Find out how to Do Load Testing with Rockset

What’s load testing and why does it matter?

Load testing is a crucial course of for any database or knowledge service, together with Rockset. By doing load testing, we intention to evaluate the system’s conduct underneath each regular and peak circumstances. This course of helps in evaluating vital metrics like Queries Per Second (QPS), concurrency, and question latency. Understanding these metrics is crucial for sizing your compute assets accurately, and guaranteeing that they’ll deal with the anticipated load. This, in flip, helps in attaining Service Stage Agreements (SLAs) and ensures a clean, uninterrupted consumer expertise. That is particularly vital for customer-facing use circumstances, the place finish customers count on a handy guide a rough consumer expertise. Load testing is usually additionally known as efficiency or stress testing.

“53% of visits are prone to be deserted if pages take longer than 3 seconds to load” — Google

Rockset compute assets (known as digital cases or VIs) come in numerous sizes, starting from Small to 16XL, and every measurement has a predefined variety of vCPUs and reminiscence accessible. Selecting an applicable measurement is dependent upon your question complexity, dataset measurement and selectivity of your queries, variety of queries which might be anticipated to run concurrently and goal question efficiency latency. Moreover, in case your VI can be used for ingestion, you must think about assets wanted to deal with ingestion and indexing in parallel to question execution. Fortunately, we provide two options that may assist with this:

• Auto-scaling – with this function, Rockset will mechanically scale the VI up and down relying on the present load. That is vital you probably have some variability in your load and/or use your VI to do each ingestion and querying.
• Compute-compute separation – that is helpful as a result of you possibly can create VIs which might be devoted solely for operating queries and this ensures that the entire accessible assets are geared in direction of executing these queries effectively. This implies you possibly can isolate queries from ingest or isolate completely different apps on completely different VIs to make sure scalability and efficiency.

We advocate doing load testing on no less than two digital cases – with ingestion operating on the primary VI and on a separate question VI. This helps with deciding on a single or multi-VI structure.

Load testing helps us establish the bounds of the chosen VI for our explicit use case and helps us decide an applicable VI measurement to deal with our desired load.

Instruments for load testing

With regards to load testing instruments, a couple of widespread choices are JMeter, k6, Gatling and Locust. Every of those instruments has its strengths and weaknesses:

• JMeter: A flexible and user-friendly software with a GUI, supreme for varied forms of load testing, however could be resource-intensive.
• k6: Optimized for prime efficiency and cloud environments, utilizing JavaScript for scripting, appropriate for builders and CI/CD workflows.
• Gatling: Excessive-performance software utilizing Scala, finest for complicated, superior scripting eventualities.
• Locust: Python-based, providing simplicity and fast script growth, nice for easy testing wants.

Every software provides a singular set of options, and the selection is dependent upon the precise necessities of the load check being performed. Whichever software you employ, you’ll want to learn via the documentation and perceive the way it works and the way it measures the latencies/response occasions. One other good tip is to not combine and match instruments in your testing – in case you are load testing a use case with JMeter, keep it up to get reproducible and reliable outcomes you can share together with your workforce or stakeholders.

Rockset has a REST API that can be utilized to execute queries, and all instruments listed above can be utilized to load check REST API endpoints. For this weblog, I’ll give attention to load testing Rockset with Locust, however I’ll present some helpful assets for JMeter, k6 and Gatling as properly.

Organising Rockset and Locust for load testing

Let’s say we now have a pattern SQL question that we wish to check and our knowledge is ingested into Rockset. The very first thing we often do is convert that question right into a Question Lambda – this makes it very straightforward to check that SQL question as a REST endpoint. It may be parametrized and the SQL could be versioned and saved in a single place, as a substitute of going backwards and forwards and altering your load testing scripts each time you should change one thing within the question.

Step 1 – Establish the question you wish to load check

In our state of affairs, we wish to discover the most well-liked product on our webshop for a selected day. That is what our SQL question seems like (notice that :date is a parameter which we are able to provide when executing the question):

--top product for a selected day
SELECT
    s.Date,
    MAX_BY(p.ProductName, s.Rely) AS ProductName,
    MAX(s.Rely) AS NumberOfClicks
FROM
    "Demo-Ecommerce".ProductStatsAlias s
    INNER JOIN "Demo-Ecommerce".ProductsAlias p ON s.ProductID = CAST(p._id AS INT)
WHERE
    s.Date = :date
GROUP BY
    1
ORDER BY
    1 DESC;

Step 2 – Save your question as a Question Lambda

We’ll save this question as a question lambda known as LoadTestQueryLambda which can then be accessible as a REST endpoint:

https://api.usw2a1.rockset.com/v1/orgs/self/ws/sandbox/lambdas/LoadTestQueryLambda/tags/newest

curl --request POST 
--url https://api.usw2a1.rockset.com/v1/orgs/self/ws/sandbox/lambdas/LoadTestQueryLambda/tags/newest 
-H "Authorization: ApiKey $ROCKSET_APIKEY" 
-H 'Content material-Kind: software/json' 
  -d '{
    "parameters": [
      {
        "name": "days",
        "type": "int",
        "value": "1"
      }
    ],
      "virtual_instance_id": "<your digital occasion ID>"
  }' 
 | python -m json.software

Step 3 – Generate your API key

Now we have to generate an API key, which we’ll use as a method for our Locust script to authenticate itself to Rockset and run the check. You possibly can create an API key simply via our console or via the API.

Step 4 – Create a digital occasion for load testing

Subsequent, we’d like the ID of the digital occasion we wish to load check. In our state of affairs, we wish to run a load check in opposition to a Rockset digital occasion that’s devoted solely to querying. We spin up an extra Medium digital occasion for this:

As soon as the VI is created, we are able to get its ID from the console:

Step 5 – Set up Locust

Subsequent, we’ll set up and arrange Locust. You are able to do this in your native machine or a devoted occasion (assume EC2 in AWS).

$ pip set up locust

Step 6 – Create your Locust check script

As soon as that’s carried out, we’ll create a Python script for the Locust load check (notice that it expects a ROCKSET_APIKEY surroundings variable to be set which is our API key from step 3).

We are able to use the script beneath as a template:

import os
from locust import HttpUser, activity, tag
from random import randrange

class query_runner(HttpUser):
    ROCKSET_APIKEY = os.getenv('ROCKSET_APIKEY') # API secret is an surroundings variable

    header = {"authorization": "ApiKey " + ROCKSET_APIKEY}

    def on_start(self):
        self.headers = {
            "Authorization": "ApiKey " + self.ROCKSET_APIKEY,
            "Content material-Kind": "software/json"
        }
        self.consumer.headers = self.headers
        self.host="https://api.usw2a1.rockset.com/v1/orgs/self" # change this together with your area's URI
        self.consumer.base_url = self.host
        self.vi_id = '<your digital occasion ID>' # change this together with your VI ID

    @tag('LoadTestQueryLambda')
    @activity(1)
    def LoadTestQueryLambda(self):
        # utilizing default params for now
        knowledge = {
            "virtual_instance_id": self.vi_id
        }
        target_service="/ws/sandbox/lambdas/LoadTestQueryLambda/tags/newest" # change this together with your question lambda
        outcome = self.consumer.publish(
            target_service,
            json=knowledge
        )

Step 7 – Run the load check

As soon as we set the API key surroundings variable, we are able to run the Locust surroundings:

export ROCKSET_APIKEY=<your api key>
locust -f my_locust_load_test.py --host https://api.usw2a1.rockset.com/v1/orgs/self

And navigate to: http://localhost:8089 the place we are able to begin our Locust load check:

Let’s discover what occurs as soon as we hit the Begin swarming button:

1. Initialization of simulated customers: Locust begins creating digital customers (as much as the quantity you specified) on the fee you outlined (the spawn fee). These customers are cases of the consumer class outlined in your Locust script. In our case, we’re beginning with a single consumer however we’ll then manually improve it to five and 10 customers, after which go down to five and 1 once more.
2. Activity execution: Every digital consumer begins executing the duties outlined within the script. In Locust, duties are sometimes HTTP requests, however they are often any Python code. The duties are picked randomly or primarily based on the weights assigned to them (if any). Now we have only one question that we’re executing (our LoadTestQueryLambda).
3. Efficiency metrics assortment: Because the digital customers carry out duties, Locust collects and calculates efficiency metrics. These metrics embrace the variety of requests made, the variety of requests per second, response occasions, and the variety of failures.
4. Actual-time statistics replace: The Locust net interface updates in real-time, displaying these statistics. This consists of the variety of customers presently swarming, the request fee, failure fee, and response occasions.
5. Check scalability: Locust will proceed to spawn customers till it reaches the entire quantity specified. It ensures the load is elevated progressively as per the required spawn fee, permitting you to look at how the system efficiency modifications because the load will increase. You possibly can see this within the graph beneath the place the variety of customers begins to develop to five and 10 after which go down once more.
6. Consumer conduct simulation: Digital customers will watch for a random time between duties, as outlined by the wait_time within the script. This simulates extra practical consumer conduct. We didn’t do that in our case however you are able to do this and extra superior issues in Locust like customized load shapes, and so forth.
7. Steady check execution: The check will proceed operating till you resolve to cease it, or till it reaches a predefined length when you’ve set one.
8. Useful resource utilization: Throughout this course of, Locust makes use of your machine’s assets to simulate the customers and make requests. It is vital to notice that the efficiency of the Locust check may depend upon the assets of the machine it is operating on.

Let’s now interpret the outcomes we’re seeing.

Deciphering and validating load testing outcomes

Deciphering outcomes from a Locust run entails understanding key metrics and what they point out in regards to the efficiency of the system underneath check. Listed below are a number of the most important metrics offered by Locust and methods to interpret them:

• Variety of customers: The entire variety of simulated customers at any given level within the check. This helps you perceive the load stage in your system. You possibly can correlate system efficiency with the variety of customers to find out at what level efficiency degrades.
• Requests per second (RPS): The variety of requests (queries) made to your system per second. The next RPS signifies a better load. Evaluate this with response occasions and error charges to evaluate if the system can deal with concurrency and excessive visitors easily.
• Response time: Often displayed as common, median, and percentile (e.g., ninetieth and 99th percentile) response occasions. You’ll probably have a look at median and the 90/99 percentile as this provides you the expertise for “most” customers – solely 10 or 1 p.c could have worse expertise.
• Failure fee: The share or variety of requests that resulted in an error. A excessive failure fee signifies issues with the system underneath check. It is essential to investigate the character of those errors.

Under you possibly can see the entire RPS and response occasions we achieved underneath completely different hundreds for our load check, going from a single consumer to 10 customers after which down once more.

Our RPS went as much as about 20 whereas sustaining median question latency beneath 300 milliseconds and P99 of 700 milliseconds.

We are able to now correlate these knowledge factors with the accessible digital occasion metrics in Rockset. Under, you possibly can see how the digital occasion handles the load by way of CPU, reminiscence and question latency. There’s a correlation between variety of customers from Locust and the peaks we see on the VI utilization graphs. It’s also possible to see the question latency beginning to rise and see the concurrency (requests or queries per second) go up. The CPU is beneath 75% on the height and reminiscence utilization seems steady. We additionally don’t see any vital queueing occurring in Rockset.

Aside from viewing these metrics within the Rockset console or via our metrics endpoint, you may as well interpret and analyze the precise SQL queries that had been operating, what was their particular person efficiency, queue time, and so forth. To do that, we should first allow question logs after which we are able to do issues like this to determine our median run and queue occasions:

SELECT
    query_sql,
    COUNT(*) as depend,
    ARRAY_SORT(ARRAY_AGG(runtime_ms)) [(COUNT(*) + 1) / 2] as median_runtime,
    ARRAY_SORT(ARRAY_AGG(queued_time_ms)) [(COUNT(*) + 1) / 2] as median_queue_time
FROM
    commons."QueryLogs"
WHERE
    vi_id = '<your digital occasion ID>'
    AND _event_time > TIMESTAMP '2023-11-24 09:40:00'
GROUP BY
    query_sql

We are able to repeat this load check on the primary VI as properly, to see how the system performs ingestion and runs queries underneath load. The method could be the identical, we’d simply use a distinct VI identifier in our Locust script in Step 6.

Conclusion

In abstract, load testing is a vital a part of guaranteeing the reliability and efficiency of any database resolution, together with Rockset. By deciding on the fitting load testing software and establishing Rockset appropriately for load testing, you possibly can achieve priceless insights into how your system will carry out underneath varied circumstances.

Locust is straightforward sufficient to get began with rapidly, however as a result of Rockset has REST API help for executing queries and question lambdas, it’s straightforward to hook up any load testing software.

Bear in mind, the objective of load testing isn’t just to establish the utmost load your system can deal with, but in addition to grasp the way it behaves underneath completely different stress ranges and to make sure that it meets the required efficiency requirements.

Fast load testing ideas earlier than we finish the weblog:

• At all times load check your system earlier than going to manufacturing
• Use question lambdas in Rockset to simply parametrize, version-control and expose your queries as REST endpoints
• Use compute-compute separation to carry out load testing on a digital occasion devoted for queries, in addition to in your most important (ingestion) VI
• Allow question logs in Rockset to maintain statistics of executed queries
• Analyze the outcomes you’re getting and evaluate them in opposition to your SLAs – when you want higher efficiency, there are a number of methods on methods to sort out this, and we’ll undergo these in a future weblog.

Have enjoyable testing 💪

Helpful assets

Listed below are some helpful assets for JMeter, Gatling and k6. The method is similar to what we’re doing with Locust: you should have an API key and authenticate in opposition to Rockset after which hit the question lambda REST endpoint for a selected digital occasion.

• JMeter net assessments: [https://jmeter.apache.org/usermanual/build-web-test-plan.html]
• Gatling pattern on our RocksetLabs GitHub web page: [https://github.com/rocksetlabs/gatling/tree/main]
• k6 API load testing: [https://k6.io/docs/testing-guides/api-load-testing/]