faster, more easily managed parts. It have no direct impact on performance, making it rarely useful. As your data grows in size, the database will continue to. Database sharding is a technique to achieve horizontal scalability in large-scale systems. If you are using mongoDB as a backend for a REST interface, the best practice is to create on collection per resource. It seemed right to share a perspective on the question of "partitioning vs. Horizontal partitioning, also known as sharding, is the process of splitting a table into smaller and more manageable chunks based on a key column or a range of values. A shard is essentially a horizontal data partition that contains a. One may choose to keep all closed orders in a single table and open ones in a separate table i. Partitioning can significantly improve the performance, availability, and manageability of large-scale systems. The partition key is part of the document ID for documents within a partitioned database. How to use Citus to shard partitions on a single node. Partitioning 1. What is Database Sharding? | Hazelcast. Horizontal partitioning is when the table is split by rows, with different ranges of rows stored on different partitions. A logical shard is an atomic unit of. After 100k user information should go second database and server. CONNECT takes this notion a step further, by providing two types of partitioning:Partitioning and sharding data is a complex task, as there is no one-size-fits-all solution. The primary tool for this in the PostgreSQL ecosystem is the Citus extension. “Vertical partitioning” refers to the practice of sharding your database into groups related tables with each group living on its own database server. Most data is distributed such that each row appears in exactly one shard. Data Partitioning with Chunks. A distributed SQL database provides a service where you can query the global database without knowing where the rows are. Understanding Sharding. 4. Sharding (also known as Data Partitioning) is the process of splitting a large dataset into many small partitions which are placed on different machines. Each partition contains a subset of rows, and the partitions are typically distributed across multiple servers or storage devices. This article explores when to use each – or even to combine them for data-intensive applications. It has more features, more active users, and every day it collects more data. I am trying to grasp the different concepts of Database Partitioning and this is what I understood of it: Horizontal Partitioning/Sharding: Splitting a table into different tables that will contain a subset of the rows that were in the initial table (an example that I have seen a lot if splitting a Users table by Continent, like a sub table for North America,. Data Partitioning divides the data set and distributes the data over multiple servers or shards. Sharding is the process of breaking up large tables into smaller chunks called shards that are spread across multiple servers. Answer → One possible option of sharding the data is based upon the Regions. Sharding your database. Partitioning is a rather general concept and can be applied in many contexts. To horizontally partition our example table, we might place the first 500 rows on the first partition and the rest of the rows on the second, like so: Database sharding fixes all these issues by partitioning the data across multiple machines. Database sharding overcomes the limitations of a single database server. For this month’s PGSQL Phriday #011, Tomasz asked us to think about PostgreSQL partitioning vs. ". Vertical partitioning: It divide columns into multiple parts as mentioned in one of the above answers eg: columns related to user info, likes, comments, friends etc in social networking application. It is useful when no single machine can handle large modern-day workloads, by allowing you to scale horizontally. When you partition a table in MySQL, the table is split up into several logical units known as partitions, which are stored separately on disk. Sharding is a method of database partitioning that is utilized by blockchain organizations to increase scalability. . It is responsible for serving a portion of the overall workload. Sharding is necessary if a dataset is too large to be stored in a single database. Document collections provide a natural mechanism for partitioning data within a single database. You can scale the system out by adding further. Data sharding is the breakdown of data spread across multiple computers, either as horizontal or vertical partitioning. e. When we say we partition a database, we split our table into smaller, individual tables, so. , The. This means that the attributes of the Database. So the data in each partition is unique but the schema remains the same. It enables distribution and replication of data. Description of "Figure 17-2 Oracle Sharding Architecture". This is the most important assumption, and is the hardest to change in future. users do not need to be aware of the necessary concepts in the sharding strategy and sharding key and other database partitioning schemes. See moreSep 14, 2023Database partitioning is normally done for manageability, performance or availability reasons, as for load balancing. Figure 1. The term “shard” refers to a partition or subset of the. Partitioning based on UserID. Database sharding is a technique used to horizontally partition data across multiple database instances, or shards. Partitioning (aka sharding) Partitioning distributes data across multiple nodes in a cluster. This allows for horizontal scaling, as more shards can be added on new servers when needed. Mark Simms discusses partitioning schemes, sharding strategies, how to implement sharding, and SQL Database Federations, starting at 19:49. Sharding and Partitioning. It is primarily employed in large-scale, high-traffic systems to improve performance, scalability, and availability. Partition an App Service web app to avoid limits on the number of instances per App Service plan. . 1. You could store those books in a single. Data partitioning is influenced by both the multi-tenant model you're adopting and the different sharding. Sharding is a method for splitting a database and storing a single logical database in multiple databases to accelerate transaction processing. When a database is sharded, a replica of the schema is created. A distributed SQL database provides a service where you can query the global database without. Database Partitioning implements very basic optimization — the easiest way to improve database performance is to scan less data. " Each shard contains a subset of the data, and together they form the complete dataset. Both methods allow you to split a large database into smaller, more manageable databases and tables, but they differ in how they accomplish this. So far, the designs we've discussed have segmented database components based on whether they respond to write requests or not. The user-selected rule by which the division of data is accomplished is known as a partitioning function, which in MariaDB can be the modulus, simple matching against a set of ranges or value lists, an internal hashing function, or a linear hashing function. It is the process of splitting up a DB/table across multiple machines to improve the manageability, performance, availability and load balancing of an application. Our application is built on J2EE and EJB 2. We call this a "shard", which can also live in a totally separate database. Sharding is a method for distributing or partitioning data across multiple machines. There are many ways to split a dataset into shards. In MySQL, the term “partitioning” means splitting up individual tables of a database. Range-based sharding involves dividing data into contiguous ranges determined by the shard key values. Horizontal Data Partitioning / Sharding is a very important concept and is used in almost every production setup. For hashed sharding: The sharding operation creates empty chunks to cover the entire range of the shard key values and performs an initial chunk distribution. Some data within a database remains present in all shards, [a] but some appear only in a single shard. There are many approaches to storing data in multi-tenant environments. It is your responsibility to ensure that the replicas are identical across the databases. Auto sharding or data sharding is needed when a dataset is too big to be stored in a single. Sharding là một mẫu kiến trúc cơ sở dữ liệu liên quan đến phân vùng ngang - thực tế tách một hàng bảng Bảng thành nhiều bảng khác nhau, được gọi là partitions. It is used to achieve better consistency and reduce contention in our systems. In a key- or hashed -based sharding architecture, a database application uses a shard key to locate a shard. It allows for faster access to data and enables a database to handle larger workloads by distributing data and processing power across multiple servers. Sharding is actually a type of database partitioning, more specifically, Horizontal Partitioning.