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hammerdb

hammerdb is a performance test kit for various databases. For now it will be used in this operator for MS-SQL databases exclusively.

Running hammerdb

Given that you followed instructions to deploy operator, you can modify cr.yaml to your needs.

The pgbench workload needs to be pointed at an existing MS-SQL databases via the CR file.

An example CR might look like this

apiVersion: ripsaw.cloudbulldozer.io/v1alpha1
kind: Benchmark
metadata:
  name: hammerdb
  namespace: my-ripsaw
spec:
  workload:
    name: "hammerdb"
    args:
      db_init: false
      db_benchmark: true
      db_server: "mssql-deployment.sql-server"
      db_port: "1443"
      db_tcp: "true"
      db_user: "SA"
      db_pass: "s3curePasswordString"
      db_warehouses: 1
      db_num_workers: 1
      transactions: 20000
      runtime: 1
      rampup: 1
      samples: 1

The db_init feature determines wether the database has already been initialized (false) or needs to be initialized (true). If the DB has been used previously to run benchmarks against it, it needs to be set to false.

The db_benchmark feature is used to run the actual benchmark when set to true. db_server either holds the name or the IP address of the DB server, db_port the port on which the DB is accessible. If db_tcp is set to true the client will use a TCP connection, if it's set to false UDP will be used.

db_user and db_pass need to be set identical to the settings on the DB server side.

The tpcc benchmark which we use can set up an arbitrary number of warehouses between which goods will be transferred in order to simulate a real-world scenario. The higher the number of warehouses is, the more complex and load-heavy the benchmark can get. db_warehouses is used to define this number. db_num_workers controls the number of virtual users, acting upon the warehouses and the goods in them. This number needs to lesser or equal to the number of warehouses.

With runtime, rampup and samples the time for a single run, the rampup time per run and the number of runs can be controlled.

Once done creating/editing the resource file, you can run it by:

# kubectl apply -f <path_to_cr_file>