Traditionally, it's been difficult to scale a MySQL-based database to an arbitrary size. Since MySQL lacks the out-of-the-box multi-instance support required to really scale an application, the process can be complex and obscure.
As the application grows, scripts emerge to back up data, migrate a master database, or run some offline data processing. Complexity creeps into the application layer, which increasingly needs to be aware of database details. And before we know it, any change needs a big engineering effort so we can keep scaling.
Vitess grew out of YouTube's attempt to break this cycle, and YouTube decided to open source Vitess after realizing that this is a very common problem. Vitess simplifies every aspect of managing a MySQL cluster, allowing easy scaling to any size without complicating your application layer. It ensures your database can keep up when your application takes off, leaving you with a database that is flexible, secure, and easy to mine.
This document talks about the process of moving from a single small database to a limitless database cluster. It explains how steps in that process influenced Vitess' design, linking to relevant parts of the Vitess documentation along the way. It concludes with tips for designing a new, highly scalable application and database schema.
Vitess sits between your application and your MySQL database. It looks at incoming queries and routes them properly. So, instead of sending a query directly from your application to your database, you send it through Vitess, which understands your database topology and constantly monitors the health of individual database instances.
While Vitess is designed to manage large, multi-instance databases, it offers features that simplify database setup and management at all stages of your product's lifecycle.
Starting out, our first step is getting a simple, reliable, durable database cluster in place with a master instance and a couple of replicas. In Vitess terminology, that's a single-shard, single-keyspace database. Once that building block is in place, we can focus on scaling it up.
We recommend a number of best practices to facilitate scaling your database as your product evolves. You might not experience the benefits of these actions immediately, but adopting these practices from day one will make it much easier for your database and product to grow:
At the outset, plan to create a database cluster that has a master instance and a couple of read-only replicas (or slaves). The replicas would be able to take over if the master became unavailable, and they might also handle read-only traffic. You'd also want to schedule regular data backups.
It's worth noting that master management is a complex and critical challenge for data reliability. At any given time, a shard has only one master instance, and all replica instances replicate from it. Your application -- either a component in your application layer or Vitess, if you are using it -- needs to be able to easily identify the master instance for write operations, recognizing that the master might change from time to time. Similarly, your application, with or without Vitess, should be able to seamlessly adapt to new replicas coming online or old ones being unavailable.
A core principle underlying Vitess' design is that your database and data management practices should always be ready to support your application's growth. So, you might not yet have an immediate need to store data in multiple data centers, shard your database, or even do regular backups. But when those needs arise, you want to be sure that you'll have an easy path to achieve them. Note that you can run Vitess in a Kubernetes cluster or on local hardware.
With that in mind, you want to have a plan that allows your database to grow without complicating your application code. For example, if you reshard your database, your application code shouldn't need to change to identify the target shards for a particular query.
Vitess has several components that keep this complexity out of your application:
Setting up these components directly -- for example, writing your own topology service or your own implementation of vtgate -- would require a lot of scripting specific to a given configuration. It would also yield a system that would be difficult and costly to support. In addition, while any one of the components on its own is useful in limiting complexity, you need all of them to keep your application as simple as possible while also optimizing performance.
Optional functionality to implement
Recommended. Vitess has basic support for identifying or changing a master, but it doesn't aim to fully address this feature. As such, we recommend using another program, like Orchestrator, to monitor the health of your servers and to change your master database when necessary. (In a sharded database, each shard has a master.)
Recommended. You should have a way to monitor your database topology and set up alerts as needed. Vitess components facilitate this monitoring by exporting a lot of runtime variables, like QPS over the last few minutes, error rates, and query latency. The variables are exported in JSON format, and Vitess also supports an InfluxDB plug-in.
Optional. Using the Kubernetes scripts as a base, you could run Vitess components with other configuration management systems (like Puppet) or frameworks (like Mesos or AWS images).
Related Vitess documentation:
Obviously, your application needs to be able to call your database. So, we'll jump straight to explaining how you'd modify your application to connect to your database through vtgate.
As of Release 2.1, VTGate supports the MySQL protocol. So, the application only needs to change where it connects to. For those using Java or Go, we additionally provide libraries that can communicate to VTGate using gRPC. Using the provided libraries allow you to send queries with bind variables, which is not inherently possible through the MySQL protocol.
The vttest library and executables provide a unit testing environment that lets you start a fake cluster that acts as an exact replica of your production environment for testing purposes. In the fake cluster, a single DB instance hosts all of your shards.
The easiest way to migrate data to your Vitess database is to take a backup of your existing data, restore it on the Vitess cluster, and go from there. However, that requires some downtime.
Another, more complicated approach, is a live migration, which requires your application to support both direct MySQL access and Vitess access. In that approach, you'd enable MySQL replication from your source database to the Vitess master database. This would allow you to migrate quickly and with almost no downtime.
Note that this path is highly dependent on the source setup. Thus, while Vitess provides helper tools, it does not offer a generic way to support this type of migration.
The final option is to deploy Vitess directly onto the existing MySQL instances and slowly migrate the application traffic to move over to using Vitess.
Related Vitess documentation:
Typically, the first step in scaling up is vertical sharding, in which you identify groups of tables that belong together and move them to separate keyspaces. A keyspace is a distributed database, and, usually, the databases are unsharded at this point. That said, it's possible that you'll need to horizontally shard your data (step 4) before scaling to multiple keyspaces.
The benefit of splitting tables into multiple keyspaces is to parallelize access to the data (increased performance), and to prepare each smaller keyspace for horizontal sharding. And, in separating data into multiple keyspaces, you should aim to reach a point where:
Several vtctl functions -- vtctl is Vitess' command-line tool for managing your database topology -- support features for vertically splitting a keyspace. In this process, a set of tables can be moved from an existing keyspace to a new keyspace with no read downtime and write downtime of just a few seconds.
Related Vitess documentation:
The next step in scaling your data is horizontal sharding, the process of partitioning your data to improve scalability and performance. A shard is a horizontal partition of the data within a keyspace. Each shard has a master instance and replica instances, but data does not overlap between shards.
In order to perform horizontal sharding, you need to identify the column that will be used to decide the target shard for each table. This is known as the Primary Vindex, which is similar to a NoSQL sharding key, but provides additional flexibility. The decision on such primary vindexes and other sharding metadata is stored in the VSchema.
Vitess offers robust resharding support, which involves updating the sharding scheme for a keyspace and dynamically reorganizing data to match the new scheme. During resharding, Vitess copies, verifies, and keeps data up-to-date on new shards while existing shards continue serving live read and write traffic. When you're ready to switch over, the migration occurs with just a few seconds of read-only downtime.
Related Vitess documentation:
In addition to the four steps discussed above, you might also want to do some or all of the following as your application matures.
Hadoop is a framework that enables distributed processing of large data sets across clusters of computers using simple programming models.
Vitess provides a Hadoop InputSource that can be used for any Hadoop MapReduce job or even connected to Spark. The Vitess InputSource takes a simple SQL query, splits that query into small chunks, and parallelizes data reading as much as possible across database instances, shards, etc.
Database query logs can help you to monitor and improve your application's performance.
To that end, each vttablet instance provides runtime stats, which can be accessed through the tablet’s web page, for the queries the tablet is running. These stats make it easy to detect slow queries, which are usually hampered by a missing or mismatched table index. Reviewing these queries regularly helps maintain the overall health of your large database installation.
Each vttablet instance can also provide a stream of all the queries it is running. If the Vitess cluster is colocated with a log cluster, you can dump this data in real time and then run more advanced query analysis.