The topic of this month's PGSQL Phriday #011 community blogging event is partitioning vs. This is termed as sharding. 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. Sharding is not implemented in MySQL, but can be done on top of MySQL. Sharding is a way to split data in a distributed database system. Sharding involves saving the partitioned data onto other computers and storage facilities. Assume we use 200 shards, we can find the shardID by userID % 200 . In Azure Data Explorer, sharding is implemented using. two horizontal partitions. A shard is an individual partition that exists on separate database server instance to spread load. Data distribution or sharding. Optimize everything else first, and then if performance still isn’t good enough, it’s time to take a very bitter medicine. Database sharding is a database architecture strategy used to divide and distribute data across multiple database instances or servers. DB Sharding (圖片來源：這篇文章)，上圖右邊兩個資料庫會儲存在不同資料庫實體中 Sharding 的方式. Breaking a large database into smaller databases is typically referred to as database partitioning. 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. " Each shard contains a subset of the data, and together they form the complete dataset. 既然要做 sharding，如何決定哪些資料要到哪個資料庫就顯得非常重要了，常見的 Sharding 方式有以下兩種： Range-based partitioning; Hash partitioning; Range-based partitioningSharding is one of several popular methods being explored by developers to increase transactional throughput. Partitioning by the hash of keys (timestamp in this case) Cassandra and MongoDB use MD5 as the Hash function for Sharding. Products like elastics database queries and elastic database jobs have been created to fill this gap. In this strategy, we split the table data horizontally based on the range of values defined by the partition key. Praveen M Dhulavvagol 1, Prasad M R 2, Niranjan C Ku ndur 3, Jagadisha N 4, S G Totad 5. A shard is an individual partition that exists on separate database server instance to spread load. The idea behind sharding is to distribute the data across multiple machines or servers, to improve scalability. These queries run in serial, not parallel execution. For 20+ years of database and application development, time-series data has always been at the heart of the products I work with. 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. Ví dụ ta có bảng dữ liệu thông tin về người dùng, ta sẽ dựa trên location của người dùng để quyết. 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. It is used to achieve better consistency and reduce contention in our systems. Platform. Each shard is held on a separate database server instance, to spread load. Then, this partition key token is used to determine and distribute the row data within the ring. SQL Server 2008 introduced a table partitioning wizard in SQL Server Management Studio. You can scale the system out by adding further. There are many ways to split a dataset into shards. Database sharding is a type of horizontal partitioning that splits large databases into smaller components, which are faster and easier to manage. This initial. A simple hashing function can be the modulus of the key and the number of shards. However, implementing sharding and data partitioning in blockchain networks comes with its own set of challenges. Sharding is the process of horizontally partitioning data across multiple nodes in a cluster. Sharding is a method of partitioning data to distribute the computational and storage workload, which helps in achieving hyperscale computing. 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. Consider the Horizontal, vertical, and functional data partitioning guidance. Oracle Sharding is implemented based on the Oracle Database partitioning feature. Database sharding is a technique used to horizontally partition data across multiple database instances, or shards. What is Database Sharding? | Hazelcast. Data is automatically distributed across shards using partitioning by consistent hash. For both indexing and searching it is necessary to select appropriate key. Database sharding is also referred to as horizontal partitioning. 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. With Oracle Sharding, data is automatically distributed across multiple nodes, while still allowing the application to treat the database as a. Download Now. For true sharding then Skype's pl/proxy is probably the best. Sharding is a way to split data in a distributed database system. It enables distribution and replication of data. We call this a "shard", which can also live in a totally separate database. But these terms are used for different architectural concepts. Replication may help with horizontal scaling of reads if you are OK to read data that potentially isn't the latest. Some data within a database remains present in all shards, [a] but some appear only in a single shard. I don't have any knowledge. The balancer migrates data between shards. database partitioning Splitting large databases into separate entities for faster retrieval. However, it does have a drawback with aggregating data across the multiple databases. The biggest problem to solve when deciding the partitioning. It have no direct impact on performance, making it rarely useful. Distributed. Partitioning 1. e. In MySQL, the term “partitioning” applies to individual tables of a database. Horizontal partitioning and 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. It seemed right to share a perspective on the question of "partitioning vs. It allows for faster access to data and enables a database to handle larger workloads by distributing data and processing power across multiple servers. by Morgon on the MySQL Performance Blog. Range based sharding involves sharding data based on ranges of a given value. two horizontal partitions. The shard catalog database also acts as a query coordinator used to process multi-shard queries and queries that do not specify a sharding key. In this article we will talk about what database sharding is and how it works. Sharding is a scale-out technique in which database tables are partitioned and each partition is hosted on its own RDBMS server. Data is automatically distributed across shards using partitioning by consistent hash. The partitions share the same data schema. It helps in managing more transactions per. Basically, a partitioner is a hash function to determine the token value by hashing the partition key of a row’s data. Finally, partitioning and sharding can simplify tasks like backup, recovery, replication, migration, and reorganization of your data by dividing it into smaller and more manageable pieces. Horizontal scaling, also known as scale-out, refers to adding machines to share the data set and load. Below are several data sharding techniques with. Understanding Data Partitioning. Each shard is held on a separate database server instance, spreading the load and reducing the response time. Sharding is to split a single table in multiple machine. This initial. The term “shard” refers to a partition or subset of the. Database Sharding vs Database Partition The terms "sharding" and "partitioning" get thrown around a lot when talking about databases. 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. Partitioning is a way to split data within each shard into non-overlapping partitions for further parallel handling. How to use range partitioning & Citus sharding together for time series. e. Each partition has the. Database partitioning is the backbone of modern system design, which helps to improve scalability, manageability, and availability. These smaller parts are called data shards. Unlike data partitioning, sharding does not require a centralized metadata management system. 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. It can also be termed as horizontal partitioning because sharding is basically horizontal partitioning across different physical machines/nodes. configure sharding using a more ideal shard key. For Cassandra, you can read it here and for MongoDB here (Btw if you don. I will use the phrase partitioning scheme to. I am new to the database system design. Sharding is usually a case of horizontal partitioning. Similar to the Failsafe series but goes into more how-to details. In case of replicating existing shards, there will be more hosts to respond to a query request. All documents are assigned to a partition, and many documents are typically. 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. The partitioning key for the data distribution is the <sharding_column_name> parameter. This article series introduces and explains the concepts of data partitioning and sharding. Each partition is a separate data store, but all of them have the same schema. In a distributed database, partitions are used to split the stored data and assign a smaller fraction of the whole database to the nodes of a cluster. The table that is divided is referred to as a partitioned table. Ensuring consensus across multiple shards, facilitating secure cross-shard communication, and maintaining data synchronization are critical considerations. Now each partition sits on an entirely different physical machine, and under the control of a separate database instance with the same database schema. For stateless services, you can think about a partition being a logical unit that contains one or more instances of a service. Jump to: What is database sharding? Evaluating. Sharding is a method for splitting a database and storing a single logical database in multiple databases to accelerate transaction processing. 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. However, a sharding key cannot be a. 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. Range-based sharding involves dividing data into contiguous ranges determined by the shard key values. It has more features, more active users, and every day it collects more data. Each physical database in such a configuration is called a shard. Database. The decision to use sharding or partitioning depends on several factors, including the scale of. In some cases, it can be a total re-architecture of how the data is being accessed and stored, so we might. Sharding is a database server partitioning technique that can be used to distribute data across different servers in order to improve performance and scalability. Sharding is closely related to partitioning, and the terms are often used interchangeably. One way to better distribute writes across a partition key space in DynamoDB is to expand the space. Horizontal Partitioning/Sharding. In this article we will talk about what database sharding is and how it works. When to apply sharding policy and partitioning policy on tables? Azure Data Explorer An Azure data analytics service for real-time analysis on large volumes of data streaming from sources including applications, websites, and internet of things devices. 1 Answer. To choose the best method, you need to consider factors such as the size and growth rate of your data. The distribution used in system-managed sharding is intended to. Hyperscale computing is a computing architecture that can scale up or down quickly to meet increased demand on the system. The proposed solution begins with the introduction of a. Database sharding is a type of database partitioning that separates large databases into smaller, faster, more easily managed parts called data shards. Then as you need to continue scaling you’re able to move. Each partition is known as a shard and holds a specific subset of the data. Database partitioning is normally done for manageability, performance or availability [1] reasons, or for load balancing. You might shard databases without also duplicating or sharding other infrastructure in your solution. Load balancing: By partitioning data, the workload can be distributed equally among several nodes,. 2 use your RDBMS "out of the box" clustering mechanism. Partitioning is a rather general concept and can be applied in many contexts. Note that the hashing algorithm is very different: PostgreSQL. . Data partitioning criteria and the partitioning strategy decide how the dataset is divided. 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. Sharding in database is the ability to horizontally partition data across one more database shards. Your database is now causing the rest of your application to slow down. Sharded Database and Shards. A data sharding method controls the placement of the data on the shards. After a failure is detected, it’s. Then I would try the regular partitioning via hash on vehicleNo first while enforcing the user_id key within the procedure. In this technique, the dataset is divided based on rows or records. It is a way of splitting data into smaller pieces so that data can be efficiently accessed and managed. If the partitioning mechanism that Azure Cosmos DB provides is not sufficient, you may need to shard the data at the application level. Database partitioning and table partitioning are two different ways to manage data in a database. 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. There are many approaches to storing data in multi-tenant environments. Sharding is a special case of data partitioning, where the partitions are distributed across different servers or clusters, called shards. In case of sharding the data might be nicely distributed and hence the queries. Each shard has the same database schema as the original database. Data Partitioning with Chunks. How to use range partitioning & Citus sharding together for time series. Sharding is a database scaling technique based on horizontal partitioning of data across multiple independent physical databases. Splitting your data in 2 dimensions gives you even smaller data and index sizes. Horizontal Partitioning - Sharding (Topology 2): Data is partitioned horizontally to distribute rows across a scaled out data tier. Each replica set (known in MongoDB as a shard) in a cluster only stores a portion of the data based on a collection sharding key (sharding strategy), which determines the distribution of the data. Each. Cassandra is NOT a column oriented database. Partitioning groups data. Once you have determined your sharding strategy, you need to create your shards. 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. Partition Service Fabric stateless services. Partition an App Service web app to avoid limits on the number of instances per App Service plan. There are three typical strategies for partitioning data: Horizontal partitioning (often called sharding). After reading many articles, I am really getting confused on what is the limit till which we should have 1 table and not go for sharding or partitioning. U think dbms can support this. In this post, SingleStore Developer Advocate, Joe Karlsson, explains the differences between database sharding vs. 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. A database can be split vertically — storing different tables & columns in a separate database or horizontally — storing rows of a same table in multiple database nodes. Partitioning is a general term, and sharding is commonly used for horizontal partitioning to scale-out the database in a shared-nothing architecture. I know that it is really hard to provide generic answer and things depend on factors like. This distribution allows for improved performance, scalability, and availability. Geo. Sharding is a common practice at companies with relational databases. Each shard has the same schema and columns like that of the original table but data stored in each shard is unique and independent of other shards. So, in this case it would be better to have a table that is un-partitioned, so that all data can be queried using the same table. It uses some key to partition the data. The table that is divided is referred to as a partitioned table. Sharding is the equivalent of “horizontal partitioning. We can partition this table. Each shard can then be hosted on a separate server,. Figure 1 is an example of a sharding database. When partitioning a table, the use should decide: a partitioning type; a partitioning expression. Sharding is a powerful technique for improving the scalability and performance of large databases. Using Sharding to Optimize Queries. Database sharding is a technique used to horizontally partition large databases into smaller, more manageable pieces called "shards. Database sharding is the process of storing a large database across multiple machines. The. How to use Citus to shard partitions on a single node. It is a "horizontal" split of the data, often by date, but could be by some other 'column'. 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. Là cách chia cùng dữ liệu của cùng một bảng (table) ra nhiều DB khác nhau. In this strategy, each partition is a separate data store, but all partitions. Sharding physically organizes the data. Each shard is a separate database, stored on a different server, and only contains a portion of the total data. Partitioning a table using the SQL Server Management Studio Partitioning wizard. Database partitioning vs. 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. Think less of sharding as a particular kind of partitioning, contrasted to vertical partitioning. Our application is built on J2EE and EJB 2. 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 is a partitioning technique where data is split and spread across multiple databases or servers to increase the scalability and efficiency and improve system performance. Database sharding and partitioning are techniques used to manage large volumes of data, improving performance and scalability. You can add a. This technique supports horizontal scaling but can be complex and requires careful planning. In fact, this means sharding of meta data, which is convenient for efficient and parallel tag filtering operations. Sharding Key: A sharding key is a column of the database to be sharded. It is primarily employed in large-scale, high-traffic systems to improve performance, scalability, and availability. A shard is a horizontal partition of data in a database. Pattern 5 - Partitioning: You know that your location database is something which is getting high write & read traffic. 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. 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 distributed SQL database provides a service where you can query the global database without knowing where the rows are. Traditional Database Sharding. ". Sharding is a form of database partitioning, also known as horizontal partitioning. Each partition has its own name. Shard Manager supports spreading shard replicas across configurable fault domains, for instance, data center buildings for regional applications and regions for global applications. A partition is a division of a logical database or its constituent elements into distinct independent parts. Within YugabyteDB partitioning is a user-defined, SQL-level concept, thus requiring an explicit definition through SQL. partitioning. Database sharding is a technique used to optimize database performance at scale. Essentially, sharding is just a fancy name given to the process of splitting the dataset along its rows. The difference between the two is that sharding generally implies a separation of the data across multiple servers. Sharding, also known as horizontal partitioning, is a popular scale-out approach for relational databases. e. The word “ Shard ” means “ a small part of a whole “. Horizontal partitioning, also known as row partitioning or sharding, is the process of splitting a table into multiple smaller tables based on a partition key, such as a customer ID, a date range. In this case, the records for stores with store IDs under 2000 are placed in one shard. It is essential to choose a sharding key that balances the load and distributes the data. Defining Database Sharding and Partitioning. Introduction Modern innovations thrive on strategic data management. 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. Within a partitioned database, documents are formed into logical partitions by use of a partition key. 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. REPLICATED means that identical copies of the table are present on each database. One may choose to keep all closed orders in a single table and open ones in a separate table i. You still have issue #1 if you use sharding. Data in each shard does not have to share resources such as CPU or memory, and can be read or written. Data Partitioning; Database Sharding; Let us first discuss indexing followed by indexing and partitioning/ sharding. / Database / Resources / Sự khác biệt giữa các khái niệm trong database: replication, partitioning, clustering và sharding. Database sharding is the optimization of large databases by splitting data from a larger database table into multiple smaller tables (shards). Sharding is possible with both SQL and NoSQL databases. It's not necessary to understand these. To find the. 3 June, 2022;. Sample application that includes a sharded database. Sharding is necessary if a dataset is too large to be stored in a single database. We can think of this like a proxy server that handles requests and connection information. Hash based partitioning: It uses hash function to decide table/node, and take key elements as input in generating hash. Database partitioning is normally done for manageability, performance or availability reasons, as for load balancing. 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. g for large database that cannot fit on a single disk. But if query needs to be done by key other then the partition key, then we need to go through each partition one by one. 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. This makes it possible to scale the storage capacity of. 4. Overall, a database is sharded and the data is partitioned. Central to this strategy is database partitioning — serving as the backbone of today’s distributed database systems. Document collections provide a natural mechanism for partitioning data within a single database. Sharding (also known as Data Partitioning) is the process of splitting a large dataset into many small partitions which are placed on different machines. In MySQL, the term “partitioning” means splitting up individual tables of a database. Neo4j sharding contains all of the fabric graphs (instances or databases) that are managed by a coordinating fabric database. 1 Benefits of sharding. In this article, we’ll cover the basics of database sharding, its best use cases, and the different ways you can implement it. A hashing function hashes the sharding key value, and the output maps data to a particular shard. , or account numbers from 00001 to 49999 in one, and 50000 to 99999 in. However, a sharding key cannot be a primary key. You can use numInitialChunks option to specify a different number of initial chunks. Similar to the Failsafe series but goes into more how-to details. 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. A single machine, or database server, can store and process only a limited amount of data. Each shard can have its own auto-increment sequence for photoID, and we prepend shardID to each photoID so that each photo has a unique global photoID. How to shard data while the business is running 24/7;. Partitioning: Splitting a big database into smaller subsets called partitions so that different partitions can be assigned to different nodes (also known as sharding). We will also contrast it with Database partitioning that is often confused with sharding. Database Sharding. For example, a database of university students may be sharded based on the first letter of. Update 3: Building Scalable Databases: Pros and Cons of Various Database Sharding Schemes by Dare Obasanjo. Later in the example, we will use a collection of books. System-managed sharding uses partitioning by consistent hash to randomly distribute data across shards. These queries run in serial, not parallel execution. Oracle Sharding supports system-managed, user defined, or composite. Document collections provide a natural mechanism for partitioning data within a single database. Consistent hashing is a technique widely used in load balancing and routing service. Each shard is an independent database, and collectively, the shard. Source: Internet. A partitioned database is the newest type of IBM Cloudant database. Database systems with large data sets or high throughput applications can challenge the capacity of a single server. Data partitioning to data. Sharding involves replicating [copying] the schema, and then dividing the data based on a shard key onto a separate database server instance, to spread load. How to use range partitioning & Citus sharding together for time series . Database sharding is the process of storing a large database across multiple machines. If the partitioning mechanism that Azure Cosmos DB provides is not sufficient, you may need to shard the data at the application level. Database. Overview. Sharding is commonly employed to improve scalability, distribute workload, and enhance performance for large-scale. Sharding vs. Partitioning assumes the partitions are on the same server. Splitting your database out into shards can help reduce the load on your database, leading to improved performance. The advantage of such a distributed database design is being able to provide infinite scalability. partitioning. In the case of MySQL, this means that each node is its own MySQL RDBMS, with its own set of data partitions. This allows for horizontal scaling, as more shards can be added on new servers when needed. Partition (database) Partitioning options on a table in MySQL in the environment of the Adminer tool. This key is responsible for partitioning the data. Sharding allows you to scale out database to many servers by splitting the data among them. When data is written to the table, a partitioning function will be used by MySQL to decide which partition to. Database sharding offers numerous benefits in performance,. sharding in PostgreSQL. It uses some key to partition the data. Sharding is also referred to as horizontal partitioning, and a shard is essentially a. Sample application that includes a sharded database. 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 spreads the workload of. 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. One may choose to keep all closed orders in a single table and open ones in a separate table i. It is a productive approach to distributed database sharding and offers a. The partitioner determines how data is distributed across the nodes in a Cassandra cluster. William McKnight, in Information Management, 2014. Sharding helps you spread the load over more computers, which reduces contention and improves performance. Sharding is a database partitioning technique that breaks a single database into smaller, more manageable parts called shards. In this post, we will examine various data sharding strategies for a distributed SQL database, analyze the tradeoffs, explain. Excellent. Each partition of data is called a shard. Horizontal Partitioning (Sharding): In horizontal partitioning, the database is divided into smaller parts or "shards" based on the. Horizontal Data Partitioning / Sharding is a very important concept and is used in almost every production setup. However, it does have a drawback with aggregating data across the multiple databases. Sharding on Azure SQL is a type of horizontal partitioning that splits large databases into smaller components, which are faster and easier to manage. Horizontal partitioning, also known as Data Sharding, splits a database by rows into separate databases. Partitioning or sharding during data extraction requires some best practices to be followed. Let me elaborate. 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. Each shard (or server) acts as the single source for this subset. By dividing data into smaller, more manageable pieces, sharding can improve performance, scalability, and resource utilization. To introduce horizontal scaling, the database is split into horizontal partitions, now called. Edit: Your interviewer is also wrong. Sharding is the process of breaking up large tables into smaller chunks called shards that are spread across multiple servers. Understanding Sharding. Table partitioning and columnstore indexes. 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. Each partition (also called a shard) contains a subset of data. Optimize everything else first, and then if performance still isn’t good enough, it’s time to take a very bitter medicine. Horizontal data partitioning or sharding is a technique for separating data into multiple partitions. The concept is simplistic and enables scalability in distributed computing, but there are many factors to consider to derive the maximum benefit from it. See moreSep 14, 2023Database partitioning is normally done for manageability, performance or availability reasons, as for load balancing. sharding. In summary, sharding and partitioning are effective database scaling techniques that can help improve database performance and handle large volumes of data. A sharding key is an attribute or column that determines how the data is distributed among the shards. Database sharding is a powerful tool for optimizing the performance and scalability of a database. Your app is getting better. Modern innovations thrive on strategic data management. Partitioning is a general term used to describe the breaking up of your logical data elements into multiple entities typically for the purpose of performance, availability, or maintainability. PostgreSQL allows you to declare that a table is divided into partitions. This means that the attributes of the Database. NHỮNG CÁCH THỨC PHÂN CHIA DỮ LIỆU. A distributed SQL database provides a service where you can query the global database without. Later in the example, we will use a collection of books. In this technique, each shard is. Each of the partitions is located on a separate server, and is called a “shard”. A shard is a horizontal data partition that contains a subset of the total data set. Sharding can improve. . Solutions. Sales data of 50 states of a country are split into four shards, each containing. Each replica set (known in MongoDB as a shard) in a cluster only stores a portion of the data based on a collection sharding key (sharding strategy), which determines the distribution of the data. Data is automatically distributed across shards using partitioning by consistent hash. Some databases have out-of-the-box support for sharding. For example, you can.