How one can 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 conduct beneath each regular and peak situations. 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 sources accurately, and making certain that they’ll deal with the anticipated load. This, in flip, helps in reaching Service Degree Agreements (SLAs) and ensures a easy, uninterrupted consumer expertise. That is particularly vital for customer-facing use instances, the place finish customers count on a quick consumer expertise. Load testing is usually additionally known as efficiency or stress testing.

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

Rockset compute sources (known 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 acceptable 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 also be used for ingestion, you need to think about sources 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 characteristic, Rockset will routinely scale the VI up and down relying on the present load. That is vital 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 working queries and this ensures that all the accessible sources 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 suggest doing load testing on no less than two digital situations – with ingestion working 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 establish the boundaries of the chosen VI for our explicit use case and helps us choose an acceptable VI measurement to deal with our desired load.

Instruments for load testing

In terms of 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, very best for numerous kinds of load testing, however will be resource-intensive.
• k6: Optimized for top 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 simple testing wants.

Every software presents a novel set of options, and the selection relies on the precise necessities of the load check being performed. Whichever software you employ, 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 which you could 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 sources for JMeter, k6 and Gatling as effectively.

Organising Rockset and Locust for load testing

Let’s say we have now a pattern SQL question that we need 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 simple to check that SQL question as a REST endpoint. It may be parametrized and the SQL will be versioned and saved in a single place, as an alternative of going backwards and forwards and altering your load testing scripts each time that you must change one thing within the question.

Step 1 – Determine 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 appears to be like like (be aware that :date is a parameter which we will 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 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-Sort: utility/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 manner for our Locust script to authenticate itself to Rockset and run the check. You’ll be able to 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 towards 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 will 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 completed, 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 are able to use the script under as a template:

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

class query_runner(HttpUser):
    ROCKSET_APIKEY = os.getenv('ROCKSET_APIKEY') # API key's 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" # exchange this along with your area's URI
        self.consumer.base_url = self.host
        self.vi_id = '<your digital occasion ID>' # exchange this along with your VI ID

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

Step 7 – Run the load check

As soon as we set the API key setting variable, we will 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 will 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 price you outlined (the spawn price). 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’ll then manually enhance it to five and 10 customers, after which go down to five and 1 once more.
2. Job 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). We have now 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 presently swarming, the request price, failure price, and response occasions.
5. Take a look at scalability: Locust will proceed to spawn customers till it reaches the overall quantity specified. It ensures the load is elevated regularly as per the required spawn price, permitting you to watch how the system efficiency adjustments because the load will increase. You’ll be able to see this within the graph under the place the variety of customers begins to develop to five and 10 after which go down once more.
6. Person conduct simulation: Digital customers will anticipate a random time between duties, as outlined by the wait_time within the script. This simulates extra reasonable 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 working till you determine to cease it, or till it reaches a predefined period when you’ve set one.
8. Useful resource utilization: Throughout this course of, Locust makes use of your machine’s sources to simulate the customers and make requests. It is vital to notice that the efficiency of the Locust check also can rely on the sources of the machine it is working 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 concerning the efficiency of the system beneath check. Listed here are a few of the predominant metrics supplied by Locust and how one can 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’ll be able to 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 the next load. Evaluate this with response occasions and error charges to evaluate if the system can deal with concurrency and excessive site visitors easily.
• Response time: Often displayed as common, median, and percentile (e.g., ninetieth and 99th percentile) response occasions. You’ll doubtless 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 may have worse expertise.
• Failure price: The share or variety of requests that resulted in an error. A excessive failure price signifies issues with the system beneath check. It is essential to investigate the character of those errors.

Beneath you may see the overall RPS and response occasions we achieved beneath 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 under 300 milliseconds and P99 of 700 milliseconds.

We are able to now correlate these knowledge factors with the accessible digital occasion metrics in Rockset. Beneath, you may see how the digital occasion handles the load when it comes to 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 under 75% on the height and reminiscence utilization appears to be like secure. We additionally don’t see any vital queueing occurring in Rockset.

Other than 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 working, what was their particular person efficiency, queue time, and so forth. To do that, we should first allow question logs after which we will do issues like this to determine our median run and queue occasions:

SELECT
    query_sql,
    COUNT(*) as rely,
    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 principle VI as effectively, to see how the system performs ingestion and runs queries beneath load. The method can 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 resolution, together with Rockset. By deciding on the fitting load testing software and organising Rockset appropriately for load testing, you may achieve invaluable insights into how your system will carry out beneath numerous situations.

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 simple to hook up any load testing software.

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

Fast load testing suggestions 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 predominant (ingestion) VI
• Allow question logs in Rockset to maintain statistics of executed queries
• Analyze the outcomes you’re getting and evaluate them towards your SLAs – when you want higher efficiency, there are a number of methods on how one can deal with this, and we’ll undergo these in a future weblog.

Have enjoyable testing 💪

Helpful sources

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