It also discusses best practices for partitioning and gives an in-depth view at how horizontal scaling works in Azure Cosmos DB. as Cassandra is column oriented DB. You can shard this data set pretty easily but you might not have to depending on the type of analysis you are trying to do. Sharding is usually a case of horizontal partitioning. It seemed right to share a perspective on the question of "partitioning vs. I was recently pointed to the article about DB Sharding (Shared Nothing). See moreThe decision to use sharding or partitioning depends on several factors, including the scale of your application, expected growth, query patterns, and data. I have three columns that seem like reasonable candidates for partitioning or indexing: Time (day or week, data spans a 4 month period)4. Later in the example, we will use a collection of books. Each partition has the same schema and columns, but also entirely different rows. Partitions, in terms of MySQL and PostgreSQL feature set, are physical segmentations of data. Auto sharding or data sharding is needed when a dataset is too big to be stored in a single. 在海量資料的儲存情境下，DB 的效能會受到影響，此時透過垂直擴充架構也許是無法滿足的，因此會需要資料分片（shard），以水平擴展的方式來提升效能（可以想像成多個公路比起一條道路，可以達到分流，減緩堵塞）。 水平擴展方式一般來說又可以分為 Horizontal Partitioning 與 Sharding，前者是在. It seemed right to share a perspective on the question of "partitioning vs. Both are methods of breaking. Non-Monotonically Changing Shard KeysThe following image illustrates a sharded cluster using the field X as the shard key. A good partition strategy should avoid Hot. Method 2: yes, the reason for having a background process break/merge/load balancing them. In that context, two words that keep on showing up with. 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 table 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. entity id, the same approach applies. The schema of the table is replicated in every shard, and a unique portion of the whole table lives in. Horizontal database partition or sharding is the mostly commonly used partitioning method in SQL databases. There is no way to perform consistent hashing because there is no way to obtain a consistent list, except by fiat. For others, tools and middleware. The basics of partitioning. 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. Data in each shard does not have to share resources such as CPU or memory, and can be read or written in parallel. The hash function can take more than one sharding. Recently, due to heavy traffic, CPU overload (over 98% utilization) in our database instance. Sharding is a database scaling technique based on horizontal partitioning of data across multiple independent physical databases. Declarative Partitioning. Based on my research, I checked that you can do indexing and partitioning to improve query performance, I seem to have known each of the concept and how to do it, but I'm not sure about the difference between both?. The simplest way to scale a database system is vertical scaling. It is the mechanism to partition a table across one or more foreign servers. For example you would split your vehicles table into multiple tables like: (assuming you want to use the vehicleNo as the "key") VehiclesNosLessThan1000After create a sharded document, when data are not evenly distributed, then mongodb will balance the data. In case of sharding the data might be nicely distributed and hence the queries. Partitions link objects in Realm Database to documents in MongoDB. A sharded database is a single logical Oracle Database that is horizontally partitioned across a pool of physical Oracle Databases (shards) that share no hardware or software. Conclusion: Sharding and partitioning are cornerstone techniques in modern database architectures. Sharding is the spreading of horizontal partitions across multiple servers. Sharding is also a 1% feature. Sharding: Partitionning over several server, allowing parallel access (of different datas as opposed to replication) and, as such, memory and cpu load. Cache, Cache, Cache. Sharding takes a different approach to spreading the load among database instances. Data is organized and presented in "rows," similar to a relational database. sharding" from someone in the Citus open source team, since we eat, sleep, and breathe sharding for Postgres. 2. A big graph is partitioned into multiple small graphs, and the storage and computation of each small graph are stored on different servers. It’s important to note. A shard is an individual partition that exists on separate database server instance to spread load. What is Database Sharding? Sharding, also often called partitioning, involves splitting data up based on keys. They exist within a single database instance, and are used to reduce the scope of data you're interacting with at a particular time, to cope with high data volume situations. It is estimated that 180 zettabytes. Horizontal. Horizontal partitioning is when the table is split by rows, with different ranges of rows stored on different partitions. Let's dive right in -. 3. Union views might provide the full original table view. Choosing a partition key is an important decision that affects your application's performance. The primary difference is one of administration. Sharding is the equivalent of “horizontal partitioning. It dispatches client requests to the relevant shards and aggregates the result from shards. Figure 1 - Horizontally partitioning (sharding) data based on a partition key. Database partitioning is the act of splitting a database into separate parts, usually for manageability, performance or availability reasons. A shard is an individual partition that exists on separate database server instance to spread load. The hash function can take more than one sharding key. Ví dụ ta có bảng dữ liệu thông tin về người dùng, ta sẽ dựa trên location của người dùng để quyết. Modulo this hash with the number of database servers, i. There are a number of base access methods: 1) Primary key access 2) Unique key access (== 2 primary key accesses) 3) Partition pruned scan access (Partition Key is provided in condition) (this can be both an ordered index scan or full scan). In this tutorial, we’ll discuss two methods for splitting databases into parts to manage them efficiently: sharding and partitioning. Particularly number 2 as Postgresql is notoriously. Data partitioning criteria and the partitioning strategy decide how the dataset is divided. Partitioning options on a table in MySQL in the environment of the Adminer tool. A bucket could be a table, a postgres schema, or a different physical database. g. ”. Horizontal partitioning is achieved in a relational database by storing rows from the same table in several database nodes. Link back to this blog post. On the above example the. The advantage of DBMS single server partitioning is that it is relatively simple to set up and manage. Sharding database allows efficient scaling and managing of massive databases. Most Citus setups I have seen primarily use Citus sharding, and not Postgres table partitioning. This increases performance because it reduces the hit on each of the individual. So that leaves two more options. With a distributed database, you can place nodes in different local regions to decrease this latency. The topic of this month's PGSQL Phriday #011 community blogging event is partitioning vs. Sharding makes it easy to generalize our data and allows for cluster computing (distributed computing). Most data is distributed such that. "Plain" MongoDB use sharding instead, and you can set up a document property that should be used as a delimiter for how your data should be sharded. As your data grows in size, the database. There are many methods to break a large dataset into shards. One of the critical benefits of database sharding is that it. 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. With the non-partitioned tables of course, you could use native foreign keys. Here you replicate the schema across (typically) multiple instances or servers, using some kind of logic or identifier to know which instance or server to look for the data. Each shard (or server) acts as the single source for this subset. An application has the option to choose the partition key that can minimize latency on a range query for a partitioned index. Second, run a platform or a program to pull and parse the database log to understand which changes happened during the partitioning process, and apply these changes to the new sharding cluster (incremental data shards). What is Database Sharding? | Hazelcast. That may be true, but you still have to do the sharding so you can split up the traffic. Sharding involves splitting a database into smaller shards, which can be distributed across multiple servers. This document captures our exploratory testing around using foreign data wrappers in combination with partitioning. Each partition has the. SQL Server requires application-level logic for sending queries to the best node . This month’s PGSQL Phriday invitation from Tomasz Gintowt is on the topic of “Partitioning vs sharding in PostgreSQL“. size of row; kind of data (strings, blobs, etc) active. cloud. It caches the shard map locally, and uses the map to route data requests to the appropriate shard. This functionality is hidden behind a series of APIs that are contained in the Elastic Database client library , which is available for Java and . Database sharding and partitioning are two similar concepts that refer to dividing a database into smaller parts or chunks in order to improve its performance and scalability. Sharding is a database scaling technique based on horizontal partitioning of data across multiple independent physical databases. Now let us discuss each partitioning in detail that is as follows: 1. This is where horizontal partitioning comes into play. Again, let's discuss whether it is even relevant. The GO command signals the end of a batch of SQL statements. “Data is distributed across multiple servers using partitioning, and each partition is further replicated to provide availability. Suppose we know that we need to spread the data of this SQL table into 4 servers. If your sharding scheme is simple it can be done in your application layer, but if its more complex you may want to use a tool. Partitioning in the context of Service Fabric stateful services refers to the process of determining that a particular service partition is responsible for a portion of the complete state of the service. Likewise, the data held in each is unique and independent of the. This will be used for sharding too. By default, the operation creates 2 chunks per shard and migrates across the cluster. Database sharding is a type of horizontal partitioning that splits large databases into smaller components, which are faster and easier to manage. The server-side system architecture uses concepts like sharding to ma. It is especially popular with cloud developers creating Software as a Service (SAAS) offerings for end customers or businesses. Compared with the partitioning problem in. Sharding Scenario: Adding a Database in a Hash-based Sharding Strategy. Consistent hash and range sharding are the most useful data sharding strategies for a distributed SQL database. partitions, with index_id = 1 for each partition used by the index. Some popular ways in SQL Server to partition data are database sharding, partitioned views and table partitioning. Database sharding isn’t anything like clustering database servers, virtualizing datastores or partitioning tables. While everything looks fine, the. Whereas, in network sharding, the entire blockchain network is partitioned into sub-networks called shards. It involves breaking down a large database into smaller, more manageable pieces called shards. Product inventory data is separated into shards in this case depending on the product key. Partitioning vs Sharding vs Scale-out. Additionally, we’ll explore the basic concept of each method, along with an example. A shard is an individual partition that exists on separate database server instance to spread load. By splitting a large table into smaller, individual tables, queries that access only a fraction of the data can run faster because there is less data to scan. Sharding and partitioning are techniques to divide and scale large databases. We would like to show you a description here but the site won’t allow us. Vertical Partitioning. Sharding involves saving the partitioned data onto other computers and storage facilities. more immediacy and money. A shard is an individual partition that exists on separate database server instance to spread load. Horizontal scaling, also known as scale-out, refers to adding machines to share the data set and load. The idea is to implement partitions as foreign tables and have other PostgreSQL clusters act as shards and hold a subset of the data. The shard catalog uses materialized views to automatically replicate changes to duplicated tables in all shards. Also if a database is partitioned, it does not imply that the database is definitely sharded. Hazelcast named in the Gartner ® Market Guide for Event Stream Processing. It is popular in distributed database management. It is a range-based sharding. If this is simply a history of what each user likes, then you can probably use database partitioning to partition the data by range on date, and then sub-partition on the user_id. Shard-Query is an OLAP based sharding solution for MySQL. There's also the issue of balancing. Database partitioning is the act of splitting a database into separate parts, usually for manageability, performance or availability reasons. 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. Take the hash of the primary key, i. In this tutorial, we’ll discuss two methods for splitting databases into parts to manage them efficiently: sharding and partitioning. Horizontal partitioning: Splitting the data by group of lines naturally given its primary keys (Row Splitting). Horizontal sharding. This article explains the relationship between logical and physical partitions. This initial. The motivation behind this is clear, it makes the task of ensuring service levels on the database easier because the data set is smaller and it allows one to prioritize the investment to improve an aspect of the system because of the logical separation (e. In a key- or hashed -based sharding architecture, a database application uses a shard key to locate a shard. It allows you to define a combination of sharded tables and unsharded tables. 어떻게 보면 샤딩은 수평 파티셔닝의 일종이다. List shard maps offer a high level of isolation for each shard, and with that, a great deal of flexibility (geography, scale, security, etc. In case of replicating existing shards, there will be more hosts to respond to a query request. Imagine a sales database, we can. }) MongoDB sets the max number of seconds to block writes to two seconds and begins the resharding operation. To introduce horizontal scaling, the database is split into horizontal partitions, now called. Problem. Each partition (also called a shard ) contains a subset of data. Consider a table that store the daily minimum and maximum temperatures. This is not a new challenge; organizations have faced it for years, and horizontal sharding is one of the key patterns for solving it. For example, high query rates can exhaust the CPU. 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. – Bill Karwin. Data in each shard does not have to share resources such as CPU or memory, and can be read or written in parallel. Sharding your database. In many cases , the terms sharding and partitioning are even used synonymously, especially when preceded by the terms “horizontal” and. This would allow parallel shard execution. This initial. Every distributed table has exactly one shard key. Auto-sharding — The chunking of data, managing the range depending on the distribution of data across chunks is automatic or called auto-sharding of data. Database systems with large data sets or high throughput applications can challenge the capacity of a single server. In today’s data-driven world, where the volume and complexity of data continue to expand at an unprecedented pace, the need for robust and scalable database solutions has become paramount. 8. Database sharding and partitioning. The partitioned table itself is a “ virtual ” table having no storage of its. The solution : Wouldn't this be a better approach? 1) It shards the data better so I don't need to use starts_with. Each machine has its CPU, storage, and memory. Partitioning is a rather general concept and can be applied in many contexts. It also discusses best practices for partitioning and gives an in-depth view at how horizontal scaling works in Azure Cosmos DB. Database normalization involves designing the tables in the database to reduce or eliminate duplicated data. Splitting your database out into shards can help reduce the load on your database, leading to improved performance. Learn about each approach and. However, a sharding key cannot be a. It is essential to choose a sharding key that balances the load and distributes the data. Table of Contents. SQL Server 2008 introduced a table partitioning wizard in SQL Server Management Studio. In this case, the records for stores with store IDs under 2000 are placed in one shard. Hashed sharding provides a more even data distribution across the sharded cluster at the cost of reducing Targeted Operations vs. Sharding. Content delivery networks are the best examples of this. Sharding and moving away from MySQL. So that leaves two more options. A chunk consists of a range of sharded data. 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. For example, a high-traffic blogging. Data partitioning or sharding is a technique of dividing data into independent components. Sharding is a database partitioning technique being considered by blockchain networks and being tested by Ethereum. Horizontal sharding, otherwise known as range partitioning, is a technique which divides the data into rows based on a determined key or range of values. Database Sharding is the process where a huge Database is partitioned horizontally. The decision to use sharding or partitioning depends on several factors, including the scale of your application, expected growth, query patterns, and data distribution requirements: Use Sharding When: Dealing with extremely large datasets that can’t be managed efficiently by a single server. What is Database Sharding? Database sharding is a horizontal partitioning of data in a database. We already planned to go for "sharding", so we'll have multiple mysql instances, in which there are multiple databases, and in each database there are multiple tables like 'table_001', 'table_002', etc. Sharding September 8,. 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. Using the FDW-based sharding, the data is partitioned to the shards in order to optimize the query for the sharded table. Sharding (or database sharding) is the process of breaking up large tables, indexes, or partitions into smaller chunks called shards (or tablets in YugabyteDB) that. It's not necessary to understand these. Sharding is a specific type of partitioning in which dat. Sharding a database is a common scalability strategy for designing server-side systems. After removing the images, the database can store 10 times as many tasks; you can go much longer before you have to think about implementing a horizontal partitioning scheme. Sharding spreads the load over more computers, which reduces contention and improves performance. Sharding is actually a type of database partitioning, more specifically, Horizontal Partitioning. What is Sharding? Sharding is a database architecture pattern related to horizontal partitioning — the practice of separating one table’s rows into multiple different tables, known as partitions. Sharding is needed if a data set is too large to be stored in a single DB. Horizontal Partitioning. Partitioning -- won't help the use case you described. Whether you're sharding by a granular uuid, or by something higher in your model hierarchy like customer id, the approach of hashing your shard key before you leverage it remains the same. execute_query. . ini file by copying the text above, and replacing the values with your new defaults. 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. Sharding, at its core, is a horizontal partitioning technique. Thus, each shard operates as an independent database, consistent with its own schema, indexes, and data subsets. In this video, we dive into the topic of Database Sharding vs Partitioning and break down the key differences between the two. For limitations of elastic query, see Preview limitations; For a vertical partitioning tutorial, see Getting started with cross-database query (vertical partitioning). When data is written to the table, a partitioning function will be used by MySQL to decide. These settings specify the default sharding parameters for newly created databases. I guess the cosmos UI behaves weirdly. Database Sharding and Database Partitioning are similar in that they both divide a larger database into smaller parts, but the way they handle and distribute data differs. It is a partitioned row store. Splitting your database out into shards can help reduce the load on your database, leading to improved performance. Yes, it's possible. Figure 1. Each partition (also called a shard ) contains a subset of data. Database sharding and partitioning are two similar concepts that refer to dividing a database into smaller parts or chunks in order to improve its performance and scalability. Figure 1 is an example. Horizontal Partitioning (sharding) stores rows of a table in multiple database clusters. The topic of this month’s PGSQL Phriday #011 community blogging event is partitioning vs. Sharding is also referred to as horizontal partitioning. In this case, the table used for the benchmark has 1. 7. Sharding at the core is splitting your data up to where it resides in smaller chunks, spread across distinct separate buckets. When. Both are methods of breaking a large dataset into smaller subsets – but there are differences. Range Based Sharding. To help customers implement partitioning on these large tables, this 2-part article goes over the details. A single DocumentDB account can contain several databases, and it specifies in which region the databases are created. 3. We distribute the data across our databases as follows: A partitioned table is split to multiple physical disks, so accessing rows from different partitions can be done in parallel. Database. In this scenario, we start with 4 databases (DB1 to DB4) and use a hash-based sharding strategy. What is Sharding? Sharding is a database architecture pattern related to horizontal partitioning — the practice of separating one table’s rows into multiple different tables, known as partitions. Sharding partitions the data-set into discrete parts. 2. Post-hash, documents with "close" shard key values are unlikely to be on the same chunk or shard - the mongos is more likely to perform Broadcast Operations to fulfill a given ranged query. We want s. Round-robin Partitioning. The guidelines for participating are as follows: Publish your blog post about “ partitioning vs sharding ” by Friday, August 4th, 2023. Key Takeaways. Sharding là một mẫu kiến trúc cơ sở dữ liệu liên quan đến phân vùng ngang - thực tế tách một hàng bảng Bảng thành nhiều bảng khác nhau, được gọi là partitions. It negates the use of any index. Jayant Chakravarti Senior Assistant Editor, Spiceworks Ziff Davis. Our application is built on J2EE and EJB 2. A database node, sometimes referred as a physical shard, contains multiple logical shards. Unlike Sharding and Replication, Partitioning is vertical scaling because each data partition is in the same. The basis for this is in PostgreSQL’s Foreign Data Wrapper (FDW) support, which has been a part of the core of PostgreSQL for a long time. A sharding key is an attribute or column that determines how the data is distributed among the shards. Historically postgres has fdw and partitioning features that can be used together to build a sharded database. Conclusion. 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. However, in some use cases it can make sense to partition your database tables where parts of the table are distributed on different servers. The declaration includes the partitioning method as described above, plus a list of columns or expressions to be used as the partition key. Partitioning could be a different database inside MySQL on the same server, or different tables, or even by column value in a singular table. Database partitioning vs. sharding in PostgreSQL. Of course, it may not be the only solution. Sharding Scenario: Adding a Database in a Hash-based Sharding Strategy. Most importantly, sharding allows a DB to scale in line with its data growth. 1 Answer. Partitioning provides very few use cases to justify its existence; sharding provides write scaling at the cost of complexity. Typically, different sets of tables reside on different databases. The main difference. Mỗi partitions có cùng schema và cột, nhưng cũng có các hàng hoàn toàn khác nhau. Data Partitioning. In that context, two words that keep on showing up with regards to databases are sharding and partitioning. What is sharding? Sharding is a type of database partitioning that separates large databases into smaller, faster, more easily managed parts. We distribute the data across our databases as follows:A partitioned table is split to multiple physical disks, so accessing rows from different partitions can be done in parallel. This article explains the relationship between logical and physical partitions. In this scenario, we start with 4 databases (DB1 to DB4) and use a hash-based sharding strategy. Database Sharding vs Partitioning – System Design Concepts . I have been reading about scalable architectures recently. In this systems design video I will be going over how to scale databases using database partitioning, in particular horizontal partitioning aka sharding and. Broadcast Operations. It is responsible for serving a portion of the overall workload. 131. Sharding is any time you split your large database into smaller pieces to limit full table scans during runtime. Later in the example, we will use a collection of books. I am happy to discuss any of the above in more detail, but only in a more focused context. This initial. You can also query across multiple tenants, even if they are in separate partitions. In this case, the table used for the benchmark has 1. You can have single partitions in the table expire, without needing to set the option to all tables in the dataset. Sharding in database is the ability to horizontally partition data across one more database shards. The difference between CockroachDB and a manually sharded database is that when you _do_ have to perform some cross-shard transactions (which you inevitably have to do at some point), in CockroachDB you can execute them (with a reasonable performance penalty) with strong consistency and 2PC between the shards, whereas in your manually. Each physical database in such a configuration is called a shard. Sharding is a method of partitioning data to distribute the computational and storage workload, which helps in achieving hyperscale computing. You can use numInitialChunks option to specify a different number of initial chunks. 차이점은 파티셔닝은 모든 데이터를. g. Sharding is a way to split data in a distributed database system. Each. 3 Answers. You separate them in another table / partition, and when you are performing updates, you do not update the. And as the app scales, your expenses grow more slowly because the bulk of your storage needs are going into very inexpensive Blob storage. sharding allows for horizontal scaling of data writes by partitioning data across. 4 Answers. A partition is a division of a logical database or its constituent elements into distinct independent parts. Distributed. Jeremy Holcombe , October 18, 2023. Partitioning is about grouping subsets of data within a single database instance. When a query is executed, the database system identifies which partition(s) to access based on the Country specified in the query conditions, thereby optimizing the query performance by limiting the data scanned. Hashed sharding provides a more even data distribution across the sharded cluster at the cost of reducing Targeted Operations vs. On the other hand, data partitioning is when the database is. For MySQL, Sharding, not partitioning, involves putting different rows on different physical servers. Figure 1 shows an overview of horizontal partitioning or sharding. I thought this might make. Benefits 🔹 Facilitate horizontal scaling. Sharded vs. However, in some use cases it can make sense to partition your database tables where parts of the table are distributed on different servers. Sharding solves various capacity challenges such as data exceeding the storage capacity of a single database. 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. We call these cross-shard queries. Sharding is a database. Data sharding is the breakdown of data spread across multiple computers, either as horizontal or vertical partitioning. Once you have identified a sharding key, it’s time to think about a sharding strategy. Or you want a separate backup machine. Whether you're sharding by a granular uuid, or by something higher in your model hierarchy like customer id, the approach of hashing your shard key before you leverage it remains the same. Postgres built-in "native" partitioning—and sharding via PG extensions like Citus—are both tools to grow your Postgres database, scale your. By sharding, you divided your collection. Actual latency for purely in-memory data could be similar. . The basis for this is in PostgreSQL’s Foreign. Each partition is known as a "shard". But these terms are used for different architectural concepts. If you are using mongoDB as a backend for a REST interface, the best practice is to create on collection per resource. Option is right there in the portal when provisioning a new collection. Sharding is also referred as horizontal partitioning. Logical partitions are formed based on the value of a partition key that is associated with each item in a container. But a partition can reside in only one shard. Right click on a table in the Object Explorer pane and in the Storage context menu choose the Create Partition command: In the Select a Partitioning. 2:Faster Access. Partitioning vs. Put another way, you Replicate shards; a data-set with no shards is a single 'shard'. It involves breaking down a large database into smaller, more manageable pieces called shards. 4) as the shard key to partition data across your sharded cluster. Database-level sharding, on the other hand, has the database system taking charge of managing shards, distributing data, and executing queries. If any of this is true, database sharding can be a potential solution to your problems. The main difference is that sharding implies the data is spread across multiple computers while partitioning is about grouping subsets of data within a single database instance. Database Sharding vs Partitioning. 1 Answer. executor-based partition pruning. Each chunk has inclusive lower and exclusive upper limits based on the shard key. Load balancing/Chunk Migration — Mongo manages an equal distribution of data across shards by migrating the chunks, so as to unleash the power of distributed computing. Sharding, also known as horizontal partitioning, is a popular scale-out approach for relational databases. Platform. In MySQL, the term “partitioning” means splitting up individual tables of a database. Partitioning is dividing large tables into multiple tables. In sharding, data is split horizontally into multiple shards. A sharding key is an attribute or column that determines how the data is distributed among the shards.