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). Using both means you will shard your data-set across multiple groups of replicas. 在海量資料的儲存情境下，DB 的效能會受到影響，此時透過垂直擴充架構也許是無法滿足的，因此會需要資料分片（shard），以水平擴展的方式來提升效能（可以想像成多個公路比起一條道路，可以達到分流，減緩堵塞）。 水平擴展方式一般來說又可以分為 Horizontal Partitioning 與 Sharding，前者是在. Modulo this hash with the number of database servers, i. Database sharding vs partitioning. return shardID. If [couch_peruser] q is set, that value is used for per-user databases. Hashed sharding provides a more even data distribution across the sharded cluster at the cost of reducing Targeted Operations vs. Sharding is a method to distribute data across multiple different servers. Sharding vs. Sharding is more general and is usually used when the database is split on several servers. This key is responsible for partitioning the data. Each partition of data is called a shard. 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. 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. However, since YugabyteDB provides both, it’s important to use the right terminology. Partitioning could be a different database inside MySQL on the same server, or different tables, or even by column value in a singular table. Shard-Query is an OLAP based sharding solution for MySQL. 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. 7. 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. It is useful when no single machine can handle large modern-day workloads, by allowing you to scale horizontally. Horizontal partitioning can be done both within a single server and across multiple servers, the latter often being referred to as sharding. PostgreSQL 11 addressed various limitations that existed with the usage of partitioned tables in PostgreSQL, such as the inability to create indexes, row-level triggers, etc. MySQL's has no built-in sharding capability. This means that the attributes of the Database will remain the same but only the records will change. UserIDs that are even would be on shard 0 and odd userIDs would be on shard 1. 4 Answers. 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. The simplest way to scale a database system is vertical scaling. g. Recently, due to heavy traffic, CPU overload (over 98% utilization) in our database instance. Replication. Sharding is also a 1% feature. In the context of scaling MongoDB: replication creates additional copies of the data and allows for automatic failover to another node. For true sharding then Skype's pl/proxy is probably the best. Its Horizontal partitioning (often called sharding). 8. Each chunk has inclusive lower and exclusive upper limits based on the shard key. 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. 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 vs partitioning vs clustering vs replication. The primary difference is one of administration. 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. Sharding vs Partitioning: Partitioning is data distribution on the same machine across tables or databases. 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. BTW, Oracle cluster is different thing from Oracle index-organized table. , user ID), which yields a range of 0 to 400. Partitioning is a rather general concept and can be applied in many contexts. Sharding vs. The balancer migrates data between shards. PostgreSQL provides a number of foreign data wrappers (FDW’s) that are used for accessing external data sources. Replication adds fault tolerance to a system. For example, a database of university students may be sharded based on the first letter of. In this case, the table used for the benchmark has 1. 3. Round-robin Partitioning. You can use Postgres table partitioning in combination with Citus, for example if you have time-based partitions that you would want to drop after the retention time has expired. Sharding is a way to split data in a distributed database system. Driver I can not find anyway to specify partitionkeys in my queries. adminCommand ( {. Horizontal data partitioning or sharding is a technique for separating data into multiple partitions. Because NoSQL databases are designed with distributed computing and automatic sharding in. Each shard (or server) acts as the single source for this subset. NET. The table that is divided is referred to as a partitioned table. Partitioning -- won't help the use case you described. April 29, 2022. 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. 28. In replication, we basically copy the database across multiple databases to provide a quicker look and less response time. Sorted by: 17. more immediacy and money. 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. 3 Answers. Database normalization involves designing the tables in the database to reduce or eliminate duplicated data. This spreads the workload of. Sharding, also known as horizontal partitioning, is a popular scale-out approach for relational databases. This technique supports horizontal scaling but can be complex and requires careful planning. After removing the images, the database can store 10 times as many tasks; you can go much longer before you have to think about implementing a horizontal partitioning scheme. I thought this might make the query. This point has been discussed ad-nauseam on Stack Overflow, specifically in this answer. Sharding is the horizontal partitioning of data where each partition resides in a separate node or a separate machine. Large databases usually have a negative impact on maintenance time, scalability and query performance. 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. Each shard holds the data for a contiguous range of shard keys (A-G and H-Z), organized alphabetically. There are two commonly used horizontal database scaling techniques: replication and horizontal partitioning (or sharding). b. Vertical partitioning, aka row splitting, uses the same splitting techniques as database normalization, but ususally the term (vertical / horizontal) data partitioning refers to a. The difference between CockroachDB and a manually sharded database is that when you _do_ have to perform some cross-shard transactions (which you inevitably have to do at some point), in CockroachDB you can execute them (with a reasonable performance penalty) with strong consistency and 2PC between the shards, whereas in your manually. 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 vs partitioning. Database partitioning is the act of splitting a database into separate parts, usually for manageability, performance or availability reasons. Why Hazelcast. List shard maps offer a high level of isolation for each shard, and with that, a great deal of flexibility (geography, scale, security, etc. Each partition is created based on the partitioning key. This article explains the relationship between logical and physical partitions. A sharded database is a collection of shards . Partitioning options on a table in MySQL in the environment of the Adminer tool. As queries become more complex, and data is stored on disk, the performance comparison becomes more confusing. In the second method, the writer chooses a random number between 1 and 10 for ten shards, and suffixes it onto the partition key before updating the item. Sharding is a specific type of partitioning in which dat. It seemed right to share a perspective on the question of "partitioning vs. Declarative Partitioning #. That may be true, but you still have to do the sharding so you can split up the traffic. This initial. When data is written to the table, a partitioning function will be used by MySQL to decide. I guess the cosmos UI behaves weirdly. By default, the operation creates 2 chunks per shard and migrates across the cluster. Normalization is a logical database design issue. Figure 1 shows an overview of horizontal partitioning or sharding. 6 GB of data for 2019 (until June in this one). This will be used for sharding too. The main of goal of partitioning is to aid in maintenance of large tables. Each chunk has inclusive lower and exclusive upper limits based on the shard key. Let's say I have two collections: users and items, where every item belongs to one user: I want to separate the documents from these two collections into different regions by using the user. Consistent hashing is a technique widely used in load balancing and routing service. Horizontal partitioning is another term for sharding. System-managed sharding is a sharding method which does not require the user to specify mapping of data to shards. In that context, two words that keep on showing up with regards to databases are sharding and partitioning. If the values for X have a large range, low frequency, and change at a non-monotonic rate,. By dividing a large table into smaller, individual tables, queries that access only a fraction of the data can run faster and use less CPU because there is less data to scan. Customer id vs. Using MySQL Partitioning that comes with version 5. 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. These end customers are often referred to as "tenants". Other query patterns may need to load large amounts of data from the remote database and may perform poorly. Sharding -- only if you need to 1000 writes per second. SQL partitioning proves beneficial in managing smaller tables, yet for enhanced scalability in SQL processing, it necessitates integration with either. 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. That feature is called shard key. Download Now. Sharding spreads the load over more computers, which reduces contention and improves performance. Some data within a database remains present in all shards, [a] but some appear only in a single shard. Hashing your partition key and keeping a mapping of how things route is key to a. Sharding at the core is splitting your data up to where it resides in smaller chunks, spread across distinct separate buckets. A shard is an individual partition that exists on separate database server instance to spread load. You put different rows into different tables, the structure of the original table stays the same in the new. Sharding, also known as horizontal partitioning, is a popular scale-out approach for relational databases. Figure 1 is an example. Learn about each approach and. For example, let’s say a query has an equality predicate based on the field sourceairport and destinationairport. 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. 2. The most basic example would be sharding by userID across 2 shards. A big graph is partitioned into multiple small graphs, and the storage and computation of each small graph are stored on different servers. Each shard is responsible for a subset of the workload, and queries can be. It is estimated that 180 zettabytes of data will be created by. Sharding distributes data across multiple servers, while partitioning splits tables within one server. The main difference. Add parallelism so FDW requests can be issued in parallel. Each partition is known as a shard. 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. In the simplest sense, sharding your database involves breaking up your big database into many, much smaller databases that share nothing and can be spread. MongoDB provides a router program mongos that will correctly route sharded queries without extra application logic. –Sharding is also referred as horizontal partitioning. Sharding, at its core, is a horizontal partitioning technique. Partitioning a table using the SQL Server Management Studio Partitioning wizard. Hashed sharding uses either a single field hashed index or a compound hashed index (New in 4. Both systems use some form of partition key for partitioning the data. If you are using mongoDB as a backend for a REST interface, the best practice is to create on collection per resource. Horizontal partitioning splits a table by rows, based on a partition key or a range of values. It’s important to note. 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. On the other hand, data partitioning is when the database is. It's not necessary to understand these. I am new to SQL and have been trying to optimize the query performances of my microservices to my DB (Oracle SQL). Most importantly, sharding allows a DB to scale in line with its data growth. database-design. I have been reading about scalable architectures recently. When data is written to the table, a. Sharding in database is the ability to horizontally partition data across one more database shards. SQL Server 2008 introduced a table partitioning wizard in SQL Server Management Studio. sharding" from someone in the Citus open source team, since we eat, sleep, and breathe sharding for Postgres. The hash function can take more than one 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. In general less REMOTE / SCATTER -> GATHER pairs means less cluster communication. Particularly number 2 as Postgresql is notoriously. Consider a table that store the daily minimum and maximum temperatures. 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. So that leaves two more options. Partitioning, also called Sharding, is a fundamental consideration in NoSQL database. What are partitioning and sharding? It has been possible to do partitioning in PostgreSQL for quite a while — splitting what is logically one large table into smaller physical tables. Sharding is a way to split data in a distributed database system. Can have up to 4000 partitions, whereas a query using date sharded tables can only query up to 1000 tables at once. Your client app creates objects in the synced realm. g. Figure 1. Hazelcast named in the Gartner ® Market Guide for Event Stream Processing. 8. When it comes to managing large databases, two common techniques are database sharding. You can use numInitialChunks option to specify a different number of initial chunks. Figure 4:Side-by-side comparison of Schema-based sharding vs. Sharding -- only if you need to 1000 writes per second. If not, there will be big changes down the line until it is. To shard Postgres, you can use Citus. 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 this tutorial, we’ll discuss two methods for splitting databases into parts to manage them efficiently: sharding and partitioning. One of the most interesting and general approach is a built-in support for sharding. User IDs 1 and 3 are in shard 1, User IDs 2 and 4 are in shard 2. 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. 샤딩은 동일한 스키마 를 가지고 있는 여러대의 데이터베이스 서버들에 데이터를 작은 단위로 나누어 분산 저장 하는 기법이다. Many modern databases have built-in sharding system. Database sharding is a strategy for scaling a database by breaking it into smaller, more manageable pieces, or “shards”. In a key- or hashed -based sharding architecture, a database application uses a shard key to locate a shard. A hashing function hashes the sharding key value, and the output maps data to a particular shard. Partitioning vs. Database Sharding vs Partitioning. Each database server in the above architecture is called a Shard while the data is said to be partitioned. Database sharding and. Both are methods of breaking. sharding" from someone in the Citus open source team, since we eat, sleep, and breathe sharding for Postgres. To sum it up. 5. "Plain" MongoDB use sharding instead, and you can set up a document property that should be used as a delimiter for how your data should be sharded. sharding in PostgreSQL. In fact, PostgreSQL has implemented sharding on top of partitioning by allowing any given partition of a partitioned table to be hosted by a remote server. Sharding Replication is not the same as sharding. Of course, it may not be the only solution. 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 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. Sharding takes a different approach to spreading the load among database instances. Product inventory data is separated into shards in this case depending on the product key. Version 10 of PostgreSQL added the declarative table partitioning feature. What is Database Sharding? Database sharding is a horizontal partitioning of data in a database. This functionality is hidden behind a series of APIs that are contained in the Elastic Database client library , which is available for Java and . 이때, 작은 단위를 샤드 (shard) 라고 부른다. Each shard is a separate database, stored on a different server, and only contains a portion of the. on the. Based on my research, I checked that you can do indexing and partitioning to improve query performance, I seem to have known each of the concept and how to do it, but I'm not sure about the difference between both?. The main difference is that partitioning groups these subsets on a single database instance, whereas sharded data can be spread across multiple. 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. The advantage of DBMS single server partitioning is that it is relatively simple to set up and manage. 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. The Pros of Database Sharding. Creating multiple servers will release a server from one another's locks. Database sharding fixes all these issues by partitioning the data across multiple machines. There are a large number of databases that businesses use today in order to perform their day-to-day operations. As I. Sharding is the equivalent of “horizontal 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. Consistent hash sharding is better for scalability and preventing hot spots, while range sharding is better for range based queries. Sharding vs Partitioning. What is MongoDB Sharding? Sharding is a method for distributing or partitioning data across multiple machines. Most Citus setups I have seen primarily use Citus sharding, and not Postgres table partitioning. Sharding literally breaks a database into little pieces, with each instance only responsible for part of the database. 4) as the shard key to partition data across your sharded cluster. Replication -- needed if you have 1000 reads per second. 2:Faster Access. The declaration includes the partitioning method as described above, plus a list of columns or expressions to be used as the partition key. Sharding involves saving the partitioned data onto other computers and storage facilities. Partitioning is a general term, and sharding is commonly used for horizontal partitioning to scale-out the database in a shared-nothing architecture. The data in all of the shards put together represent the original complete database. It is a range-based sharding. It is often used with NoSQL databases and extensive data systems. Using some kind of third party library that encapsulates the partitioning of the data (like hibernate shards) Implementing it ourselves inside our application. BTW, Oracle cluster is different thing from Oracle index-organized table. It seemed right to share a perspective on the question of "partitioning vs. 16. MongoDB uses sharding to support deployments with very large data sets and high throughput operations. 1. 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. Each partition (also called a shard) contains a subset of data. 2. Sharding involves splitting a database into smaller shards, which can be distributed across multiple servers. I have been reading about scalable architectures recently. Sharded vs. In this scenario, we start with 4 databases (DB1 to DB4) and use a hash-based sharding strategy. This is the twenty-first video in the series of System Design Primer Course. Again, let's discuss whether it is even relevant. Replication duplicates the data-set. reshardCollection: "<database>. Splitting your data in 2 dimensions gives you even smaller data and index sizes. Data is automatically distributed across shards using partitioning by consistent hash. Sharding is any time you split your large database into smaller pieces to limit full table scans during runtime. 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. Later in the example, we will use a collection of books. Sharding is a method for distributing a single dataset across multiple databases, which can then be stored on multiple machines. Horizontal data partitioning or sharding is a technique for separating data into multiple partitions. Oracle Sharding is a feature of Oracle Database that lets you automatically distribute and replicate data across a pool of Oracle databases that share no hardware or software. Then as you need to continue scaling you’re able to move your shards to new physical nodes thus improving performance. Sharding / partitioning ≠ replication DB shard 1 shard 3 shard 2 replica 2 replica 2DB replica 3DB 3 partitions vs. What is sharding? Sharding is a type of database partitioning that separates large databases into smaller, faster, more easily managed parts. PostgreSQL allows you to declare that a table is divided into partitions. However I also want to store the items of every user in the same region. The closer FILTER nodes can be deployed to *CollectionNodes to reduce the amount of the. MongoDB Sharding by foreign key. Likewise, the data held in each is unique and independent of the. . Each partition has the. This is not a new challenge; organizations have faced it for years, and horizontal sharding is one of the key patterns for solving it. sharding” from someone in the Citus open source team, since we eat, sleep, and breathe sharding for Postgres. g. 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. It is effective when queries tend to return only a subset of columns of the data. partitioning. 1M WordPress "users", each owning Database with. Sharding on Azure SQL is a type of horizontal partitioning that splits large databases into smaller components, which are faster and easier to manage. A primary key can be used as a sharding key. 4) Ordered index scan This scan will scan all. 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. Horizontal partitioning is achieved in a relational database by storing rows from the same table in several database nodes. The replication strategy determines where replicas are stored in the cluster. Then place that row in the corresponding server number. Like partitioning, sharding is also a method to divide off a database to be saved separately. Database partitioning is normally done for manageability, performance or availability reasons, as for load balancing. This is a topic near and dear to me and I’m excited to think about it some this month. We would like to show you a description here but the site won’t allow us. The correct way to scale writes is sharding as you gave. 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. 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. And indeed, these are very similar terms that deal with dividing large data sets into smaller subsets. Sharding and moving away from MySQL. When you shard a database, you create replications of the table schema, then divide what. This article explains the relationship between logical and physical partitions. The motivation behind this is clear, it makes the task of ensuring service levels on the database easier because the data set is smaller and it allows one to prioritize the investment to improve an aspect of the system because of the logical separation (e. In DBMS, Sharding is a type of DataBase partitioning in which a large database is divided or. Hashing your partition key and keeping a mapping of how things route is key to a scalable sharding. The shard catalog database also acts as a query coordinator used to process multi-shard queries and queries that do not specify a sharding key. Partitioning -- won't help the use case you described. Vertical sharding — Vertical partitioning on the other hand refers to division of columns into multiple tables. For MySQL, Sharding, not partitioning, involves putting different rows on different physical servers. So we decided to do shard our db into multiple instances. 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. Scaling vertically, also called scaling up, means adding capacity to the server that manages your database. It relies on separating data into logical chunks so that they can be separat. The guidelines for participating are as follows: Publish your blog post about “ partitioning vs sharding ” by Friday, August 4th, 2023. The declaration includes the partitioning method as described above, plus a list of columns or expressions to be used as the partition key. Within YugabyteDB partitioning is a user-defined, SQL-level concept, thus requiring an explicit definition through SQL. Recently, due to heavy traffic, CPU overload (over 98% utilization) in our database instance. Sharding involves saving the partitioned data onto other computers and storage facilities. The mongos acts as a query router for client applications, handling both read and write operations. 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. Table A holds items 1–5000 and Table B holds items 5001–10000. We would like to show you a description here but the site won’t allow us. MongoDB – Replication and Sharding. This article will help you understand what Database Sharding is and how MySQL Sharding works. 3:Data Synchronizations. Horizontal data partitioning or sharding is a technique for separating data into multiple partitions. However, in some use cases it can make sense to partition your database tables where parts of the table are distributed on different servers. 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. For 20+ years of database and application development, time-series data has always been at the heart of the products I work with. It is the mechanism to partition a table across one or more foreign servers. Splitting your database out into shards can help reduce the load on your database, leading to improved performance. Therefore, the query performance improves significantly, and multiple queries can run in parallel on different machines. Additionally,. 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. It seemed right to share a perspective on the question of “partitioning vs. This increases performance because it reduces the hit on each of the individual. 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. At this time, MongoDB still uses a global lock per mongodb server. Each node is assigned a set of partitions and hence the read/write throughput could be increased with parallelization. Partitioning Azure SQL Database. Sharding and partitioning are techniques to divide and scale large databases. The Cons of Database. Table of Contents. 1. If everything is in the same database node, user requests for data can. Horizontal partitioning or sharding. Unlike Sharding and Replication, Partitioning is vertical scaling because each data partition is in the same. 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. Partitioning is dividing large tables into multiple tables. Distributed. The nature of how data is scoped and managed by DynamoDB adds some new twists to how you approach multitenancy. Sharding is one specific type of. As with clustering, there are multiple approaches to sharding, not all of which are called sharding by database administrators. Elastic clusters use the separation, or “decoupling”, of compute and storage in Amazon DocumentDB enabling you to scale independently of each other. Partitioning is the idea of splitting something large into smaller chunks. Database sharding is the optimization of large databases by splitting data from a larger database table into multiple smaller tables (shards). Partitioning is about grouping subsets of data within a single database instance. Content delivery networks (CDNs) use sharding to store web content like images, videos, and JavaScript files, ensuring fast and efficient content delivery to users. A shard is an individual partition that exists on separate database server instance to spread load. Database. Hybrid Sharding. Partitioning vs. 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. We apply a hash function to our data key (e. Sharding is possible with both SQL and NoSQL databases. Sharding and moving away from MySQL. The technique divides the data into buckets using some type of hash key such as a date and/or a natural key. Horizontal partitioning is when the table is split by rows, with different ranges of rows stored on different partitions. Partitioning creates separate physical units within the same database in the same server, while sharding distributes data across multiple databases in different server. Sharding -- only if you need to 1000 writes per second. 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. The topic of this month's PGSQL Phriday #011 community blogging event is partitioning vs. Sharding is a technique to distribute large amounts of identically structured data across a number of independent databases. 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. For performance, tables without correct indexes result in full table or clustered index scans.