Partition and clustering is key to fully maximize BigQuery performance and cost when querying over a specific data range. The first shard contains the following rows: store_ID. Horizontal partitioning means dividing the rows of a table into multiple tables, known as partitions. In this case, the records for stores with store IDs under 2000 are placed in one shard. 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. An important point when you are using Sharding is to choose a good shard key that distributes the data between the nodes in the best way. Sharding: Partitionning over several server, allowing parallel access (of different datas as opposed to replication) and, as such, memory and cpu load. 2 use your RDBMS "out of the box" clustering mechanism. Again, let's discuss whether it is even relevant. sharding" from someone in the Citus open source team, since we eat, sleep, and breathe sharding for Postgres. Almost always a single table is better than splitting up the table (multiple tables; PARTITIONing; sharding). Rather, you can choose to use Postgres native partitioning, or you can shard Postgres with an extension like Citus to distribute Postgres across multiple nodes—or you can use both. Sharding: Partitionning over several server, allowing parallel access (of different datas as opposed to replication) and, as such, memory and cpu load. To put it simply, indexes allow fast access to small proportions of a table. If the values for X have a large range, low frequency, and change at a non-monotonic rate,. We also have quite a few databases of all sizes. Do đó. We can partition a table based on a date, by the hour, or integers with a fixed range. In this strategy, each partition is a separate data store, but all partitions have the same schema. Sharding vs. Each database shard is kept on a separate database server instance to help in spreading the load. However, a sharding key cannot be a. Sharding can improve. Instead, the SolrCloud feature of the. Declarative Partitioning #. What’s more, sharding can be viewed as a very specific type of partitioning, namely — horizontal partitioning. Primary shards & Replica shards in. Sharding and moving away from MySQL. The database hotspot problem arises when one shard is accessed more as compared to all other shards and hence, in this case, any benefits of sharding the. The data of partitioned tables and indexes is divided into units that may be spread across more than one filegroup in a database or stored in a. Partioning implies breaking up the data across multiple tables. In the first method, the data sits inside one shard. However, since YugabyteDB provides both, it’s important to use the right terminology. To sum it up. It shouldn't be based on data that might change. Database Shard: A database shard is a horizontal partition in a search engine or database. A partition is a physically separate file that comprises a subset of rows of a logical file, which occupies the same CPU+memory+storage node as its peer partitions. Vertical Partitioning In contrast to horizontal partitioning, vertical partitioning lets you restrict which columns you send to other destinations, so you can replicate a limited subset of a table's columns to other machines. Both the techniques split a huge data set into different chunks and store it on different database servers. It can be either a single indexed column or multiple columns denoted by a value that determines the data division between the shards. PostgreSQL allows you to declare that a table is divided into partitions. 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. Sharding on the other hand, and the load balancing of shards, is a storage level concept that is performed automatically by YugabyteDB based on your replication factor. Partitioning vs. With sharding (in this context) being “distributed” partitioning, the essence of a successful (performant) sharded environment lies in choosing the right shard key – and by “right,” I mean one that will distribute your data across the shards in a way that will benefit most of your queries. Sharding is typically used to improve query performance by distributing the workload across multiple nodes. We would like to show you a description here but the site won’t allow us. A common interview question is the difference between partitioning and sharding especially in relation to Big Data systems. This means that all SELECT, UPDATE, and DELETE should include that column in the WHERE clause. 1y. Each partition of data is called a shard. It limits you in data joining/intersecting/etc. But if a database is sharded, it implies that the database has definitely been partitioned. These shards are not only smaller, but also faster and hence easily manageable. In other words, a query that specifies a filter predicate on a range of values that accesses 10% of the values in the range should ideally only scan 10% of the micro. 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. Sharding can be used in system design interviews to help demonstrate a candidate’s understanding of scalability. Horizontal Partitioning (Sharding) Each partition is a separate data store, but all partitions have the same schema. The question of partitioning vs. a. shardID = identifier % numShards. Partitioning vs. Many modern databases have built-in sharding system. This article series introduces and explains the concepts of data partitioning and sharding. Each physical database in such a configuration is called a shard. Table partitioning is the process of splitting a single table into multiple tables. Sharding is a method of partitioning data to distribute the computational and storage workload, which helps in achieving hyperscale computing. . BigQuery’s decoupled storage and compute architecture leverages column-based partitioning simply to minimize the amount of data that slot workers read from disk. Through partitioning, databases are thoughtfully. For stateless services, you can think about a partition being a logical unit that contains one or more instances of a service. Here, I will focus on date type partitioning. Kinesis Data Streams segregates the data records belonging to a stream into multiple shards. There are many ways to split a dataset into shards. 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. These queries run in serial, not parallel execution. Distributed. However, to take full advantage of sharding, the application needs to be fully aware of it. Each partition contains a subset of rows, and the partitions are typically distributed across multiple servers or storage devices. Others describe it as using partitions. Such databases don’t have traditional rows and columns, and so it is interesting to learn how they implement partitioning. Sharding is complementary to other forms of partitioning, such as vertical partitioning and functional partitioning. In this partitioning, each partition is a separate data store , but all partitions have the same schema . So far, I've tried 3 scenarios and executed an explain analyze on my slowest queries that are impacted by these tables after each partitioning. Database Sharding vs. Big Data: Partitioning vs Sharding Adjust Here at Adjust we use both. Each partition is known as a shard and holds a specific subset of the data. If Database sharding sounds a bit complicated, it implies partitioning an on-prem server into multiple smaller servers, known as shards, each of which can carry different records. The shard key should be static. Sharding - What about SQL Features? 2 Citus is not ACID but Eventually Consistent 3 YugabyteDB is Distributed SQL: resilient and consistent. Horizontal partitioning: Splitting the data by group of lines naturally given its primary keys (Row Splitting). 4 and basically is a monitoring service for master and slaves. Sharding and moving away from MySQL. Là cách chia cùng dữ liệu của cùng một bảng (table) ra nhiều DB khác nhau. Redis Cluster data sharding. Horizontal Partitioning (sharding) stores rows of a table in multiple database clusters. As I understand the strategy Cosmos DB use is partitioning with partition keys, but since we use the MongoDB. Database sharding is the process of dividing the data into partitions which can then be stored in multiple database instances. Each time-based partition could be a separate distributed table in the. Every shard will get. # Example of. There are two broad ways by which we partition/shard data : Partition by key-range. Both are used to improve query performance, but they achieve this in different ways. Essentially, sharding is just a fancy name given to the process of splitting the dataset along its rows. The partitioning policy defines if and how extents (data shards) should be partitioned for a specific table or a materialized view. In Azure Data Explorer, sharding is implemented using. We achieve horizontal scalability through sharding”. The following example is employee name data that uses a shard key named "user_id": DocumentDB uses hash sharding to partition your data across underlying. Splitting your database out into shards can help reduce the. While partitioning and sharding are pretty similar in concept, the difference becomes much more apparent regarding No-SQL databases like MongoDB. In upcoming release Oracle 12. The Ethereum Wiki’s Sharding FAQ suggests random sampling of validators on each shard. On the Citus blog, we write about Postgres, Postgres extensions, and of course, scaling out Postgres horizontally with Citus—the open source extension that transforms Postgres into a distributed database. For example, high query rates can exhaust the CPU. Sharding is needed if a data set is too large to be stored in a single DB. So that leaves two more options. The database sharding examples below demonstrate how range sharding might work using the data from the store database. . Each shard is typically assigned to a different database server, which allows for parallel processing and faster query execution times. Horizontal partitioning is achieved in a relational database by storing rows from the same table in several database nodes. Even 1 billion rows may not need any of those fancy actions. Sharding is a specific type of partitioning in which dat. By contrast, sharding offers unlimited scalability. 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. This is where PostgreSQL foreign data wrappers come in and provide a way to access a foreign table just like we are accessing regular tables in the local database. Sharding distributes data across multiple servers, while partitioning splits tables within one server. Partitioning -- won't help the use case you described. If the sharding is based on some real-world aspect of the data (e. Hence Sharding means dividing a larger part into smaller parts. 2. 1Also known as "index-organized table" under Oracle. Load balancing/Chunk Migration — Mongo manages an equal distribution of data across shards by migrating the chunks, so as to unleash the power of distributed computing. A hashing function hashes the sharding key value, and the output maps data to a. BTW, Oracle cluster is different thing from Oracle index-organized table. What is Database Sharding? | Hazelcast. Sharding Keys ("Partitioning Keys") Weaviate uses specific characteristics of an object to decide which shard it belongs to. sharding in PostgreSQL. • Sharding algorithm: an algorithm to distribute your data to one or more shards. If you’ve used Google or YouTube, you’ve probably accessed sharded data. Vertical partitioning: Each partition is a proper subset of the original database schema - i. As aggregation query will always be on time range than it will go to multiple shards/ partitions always. Using the FDW-based sharding, the data is partitioned to the shards in order to optimize the query for the sharded table. If not, there will be big changes down the line until it is. A SQL table is decomposed into multiple sets of rows according to a specific sharding strategy. 1M rows in a table -- no problem. The guidelines for participating are as follows: Publish your blog post about “ partitioning vs sharding ” by Friday, August 4th, 2023. A sharding key is an attribute or column that determines how the data is distributed among the shards. I feel. As of writing, we can only choose one (1) partition among all of these partitioning types. Partitioning and segmenting are essentially the same and are equally obsolete. Splitting your data in 2 dimensions gives you even smaller data and index sizes. 2 , the Oracle Sharding feature provides the exact capability of shared nothing architecture with. Horizontal partitioning is what we term as "Sharding". So that leaves two more options. 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. Horizontal Partitioning - Sharding (Topology 2): Data is partitioned horizontally to distribute rows across a scaled out data tier. We call these cross-shard queries. A partition is a division of a logical database or its constituent elements into distinct independent parts. This pattern is a typical multi-tenant sharding pattern - and it may be driven by the fact that an application manages large numbers of small tenants. In this video, we dive into the topic of Database Sharding vs Partitioning and break down the key differences between the two. Both the techniques split a huge data set into different chunks and store it on different database servers. The replication strategy determines where replicas are stored in the cluster. This process includes reingesting data from the source extents and. With partitioning, we accomplish this scaling by inserting data into many small tables (with associated indexes) and limited scopes of data per table. It is useful for large, high-traffic applications that require high availability and fast response times. The partitioned table itself is a “ virtual ” table having no storage of its. (Seems not applicable to you. it contains all of the rows, but only a subset of the original columns. This will be used for sharding too. It evolves out of horizontal partitioning in which you separate the rows of one table into multiple different tables, known as partitions. (As mentioned before, a partition is a set of replicas ). partitioning. Most importantly, sharding allows a DB to scale in line with its data growth. Sharding (also known as Data Partitioning) is the process of splitting a large dataset into many small partitions which are placed on different machines. The Backend systems function as intermediate storage of data, anything between. 5. On the other hand, data partitioning is when the database is. Sharding is performed by exchanges, that is, messages will be partitioned across "shard" queues by one exchange that we should define as sharded. Step 1: Analyze scenario query and data distribution to find sharding key and sharding algorithm. Consider the following points: There are three typical strategies for partitioning data: Firstly, Horizontal partitioning (often called sharding). Sharding, also known as horizontal partitioning, is a popular scale-out approach for relational databases. Horizontal data partitioning or sharding is a technique for separating data into multiple partitions. Hashed sharding uses either a single field hashed index or a compound hashed index (New in 4. . What is Sharding? What is Partitioning? Difference Between Sharding and Partitioning; Key Aspects Of Sharding: Key Aspects Of Partitioning: Which One Should Be Used When? Database partitioning is normally done for manageability, performance or availability reasons, as for load balancing. Figure 1 is an example of a sharding database. 1 (hopefully we’re switching to EJB 3 some day). The table is partitioned into “ranges” defined by a key column or set of columns, with no overlap between the ranges of values assigned to different partitions. Unlike Sharding and Replication, Partitioning is vertical scaling because each data partition is in the same. sharding is a bit of a false dichotomy. Q&A: Partitioning vs Sharding, Scaling Behavior, and Visualization Tools for YugabyteDB. Its Horizontal partitioning (often called sharding). Distributed. Database sharding is a technique used to distribute the data in a database across multiple servers, or shards, in order to improve scalability and performance. Choose a scheme that matches the data characteristics and query patterns, and avoid schemes that cause. We can easily add new table/node in this approach. The declaration includes the partitioning method as described above, plus a list of columns or expressions to be used as the partition key. This allows for the querying of smaller sets of data by using WHERE constraints to limit the number of tables or indexes scanned, resulting in much faster query response time despite large. e. Sharding makes it easy to generalize our data and allows for cluster computing (distributed computing). Partitioning vs sharding. You can partition your data using 2 main strategies: on the one hand you can use a table column, and on the other, you can use the data time of ingestion. Partitioning, also called Sharding, is a fundamental consideration in NoSQL database. The idea is to distribute data that can’t fit on a. In terms of latency, MySQL Cluster should have more stable latency than sharded MySQL. In this post, I describe how to use Amazon RDS to implement a. The advantage is the number of rows in each table is reduced (this reduces index size, thus improves search performance). Note: In addition to the BigQuery web UI, you can use the bq command-line tool to perform operations on BigQuery datasets. Announce your blog post on one or more of these platforms: Twitter/Linkedin/FB using the #. You can use numInitialChunks option to specify a different number of initial chunks. . Unfortunately, the terms "partitioning" and "sharding" are used at. Vertical partitioning was somewhat useful in MyISAM, but rarely useful in InnoDB, since that engine automatically does such. August 4, 2023 The topic of this month's PGSQL Phriday #011 community blogging event is partitioning vs. This is a topic near and dear to me and I’m excited to think about it some this month. Database partitioning is normally done for manageability, performance or availability reasons, or for load balancing. If you are using mongoDB as a backend for a REST interface, the best practice is to create on collection per resource. If you get this right, database works beautifully. Algorithmically sharded databases use a sharding function (partition_key) -> database_id to locate data. However, since YugabyteDB provides both, it’s important to use the right terminology. Partitioning assumes the partitions are on the same server. Jayant Chakravarti Senior Assistant Editor, Spiceworks Ziff Davis. It's not a choice of one or the other, since the two techniques are not mutually exclusive. It seemed right to share a perspective on the question of "partitioning vs. MongoDB provides a router program mongos that will correctly route sharded queries without extra application logic. Allow lighter joins. Horizontal Partitioning: Also known as sharding, horizontal data partitioning involves dividing a database table into multiple partitions or shards, with each partition containing a subset of rows. In MySQL, the term “partitioning” applies to individual tables of a database. 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. Various parts of the query e. Sharding -- only if you need to 1000 writes per second. partitioning. , aggregates, joins, are pushed down to the shards. We should specifically mention here that in partitioning , the partitions lies within a single database instance whereas in sharding the shards lies across different database servers. Hash Sharding: use a hashed index of a single field as the shard key to partition data across your sharded cluster. Sharding is a specific type of partitioning in which dat. 0:00. Sharding" recently, particularly. By default, the operation creates 2 chunks per shard and migrates across the cluster. Partitioning is dividing large tables into multiple tables. Here, each partition is known as a shard and holds a specific subset of the data, such as all the orders for a specific set of. Partitioning Vs Sharding. PostgreSQL allows you to declare that a table is divided into partitions. Horizontal partitioning or sharding. Both partitioning and sharding involve distributing data across multiple physical or logical storage devices, with the goal of improving data processing and query performance. The main difference is that sharding explicitly imposes the necessity to split. Horizontal partitioning is often referred as Database Sharding. However, in case of Partitioning, the data is stored on a single machine and managed by different database servers running on the same machine. Horizontal scaling, also known as scale-out, refers to adding machines to share the data set and load. 3. A table can be clustered or partitioned or both (depending on DBMS). In most systems the disk space is allocated before the memory is allocated. This tool runs as an Azure web service, and migrates data safely between shards. We’re using the partitioning. Partitioning can help with larger tables but only when a small part of the data is hot. Imagine a sales database, we can. Cassandra is NOT a column oriented database. Table sharding is the practice of storing data in multiple tables, using a naming prefix such as [PREFIX]_YYYYMMDD. –The question of partitioning vs. Driver I can not find anyway to specify partitionkeys. Horizontal partitioning: Splitting the data by group of lines naturally given its primary keys (Row Splitting). Replication -- needed if you have 1000 reads per second. The concept is simplistic and enables scalability in distributed computing, but. In sharding, data is split horizontally into multiple shards. 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. It seemed right to share a perspective on the question of "partitioning vs. UserIDs that are even would be on shard 0 and odd userIDs would be on shard 1. Data is automatically distributed across shards using partitioning by consistent hash. Apache Spark supports two types of partitioning “hash partitioning” and “range partitioning”. Horizontal partitioning (or row-based partitioning) means that data is split in multiple tables based on predicate you define (most often it relates to dates, so data is being partitioned by year, month, even day – if it makes. It is essential to choose a sharding key that balances the load and distributes the data. There are two typical strategies for partitioning data. The main downside of both sharding and partitioning is added complexity, albeit in different ways. The following topics describe the sharding methods supported by Oracle Sharding: System-managed sharding is a sharding method which does not require the user to specify mapping of data to shards. . For example, you might have a collection. In this systems design video I will be going over how to scale databases using database partitioning, in particular horizontal partitioning aka sharding and. With sharded tables, BigQuery must maintain a copy of the schema and metadata for each table. . For example, if you intend on having a /api/users endpoint, you should have users collection and it should contain any and everything you intend to return on that endpoint. Figure 4:Side-by-side comparison of Schema-based sharding vs. 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. This defeats the purpose of sharding/partitioning. For a horizontal partitioning (sharding) tutorial, see Getting started with elastic query for horizontal partitioning (sharding). partitioning. It also discusses best practices for partitioning and gives an in-depth view at how horizontal scaling works in Azure Cosmos DB. Data partitioning criteria and the partitioning strategy decide how the dataset is divided. This enhances parallel processing and data management efficiency. This means that if we partition by the order_date, we cannot. By reducing the. In traditional database structures, sharding is a form of data partitioning (horizontal partitioning) which allows data from a single database to be stored across multiple servers. [Optional] An integer that defines the number of partitions to divide into. 28. sharding in PostgreSQL. Scaling a server cluster is easy and flexible; you keep adding machines as the size of your data increases. We also did a whole Postgres FM episode on partitioning. Horizontal partitioning is when the table is split by rows, with different ranges of rows stored on different partitions. Additionally, we’ll explore the basic concept of. Assuming that we have our data partitioned by the date, we can split that data into multiple nodes. Include “PGSQL Phriday #011” in the title or first paragraph of your blog post. Horizontal partitioning or sharding. Both are methods of breaking. Partitioning vs. Sharding on a Single Field Hashed Index. Partitioning data is often used for distributing load horizontally, this has performance benefit, and helps in organizing data in a logical fashion. One of the most important features of VoltDB is partitioning. Partitioning is recommended over table sharding, because partitioned tables perform better. 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. A sharding key is an attribute or column that determines how the data is distributed among the shards. Database sharding and. Sharding vs. This point has been discussed ad-nauseam on Stack Overflow, specifically in this answer. This technique supports horizontal scaling but can be. This architecture innovation was originally driven by internet giants that run. Database sharding is the optimization of large databases by splitting data from a larger database table into multiple smaller tables (shards). Sharding is a database partitioning technique used by blockchain companies with the purpose of scalability, enabling them to process more transactions per second. In this strategy each partition is a data store in its own right, but all partitions have the same schema. Horizontal Partitioning (sharding) stores rows of a table in multiple database clusters. Here, each partition is known as a shard and holds a specific subset of the data, such as all the orders for a specific set of. In the example above, using the customer ZIP. executor-based partition pruning. This allows for size growth and possibly performance scaling. whether Cassandra follows Horizontal partitioning (sharding) It may be clear that a shard can have multiple partitions in it. This makes it possible for parallell resolution of queries. These attributes form the shard key (sometimes referred to as the partition key). This article explores when to use each – or even to combine them for data-intensive applications. It can also affect the rate at which shards have to be added or removed, or that data must be repartitioned across shards. Sharding is the process of splitting a database into multiple smaller and independent databases, called shards, that share the same schema but store different subsets of data. In general, partitioning is a technique that is used within a single database instance to improve performance and manageability, while sharding is a technique that is used to scale a database across multiple servers. The question of partitioning vs. Rather, you can choose to use Postgres native partitioning, or you can shard Postgres with an extension like Citus to distribute Postgres across multiple nodes—or you can use. Database sharding is the process of breaking up large database tables into smaller chunks called shards. Multiple instances contain the same data. Database sharding is a type of horizontal partitioning that splits large databases into smaller components, which are faster and easier to manage. When partitioning a table, you need to consider having enough data for each partition. Database replication, partitioning and clustering are concepts related to sharding. Tag Aware Sharding: Assign specific ranges of a shard key with a specific shard or subset of shards. Each shard contains a subset of the data and can be processed independently. Understanding MongoDB Sharding & Difference From Partitioning. While partitioning and sharding are pretty similar in concept, the difference becomes much more apparent regarding No-SQL databases like MongoDB. In such a scenario, we are putting a subset of all partition keys in a physical node. Distributed. sharding. Partitioning is a. In the context of scaling MongoDB: replication creates additional copies of the data and allows for automatic failover to another node. The table that is divided is referred to as a partitioned table. However, in. System Design for Beginners: Design for Experienced Engineers: a member fo. Create a shard key that has many unique values. All data fits in-memory. Each partition has the same schema and columns, but also entirely different rows. For example, one might partition by date ranges, or by ranges of identifiers for particular business objects. Whether organizing data within a database or distributing it across servers, understanding their nuances and. We would like to show you a description here but the site won’t allow us. PostgreSQL has some sharding plug-ins or mpp products that closely integrate with databases, such as Citus, PG-XC, PG-XL, PG-X2, AntDB, Greenplum, Redshift, Asterdata, pg_shardman, and PL/Proxy. MongoDB uses sharding to support deployments with very large data sets and high throughput operations. . This Distributed SQL Tips & Tricks post looks at partitioning vs sharding, scaling limitations in RocksDB. Amazon Relational Database Service (Amazon RDS) is a managed relational database service that provides great features to make sharding easy to use in the cloud. Broadcast. Or you want a separate backup machine. However, they are. Hyperscale computing is a computing architecture that can scale up or down quickly to meet increased demand on the system. Low Shard Key Frequency. Horizontal partitioning (sharding) Horizontal portioning is like splitting up a table by rows: one set of rows goes into one data store, and another set of rows goes into a different. It is a range-based sharding. I described the PDP as using segments. 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. People often get confused between partitioning and sharding. Version 10 of PostgreSQL added the declarative table partitioning feature. 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 is the act of creating shards. However, to take full advantage of sharding, the application needs to be fully aware of it. 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. 1. 1M rows in a table -- no problem. The Backend systems function as intermediate storage of data, anything between. A distributed SQL database needs to automatically partition the data in a table and distribute it across nodes. The main reason to have vertical partition is when there are columns in the table that are updated more often than the rest. Partition keys are Unicode strings, with a maximum length limit. In this strategy, each partition is a data store in its own right, but all partitions have the same schema. What is MongoDB Sharding? Sharding is a method for distributing or partitioning data across multiple machines. Jayant Chakravarti Senior Assistant Editor, Spiceworks Ziff Davis. The common solution to this problem is using a hybrid between shared database and isolated databases - it's called database sharding, and basically, it means splitting your data into different databases, according to a sharding criterion (which in our case will by the TenantId) - but without having to keep each tenant on in a dedicated. A simple sharding function may be “ hash (key) % NUM_DB ”. Shard-Key. And if you are this far, go to method 2. If you end up sharding, the forum_id may be the best. 3. Our application is built on J2EE and EJB 2. The topic of this month's PGSQL Phriday #011 community blogging event is partitioning vs. To choose the best method, you need to consider factors such as the size and growth rate of your data. The declaration includes the partitioning method as described above, plus a list of columns or expressions to be used as the partition key.