There are various ways to manage user sessions including storing those sessions locally to the node responding to the HTTP request or designating a layer in your architecture which can store those sessions in a scalable and robust manner. Common approaches used include utilizing Sticky sessions or using a Distributed Cache for your session management. These approaches are described below.
Sticky sessions, also known as session affinity, allow you to route a site user to the particular web server that is managing that individual user’s session. The session’s validity can be determined by a number of methods, including a client-side cookies or via configurable duration parameters that can be set at the load balancer which routes requests to the web servers.
Some advantages with utilizing sticky sessions are that it’s cost effective due to the fact you are storing sessions on the same web servers running your applications and that retrieval of those sessions is generally fast because it eliminates network latency. A drawback for using storing sessions on an individual node is that in the event of a failure, you are likely to lose the sessions that were resident on the failed node. In addition, in the event the number of your web servers change, for example a scale-up scenario, it’s possible that the traffic may be unequally spread across the web servers as active sessions may exist on particular servers. If not mitigated properly, this can hinder the scalability of your applications.
In order to address scalability and to provide a shared data storage for sessions that can be accessible from any individual web server, you can abstract the HTTP sessions from the web servers themselves. A common solution to for this is to leverage an In-Memory Key/Value store such as Redis and Memcached.
While Key/Value data stores are known to be extremely fast and provide sub-millisecond latency, the added network latency and added cost are the drawbacks. An added benefit of leveraging Key/Value stores is that they can also be utilized to cache any data, not just HTTP sessions, which can help boost the overall performance of your applications.
A consideration when choosing a distributed cache for session management is determining how many nodes may be needed in order to manage the user sessions. Generally speaking, this decision can be determined by how much traffic is expected and/or how much risk is acceptable. In a distributed session cache, the sessions are divided by the number of nodes in the cache cluster. In the event of a failure, only the sessions that are stored on the failed node are affected. If reducing risk is more important than cost, adding additional nodes to further reduce the percent of stored sessions on each node may be ideal even when fewer nodes are sufficient.
Another consideration may be whether or not the sessions need to be replicated or not. Some key/value stores offer replication via read replicas. In the event of a node failure, the sessions would not be entirely lost. Whether replica nodes are important in your individual architecture may inform as to which key/value store should be used. ElastiCache offerings for In-Memory key/value stores include ElastiCache for Redis, which can support replication, and ElastiCache for Memcached which does not support replication.
There are a number of ways to store sessions in Key/Value stores. Many application frameworks provide libraries which can abstract some of the integration plumbing required to GET/SET those sessions in memory. In other cases, you can write your own session handler to persist the sessions directly.
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Additional Resources: Technical Whitepaper on In-memory Caching | Amazon ElastiCache | Amazon CloudFront