A simple hashing function can be the modulus of the key and the number of shards. Database normalization ensures data efficiency by eliminating redundancy and ensuring. Shard & shard key: To make partition or distribute data we need to make a base feature (attribute) on which we can partition the data. I thought this might make. Horizontal partitioning is achieved in a relational database by storing rows from the same table in several database nodes. In the world of databases, two commonly used techniques for managing large amounts of data are database sharding and partitioning. Horizontally partitioning (sharding) data based on a partition key That data is heavily written. 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). So that leaves two more options. The balancer migrates data between shards. 어떻게 보면 샤딩은 수평 파티셔닝의 일종이다. . Sharding is a database. g. Sharding is a type of partitioning, such as Horizontal Partitioning (HP) There is also Vertical Partitioning (VP) whereby you split a table into smaller distinct parts. In this scenario, we start with 4 databases (DB1 to DB4) and use a hash-based sharding strategy. Replication vs. For a horizontal partitioning (sharding) tutorial, see Getting started with elastic query for horizontal partitioning (sharding). The schema of the table is replicated in every shard, and a unique portion of the whole table lives in. Hazelcast named in the Gartner ® Market Guide for Event Stream Processing. Sharding is a method for distributing a single dataset across multiple databases, which can then be stored on multiple machines. Partitioning: Splitting a big database into smaller subsets called partitions so that different partitions can be assigned to different nodes (also known as sharding). Hashed sharding provides a more even data distribution across the sharded cluster at the cost of reducing Targeted Operations vs. This article explores when to use each – or even to combine them for data-intensive applications. 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). Vertical sharding — Vertical partitioning on the other hand refers to division of columns into multiple tables. Final step in search of the limits of the scalability of the relational databases is to sacrifice one of the core principles of the relational model, the database normalization. . The less number of records a query has to run over, the more performant it will be. sharding allows for horizontal scaling of data writes by partitioning data across. Sharding is usually a case of horizontal partitioning. DrawbacksA shard is essentially a horizontal data partition that contains a subset of the total data set, and hence is responsible for serving a portion of the overall workload. Database sharding isn’t anything like clustering database servers, virtualizing datastores or partitioning tables. Learn the similarities and differences between sharding and partitioning, understand the use. sharding vs partitioning vs clustering vs replication. Like partitioning, sharding is also a method to divide off a database to be saved separately. Horizontal partitioning is another term for sharding. Database Sharding vs Database Partition The terms "sharding" and "partitioning" get thrown around a lot when talking about databases. The problem of data partitioning in graph databases - graph partitioning. If you run a multiple core machine with seperate NUMAs, this can also increase performance. 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. partitioning. As your data grows in size, the database will continue to. 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. A range can be a portion of the chunk or the whole chunk. Database sharding is a type of horizontal partitioning that splits large databases into smaller components, which are faster and easier to manage. 131. I thought this might make the query. Think of each partition like being a different file - and opening 365 files might be slower than having a huge one. Trong nhiều trường hợp, các thuật ngữ Sharding và Partitioning thậm chí còn được sử dụng đồng nghĩa, đặc biệt là khi đi trước các thuật ngữ “horizontal” và “vertical”. Jeremy Holcombe , October 18, 2023. For example, a database of university students may be sharded based on the first letter of. Distributed. 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. cloud. 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. In replication, we basically copy the database across multiple databases to provide a quicker look and less response time. Then it's like using a database with a much smaller dataset, and that by itself is likely to improve performance a little bit. ini file by copying the text above, and replacing the values with your new defaults. Partitions link objects in Realm Database to documents in MongoDB. While everything looks fine, the. Replication may help with horizontal scaling of reads if you are OK to read data that potentially isn't the latest. 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. For example, high query rates can exhaust the CPU. This functionality is hidden behind a series of APIs that are contained in the Elastic Database client library , which is available for Java and . Sharding is a database scaling technique based on horizontal partitioning of data across multiple independent physical databases. Thus, each shard operates as an independent database, consistent with its own schema, indexes, and data subsets. Sharding and partitioning is great if your query logically touches only one of the shards or partitions. Lastly maybe consider a NoSQL option (highly doubt you need to do this) If you have not done at least 3/5 options I mentioned you probably should not do sharding and look at the alternatives. Starting in PostgreSQL 10, we have declarative partitioning. But if your query has to visit every shard or partition, then it's more costly. Distributed. }) MongoDB sets the max number of seconds to block writes to two seconds and begins the resharding operation. For true sharding then Skype's pl/proxy is probably the best. Case 1 — Algorithmic Sharding One way to categorize sharding is algorithmic versus dynamic . . This is a topic near and dear to me and I’m excited to think about it some this month. ". It involves breaking down a large database into smaller, more manageable pieces called shards. In this case, the table used for the benchmark has 1. 1. The distinction of horizontal vs vertical comes from the traditional tabular view of a database. Database partitioning deals with a single database instance, whereas sharding splits partitions (shards) across multiple database instances for scalability and availability. However, since YugabyteDB provides both, it’s important to use the right terminology. This document captures our exploratory testing around using foreign data wrappers in combination with partitioning. Horizontal partitioning is what we term as "Sharding". A database can be split vertically — storing different tables & columns in a separate database or horizontally — storing rows of a same table in multiple database nodes. However, in some use cases it can make sense to partition your database tables where parts of the table are distributed on different servers. However, a sharding key cannot be a. A hashing function hashes the sharding key value, and the output maps data to a particular shard. A sharding key is an attribute or column that determines how the data is distributed among the shards. Partitioning is a general term, and sharding is commonly used for horizontal partitioning to scale-out the database in a shared-nothing architecture. Partitioning provides very few use cases to justify its existence; sharding provides write scaling at the cost of complexity. In terms of latency, MySQL Cluster should have more stable latency than sharded MySQL. 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. Imagine a sales database, we can. Partitioning assumes the partitions are on the same server. The topic of this month's PGSQL Phriday #011 community blogging event is partitioning vs. It is popular in distributed database management. Now, I need to have a way to access the data in this table quickly, so I'm researching partitions and indexes. Creating multiple servers will release a server from one another's locks. Figure 4:Side-by-side comparison of Schema-based sharding vs. 1 Horizontal partitioning — also known as sharding. 2. I may be wrong here but my understanding is that partitioning is a kind of sharding, usually referring to horizontal or row level sharding (although that may be platform specific). Horizontal sharding. Database sharding is a powerful tool for optimizing the performance and scalability of a database. adminCommand ( {. Sharding is a way to split data in a distributed database system. Database partitioning is normally done for manageability, performance or availability reasons, as for load balancing. The word shard means "a small part of a whole. In MySQL, the term “partitioning” means splitting up individual tables of a database. Each machine has its CPU, storage, and memory. partitioning. In this blog post, we’ll discuss the relevant terms and definitions behind sharding and partitioning in YugabyteDB and show you how to use both correctly. Sharding is a common practice at companies with relational databases. Horizontal and vertical sharding. Each shard is responsible for a subset of the workload, and queries can be. You can also query across multiple tenants, even if they are in separate partitions. This is the twenty-first video in the series of System Design Primer Course. Using the FDW-based sharding, the data is partitioned to the shards in order to optimize the query for the sharded table. 1Also known as "index-organized table" under Oracle. Partitions, in terms of MySQL and PostgreSQL feature set, are physical segmentations of data. 3) I will consume much less capacity on queries since it won't have to go through items I don't need. 8. It seems to me a bit like Sharding to Oracle RAC is like SQL Server partitioning is to Oracle Partitioning. Sharding vs. Database Sharding vs Partitioning – System Design Concepts . A shard is. 5. Sharding is a form of partitioning, with the emphasis being that each shard is located on a separate physical node. Additionally,. Some data within a database remains present in all shards, [a] but some appear only in a single shard. It is estimated that 180 zettabytes of data will be created by. Hence Sharding means dividing a larger part into smaller parts. Partitioning. Sharding is a database partitioning technique being considered by blockchain networks and being tested by Ethereum. PartitioningData partitioning can be done horizontally or vertically, while sharding is usually done horizontally. partitioning. For example, in an ecommerce application, you might have one database node serving product catalog data, and another database node capturing and processing orders. Sharding is actually a type of database partitioning, more specifically, Horizontal Partitioning. Sharding vs Partitioning: Partitioning is data distribution on the same machine across tables or databases. In the first method, the data sits inside one shard. In this post, SingleStore Developer Advocate, Joe Karlsson, explains the differences between database sharding vs. . So we decided to do shard our db into multiple instances. Link back to this blog post. In general less REMOTE / SCATTER -> GATHER pairs means less cluster communication. So the data in each partition is unique but the schema remains the same. Vertical partitioning - Cross-database queries (Topology 1): The data is partitioned vertically between a number of databases in a data tier. e. UserIDs that are even would be on shard 0 and odd userIDs would be on shard 1. A simple hashing function can be the modulus of the key and the number of shards. This defeats the purpose of sharding/partitioning. Sharding is the spreading of horizontal partitions across multiple servers. Microservices that use the same database; Vertical partitioning by groups of tables; Each of these scenarios can now be enabled on Citus using regular CREATE SCHEMA commands. Partitioning is the idea of splitting something large into smaller chunks. . Partitioning involves dividing a database into smaller, logical partitions based on specific criteria. Vertical partitioning, aka row splitting, uses the same splitting techniques as database normalization, but ususally the. Sharding vs. Sharding and Partitioning. Sharding involves saving the partitioned data onto other computers and storage facilities. Sharding, also known as horizontal partitioning, is a popular scale-out approach for relational databases. For performance, tables without correct indexes result in full table or clustered index scans. 1 (hopefully we’re switching to EJB 3 some day). Likewise, the data held in each is unique and independent of the. System Design for Beginners: Design for Experienced Engineers: a member fo. 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. Auto sharding or data sharding is needed when a dataset is too big to be stored in a single. Then place that row in the corresponding server number. : Confusing terminology! network partitioning ≠ data partitioning consistent hashing ≠ consistency. Database partitioning is normally done for manageability, performance or availability reasons, or for load balancing. 4) Ordered index scan This scan will scan all. Sharding solves various capacity challenges such as data exceeding the storage capacity of a single database. Sharding and moving away from MySQL. Partitioning is about grouping subsets of data within a single database instance. A single DocumentDB account can contain several databases, and it specifies in which region the databases are created. In MongoDB, a sharded cluster consists of: Shards; Mongos; Config servers ; A shard is a replica set that contains a subset of the cluster’s data. Replication. you are leveraging database sharding. PostgreSQL allows you to declare that a table is divided into partitions. This is particularly the case when it comes to heavy write contention, database locking and heavy queries. On the other hand, data partitioning is when the database is. database-design. For hashed sharding: The sharding operation creates empty chunks to cover the entire range of the shard key values and performs an initial chunk distribution. Sharding 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 vs. In this example, product inventory data is divided into shards based on the product key. What is Database Sharding? | Hazelcast. Third, choose a data-check strategy to compare the data between the original database and new sharding cluster. Jayant Chakravarti Senior Assistant Editor, Spiceworks Ziff Davis. The data-based partitioning allows for features that might be impossible to implement with sharded tables. Sharding vs Partitioning. Because xa transaction and partitioning is supported, it can do decentralized arrangement to two or more servers of data of same table. Non-Monotonically Changing Shard KeysThe following image illustrates a sharded cluster using the field X as the shard key. The partitioned table itself is a “ virtual ” table having no storage of its. Hashed sharding uses either a single field hashed index or a compound hashed index (New in 4. Horizontal data partitioning or sharding is a technique for separating data into multiple partitions. Database sharding is a type of horizontal partitioning that splits large databases into smaller components, which are faster and easier to manage. 16. Sharding is a technique of partitioning database tables by row ("horizontally"); typically this technique requires a key to be selected that determines how the rows are to be partitioned. 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. . Sharding is more general and is usually used when the database is split on several servers. The following topics describe the physical organization of a sharded database: Sharding as Distributed Partitioning. The list of popular data partitioning techniques is as follows: Horizontal Partitioning. But a partition can reside in only one shard. Database denormalization. Compared with the partitioning problem in. Learn about each approach and. The distribution used in system-managed sharding is intended to. This initial. What I would like to confirm is, if partitioning is still needed in the sub-tables (table_001, table_002, etc). Again, let's discuss whether it is even relevant. Method 1: Yes the reason why every shard has to be checked. It involves breaking down a large database into smaller, more manageable pieces called shards. Each DocumentDB account also enforces its own access control. Horizontal partitioning and sharding. Each shard is held on a separate database server instance, to spread load. Download Now. ”. Sharding can be used in system design interviews to help demonstrate a candidate’s understanding of scalability. Sharding refers to horizontal scaling, and was introduced to Weaviate in v1. Hashed sharding provides a more even data distribution across the sharded cluster at the cost of reducing Targeted Operations vs. shardID = identifier % numShards. It is a way of splitting data into smaller pieces so that data can be efficiently accessed and managed. But as a backend developer. A lot of the options are described on our site here, as well as the advanced options we support. Partitioning allows each partition to be deployed on a different type of data store, based on cost and the built-in features that data store offers. 5. But these terms are used for different architectural concepts. If you will frequently update the date (users can. It seemed right to share a perspective on the question of “partitioning vs. Sharding facilitates the possibility of adding more machines to spread out the load. , user ID), which yields a range of 0 to 400. Driver I can not find anyway to specify partitionkeys in my queries. First of all try to optimize the database/queries (can be combined with vertical scaling - by using more powerful server for the database) Enable replication (if not already) and use secondary instances for read queries; Use partitioning and/or shardingMake sure you're interview-ready with Exponent's system design interview prep course: the basics of database sharding and partitio. executor-based partition pruning. Partitioning is a general term, and sharding is commonly used for horizontal partitioning to scale-out the database in a shared-nothing architecture. Overall, a database is sharded and the data is partitioned. partitioning. Here's is a figure from MySQL's official documentation on shard key. Edit: Your interviewer is also wrong. The replication strategy determines where replicas are stored in the cluster. 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" from someone in the Citus open source team, since we eat, sleep, and breathe sharding for Postgres. Key-based Partitioning. Hash vs Range-Based Sharding The biggest pro of hash-based sharding is that it greatly increases the chances of having evenly distributed shards . The main difference. For example, let’s say a query has an equality predicate based on the field sourceairport and destinationairport. Although some storage services align nicely with the traditional data partitioning strategies, DynamoDB has a slightly less direct mapping to the silo, bridge, and pool models. System-managed sharding is a sharding method which does not require the user to specify mapping of data to shards. MongoDB is a database that supports this method. It is essential to choose a sharding key that balances the load and distributes the data. . Sharding is possible with both SQL and NoSQL databases. It allows for faster access to data and enables a database to handle larger workloads by distributing data and processing power across multiple servers. The technique divides the data into buckets using some type of hash key such as a date and/or a natural key. Replication refers to creating copies of a database or database node. 2. In this case, the table used for the benchmark has 1. – Kain0_0. A shard is an individual partition that exists on separate database server instance to spread load. High cardinality keys are preferable to low cardinality keys to avoid un-splittable chunks. For example, a table of customers can be. Q&A: Partitioning vs Sharding, Scaling Behavior, and Visualization Tools for YugabyteDB This Distributed SQL Tips & Tricks post looks at partitioning vs sharding, scaling limitations in RocksDB. This would allow parallel shard execution. Each partition of data is called a shard. Each partition (also called a shard ) contains a subset of data. Sharding is needed if a data set is too large to be stored in a single DB. So we decided to do shard our db into multiple instances. High Availability: If an outage happens in sharded architecture, then only some specific shards will be. We talk about one more important component of System Design: Sharding. Horizontal partitioning or sharding. Many modern databases have built-in sharding system. Horizontal partitioning can be done both within a single server and across multiple servers, the latter often being referred to as sharding. Data Partitioning. A primary key can be used as a sharding key. Sharding / partitioning ≠ replication DB shard 1 shard 3 shard 2 replica 2 replica 2DB replica 3DB 3 partitions vs. Horizontal scaling, also known as scale-out, refers to adding machines to share the data set and load. Recently, due to heavy traffic, CPU overload (over 98% utilization) in our database instance. Recently, due to heavy traffic, CPU overload (over 98% utilization) in our database instance. A single SQL database has a limit to the volume of data that it can contain. Postgres built-in “native” partitioning—and sharding via PG extensions like Citus—are both tools to grow your Postgres database, scale your. 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. Database sharding needs to be done in such a way that the incoming data should be inserted into a correct shard, there should not be any data loss and the result queries should not be slow. Each chunk has inclusive lower and exclusive upper limits based on the shard key. Each partition is a separate data store, but all of them have the same schema. Some data within a database remains present in all shards, [a] but some appear only in a single shard. Horizontal partitioning (sharding) Figure 1 shows horizontal partitioning or sharding. Step 2: Create New Databases for Sharding. Hybrid sharding, as the name goes, is the hybrid of two or more of the aforementioned. 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. Table of Contents. Postgres built-in "native" partitioning—and sharding via PG extensions like Citus—are both tools to grow your Postgres database, scale your. Sharding makes it easy to generalize our data and allows for cluster computing (distributed computing). –Sharding is also referred as horizontal partitioning. Sharding and partitioning are techniques to divide and scale large databases. 샤딩은 동일한 스키마 를 가지고 있는 여러대의 데이터베이스 서버들에 데이터를 작은 단위로 나누어 분산 저장 하는 기법이다. 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. Partitioning vs. NET. In a database, horizontal partitioning, also known as sharding, involves dividing the rows of a table into smaller tables and storing them on different servers or database instances. 131. Sharded vs. You put different rows into different tables, the structure of the original table stays the same in the new. Each partition is a separate data store, but all of them have the same schema. 1M rows in a table -- no problem. This article will help you understand what Database Sharding is and how MySQL Sharding works. Suppose we know that we need to spread the data of this SQL table into 4 servers. Each shard is a separate database, stored on a different server, and only contains a portion of the. Consistent hash and range sharding are the most useful data sharding strategies for a distributed SQL database. The technique for distributing (aka partitioning) is consistent hashing”. Sharding your database. In comparison, when using range-based sharding. Horizontal data partitioning or sharding is a technique for separating data into multiple partitions. List shard maps offer a high level of isolation for each shard, and with that, a great deal of flexibility (geography, scale, security, etc. Partitioning is a generic term used for dividing a large database table into multiple smaller parts. Declarative Partitioning #. Sharding is a way to split data in a distributed database system. Database sharding vs partitioning. Data is organized and presented in "rows," similar to a relational database. This initial. It seemed right to share a perspective on the question of “partitioning vs. Range Based Sharding. Since version 10, a huge leap was made with. For a horizontal partitioning (sharding) tutorial, see Getting started with elastic query for horizontal partitioning (sharding). The new storage engine "Spider" does work for its strong scalability to access other storage engine of MySQL, to idea to the most considerations are below; 1:Scalability. Federating a database is how to provide the abstraction of a. Different relational DB worlds do replication differently; some directly send queries to replicas using network connections, others stream queries (or rows to be updated) as files that are “played”, etc. 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. 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. The most important factor is the choice of a sharding key. Customer id vs. 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. Problem. Once connected, create two new databases that will act as our data shards. This technique supports horizontal scaling but can be complex and requires careful planning. A simple way to shard the data is -. Each shard in the sharded database is an independent Oracle Database instance that hosts subset of a sharded database's data. Include “PGSQL Phriday #011” in the title or first paragraph of your blog post. How do I know which server is responsible for/ stores a certain2 Answers. A sharding key that has only 50 possible values, is considered low cardinality, while one that might be able to express several million values might be considered a high cardinality key. Cassandra is NOT a column oriented database. Database sharding vs partitioning. 4 Answers. One of the critical benefits of database sharding is that it. Partitioning and clustering play an important role when we have a huge amount of data and this huge data needs to be stored in the database or data warehouse. When it comes to managing large databases, two common techniques are database sharding. To shard Postgres, you can use Citus. Sharding is a technique to distribute large amounts of identically structured data across a number of independent databases. Shard-Query is an OLAP based sharding solution for MySQL. Solutions. Distributed. In the context of scaling MongoDB: replication creates additional copies of the data and allows for automatic failover to another node. Sorted by: 17. By default, the operation creates 2 chunks per shard and migrates across the cluster. In a key- or hashed -based sharding architecture, a database application uses a shard key to locate a shard. It is a partitioned row store. PostgreSQL provides a number of foreign data wrappers (FDW’s) that are used for accessing external data sources. Each chunk has inclusive lower and exclusive upper limits based on the shard key. Mike Grayson: Sharding is the act of partitioning your collections so that parts of your data are dispersed among multiple servers called shards. What is sharding? Sharding is a type of database partitioning that separates large databases into smaller, faster, more easily managed parts. Sharding Architecture. Product inventory data is separated into shards in this case depending on the product key. Each partition contains a single copy of the data in the database and functions as a separate database in its own right. The unsharded tables (like lookup tables) are freely joinable to sharded tables, and sharded tables may be joined to each other as long as the tables are joined by the shard key (no cross shard or self joins. Each partition has the. Both are methods of breaking. YugabyteDB supports both hash and range sharding of data across nodes to enable the. Sharding at the core is splitting your data up to where it resides in smaller chunks, spread across distinct separate buckets. In many cases , the terms sharding and partitioning are even used synonymously, especially when preceded by the terms “horizontal” and. We would like to show you a description here but the site won’t allow us. Sharding Typically, when we think of partitioning, we’re describing the process of breaking a table into smaller, more manageable tables on the same database server. The only difference is that in transaction sharding, the partitioning and creation of shards are done based on the transactions. Take the hash of the primary key, i. When data is written to the table, a. Partitioning Azure SQL Database. 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. It also discusses best practices for partitioning and gives an in-depth view at how horizontal scaling works in Azure Cosmos DB. Sharding is also a 1% feature. It seemed right to share a perspective on the question of "partitioning vs. Partitioning creates separate physical units within the same database in the same server, while sharding distributes data across multiple databases in different server. Each database server in the above architecture is called a Shard while the data is said to be partitioned. I am happy to discuss any of the above in more detail, but only in a more focused context. 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. g. Choosing a partition key is an important decision that affects your application's performance. This means that the attributes of the Database will remain the same but only the records will change. . Horizontal partitioning: Splitting the data by group of lines naturally given its primary keys (Row Splitting). Union views might provide the full original table view. Queries are simple. 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. The concept is simplistic and enables scalability in distributed computing, but. What is Database Sharding? Sharding, also often called partitioning, involves splitting data up based on keys. Like partitioning, sharding is also a method to divide off a database to be saved separately. SQL Server 2008 introduced a table partitioning wizard in SQL Server Management Studio.