Session API

Concept

Going Stateful

In most load testing use cases, it’s important that the virtual users don’t play the same data. Otherwise, you might end up not testing your application but your caches.

Moreover, if you’re running an application on a Java Virtual Machine, the Just In Time compiler (JIT) will make dramatic optimizations and your system will behave very differently from your actual one.

Though, you have to make your scenario steps dynamic, based on virtual user specific data.

Session

Session is a virtual user’s state.

Basically, it’s a Map[String, Any]: a map with key Strings. In Gatling, entries in this map are called Session attributes.

Note

Remember that a Gatling scenario is a workflow where every step is backed by an Akka Actor?

A Session is actually the message that are passed along a scenario workflow.

Injecting Data

The first step is to inject state into the virtual users.

There’s 3 ways of doing that:

• using Feeders
• extracting data from responses and saving them, e.g. with HTTP Check’s saveAs
• manually with the Session API

Fetching Data

Once you have injected data into your virtual users, you’ll naturally want to retrieve and use it.

There’s 2 ways of doing that:

• using Gatling’s Expression Language
• manually with the Session API

Note

If Gatling complains that an attribute could not be found, check that:

• you don’t have a typo in a feeder file header
• you don’t have a typo in a Gatling EL expression
• your feed action is properly called (e.g. could be properly chained with other action because a dot is missing)
• the check that should have saved it actually failed

Session API

Setting Attributes

Session has the following methods:

• set(key: String, value: Any): Session: add or replace an attribute
• setAll(newAttributes: (String, Any)*): Session: bulk add or replace attributes
• setAll(newAttributes: Iterable[(String, Any)]): Session: same as above but takes an Iterable instead of a varags
• reset: reset all attributes but loop counters, timestamps and Gatling internals (baseUrl, caches, etc)

Warning

Session instances are immutable!

Why is that so? Because Sessions are messages that are dealt with in a multi-threaded concurrent way, so immutability is the best way to deal with state without relying on synchronization and blocking.

A very common pitfall is to forget that set and setAll actually return new instances.

val session: Session = ???

// wrong usage
session.set("foo", "FOO") // wrong: the result of this set call is just discarded
session.set("bar", "BAR")

// proper usage
session.set("foo", "FOO").set("bar", "BAR")

Getting Attributes

Let’s say a Session instance variable named session contains a String attribute named “foo”.

val session: Session = ???

Then:

val attribute: SessionAttribute = session("foo")

Warning

session("foo") doesn’t return the value, but a wrapper.

You can then access methods to retrieve the actual value in several ways:

session("foo").as[Int]:

• returns a Int,
• throws a NoSuchElementException if the foo attribute is undefined,
• throws a NumberFormatException if the value is a String and can’t be parsed into an Int,
• throws a ClassCastException otherwise.

session("foo").asOption[Int]:

• returns an Option[Int],
• which is None if the foo attribute is undefined,
• which is Some(value) otherwise and value is an Int, or is a String that can be parsed into an Int,
• throws a NumberFormatException if the value is a String and can’t be parsed into an Int,
• throws a ClassCastException otherwise.

session("foo").validate[Int]:

• returns a Validation[Int],
• which is Success(value) if the foo attribute is defined and value is an Int or is a String that can be parsed into an Int,
• which is Failure(errorMessage) otherwise.

Note

Trying to get a [String] actually performs a toString conversion and thus, always works as long as the entry is defined.

Note

If the value is a [String], Gatling will try to parse it into a value of the expected type.

Note

Using as will probably be easier for most users. It will work fine, but the downside is that they might generate lots of expensive exceptions once things starts going wrong under load.

We advise considering validate once accustomed to functional logic as it deals with unexpected results in a more efficient manner.