Also if a database is partitioned, it does not imply that the database is definitely sharded. In Elastic Scale, data is sharded (split into fragments) according to a key. e. Sharding is a strategy that can mitigate this by distributing the database data across multiple machines. And if you are this far, go to method 2. Data Distribution: The distribution of data is an important process in which sharding comes into play. It is essential to choose a sharding key that balances the load and distributes the data. In this article, I demonstrate how to build a distributed database load-balancing architecture based on ShardingSphere and the. It’s important to note. Each shard (or server) acts as the single source for this subset. Stores possessing IDs of 2001 and greater go in the other. In comparison, when using range-based sharding. Sharding allows you to scale out database to many servers by splitting the data among them. EstructuraDatabase sharding is a database architecture strategy used to divide and distribute data across multiple database instances or servers. Apache ShardingSphere, as Apache’s first Top-Level open source database sharding project, can tackle all the above-mentioned challenges. use sharding. When making a sharding choice, you need to think about two things: 1) as many data access points as possible should go into a single shard, because cross-shard access is expensive if supported at. Database Sharding is a technique used to horizontally partition a database into smaller, more manageable pieces called shards. The. The term "sharding" refers to the data fragments that result from breaking a database into many smaller databases. The idea is to distribute data that can’t fit on a single node onto a cluster of database nodes. This DB contains data of near about 10 different clients so I am planning to move on Azure. This will enable sharding for the specified database, allowing you to distribute its data across. In sharding, you're just taking a given schema (normalized or not) and distributing it across a number of physical/logical data stores. Partitioning can be applied to databases at many levels. Sharding is a data tier architecture in which data is horizontally partitioned across independent databases. HDFS federation provides MapReduce with the ability to start multiple HDFS namespaces in the cluster, monitor their health, and fail over in case of daemon or host failure. Each partition has the same schema and columns, but also entirely different rows. In databases, it means that several databases hold information,A sharding key is an attribute or column that determines how the data is distributed among the shards. Sharding allows you to scale larger than federation, but it requires more logic in your application to dynamically change the target database depending on the. Sharding refers to horizontal scaling, and was introduced to Weaviate in v1. 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. Neo4j scales out as data grows with sharding. (Your simplified example will probably work. Let’s add 2 more Citus worker nodes and scale out the database:A federated database system (FDBS) is a type of meta-database management system (DBMS), which transparently maps multiple autonomous database systems into a single federated database. Since shards are. These attributes form the shard key (sometimes referred to as the partition key). Sharding involves dividing a large dataset horizontally, creating smaller and independent subsets known as shards. UserIDs that are even would be on shard 0 and odd userIDs would be on shard 1. The shards can reside on different servers. For static sharding, i. Sharding is a powerful technique for improving the scalability and performance of large databases. '5400'); //at the. Database sharding overcomes this limitation by splitting data into smaller chunks, called shards, and storing them across several database servers. In a key- or hashed -based sharding architecture, a database application uses a shard key to locate a shard. What is Sharding or Data Partitioning? Sharding (also known as Data Partitioning) is the process of splitting a large dataset into many small partitions which are placed on different machines. This is not a new challenge; organizations have faced it for years, and horizontal sharding is one of the key patterns for solving it. We can think of a shard as a little c…Sharding is a database architecture pattern related to horizontal partitioning — the practice of separating one table’s rows into multiple different tables, known as. In an ideal world, sharding would be understood not only at the data tier of an application but also by the application itself. Sharding, even when done correctly, is likely to have a significant influence on your team’s processes. Sharding is a technique that divides a large database into smaller, more manageable parts called shards. In general, it is best to prototype in InnoDB, grow the dataset until. Compare price, features, and reviews of the software side-by-side to make the best choice for your business. Sharding in Postgres is: a technique of splitting Postgres database tables into smaller tables (called “shards”) that is typically used to distribute data horizontally across multiple nodes comprising a cluster of database instances. Spectrum Data Federation vs. Stores possessing IDs of 2001 and greater go in the other. Row-based sharding. Simply put, federation is the ability of one Prometheus server to scrape time-series data from another Prometheus server. Used for basic computations about user behaviour that do not need. One common. With Fabric, you. ScaleGrid vs. Junta Local. Sharding Key: Sharding typically uses a sharding key, which is a chosen attribute or criterion (e. It can be either a single indexed column or multiple columns denoted by a value that determines the data division between the shards. g. 3 Create. Oracle Sharding builds on the generic sharding concept and extends it to offer an enterprise-grade distributed database solution that can handle massive amounts of data with ease. Partitioning vs. Also, can send notifications, automatically switch masters and slaves roles if a master is down and so on. Oracle Sharding is a feature of Oracle Database that lets you automatically distribute and replicate data across a pool of Oracle databases that share no hardware or software. The sharding extension is currently in transition from a seperate Project into DBAL. The DataNodes are used as common storage by all the namespaces,. Federation. Let each shard write locally to these tables and utilize sql merge replication to update/sync this data on all other shards. Sharding is referred to as horizontal scaling, and it makes it easier to scale as you can increase the number of machines to handle user traffic as it increases. The same code runs for all customers, but each customer sees. Range-based sharding produces a shard key using multiple fields and creates contiguous data ranges based on the shard key values. This usually requires that a single job has thousands of instances, a scale that most users never reach. For example, MySQL can be sharded through a driver, PostgreSQL has the Postgres-XC project, and other databases. 97 times compared to random data sharding with various query types. Yet, in my mind I think of partitioning as a basic level category and federation and sharding as more specific (subordinate) instances of partitioning. Sharding physically organizes the data. On the above example the. We can set up sharding (sometimes called database federation) pretty easily at one of many levels. The shard map manager is a special database that maintains global mapping information about all shards (databases) in a shard set. Then as you need to continue scaling you’re able to move. enableSharding("exampleDB") Sharding Strategy. 4. The pros and cons of graph system leveraging distributed consensus include: Small hardware footprint (cheaper). Keywords: Big Data, Hadoop 3. You still have issue #1 if you use sharding. Sharding is needed if a data set is too large to be stored in a single DB. But if a database is sharded, it implies that the database has definitely been partitioned. The constituent databases are interconnected via a computer network and may be geographically decentralized. It involves one database getting all of the writes from. See Partitioning: how to split data among multiple Redis instances and Redis Cluster data sharding. While sharding helps ease the load on a database and ensures a backup is in place, Gelvan says that sharding can only be a short-term option for scaling. Database Sharding takes more work, but has the advantage. tenant-federation. Database sharding is a type of horizontal partitioning that splits large databases into smaller components, which are faster and easier to manage. Database sharding can be simply defined as a 'shared-nothing' partitioning scheme for large databases across a number of servers, enabling new levels. They go on to describe it as “Sharding and federation: Neo4j 4. But you can also handle the sharding logic at the application level, as recent posts from the likes of Notion and Figma have described. spring. The most important factor is the choice of a sharding key. 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. 84 (sim) 3. Also, failure of one shard only impacts the users whose data resides in that shard. Hash Sharding is greatly used for targeted data operations. Instead of routing all writes to one server and scaling up, it’s possible to write to many servers and scale out. Federating data on a single machine is an inappropriate use of the term. Sharding. Đây là mô hình mà nhiều cơ sở dữ liệu NoSQL sử dụng. This approach allows for improved scalability, performance, and availability in. In this diagram, the same colors are used on both sides of the diagram to depict data for each of the 5 tenants (green for tenant1, blue for tenant2, yellow for tenant3, grey for tenant4, orange for tenant5)—so you can visually see how the tenant data is. Prometheus offers two types of federation: hierarchical and cross-service. In sharding, each shard is stored on a separate server, and queries are sent directly to the. Each partition of data is called a shard. And partitioning is a more specific instance of the more more general (superordinate) category divide-and-conquer. Sharding represents a technique used to enhance the scalability and performance of database management for handling large amounts of data. Auto sharding or data sharding is needed when a dataset is too big to be stored in a single. The sharding extension is currently in transition from a seperate Project into DBAL. Each shard contains a subset of the data, allowing for improved performance and scalability. In this case, the records for stores with store IDs under 2000 are placed in one shard. A primary key can be used as a sharding key. Having a large number of clients performing high-throughput operations can really test the limits of a single database instance. Please explain in simple words. The shard key should be static. the "employee id" here. In this first release it contains a ShardManager interface. El sharding es un concepto que se está poniendo de moda dentro de la comunidad criptográfica, debido a los grandes problemas de escalabilidad que tienen las principales plataformas como Bitcoin o Ethereum. Step 1: Make a PostgreSQL database backup. Users must manage data across numerous shard locations rather than accessing and managing it from a single entry point, which could be disruptive to some teams. FOCUS ON: Blog, Azure. ShardingSphere 数据分片的原理如下图所示，按照是否需要进行查询优化，可以分为 Simple Push Down 下推流程和 SQL Federation 执行引擎流程。. Sharing the Load. This is more complex setup and is much more involved to manage than a normal Prometheus deployment, so should be avoided. How to replay incremental data in the new sharding cluster. Step 2: Create New Databases for Sharding. In fact, PostgreSQL has implemented sharding on top of partitioning by allowing any given partition of a partitioned table to be hosted by a remote server. Modulo this hash with the number of database servers, i. The GO command signals the end of a batch of SQL statements. This allows for horizontal scaling, as more shards can be added on new servers when needed. In this first release it contains a ShardManager interface. Sharding Key: A sharding key is a column of the database to be sharded. Furthermore, we can distribute them across multiple servers or nodes in a cluster. Instead of routing all writes to one server and scaling up, it’s possible to write to many servers and scale out. 3. A shard is an individual partition that exists on separate database server instance to spread load. 5 exabytes of data are generated and processed by the IT. 3 Doctrine DBAL contains some functionality to simplify the development of horizontally sharded applications. Sharding can also improve geographic distribution, storing data closer to the users who. Sharding: Take one database and slice it to create shards of the same database. This requires the application to be aware of the modification to the data storage to work efficiently, as it needs to know where to find the information it needs. This point has been discussed ad-nauseam on Stack Overflow, specifically in this answer. Database Sharding takes more work, but has the advantage. Database shards are based on the fact that after a certain point it is feasible and. The main difference between them is the way the distribution happens. denormalization. It affords the ability to accommodate additional storage needs and more efficiently handle requests. The users have no idea where the data is stored. In fact, PostgreSQL has implemented sharding on top of partitioning by allowing any given partition of a partitioned table to be hosted by a remote server. A single machine, or database server, can store and process only a limited amount of data. It is a mechanism to achieve distributed systems. If we apply sharding to. sharding# Database partitioning deals with a single database instance, whereas sharding splits partitions (shards) across multiple database instances for scalability and availability. Sharding is splitting one group of data onto separate servers, while a federation is a group of humans, Vulcans, and Andorians. Both sharding and partitioning mean distributing data into smaller and more manageable chunks or subsets. This tutorial builds upon the Brian Swans tutorial on SQLAzure Sharding and turns all the examples into examples using the Doctrine Sharding support. It involves partitioning a large database into smaller, more manageable parts, known as shards. 4 and basically is a monitoring service for master and slaves. The federation architecture makes several distinct physical databases appear as one logical database to end-users. This tutorial demonstrates how to create your first cluster in Atlas from Helm Charts with Atlas Kubernetes Operator . Some databases have out-of-the-box support for sharding. These individual shards are then hosted on separate servers or nodes. Both data and query replacements are. Each shard is stored on a separate server, allowing the database to scale horizontally as the data grows. Instead, focus on your. Sharding manages the metadata using locality-preserving hashing and consistent hashing methods. Because NoSQL databases are designed with distributed computing and automatic sharding in. ago. Sharding on Azure SQL is a type of horizontal partitioning that splits large databases into smaller components, which are faster and easier to manage. Most probably YES. Method 2: yes, the reason for having a background process break/merge/load balancing them. Some databases have out-of-the-box support for sharding. For me this was one of the most confusing aspects of learning this stuff because they are often used interchangeably and there is a certain amount of overlap between the terms. Sharding. ”. This tutorial explains what database sharding is and walks through its pros and cons. ) The typical shard+repl setup is each shard is composed of several servers. In this first release it contains a ShardManager interface. Data sharding means breaking the huge database into smaller databases so that the latency and throughput are maintained after the database replication. Finally, we’ll enable sharding for a database by running the following command: sh. 2 Referential integrityDatabase 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 is particularly the case when it comes to heavy write contention, database locking and heavy queries. Differences between Database Sharding and Federation. The biggest pro of hash-based sharding is that it greatly increases the chances of having evenly distributed shards. Scalability with Sharding: A Real-World Marvel!🚀 Let's dive into the fascinating world of sharding and how it's. In sharding, each shard is stored on a separate server,. Any microservice can accept any request. Sharding is horizontal ( row wise) database partitioning as opposed to vertical ( column wise) partitioning which is Normalization. In this first release it contains a ShardManager interface. Data partitioning is a kind of Database architecture that is gaining popularity. Horizontal sharding refers to taking a single MySQL database and partitioning the data across several database servers, each with an identical schema. data consolidation. A simple way to shard the data is -. Sharding is the so-called umbrella term for all types of horizontal data partitioning schemes. Sharding is a way to split data in a distributed database system. Hence Sharding means dividing a larger part into smaller parts. So, think those individual shards as individual RS's. A manually sharded database, however, requires writing new database logic into your application code. Figure 4:Side-by-side comparison of Schema-based sharding vs. – Kain0_0. It is essential to choose a sharding key that balances the load and distributes the data. Sharding is a technique of splitting a large database into smaller and more manageable chunks, called shards, that can be distributed across multiple servers. Each partition (also called a shard ) contains a subset of data. By partitioning data across multiple servers, it allows for better load balancing and faster query response times. Performance Enhancement of Distributed System Using HDFS Federation and Sharding. Sharding is a common practice at companies with relational databases. Data federation is a data management strategy that can help you connect data from different sources. Clustering usually means to establish a tight bond between several machines, so that services can run on either of the machines and be relocated to a different machine in case one machine has. Federation does basic scaling of objects in a SQL Azure. 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. In databases, it means that several databases hold information, The database sharding examples below demonstrate how range sharding might work using the data from the store database. A shard is an individual partition that exists on separate database server instance to spread load. Sharding involves dividing a large dataset horizontally, creating smaller and independent subsets known as shards. First, accessing data from memory is faster than from a disk, and second, the data structures used to store data in memory are more. Data virtualization is an interface that provides a single point of access to data that hides its distributed and heterogeneous storage details. When a database is sharded, partitions are stored and managed by discrete servers that may run in different VMs, zones, or regions. Sharding makes it easy to generalize our data and allows for cluster computing (distributed computing). Database sharding is a process of breaking up large tables into multiple smaller tables, or chunks called shards, and distributing data across multiple machines or clusters. remy_porter • 6 mo. However sharding is a trade-off. In DBMS, Sharding is a type of DataBase partitioning in which a large database is divided or partitioned into smaller data and different nodes. 3 Doctrine DBAL contains some functionality to simplify the development of horizontally sharded applications. Database partitioning vs. By distributing the data among multiple machines, a cluster of database systems can store larger. Class names may differ. If scalability is the primary concern, database sharding is often the best choice, as it allows for easy. It provides high performance, high availability, and easy. Sharding can be used in system design interviews to help demonstrate a candidate’s understanding of scalability. Each shard contains a subset of the data, which is then distributed across multiple servers or nodes. Partitioning vs. However, sharding on graph data can be a Pandora box, and here is why: · Multiple shards will increase I/O performance, particularly data ingestion speed. 6. In Oracle 20c, Oracle came with 2 new advisors: Oracle Autonomous Database Advisor and the Oracle Sharding Advisor . Abstract. Sharding is nothing new from a traditional SQL or NoSQL big-data framework design perspective. The requirement to increase the capacity for writing usually prompts the use of. In a key- or hashed -based sharding architecture, a database application uses a shard key to locate a shard. , customer ID, geographic location) that determines which shard a piece of data belongs to. Sharding is a method for distributing data across multiple machines. This post will teach you how to shard in the simplest of ways. Sharding in Postgres is: a technique of splitting Postgres database tables into smaller tables (called “shards”) that is typically used to distribute data horizontally across multiple nodes comprising a cluster of database instances. e. partitioning. Both sharding and partitioning mean distributing data into smaller and more manageable chunks or subsets. What is Sharding? An Overview of Database Sharding. OPTIONS (dbname 'postgres', host 'hosturl. Sharding A federation is a set of things (usually states or regions) that together compose a centralized unit but each individually maintains some aspect of autonomy. Now part of tenant-b’s data is copied to tenant-a (albeit aggregated). In Range Sharding the data is divided based on ranges or keyspaces, and the nearer the shard keys, the more likely for data to place under the. The main benefit of directory-based sharding is higher flexibility when compared to the other strategies. Partitioning criteria A shard typically contains items that fall within a specified range determined by one or more attributes of the data. What is Sharding? Businesses that rely on monolithic Relational Database Management Systems (RDBMS) will have bottlenecks as the amount of data stored grows. Tablet sharding applies to YCQL and YSQL but partitioning is a YSQL feature. Database sharding is an architecture pattern for horizontal scaling. Replication, or Replica Sets in MongoDB parlance, is how MongoDB achieves high availability, Replica Sets are a Primary, and 0 to n amount of secondaries which have read-only copies of the data and. Those servers are configured in some replication (M-S, Galera, Group Replication, etc) for HA and/or read scaling. When Sharding is the Problem, not the Answer. It allows for faster access to data and enables a database to handle larger workloads by distributing data and processing power across multiple servers. Before we enable sharding for a collection, we’ll need to decide on a sharding strategy. Sharding vs. Range Based Sharding. Database sharding is a powerful technique employed to manage large databases more effectively. This is what database sharding is. Just to recap, sharding in database is the ability to horizontally partition the data across one more database shards. 0, featuring their Fabric database, advertised as offering “unlimited scalability. As I understand, in postgres, db level sharding is mostly done by partitioning the tables and moving each partition into seperate instance like shown bellow. A SQL table is decomposed into multiple sets of rows according to a specific sharding strategy. I deal with a lot of large systems and many large systems are complicated. 3 Doctrine DBAL contains some functionality to simplify the development of horizontally sharded applications. Within YugabyteDB partitioning is a user-defined, SQL-level concept, thus requiring an explicit definition through SQL. Database Plus is a concept for creating a distributed database system for more than sharding, positioned above DBMS. Allowing customers to have their own database, to share databases or to access many databases. This week, Neo4j announced version 4. Horizontal partitioning is when the table is split by rows, with different ranges of rows stored on different partitions. High Availability - With sharding, your data is spread across a fleet of database servers. CL#6-1 Sharding Federation vs. The tools are used to manage shard maps, and include the client library, the split-merge tool, elastic pools, and queries. That means, instead of one server acting as a primary (as in the case of replication) we now have several sharded servers with each one only holding part of the data. The term “shard” refers to a partition or subset of the. In general the shard catalog database is small (< 100 GBs) and read-only. Cassandra is NOT a column oriented database. If scalability is the primary concern, database sharding is often the best choice, as it allows for easy. Sharding at the Data Layer . 1. Applies to: Azure SQL Database. Learn about each approach and. As such, data federation has fewer points of potential failure. While modern database servers. Different databases use the term sharding: from manually isolating data into a few monolithic databases, to distributing little chunks of data across multiple servers. Learn about each approach and. Doctrine Database Abstraction Layer Documentation: Sharding . I am just confuse about the Sharding and Replication that how they works. The primary tool for this in the PostgreSQL ecosystem is the Citus extension . Sharding, or say partitioning, is a technique widely used in distributed systems which logically splits data into partitions. I've never partitioned data into multiple tables, because most RDBMS systems have the ability to partition the data in a table into separate storage configurations. Federation does basic scaling of objects in a SQL Azure Database. Due to restricted CPU power, memory, storage capacity, and throughput, response time will inevitably deteriorate. Compare Oracle Database vs. Sharing the Load. Database sharding is a powerful tool for optimizing the performance and scalability of a database. Hashed sharding forms a shard key using a single field's hashed index. Sharding and Partitioning. 2. Database Sharding is the process where a huge Database is partitioned horizontally. 5 exabytes of data are generated and processed by the IT industry. Sharding enables effective scaling and management of large datasets. Recap on FDW based Sharding. Sharding provides linear scalability and complete fault isolation for the most demanding applications. Configuration Item Explanation. Because of the large shard size, this mechanism can be prone to imbalances due to hot spots and unequal growth as was evidenced by the Foursquare. a capability available via the Citus open source extension to Postgres. Database sharding involves splitting a large database into smaller, more manageable parts known as shards. Introduction Apache Hadoop [1], the BD landmark, has become a large-scale data analyt-ics operating system. With Oracle Sharding, data is automatically distributed across multiple nodes, while still allowing the application to treat the database as a single instance. This technique divides a single logical database into. Great data consistency (easier to implement). Real-time access. This data will then be replicated down to each shard allowing each shard to read this data and inner join to this data in t-sql procs. Partitioning is a more general concept and federation is a means of partitioning. Polkadot utilises a sharding model that differs entirely from the Ethereum-based sharding mechanism and makes use of its cross-chain composability features to activate sharding through parachains. Sharding is a technique to distribute large amounts of identically structured data across a number of independent databases. Data federation is a software process that collects data from diverse sources and converts it into a common model. Data federation is an approach to collecting, storing, and making use of data through virtualization rather than by physical storage of a dedicated database. datasource. As long as you don't shard individual collection, collection must have primary location, at one of the replica sets. Database sharding is an architecture designed to help applications meet scaling needs through horizontal expansion. This is particularly the case when it comes to heavy write contention, database locking and heavy queries. Database sharding overcomes this limitation by splitting data into smaller chunks, called shards, and storing them across several database servers. Indexing, Replicating, and Sharding in MongoDB [Tutorial] MongoDB is an open source, document-oriented, and cross-platform database. Federated analytics: Decentralised analysis of the raw data stored on user devices. Auto sharding or data sharding is needed when a dataset is too big to be stored in a single. See full list on baeldung. Database sharding is the process of dividing the data into partitions which can then be stored in multiple database instances. A federated database can have multiple hardware, network protocols, data models, etc. Sharding Graph Data With Neo4j Fabric Fabric provides unlimited scalability by simplifying the data model to reduce complexity. It is especially popular with cloud developers creating Software as a Service (SAAS) offerings for end customers or businesses. rules. Latency reduction is due to two main reasons. This data will then be replicated down to each shard allowing each shard to read this data and inner join to this data in t-sql procs. g. Sharding is a MariaDB technique for dividing a single database server into many pieces. 0 now allows for horizontal scaling. It is a mechanism to achieve distributed systems. , last name in 'A-D') to live on a given database instance. Each partition of data is called a shard. The disadvantage is ultimately you are limited by what a single server can do. With sharding, you store data across multiple databases and spread the records evenly. RethinkDB uses the table's primary key to perform all sharding operations and it cannot use any other keys to do so. A bucket could be a table, a postgres schema, or a different physical database. Make sure you backup your PostgreSQL database before beginning the transfer procedure. Data federation makes the Oracle and Azure databases accessible under a common, federated data model so you can accomplish your goal with a single query. Another common (and practical) example is federating based on quality of service (paying users vs. Furthermore, it can be almost completely alleviated in a SQL database with proper isolation level usage and other techniques such as data replication (akin to sharding). · Hi Rajesh, Sharding logic needs to be. A hashing function hashes the sharding key value, and the output maps data to a particular shard. What is important to know is that you can shard database tables by consistent hash (system-managed sharding), by range or list (user-defined sharding), or a combination (composite sharding). Sharding in Postgres is: a technique of splitting Postgres database tables into smaller tables (called “shards”) that is typically used to distribute data horizontally across multiple nodes comprising a cluster of database instances. However, implementing sharding can be complex, and the specific strategy used will depend on the needs of the application and the. jBASE using this comparison chart. YugabyteDB distributes data by splitting the table rows and index entries into tablets. Abstract. Workaround: denormalize the database so that queries can be performed from a single table. Data sharding according to the z order, which is one of space-filling curves, improves the performance of MongoDB by 1. Sharding graph data is a notoriously hard problem. Once a logical shard is stored on another node, it is known as a physical shard. Figure 1: General Concept of Database Sharding. Keywords: Big Data, Hadoop 3. Difference between Database Sharding vs Partitioning. Distributed. Sharding implies breaking up the data across physical machines. Partitioning vs. Let each shard write locally to these tables and utilize sql merge replication to update/sync this data on all other shards. Atlas distributes the sharded data evenly by hashing the second field of the shard key. So we decided to do shard our db into multiple instances.