The foreign data wrapper functionality has existed in Postgres for some time. Horizontal partitioning can be done both within a single server and across multiple servers, the latter often being referred to as sharding. MariaDB supports partitioning via sharding, whereas PostgreSQL does not support partitioning of its table(s). Download Now. Either way, after adding a node to an existing cluster it will not contain any. With hypertables, Timescale makes it easy to improve insert and query performance by partitioning time-series data on its time parameter. By default, a clustered index has a single partition. PostgreSQL offers a way to specify how to divide a table into pieces called partitions. Partition tolerance means that the cluster continues to function even if there is a "partition" (communication break) between two nodes (both nodes are up, but can't communicate). PostgreSQL has a rich set of semi-structured data types that include hstore, json, and jsonb. By using partitioning in this way, you can improve query performance and reduce the amount of data that needs to. 4 release in Nov 2016, MongoDB has made improvements in its sharding and replication architecture that has allowed it to be re-classified as a Consistent and Partition-tolerant. MariaDB vs PostgreSQL Parameters: Partitioning. Even if 1 server containing the data we need fails, our. You can now represent. Both read and write queries can be routed to the shards using this pooler. g. The reason for this is reliability. However, they are. Kumar added: “We really liked their approach of using the extensibility model of Postgres to maintain compat[ability] while enabling… a database that underneath the covers was sharded. Each shard holds the data for a contiguous range of shard keys (A-G and H-Z), organized alphabetically. Partitioning and sharding are essentially about breaking up large datasets into smaller subsets. 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. Likewise, the data held in each is unique and independent of the data held in other. PostgreSQL is a powerful, open source object-relational database system that uses and extends the SQL language combined with many features that safely store and scale the most complicated data workloads. Robert M. You can find them in the pg_amproc system catalog; join with pg_opfamily and restrict the query to operator families for the hash access method. Because Citus is an open source extension to Postgres, you can leverage the Postgres features, tooling, and ecosystem you love. The most basic example would be sharding by userID across 2 shards. The first shard contains the following rows: store_ID. Partitioning involves dividing a database into smaller, logical partitions based on specific criteria. Jeremy Holcombe , October 18, 2023. PostgreSQL, MySQL, MongoDB, and Cassandra are examples of database systems that provide. Partitioning is an optimization technique in databases where a single table is divided into smaller segments called partitions. Consider data distribution: In distributed databases, data distribution or sharding is an extension of partitioning, turning the database into smaller, more manageable partitions and then distributing (sharding) them across multiple cluster nodes. Instead of routing all writes to one server and scaling up, it’s possible to write to many servers and scale out. Common partitioning methods including partitioning by date, gender, user age, and more. “Partitioning” is usually referring to the concept of row level sharding which is like a bunch of equivalent tables unioned together (that’s basically how Oracle treats it in the back end). Partitioning Example: Range Partitioning 2. Horizontal Partitioning (sharding) stores rows of a table in multiple database clusters. To improve query response will it be better to shard the data or replicate existing shards for faster response. Q&A for database professionals who wish to improve their database skills and learn from others in the communitySurrealDB vs. Write performance via partitioning or sharding; PostgreSQL supports horizontal scalability across multiple servers using features like replication, clustering, partitioning, and sharding. Secondary replicas can handle read operations, which helps to distribute the read workload and increase performance. Both concepts are integral components of the same methodology for achieving horizontal scalability. These individual shards are then hosted on separate servers or nodes. 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 . Partitioning versus sharding. 1y. g. execute () with 2. Let me clarify what I mean by “table”. 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. So, what I would ideally request from a PostgreSQL sharding solution: Automatically keep several copies of every user's data around (on different machines). The assignment is made deterministically based on the value of a table column called the distribution column. Prisma then connects to a single endpoint and doesn't know that it's a sharded database. As of this writing, native PostgreSQL partitioning handles table inheritance (table structure, indexes, primary keys, foreign keys, constraints, and so on) efficiently from major version 11 and higher. For others, tools and middleware are available to assist in sharding. If you give that a try, please let us know how it goes because we definitely want to support this use case. The pgvector extension adds an open-source vector similarity search to PostgreSQL. sharding” from someone in the Citus open source team, since we eat, sleep, and breathe sharding for Postgres. 11. In Postgres, database partitioning and sharding are both techniques for splitting collections of data into smaller sets, so the database only needs to process smaller chunks of data at a time. The system knows how to access the data in a seamless and transparent way. Implement a sharding-only multi-tenant application. ! To partition each table (a single entity) we break it down into multiple smaller tables. Some data within a database remains present in all shards, [a] but some appear only in a single shard. There is a concept of “partitioned tables” in PostgreSQL that can make horizontal data partitioning/sharding confusing to PostgreSQL developers. This is known as data sharding and it can be achieved through different strategies, each with its own tradeoffs. For example, one might partition by date ranges, or by ranges of identifiers for particular business objects. We want to shard a single PostgreSQL 10. All data is ordered by the row key in each partition. Or you want a separate backup machine. The hard part will be moving the data without eexcessive downtime. No postgres_fdw extension is needed on the source server. While both sharding and partitioning are essentially about breaking a large dataset into smaller subsets, sharding implies that the data is spread across multiple computers while partitioning doesn’t. Horizontal partitioning, also known as row partitioning or sharding, is the process of splitting a table into multiple smaller tables based on a partition key, such as a customer ID, a date range. Then, Azure Cosmos DB allocates the key space of partition key hashes evenly across the physical partitions. The basis for this is in PostgreSQL’s. The main reason for partitioning, besides partition pruning, is information lifecycle management. Compared to PostgreSQL alone, TimescaleDB can dramatically improve query performance by 1000x or more, reduce storage utilization by 90 %, and provide features essential for time-series and analytical applications. # Example of. 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. Databases. Here are the steps to use the pg_proctab extension to enable the pg_top utility: In the psql tool, run the CREATE EXTENSION command for pg_proctab. To sum it up. Also note that postgres_fdw currently inhibits parallel query execution, which is also pretty disappointing if your purpose in sharding is to bring more CPU to bear on the task. Here we discussed default partitioning techniques in PostgreSQL using single columns, and we can also create multi-column partitioning. Postgres typically stores data using the heap access method, which is row-based storage. Connect to destination server, and create the postgres_fdw extension in the destination database from where you wish to access the tables of source server. Horizontal Scaling (scale-out): This is done through adding more individual machines in some way. Sharding vs Partitioning. In this strategy, each partition is a separate data store, but all partitions have the same schema. , customer ID). 1. My questions are , is there any good tutorials or places to learn about PostgreSQL auto sharding (I found results of firms like sykpe doing auto sharding but no tutorials, I want to play with this myself)?. May 11, 2021. Each of. Last but not the least the blog will continue to emphasise the importance of this feature in the core of PostgreSQL. I am using Mongo Sharding to register page views on my website. For 20+ years of database and application development, time-series data has always been at the heart of the products I work with. Citus uses the distribution column in distributed tables to assign table rows to shards. Sharding is also a 1% feature. Last but not the least the blog will continue to emphasise the importance of this feature in the core of PostgreSQL. 27. 어떻게 보면 샤딩은 수평 파티셔닝의 일종이다. It shouldn't be based on data that might change. But a partition can reside in only one shard. If the desired key happens to be the distribution column, then it’s quite easy, just add the constraint. PostgreSQL is a powerful, open source object-relational database system that uses and extends the SQL language combined with many features that safely store and scale the most complicated data workloads. ” (Sharding is a foundational technique in scaling out and partitioning databases across multiple servers. There are three typical strategies for partitioning data: Firstly, Horizontal partitioning (often called sharding). 3. Let’s add 2 more Citus worker nodes and scale out the database: The database sharding examples below demonstrate how range sharding might work using the data from the store database. Replication. We therefore introduced local execution, to execute Postgres queries within a function locally, over the same connection that issued the function call. Sharding makes it easy to generalize our data and allows for cluster computing (distributed computing). Developers are busy creatures who don’t always have the time to find helpful, productive PostgreSQL tools. MySQL requires tables with pre-defined rows and columns. 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. 1y. To horizontally partition our example table, we might place the first 500 rows on the first partition and the rest of the rows on the second, like so:Azure Cosmos DB for PostgreSQL uses algorithmic sharding to assign rows to shards. At Citus we make it simple to shard PostgreSQL. If you partition by month or years, purging old data is as simple as dropping a partition. It can handle high-traffic applications with 100s to 1000s of concurrent users. Sharding implies that the data is stored across multiple computers while partitioning groups this data within a single database instance. Let’s just mention some interesting possibilities. Scaling up –– or vertical scaling –– is relatively easy. Add more CPU and, broadly speaking, Postgres can handle more concurrent connections. There are many ways to split a dataset into shards. The specification consists of the partitioning method and a list of columns or expressions to be used as the partition key. sharding. Q&A for database professionals who wish to improve their database skills and learn from others in the communityStack Overflow Public questions & answers; Stack Overflow for Teams Where developers & technologists share private knowledge with coworkers; Talent Build your employer brand ; Advertising Reach developers & technologists worldwide; Labs The future of collective knowledge sharing; About the company1. Citus Sharding and PostgreSQL table partitioning on the same column. g. Built-in sharding is something that many people have wanted to see in PostgreSQL for a long time. 2, you can update a document's shard key value unless your shard key field is the immutable _id field. Partitioning is another term for physically dividing large tables in YugabyteDB into smaller, more manageable tables to improve performance. Sharding in PostgreSQL can be performed at the database, table, or even row level, allowing for fine-grained control over data placement. 0, PostgreSQL supports declarative partitioning — partitioning by range, list, or hash. Way 1: execute queries: INSERT INTO test_2 (SELECT * FROM ltest_2); INSERT INTO test_3 (SELECT * FROM ltest_3); Execution time: 357 seconds. Then as you need to continue scaling you’re able to move. A few of our early users have chosen to build their new cloud applications on YugabyteDB even though their current primary datastore is MongoDB. , aggregates, joins, are pushed down to the shards. 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. database-design. CREATE SERVER shard_eu FOREIGN DATA WRAPPER postgres_fdw. Replication: PostgreSQL provides synchronous and asynchronous replication, allowing data to be synchronized between multiple servers for high availability and disaster recovery. To connect to a PostgreSQL cluster, you can use the following command: psql -U Postgres -p 5436 -h localhost. Scale-out: you add more database instances. Patterns for Distribute Data. Database replication, partitioning and clustering are concepts related to sharding. Or range partitioning: put IDs 1 - 1000 into one partition, 1001 to 2000 in the next and so on. This is generally done to scale horizontally (more hosts) as opposed to vertically (more powerful hosts) and can provide significant cost. Splitting your data in 2 dimensions gives you even smaller data and index sizes. 1. A database shard, or simply a shard, is a horizontal partition of data in a database or search engine. Postgres partitioning implementation. No standard sharding implementation. 9. Sharding is a way to split data in a distributed database system. Partitioning splits based on the column value (s). This means that the attributes of the Database will remain the same but only the records will change. Share. All rows inserted into a partitioned table will be routed to one of the partitions based on. So your sharding should help your query remain on the logical partition (shard)• PostgreSQL compatible • Re-uses PostgreSQL query layer • New changes do not break existing PostgreSQL functionality • Enable migrating to newer PostgreSQL versions • New features are implemented in a modular fashion • Integrate with new PostgreSQL features as they are available • E. Sharding with declarative partitioning Create partition table definition on Data node with appropriate partition boundaries using CHECK constraint on partition column. Both systems use some form of partition key for partitioning the data. Therefore, when we refer to partitioning below, we refer to the partitions on a single machine. Partitioning by range, usually a date range, is the most common, but partitioning by list can be useful if the variables that is the partition are static and not skewed. 6 & 11 SQL Queries PG FDW Foreign Server Foreign Server. The declaration includes the partitioning method as described above, plus a list of columns or expressions to be used as the partition key. For example, if a clustered index has four partitions, there are four B-tree structures; one in each partition. References tables are replicated to all nodes for joins and foreign keys from distributed tables and maximum read performance. It may be clear that a shard can have multiple partitions in it. It stores. ago. Overview #. Citus 10 extends Postgres (12 and 13) with many new superpowers: Columnar storage for Postgres: Compress your PostgreSQL and Citus tables to reduce storage cost and speed up your analytical queries. This is particularly the case when it comes to heavy write contention, database locking and heavy queries. In this post, I describe how to use Amazon RDS to implement a sharded database. Scale-up: you have one database instance but give it more memory, CPU, disk. Data in each shard does not have to share resources such as CPU or memory, and can be read or written. g. Stack Overflow | The World’s Largest Online Community for DevelopersTo avoid this altogether, it is advisable to enforce partitioning also at DB level. Splitting your data in 2 dimensions gives you even smaller data and index sizes. Scale-up: you have one database instance but give it more memory, CPU, disk. In this setup, each partition can be put on a different machine. Tomasz is a new PostgreSQL friend for me and I love the topic he’s picked: Partitioning vs. There can be multiple copies of each logical shard spread across multiple physical instances. In this systems design video I will be going over how to scale databases using database partitioning, in particular horizontal partitioning aka sharding and. [UPDATE as of October 2019: To read more about. You may also want to refer to the official. We came across Kafka for write distribution for heavy load and this kind of streaming. In Citus Community edition you can add nodes manually by calling the citus_add_node UDF with the hostname (or IP address) and port number of the new node. Each partition has the same schema and columns, but also entirely different rows. Keeping all messages in a table makes queries slower even after tuning, 0. In this walkthrough you will understand how to use write sharding combined with a scatter-gather query to satisfy the leaderboard use case. This will be used for sharding too. Sharding implies breaking up the data across physical machines. Then as you need to continue scaling you’re able to move your shards to new physical nodes thus improving performance. Every row will be in exactly one shard, and every shard can contain multiple rows. Partitioning -- won't help the use case you described. Within indexing. With sharded tables, BigQuery must maintain a copy of the schema and metadata for each table. Sorted by: 4. If you want to CLUSTER all the sub-tables you have to do each individually. We will use citus which extends PostgreSQL capability to do sharding and replication. A sharding key is an attribute or column that determines how the data is distributed among the shards. I have three columns that seem like reasonable candidates for partitioning or indexing: Time (day or week, data spans a 4 month period)We have always used EXT4, so this turned out to be an unfounded concern. As of SQLAlchemy 1. Partitioning data is often used for distributing load horizontally, this has performance benefit, and helps in organizing data in a logical fashion. Database sharding is a technique for horizontally partitioning a large database into smaller and more manageable subsets. Table sharding is the practice of storing data in multiple tables, using a naming prefix such as [PREFIX]_YYYYMMDD. PostgreSQL allows you to declare that a table is divided into partitions. Create the initial partitions. The Citus database gives you the superpower of distributed tables. Database sharding vs partitioning. Sharding" recently, particularly in the context of PostgreSQL, largely due to the recent PGSQL Phriday #011 and I was surprised by the low coverage of the limitations with the most basic SQL database features: Postgres 10 will include an overhaul of partitioning for single-node use to improve performance and enable more optimizations, e. We can think of a shard as a little c…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. Just to recap, sharding in database is the ability to horizontally partition the data across one more database shards. Use list partitioning to split the table in something like at most 600 partitions. Sorted by: 1. Within YugabyteDB partitioning is a user-defined, SQL-level concept, thus requiring an explicit definition through SQL. 392 Create unique constraint with null columns. But if a database is sharded, it implies that the database has definitely been partitioned. Defining your partition key (also called a 'shard key' or 'distribution key') Sharding at the core is splitting your data up to where it resides in smaller chunks, spread across distinct separate buckets. Read more here. If you are using mongoDB as a backend for a REST interface, the best practice is to create on collection per resource. Perhaps you can use triggers to capture changes while you INSERT INTO. In PostgreSQL, you create a list partition to store the data of the partitioned table for predefined values. This article explores the limitations and tradeoffs of pgvector and shows how to use partitioning, indexing and search settings to improve performance. As I understand the strategy Cosmos DB use is partitioning with partition keys, but since we use the MongoDB. Note: As mentioned above, sharding is a subset of partitioning where data is distributed over multiple machines. Figure 1: Sharding Postgres on a single Citus node and adopting a distributed data model from the beginning can make it easy for you to scale out your Postgres database at any time, to any scale. Further Notes: Sharding vs Partitioning: Partitioning is data distribution on the same machine across tables or databases. application_name. PostgreSQL allows you to declare that a table is divided into partitions. Rather than horizontally shard, we decided to vertically partition the database by table(s). Examples include demonstrations of the with_loader_criteria () option as well as the SessionEvents. . Let's assume all the shards have ~1 million rows individually and there might be more than one DB on the Master Node. The disadvantage is ultimately you are limited by what a single server can do. However, without the use of extensions, the process of creating and managing partitions is still a manual process. For Example, PostgreSQL doesn’t support automatic sharding features, though it is possible to manually shard it, again it will increase the complexity. By default create_distributed_table() makes 32 shards, as we can see by counting in the metadata. MongoDB Consistency and Availability. In case of replicating existing shards, there will be more hosts to respond to a query request. Distributed Queries Example: Creating a Foreign Table 4. The hashed result determines the physical partition. If you are interested in sharding, consider checking out shard_manager, which is available on PGXN. And in Citus 12, thanks to schema-based sharding you can now onboard existing apps with minimal changes and support. Sorted by: 3. executor-based partition pruning. Our latest Citus open source release, Citus 12, adds a new and easy way to transparently scale your Postgres database: Schema-based sharding, where the database is transparently sharded by schema name. Source: Postgres Pro Team Subscribe to blog. It would be a gross exaggeration to say that PostgreSQL 11 (due to be released this fall) is capable of real sharding, but it seems pretty clear that the momentum is building. Currently I'm experimenting on Postgres Sharding. It is estimated that 180 zettabytes. 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. Partitioning vs. We also did a whole Postgres FM episode on partitioning. 2 and earlier, the choice of shard key cannot be changed after sharding. Partitioning is dividing large tables into multiple tables. Email us at postgres@heroku. To determine which shard to store any given row, apply the sharding algorithm to the sharding key. 0. And as you might imagine, work gets done faster when you’re processing less data. 1 In hash sharding, is there an algorithm that enables hash partitioning twice on a UUID V1?. To determine which shard to store any given row, apply the sharding algorithm to the sharding key. Horizontal Partitioning - Sharding (Topology 2): Data is partitioned horizontally to distribute rows across a scaled out data tier. It can handle high-traffic applications with 100s to 1000s of concurrent users. They solve (or fail to solve) different problems. . Data sharding is the breakdown of data spread across multiple computers, either as horizontal or vertical partitioning. Consider a table that store the daily minimum and maximum temperatures. g. The topic of this month's PGSQL Phriday #011 community blogging event is partitioning vs. Generally if you are sharding you would also want to have each shard backed by a replica set, but the two concepts are in fact orthogonal. This would allow parallel shard execution. user, password and sslpassword (specify these in a user mapping, instead, or use a service file). k. Also if a database is partitioned, it does not imply that the database is definitely sharded. Below table has a primary key and 2 unique keys. 7 Answers Sorted by: 259 Partitioning is more a generic term for dividing data across tables or databases. Apr 27, 2022 at 12:38 Add a comment 1 Answer Sorted by: 2 If partitioning is done correctly, then querying data from all shards need not be slower, because all those. Data sharding helps in scalability and geo-distribution by horizontally partitioning data. 4, the Query construct is. As of this writing, native PostgreSQL partitioning handles table inheritance (table structure, indexes, primary keys, foreign keys, constraints, and so on) efficiently from major version 11 and higher. 4 → 11. See full list on baeldung. Our latest Citus open source release, Citus 12, adds a new and easy way to transparently scale your Postgres database: Schema-based sharding, where the database is transparently sharded by schema name. a distributing tables). When to partition tables on Databricks. MySQL user support, both database systems have helpful communities to provide support to users. Partitioning. 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. js, partition. Our unpartitioned table ran the query in 4. It would be a gross exaggeration to say that PostgreSQL 11 (due to be released this fall) is capable of real sharding, but it seems pretty clear that the momentum is building. It will looks like: We have a single "master" and several data nodes with equal schema. In addition, some non-relational databases also are ACID compliant to a certain. Nevermind if they all share the same password; the important is that they simply can't access other schemas. Each shard is held on a separate database server instance, to spread load. So, what I would ideally request from a PostgreSQL sharding solution: Automatically keep several copies of every user's data around (on different machines). And as you might imagine, work gets done faster when. Auto sharding or data sharding is needed when a dataset is too big to be stored in a single. Today, for the first time, we are publicly sharing our design, plans, and benchmarks for the distributed version of TimescaleDB. events', 'created_at', 'time', 'daily'); After invoking this command, pg_partman creates a number of control tables and. Learn the similarities and. 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. APPLIES TO: Azure Cosmos DB for PostgreSQL (powered by the Citus database extension to PostgreSQL) Azure Cosmos DB for PostgreSQL includes features beyond standard PostgreSQL. This is where horizontal partitioning comes into play. Partitioning can be done on multiple columns, such as both a ‘date’ and a ‘country’ column. Standard PostgreSQL partitioning creates all partitions equal and on the same physical cluster. Horizontal data partitioning or sharding is a technique for separating data into multiple partitions. Now we'll convert the table to a partitioned table via Postgres Declarative Table Partitioning. Postgres 10 will include an overhaul of partitioning for single-node use to improve performance and enable more optimizations, e. com', port. Consider the following points when you design your entities for Azure Table storage: Select a partition key and row key by how the data is accessed. Haas. Choosing the distribution column for each table is one of the most important modeling decisions because it determines how data is spread across nodes. Oracle Globally Distributed Database can be used to store massive amounts of structured and unstructured data and to eliminate data fragmentation. Mỗi partitions có cùng schema và cột, nhưng cũng có các hàng hoàn toàn khác nhau. Be able to dynamically switch the master node per user/shard (if the previous master goes down). Choose a partition key/row key combination that supports the majority of. The idea is to distribute large amount of data across multiple partitions that can run on the same node or different nodes using a shared-nothing architecture, where each node operates independently without sharing memory or storage. It has high availability built in, is easily scalable, and distributes. Sorted by: 20. Behind the scenes, the database performs the work of setting up and maintaining the hypertable's partitions. The partitioned table itself is a “ virtual ” table having no storage of its. After our blog post on sharding a multi-tenant app with Postgres, we received a number of questions on architectural patterns for multi-tenant databases and when to use which. Choose a column with high cardinality as the distribution column. "Vertical partitioning" involves dividing up the. While Azure SQL doesn't natively support sharding, it provides sharding tools to support this type of architecture. Sharding at the core is splitting your data up to where it resides in smaller chunks, spread across distinct separate buckets. With Citus, you extend your PostgreSQL database with new superpowers: Distributed tables are sharded across a cluster of PostgreSQL nodes to combine their CPU, memory, storage and I/O capacity. Data sharding helps in scalability and geo-distribution by horizontally partitioning data. However, I'm getting confused on when I'd want to create a partition vs. Horizontal Scaling (scale-out): This is done through adding more individual machines in some way. Every shard is stored as a regular PostgreSQL table on another PostgreSQL server and replicated to other servers. Database replication, partitioning and clustering are concepts related to sharding. , serially. That may be true, but you still have to do the sharding so you can split up the traffic. In order to get both availability and partition tolerance, you have. Greenplum Partitioning. Step 1: Analyze scenario query and data distribution to find sharding key and sharding algorithm. Partitioning. This architecture innovation was originally driven by internet giants that run. This enhances parallel processing and data. Often people refer to this as “sharding” the Postgres table across multiple nodes in a cluster. I have absolutely no idea how it is possible to somehow optimize such a request. To shard Postgres, you can use Citus. Further Notes: Sharding vs Partitioning: Partitioning is the distribution of data on the same machine across tables or databases. Each shard is responsible for a subset of the workload, and queries can be. On Azure Database for PostgreSQL - Hyperscale (Citus) it’s as easy as dragging a slider in the user interface. Note: I am not allowed to change the table structure. Horizontal partitioning or sharding.