Sharding vs. It is a way of splitting data into smaller pieces so that data can be efficiently accessed and managed. size of row; kind of data (strings, blobs, etc) active. The distribution used in system-managed sharding is intended to. A shard is a horizontal data partition that holds a portion of the complete data set and is thus in the responsibility of serving a portion of the overall demand. When data is written to the table, a partitioning function will be used by MySQL to decide. In this post, SingleStore Developer Advocate, Joe Karlsson, explains the differences between database sharding vs. Sharding is horizontal ( row wise) database partitioning as opposed to vertical ( column wise) partitioning which is Normalization. Database sharding is the easiest partition technique that can be used with SQL Server. The partitioner determines how data is distributed across the nodes in a Cassandra cluster. Database Sharding vs. Database sharding is the optimization of large databases by splitting data from a larger database table into multiple smaller tables (shards). Sample code: Cloud Service Fundamentals in Windows Azure. 2 Vertical partitioningDistributed SQL: Sharding and Partitioning in YugabyteDB. On the other hand, data partitioning is when the database is broken down. Database. But I didn't find any article about SQL Server. ; Product inventory data is separated into shards in this case depending on the product key. 1. Sharding is a form of horizontal partitioning, which means dividing a table or a collection of data by rows, not by columns. Sharding is a method of database partitioning that is utilized by blockchain organizations to increase scalability. configure sharding using a more ideal shard key. Again, let's discuss whether it is even relevant. Each shard contains a subset of the data, and together, they make up the complete dataset. 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. Data is automatically distributed across shards using partitioning by consistent hash. Step 4 — Partitioning Collection Data. Conclusion131. However, it does have a drawback with aggregating data across the multiple databases. Indexing is the process of storing the column values in a datastructure like B-Tree or Hashing. Oracle S harding is a data distribution system that provides advanced ways to partition the data across multiple servers, or shards, to deliver exceptional performance, availability, and scalability. Sharding, or say partitioning, is a technique widely used in distributed systems which logically splits data into partitions. ”. Data sharding and partitioning are techniques to distribute and store data across multiple servers or nodes, improving performance, scalability, and availability. shards and replication, system managed partitioning, single command deployment, and fine-grained rebalancing. This allows for the querying of smaller sets of data by using WHERE constraints to limit the number of tables or indexes scanned, resulting in much faster query response time despite large. 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. In this model, documents with "close" shard key values are likely to be in the same chunk or shard. 3. A simple hashing function can be the modulus of the key and the number of shards. 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. Partitioning solve some of the size challenges and reads from tables, but sharding is only way to really address all aspects of big databases including reads and. A shard is a horizontal data partition that holds a portion of the complete data set and is thus in the responsibility of serving a portion of the overall demand. Modern innovations thrive on strategic data management. Using Oracle Data Guard for shard catalog high availability is a recommended best practice. sharding" from someone in the Citus open source team, since we eat, sleep, and breathe sharding for Postgres. Partitioning or sharding during data extraction requires some best practices to be followed. A partition is a division of a logical database or its constituent elements into distinct independent parts. Table partitioning and columnstore indexes. Most importantly, sharding allows a DB to scale in line with its data growth. Introduction Modern innovations thrive on strategic data management. In the example above, using the customer ZIP. Database sharding is a strategy for scaling a database by breaking it into smaller, more manageable pieces, or “shards”. Partition (database) Partitioning options on a table in MySQL in the environment of the Adminer tool. Both methods allow you to split a large database into smaller, more manageable databases and tables, but they differ in how they accomplish this. whether Cassandra follows Horizontal partitioning (sharding) Technically, Cassandra is what you would call a "sharded" database, but it's almost never referred to in this way. Update 4: Why you don’t want to shard. In this partitioning, each partition is a separate data store , but all partitions have the same schema . Data sharding is the breakdown of data spread across multiple computers, either as horizontal or vertical partitioning. In a traditional database setup, we store in a single server. On the other hand, data partitioning is when the database is broken down. Central to this strategy is database partitioning — serving as the backbone of today’s distributed database systems. Horizontal partitioning is when the table is split by rows, with different ranges of rows stored on different partitions. The unsharded tables (like lookup tables) are freely joinable to sharded tables, and sharded tables may be joined to each other as long as the tables are joined by the shard key (no cross shard or self joins. Figure 1 shows a stateless service with five instances distributed across a cluster using. The meda data of each table (including schema, tags, etc. For example, a database of university students may be sharded based on the first letter of. Sharding is more general and is usually used when the database is split on several servers. partitioning. Sharding is to split a single table in multiple machine. Partitioning data into shards and distributing copies of each shard (called “shard. Overview. It is seen in CREATE TABLE (. Each node is assigned a set of partitions and hence the read/write throughput could be increased with parallelization. It’s an architectural pattern involving a process of splitting up (partitioning. This makes it possible to scale the storage capacity of. Sharding and Partitioning. Shard Management¶ 4. The database sharding examples below demonstrate how range sharding might work using the data from the store database. Replication may help with horizontal scaling of reads if you are OK to read data that potentially isn't the latest. 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;. Database Design and Management Database Schema. This process of partitioning is known as Vertical Sharding or Vertical Partitioning. Learn the similarities and differences between sharding and partitioning, understand the use cases. For example, if some queries request only names, and others request only addresses, then the names and addresses can be sharded onto separate servers. Horizontal sharding, otherwise known as range partitioning, is a technique which divides the data into rows based on a determined key or range of values. 1. Think less of sharding as a particular kind of partitioning, contrasted to vertical partitioning. The hash function can take more than one sharding key. Each physical node in the cluster stores several sharding units. Update 4: Why you don’t want to shard. Horizontal scaling allows for near-limitless. So far, the designs we've discussed have segmented database components based on whether they respond to write requests or not. Choose a scheme that matches the data characteristics and query patterns, and avoid schemes that cause. Sharded vs. Oracle Sharding supports system-managed, user defined, or composite sharding methods. Database Sharding takes more work, but has the advantage. MongoDB uses the shard key associated to the collection to partition the data into chunks owned by a specific shard. Database sharding is a database architecture strategy used to divide and distribute data across multiple database instances or servers. Data sharding is a type of horizontal partitioning, which means splitting a large table or collection into smaller chunks, called shards, based on a key or a range of values. Database sharding is the easiest partition technique that can be used with SQL Server. It is used to achieve better consistency and reduce contention in our systems. A distributed SQL database provides a service where you can query the global database without knowing where the rows are. Its Horizontal partitioning (often called sharding). I am happy to discuss any of the above in more detail, but only in a more focused context. The partitioning algorithm evenly and randomly. For example, a range partitioning scheme for a customer database might partition customers based on their country or region of residence. In horizontal partitioning, also called sharding, each partition holds data for a subset of the total data set. sharding in PostgreSQL. For the open orders, order data may be in one vertical partition and fulfilment data in a separate partition. partitioning. Sample code: Cloud Service Fundamentals in Windows Azure. Sharding physically organizes the data. This article explores when to use each – or even to combine them for data-intensive applications. sharding# Database partitioning deals with a single database instance, whereas sharding splits partitions (shards) across multiple database instances for scalability and availability. The unit for data movement and balance is a sharding unit. Sharding is a technique to distribute large amounts of identically structured data across a number of independent databases. Sharding is a method for distributing data across multiple machines. Choose a scheme that matches the data characteristics and query patterns, and avoid schemes that cause. For true sharding then Skype's pl/proxy is probably the best. It separates very large databases into smaller, faster and more easily managed parts called data shards. Database sharding is a technique used to distribute the data in a database across multiple servers, or shards, in order to improve scalability and performance. These shards are not only smaller, but also faster and hence easily manageable. Take the example of Pizza (yes!!! your favorite food). The advantage of such a distributed database design is being able to provide infinite scalability. It is primarily employed in large-scale, high-traffic systems to improve performance, scalability, and availability. Horizontal partitioning is another term for sharding. For data belonging to Europe region, we can house all the data at Shard-B. Sharding is a database partitioning technique that involves horizontally breaking a large database into smaller, more manageable pieces called “shards. Solutions. Horizontal Partitioning or Database Sharding. Horizontal Partitioning (Sharding): In horizontal partitioning, the database is divided into smaller parts or "shards" based on the. Partitioning and Sharding are similar concepts. Jump to: What is database sharding? Evaluating. Each shard is a separate database, stored on a different server, and only contains a portion of the total data. Relational schemas; Database partitioningSharding is a data tier architecture in which data is horizontally partitioned across independent databases. For Cassandra, you can read it here and for MongoDB here (Btw if you don. We can think of this like a proxy server that handles requests and connection information. With schema-based sharding, you can easily achieve this or prepared for it upfront by assigning each group to its own schema and scale out only when necessary (and avoid all the growing. Sharding is a type of partitioning, such as. Figure 1 is an example of a sharding database. Sales data of 50 states of a country are split into four shards, each containing. Database sharding is a technique used to horizontally partition data across multiple database instances, or shards. A chunk consists of a range of sharded data. Sharding is a type of database partitioning that separates large databases into smaller, faster, and more easily managed parts. 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. 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. Sharding is a method for splitting a database and storing a single logical database in multiple databases to accelerate transaction processing. Sharding. 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. Document collections provide a natural mechanism for partitioning data within a single database. This article series introduces and explains the concepts of data partitioning and sharding. However, a sharding key cannot be a primary key. Now each partition sits on an entirely different physical machine, and under the control of a separate database instance with the same database schema. This article explains the relationship between logical and physical partitions. Data partitioning or sharding is a technique of dividing data into independent components. Overall, a database is sharded. This article explores when to use each – or even to combine them for data-intensive applications. With partitioning, we accomplish this scaling by inserting data into many small tables (with associated indexes) and limited scopes of data per table. Once you have determined your sharding strategy, you need to create your shards. Database sharding is also referred to as horizontal partitioning. In the example provided by Digital Ocean, data A and B are placed in one shard, while data C and D are placed in another. Then, this partition key token is used to determine and distribute the row data within the ring. By contrast, sharding offers unlimited scalability. To introduce horizontal scaling, the database is split into horizontal partitions, now called. Each shard is held on a separate database server instance, to spread load. Limitation of Horizontal Partitioning Horizontal Partitioning is frequently used in Distributed Systems. As your data grows in size, the database will continue to. Introduction. The following topics describe the physical organization of a sharded database: Sharding as Distributed Partitioning. It shouldn't be based on data that might change. A primary key can be used as a sharding key. Database sharding allows you to distribute a single data set across multiple databases. Sharding and partitioning both separate large datasets into smaller subsets. Vertical sharding — Vertical partitioning on the other hand refers to division of columns into multiple tables. If the partitioning mechanism that Azure Cosmos DB provides is not sufficient, you may need to shard the data at the application level. Figure 1. Oracle Sharding supports system-managed, user defined, or composite. When we say we partition a database, we split our table into smaller, individual tables, so. Database sharding is a powerful tool for optimizing the performance and scalability of a database. The schema of the table is replicated in every shard, and a unique portion of the whole table lives in. Horizontal partitioning and sharding. Each partition of data is called a shard. Sharding can offer several advantages for data partitioning and replication, such as reducing the load and contention on a single server or database, increasing the. In addition to the partitioned data stored across every shard in the cluster. Amazon Relational Database Service (Amazon RDS) is a managed relational database service that provides great features to make sharding easy to use in the cloud. The partitioning algorithm evenly and randomly. 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. A shard is an individual partition that exists on separate database server instance to spread load. 1 Answer. For a vertical partitioning tutorial, see Getting started with cross-database query (vertical partitioning). I know that it is really hard to provide generic answer and things depend on factors like. Sharding, also known as horizontal partitioning, is a database partition approach that divides the database schema and distributes them across multiple instances or servers into smaller parts that are faster and easier. While partitioning is a generic term for data splitting in a database, sharding is used for a specific type of partitioning, popularly known as horizontal partitioning. You can scale the system out by adding further. There are many ways to split a dataset into shards. There are three typical strategies for partitioning data: Horizontal partitioning (often called sharding). Each partition contains a subset of rows, and the partitions are typically distributed across multiple servers or storage devices. Horizontal Partitioning - Sharding (Topology 2): Data is partitioned horizontally to distribute rows across a scaled out data tier. 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. Data in each shard does not have to share resources such as CPU or memory, and can be read or written. For data belonging to Asia region, we can house all the data at Shard-A. For 20+ years of database and application development, time-series data has always been at the heart of the products I work with. A distributed SQL database provides a service where you can query the global database without. Data partitioning criteria and the partitioning strategy decide how the dataset is divided. For example, a table of customers can be. MongoDB uses sharding to support deployments with very large data sets and high throughput operations. Sharding is a more complex and powerful technique that can distribute data across multiple servers, providing better scalability, availability, and performance. Data sharding. It is essential to choose a sharding key that balances the load and distributes the data. Then I would try the regular partitioning via hash on vehicleNo first while enforcing the user_id key within the procedure. , The. Sharding is closely related to partitioning, and the terms are often used interchangeably. A shard typically contains items that fall within a specified range determined by one or more attributes of the data. Horizontal partitioning, also known as Data Sharding, splits a database by rows into separate databases. This means that the attributes of the Database will remain the same but only the records will change. The correct way to scale writes is sharding as you gave. The shard catalog uses materialized views to automatically replicate changes to duplicated tables in all shards. 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. " Each shard contains a subset of the data, and together they form the complete dataset. Horizontal Data Partitioning / Sharding is a very important concept and is used in almost every production setup. Each shard can then be hosted on a separate server,. This allows for horizontal scaling, as more shards can be added on new servers when needed. A distributed SQL database needs to automatically partition the data in a table and distribute it across nodes. Considering performance only, can a MySQL Cluster beat a custom data sharding MySQL solution? sharding = horizontal partitioning. Sharding in database is the ability to horizontally partition data across one more database shards. sharding in PostgreSQL. Sharding can improve. Sharding, also known as horizontal partitioning, is a database partition approach that divides the database schema and distributes them across multiple instances or servers into smaller parts that are faster and easier to manage. It's not necessary to understand these. As your data grows in size, the database. Sharding is a database scaling technique based on horizontal partitioning of data across multiple independent physical databases. What is Database Sharding? | Hazelcast. A distributed SQL database provides a service where you can query the global database without knowing where the rows are. partitioning. A shard is a horizontal partition of data in a database. This is termed as sharding. Database sharding is a type of horizontal partitioning that splits large databases into smaller components, which are faster and easier to manage. Each. It can also be termed as horizontal partitioning because sharding is basically horizontal partitioning across different physical machines/nodes. Each partition is known as a "shard". Each partition (also called a shard) contains a subset of data. Auto sharding or data sharding is needed when a dataset is too big to be stored in a single. The partitioning algorithm evenly and randomly distributes data across shards. Pattern 5 - Partitioning: You know that your location database is something which is getting high write & read traffic. Each shard has the same database schema as the original database. Database sharding is a type of horizontal partitioning that splits large databases into smaller components, which are faster and easier to manage. We want to keep all data of a user on the same shard. 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. 4: Table A is split horizontally into two tables. / Database / Resources / Sự khác biệt giữa các khái niệm trong database: replication, partitioning, clustering và sharding. However, instead of simply. Partitioning can help with larger tables but only when a small part of the data is hot. The balancer migrates data between shards. It is a mechanism to achieve distributed systems. This is particularly the case when it comes to heavy write contention, database locking and heavy queries. Horizontal partitioning is often referred as Database Sharding. Consistent hashing is a technique widely used in load balancing and routing service. 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. 1. Partitioning, Sharding là một hình thức của clustering trong đó tất cả các node trong cluster có schema và data giống nhau / giống hệt nhau/ được chia nhỏ và. Database sharding overcomes the limitations of a single database server. When doing a join across sharded tables what you generally want to optimize for is the amount of data being transferred across the shards. It uses some key to partition the data. For data belonging to America region, we can house this data at Shard-C. Sharding can be performed and managed using (1) the elastic database tools libraries or (2) self. What is sharding? Sharding is a type of database partitioning that separates large databases into smaller, faster, more easily managed parts. Sharding is possible with both SQL and NoSQL databases. One way to better distribute writes across a partition key space in DynamoDB is to expand the space. 5. Sharding is a database server partitioning technique that can be used to distribute data across different servers in order to improve performance and scalability. It helps in managing more transactions per. Partitioning schemes and data replication strategies. The partition key is part of the document ID for documents within a partitioned database. Assume we use 200 shards, we can find the shardID by userID % 200 . Sharding is a form of horizontal partitioning, which means dividing a table or a collection of data by rows, not by columns. It enables distribution and replication of data. Difference between sharding and partitioning. When a database is sharded, partitions are stored and managed by discrete servers that may run in different VMs, zones, or regions. It allows you to define a combination of sharded tables and unsharded tables. Sharding is used when Partitioning is not possible any more, e. This partitioning technique offers several. - Horizontally partitioning (sharding) data based on a partition key . database partitioning Splitting large databases into separate entities for faster retrieval. 1 do sharding by yourself. When you partition a database, you provide the database system. Because Oracle Sharding is based on table partitioning, all of the sub-partitioning methods provided by Oracle Database are also supported by Oracle Sharding. “Vertical partitioning” refers to the practice of sharding your database into groups related tables with each group living on its own database server. You can use numInitialChunks option to specify a different number of initial chunks. The topic of this month's PGSQL Phriday #011 community blogging event is partitioning vs. In the case of MySQL, this means that each node is its own MySQL RDBMS, with its own set of data partitions. In Redis, data sharding (partitioning) is the technique to split all data across multiple Redis instances so that every instance will only contain a subset of the keys. Ta có 3 cách thức Sharding dữ liệu như sau: Horizontal sharding. Your database is now causing the rest of your application to slow down. A database can be partitioned horizontally, vertically, or functionally. 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 enables you to spread the load over more computers; reducing contention, and improving performance. Data Partitioning. We’ll detail the tooling, linters, and Rails improvements related to this in a future blog post. Hyperscale computing is a computing architecture that can scale up or down quickly to meet increased demand on the system. two horizontal partitions. If the partitioning mechanism that Azure Cosmos DB provides is not sufficient, you may need to shard the data at the application level. Database sharding is a technique used to horizontally partition large databases into smaller, more manageable pieces called "shards. It seemed right to share a perspective on the question of "partitioning vs. Database sharding overcomes this limitation by splitting data into smaller chunks, called shards, and storing them across several database servers. Database sharding might be the answer to your problems, but many people. This reduces the reading of unnecessary data, and allows for efficiently implementing. U think dbms can support this. However, while both are often used interchangeably, partitioning expects the data divided off to be stored on the same computer. And I want copy the database to 10 databases in 10 dedicated servers. In this article, we will explore the concept of database sharding in Java and discuss some design patterns that can be. But these terms are used for different architectural concepts. I say this having worked with tables that were in the 10s of billions of rows without partitioning and were. Sharding is an alternative approach for scaling databases, which divides the database into smaller pieces called shards. e. It is a productive approach to distributed database sharding and offers a. Sample application that includes a sharded database. Each partition is known as a "shard". Although sharding and partitioning both break up a large database into smaller databases, there is a difference between the two methods. 1 Benefits of sharding. SaaS architects must identify the mix of data partitioning strategies that will align the scale, isolation, performance, and compliance needs of your SaaS environment. Database sharding is a technique to achieve horizontal scalability in large-scale systems. How to use Citus to shard partitions on a single node. » All of the advantages of sharding without sacrificing the capabilities of an enterprise RDBMS, including: relational schema, SQL, and other programmatic. Database Sharding. Each shard (or server) acts as the single source for this subset. Database Sharding is the process where a huge Database is partitioned horizontally. Stores possessing IDs of 2001 and greater go in the other. Range-based sharding involves dividing data into contiguous ranges determined by the shard key values. PostgreSQL allows you to declare that a table is divided into partitions. Data partitioning to data. The topic of this month's PGSQL Phriday #011 community blogging event is partitioning vs. Neo4j sharding contains all of the fabric graphs (instances or databases) that are managed by a coordinating fabric database. With this approach, the schema is identical on all participating databases. The difference between the two is that sharding generally implies a separation of the data across multiple servers. A PARTITION is a specific way to lay out a table (in a database). Each partition has the same schema and columns, but also entirely different rows. In this technique, each shard is. After a failure is detected, it’s. . Such a process allows mitigating data grown by adding more and more instances and dividing the data to smaller parts (shards or partitions). Database sharding and partitioning are techniques used to manage large volumes of data, improving performance and scalability. A primary key can be used as a sharding key. Hazelcast named in the Gartner ® Market Guide for Event Stream Processing. However, implementing sharding and data partitioning in blockchain networks comes with its own set of challenges. In this post, I describe how to use Amazon RDS to implement a sharded database. I searched : mysql can use sharding platform. Splitting your database out into shards can help reduce the load on your database, leading to improved performance. The table that is divided is referred to as a partitioned table. When it considers the partitioning of relational data, it usually refers to decomposing your tables either row-wise (horizontally) or column-wise (vertically). Data is organized and presented in "rows," similar to a relational database. Each shard operates independently, allowing for greater scalability and fault tolerance. The proposed solution begins with the introduction of a. 5. Later in the example, we will use a collection of books. Database sharding is a strategy for scaling a database by breaking it into smaller, more manageable pieces, or “shards”. It’s a partitioning pattern that places each partition in potentially separate servers—potentially all over the world. Unlike data partitioning, sharding does not require a centralized metadata management system. Ensuring consensus across multiple shards, facilitating secure cross-shard communication, and maintaining data synchronization are critical considerations. ReplicationThe distinction of horizontal vs vertical comes from the traditional tabular view of a database. The simplest way to implement sharding is to create a collection for each shard. In figure 4, Imagine we have a database with one table, Table A, and it has 10000 rows. Horizontal Partitioning/Sharding. Sharding is a common practice at companies with relational databases. Sharding is a type of horizontal partitioning where a large database is divided into smaller partitions or shards. Without sharding, the database is limited to vertical scaling alone, which is beneficial but limited. sharding. For example, high query rates can exhaust the CPU. Using MySQL Partitioning that comes with version 5. System Design for Beginners: Design for Experienced Engineers: a member fo. The above figure shows horizontal partitioning or sharding. Sharding is the process of breaking up large tables into smaller chunks called shards that are spread across multiple servers. Partitioning is dividing large tables into multiple tables. These end customers are often referred to as "tenants". The advantage of DBMS single server partitioning is that it is relatively simple to set up and manage. 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. Partitioning: Splitting a big database into smaller subsets called partitions so that different partitions can be assigned to different nodes (also known as sharding). Below are several data sharding techniques with. Design a compression strategy based on the type of data residing in each partition. Horizontal partitioning or sharding. Sharding is a method of partitioning data to distribute the computational and storage workload, which helps in achieving hyperscale computing. . How to use range partitioning & Citus sharding together for time series. To introduce horizontal scaling, the database is split into horizontal partitions, now called. Sharding is a way to split data in a distributed database system. 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. Unfortunately, the terms "partitioning" and "sharding" are used at. Sharding. This architecture innovation was originally driven by internet giants that run. In this context, "partitioning" refers to the division of rows based on their primary key, while "sharding" involves dispersing these rows across multiple key-value data stores. Each shard contains a subset of the data, allowing for better performance and scalability. Sharding is a database partitioning technique that involves breaking up a large database into smaller, more manageable parts called shards.