BigQuery offers strong query performance, but it is also a complex distributed system with many internal and external factors that can affect query speed. When your queries are running slower than expected or are slower than prior runs, understanding what happened can be a challenge.

The query execution graph provides an intuitive interface for inspecting query execution details. By using it, you can review the query plan information in graphical format for any query, whether running or completed.

You can also use the query execution graph to get performance insights for queries. Performance insights provide best-effort suggestions to help you improve query performance. Since query performance is multi-faceted, performance insights might only provide a partial picture of the overall query performance.

Execution graph

When BigQuery executes a query job, it converts the declarative SQL statement into a graph of execution, broken up into a series of query stages, which themselves are composed of more granular sets of execution steps. The query execution graph provides a visual representation of the execution stages and shows the corresponding metrics. Not all stages are made equal. Some are more expensive and time consuming than others. The execution graph provides toggles for highlighting critical stages, which makes it easier to spot the potential performance bottlenecks in the query.

Query performance insights

In addition to the detailed execution graph BigQuery also provides specific insights on possible factors that might be slowing query performance.

Slot contention

When you run a query, BigQuery attempts to break up the work needed by your query into tasks. A task is a single slice of data that is input into and output from a stage. A single slot picks up a task and executes that slice of data for the stage. Ideally, BigQuery slots execute tasks in parallel to achieve high performance. Slot contention occurs when your query has many tasks ready for slots to start executing, but BigQuery can’t get enough available slots to execute them.

Insufficient shuffle quota

Before running your query, BigQuery breaks up your query’s logic into stages. BigQuery slots execute the tasks for each stage. When a slot completes the execution of a stage’s tasks, it stores the intermediate results in shuffle. Subsequent stages in your query read data from shuffle to continue your query’s execution. Insufficient shuffle quota occurs when you have more data that needs to get written to shuffle than you have shuffle capacity.

Data input scale change

Getting this performance insight indicates that your query is reading at least 50% more data for a given input table than the last time you ran the query and hence experiencing query slowness. You can use table change history to see if the size of any of the tables used in the query has recently increased.

What’s next?

We continue to work on improving the visualization of the graph. We are working on adding additional metrics to each step and adding more performance insights that will make query diagnosis significantly easier. We are just getting started.

May is Asian American Pacific Islander 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 founder Georgene Huang and how her startup Fairygodboss uses easy to use Google Cloud tools to grow and innovate. 

Coming from a male-dominated industry like Wall Street, I never thought much about my gender. I was so focused on work that I didn’t feel like I could focus on my identity as a woman of color. That all changed when I found myself thrust into the job market while two months pregnant.

Traditionally, job-seeking sites are built by – and geared towards – men and their professional needs. There was no recruiting site that broke down what candidates who identify as women might expect from a company, let alone women of color like myself. I founded Fairygodboss to create a space where women can crowdsource information about opportunities and employers in order to make informed career decisions. 

Fairygodboss 

Fair•y•god•boss (noun): A person who elevates women at work.

Fairygodboss is the largest career community for women. In researching potential employers, I had many questions surrounding company culture, maternity leave, and women in leadership positions. While sites like Glassdoor provide high-level details for job seekers, I wanted more concrete examples and data than general salary information and anecdotes about work-life balance. 

Fairygodboss began as a job review site geared towards women, but has expanded significantly since our initial launch. In addition to providing free resources like career connections, job listings, virtual recruiting events, community advice, and real research on how companies treat women, we also proactively help companies improve gender diversity. Fairygodboss partners with hundreds of top employers to create a 360-degree recruiting and employer branding program designed to drive female applicants to job postings by showcasing a company’s track record of women in leadership within a company, and their diversity investments. 

Google Cloud + Fairygodboss: Data defined 

At our core, Fairygodboss is a data-driven company, and we rely on Google Cloud to help us process, store, and analyze user-driven events occurring on the site. As a highly scalable, serverless, and cost-effective data warehousing solution, BigQuery collect millions of events each day, helping us identify where traffic is coming from (and where it goes next), and it produces actionable insights to provide better recommendations, information, and opportunities for women visiting our platform. 

After seamlessly connecting our BigQuery data to Data Studio, we have been able to visualize and make sense of all the data we receive on our platform. Unlike some other data visualization tools, Data Studio allows us to analyze data in real time. With tools like BigQuery, we are now able to identify trends and gain a deep understanding for how our users interact with our site. 

Google for Startups Accelerator: Women Founders 

In order to provide more accurate job recommendations for our users, the Fairygodboss team wanted to build a machine learning prototype to classify our users and jobs into categories to facilitate more effective matching. So we applied for the Google for Startups Accelerator: Women Founders, a three-month digital accelerator program for high-potential Seed to Series A tech startups based in the U.S. and Canada. Along with tailored mentorship and product support from Googlers and industry experts, the Google for Startups Accelerator provided $100K in Google Cloud credits to scale our businesses. The mentorship we received from the Google Cloud technical experts as part of the Accelerator – special shout out to Peter Novig! – empowered us to integrate Cloud AutoML into our systems and ultimately curate more accurate job suggestions for our users.  

Not only did the Google for Startups Accelerator program help to achieve our business goals for the year, it was also extremely beneficial to be connected with a cohort of other women founders of color. While we are all building different kinds of businesses across different industries, the guidance around fundraising, scaling teams, and coping with the struggles of being a founder rang true for all of us. 

Advancing the community

It can be daunting to launch your own business – and even more so as an AAPI woman. Throughout my career, I am certain I have experienced unconscious bias around my race. Specifically, there have likely been “model minority” stereotypes about my demeanor and math abilities, assumptions that I may be mild-mannered or agreeable. While frustrating, these unfair tropes actually inspired me to see myself beyond others’ perceptions. Why be either analytical or creative, meek or brash, inspiring or agreeable? It’s the ors of life that prevent us from seeing ourselves as truly multi-dimensional individuals. I aspired to be more instead of or, and want my company to be as well. 

As an inclusion-focused business, it is extremely important that Fairygodboss mindfully engage with all underrepresented groups. Diversity can be sliced in many different ways, and these intersections lead to great opportunities for change. We have a team of volunteer ‘culture wizards’ at work that provide educational resources and videos for those wanting to learn more about different cultures and identities from members of under-represented groups. We also practice what we preach at Fairygodboss by prioritizing diverse slates and diversity in our own hiring process. We aim for the talent we hire to be representative of the diverse communities we support. It is imperative to take an intentional approach to hiring diverse talent, as it falls on all employers to promote equity and inclusion—there are always ways in which we can all improve. Fairygodboss looks forward to evolving with Google Cloud in 2021 and beyond as we work together to foster inclusive teams around the world. 

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.

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.