The value of streaming analytics comes from the insights a business draws from instantaneous data processing, and the timely responses it can implement to adapt its product or service for a better customer experience. “Instantaneous data insights,” however, is a concept that varies with each use case. Some businesses optimize their data analysis for speed, while others optimize for execution cost. 

In this post, we’ll offer some tips on estimating the cost of a job in Dataflow, Google Cloud’s fully managed streaming and batch analytics service. Dataflow provides the ability to optimize a streaming analytics job through its serverless approach to resource provisioning and management. It automatically partitions your data and distributes your worker code to Compute Engine instances for parallel processing, optimizes potentially costly operations such as data aggregations, and provides on-the-fly adjustments with features like autoscaling and dynamic work rebalancing. 

The flexibility that Dataflow’s adaptive resource allocation offers is powerful; it takes away the overhead of estimating workloads to avoid paying for unutilized resources or causing failures due to the lack of processing capacity. Adaptive resource allocation can give the impression that cost estimation is unpredictable too. But it doesn’t have to be. To help you add predictability, our Dataflow team ran some simulations that provide useful mechanisms you can use when estimating the cost of any of your Dataflow jobs. 

The main insight we found from the simulations is that the cost of a Dataflow job increases linearly when sufficient resource optimization is achieved. Under this premise, running small load experiments to find your job’s optimal performance provides you with a throughput factor that you can then use to extrapolate your job’s total cost. At a high level, we recommend following these steps to estimate the cost of your Dataflow jobs: 

1. Design small load tests that help you reach 80% to 90% of resource utilization
2. Use the throughput of this pipeline as your throughput factor
3. Extrapolate your throughput factor to your production data size and calculate the number of workers you’ll need to process it all
4. Use the Google Cloud Pricing Calculator to estimate your job cost

This mechanism works well for simple jobs, such as a streaming job that moves data from Pub/Sub to BigQuery or a batch job that moves text from Cloud Storage to BigQuery. In this post, we will walk you through the process we followed to prove that throughput factors can be linearly applied to estimate total job costs for Dataflow.  

Finding the throughput factor for a streaming Dataflow job

To calculate the throughput factor of a streaming Dataflow job, we selected one of the most common use cases: ingesting data from Google’s Pub/Sub, transforming it using Dataflow’s streaming engine, then pushing the new data to BigQuery tables. We created a simulated Dataflow job that mirrored a recent client’s use case, which was a job that read 10 subscriptions from Pub/Sub as a JSON payload. Then, the 10 pipelines were flattened and pushed to 10 different BigQuery tables using dynamic destinations and BigQueryIO, as shown in the image below.

The team ran 11 small load tests for this job. The first few tests were focused on finding the job’s optimal throughput and resource allocation to calculate the job’s throughput factor. 

For the tests, we generated messages in Pub/Sub that were 500 KB on average, and we adjusted the number of messages per topic to obtain the total loads to feed each test. We tested a range of loads from 3MB/s to 250MB/s. The table below shows five of the most representative jobs with their adjusted parameters:

In order to ensure maximum resource utilization, we monitored the backlog of each test using the backlog graph in the Dataflow interface. We recommend targeting an 80% to 90% utilization so that your pipeline has enough capacity to handle small load increases. We considered 86% to 91% of CPU utilization to be our optimal utilization. In this case, it meant a 2.5MB/s per virtual CPU (vCPU) load. This is job #4 on the table above. In all tests, we used n1-standard-2 machines, which are the recommended type for streaming jobs and have two vCPUs. The rest of the tests were focused on proving that resources scale linearly using the optimal throughput, and we confirmed it.

Using the throughput factor to estimate the approximate total cost of a streaming job

Let’s assume that our full-scale job runs with a throughput of 1GB/s and runs five hours per month. Our throughput factor estimates that 2.5MB/s is the ideal throughput per worker using the n1-standard-2 machines. To support a 1GB/s throughput, we’ll need approximately 400 workers, so 200 n1-standard-2 machines.

We entered this data in the Google Cloud Pricing Calculator and found that the total cost of our full-scale job is estimated at $166.30/month. In addition to worker costs, there is also the cost of streaming data processed when you use the streaming engine. This data is priced by volume measured in gigabytes, and is typically between 30% to 50% of the worker costs. For our use case, we took a conservative approach and estimated 50%, totaling $83.15 per month. The total cost of our use case is $249.45 per month.

Finding the throughput factor for a simple batch Dataflow job

The most common use case in batch analysis using Dataflow is transferring text from Cloud Storage to BigQuery. Our small load experiments read a CSV file from Cloud Storage and transformed it into a TableRow, which was then pushed into BigQuery in batch mode. The source was split into 1 GB files. We ran tests with file sizes from 10GB to 1TB to demonstrate that optimal resource allocation scales linearly. Here are the results of these tests:

These tests demonstrated that batch analysis applies autoscaling efficiently. Once your job finds an optimized resource utilization, it scales to allocate the resources needed to complete the job with a consistent price per unit of processed data in a similar processing time.

Let’s assume that our real scale job here processes 10TB of data, given that our estimated cost using resources in us-central1 is about $0.0017/GB of processed data. The total cost of our real scale job would be about $18.06. This estimation follows this equation: cost(y) = cost(x) * Y/X, where cost(x) is the cost of your optimized small load test, X is the amount of data processed in your small load test, and Y is the amount of data processed in your real scale job. 

The key in this and the previous examples is to design small-load experiments to find your optimized pipeline setup. This setup will give you the parameters for a throughput factor that you can scale to estimate the resources needed to run your real scale job. You can then input these resource estimations in the Pricing Calculator to calculate your total job cost. 

May is Asian Pacific American Heritage Month —a time for us to come together to celebrate and remember the important people and history of Asian and Pacific Island heritage. This feature highlights three AAPI founders from the Google For Startups community.

Read on to learn how these three founders built their businesses, leverage Google tech, and suggestion they have for aspiring entrepreneurs.

CultivatePeople 

Founder: Lola Han

Description: CultivatePeople’s compensation software, Kamsa, provides global market pay rate data and helps companies make data-driven salary decisions so they can attract and retain their most valuable asset: employees. 

Why GCP: “CultivatePeople began using GCP when integrating SSO/SAML authentication after  our SaaS product became a high priority. We were able to exceed our clients’ expectations and increase their level of trust in our platform’s capabilities. We’re currently investigating additional machine learning products, such as Cloud AutoML, that will allow us to quickly deliver exciting features.” 

Note from the Founder: “I grew up with immigrant parents who value stability and are risk-averse. My parents discouraged me from starting my own company because they didn’t want to see me struggle financially or see my health suffer (due to stress). I felt strongly about what CultivatePeople could do, so I started the company as a sole founder in 2017 and watched it double in size year over year since. While the ones I love most may not have cheered me on initially, it was important for me to hang on to the encouraging words of former bosses, executives, and founders to keep me focused on my mission. 

My advice for other AAPI founders is to be a “silent assassin” and believe in the mission and values of your organization. Always remember to stop along the way and: 

 1) Enjoy the journey by celebrating wins and giving yourself credit;

2) Follow your intuition—it’s (almost) always right; 

3) Recognize and invest in your people regularly (ie. give increases more than once a year, if warranted);

4) Give regular words of affirmation to employees on even small achievements.”

Swit

Founder: Josh Lee and Max Lim

Description: Swit is a team collaboration platform that seamlessly combines team chat with task management by allowing teams to turn their conversations into trackable tasks and share tasks to chat with simple drag-and-drop functionality, ensuring everyone is on the same page and projects get done faster.

Why GCP: “Swit is a cross-category hybrid work tool for chat and tasks. This functionality requires more complicated and heavier architecture for performance. So, configuring and managing virtual machines was really challenging to scale up our systems, while handling occasional unexpected traffic surges and frequent updates. Eventually we divided our monolithic architecture into 35 microservices when we launched our official product. The migration to GKE took around one month, and it turned out to be well worth the effort—our systems became able to offer high scalability and enough resilience to keep its uptime no matter what happened. Now we’re operating 84 workloads and 252 microservices with high stability with remarkably low downtime – less than 0.00001%/year.”

Note from the Founder: “As an AAPI founder based in Silicon Valley, I feel proud of the work ethics and diligence fellow Korean American entrepreneurs and professionals have long demonstrated here. Especially with K-pop breaking into the mainstream, I feel even more proud of our culture that strives toward an absolute perfection molded through years of training and dedication. The mission-driven culture of Silicon Valley coupled with Google’s edging technology and creativity really helped us build a product that not only encompasses verticals but also transcends cultures. Swit is growing at an unprecedented rate, and we hope  to join the long list of successful AAPI entrepreneurs here. Swit’s close network with the AAPI community wouldn’t have been possible without Google support. We are grateful for this collaborative environment, and we hope to become the next-generation ambassador for collaboration after Google.” 

Check out more from Swit in their founder story.

WISY 

Founder: Min Chen

Description: Wisy develops technology to bring digital efficiency into the physical world, supporting consumer products businesses and making them thrive in the new economy. All of us have a bad experience when we can’t find the product we want to buy. That is a $1.9T problem in the consumer-packaged goods industry that Wisy is solving with AI and analytics to help manufacturers and retailers sell more by reducing out-of-stocks and waste at a global scale.

Why GCP: “GCP has an intuitive, easy to use interface, was lower cost, and offered preemptible instances with flexible compute options. Some of the reasons why Wisy decided to use GCP include instance and payment configurability, privacy and traffic security, cost-efficiency, and Machine Learning.

Wisy has been able to advance quickly with product development, as well as collaborate better and iterate faster in the creation of our AI models, while reducing costs by 40%. At Wisy, we are solving a problem that affects everyone who shops at a store.“

Note from the Founder: “Two years ago, I moved to San Francisco to expand my second startup, Wisy. This is when I learned that my name ‘Min’ stands for ‘minority.’ I was born in China, raised in a Black community in Panama, received scholarships to attend both Carnegie Mellon and UC Berkeley. I worked for 20 years in several countries, but I have never felt so discriminated against due to my race, ethnicity, gender and age than during my time in Silicon Valley. However, this is also the place I learned that my diverse life experience is my competitive advantage. My background enables me to recruit and relate to people in different countries, create scalable and flexible products for multinational customers, and run global operations efficiently.

My recommendation to AAPI founders is to find strength in their multicultural background. Don’t hide what makes you unique, do not limit yourselves, and do not let others limit you. You will lose your edge when trading authenticity for validation. Be proud and own your story.”

If you want to learn more about how Google Cloud can help your startup, visit our Startup Program application page here and sign up for our monthly startup newsletter to get a peek at our community activities, digital events, special offers, and more.To learn more about how you can help #StopAsianHate during Asian Pacific American Heritage Month and beyond, visit their website here.

We are excited to announce that Google has been named a Leader in The Forrester Wave™: Streaming Analytics, Q2 2021 report. Thank you to our strong community of customers and partners for working with us to deliver a customer focused product. We believe Forrester’s recognition is an acknowledgement of our leadership across an integrated set of capabilities that rely on data to drive transformation. We were also honored to be named a leader in The Forrester Wave™: Cloud Data Warehouse, Q1 2021. 

Forrester gave Dataflow a score of 5 out of 5 across 12 different criteria and according to the report: “Google Cloud Dataflow has strengths in data sequencing, advanced analytics, performance, and high-availability. Google Dataflow’s sweet spot is for enterprises that have a preponderance of real-time data generated on Google Cloud Platform or wish to simplify all data processing by using a single platform that unifies both streaming and batch jobs.” 

Harnessing the power of real-time data

The speed with which businesses are able to respond to change is the difference between those that successfully navigate the future and those that get left behind. In order to accelerate their digital transformation, reimagine their business and leverage the power of real-time data,  today’s data leaders require a streaming analytics platform that provides both depth and breadth.  

Cloud Pub/Sub and Cloud Dataflow, based on more than a decade of experience in internet scale systems for Google’s own needs, provide customers with a reliable, scalable, performant platform. In addition, we’ve designed these products for ease of use to make streaming analytics accessible to more users, which is why customers such as Sky and others from across all industries use Dataflow to run streaming analytics workloads.

5 out of 5 across key streaming analytics criteria

While Forrester gave Dataflow a score of 5 out of 5 in 12 criteria, the product achieved the highest possible scores in areas that are top of mind for our customers.

We continue to be focused on solving problems that matter to you. For example, just in the last month we announced Dataflow Prime and  Auto Sharding for BigQuery – two new auto tuning capabilities that bring efficiency and simplicity to your streaming pipelines. 

Dataflow achieves highest score possible in strategy 

With Google, organizations gain an industry leading product and a partner that has the vision and strategy to help you tackle new business challenges and provide delightful experiences to your customers.

In summary, we are honored to be a Leader in The Forrester Wave™, Streaming Analytics, and look forward to continuing to innovate and partner with you on your digital transformation journey. 

Download the full report: The Forrester Wave™: Streaming Analytics, Q2 2021 and check out these smart analytics reference patterns. To learn more about Dataflow, visit our website and get to know the product by taking an interactive tutorial. You can also watch recordings from the Data Cloud Summit event (May 2021), where we provided an in-depth view of new product innovations in Dataflow and other data analytics products.