vaadin-framework/documentation/articles/ScalableWebApplications.asciidoc

---
title: Scalable Web Applications
order: 22
layout: page
---

[[scalable-web-applications]]
Scalable web applications
-------------------------

[[introduction]]
Introduction
^^^^^^^^^^^^

Whether you are creating a new web application or maintaining an
existing one, one thing you certainly will consider is the scalability
of your web application. Scalability is your web application’s ability
to handle a growing number of concurrent users. How many concurrent
users can, for instance, your system serve at its peak usage? Will it be
intended for a small scale intranet usage of tens to hundreds of
concurrent users, or do you plan to reach for a medium size global web
application such as 500px.com with about 1000-3000 concurrent users. Or
is your target even higher? You might wonder how many concurrent users
there are in Facebook or Linkedin. We wonder about that too, and thus
made a small study to estimate it. You can see the results in Figure 1
below. The estimates are derived from monthly visitors and the average
visit time data found from Alexa.com. 

image:img/webusers.png[image]

_Figure 1: Popular web applications with estimated number of concurrent
users._

The purpose of this article is to show you common pain points which tend
to decrease the scalability of your web applications and help you find
out ways to overcome those. We begin by introducing you to our example
application. We will show you how to test the scalability of this
example application with Gatling, a tool for stress testing your web
application. Then, in the next chapters, we will go through some pain
points of scalability, such as memory, CPU, and database, and see how to
overcome these.

[[book-store-inventory-application]]
Book store inventory application
--------------------------------

Throughout this example we’ll use a book store inventory application
(see Figure 2) as an example application when deep diving into the world
of web application scalability. The application contains a login view
and an inventory view with CRUD operations to a mockup data source. It
also has the following common web application features: responsive
layouts, navigation, data listing and master detail form editor. This
application is publicly available as a Maven archetype
(`vaadin-archetype-application-example`). We will first test how many
concurrent users the application can serve on a single server.

image:img/mockapp-ui.png[image]

_Figure 2: Book store inventory application_

The purpose of scalability testing is to verify whether the
application's server side can survive with a predefined number of
concurrent users or not. We can utilize a scalability test to find the
limits, a breaking point or server side bottlenecks of the application.
There are several options for scalability testing web applications. One
of the most used free tools is Apache JMeter. JMeter suits well for
testing web applications, as long as the client-server communication
does not use websockets. When using asynchronous websocket
communication, one can use the free Gatling tool or the commercial
NeoLoad tool.

You can find a lot of step by step tutorials online on how to use
Gatling and JMeter. There is typically a set of tips and tricks that one
should take into account when doing load testing on certain web
frameworks, such as the open source Vaadin Framework. For more
information on Vaadin specific tutorials, check the wiki pages on
https://vaadin.com/scalability[vaadin.com/scalability].

Gatling and JMeter can be used to record client to server requests of a
web application. After recording, the recorded requests can be played
back by numbers of concurrent threads. The more threads (virtual users)
you use the higher the simulated load generated on the tested
application.

Since we want to test our application both in synchronous and
asynchronous communication modes, we will use Gatling. Another benefit
of Gatling compared to JMeter is that it is less heavy for a testing
server, thus more virtual users can be simulated on a single testing
server. Figure 3 shows the Gatling settings used to record the example
scenario of the inventory application. Typically all static resources
are excluded from the recording (see left bottom corner of the figure),
since these are typically served from a separate http server such as
Nginx or from a CDN (Content Delivery Network) service. In our first
test, however, we still recorded these requests to see the worst case
situation, where all requests are served from a single application
server.

image:img/figure3s2.png[image]

_Figure 3: Gatling recorder._

Gatling gathers the recorded requests as text files and composes a Scala
class which is used to playback the recorded test scenario. We planned a
test scenario for a typical inventory application user: The user logs in
and performs several updates (in our case 11) to the store in a
relatively short time span (3 minutes). We also assumed that they leave
the inventory application open in their browser which will result in the
HttpSession not closing before a session timeout (30min in our case).

Let’s assume that we have an extremely large bookstore with several
persons (say 10000) responsible for updating the inventory. If one
person updates the inventory 5 times a day, and an update session takes
3 minutes, then with the same logic as we calculated concurrent users in
the Introduction, we will get a continuous load of about 100 concurrent
users. This is of course not a realistic assumption unless the
application is a global service or it is a round-the-clock used local
application, such as a patient information system. For testing purposes
this is, however, a good assumption.

A snippet from the end of our test scenario is shown in Figure 4. This
test scenario is configured to be run with 100 concurrent users, all
started within 60 seconds (see the last line of code).

[source,scala]
....
.pause(9)
.exec(http(>"request_45")
    .post(>"/test/UIDL/?v-uiId=0")
    .headers(headers_9)
    .body(RawFileBody(>"RecordedSimulation_0045_request.txt")))
.pause(5)
.exec(http(>"request_46")
    .post(>"/test/UIDL/?v-uiId=0")
    .headers(headers_9)
    .body(RawFileBody(>"RecordedSimulation_0046_request.txt"))
    .resources(http(>"request_47")
    .post(uri1 + >"/UIDL/?v-uiId=0")
    .headers(headers_9)
    .body(RawFileBody(>"RecordedSimulation_0047_request.txt"))))}
setUp(scn.inject(rampUsers(100) over (60 seconds))).protocols(httpProtocol)
....

_Figure 4: Part of the test scenario of inventory application._

To make the test more realistic, we would like to execute it several
times. Without repeating we do not get a clear picture of how the server
will tolerate a continuous high load. In a Gatling test script, this is
achieved by wrapping the test scenario into a repeat loop. We should
also flush session cookies to ensure that a new session is created for
each repeat. See the second line of code in Figure 5, for an example of
how this could be done.

[source,scala]
....
val scn = scenario("RecordedSimulation")
    .repeat(100,"n"){exec(flushSessionCookies).exec(http("request_0")
    .get("/test/")
        .resources(http("request_1")
        .post(uri1 + "/?v-1440411375172")
        .headers(headers_4)
        .formParam("v-browserDetails", "1")
        .formParam("theme", "mytheme")
        .formParam("v-appId", "test-3556498")
....

_Figure 5: Repeating 100 times with session cookie flushing (small part
of whole script)_

We tested how well this simple example application tolerated several
concurrent users. We deployed our application in Apache Tomcat 8.0.22 on
a Windows 10 machine with Java 1.7.0 and an older quad core mobile Intel
i7 processor. With its default settings (using the default heap size of
2GB), Tomcat was able to handle about 200 concurrent users for a longer
time. The CPU usage for that small number of concurrent users was not a
problem (it was lower than 5%), but the server configurations were a
bottleneck. Here we stumbled upon the first scalability pain point:
server configuration issues (see next chapter). It might sound
surprising that we could only run a such small number of concurrent
users by default, but do not worry, we are not stuck here. We will see
the reasons for this and other scalability pain points in the following
chapter.

[[scalability-pain-points]]
Scalability pain points
-----------------------

We will go through typical pain points of a web application developer
which she (or he) will encounter when developing a web application for
hundreds of concurrent users. Each pain point is introduced in its own
subchapter and followed by typical remedies.

[[server-configuration-issues]]
Server configuration issues
~~~~~~~~~~~~~~~~~~~~~~~~~~~

One typical problem that appears when there are lots of concurrent
users, is that the operating system (especially the *nix based ones) run
out of file descriptors. This happens since most *nix systems have a
pretty low default limit for the maximum number of open files, such as
network connections. This is usually easy to fix with the `ulimit`
command though sometimes it might require configuring the `sysctl` too.

A little bit unexpected issues can also surface with network bandwidth.
Our test laptop was on a wireless connection and its sending bandwidth
started choking at about 300 concurrent users. (Please note that we use
an oldish laptop in this entire test to showcase the real scalability of
web apps –your own server environment will no doubt be even more
scalable even out of the box.) One part of this issue was the wifi and
another part was that we served the static resources, such as javascript
files, images and stylesheets, from Tomcat. At this point we stripped
the static resources requests out of our test script to simulate the
situation where those are served from a separate http server, such as
nginx. Please read the blog post
“https://vaadin.com/blog/-/blogs/optimizing-hosting-setup[Optimizing
hosting setup]” from our website for more information about the topic.

Another quite typical configuration issue is that the application server
is not configured for a large number of concurrent users. In our
example, a symptom of this was that the server started rejecting
(“Request timed out”) new connections after a while, even though there
were lots of free memory and CPU resources available.

After we configured our Apache Tomcat for high concurrent mode and
removed static resource requests, and connected the test laptop into a
wired network, we were able to push the number of concurrent users from
200 up to about 500 users. Our configuration changes into the server.xml
of Tomcat are shown in Figure 6, where we define a maximum thread count
(10240), an accepted threads count (4096), and a maximum number of
concurrent connections (4096).

image:img/figure6a.png[image]

_Figure 6: Configuring Tomcat’s default connector to accept a lot of
concurrent users._

The next pain point that appeared with more than 500 users was that we
were out of memory. The default heap size of 2GB eventually ran out with
such high number of concurrent users. On the other hand, there was still
a lot of CPU capacity available, since the average load was less than
5%.

[[out-of-memory]]
Out of memory
~~~~~~~~~~~~~

Insufficient memory is possibly the most common problem that limits the
scalability of a web application with a state. An http session is used
typically to store the state of a web application for its user. In
Vaadin an http session is wrapped into a `VaadinSession`. A
VaadinSession contains the state (value) of each component (such as
`Grid`, `TextFields` etc.) of the user interface. Thus,
straightforwardly the more components and views you have in your Vaadin
web application, the bigger is the size of your session.

In our inventory application, each session takes about 0.3MB of memory
which is kept in memory until the session finally closes and the garbage
collectors free the resources. The session size in our example is a
little bit high. With constant load of 100 concurrent users, a session
timeout of 30 minutes and an average 3 minutes usage time, the expected
memory usage is about 350MB. To see how the session size and the number
of concurrent users affect the needed memory in our case, we made a
simple analysis which results are shown in Figure 7. We basically
calculated how many sessions there can exist at most, by calculating how
many users there will be within an average usage time plus the session
timeout.

image:img/figure6s.png[image]

_Figure 7: Memory need for varying size sessions and a different number
of concurrent users._

[[remedies]]
Remedies
^^^^^^^^

[[use-more-memory]]
Use more memory
+++++++++++++++

This might sound simplistic, but many times it might be enough to just
add as much memory as possible to the server. Modern servers and server
operating systems have support for hundreds of gigabytes of physical
memory. For instance, again in our example, if the size of a session
would be 0.5MB and we had 5000 concurrent users, the memory need would
be about 28GB.

You also have to take care that your application server is configured to
reserve enough memory. For example, the default heap size for Java is
typically 2GB and for example Apache Tomcat will not reserve more memory
if you do not ask it to do it with **`-Xmx`** JVM argument. You might
need a special JVM for extremely large heap sizes. We used the following
Java virtual machine parameters in our tests:

....
-Xms5g -Xmx5g -Xss512k -server
....

The parameters **`-Xms`** and **`-Xmx`** are for setting the minimum and
the maximum heap size for the server (5 GB in the example), the `-Xss`
is used to reduce the stack size of threads to save memory (typically
the default is 1MB for 64bit Java) and the `-server` option tells JVM
that the Java process is a server.

[[minimize-the-size-of-a-session]]
Minimize the size of a session
++++++++++++++++++++++++++++++

The biggest culprit for the big session size in the inventory
application is the container (BeanItemContainer) which is filled with
all items of the database. Containers, and especially the built in fully
featured BeanItemContainer, are typically the most memory hungry parts
of Vaadin applications. One can either reduce the number of items loaded
in the container at one time or use some lightweight alternatives
available from Vaadin Directory
(https://vaadin.com/directory[vaadin.com/directory]) such as Viritin,
MCont, or GlazedLists Vaadin Container. Another approach is to release
containers and views to the garbage collection e.g. every time the user
switches into another view, though that will slightly increase the CPU
load since the views and containers have to be rebuilt again, if the
user returns to the view. The feasibility of this option is up to your
application design and user flow –usually it’s a good choice.

[[use-a-shorter-session-time-out]]
Use a shorter session time out
++++++++++++++++++++++++++++++

Since every session in the memory reserves it for as long as it stays
there, the shorter the session timeout is, the quicker the memory is
freed. Assuming that the average usage time is much shorter than the
session timeout, we can state that halving the session timeout
approximately halves the memory need, too. Another way to reduce the
session’s time in the memory could be instructing users to logout after
they are done.

The session of a Vaadin application is kept alive by requests (such as
user interactions) made from the client to the server. Besides user
interaction, the client side of Vaadin application sends a heartbeat
request into the server side, which should keep the session alive as
long as the browser window is open. To override this behaviour and to
allow closing idle sessions, we recommend that the `closeIdleSessions`
parameter is used in your servlet configuration. For more details, see
chapter
https://vaadin.com/book/-/page/application.lifecycle.html[Application
Lifecycle] in the Book of Vaadin.

[[use-clustering]]
Use clustering
++++++++++++++

If there is not enough memory, for example if there is no way to reduce
the size of a session and the application needs a very long session
timeout, then there is only one option left: clustering. We will discuss
clustering later in the Out of CPU chapter since clustering is more
often needed for increasing CPU power.

[[out-of-cpu]]
Out of CPU
~~~~~~~~~~

We were able to get past the previous limit of 500 concurrent users by
increasing the heap size of Tomcat to 5GB and reducing the session
timeout to 10 minutes. Following the memory calculations above, we
should theoretically be able to serve almost 3000 concurrent users with
our single server, if there is enough CPU available.

Although the average CPU load was rather low (about 10%) still with 800
concurrent users, it jumped up to 40% every now and then for several
seconds as the garbage collector cleaned up unused sessions etc. That is
also the reason why one should not plan to use full CPU capacity of a
server since that will increase the garbage collection time in worst
case even to tens of seconds, while the server will be completely
unresponsive for that time. We suggest that if the average load grows to
over 50% of the server’s capacity, other means have to be taken into use
to decrease the load of the single server.

We gradually increased the number of concurrent users to find out the
limits of our test laptop and Tomcat. After trial and error, we found
that the safe number of concurrent users for our test laptop was about
1700. Above that, several request timeout events occurred even though
the CPU usage was about 40-50% of total capacity. We expect that using a
more powerful server, we could have reached 2000-3000 concurrent users
quite easily.

[[remedies-1]]
Remedies
^^^^^^^^

[[analyze-and-optimize-performance-bottlenecks]]
Analyze and optimize performance bottlenecks
++++++++++++++++++++++++++++++++++++++++++++

If you are not absolutely sure about the origin of the high CPU usage,
it is always good to verify it with a performance profiling tool. There
are several options for profiling, such as JProfiler, XRebel, and Java
VisualVM. We will use VisualVM in this case since it comes freely with
every (Oracle’s) JDK since the version 1.5.

Our typical procedure goes like this: 1. Deploy your webapp and start
your server, 2. Start VisualVM and double click your server’s process
(“e.g. Tomcat (pid 1234)”) on the Applications tab (see Figure 8), 3.
Start your load test script with, for instance, 100 concurrent users, 4.
Open the Sampler tab to see where the CPU time is spent, 5. Use the
filter on the bottom to show the CPU usage of your application (e.g.
“`biz.mydomain.projectx`”) and possible ORM (Object-relational mapping)
framework (e.g. “`org.hibernate`”) separately.

Typically, only a small part (e.g. 0.1 - 2 %) of CPU time is spent on
the classes of your webapp, if your application does not contain heavy
business logic. Also, CPU time spent on the classes of Vaadin should be
very small (e.g. 1%). You can be relaxed about performance bottlenecks
of your code if the most time (>90%) is spent on application server’s
classes (e.g. “`org.apache.tomcat`”).

Unfortunately, quite often database functions and ORM frameworks take a
pretty big part of CPU time. We will discuss how to tackle heavy
database operations in the Database chapter below.

image:img/figure7s.png[image]

_Figure 8: Profiling CPU usage of our inventory application with Java
VisualVM_

[[use-native-application-server-libraries]]
Use native application server libraries
+++++++++++++++++++++++++++++++++++++++

Some application servers (at least Tomcat and Wildfly) allow you to use
native (operating system specific) implementation of certain libraries.
For example, The Apache Tomcat Native Library gives Tomcat access to
certain native resources for performance and compatibility. Here we
didn’t test the effect of using native libraries instead of standard
ones. With little online research, it seems that the performance benefit
of native libraries for Tomcat is visible only if using secured https
connections.

[[fine-tune-java-garbage-collection]]
Fine tune Java garbage collection
+++++++++++++++++++++++++++++++++

We recommended above not to strain a server more than 50% of its total
CPU capacity. The reason was that above that level, a garbage collection
pause tends to freeze the server for too long a time. That is because it
typically starts not before almost all of the available heap is already
spent and then it does the full collection. Fortunately, it is possible
to tune the Java garbage collector so that it will do its job in short
periods. With little online study, we found the following set of JVM
parameters for web server optimized garbage collection

....
-XX:+UseCMSInitiatingOccupancyOnly
-XX:CMSInitiatingOccupancyFraction=70
....

The first parameter prevents Java from using its default garbage
collection strategy and makes it use CMS (concurrent-mark-sweep)
instead. The second parameter tells at which level of “occupancy” the
garbage collection should be started. The value 70% for the second
parameter is typically a good choice but for optimal performance it
should be chosen carefully for each environment e.g. by trial and error.

The CMS collector should be good for heap sizes up to about 4GB. For
bigger heaps there is the G1 (Garbage first) collector that was
introduced in JDK 7 update 4. G1 collector divides the heap into regions
and uses multiple background threads to first scan regions that contain
the most of garbage objects. Garbage first collector is enabled with the
following JVM parameter.

....
-XX:+UseG1GC
....

If you are using Java 8 Update 20 or later, and G1, you can optimize the
heap usage of duplicated Strings (i.e. their internal `char[]` arrays)
with the following parameter.

....
-XX:+UseStringDeduplication
....

[[use-clustering-1]]
Use clustering
++++++++++++++

We have now arrived at the point where a single server cannot fulfill
our scalability needs whatever tricks we have tried. If a single server
is not enough for serving all users, obviously we have to distribute
them to two or more servers. This is called clustering.

Clustering has more benefits than simply balancing the load between two
or more servers. An obvious additional benefit is that we do not have to
trust a single server. If one server dies, the user can continue on the
other server. In worst case, the user loses her session and has to log
in again, but at least she is not left without the service. You probably
have heard the term “session replication” before. It means that the
user’s session is copied into other servers (at least into one other) of
the cluster. Then, if the server currently used by the user goes down,
the load balancer sends subsequent requests to another server and the
user should not notice anything.

We will not cover session replication in this article since we are
mostly interested in increasing the ability to serve more and more
concurrent users with our system. We will show two ways to do clustering
below, first with Apache WebServer and Tomcats and then with the Wildfly
Undertow server.

[[clustering-with-apache-web-server-and-tomcat-nodes]]
Clustering with Apache Web Server and Tomcat nodes
++++++++++++++++++++++++++++++++++++++++++++++++++

Traditionally Java web application clustering is implemented with one
Apache Web Server as a load balancer and 2 or more Apache Tomcat servers
as nodes. There are a lot of tutorials online, thus we will just give a
short summary below.

1.  Install Tomcat for each node
2.  Configure unique node names with jvmRoute parameter to each Tomcat’s
server.xml
3.  Install Apache Web Server to load balancer node
4.  Edit Apache’s httpd.conf file to include mod_proxy, mod_proxy_ajp,
and mod_proxy_balancer
5.  Configure balancer members with node addresses and load factors into
end of httpd.conf file
6.  Restart servers

There are several other options (free and commercial ones) for the load
balancer, too. For example, our customers have used at least F5 in
several projects.

[[clustering-with-wildfly-undertow]]
Clustering with Wildfly Undertow
++++++++++++++++++++++++++++++++

Using Wildfly Undertow as a load balancer has several advantages over
Apache Web Server. First, as Undertow comes with your WildFly server,
there is no need to install yet another software for a load balancer.
Then, you can configure Undertow with Java (see Figure 8) which
minimizes the error prone conf file or xml configurations. Finally,
using the same vendor for application servers and for a load balancer
reduces the risk of intercompatibility issues. The clustering setup for
Wildfly Undertow is presented below. We are using sticky session
management to maximize performance.

1.  Install Wildfly 9 to all nodes
2.  Configure Wildfly’s standalone.xml
1.  add `“instance-id=”node-id”` parameter undertow subsystem, e.g:
`<subsystem xmlns="urn:jboss:domain:undertow:2.0" instance-id="node1"> `(this
is needed for the sticky sessions).
2.  set http port to something else than 8080 in socket-binding-group,
e.g: `<socket-binding name="http" port="${jboss.http.port:8081}"/>`
3.  Start your node servers accepting all ip addresses:
`./standalone.sh -c standalone.xml -b=0.0.0.0`
4.  Code your own load balancer (reverse proxy) with Java and Undertow
libraries (see Figure 9) and start it as a Java application.

[source,java]
....
public static void main(final String[] args) {
  try {
    LoadBalancingProxyClient loadBalancer = new LoadBalancingProxyClient()
      .addHost(new URI("http://192.168.2.86:8081"),"node1")
      .addHost(new URI("http://192.168.2.216:8082"),"node2")
      .setConnectionsPerThread(1000);
    Undertow reverseProxy = Undertow.builder()
      .addHttpListener(8080, "localhost")
      .setIoThreads(8)
      .setHandler(new ProxyHandler(loadBalancer, 30000, ResponseCodeHandler.HANDLE_404))
      .build();
      reverseProxy.start();
  } catch (URISyntaxException e) {
    throw new RuntimeException(e);
  }
}
....

_Figure 9: Simple load balancer with two nodes and sticky sessions._

[[database]]
Database
~~~~~~~~

In most cases, the database is the most common and also the most tricky
to optimize. Typically you’ll have to think about your database usage
before you actually need to start optimizing the memory and CPU as shown
above. We assume here that you use object to relational mapping
frameworks such as Hibernate or Eclipselink. These frameworks implement
several optimization techniques within, which are not discussed here,
although you might need those if you are using plain old JDBC.

Typically profiling tools are needed to investigate how much the
database is limiting the scalability of your application, but as a rule
of thumb: the more you can avoid accessing the database, the less it
limits the scalability. Consequently, you should generally cache static
(or rarely changing) database content.

[[remedies-2]]
Remedies
^^^^^^^^

[[analyze-and-optimize-performance-bottlenecks-1]]
Analyze and optimize performance bottlenecks
++++++++++++++++++++++++++++++++++++++++++++

We already discussed shortly, how to use Java VisualVM for finding CPU
bottlenecks. These same instructions also apply for finding out at what
level the database consumes the performance. Typically you have several
Repository-classes (e.g. `CustomerRepository`) in your web application,
used for CRUD (create, read, update, delete) operations (e.g.
`createCustomer`). Commonly your repository implementations either
extend Spring’s JPARepository or use `javax.persistence.EntityManager`
or Spring’s `Datasource` for the database access. Thus, when profiling,
you will probably see one or more of those database access methods in
the list of methods that are using most of your CPU’s capacity.

According to our experience, one of the bottlenecks might be that small
database queries (e.g. `findTaskForTheDay`) are executed repeatedly
instead of doing more in one query (e.g. `findTasksForTheWeek`). In some
other cases, it might be vice versa: too much information is fetched and
only part of it is used (e.g. `findAllTheTasks`). A real life example of
the latter happened recently in a customer project, where we were able
to a gain significant performance boost just by using JPA Projections to
leave out unnecessary attributes of an entity (e.g. finding only Task’s
name and id) in a query.

[[custom-caching-and-query-optimization]]
Custom caching and Query optimization
+++++++++++++++++++++++++++++++++++++

After performance profiling, you have typically identified a few queries
that are taking a big part of the total CPU time. A part of those
queries might be the ones that are relatively fast as a single query but
they are just done hundreds or thousands of times. Another part of
problematic queries are those that are heavy as is. Moreover, there is
also the __N__+1 query problem, when, for example, a query for fetching
a Task entity results __N__ more queries for fetching one-to-many
members (e.g. assignees, subtasks, etc.) of the Task.

The queries of the first type might benefit from combining to bigger
queries as discussed in the previous subchapter (use
`findTasksForTheWeek` instead of `findTaskForTheDay`). I call this
approach custom caching. This approach typically requires changes in
your business logic too: you will need to store (cache) yet unneeded
entities, for example in a `HashMap` or `List` and then handle all these
entities sequentially.

The queries of the second type are typically harder to optimize.
Typically slow queries can be optimized by adding a certain index or
changing the query logic into a little bit different form. The difficult
part is to figure out what exactly makes the query slow. I recommend
using a logging setting that shows the actual sql query made in your log
file or console (e.g. in Hibernate use `show_sql=true`). Then you can
take the query and run it against your database and try to vary it and
see how it behaves. You can even use the `EXPLAIN` keyword to ask MySQL
or PostgreSql (`EXPLAIN PLAN FOR` in Oracle and `SHOWPLAN_XML` in SQL
Server) to explain how the query is executed, what indexes are used etc.

The __N__+1 queries can be detected by analysing the executed sqls in
the log file. The first solution for the issue is redesigning the
problematic query to use appropriate join(s) to make it fetch all the
members in a single sql query. Sometimes, it might be enough to use
`FetchType.EAGER` instead of `LAZY` for the problematic cases. Yet
another possibility could be your own custom caching as discussed above.

[[second-level-cache]]
Second-level cache
++++++++++++++++++

According to Oracle’s Java EE Tutorial: a second-level cache is a local
store of entities managed by the persistence provider. It is used to
improve the application performance. A second-level cache helps to avoid
expensive database queries by keeping frequently used entities in the
cache. It is especially useful when you update your database only by
your persistence provider (Hibernate or Eclipselink), you read the
cached entities much more often than you update them, and you have not
clustered your database.

There are different second-level cache vendors such as EHCache, OSCache,
and SwarmCache for Hibernate. You can find several tutorials for these
online. One thing to keep in mind is that the configuration of, for
example, EHCache varies whether you use Spring or not. Our experience of
the benefits of second-level caches this far is that in real world
applications the benefits might be surprisingly low. The benefit gain
depends highly on how much your application uses the kind of data from
the database that is mostly read-only and rarely updated.

[[use-clustering-2]]
Use clustering
++++++++++++++

There are two common options for clustering or replication of the
database: master-master replication and master-slave replication. In the
master-master scheme any node in the cluster can update the database,
whereas in the master-slave scheme only the master is updated and the
change is distributed to the slave nodes right after that. Most
relational database management systems support at least the master-slave
replication. For instance, in MySql and PostgreSQL, you can enable it by
few configuration changes and by granting the appropriate master rights
for replication. You can find several step-by-step tutorials online by
searching with e.g. the keywords “postgresql master slave replication”.

[[nosql]]
NoSQL
+++++

When looking back to the first figure (Figure 1) of the article, you
might wonder what kind of database solutions the world's biggest web
application’s use? Most of them use some relation database, partly, and
have a NoSQL database (such as Cassandra, MongoDB, and Memcached) for
some of the functionality. The big benefit of many NoSQL solutions is
that they are typically easier to cluster, and thus help one to achieve
extremely scalable web applications. The whole topic of using NoSQL is
so big that we do not have the possibility to discuss it in this
article.

[[summary]]
Summary
-------

We started the study by looking at typical applications and estimated
their average concurrent user number. We then started with a typical
Vaadin web application and looked at what bottlenecks we hit on the way,
by using a standard laptop. We discussed different ways of overcoming
everything from File Descriptors to Session size minimization, all the
way to Garbage collection tweaking and clustering your entire
application. At the end of the day, there are several issues that could
gap you applications scalability, but as shown in this study, with a few
fairly simple steps we can scale the app from 200 concurrent users to
3000 concurrent users. As a standard architectural answer, however: the
results in your environment might be different, so use tools discussed
in this paper to find your bottlenecks and iron them out.