BigQuery’s decoupled storage and compute architecture leverages column-based partitioning simply to minimize the amount of data that slot workers read from disk. A method of splitting and storing a single logical dataset in multiple database instances. Database denormalization. A partition key is used to group data by shard within a stream. Sharding is a method to distribute data across multiple different servers. As of writing, we can only choose one (1) partition among all of these partitioning types. European customers vs. Modern innovations thrive on strategic data management. Each partition is known as a shard and holds a specific subset of the data, such as all the orders for a specific set of customers in an e-commerce application. –The question of partitioning vs. 1M rows in a table -- no problem. The Ethereum Wiki’s Sharding FAQ suggests random sampling of validators on each shard. 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. Spark Shuffle operations move the data from one partition to other partitions. Choosing a partition key is an important decision that affects your application's performance. partitioning. The topic of this month's PGSQL Phriday #011 community blogging event is partitioning vs. expr. A well-known form of partitioning is data partitioning, also known as sharding. Some of these databases are highly commercialized and are suitable for a broader range of scenarios. 4 and basically is a monitoring service for master and slaves. Sharding can be used in system design interviews to help demonstrate a candidate’s understanding of scalability. If you’ve used Google or YouTube, you’ve probably accessed sharded data. 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. Replication and Clustering. By default, a clustered index has a single partition. I thought this might. The common solution to this problem is using a hybrid between shared database and isolated databases - it's called database sharding, and basically, it means splitting your data into different databases, according to a sharding criterion (which in our case will by the TenantId) - but without having to keep each tenant on in a dedicated. Let’s look at some examples. In this case, the table used for the benchmark has 1. Horizontal partitioning is another term for sharding. We would like to show you a description here but the site won’t allow us. Other properties and other algorithms for sharding may be added in the future. In a key- or hashed -based sharding architecture, a database application uses a shard key to locate a shard. e. If you managed to bare reading until this last paragraph, please check also Partitioning vs. And if you are this far, go to method 2. partitioning. Tuples in the same partition are guaranteed to be on the same machine. sharding# Database partitioning deals with a single database instance, whereas sharding splits partitions (shards) across multiple database instances for scalability and availability. Big Data: Partitioning vs Sharding Adjust Here at Adjust we use both. “Horizontal partitioning”, or sharding, is replicating the schema, and then dividing the data based on a shard key. In the third method, to determine the shard number. For example, one might partition by date ranges, or by ranges of identifiers for particular business objects. Partitioning is dividing large tables into multiple tables. Distributed. In this post, SingleStore Developer Advocate, Joe Karlsson, explains the differences between database sharding vs. Compare postgresql execution plan. It's not a choice of one or the other, since the two techniques are not mutually exclusive. In a segment/partition system, it is possible to go back the same memory after swapping but the larger the physical memory, the less likely it will be to return to the same place. [Optional] An integer that defines the number of partitions to divide into. The micro-partition metadata maintained by Snowflake enables precise pruning of columns in micro-partitions at query run-time, including columns containing semi-structured data. One of the most important features of VoltDB is partitioning. In the context of scaling MongoDB: replication creates additional copies of the data and allows for automatic failover to another node. By default, the operation creates 2 chunks per shard and migrates across the cluster. "Partitioning" splits up the data, but only within a single server; it does not appear that there is any advantage for your use case. If Database sharding sounds a bit complicated, it implies partitioning an on-prem server into multiple smaller servers, known as shards, each of which can carry different records. range partitioning in Apache Spark. Horizontal partitioning can be done both within a single server and across multiple servers, the latter often being referred to as sharding. This month’s PGSQL Phriday invitation from Tomasz Gintowt is on the topic of “Partitioning vs sharding in PostgreSQL“. This is where PostgreSQL foreign data wrappers come in and provide a way to access a foreign table just like we are accessing regular tables in the local database. We leverage four primary database. Since version 10, a huge leap was made with. Horizontal partitioning (or row-based partitioning) means that data is split in multiple tables based on predicate you define (most often it relates to dates, so data is being partitioned by year, month, even day – if it makes. Sharding on a Single Field Hashed Index. Download Now. Others describe it as using partitions. Instead, the SolrCloud feature of the. Database partitioning is normally done for manageability, performance or availability reasons, as for load balancing. Sharding can be used in system design interviews to help demonstrate a candidate’s understanding of scalability. We also have quite a few databases of all sizes. This would allow parallel shard execution. There are multiple versions of partitions. Azure's best practices on data partitioning says: All databases are created in the context of a DocumentDB account. Data sharding is the breakdown of data spread across multiple computers, either as horizontal or vertical partitioning. As of v1. For a faster query response Hive table. Sharding, a side-by-side comparison How to use range partitioning & Citus sharding together for time series What about sharding using partitioned tables with postgres_fdw? The question of partitioning vs. To introduce horizontal scaling, the database is split into horizontal partitions, now called. Each partition has the same schema and columns, but also entirely different rows. Here the data is divided based on a shard key onto a separate database server instance. You put different rows into different tables, the structure of the original table stays the same in the new. This is where horizontal partitioning comes into play. Database. For MySQL, Sharding, not partitioning, involves putting different rows on different physical servers. In this strategy, each partition is a data store in its own right, but all partitions have the same schema. Somehow, somewhere somebody decided that what they were doing was so cool that they had to make up a new term for what people have been doing for many many years. Sharding and partitioning are cornerstone techniques in modern database architectures. Sharding is the act of creating shards. However, it does have a drawback with aggregating data across the multiple databases. Each shard is responsible for a subset of the workload, and queries can be. Think of each partition like being a different file - and opening 365 files might be slower than having a huge one. Intel kept (and keeps in 32-bit mode) segmentation alive long after it should have died out in its processors. For example, half the table can be searched on one machine and the other half on another machine. A shard is a horizontal data partition that contains a subset of the total data set. In this partitioning, each partition is a separate data store , but all partitions have the same schema . Each shard has the same database schema as the original database. Sharding in database is the ability to horizontally partition data across one more database shards. If not, there will be big changes down the line until it is. We call this a "shard", which can also live in a totally separate database. 1. Partitioning is about grouping subsets of data within a single database instance. You may need to partition on an attribute of the data if: The consumers of the topic need to aggregate by some attribute of the data. Algorithmically sharded databases use a sharding function (partition_key) -> database_id to locate data. Database Sharding vs. Each shard contains a subset of the data and can be processed independently. Here, each partition is known as a shard and holds a specific subset of the data, such as all the orders for a specific set of. Low Shard Key Frequency. sharding" from someone in the Citus open source team, since we eat, sleep, and breathe sharding for Postgres. A single DocumentDB account can contain several databases, and it specifies in which region the databases are created. Orthogonally to partitioning or sharding. Another resource is a bottleneck and you need to shard data. You can partition your data using 2 main strategies: on the one hand you can use a table column, and on the other, you can use the data time of ingestion. One index satisfies the needs of most Sitecore solutions but multiple indexes offer better scaling when needed. Imagine that the sales leads table has an extra column, revenue_ potential, as you see in Table 2. Partitioning is a generic term used for dividing a large database table into multiple smaller parts. Sharding makes it easy to generalize our data and allows for cluster computing (distributed computing). Database. Sharding is a method of partitioning data to distribute the computational and storage workload, which helps in achieving hyperscale computing. Each shard will have its replica in order to save data from data loss. The sharding algorithm is a 64bit Murmur-3 hash. Each individual partition is known as shard or database shard. When partitioning a table, you need to consider having enough data for each partition. Data sharding is a type of horizontal partitioning, which means splitting a large table or collection into smaller chunks, called shards, based on a key or a range of values. To choose the best method, you need to consider factors such as the size and growth rate of your data. It is essential to choose a sharding key that balances the load and distributes the data. Each partition is created based on the partitioning key. A partition is a division of a logical database or its constituent elements into distinct independent parts. Announce your blog post on one or more of these platforms: Twitter/Linkedin/FB using the #. Partitions, Tablespaces, and Chunks. The three Vs of data storage. Horizontal partitioning, also known as sharding, is the process of splitting a table into smaller and more manageable chunks based on a key column or a range of values. For sharding, the data model should ensure that data and queries are distributed evenly across the shards. There are very few cases where performance is enhanced by such. In context to the scaling of the MongoDB database, it has some features know as Replication and Sharding. However they’re still somewhat common, the google analytics 360 bigquery export for example, provides a new table shard each day, for the new data from the prior day. A database can be split vertically — storing different. A shard is a horizontal data partition that holds a portion of the complete data set and is thus in the responsibility of serving a portion of the overall demand. Sharded vs. In Azure Data Explorer, sharding is implemented using. By default, Spark/PySpark creates partitions that are equal to the number of CPU cores in the machine. We would like to show you a description here but the site won’t allow us. There's also the issue of balancing. Hashed sharding provides a more even data distribution across the sharded cluster at the cost of reducing Targeted Operations vs. It is responsible for serving a portion of the overall workload. Oracle Sharding: Part 1 – Overview. Using the FDW-based sharding, the data is partitioned to the shards in order to optimize the query for the sharded table. Partitioning Vs Sharding. Some databases have out-of-the-box support for sharding. . It’s not a choice of one or the other, since the two techniques are not mutually exclusive. The shard key should be static. Why Use Sharding? • Only sharding can reduce I/O, by splitting data across servers • Sharding benefits are only possible with a shardable workload • The shard key should be one that evenly spreads the data • Changing the sharding layout can cause downtime • Additional hosts reduce reliability; additional standby servers might be. A shard is an individual partition that exists on separate database server instance to spread load. Horizontal Partitioning (sharding) stores rows of a table in multiple database clusters. The advantage is the number of rows in each table is reduced (this reduces index size, thus improves search performance). The main downside of both sharding and partitioning is added complexity, albeit in different ways. . Both the techniques split a huge data set into different chunks and store it on different database servers. Multiple instances contain the same data. It results in scanning less data per query, and pruning is determined before query start time. Algorithmically sharded databases use a sharding function (partition_key) -> database_id to locate data. It seemed right to share a perspective on the question of "partitioning vs. It also discusses best practices for partitioning and gives an in-depth view at how horizontal scaling works in Azure Cosmos DB. In our exploratory scheme, each partition is a foreign table and physically lives in a separate database. In this tutorial, we’ll discuss two methods for splitting databases into parts to manage them efficiently:. See more on the basics of sharding here. ; Purpose: The difference is that sharding implies the data is spread across multiple computers while partitioning does not. Database sharding is the process of storing a large database across multiple machines. Partitioning and segmenting are essentially the same and are equally obsolete. entity id, the same approach applies. ”. A single machine, or database server, can store and process only a limited amount of data. 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. Sharding, a side-by-side comparison How to use range partitioning & Citus sharding together for time series What about sharding using. Replication -- needed if you have 1000 reads per second. Sharding (Horizontal Partitioning)— A type of horizontal partitioning that splits large databases into smaller components, which are faster and easier to manage. It limits you in data joining/intersecting/etc. The question of partitioning vs. It is useful for large, high-traffic applications that require high availability and fast response times. This article explains the relationship between logical and physical partitions. In order to determine whether you need a partitioning strategy and what it should be, consider three questions about your data:. With partitioning, we accomplish this scaling by inserting data into many small tables (with associated indexes) and limited scopes of data per table. Vertical partitioning: Each partition is a proper subset of the original database schema - i. See Partitioning: how to split data among multiple Redis instances and Redis Cluster data 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. Solutions. Hash-based Sharding. Products like elastics database queries and elastic database jobs have been created to fill this gap. Each cluster is further divided into multiple nodes. A partition is an allocation of storage for a table, backed by solid state drives (SSDs) and automatically replicated across multiple Availability Zones within an AWS Region. Partition management is handled entirely by DynamoDB—you never have to manage partitions yourself. Table partitioning is the process of splitting a single table into multiple tables. Example: if we are dealing with a large employee table and often run queries with WHERE clauses that restrict the results to a particular country or department . In this case, the records for stores with store IDs under 2000 are placed in one shard. Once slot workers read their data from disk, BigQuery can automatically determine more optimal data sharding and quickly repartition data using BigQuery’s in-memory shuffle. . Data is not only read but is partially processed on the remote servers (to the extent that this. Let’s look at some examples. The decision on what data to partition. Partitioning assumes the partitions are on the same server. Each table contains the same number of rows but fewer columns (see diagram below). It is the mechanism to partition a table across one or more foreign servers. Là cách chia cùng dữ liệu của cùng một bảng (table) ra nhiều DB khác nhau. Big Data: Partitioning vs Sharding Adjust Here at Adjust we use both. We also have quite a few databases of all sizes. The database hotspot problem arises when one shard is accessed more as compared to all other shards and hence, in this case, any benefits of sharding the. 131. Shard-Query is an OLAP based sharding solution for MySQL. Each shard is held on a separate database server instance, to spread load. We should specifically mention here that in partitioning , the partitions lies within a single database instance whereas in sharding the shards lies across different database servers. Essentially, sharding is just a fancy name given to the process of splitting the dataset along its rows. – Application sharding key-based routing is not supported – The existing databases, before being added to a federated sharding configuration, must be upgraded to Oracle Database 20c or later. The disadvantage is ultimately you are limited by what a single server can do. The goal is so these validators will not know which shard they will get in advance. In this technique, the dataset is divided based on rows or records. Without sharding, the database is limited to vertical scaling alone, which is beneficial but limited. Some data within a database remains present in all shards, [a] but some appear only in a single shard. a. See moreSharding vs. It’s no secret that PlanetScale has a focus on the ability to shard databases, but how does that differ from partitioning? The concepts behind partitioning and sharding are very similar. 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: Partitionning over several server, allowing parallel access (of different datas as opposed to replication) and, as such, memory and cpu load. Partitioning. Table sharding is the practice of storing data in multiple tables, using a naming prefix such as [PREFIX]_YYYYMMDD. Scaling a server cluster is easy and flexible; you keep adding machines as the size of your data increases. sharding Scalability. 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. Shard-Key. In terms of latency, MySQL Cluster should have more stable latency than sharded MySQL. Database sharding is a technique for horizontal scaling of databases, where the data is split across multiple database instances, or shards, to improve performance and reduce the impact of large amounts of data on a single database. Database sharding and partitioning. What’s more, sharding can be viewed as a very specific type of partitioning, namely — horizontal partitioning. Understanding MongoDB Sharding & Difference From Partitioning. (As mentioned before, a partition is a set of replicas ). Horizontal Partitioning: Also known as sharding, horizontal data partitioning involves dividing a database table into multiple partitions or shards, with each partition containing a subset of rows. Both partitioning and sharding are techniques used in database management…1. Such databases don’t have traditional rows and columns, and so it is interesting to learn how they implement partitioning. Each shard (or server) acts as the. Sharding is a specific type of partitioning in which dat. Different sharding strategies fit different scenarios. Sharding involves splitting a database into smaller shards, which can be distributed across multiple servers. It seemed right to share a perspective on. In the case of MySQL, this means that each node is its own MySQL RDBMS, with its own set of data partitions. 5. It also discusses best practices for partitioning and gives an in-depth view at how horizontal scaling works in Azure Cosmos DB. Learn about each approach and. By sharding, you divided your collection. 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. Similar to sharding, VoltDB partitioning is unique because: VoltDB partitions the database tables automatically, based on a partitioning column you specify. To handle the high data volumes of time series data that cause the database to slow down over time, you can use sharding and partitioning together, splitting your data in 2 dimensions. Our application is built on J2EE and EJB 2. Recently, due to heavy traffic, CPU overload (over 98% utilization) in our database instance. Federation vs. Sharding is the equivalent of “horizontal partitioning. Allow lighter joins. Horizontal Partitioning (sharding) stores rows of a table in multiple database clusters. This allows for larger datasets to be split into smaller chunks and stored in multiple data nodes, increasing the total storage capacity of the system. It seemed right to share a perspective on the question of "partitioning vs. 1. This is a topic near and dear to me and I’m excited to think about it some this month. Distributed. 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. Each shard is typically assigned to a different database server, which allows for parallel processing and faster query execution times. Various parts of the query e. To sum it up. Partitioning vs. When it considers the partitioning of relational data, it usually refers to decomposing your tables either row-wise (horizontally) or column-wise (vertically). This reduces the reading of unnecessary data, and. We leverage four primary database systems, termed as “Backends”, “Shards”, “Bagger” and “Tracker”. Hash Sharding: use a hashed index of a single field as the shard key to partition data across your sharded cluster. If you’ve used Google or YouTube, you’ve probably accessed sharded data. For a horizontal partitioning (sharding) tutorial, see Getting started with elastic query for horizontal partitioning (sharding). We achieve horizontal scalability through sharding”. However, sharding requires a high level of cooperation between an application and the database. Sharding is needed if a data set is too large to be stored in a single DB. Database Sharding vs Database Partition The terms "sharding" and "partitioning" get thrown around a lot when talking about databases. This will be used for sharding too. sharding allows for horizontal scaling of data writes by partitioning data across. 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. Difference between Database Sharding vs Partitioning. 4) Ordered index scan This scan will scan all. Data in each shard does not have to share resources such as CPU or memory, and can be read or written. Rather, you can choose to use Postgres native partitioning, or you can shard Postgres with an extension like Citus to distribute Postgres across multiple nodes—or you can use both. A distributed SQL database needs to automatically partition the data in a table and distribute it across nodes. It seemed right to share a perspective on the question of "partitioning vs. Imagine a sales database, we can. For this month’s PGSQL Phriday blogging challenge, Tomasz Gintowt asks if people rather use partitioning or sharding to solve business problems. Horizontal scaling allows. A good partition strategy should avoid Hot spots. Horizontal Partitioning: Also known as sharding, horizontal data partitioning involves dividing a database table into multiple partitions or shards, with each partition containing a subset of rows. Tomasz is a new PostgreSQL friend for me and I love the topic he’s picked: Partitioning vs. Let’s look at some examples. A table can be clustered or partitioned or both (depending on DBMS). Federating a database is how to provide the abstraction of a. Hashed sharding uses either a single field hashed index or a compound hashed index (New in 4. Unfortunately, the terms "partitioning" and "sharding" are used at. Do đó. A sharding key is an attribute or column that determines how the data is distributed among the shards. MongoDB provides a router program mongos that will correctly route sharded queries without extra application logic. The declaration includes the partitioning method as described above, plus a list of columns or expressions to be used as the partition key. 4) as the shard key to partition data across your sharded cluster. When you create date-named tables, BigQuery must maintain a copy of the schema and metadata for each date-named table. Consider the following points: There are three typical strategies for partitioning data: Firstly, Horizontal partitioning (often called sharding). whether Cassandra follows Horizontal partitioning (sharding) It may be clear that a shard can have multiple partitions in it. For example, a single shard can contain entities that have been partitioned vertically, and a functional. These two things can stack since they're different. This point has been discussed ad-nauseam on Stack Overflow, specifically in this answer. g. Show 3 more. You can use DocumentDB accounts to. Partitioning or sharding during data extraction requires some best practices to be followed. Partitioning Vs Sharding. Driver I can not find anyway to specify partitionkeys. The most basic example would be sharding by userID across 2 shards. Partitioning vs. Sharding vs. This makes it possible for parallell resolution of queries. The question of partitioning vs. Sharding is a method for distributing a single dataset across multiple databases, which can then be stored on multiple machines. Partitioning and Sharding in PostgreSQL are good features. But it's also possible to have a "shared nothing" architecture without partitioning. However, in case of Partitioning, the data is stored on a single machine and managed by different database servers running on the same machine. As I understand the strategy Cosmos DB use is partitioning with partition keys, but since we use the MongoDB. System Design for Beginners: Design for Experienced Engineers: a member fo. Now that I'm looking at the data I gathered, I'm asking my self if choosing. Sharding is performed by exchanges, that is, messages will be partitioned across "shard" queues by one exchange that we should define as sharded. If you want to filter rows where this date is equal to a value then you can do a partition full table scan to read all of the partition that houses this data with a full scan. Partitioning is a general term, and sharding is commonly used for horizontal partitioning to scale-out the database in a shared-nothing architecture. Database sharding is a type of horizontal partitioning that splits large databases into smaller components, which are faster and easier to manage. In this post, we will examine various data sharding strategies for a distributed SQL database, analyze the tradeoffs, explain. Partitioning Vs Sharding. Partitioning is a general term used to describe the breaking up of your logical data elements into multiple entities typically for the purpose of performance, availability, or maintainability. Broadcast. Union views might provide the full original table view. 1. 3. 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. 0, a sharding key is always the object's UUID. Within YugabyteDB partitioning is a user-defined, SQL-level concept, thus requiring an explicit definition through SQL. A sharding key is an attribute or column that determines how the data is distributed among the shards. The declaration includes the partitioning method as described above, plus a list of columns or expressions to be used as the partition key. Partitioning vs shards: Partitioning and sharding are similar techniques used to divide large datasets into smaller, more manageable subsets. Amazon Relational Database Service (Amazon RDS) is a managed relational database service that provides great features to make sharding easy to use in the cloud. The schema of the table is replicated in every shard, and a unique portion of the whole table lives in. Sharding is a technique to split the table up between different machines. I described the PDP as using segments. There are many ways to split a dataset into shards. It can also be functional (which maps rows of data into one partition or the other depending on their value). 2 , the Oracle Sharding feature provides the exact capability of shared nothing architecture with. Sharded vs. Partition keys are Unicode strings, with a maximum length limit. Replication may help with horizontal scaling of reads if you are OK to read data that potentially isn't the latest. See sp_execute _remote for a stored procedure that executes a Transact-SQL statement on a single remote Azure SQL Database or set of databases serving as shards in a horizontal partitioning scheme. By contrast, sharding offers unlimited scalability. Every distributed table has exactly one shard key. We talk about one more important component of System Design: Sharding. Database sharding is the easiest partition technique that can be used with SQL Server. For example, a table of customers can be. In the example above, using the customer ZIP. The table is partitioned into “ranges” defined by a key column or set of columns, with no overlap between the ranges of values assigned to different partitions. Unlike Sharding and Replication, Partitioning is vertical scaling because each data partition is in the same. Or you want a separate backup machine. Key Takeaways. sharding in PostgreSQL. When you create a table, the initial status of the table is CREATING . Sharding and Solr. This article explores when to use each – or even to combine them for data-intensive applications. Sharding on Azure SQL is a type of horizontal partitioning that splits large databases into smaller components, which are faster and easier to manage. Shard (database architecture) A database shard, or simply a shard, is a horizontal partition of data in a database or search engine. Just to recap, sharding in database is the ability to horizontally partition the data across one more database shards. In general, it is best to prototype in InnoDB, grow the dataset until. System Design for Beginners: Design for Experienced Engineers: a member fo. 5. Sharding distributes data across multiple servers, while partitioning splits tables within one server. As your data grows in size, the database. Union views might provide the full original table view. Hence Sharding means dividing a larger part into smaller parts. Database sharding is a useful database architecture pattern to use when the data stored in a database grows to an extent that it starts impacting the performance of the application. Choosing a partition key is an important decision that affects your application's performance. Partitioning is a general term, and sharding is commonly used for horizontal partitioning to scale-out the database in a shared-nothing architecture. But a partition can reside in only one shard. Step 1: Analyze scenario query and data distribution to find sharding key and sharding algorithm. Each partition of data is called a shard. Replication -- needed if you have 1000 reads per second. Sharding implies breaking up the data across physical machines. Sharding is the process of horizontally partitioning data across multiple nodes in a cluster. The partitioning algorithm evenly and randomly. ; Vertical partitioning. Horizontal Partitioning/Sharding. The machinery used behind the scenes implies defining an exchange that will partition, or shard messages across queues. Horizontal partitioning is what we term as "Sharding". Partitioning is a generic term used for dividing a large database table into multiple smaller parts. Central to this strategy is database partitioning — serving as the backbone of today’s distributed database systems. Here’s an illustration that shows how horizontal partitioning works in practice. You can use numInitialChunks option to specify a different number of initial chunks. Sharding is a very important concept that helps the system to keep data in different resources according to the sharding process.