The data that has close shard keys are likely to be placed on the same shard server. Hash Sharding is greatly used for targeted data operations. In Range Sharding the data is divided based on ranges or keyspaces, and the nearer the shard keys, the more likely for data to place under the same range and shard.
Every shard has an identical schema taken from the original database. It allows users to easily run queries to read data within contiguous data ranges. Choosing the shard keys poorly can result in database hotspots. The Geo-based sharding first partitions data according to the user-specified column so that it can map range shards to specific locations and the nodes in those regions.
For every location, the data is sharded using range sharding or hash. As the data increases the complexity to handle MySQL sharding becomes an additional development task. A few more challenges of Manual Sharding are listed below:. This means that if something like MySQL Sharding has to be implemented, it has to be done manually in the Application Layer which requires immense Engineering bandwidth since an entire Sharding logic that determines how data is distributed and how it is fetched has to be set up.
Since the implementation has to be done manually, there are certain design decisions that have to be made. The decisions that need to be made are as follows:.
For example, Referential Integrity i. MySQL allows users to change table Schemas in the database at any point in time after creation. If MySQL Sharding has been implemented, this change in the Schemas has to be implemented across all Shards before any application can leverage the new Schemas.
If the change is not implemented in even one of the Shards, it could lead to application errors or data inconsistency. Hence, the users should either develop a logic that can ensure that Schema changes are implemented across all Shards or not make any changes in the Schema once MySQL Sharding is implemented. This mapping might have to be updated regularly as the number of Shards increase or the Schemas change.
Also, this mapping would have to be accessed for almost every query that is run on the database and hence, should be stored in a location where fast lookups are possible. Therefore, it is considered ideal to store it in an in-memory database.
Implementation of MySQL Sharding has certain drawbacks because of which its implementation for most applications in production is not recommended. This article provided you with an in-depth analysis of what MySQL is, how Sharding works and why it is required along with the design decisions that have to be made while implementing MySQL Sharding.
It also provided you with a comprehensive understanding of why Sharding is considered to be a bad idea for most businesses. Hence, if Sharding is of paramount importance for your business and data requirements, it would be ideal to transfer all the required data to a NoSQL Database and then implement Sharding on it.
Hevo helps you directly transfer data from a source of your choice to a Data Warehouse or desired destination in a fully automated and secure manner without having to write the code or export data repeatedly.
If the application can live with eventually consistent data, scaling read traffic can be solved with relative ease by adding more replicas. However, scaling write traffic is more challenging; for example at a certain point even the largest MySQL database will see performance issues. This is not a new challenge, organizations have faced it for years, and one of the key patterns for solving it is horizontal sharding. Horizontal sharding refers to taking a single MySQL database and partitioning the data across several database servers each with identical schema.
This spreads the workload of a given database across multiple database servers, which means you can scale linearly simply by adding more database servers as needed. Having multiple shards reduces the read and write traffic handled by a single database server and makes it possible to keep the data on a single database server at an optimal size. However, now, since you are dealing with multiple servers rather than one this adds additional complexity to query routing and to the operational tasks like backup and restore, schema migration, and monitoring.
Many companies implemented horizontal sharding at the application level. In this approach, all of the logic for routing queries to the correct database server lives in the application. Each of these is critical to both the performance of the sharded array, as well as the flexibility of the implementation going forward. These design decisions include the following:. The sharding key controls how data is distributed across the shards. When sharding a MySQL database, care must be taken when choosing the sharding key, because that decision can later cause system inflexibility.
Another reason choosing the sharding key is critical is because changing an in-place sharding key can be very involved and troublesome. Since sharding key changes can have a knock-on effect across application, data location, and transactionality ACID as well across nodes, they are usually avoided if at all possible. Each MySQL shard can definitely deploy an online schema change, allowing no lost transactions. Each of the shards has to successfully complete their online schema change s before the application code leveraging the new schema s can be enabled.
This kind of coordination across multiple separate MySQL instances is a very involved process, exposing the application to potential errors and downtime. As a result, most administrators of sharded arrays seek to avoid schema changes if at all possible. For example, at Pinterest, online schema changes were found to be so costly they chose to handle attribute type changes via JSON. Instead of saving new attributes in their own column s , requiring schema changes to all the shards, Pinterest creates, modifies, and captures new attribute types ad hoc in JSON objects.
In other words, the performance tradeoff is beneficial compared to changing the schema, especially across a high number of shards. Part of sharding is creating and maintaining the mapping between sharding keys, data partitions, databases, and nodes. Shard splits, shard migrations, instance replacement, and sharding key changes all will change this mapping. Ideally, this mapping should be done in a very fast lookup because potentially, this information is needed for each query.
Sharding usually requires significant application changes as well. Applications moving to a MySQL sharded array now have to handle the following:. Specifically, queries have to be modified to leverage sharding key, and the mapping between that sharding key, shard id, and the server on which that shard currently resides.
As described before, the latter information is typically contained in a lookup, requiring additional code to keep up-to-date local version s of that mapping data in the application.
Having the query access the correct shard is only part of the challenge. Application queries typically need to access lots of data in many different tables, not all of which will be local to the shard.
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