; Vertical partitioning. return shardID. We achieve horizontal scalability through sharding”. A partitioned table is split to multiple physical disks, so accessing rows from different partitions can be done in parallel. partitioning. Additionally, we’ll explore the basic concept of. Build vs Buy for a Sharding Solution Meme Image (Image Source: LinkedIn) To make this choice, you need to consider the cost of 3rd party integration, keeping in mind. Sharding, at its core, is a horizontal partitioning technique. 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. In this step, you convert MongoDB servers into replica sets and configure them to serve as shard servers. To shard Postgres, you can use Citus. For stateless services, you can think about a partition being a logical unit. an index. April 29, 2022. Final step in search of the limits of the scalability of the relational databases is to sacrifice one of the core principles of the relational model, the database normalization. The schema of the table is replicated in every shard, and a unique portion of the whole table lives in. Even 1 billion rows may not need any of those fancy actions. e. Data sharding is a type of horizontal partitioning, which means splitting a large table or collection into smaller chunks, called shards, based on a key or a range of values. Redis Cluster data sharding. Hashing your partition key and keeping a mapping of how things route is key to a. 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. Include “PGSQL Phriday #011” in the title or first paragraph of your blog post. So the data in each partition is unique but the schema remains the same. Sharding vs. Horizontal data partitioning or sharding is a technique for separating data into multiple partitions. 데이터베이스를 분할하는 방법은 크게 샤딩(sharding)과 파티셔닝(partitioning)이 있다. If you are using mongoDB as a backend for a REST interface, the best practice is to create on collection per resource. Sharding is horizontal ( row wise) database partitioning as opposed to vertical ( column wise) partitioning which is Normalization. The partitioning policy defines if and how extents (data shards) should be partitioned for a specific table or a materialized view. Show 3 more. Database sharding involves partitioning data across multiple servers, so each server contains a subset of the data. Partition: Physical storage and I/O for read/write operations (for example, when rebuilding or refreshing an index). When you create a table, the initial status of the table is CREATING . This will reduce the risk of imbalanced shards while reducing the search impact. For example, high query rates can exhaust the CPU. Sharding and moving away from MySQL. Uncomment the replication and sharding section. A partition is an allocation of storage for a table, backed by solid state drives (SSDs) and automatically replicated across multiple Availability Zones within an AWS Region. Hashed sharding uses either a single field hashed index or a compound hashed index (New in 4. Database sharding vs partitioning I have been reading about scalable architectures recently. But that assumes no forum is too big to fit on one server. Both methods aim to improve performance and scalability, but they differ in how they handle data distribution. Là cách chia cùng dữ liệu của cùng một bảng (table) ra nhiều DB khác nhau. • Sharding algorithm: an algorithm to distribute your data to one or more shards. Sharding, also known as horizontal partitioning, is a popular scale-out approach for relational databases. These shards are not only smaller, but also faster and hence easily manageable. We are thinking of sharding our database with replication. 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. 1. One index satisfies the needs of most Sitecore solutions but multiple indexes offer better scaling when needed. PartitioningBy default, a clustered index has a single partition. Here the data is divided based on a shard key onto a separate database server instance. Vertical partitioning (schema per table group):. Introduction. Data in each shard does not have to share resources such as CPU or memory, and can. Sharding is a good option for handling a situation like this. Partitioning is a. This provides better load balancing compared to user-defined sharding that uses partitioning by range or list. There are two commonly used horizontal database scaling techniques: replication and horizontal partitioning (or sharding). Sharding is necessary as the number of records in the relationship table can easily exceed the storage space of any drive. 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. range partitioning in Apache Spark. PostgreSQL allows you to declare that a table is divided into partitions. Sharding. Allow lighter joins. Sharding is usually a case of horizontal partitioning. Sharding and partitioning is great if your query logically touches only one of the shards or partitions. • Sharding algorithm: an algorithm to distribute your data to one or more shards. Both systems use some form of partition key for partitioning the data. Data is automatically distributed across shards using partitioning by consistent hash. A simple sharding function may be “ hash (key) % NUM_DB ”. The partitions share the same data schema. 1 Answer. Sharding vs. MongoDB provides a router program mongos that will correctly route sharded queries without extra application logic. ". It also discusses best practices for partitioning and gives an in-depth view at how horizontal scaling works in Azure Cosmos DB. Each shard is responsible for a subset of the workload, and queries can be. It separates very large databases into smaller, faster and more easily managed parts called data shards. It results in scanning less data per query, and pruning is determined before query start time. ReplicationReplication & sharding can be part of either. sharding is a bit of a false dichotomy. Conclusion. A table can be clustered or partitioned or both (depending on DBMS). Note: In addition to the BigQuery web UI, you can use the bq command-line tool to perform operations on BigQuery datasets. Horizontal partitioning: Splitting the data by group of lines naturally given its primary keys (Row Splitting). I say this having worked with tables that were in the 10s of billions of rows without partitioning and were. 1. Database Sharding takes more work, but has the advantage. Horizontal partitioning or sharding. 131. the "employee id" here. So we decided to do shard our db into multiple instances. Actual latency for purely in-memory data could be similar. Sharded vs. Differences in Usage: Sharding vs Partitioning Now that you have a fundamental understanding of the differences in structure, let's move forward and explore the divergent usages of Sharding and Partitioning. Then it's like using a database with a much smaller dataset, and that by itself is likely to improve performance a little bit. Hence Sharding means dividing a larger part into smaller parts. I thought this might. Almost always a single table is better than splitting up the table (multiple tables; PARTITIONing; sharding). Again, the application tier is responsible for routing a. There are a number of base access methods: 1) Primary key access 2) Unique key access (== 2 primary key accesses) 3) Partition pruned scan access (Partition Key is provided in condition) (this can be both an ordered index scan or full scan). Partitioning -- won't help the use case you described. It is the simplest sharding algorithm and can be used to evenly distribute data among shards and prevent the risk of having a database hotspot. partitioning. Choose a scheme that matches the data characteristics and query patterns, and avoid schemes that cause. Sharding as a concept tends to work well for proof-of-stake. conf file with the following command. Partitioning and bucketing are complementary and can be used together. entity id, the same approach applies. Sharding is a specific type of partitioning in which dat. g. Database sharding is a powerful tool for optimizing the performance and scalability of a database. Range Partitioning. migrate to a NoSQL solution. Horizontal (sharding) and Vertical (increase server size. 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. By default, the operation creates 2 chunks per shard and migrates across the cluster. 水平擴展方式一般來說又可以分為 Horizontal Partitioning 與 Sharding，前者是在同一個資料庫中將 table 拆成數個小 table，後者則是將 table 放到數個資料庫中。Horizontal Partitioning 的 table 與 schema 可. However sharding is a trade-off. 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. 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. In general less REMOTE / SCATTER -> GATHER pairs means less cluster communication. 1y. How long the delays would be in replication? Will there be any data redundancy if one server goes down and comes back (because of delay in replication)?Tuples in the same partition are guaranteed to be on the same machine. This article explains the relationship between logical and physical partitions. NHỮNG CÁCH THỨC PHÂN CHIA DỮ LIỆU. 2. 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. Also if a database is partitioned, it does not imply that the database is definitely sharded. In this case, the table used for the benchmark has 1. But a partition can reside in only one shard. Should I do a Sharding? Sharding should be done only when it’s absolutely. Within YugabyteDB partitioning is a user-defined, SQL-level concept, thus requiring an explicit definition through SQL. Some databases have out-of-the-box support for sharding. Sharding is a method to distribute data across multiple different servers. Replication may help with horizontal scaling of reads if you are OK to read data that potentially isn't the latest. Sharding is the so-called umbrella term for all types of horizontal data partitioning schemes. It involves breaking down a large database into smaller, more manageable pieces called shards. To illustrate, let’s say you have a database that stores information about all the products. Sharding: Partitionning over several server, allowing parallel access (of different datas as opposed to replication) and, as such, memory and cpu load. Comparison of database sharding and partitioning. This is useful for 'write scaling'. When creating a partitioned index, you can use the WITH clause to specify additional options for the partitions. While the declarative partitioning feature allows users to partition tables into multiple partitioned tables living on the same database server, sharding allows tables. Partitioning options on a table in MySQL in the environment of the Adminer tool. This horizontal architecture creates a more dynamic ecosystem as it allows shards to perform specialised actions based on their characteristics. In this article, we will explore the. Another resource is a bottleneck and you need to shard data. Each partition is a separate data store, but all of them have the same schema. Each shard (or server) acts as the. There are three typical strategies for partitioning data: Firstly, Horizontal partitioning (often called sharding). However they’re still somewhat common, the google analytics 360 bigquery export for example, provides a new table shard each day, for the new data from the prior day. By default, the operation creates 2 chunks per shard and migrates across the cluster. Each shard (or server) acts as the. Later in the example, we will use a collection of books. The decision to use sharding or partitioning depends on several factors, including the scale of your application, expected growth, query patterns, and data distribution requirements: Use Sharding When: Dealing with extremely large datasets that can’t be managed efficiently by a single server. Figure 4:Side-by-side comparison of Schema-based sharding vs. 🔹 Vertical partitioning: it means some columns are moved to new tables. Sharding on a Single Field Hashed Index. This architecture innovation was originally driven by internet giants that run. Database partitioning is normally done for manageability, performance or availability reasons, or for load balancing. Its Horizontal partitioning (often called sharding). Each shard holds a subset of the data, and no shard has. An object with the following properties: num_partition. Imagine that the sales leads table has an extra column, revenue_ potential, as you see in Table 2. Sharding makes it easy to generalize our data and allows for cluster computing (distributed computing). In that context, two words that keep on showing up with regards to databases are sharding and partitioning. partitioning. A sharding key is an attribute or column that determines how the data is distributed among the shards. Sharding -- only if you need to 1000 writes per second. There are two broad ways by which we partition/shard data : Partition by key-range. When automatic sharding finds an uneven distribution of data (or queries) among the shards, it will automatically re-partition the data, resulting in improved performance and scalability. In this case, the records for stores with store IDs under 2000 are placed in one shard. Sharded vs. We’re using the partitioning. 4) as the shard key to partition data across your sharded cluster. Database Sharding vs Partitioning – System Design Concepts . In many cases , the terms sharding and partitioning are even used synonymously, especially when preceded by the terms “horizontal” and. Database sharding is a type of horizontal partitioning that splits large databases into smaller components, which are faster and easier to manage. In a segment/partition system, it is possible to go back the same memory after swapping but the larger the physical memory, the less likely it will be to return to the same place. While partitioning and sharding are pretty similar in concept, the difference becomes much more apparent regarding No-SQL databases like MongoDB. Cassandra achieves high availability and fault tolerance by replication of the data across nodes in a cluster. Replication -- needed if you have 1000 reads per second. 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. Horizontal partitioning is another term for sharding. ENGINE = Distributed(logs, default, hits[, sharding_key[, policy_name]]) SETTINGS. sharding is a bit of a false dichotomy. The difference is that sharding implies the data is spread across multiple computers while partitioning does not. But I didn't find any article about SQL Server. In DBMS, Sharding is a type of DataBase partitioning in which a large database is divided or partitioned into smaller data and different nodes. The topic of this month’s PGSQL Phriday #011 community blogging event is partitioning vs. sharding allows for horizontal scaling of data writes by partitioning data across. Content delivery networks (CDNs) use sharding to store web content like images, videos, and JavaScript files, ensuring fast and efficient content delivery to users. For example, if a clustered index has four partitions, there are four B-tree structures; one in each partition. Horizontal scaling, also known as scale-out, refers to adding machines to share the data set and load. Learn about each approach and. Sharding is typically used to scale storage and query processing, with the goal being that the database 'as a whole' provides the abstraction of a single, unified logical repository of data, typically managed by a single organization. With sharded tables, BigQuery must maintain a copy of the schema and metadata for each table. Sharding, also known as horizontal partitioning, is a popular scale-out approach for relational databases. Both are used to improve query performance, but they achieve this in different ways. Most Citus setups I have seen primarily use Citus sharding, and not Postgres table partitioning. Sharding -- only if you need to 1000 writes per second. Apache Spark supports two types of partitioning “hash partitioning” and “range partitioning”. Database Application level sharding is the process of splitting a table into multiple database instances in order to distribute the load. 1. 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. 1. 1. This Distributed SQL Tips & Tricks post looks at partitioning vs sharding, scaling limitations in RocksDB. g. The table that is divided is referred to as a partitioned table. However, system-managed sharding does not give the user any control on assignment of data to shards. When data is written to the table, a partitioning function will be used by MySQL to decide. Database partitioning is normally done for manageability, performance or availability reasons, as for load balancing. Sharding is a specific type of partitioning, where each partition is independent and self-contained. Hash-based Sharding. Announce your blog post on one or more of these platforms: Twitter/Linkedin/FB using the #. 131. sharding in PostgreSQL. date partitioning. When partitioning a table, you need to consider having enough data for each partition. Both partitioning and sharding involve distributing data across multiple physical or logical storage devices, with the goal of improving data processing and query performance. Whether organizing data within a database or distributing it across servers, understanding their nuances and. Some data within a database remains present in all shards, [a] but some appear only in a single shard. Keep in mind that indexes are sharded in the same way as tables. The CAP always applies, it says user failure to acces data means either interruptions or inconsistencies. However, in some use cases it can make sense to partition your database tables where parts of the table are distributed on different servers. The main difference is that partitioning groups these subsets on a single database instance, whereas sharded data can be spread across multiple. The terms Sharding and Partitioning are used interchangeably nowadays. In this post, I describe how to use Amazon RDS to implement a. Both approaches have their own strengths and weaknesses, and the best approach for a given situation will depend on the specific. So, bucketing works well when the field has high cardinality and data is evenly distributed among buckets. . On the other hand, Partitioning divides data into smaller, more manageable chunks within a single server. Imagine a sales database, we can. Choosing a partition key is an important decision that affects your application's performance. Additionally, we’ll explore the basic concept of each method, along with an example. Sharding is used when Partitioning is not possible any more, e. shardID = identifier % numShards. Horizontal partitioning: Splitting the data by group of lines naturally given its primary keys (Row Splitting). Whereas, in network sharding, the entire blockchain network is partitioned into sub-networks called shards. In this tutorial, we’ll discuss two methods for splitting databases into parts to manage them efficiently: sharding and partitioning. This brings me to my last point, and the motivation for this post. Partition an App Service web app to avoid limits on the number of instances per App Service plan. Later in the example, we will use a collection of books. Comparison of database sharding and partitioning. 1. But there’s two new things: There’s a new shard_axes argument being passed into the layer definition on lines 11 and 21. It is a partitioned row store. To improve query response will it be better to shard the data or replicate existing shards for faster response. There's also the issue of balancing. Driver I can not find anyway to specify partitionkeys in my queries. Sharding is a type of database partitioning that separates large databases into smaller, faster, and more easily managed parts. 1. By default, the operation creates 2 chunks per shard and migrates across the cluster. It is popular in distributed database. The key differences are that partitioning occurs on the same server and is supported by MySQL natively, whereas sharding a. sharding allows for horizontal scaling of data writes by partitioning data across. The criteria used to partition the data could be a specific range of values, a list of values, or a. Sharding is useful to increase performance, reducing the hit and memory load on any one resource. In this video, we dive into the topic of Database Sharding vs Partitioning and break down the key differences between the two. 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. The partitioning scheme can significantly affect the performance of your system. 1 do sharding by yourself. Partitioning is a word used to describe the process of breaking your data elements logically into different entities for purposes of efficiency. Horizontal partitioning and sharding. The table that is divided is referred to as a partitioned table. Sharding is a pattern that divides a data store into horizontal partitions or shards to improve scalability and performance. With partitioning, we accomplish this scaling by inserting data into many small tables (with associated indexes) and limited scopes of data per table. However, a sharding key cannot be a. In this post, SingleStore Developer Advocate, Joe Karlsson, explains the differences between database sharding vs. 2. By dividing the data into. Using MySQL Partitioning that comes with version 5. There are 5 types of distributed joins, as explained here, ordered from most preferred to least: This is the example you mentioned with the Countries table. The word “ Shard ” means “ a small part of a whole “. Replication -- needed if you have 1000 reads per second. To make sure all of our important data fits into memory and is available quickly for our users, we’ve begun to shard our data — in other words, place the data in many smaller buckets, each holding a part of the data. In this partitioning, each partition is a separate data store , but all partitions have the same schema . Mỗi partitions có cùng schema và cột, nhưng cũng có các hàng hoàn toàn khác nhau. The disadvantage is ultimately you are limited by what a single server can do. Horizontal data partitioning or sharding is a technique for separating data into multiple partitions. One of the primary differences between sharding and partitioning is how they distribute data. Step 1: Analyze scenario query and data distribution to find sharding key and sharding algorithm. It can also be functional (which maps rows of data into one partition or the other depending on their value). Horizontal scaling, also known as scale-out, refers to adding machines to share the data set and load. Create a shard key that has many unique values. It's not necessary to understand these. 🔹 Horizontal partitioning (often called sharding): it divides a table into multiple smaller tables. Horizontal partitioning: Each partition uses the same database schema and has the same columns, but contains different rows. Each individual partition is known as shard or database shard. 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. Horizontal partitioning or sharding. For a horizontal partitioning (sharding) tutorial, see Getting started with elastic query for horizontal partitioning (sharding). sharding. Database Sharding is the process where a huge Database is partitioned horizontally. In terms of Database Partitioning, its intent is predominantly to enhance query performance in a database. ) "Partitioning" -- a special syntax that builds sub-tables, but reference it as if it were a single table. 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. 1M WordPress "users", each owning Database with. In this post, I describe how to use Amazon RDS to implement a sharded database. We achieve horizontal scalability through sharding”. It can also affect the rate at which shards have to be added or removed, or that data must be repartitioned across shards. Each shard contains a subset of the total rows and functions as a smaller independent database. Partitioning is recommended over table sharding, because partitioned tables perform better. Database sharding and partitioning. The primary difference is one of administration. . Sharding key is only. It helps you in case you need to separate data in a big table to improve performance, or even to purge data in an easy way, among other situations. This makes it possible for parallell resolution of queries. Driver I can not find anyway to specify partitionkeys in my queries. Horizontal partitioning or sharding. If you’ve used Google or YouTube, you’ve probably accessed sharded data. If the number of shards is changed, then the allocation will be different. Partition Service Fabric stateless services. Unstructured data, including images, video, audio, and natural language, is information that doesn't follow a predefined model or manner of organization. Sharding is the spreading of horizontal partitions across multiple servers. Each partition is known as a shard and holds a specific subset of the data, such as all the orders for a specific set of customers in an ecommerce application. If, however, Alice that resides on shard #1 wants to send money to Bob who resides on shard #2, neither validators on shard #1(they won’t be able to credit Bob’s account) nor the validators on. 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:In this context, "partitioning" refers to the division of rows based on their primary key, while "sharding" involves dispersing these rows across multiple key-value data stores. Horizontal partitioning is achieved in a relational database by storing rows from the same table in several database nodes. 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. Each partition is known as a "shard". 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. With this approach, the schema is identical on all participating databases. 1 Answer. 2 Answers. Central to this strategy is database partitioning — serving as the backbone of today’s distributed database systems. Horizontal database partition or sharding is the mostly commonly used partitioning method in SQL databases. In MySQL, the term “partitioning” applies to individual tables of a database. Primary shards & Replica shards in. See sp_execute _remote for a stored procedure that executes a Transact-SQL statement on a single remote Azure SQL Database or set of databases serving as shards in a horizontal partitioning scheme. Also, you can partition on multiple fields, with an order (year/month/day is a good example), while you can bucket on only one field. Database sharding is a technique used to optimize database performance at scale. It uses the partition key that is associated with each data record to determine which shard a given data record belongs to. What is Database Sharding? | Hazelcast. A good partition strategy should avoid Hot spots. Queries are simple. A hashing function hashes the sharding key value, and the output maps data to a particular shard. This is not a new challenge; organizations have faced it for years, and horizontal sharding is one of the key patterns for solving it. 2. 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, a side-by-side comparison How to use range partitioning & Citus sharding together for time series What about sharding using. Sharding vs Partitioning. We would like to show you a description here but the site won’t allow us. 1M rows in a table -- no problem. Sharding is a method for distributing a single dataset across multiple databases, which can then be stored on multiple machines. Hybrid Sharding. It can also be functional (which maps rows of data into one partition or the other depending on their value). entity id, the same approach applies . In upcoming release Oracle 12. As your data grows in size, the database will continue to. PostgreSQL allows you to declare that a table is divided into partitions. For a more detailed explanation of sharding and the auto-sharding mechanics in YugabyteDB, check out Distributed SQL Sharding: How Many Tablets, and at What Size? P. Size of row and kinds of data -- Large columns (TEXT/BLOB/JSON) are stored "off-record", thereby leading to [potentially] an extra disk. Sharding vs. 1 Horizontal partitioning — also known as sharding. These smaller parts are called data shards. It has nothing to do with SQL vs NoSQL. Well, if the question is about sharding, then pgpool and postgresql partitioning features are not valid answers. The advantage of DBMS single server partitioning is that it is relatively simple to set up and manage. Such databases don’t have traditional rows and columns, and so it is interesting to learn how they implement partitioning. Download Now. Each node further gets split into multiple shards. Horizontal vs Vertical partitioning First of all, there are two ways of partitioning – horizontal and vertical. Database sharding is the easiest partition technique that can be used with SQL Server. Hazelcast named in the Gartner ® Market Guide for Event Stream Processing. sharding” from someone in the Citus open source team, since we eat, sleep, and breathe sharding for Postgres. If you end up sharding, the forum_id may be the best. Sharding is the equivalent of “horizontal partitioning. cloud. Data is not only read but is partially processed on the remote servers (to the extent that this. 5. In Mongodb each secondary node contains full data of primary node but in Cassandra, each secondary node has responsibility of keeping only some key partitions of data. partitioning. For general guidelines about Athena query performance, see Top 10 performance. The declaration includes the partitioning method as described above, plus a list of columns or expressions to be used as the partition key. Sharding and partitioning are cornerstone techniques in modern database architectures. All data fits in-memory. So that leaves two more options. Algorithmically sharded databases use a sharding function (partition_key) -> database_id to locate data. I don't have any knowledge. Create secondary filegroups and add data files into each filegroup. Horizontal scaling vs vertical scaling: When we design any application, we need to think of scaling as well. remy_porter • 6 mo. Hyperscale computing is a computing architecture that can scale up or down quickly to meet increased demand on the system. MongoDB – Replication and Sharding. The only difference is that in transaction sharding, the partitioning and creation of shards are done based on the transactions. What is the difference between replication and sharding? Replication: The primary server node copies data onto secondary server nodes. Now the requests will be routed across shards in the partition rather than one (basic routing) or all shards (no routing) in the index. However, Sharding a. It is similar to partitioning, but with an added functionality of hashing technique. Suppose we know that we need to spread the data of this SQL table into 4 servers.