Resin's cluster protocol for distributed sessions can
is an alternative to JDBC-based distributed sessions. In some
configurations, the cluster-stored sessions will be more efficient
than JDBC-based sessions.
Because sessions are always duplicated on separate servers, cluster
sessions do not have a single point of failure.
As the number of
servers increases, JDBC-based sessions can start overloading the
backing database. With clustered sessions, each additional server
shares the backup load, so the main scalability issue reduces to network
bandwidth. Like the JDBC-based sessions, the cluster store sessions
uses sticky-session caching to avoid unnecessary network traffic.
The cluster configuration must tell each host the servers in the
cluster
and it must enable the persistent in the session configuration
with use-persistent-store.
Because session configuration is specific to a virtual host and a
web-application, each web-app needs use-persistent-store enabled
individually. The web-app-default
tag can be used to enable distributed sessions across an entire site.
Most sites using Resin's load balancing will already have the cluster
<srun> configured. Each <srun> block corresponds to a
host, including the current host. Since cluster sessions uses
Resin's srun protocol, each host must listen for srun requests.
Usually, hosts will share the same resin.conf. Each host will be
started with a different -server xx to select the correct
block. On Unix, startup will look like:
resin-3.0.x> bin/httpd.sh -conf conf/resin.conf -server c start
On Windows, Resin will generally be configured as a service:
resin-3.0.x> bin/httpd -conf conf/resin.conf -server c -install-as ResinC
Resin's distributed sessions needs to know when a session has
changed in order to save the new session value. Although Resin can
detect when an application calls HttpSession.setAttribute, it
can't tell if an internal session value has changed. The following
Counter class shows the issue:
package test;
public class Counter implements java.io.Serializable {
private int _count;
public int nextCount() { return _count++; }
}
Assuming a copy of the Counter is saved as a session attribute,
Resin doesn't know if the application has called nextCount. If it
can't detect a change, Resin will not backup the new session, unless
always-save-session is set. When always-save-session is
true, Resin will back up the session on every request.
Like the JDBC-based sessions, Resin will ignore the
always-load-session flag for cluster sessions. Because the
cluster protocol notifies servers of changes, always-load-session is
not needed.
Resin's distributed sessions relies on Java serialization to save and
restore sessions. Application object must implement
java.io.Serializable for distributed sessions to work.
To see how cluster sessions work, consider a case where
the load balancer sends the request to a random host. Host C owns the
session but the load balancer gives the request to Host A. In the
following figure, the request modifies the session so it must be saved
as well as loaded.
The session id encodes the owning host. The example session
id, ca8MbyA, decodes to an srun-index of 3, mapping
to Host C. Resin determines the backup host from the cookie
as well.
Host A must know the owning host
for every cookie so it can communicate with the owning srun.
The example configuration defines all the sruns Host A needs to
know about. If Host C is unavailable, Host A can use its
configuration knowledge to use Host D as a backup
for ca8MbyA instead..
When the request first accesses the session, Host A asks
Host C for the serialized session data (2:load).
Since Host A doesn't cache the session data, it must
ask Host C for an update on each request. For requests that
only read the session, this TCP load is the only extra overhead,
i.e. they can skip 3-5. The always-save-session
flag, in contrast, will always force a write.
At the end of the request, Host A writes any session
updates to Host C (3:store). If always-save-session
is false and the session doesn't change, this step can be skipped.
Host A sends
the new serialized session contents to Host C. Host C saves
the session on its local disk (4:save) and saves a backup
to Host D (5:backup).
Smart load balancers that implement sticky sessions can improve
cluster performance. In the previous request, Resin's cluster
sessions maintain consistency for dumb load balancers or twisted
clients like the AOL browsers. The cost is the additional network
traffic for 2:load and 3:store. Smart load-balancers
can avoid the network traffic of 2 and 3.
Host C decodes the session id, caaMbyA. Since it owns
the session, Host C gives the session to the servlet with no work
and no network traffic. For a read-only request, there's zero
overhead for cluster sessions. So even a semi-intelligent load
balancer will gain a performance advantage. Normal browsers will have
zero overhead, and bogus AOL browsers will have the non-sticky
session overhead.
A session write saves the new serialized session to disk
(2:save) and to Host D (3:backup).
always-save-session will determine if Resin can take advantage
of read-only sessions or must save the session on each request.
Since the session always has a current copy on two servers, the load
balancer can direct requests to the next server in the ring. The
backup server is always ready to take control. The failover will
succeed even for dumb load balancers, as in the non-sticky-session
case, because the srun hosts will use the backup as the new owning
server.
In the example, either Host C or Host D can stop and
the sessions will use the backup. Of course, the failover will work
for scheduled downtime as well as server crashes. A site could
upgrade one server at a time with no observable downtime.
When Host C restarts, possibly with an upgraded version of Resin,
it needs to use the most up-to-date version of the session; its
file-saved session will probably be obsolete. When a "new" session
arrives, Host C loads the saved session from both the file and
from Host D. It will use the newest session as the current
value. Once it's loaded the "new" session, it will remain consistent
as if the server had never stopped.
Resin's cluster sessions does not lock sessions. For browser-based
sessions, only one request will execute at a time. Since browser
sessions have no concurrently, there's no need for distributed
locking. However, it's a good idea to be aware of the lack of
distributed locking.
Although reliability generally will end up costing some performance,
the trick for a good implementation is to increase reliability with a
minimal cost. In some environments, using JDBC distributed sessions
or simple file-based persistent sessions will improve a site's
robustness with low-enough cost. Because of its scalability, it's
more likely that Resin's cluster distributed sessions will be a
better choice for most deployment configurations.
Copyright (c) 1998-2009 Caucho Technology, Inc. All rights reserved. caucho® ,
resin® and
quercus®
are registered trademarks of Caucho Technology, Inc.
Copyright (c) 1998-2009 Caucho Technology, Inc. All rights reserved. caucho® ,
resin® and
quercus®
are registered trademarks of Caucho Technology, Inc.