Design Goals

Nick edited this page Dec 11, 2017 · 3 revisions

ProxySQL has been built with a few key design choices in mind.

Maximum uptime

Since ProxySQL is a proxy, it's expected to be up there as close to 100% of the time as possible. This means that we should be handling differently the usual suspects when it comes to downtime:

  • configuration changes. This is usually done by changing the configuration file and restarting the daemon. In ProxySQL, by design, the user is able to modify most configuration variables through the admin interface, without having to restart the server. Example things that can be modified at runtime:
    • interfaces on which ProxySQL is listening
    • backend servers to which ProxySQL is connecting
    • timeouts for the different operations performed by ProxySQL
  • crashes. ProxySQL has an extensive suite of integration tests that are ran using Docker, to ensure that it does not crash. Also, each major release is performance-tested using sysbench, and memory-leak tested using valgrind. In addition to this, an angel process has been implemented that monitors and restarts ProxySQL when needed, in order to keep downtimes to a minimum, if they end up occuring.

Maximum scalability

There are several key scenarios that we wanted to run as fast as possible when interacting with ProxySQL:

  • time to complete a new MySQL connection to it: this is why ProxySQL has a pool of threads all waiting via the system call accept() to receive a new connection. Because of this, the probability of the new TCP connection of being established faster is increased
  • time to connect to a MySQL backend: this is why ProxySQL has a backend connection pool in which it keeps some idle connections alive to the backend servers, according to the configuration. When it needs to send a packet to those servers, most of the time the connection is already open
  • multi-core scalability: ProxySQL has a multi-threaded design where all threads do the same thing (marshal messages back and forth between the backend servers and the MySQL client connections), and our tests show that it scales very well with the number of cores.

Cascade possibilities

ProxySQL can be cascaded to as many layers as required. For example, one common scenario is to have a ProxySQL instance as close to the servers running the application as possible, pointing to another middle layer cluster of ProxySQL servers that routes all traffic to a farm of backend MySQL servers.

A -> P1 -----> {P2, P3, P4 ..} ------> {B1, B2, B3, ..}

The advantage in this case is that it is completely fault tolerant with respect to the MySQL access, given that the app is not a single point of failure. There is no modification needed at the application level for the application to connect to a cluster of proxies instead of a single one. If any proxy from the middle layer fails, the P1 proxy will detect that and will route traffic through the other ones. If any of the backends B1, B2, B3, ... fails, then the middle layer proxies will take care of the job in a way that is transparent to P1.

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