Each. SQL Server 2008 introduced a table partitioning wizard in SQL Server Management Studio. Replication -- needed if you have 1000 reads per second. Ta có 3 cách thức Sharding dữ liệu như sau: Horizontal sharding. I guess the cosmos UI behaves weirdly. See other posts by Luka. Horizontal data partitioning or sharding is a technique for separating data into multiple partitions. Ranged sharding is most efficient when the shard key displays the following traits: Large Shard Key Cardinality. But these terms are used for different architectural concepts. System Design for Beginners: Design for Experienced Engineers: a member fo. All data fits in-memory. In terms of latency, MySQL Cluster should have more stable latency than sharded MySQL. Partitioning creates separate physical units within the same database in the same server, while sharding distributes data across multiple databases in different server. Additionally,. By increasing the processing power, memory allocation, or storage capacity, you can increase the performance and volume that a database system can handle without increasing. Third, choose a data-check strategy to compare the data between the original database and new sharding cluster. The technique for distributing (aka partitioning) is consistent hashing”. Horizontal partitioning can be done both within a single server and across multiple servers, the latter often being referred to as sharding. Each DocumentDB account also enforces its own access control. The balancer migrates data between shards. A Comprehensive Guide To Understanding MongoDB Sharding. The less number of records a query has to run over, the more performant it will be. e. Sharding would generally be considered entirely separate servers with separate IPs. Low Shard Key Frequency. Using some kind of third party library that encapsulates the partitioning of the data (like hibernate shards) Implementing it ourselves inside our application. 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. Both are methods of breaking. Various parts of the query e. Partitioning vs. Each partition (also called a shard ) contains a subset of 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. Database Sharding vs Partitioning – System Design Concepts . This increases performance because it reduces the hit on each of the individual. Replication vs. Sharding solves various capacity challenges such as data exceeding the storage capacity of a single database. Yes, sharding is splitting data into a subset per cluster. 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. About Oracle Sharding. (As mentioned before, a partition is a set of replicas ). 2. Auto sharding or data sharding is needed when a dataset is too big to be stored in a single. Benefits 🔹 Facilitate horizontal scaling. Furthermore, we’ll also list some advantages and disadvantages of each method. It seemed right to share a perspective on the question of "partitioning vs. Hazelcast named in the Gartner ® Market Guide for Event Stream Processing. Sharding and partitioning is great if your query logically touches only one of the shards or partitions. shardID = identifier % numShards. horizontal partitioning or sharding. Database sharding is a type of horizontal partitioning that splits large databases into smaller components, which are faster and easier to manage. By default, the operation creates 2 chunks per shard and migrates across the cluster. Data sharding is the breakdown of data spread across multiple computers, either as horizontal or vertical partitioning. Sharding, at its core, is a horizontal partitioning technique. Once connected, create two new databases that will act as our data shards. Partitioning is about grouping subsets of data within a single database instance. Horizontal data partitioning or sharding is a technique for separating data into multiple partitions. Sharding and partitioning are techniques to divide and scale large databases. Sharding and moving away from MySQL. Horizontally partitioning (sharding) data based on a partition key That data is heavily written. In this article, we will explore the. You can shard by list (one shard for each unique key) or range (consecutive ranges of keys housed in the same shard). Database sharding is the optimization of large databases by splitting data from a larger database table into multiple smaller tables (shards). 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. The shard catalog uses materialized views to automatically replicate changes to duplicated tables in all shards. sharding. # Example of. In replication, we basically copy the database across multiple databases to provide a quicker look and less response time. Vertical sharding — Vertical partitioning on the other hand refers to division of columns into multiple tables. 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. 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. This article explains the relationship between logical and physical partitions. This document captures our exploratory testing around using foreign data wrappers in combination with partitioning. Sharding is needed if a data set is too large to be stored in a single DB. Recently, due to heavy traffic, CPU overload (over 98% utilization) in our database instance. That feature is called shard key. The only thing I can think of is to partition the table based on length of code. 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. 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. You can use numInitialChunks option to specify a different number of initial chunks. Large databases usually have a negative impact on maintenance time, scalability and query performance. These end customers are often referred to as "tenants". Sharding is any time you split your large database into smaller pieces to limit full table scans during runtime. The declaration includes the partitioning method as described above, plus a list of columns or expressions to be used as the partition key. We would like to show you a description here but the site won’t allow us. Like partitioning, sharding is also a method to divide off a database to be saved separately. Typically, different sets of tables reside on different databases. 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. Just like many database strategies, partitioning also aims to reduce the effort of querying data. With it, there is dedicated syntax to create range and list *partitioned* tables and their partitions. This initial. A shard is an individual partition that exists on separate database server instance to spread load. The disadvantage is ultimately you are limited by what a single server can do. executor-based partition pruning. 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. However, to take full advantage of sharding, the application needs to be fully aware of it. sharding in PostgreSQL. Federation vs. 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. Sharding is used when Partitioning is not possible any more, e. The topic of this month's PGSQL Phriday #011 community blogging event is partitioning vs. In the world of databases, two commonly used techniques for managing large amounts of data are database sharding and partitioning. Partitioning is dividing large tables into multiple tables. Database sharding vs partitioning. The first shard contains the following rows: store_ID. In sharding, data is split horizontally into multiple shards. 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. Implementing table partitioning on a table that is exceptionally large in Azure SQL Database Hyperscale is not trivial due to the large data movement operations involved, and potential downtime needed to accomplish them efficiently. Each shard is responsible for a subset of the workload, and queries can be. A sharded database is a collection of shards . 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. In MySQL, the term “partitioning” applies to individual tables of a database. You are conflating MongoDB replication (where secondaries contain a full copy of the data for redundancy) with sharding (partitioning of a logical database across a cluster of machines). You can also query across multiple tenants, even if they are in separate partitions. There are many ways to split a dataset into shards. –Sharding is also referred as horizontal partitioning. Sharding Scenario: Adding a Database in a Hash-based Sharding Strategy. On the above example the. The GO command signals the end of a batch of SQL statements. 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. However, to take full advantage of sharding, the application needs to be fully aware of it. Postgres built-in “native” partitioning—and sharding via PG extensions like Citus—are both tools to grow your Postgres database, scale your. Partitioning and Sharding are similar concepts. By using separate partition keys for each tenant, you can easily query the data for a single tenant. sharding allows for horizontal scaling of data writes by partitioning data across. This article explores when to use each – or even to combine them for data-intensive applications. Because NoSQL databases are designed with distributed computing and automatic sharding in. What is Database Sharding? | Hazelcast. In many cases , the terms sharding and partitioning are even used synonymously, especially when preceded by the terms “horizontal” and. To illustrate, let’s say you have a database that stores information about all the products. Additionally, we’ll explore the basic concept of each method, along with an example. Sharding Scenario: Adding a Database in a Hash-based Sharding Strategy. Partitions can co-exist on a single machine, whereas shards. sharding in PostgreSQL. The word “Shard” means “a small part of a whole“. To help customers implement partitioning on these large tables, this 2-part article goes over the details. Shard & shard key: To make partition or distribute data we need to make a base feature (attribute) on which we can partition the data. 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. sharding” from someone in the Citus open source team, since we eat, sleep, and breathe sharding for Postgres. Sharding vs Partitioning. Database partitioning deals with a single database instance, whereas sharding splits partitions (shards) across multiple database instances for scalability and availability. 4. The nature of how data is scoped and managed by DynamoDB adds some new twists to how you approach multitenancy. Database partitioning is normally done for manageability, performance or availability reasons, as for load balancing. Both systems use some form of partition key for partitioning the data. Step 2: Create New Databases for Sharding. A partition is a division of a logical database or its constituent elements into distinct independent parts. For performance, tables without correct indexes result in full table or clustered index scans. Key Differences Between Database Sharding and Partitioning. If the index is also partitioned by the index keys on sourceairport and destinationairport, then the query will only need to read. 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. Conclusion. You can use DocumentDB accounts to. Sharded vs. MySQL's has no built-in sharding capability. I was recently pointed to the article about DB Sharding (Shared Nothing). Horizontal and vertical sharding. Figure 4:Side-by-side comparison of Schema-based sharding vs. We call these cross-shard queries. function executes a query on the appropriate shard and handles any errors that may occur. But if a database is sharded, it implies that the database has definitely been partitioned. In the first method, the data sits inside one shard. 2. Horizontal partitioning is what we term as "Sharding". Hash vs Range-Based Sharding The biggest pro of hash-based sharding is that it greatly increases the chances of having evenly distributed shards . 2. Hashed sharding uses either a single field hashed index or a compound hashed index (New in 4. MongoDB – Replication and Sharding. We achieve horizontal scalability through sharding”. Suppose we know that we need to spread the data of this SQL table into 4 servers. A database can be split vertically. The shard catalog also contains the master copy of all duplicated tables in an SDB. Thus, each shard operates as an independent database, consistent with its own schema, indexes, and data subsets. size of row; kind of data (strings, blobs, etc) active. Whereas, in network sharding, the entire blockchain network is partitioned into sub-networks called shards. <collection>", key: < shardkey >. For true sharding then Skype's pl/proxy is probably the best. Sharding vs. sharding” from someone in the Citus open source team, since we eat, sleep, and breathe sharding for Postgres. . 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. Database Sharding takes more work, but has the advantage. Stores possessing IDs of 2001 and greater go in the other. Sharding Key: A sharding key is a column of the database to be sharded. Actual latency for purely in-memory data could be similar. Mỗi partitions có cùng schema và cột, nhưng cũng có các hàng hoàn toàn khác nhau. I am new to the database system design. You can definitely implement database sharding with MySQL very effectively. sharding" from someone in the Citus open source team, since we eat, sleep, and breathe sharding for Postgres. Ví dụ ta có bảng dữ liệu thông tin về người dùng, ta sẽ dựa trên location của người dùng để quyết. Can have up to 4000 partitions, whereas a query using date sharded tables can only query up to 1000 tables at once. Consistent hash and range sharding are the most useful data sharding strategies for a distributed SQL database. So you would need to go back and rewrite all the database accessing code to pick the right server to talk to for each query. When partitioning a table, you need to consider having enough data for each partition. A primary key can be used as a sharding key. So that leaves two more options. When you partition a table in MySQL, the table is split up into several logical units known as partitions, which are stored separately on disk. However, in some use cases it can make sense to partition your database tables where parts of the table are distributed on different servers. Database sharding is a technique used to optimize database performance at scale. Azure's best practices on data partitioning says: All databases are created in the context of a DocumentDB account. Case 1 — Algorithmic Sharding One way to categorize sharding is algorithmic versus dynamic . Partitioning allows relational database schemas to scale with customer usage and application growth, without negatively affecting database performance. Version 10 of PostgreSQL added the declarative table partitioning feature. In that context, two words that keep on showing up with regards to databases are sharding and partitioning. g. 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. sharding allows for horizontal scaling of data writes by partitioning data across. This month’s PGSQL Phriday invitation from Tomasz Gintowt is on the topic of “Partitioning vs sharding in PostgreSQL“. Then as you need to continue scaling you’re able to move your shards to new physical nodes thus improving performance. An application has the option to choose the partition key that can minimize latency on a range query for a partitioned index. Sharding distributes data across multiple servers, while partitioning splits tables within one server. The most important factor is the choice of a sharding key. The list of popular data partitioning techniques is as follows: Horizontal Partitioning. BTW, Oracle cluster is different thing from Oracle index-organized table. In graph databases, the distribution process is imaginatively called graph partitioning. 3. Sharding is actually a type of database partitioning, more specifically, Horizontal Partitioning. In this systems design video I will be going over how to scale databases using database partitioning, in particular horizontal partitioning aka sharding and. Sharding spreads the load over more computers, which reduces contention and improves performance. Sharding involves saving the partitioned data onto other computers and storage facilities. What I would like to confirm is, if partitioning is still needed in the sub-tables (table_001, table_002, etc). Sharding Architecture. 차이점은 파티셔닝은 모든 데이터를. The schema of the table is replicated in every shard, and a unique portion of the whole table lives in. Database partitioning is the act of splitting a database into separate parts, usually for manageability, performance or availability reasons. . 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. “Data is distributed across multiple servers using partitioning, and each partition is further replicated to provide availability. This technique supports horizontal scaling but can be complex and requires careful planning. The correct way to scale writes is sharding as you gave. Horizontal partitioning is another term for sharding. These can be overridden in the etc/local. Here's is a figure from MySQL's official documentation on shard key. The main difference. The Pros of Database Sharding. sharding# Database partitioning deals with a single database instance, whereas sharding splits partitions (shards) across multiple database instances for scalability and availability. 4 here. 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. Replication adds fault tolerance to a system. Figure 1 shows an overview of horizontal partitioning or sharding. If you will frequently update the date (users can. 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. result = execute_query("SELECT * FROM my_table") This code snippet demonstrates how to handle errors in sharded databases using psycopg2, a PostgreSQL adapter for Python. Sharding is replicating [copying] the schema, and then dividing the data based on a shard key onto a separate database server instance, to spread the load. 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. In comparison, when using range-based sharding. Sharding vs Partitioning. Replication can be simply understood as the duplication of the data-set whereas sharding is partitioning the data-set into discrete parts. You need to make subsequent reads for the partition key against each of the 10 shards. However I also want to store the items of every user in the same region. The distribution used in system-managed sharding is intended to. Database sharding is a technique used to optimize database performance at scale. Each shard holds the data for a contiguous range of shard keys (A-G and H-Z), organized alphabetically. You can use numInitialChunks option to specify a different number of initial chunks. 5. The solution : Wouldn't this be a better approach? 1) It shards the data better so I don't need to use starts_with. The server-side system architecture uses concepts like sharding to ma. Or you want a separate backup machine. Think of each partition like being a different file - and opening 365 files might be slower than having a huge one. The data in all of the shards put together represent the original complete database. The idea is to implement partitions as foreign tables and have other PostgreSQL clusters act as shards and hold a subset of the data. Partitioning -- won't help the use case you described. A shard is a data store in its own right (it can contain the data for many entities of. When you partition a table in MySQL, the table is split up into several logical units known as partitions, which are stored separately on disk. The document you're quoting from is speaking of a more abstract concept of. Partitioning is the idea of splitting something large into smaller chunks. There are two types of Sharding: Horizontal Sharding: Each new table has the same schema as the big table. When data is written to the table, a partitioning function will be used by MySQL to decide. In this case, the table used for the benchmark has 1. While partitioning is a generic term for data splitting in a database, sharding is used for a specific type of partitioning, popularly known as horizontal partitioning. When I try to create a new collection by clicking on the ellipses button on a DB or choose existing DB, it doesn't provide the option to create collection without supplying shard key. Each shard is responsible for a subset of the workload, and queries can be. Sharding is a form of partitioning, with the emphasis being that each shard is located on a separate physical node. Declarative Partitioning #. Partitions, in terms of MySQL and PostgreSQL feature set, are physical segmentations of data. execute_query. Broadcast. If sharding is unfair, then a single node might be taking all the load and other nodes might sit idle. This is particularly the case when it comes to heavy write contention, database locking and heavy queries. Each machine has its CPU, storage, and memory. Figure 1 - Horizontally partitioning (sharding) data based on a partition key. It is often used with NoSQL databases and extensive data systems. However, while both are often used interchangeably, partitioning expects the data divided off to be stored on the same computer. However, a sharding key cannot be a. Replication may help with horizontal scaling of reads if you are OK to read data that potentially isn't the latest. Oracle Sharding provides the best features and capabilities of mature RDBMS and NoSQL databases, as described here. It relies on separating data into logical chunks so that they can be separat. Each node is assigned a set of partitions and hence the read/write throughput could be increased with parallelization. 3:Data Synchronizations. ini file by copying the text above, and replacing the values with your new defaults. Row-based sharding. But if your query has to visit every shard or partition, then it's more costly. Partitioning provides very few use cases to justify its existence; sharding provides write scaling at the cost of complexity. MongoDB is a database that supports this method. Each database server in the above architecture is called a Shard while the data is said to be partitioned. Sharding: Partitionning over several server, allowing parallel access (of different datas as opposed to replication) and, as such, memory and cpu load. Because xa transaction and partitioning is supported, it can do decentralized arrangement to two or more servers of data of same table. 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 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. If not, there will be big changes down the line until it is. 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. Replication, or Replica Sets in MongoDB parlance, is how MongoDB achieves high availability, Replica Sets are a Primary, and 0 to n amount of secondaries which have read-only copies of the. Now, I need to have a way to access the data in this table quickly, so I'm researching partitions and indexes. 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. The main reason to have vertical partition is when there are columns in the table that are updated more often than the rest. The balancer migrates data between shards. This will only scan one partition of the table. In a key- or hashed -based sharding architecture, a database application uses a shard key to locate a shard. In this post, SingleStore Developer Advocate, Joe Karlsson, explains the differences between database sharding vs. 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. 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. Queries are simple. Declarative Partitioning. Take the hash of the primary key, i. Each partition of data is called a shard. Sharding facilitates the possibility of adding more machines to spread out the load. Social media platforms rely on sharding to manage user profiles, posts, and comments, enabling them to scale to millions of users. 1M rows in a table -- no problem. Both methods aim to improve performance and scalability, but they differ in how they handle data distribution. On the other hand, data partitioning is when the database is. Database Sharding vs Partitioning. Splitting your database out into shards can help reduce the load on your database, leading to improved performance. 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. 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. Sharding. Horizontal partitioning is another term for sharding. A simple hashing function can be the modulus of the key and the number of shards. 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. Sharding is typically used to scale storage and query processing, with the goal being that the database 'as a whole' provides the abstraction of a single, unified logical repository of data, typically managed by a single organization. By default, the operation creates 2 chunks per shard and migrates across the cluster. It relies on separating data into logical chunks so that they can be separat. The items in a container are divided into distinct subsets called logical partitions. It dispatches client requests to the relevant shards and aggregates the result from shards. , user ID), which yields a range of 0 to 400. e. Each partition is known as a "shard". For example, if some queries request only names, and others request only addresses, then the names and addresses can be sharded onto separate servers. Partitioning is a generic term used for dividing a large database table into multiple smaller parts. Hash-based Partitioning. BTW, Oracle cluster is different thing from Oracle index-organized table. Then place that row in the corresponding server number. Partitioning options on a table in MySQL in the environment of the Adminer tool. Sharding can be used in system design interviews to help demonstrate a candidate’s understanding of scalability. Think of each partition like being a different file - and opening 365 files might be slower than having a huge one. In this post, I describe how to use Amazon RDS to implement a. The distinction ofhorizontal vs vertical comes from the traditional tabular view of a database. Sharding is also a 1% feature. In that context, two words that keep on showing up with. Another option would be to do the partitioning manually (i. Sharding Replication is not the same as sharding. 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. Include “PGSQL Phriday #011” in the title or first paragraph of your blog post. Shard-Key. Unlike Sharding and Replication, Partitioning is vertical scaling because each data partition is in the same. In this simple query the RETURN & GATHER -nodes are on the coordinator; the nodes upwards including the REMOTE -node are deployed to the DB-server. Database sharding is the process of breaking up large database tables into smaller chunks called shards. This initial. A shard key is selected to decide which shard a data row should go into. 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. This is a topic near and dear to me and I’m excited to think about it some this month. Range based sharding involves sharding data based on ranges of a given value. Use a message queue (Redis (pub/sub) or RabbitMQ) to throttle db writes. Each shard holds the data for a contiguous range of shard keys (A-G and H-Z), organized alphabetically. Sharding is a method for distributing a single dataset across multiple databases, which can then be stored on multiple machines. Database sharding vs partitioning. Horizontal database partition or sharding is the mostly commonly used partitioning method in SQL databases. What is Database Sharding? Sharding, also often called partitioning, involves splitting data up based on keys. Most Citus setups I have seen primarily use Citus sharding, and not Postgres table partitioning. Normalization is a logical database design issue. With partitioning, we accomplish this scaling by inserting data into many small tables (with associated indexes) and limited scopes of data per table. 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. These two things can stack since they're different. 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. Sharded vs. Horizontal sharding. PostgreSQL allows you to declare that a table is divided into partitions. Your app had better know exactly where to find the data (or at least where to find where to find the data). Sharding vs. Table of Contents. 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. Each node in the cluster owns not only the data within an assigned token range but also the replica for a different range of data. This will be used for sharding too. The value of this field determines which MongoDB. At this time, MongoDB still uses a global lock per mongodb server. Yes, it's possible. When you initialize a synced realm file, one of its parameters is a partition value. Sharding and Partitioning. Once you have identified a sharding key, it’s time to think about a sharding strategy. Horizontal partitioning or sharding. A range can be a portion of the chunk or the whole chunk.