Skip to main content

Load testing MongoDB using JMeter

Apache JMeter (http://jmeter.apache.org/) added support for MongoDB since its 2.10 release. In this post I am referring to the latest JMeter release (2.13).
A preliminary JMeter setup is needed before starting your first test plan for MongoDB. It uses Groovy as scripting reference language, so Groovy needs to be set up for our favorite load testing tool. Follow these steps to complete the set up:
  • Download Groovy from the official website (http://www.groovy-lang.org/download.html). In this post I am referring to the Groovy release 2.4.4, but using later versions is fine.
  • Copy the groovy-all-2.4.4.jar to the $JMETER_HOME/lib folder.
  • Restart JMeter if it was running while adding the Groovy JAR file.
Now you can start creating a test plan for MongoDB load testing. From the UI select the MongoDB template (File -> Templates...). The new test plan has a MongoDB Source Config element. Here you have to setup the connection details for the database to be tested:



The Thread Group associated with the template comes with two JSR223 Samplers, one for insert and the other one for queries/counts:


In order to run load tests against a database please always use JSR223 Samplers and not MongoDB Script Samplers. These samplers invoke the db.eval() function in order to run the scripts. It has been deprecated (https://docs.mongodb.org/manual/reference/method/db.eval/) since MongoDB 3.0 (mostly for performance and security reasons) and any way it doesn't work with sharded collections. The JSR223 Samplers use the MongoDB Java driver through Groovy instead. The next figure shows the layout of a JSR223 Sampler:



You have to select the reference scripting language (Groovy) through the combobox highlighted in the figure. Then you can choose to edit your script in the Script text area or load the code from an external Groovy file. This second way a script will get compiled into bytecode reducing the overhead at execution time. The code in the example explains itself: please read the comment inside. Most part of the work preparing a MongoDB load test would be cloning the JSR223 script templates and setting the collection names and field names and values of your database.
The JSR223 samplers in the template come with associated Response Assertions:


There you can apply assertions to the responses using custom patterns:

The results of each run can be viewed in the View Result Tree and Aggregate Report listeners as usual for most part of the JMeter test plans.
A good practice for the results could be to use a specific View Result Tree listener for each JSR223 Sampler (rather than one for the Thread Group) and then add an Aggregate Graph Result listener to aggregate the result and some stats in a chart.
The execution of the MongoDB test plans created through JMeter can be also automated through Jenkins as explained in one of my old posts in this blog.

Comments

  1. I wanted to thank you for this great read!! I definitely enjoying every little bit of it and I have you bookmarked to check out new stuff you post.
    MongoDB Training in Bangalore

    ReplyDelete
  2. I got wonderful information from this blog. I enjoyed your blog... the way you presented is really awesome... Thanks for sharing with us...If someone wants to know about load testing services this is the right place for you.



    ReplyDelete
  3. Thank you for sharing this informative post. Betal Engineering Pvt Ltd provides Load Testing and Certification, Lifting Equipment Certification in India and across the globe.
    Lifting Equipment Certification in India

    ReplyDelete
  4. Nice article, its very informative content..thanks for sharing...Waiting for the next update.

    swift developer training in chennai
    swift developer course certification in chennai

    ReplyDelete
  5. Axure RP Pro / Team is a software for creating prototypes and specifications for websites and applications. It offers drag and drop placement, User easily Axure RP Pro

    ReplyDelete
  6. The free version of Kaspersky Security Cloud allows you to store up to 15 passwords in its password manager; the paid version removes Kaspersky Total Security Activation Code

    ReplyDelete

Post a Comment

Popular posts from this blog

Streamsets Data Collector log shipping and analysis using ElasticSearch, Kibana and... the Streamsets Data Collector

One common use case scenario for the Streamsets Data Collector (SDC) is the log shipping to some system, like ElasticSearch, for real-time analysis. To build a pipeline for this particular purpose in SDC is really simple and fast and doesn't require coding at all. For this quick tutorial I will use the SDC logs as example. The log data will be shipped to Elasticsearch and then visualized through a Kibana dashboard. Basic knowledge of SDC, Elasticsearch and Kibana is required for a better understanding of this post. These are the releases I am referring to for each system involved in this tutorial: JDK 8 Streamsets Data Collector 1.4.0 ElasticSearch 2.3.3 Kibana 4.5.1 Elasticsearch and Kibana installation You should have your Elasticsearch cluster installed and configured and a Kibana instance pointing to that cluster in order to go on with this tutorial. Please refer to the official documentation for these two products in order to complete their installation (if you do

Exporting InfluxDB data to a CVS file

Sometimes you would need to export a sample of the data from an InfluxDB table to a CSV file (for example to allow a data scientist to do some offline analysis using a tool like Jupyter, Zeppelin or Spark Notebook). It is possible to perform this operation through the influx command line client. This is the general syntax: sudo /usr/bin/influx -database '<database_name>' -host '<hostname>' -username '<username>'  -password '<password>' -execute 'select_statement' -format '<format>' > <file_path>/<file_name>.csv where the format could be csv , json or column . Example: sudo /usr/bin/influx -database 'telegraf' -host 'localhost' -username 'admin'  -password '123456789' -execute 'select * from mem' -format 'csv' > /home/googlielmo/influxdb-export/mem-export.csv

Using Rapids cuDF in a Colab notebook

During last Spark+AI Summit Europe 2019 I had a chance to attend a talk from Miguel Martinez  who was presenting Rapids , the new Open Source framework from NVIDIA for GPU accelerated end-to-end Data Science and Analytics. Fig. 1 - Overview of the Rapids eco-system Rapids is a suite of Open Source libraries: cuDF cuML cuGraph cuXFilter I enjoied the presentation and liked the idea of this initiative, so I wanted to start playing with the Rapids libraries in Python on Colab , starting from cuDF, but the first attempt came with an issue that I eventually solved. So in this post I am going to share how I fixed it, with the hope it would be useful to someone else running into the same blocker. I am assuming here you are already familiar with Google Colab. I am using Python 3.x as Python 2 isn't supported by Rapids. Once you have created a new notebook in Colab, you need to check if the runtime for it is set to use Python 3 and uses a GPU as hardware accelerator. You