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Package consistent provides a consistent hashing function. Consistent hashing is often used to distribute requests to a changing set of servers. For example, say you have some cache servers cacheA, cacheB, and cacheC. You want to decide which cache server to use to look up information on a user. You could use a typical hash table and hash the user id to one of cacheA, cacheB, or cacheC. But with a typical hash table, if you add or remove a server, almost all keys will get remapped to different results, which basically could bring your service to a grinding halt while the caches get rebuilt. With a consistent hash, adding or removing a server drastically reduces the number of keys that get remapped. Read more about consistent hashing on wikipedia: http://en.wikipedia.org/wiki/Consistent_hashing

Package pop wraps the absolutely amazing https://github.com/jmoiron/sqlx library. It cleans up some of the common patterns and workflows usually associated with dealing with databases in Go. Pop makes it easy to do CRUD operations, run migrations, and build/execute queries. Is Pop an ORM? I'll leave that up to you, the reader, to decide. Pop, by default, follows conventions that were defined by the ActiveRecord Ruby gem, http://www.rubyonrails.org. What does this mean? * Tables must have an "id" column and a corresponding "ID" field on the `struct` being used. * If there is a timestamp column named "created_at", "CreatedAt" on the `struct`, it will be set with the current time when the record is created. * If there is a timestamp column named "updated_at", "UpdatedAt" on the `struct`, it will be set with the current time when the record is updated. * Default databases are lowercase, underscored versions of the `struct` name. Examples: User{} is "users", FooBar{} is "foo_bars", etc...

Package consistent provides a consistent hashing function. Consistent hashing is often used to distribute requests to a changing set of servers. For example, say you have some cache servers cacheA, cacheB, and cacheC. You want to decide which cache server to use to look up information on a user. You could use a typical hash table and hash the user id to one of cacheA, cacheB, or cacheC. But with a typical hash table, if you add or remove a server, almost all keys will get remapped to different results, which basically could bring your service to a grinding halt while the caches get rebuilt. With a consistent hash, adding or removing a server drastically reduces the number of keys that get remapped. Read more about consistent hashing on wikipedia: http://en.wikipedia.org/wiki/Consistent_hashing

Package strcase converts strings to various cases. See the conversion table below:

Package dht implements a distributed hash table that satisfies the ipfs routing interface. This DHT is modeled after kademlia with S/Kademlia modifications.