The Geo-based sharding first partitions data according to the user-specified column so that it can map range. It is a mechanism to achieve distributed systems. Sharding (also known as Data Partitioning) is the process of splitting a large dataset into many small partitions which are placed on different machines. Design a compression strategy based on the type of data residing in each partition. Indexing is the process of storing the column values in a datastructure like B-Tree or Hashing. Database sharding is a powerful tool for optimizing the performance and scalability of a database. Sharding is used when Partitioning is not possible any more, e. Hash based partitioning: It uses hash function to decide table/node, and take key elements as input in generating hash. Database sharding is the process of storing a large database across multiple machines. Partitioning Types. 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. Basically, a partitioner is a hash function to determine the token value by hashing the partition key of a row’s data. 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 partitioning criteria and the partitioning strategy decide how the dataset is divided. Each partition in our store is contained in a single shard, and each shard is replicated to a set of nodes. In this model, documents with "close" shard key values are likely to be in the. Data is organized and presented in "rows," similar to a relational database. 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. The word “ Shard ” means “ a small part of a whole “. The partitioning algorithm evenly and randomly distributes data across shards. Database Sharding is the process where a huge Database is partitioned horizontally. ReplicationThe distinction of horizontal vs vertical comes from the traditional tabular view of a database. A data sharding method controls the placement of the data on the shards. Database sharding is a technique used to optimize database performance at scale. These attributes form the shard key (sometimes referred to as the partition key). 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. Database sharding overcomes this limitation by splitting data into smaller chunks, called shards, and storing them across several database servers. The partitioning key for the data distribution is the <sharding_column_name> parameter. This initial. migrate to a NoSQL solution. shards and replication, system managed partitioning, single command deployment, and fine-grained rebalancing. By contrast, sharding offers unlimited scalability. 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 partitioning or sharding is a technique of dividing data into independent components. In Sharding, the data in a database is distributed across multiple servers or nodes, each responsible for a specific subset of the data. Figure 1 is an example of a sharding database. 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. However, sharding requires a high level of cooperation between an application. In this article we will talk about what database sharding is and how it works. Partitioning is an important strategy to segregate the data based on the partition key and distribute the data evenly across partitions for efficient querying and analysis. You query your tables, and the database will determine the best access to. Sharding is more general and is usually used when the database is split on several servers. It have no direct impact on performance, making it rarely useful. High Availability: If an outage happens in sharded architecture, then only some specific shards will be. With sharding or partitioning, you are not restricted to storing data on the memory of a single computer. Range based sharding involves sharding data based on ranges of a given value. Sharding can be used in system design interviews to help demonstrate a candidate’s understanding of scalability. In horizontal partitioning, also called sharding, each partition holds data for a subset of the total data set. But these terms are used for different architectural concepts. Simply stated, sharding is a way of partitioning to spread out the computational and. Each partition has the same schema and columns, but also entirely different rows. sharding# Database partitioning deals with a single database instance, whereas sharding splits partitions (shards) across multiple database instances for scalability and availability. It is essential to choose a sharding key that balances the load and distributes the data. One shard within every sharded MongoDB cluster will be elected to be the cluster’s primary shard. Each physical node in the cluster stores several sharding units. Shard Manager supports spreading shard replicas across configurable fault domains, for instance, data center buildings for regional applications and regions for global applications. This scale out works well for supporting people all over the world accessing different parts of the data. Sharding is a database partitioning technique that involves breaking up a large database into smaller, more manageable parts called shards. Each shard is an independent database, and collectively, the shard. Note that the hashing algorithm is very different: PostgreSQL. Sharding involves saving the partitioned data onto other computers and storage facilities. Partitioning (aka sharding) Partitioning distributes data across multiple nodes in a cluster. Database sharding is a strategy for scaling a database by breaking it into smaller, more manageable pieces, or “shards”. The partitioned table itself is a “ virtual ” table having no storage of its. Sharding is a technique of splitting some arbitrary set of entities into smaller parts known as shards. 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. Database Sharding is the process where a huge Database is partitioned horizontally. Partitioning is a rather general concept and can be applied in many contexts. All documents are assigned to a partition, and many documents are typically. e. Sharding is a way to split data in a distributed database system. Sharding, also known as partitioning, splits large data sets into small data sets across multiple nodes enabling you to scale out your database beyond vertical scaling limits. Partitioning assumes the partitions are on the same server. Sharding is a common practice at companies with relational databases. We would like to show you a description here but the site won’t allow us. Horizontal Data Partitioning / Sharding is a very important concept and is used in almost every production setup. ) is also stored in vnode instead of centralized storage in mnode. 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. It is a way of splitting data into smaller pieces so that data can be efficiently accessed and managed. For both indexing and searching it is necessary to select appropriate key. What is Database Sharding? | Hazelcast. Even if you have not worked directly with this yet, this is a very important topic. Database. Database Partitioning implements very basic optimization — the easiest way to improve database performance is to scan less data. These queries run in serial, not parallel execution. Sharding Key: A sharding key is a column of the database to be sharded. It is used to achieve better consistency and reduce contention in our systems. This is known as data sharding and it can be achieved through different strategies, each with its own tradeoffs. This approach is also called "sharding". One may choose to keep all closed orders in a single table and open ones in a separate table i. 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. It can be either a single indexed column or multiple columns denoted by a value that determines the data division between the shards. Data is automatically distributed across shards using partitioning by consistent hash. Database replication, partitioning and clustering are concepts related to sharding. In figure 4, Imagine we have a database with one table, Table A, and it has 10000 rows. Database sharding is a type of horizontal partitioning that splits large databases into smaller components, which are faster and easier to manage. Similar to the Failsafe series but goes into more how-to details. Sharding Key: A sharding key is a column of the database to be sharded. However, system-managed sharding does not give the user any control on assignment of data to shards. It can also be termed as horizontal partitioning because sharding is basically horizontal partitioning across different physical machines/nodes. Data sharding is a specific type of data partitioning, where the partitions are distributed across multiple servers or clusters, called shards. The concept is simplistic and enables scalability in distributed computing, but there are many factors to consider to derive the maximum benefit from it. MongoDB uses the shard key associated to the collection to partition the data into chunks owned by a specific shard. Database Sharding. A primary key can be used as a sharding key. Table A holds items 1–5000 and Table B holds items 5001–10000. 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. In a traditional database setup, we store in a single server. This is not a new challenge; organizations have faced it for years, and horizontal sharding is one of the key patterns for solving it. Suppose you own a company and. How to use range partitioning & Citus sharding together for time series. 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. Data Partitioning divides the data set and distributes the data over multiple servers or shards. To introduce horizontal scaling, the database is split into horizontal partitions, now called. This kind of information is incredibly important to know and understand before starting down the path of with SQL Server—primarily because sharding isn’t a simple venture involving changing a configuration option or flipping a switch. Partitioning by the hash of keys (timestamp in this case) Cassandra and MongoDB use MD5 as the Hash function for Sharding. NHỮNG CÁCH THỨC PHÂN CHIA DỮ LIỆU. Sharding. The idea behind sharding is to distribute the data across multiple machines or servers, to improve scalability. The. It also discusses best practices for partitioning and gives an in-depth view at how horizontal scaling works in Azure Cosmos DB. A distributed SQL database provides a service where you can query the global database without knowing where the rows are. However, both read and write performance may decrease. 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. I am new to the database system design. Sharding is a way to split data in a distributed database system. Optimize everything else first, and then if performance still isn’t good enough, it’s time to take a very bitter medicine. In addition to the partitioned data stored across every shard in the cluster. Partition Service Fabric stateless services. database partitioning Splitting large databases into separate entities for faster retrieval. Unlike data partitioning, sharding does not require a centralized metadata management system. two horizontal partitions. Load balancing: By partitioning data, the workload can be distributed equally among several nodes,. Sharding is horizontal ( row wise) database partitioning as opposed to vertical ( column wise) partitioning which is Normalization. The balancer migrates data between shards. I say this having worked with tables that were in the 10s of billions of rows without partitioning and were. e. In contrast, sharding involves horizontally splitting a dataset into multiple pieces, each of which is stored on a separate node or cluster of nodes. 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. You can use numInitialChunks option to specify a different number of initial chunks. Another advantage of sharding is being able to use the computational. Each physical database in such a configuration is called a shard. It is useful when no single machine can handle large modern-day workloads, by allowing you to scale horizontally. You can add a. Each of the partitions is located on a separate server, and is called a “shard”. Automatic failure detection and shard failover: Shard Manager can automatically detect server failures and network partition. Horizontal Partitioning (sharding) stores rows of a table in multiple database clusters. Sharding is a database partitioning technique that breaks a single database into smaller, more manageable parts called shards. While the declarative partitioning feature allows users to partition tables into multiple partitioned tables living on the same database server, sharding allows tables. Sharding is also referred to as horizontal partitioning, and a shard is essentially a. 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. Conclusion131. 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. Horizontal Partitioning (Sharding): In horizontal partitioning, the database is divided into smaller parts or "shards" based on the. Partitioning or sharding during data extraction requires some best practices to be followed. Then as you need to continue scaling you’re able to move. In some cases, it can be a total re-architecture of how the data is being accessed and stored, so we might. In this strategy, each partition is a separate data store, but all partitions have the same schema. Sharding, also known as horizontal partitioning, is a popular scale-out approach for relational databases. The declaration includes the partitioning method as described above, plus a list of columns or expressions to be used as the partition key. Sharding vs. Each partition is known as a shard and holds a specific subset of the data. Vertical sharding — Vertical partitioning on the other hand refers to division of columns into multiple tables. Horizontal Data Partitioning / Sharding is a very important concept and is used in almost every production setup. configure sharding using a more ideal shard key. Sharding is a type of partitioning, such as. Each partition. 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. When data is written to the table, a partitioning function will be used by MySQL to decide. William McKnight, in Information Management, 2014. Stores possessing IDs of 2001 and greater go in the other. Each of the nodes stores only a part of the dataset. If the partitioning mechanism that Azure Cosmos DB provides is not sufficient, you may need to shard the data at the application level. The advantage of such a distributed database design is being able to provide infinite scalability. Later in the example, we will use a collection of books. Sharding allows you to scale out database to many servers by splitting the data among them. Second, run a platform or a program to pull and parse the database log to. Partitioning can help with larger tables but only when a small part of the data is hot. Unlike Sharding and Replication, Partitioning is vertical scaling because each data partition is in the same. Horizontal partitioning is achieved in a relational database by storing rows from the same table in several database nodes. Update 4: Why you don’t want to shard. A hashing function hashes the sharding key value, and the output maps data to a particular shard. Vertical and horizontal partitioning can be mixed. Sharding and Partitioning. Introduction. Firstly, Horizontal partitioning (often called sharding). Sharding is closely related to partitioning, and the terms are often used interchangeably. Sharding is complementary to other forms of partitioning, such as vertical partitioning and functional partitioning. Because NoSQL databases are designed with distributed computing and automatic sharding in. Breaking a large database into smaller databases is typically referred to as database partitioning. You query your tables, and the database will determine the best access to your data, whether it. 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. Oracle Sharding supports system-managed, user defined, or composite. partitioning. Auto sharding or data sharding is needed when a dataset is too big to be stored in a single. 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. Horizontal sharding. Database sharding is the process of storing a large database across multiple machines. On the other hand, data partitioning is when the database is broken down. Optimize everything else first, and then if performance still isn’t good enough, it’s time to take a very bitter medicine. 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. partitioning. 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,. 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. ” Each shard is essentially a separate. In this partitioning, each partition is a separate data store , but all partitions have the same schema . Sharding is the process of breaking up large tables into smaller chunks called shards that are spread across multiple servers. Range-based sharding involves dividing data into contiguous ranges determined by the shard key values. This approach allows for improved scalability, performance, and availability in. 5. 4. Sharded Database and Shards. Horizontal and vertical sharding. This means that the attributes of the Database. The distribution used in system-managed sharding is intended to eliminate hot spots and provide uniform performance across shards. The concept of partitioning is the same whether a table has a clustered index, is a heap, or has a columnstore index. 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. Database sharding and partitioning are techniques used to manage large volumes of data, improving performance and scalability. For example, if you intend on having a /api/users endpoint, you should have users collection and it should contain any and everything you intend to return on that endpoint. Sharding vs. This key is responsible for partitioning the data. For data belonging to Europe region, we can house all the data at Shard-B. It is a "horizontal" split of the data, often by date, but could be by some other 'column'. Sample code: Cloud Service Fundamentals in Windows Azure. Each partition of data is called a shard. Defining Database Sharding and Partitioning. Sharding is the process of horizontally partitioning data across multiple nodes in a cluster. sharding" from someone in the Citus open source team, since we eat, sleep, and breathe sharding for Postgres. But I didn't find any article about SQL Server. 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 way to split data in a distributed database system. Partition an App Service web app to avoid limits on the number of instances per App Service plan. Horizontal partitioning is another term for sharding. In Azure Data Explorer, sharding is implemented using. With this approach, the schema is identical on all participating databases. Database sharding might be the answer to your problems, but many people. Choose a scheme that matches the data characteristics and query patterns, and avoid schemes that cause. 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. Sharding is a form of database partitioning, also known as horizontal partitioning. A simple hashing function can be the modulus of the key and the number of shards. Database Sharding and Partitioning both offer intuitive solutions to address a common challenge — managing and querying the vast volumes of data generated by modern applications. Sharding is possible with both SQL and NoSQL databases. It is effective when queries tend to return only a subset of columns of the data. Sharding is a partitioning pattern for the NoSQL age. This key is responsible for partitioning the data. 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 form of horizontal partitioning, which means dividing a table or a collection of data by rows, not by columns. Database Sharding. 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. Sharding (also known as Data Partitioning) is the process of splitting a large dataset into many small partitions which are placed on different machines. We want to keep all data of a user on the same shard. A shard is a horizontal data partition that contains a subset of the total data set. Partitioning is more of a generic term for splitting a database and Sharding is a type of partitioning. 2 Vertical partitioning Distributed SQL: Sharding and Partitioning in YugabyteDB. However sharding is a trade-off. . Hence Sharding means dividing a larger part into smaller parts. Both are methods of breaking a large dataset into smaller subsets – but there are differences. When I refer to sharding, I'm considering sharding made in the application layer, for instance, distributing records evenly across independent MySQL instances. Let me elaborate. If the partitioning mechanism that Azure Cosmos DB provides is not sufficient, you may need to shard the data at the application level. How to use Citus to shard partitions on a single node. Sharding physically organizes the data. Hyperscale computing is a computing architecture that can scale up or down quickly to meet increased demand on the system. Sharding would generally be considered entirely separate servers with separate IPs. Data sharding. Database. users do not need to be aware of the necessary concepts in the sharding strategy and sharding key and other database partitioning schemes. It is responsible for serving a portion of the overall workload. Data partitioning is influenced by both the multi-tenant model you're adopting and the different sharding. In this course, Implement Partitioning with Azure, you’ll learn to apply efficient partitioning, sharding, and data distribution techniques over Azure Cloud Portal for. A logical shard is an atomic unit of. 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. This reduces the reading of unnecessary data, and allows for efficiently implementing. You still have issue #1 if you use sharding. This makes it possible to scale the storage capacity of. This is a topic near and dear to me and I’m excited to think about it some this month. use sharding. For stateless services, you can think about a partition being a logical unit that contains one or more instances of a service. “Vertical partitioning” refers to the practice of sharding your database into groups related tables with each group living on its own database server. Without sharding, the database is limited to vertical scaling alone, which is beneficial but limited. Data Partitioning with Chunks. When we say we partition a database, we split our table into smaller, individual tables, so. 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. Modern innovations thrive on strategic data management. Some databases have out-of-the-box support for sharding. 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. This provides better load balancing compared to user-defined sharding that uses partitioning by range or list. Two commonly-used sharding strategies are range-based sharding and hash-based. Each shard is a separate database, stored on a different server, and only contains a portion of the total data. 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. After 100k user information should go second database and server. To choose the best method, you need to consider factors such as the size and growth rate of your data. In this strategy, selecting the sharding key is essential because it is responsible for distributing the workload among. Sharding is a database partitioning technique where a large database is divided horizontally into smaller and more manageable parts called shards or partitions. A chunk consists of a range of sharded data. It helps in managing more transactions per. 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. In this technique, the dataset is divided based on rows or records. The database sharding examples below demonstrate how range sharding might work using the data from the store database. 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 vs. How to shard data while the business is running 24/7;. Partitioning is a way to split data within each shard into non-overlapping partitions for further parallel handling. 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. 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. Partitioning based on UserID. Neo4j sharding contains all of the fabric graphs (instances or databases) that are managed by a coordinating fabric database. Document collections provide a natural mechanism for partitioning data within a single database. Later in the example, we will use a collection of books. 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. Database sharding allows you to distribute a single data set across multiple databases. Table partitioning and columnstore indexes. Elastic clusters use the separation, or “decoupling”, of compute and storage in Amazon DocumentDB enabling you to scale independently of each other. The shard key should be static. A distributed SQL database provides a service where you can query the global database without. It separates very large databases into smaller, faster and more easily managed parts called data shards. ". For example, a range partitioning scheme for a customer database might partition customers based on their country or region of residence. Each shard contains a subset of the data and can be processed independently. It currently supports hash and range sharding. Then I would try the regular partitioning via hash on vehicleNo first while enforcing the user_id key within the procedure. Partitioning could be a different database inside MySQL on the same server, or different tables, or even by column value in a singular table. » All of the advantages of sharding without sacrificing the capabilities of an enterprise RDBMS, including: relational schema, SQL, and other programmatic. In the example above, using the customer ZIP. Database sharding is a technique used to horizontally partition data across multiple database instances, or shards. It can be either a single indexed column or multiple columns denoted by a value that determines the data division between the shards. Each shard is an independent database responsible for storing a subset of the overall data. See moreSep 14, 2023Database partitioning is normally done for manageability, performance or availability reasons, as for load balancing. In most distributed databases, the terms partitioning and sharding are used as synonyms. This process of partitioning is known as Vertical Sharding or Vertical Partitioning. A primary key can be used as a sharding key. Both methods allow you to split a large database into smaller, more manageable databases and tables, but they differ in how they accomplish this. I am happy to discuss any of the above in more detail, but only in a more focused context. Each physical database in such a configuration is called a shard. Sharding your database. 2. 2 Vertical partitioningDistributed SQL: Sharding and Partitioning in YugabyteDB. Data sharding and partitioning are techniques to distribute and store data across multiple servers or nodes, improving performance, scalability, and availability. Partitioning or sharding during data extraction requires some best practices to be followed. It shouldn't be based on data that might change. Database partitioning is the backbone of modern system design, which helps to improve scalability, manageability, and availability. One way to better distribute writes across a partition key space in DynamoDB is to expand the space. 1 Benefits of sharding. You can scale the system out by adding further. Step 2: Create Your Shards. For example :-. 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. Hashed sharding uses either a single field hashed index or a compound hashed index as the shard key to partition data across your sharded cluster. Database sharding is the process of dividing the data into partitions which can then be stored in multiple database instances. MongoDB uses the shard key associated to the collection to partition the data into chunks owned by a specific shard. Each. It is primarily employed in large-scale, high-traffic systems to improve performance, scalability, and availability. It is your responsibility to ensure that the replicas are identical across the databases. Platform. Partitioning is a general term, and sharding is commonly used for horizontal partitioning to scale-out the database in a shared-nothing architecture. By dividing data into smaller, more manageable pieces, sharding can improve performance, scalability, and resource utilization. Using MySQL Partitioning that comes with version 5. Some data within a database remains present in all shards, [a] but some appear only in a single shard. Sharding vs. Database sharding and partitioning are techniques used to manage large volumes of data, improving performance and scalability. The advantage of DBMS single server partitioning is that it is relatively simple to set up and manage. This spreads the workload of. Data is automatically distributed across shards using partitioning by consistent hash. Your app is getting better. The partitioning algorithm evenly and randomly distributes data across shards. partitioning. 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. horizontal partitioning or sharding. Sharding is an alternative approach for scaling databases, which divides the database into smaller pieces called shards. Database sharding involves partitioning data across multiple servers, so each server contains a subset of the data. In this post, SingleStore Developer Advocate, Joe Karlsson, explains the differences between database sharding vs. If you are using mongoDB as a backend for a REST interface, the best practice is to create on collection per resource. The. When a database is sharded, a replica of the schema is created. However, it does have a drawback with aggregating data across the multiple databases. For a vertical partitioning tutorial, see Getting started with cross-database query (vertical partitioning). 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. But you can also handle the sharding logic at the application level, as recent posts from the likes of Notion and Figma have described. Shard Generation and Data Partitioning .