It's only suboptimal if you choose the wrong column type for the task at hand. For storing URLs, you almost certainly don't want collation behaviors, such as accent insensitivity or case insensitivity. So VARBINARY is a better choice here anyway.
And as several other commenters have mentioned, at large scale, indexing a bunch of long URLs in b-trees is indeed a bad practice performance-wise in any relational database. You won't be able to fit many entries per page, so read performance will be slow, especially for range scans.
In that situation it's almost always better to use a non-unique index over a prefix (if you need sorting and range scans) or a hash (if you don't), and disambiguate collisions by having the full value in an unindexed column. And/or split the URL up between the domain name and path in separate columns. If needed, normalize the domain names into a separate table so that the URL table can refer to them by numeric ID. etc. All depends on the specific use-case.
It's only suboptimal if you choose the wrong column type for the task at hand. For storing URLs, you almost certainly don't want collation behaviors, such as accent insensitivity or case insensitivity. So VARBINARY is a better choice here anyway.
And as several other commenters have mentioned, at large scale, indexing a bunch of long URLs in b-trees is indeed a bad practice performance-wise in any relational database. You won't be able to fit many entries per page, so read performance will be slow, especially for range scans.
In that situation it's almost always better to use a non-unique index over a prefix (if you need sorting and range scans) or a hash (if you don't), and disambiguate collisions by having the full value in an unindexed column. And/or split the URL up between the domain name and path in separate columns. If needed, normalize the domain names into a separate table so that the URL table can refer to them by numeric ID. etc. All depends on the specific use-case.