It is best to keep every workload template self-contained within a single YAML file, including schema, rampup, and the main steps in your testing workflow. These steps in a typical testing workflow are controlled by tags as described below.

👉 The step names described below have been adopted as a convention within the built-in workloads templates. It is strongly advised that new workload templates use the same naming scheme so that they are easier to re-use..

Automatic tags

To make tag filters more useful, every op template in NoSQLBench is given a set of automatic tags based on the block and op template names:

• block: <blockname>
• name: <blockname>--<op name>

For example, if you had a block named block42 and an op named op007, then you would be able to match it with tags=block:block42, or tags=name:block42--op007, or any regex which also matched. The difference between the first example and the second is this: There is only one op template which will have the name shown, but multiple statements could be in block42. ¹

👉 In previous versions of NoSQLBench, you had to add tags directly to your docs, blocks, or op templates. This is still supported if you need, but most cases will only require that you group statements together in named blocks. When used with the regex matching pattern demonstrated above, you get quite a bit of flexibility without having to create boilerplate tags everywhere.

A Standard Workflow

These steps are very commonly used by nb5 users. The standard test workflow described here is understood as lingua franca for seasoned NoSQLBench users.

Schema step

The schema step is simply where you create the necessary schema on your target system. For CQL, this generally consists of a keyspace and one or more table statements. There is no special schema layer in NoSQLBench. All statements executed are simply statements. This provides the greatest flexibility in testing since every activity type is allowed to control its DDL and DML using the same machinery.

The schema step is normally executed with defaults for most parameters. This means that operations will execute in the order specified in the workload template, serially, exactly once. This is a welcome side effect of how the initial parameters like cycles are set from the op templates which are activated by tagging.

The nb5 way of selecting all op templates in a block is to use the built-in block name in a tag filter, like this:

# select all op templates in the block named schema
./nb5 ... tags=block:schema ...

# select all op templates in all blocks that have a name matching the regex
./nb5 ... tags='block:schema-.*'

Rampup step

When you run a performance test, it is very important to be aware of how much data is present. Higher density tests are more realistic for systems which accumulate data over time, or which accumulate a larger working set every day. The amount of data on the system you are testing should recreate a realistic amount of data that you would run in production.

It is the purpose of the rampup activity is to create the backdrop data on a target system that makes a test meaningful for some level of data density. Data density is normally discussed as average per node, but it is also important to consider distribution of data as it varies from the least dense to the most dense nodes in your target system.

Because it is useful to be able to add data to a target system in an incremental way, the bindings which are used with a rampup step may actually be different from the ones used for a main step. In most cases, you want the rampup step to create data in a way that incrementally adds to the working set. This allows you to add some data to a cluster with cycles=0..1M and then decide whether to continue adding data using the next contiguous range of cycles, with cycles=1M..2M and so on.

Main step

The main step of a performance testing scenario is the one during which you really care about recording the metrics. This is the actual test that everything else has prepared your system for.

You will want to run your main workload for a significant amount of time. This doesn't mean a long time, but it may. What is significant in terms of getting realistic results is a question of statistical significance. If you have a very small system which you can push into steady-state performance in 20 minutes, then 30 minutes may be enough testing time. However, most modern systems of scale, even with a few nodes, will take longer to get reasonably accurate measurements. It depends on how you are measuring.

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