Methods to Do Load Testing with Rockset

What’s load testing and why does it matter?

Load testing is a important course of for any database or knowledge service, together with Rockset. By doing load testing, we purpose to evaluate the system’s habits below each regular and peak circumstances. This course of helps in evaluating necessary metrics like Queries Per Second (QPS), concurrency, and question latency. Understanding these metrics is important for sizing your compute assets appropriately, and making certain that they will deal with the anticipated load. This, in flip, helps in reaching Service Degree Agreements (SLAs) and ensures a clean, uninterrupted consumer expertise. That is particularly necessary for customer-facing use circumstances, the place finish customers anticipate a handy guide a rough consumer expertise. Load testing is usually additionally referred to 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 (referred to as digital situations or VIs) come in several sizes, starting from Small to 16XL, and every measurement has a predefined variety of vCPUs and reminiscence accessible. Selecting an applicable measurement relies on 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, it is best to 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 routinely scale the VI up and down relying on the present load. That is necessary if in case you 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 may create VIs which might be devoted solely for operating queries and this ensures that all the accessible assets are geared in the direction of executing these queries effectively. This implies you may 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 at the very least two digital situations – with ingestion operating on the principle VI and on a separate question VI. This helps with deciding on a single or multi-VI structure.

Load testing helps us determine the boundaries of the chosen VI for our explicit use case and helps us choose an applicable VI measurement to deal with our desired load.

Instruments for load testing

On the subject of load testing instruments, a couple of standard choices are JMeter, k6, Gatling and Locust. Every of those instruments has its strengths and weaknesses:

• JMeter: A flexible and user-friendly device with a GUI, ideally suited for varied kinds of load testing, however might 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 device utilizing Scala, finest for complicated, superior scripting situations.
• Locust: Python-based, providing simplicity and speedy script growth, nice for easy testing wants.

Every device affords a singular set of options, and the selection relies on the precise necessities of the load check being carried out. Whichever device you utilize, make sure to learn by way of 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 – if you’re load testing a use case with JMeter, keep it up to get reproducible and reliable outcomes that you would be able to share along with your group 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 concentrate on load testing Rockset with Locust, however I’ll present some helpful assets for JMeter, k6 and Gatling as effectively.

Organising Rockset and Locust for load testing

Let’s say we’ve a pattern SQL question that we need to check and our knowledge is ingested into Rockset. The very first thing we normally 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 might be versioned and saved in a single place, as an alternative of going forwards and backwards and altering your load testing scripts each time you have to change one thing within the question.

Step 1 – Establish the question you need to load check

In our situation, we need to discover the preferred product on our webshop for a selected day. That is what our SQL question seems to be like (be aware 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.Depend) AS ProductName,
    MAX(s.Depend) 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 referred to as LoadTestQueryLambda which is able to 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-Sort: utility/json' 
  -d '{
    "parameters": [
      {
        "name": "days",
        "type": "int",
        "value": "1"
      }
    ],
      "virtual_instance_id": "<your digital occasion ID>"
  }' 
 | python -m json.device

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 by way of our console or by way of the API.

Step 4 – Create a digital occasion for load testing

Subsequent, we want the ID of the digital occasion we need to load check. In our situation, we need to run a load check in opposition to a Rockset digital occasion that’s devoted solely to querying. We spin up a further 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 (suppose EC2 in AWS).

$ pip set up locust

Step 6 – Create your Locust check script

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

We will 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 setting variable

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

    def on_start(self):
        self.headers = {
            "Authorization": "ApiKey " + self.ROCKSET_APIKEY,
            "Content material-Sort": "utility/json"
        }
        self.consumer.headers = self.headers
        self.host="https://api.usw2a1.rockset.com/v1/orgs/self" # substitute this along with your area's URI
        self.consumer.base_url = self.host
        self.vi_id = '<your digital occasion ID>' # substitute this along 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" # substitute this along with your question lambda
        outcome = self.consumer.submit(
            target_service,
            json=knowledge
        )

Step 7 – Run the load check

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

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 charge you outlined (the spawn charge). These customers are situations of the consumer class outlined in your Locust script. In our case, we’re beginning with a single consumer however we are going to then manually improve it to five and 10 customers, after which go down to five and 1 once more.
2. Process 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 based mostly on the weights assigned to them (if any). We’ve got 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 embody 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 contains the variety of customers at the moment swarming, the request charge, failure charge, and response occasions.
5. Take a look at scalability: Locust will proceed to spawn customers till it reaches the whole quantity specified. It ensures the load is elevated regularly as per the required spawn charge, 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. Person habits simulation: Digital customers will watch for a random time between duties, as outlined by the wait_time within the script. This simulates extra lifelike consumer habits. 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 period in the event 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 necessary to notice that the efficiency of the Locust check also can depend upon the assets of the machine it is operating on.

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

Decoding and validating load testing outcomes

Decoding outcomes from a Locust run entails understanding key metrics and what they point out in regards to the efficiency of the system below check. Listed here are a number of the principal metrics supplied by Locust and the best way to interpret them:

• Variety of customers: The whole variety of simulated customers at any given level within the check. This helps you perceive the load degree 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: Normally displayed as common, median, and percentile (e.g., ninetieth and 99th percentile) response occasions. You’ll possible take 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 charge: The share or variety of requests that resulted in an error. A excessive failure charge signifies issues with the system below check. It is essential to research the character of those errors.

Under you may see the whole RPS and response occasions we achieved below completely different masses 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 will now correlate these knowledge factors with the accessible digital occasion metrics in Rockset. Under, you may 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. You can too 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 to be steady. We additionally don’t see any vital queueing occurring in Rockset.

Aside from viewing these metrics within the Rockset console or by way of our metrics endpoint, you too can 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 will repeat this load check on the principle VI as effectively, to see how the system performs ingestion and runs queries below load. The method could be the identical, we might simply use a special VI identifier in our Locust script in Step 6.

Conclusion

In abstract, load testing is a crucial a part of making certain the reliability and efficiency of any database answer, together with Rockset. By choosing the best load testing device and establishing Rockset appropriately for load testing, you may achieve beneficial insights into how your system will carry out below varied circumstances.

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

Keep in mind, the objective of load testing is not only to determine the utmost load your system can deal with, but in addition to grasp the way it behaves below 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:

• All the time 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 principal (ingestion) VI
• Allow question logs in Rockset to maintain statistics of executed queries
• Analyze the outcomes you’re getting and examine them in opposition to your SLAs – in the event you want higher efficiency, there are a number of methods on the best way to sort out this, and we’ll undergo these in a future weblog.

Have enjoyable testing 💪

Helpful assets

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