Editor’s note: Today we’re hearing from Ryan Mason, Director, Head of DTC Growth & Strategy, at alcoholic beverage firm, Constellation Brands on the company’s shift to Direct-to-Consumer (DTC) sales and how Google Cloud’s powerful technology stack helped with this transformation. 

It’s no secret that consumer businesses have been up-ended in a lasting manner after 18 months of the pandemic. Consumers have been forced to shop differently over the past year – and as a result, they’ve evolved to be more comfortable with online spending and have grown to expect a certain level of convenience. While the e-commerce share of consumer sales has grown steadily over the past decade, the pandemic was the catalyst for the famous “10 years of growth in 3 months” which many argue is here to stay. 

Facing this reality head-on,  we placed a new emphasis on Direct-to-Consumer (DTC) with our acquisition of Empathy Wines, a DTC-native wine brand that sells directly to consumers via e-commerce. To accelerate our innovation in the DTC space, we added headcount and new functions to the existing Empathy team and empowered the newly-minted DTC group to apply their digital commerce operating model across the rest of the wine and spirits portfolio, which includes Robert Mondavi Winery, Meiomi Wines, The Prisoner Wine Company, High West Whiskey, and more. 

One pandemic and one year later, DTC sales have surged in the wine and spirits category with Constellation positioned as a leader armed with a unique and powerful cloud technology stack, best-in-class e-commerce user experiences, modernized fulfillment solutions, and data-driven growth marketing. 

Benefits of Going DTC 

A report from McKinsey estimates that the strategic business shift to DTC has been accelerated by two years because of the pandemic and argues that consumer brands that want to thrive will need to aim for a 20% DTC business or higher, which is already taking shape in the market: Nike’s direct digital channels are on track to make up 21.5% of the total business by the end of 2021, up from 15.5% in the last fiscal year, and Adidas is aiming for 50% DTC by 2025. But outside of the clear revenue upside, the auxiliary benefits of going DTC are robust.

For Constellation Brands, each of these four pillars ring true, and our shift toward DTC is as much about margin accretion and revenue mix management as it is about consumer insights and data. The added complexities of the alcohol space add wrinkles to our DTC approach and manifest in many areas like consumer shopping preference, shipping and logistics hurdles, and more. In order to win share early and continue to lead the category, we recognized the need to harness the immense amount of first-party data to power impactful and actionable insights. 

Our DTC technology architecture has fostered a value chain that is completely digitized: website traffic, marketing expenditures, tasting room transactions, e-commerce transactions, logistics and fulfillment events, cost of goods sold (COGS) and margin profiles, etc. are recorded and stored in a data warehouse in real time.  For the first time, at any given moment, we can easily and deterministically answer complex business questions like “what is the age and gender distribution of my customers from Los Angeles who have purchased SKU X from Brand.com Y in the last 6 months? What is the cohort net promoter score? Did that increase after we introduced same-day shipping in this zip code? By how much?” 

The ability to answer these questions and understand the root causes allows us to stay nimble with product offerings and iterate marketing strategies at the speed of consumer preference. Further, it enables us to optimize our omnichannel presence in the same manner by leaning on DTC consumer insights to develop valuable strategies with key wholesale distribution partners and 3-Tier eCommerce partners like Drizly and Instacart. At its core, Constellation’s DTC practice is designed to be the consumer-centric “tip-of-the-spear” responsible for generating insights from which all sales channels, including wholesale, can benefit. 

Constellation’s DTC technology approach prioritizes consumer-centricity and insights generation

We have taken a modern approach to building a digital commerce technology stack, leveraging a hub-and-spoke model built around Shopify Plus and other key emergent technology providers like email provider Klaviyo, loyalty platform Yotpo, Net Promoter Score measurer Delighted, Customer Service module Gorgias, payments processor Stripe, event reservations platform Tock, and many more. For digital marketing and analytics, we use Google Cloud and Google Marketing Platform, which includes products like Analytics 360, Tag Manager 360, and Search Ads 360.

To help gather, organize, and store all of the inbound data from the ecosystem, we partnered with SoundCommerce, a data processing platform for eCommerce businesses. Together with SoundCommerce, we are able to automate data ingestion from all endpoints into a central data warehouse in Google BigQuery. With BigQuery, our data team is able to break data silos and quickly analyze large volumes of data that help unlock actionable insights about our business.  BigQuery itself allows for out-of-the-box predictive analytics using SQL via BigQuery ML, and a key differentiator for us is that all Google Marketing Platform data is natively accessible for analysis within BigQuery. 

But data possession only addresses half of the opportunity: we needed a powerful and modern business intelligence platform to help make sense of the vast amounts of data flowing into the system. Core to the search was to find a partner that approached BI in a way that fit with our future-looking strategy.

Our DTC team relies on the accurate measurement of variable metrics like Customer Acquisition Cost (CAC), Customer Lifetime Value (CLV), Churn, and Net Promoter Score (NPS) as a bellwether of the health of the business and monitoring these figures on a daily basis is paramount to success. To enable us to keep an accurate pulse on strategic KPIs, we considered several incumbent BI platforms. Ultimately we selected Google Cloud’s Looker for a range of benefits that separated it from the rest of the pack.

From a vision perspective, in this particular case we felt Looker was most aligned with our belief that better decisions are made when everyone has access to accurate, up-to-date information. Looker allows us to realize that vision by surfacing data in a simple web-based interface that empowers everyone to take action with real-time data on critical commercial activities. Furthermore, Looker’s ability to automate and distribute formatted modules to a myriad of stakeholders on a regular cadence increases data literacy and business performance transparency.

From a product perspective, we chose Looker for it’s cloud offering, web-based interface, and  centralized, agile modeling layer that creates a trusted environment for all users to confidently interact with data — without any actual data extraction. While other BI tools have centralized semantic layers that require skilled IT resources, we’ve experienced that those can lead to bottlenecks and limited agility. With Looker’s semantic layer, LookML, our BI Team, led by Peter Donald, can easily build upon their SQL knowledge to add both a high degree of control as well as flexibility to our data model.  The fully browser-based development environment allows the data team to rapidly develop, test, and deploy code and is backed by robust and seamless Git source code management. 

In parallel, LookML empowers  business users to collaborate without the need for advanced SQL knowledge. Our data team curates interactive data experiences with Looker to help scale access and adoption. Business users can explore ad hoc analysis, create dashboards, and develop custom data experiences in the web-based environment to get the answers they need without relying on IT resources each time they have a new question, while also maintaining the confidence that the underlying data will always be accurate. This helps us meet our primary goal of providing all businesses users with the data access they need to monitor the pulse of key metrics in near real-time.

Impact and future of DTC BI at Constellation

In short order, taking a modern and integrated approach to the DTC technology stack has delivered economic impact across the portfolio, helping our team understand and combat customer churn, increase conversion rates, and optimize the customer acquisition cost (CAC) and customer lifetime value (CLV) ratios. Perhaps most important is the benefit it can provide to the customer base. Mining customer data and consumer behavior generates data into what our customers are seeking, giving us insights to supply more, or less of it. For example, observing sales velocity and conversion rates by SKU or by region can help us better understand changes in customer taste profiles and fluctuations in demand, providing the foundation for a more powerful innovation pipeline and more effective sales and distribution tactics in wholesale. Our team has also been an early pilot tester for Looker’s new integration with Customer Match, which contributes to the virtuous cycle between data insight and data activation. In the future, our plan is to leverage this cycle to amplify the impact of Google Ads across Search, Shopping, and YouTube placements for the wine and spirits portfolio. 

The operational impact of Looker is also substantial: our team estimates that the number of hours needed to reach critical business decisions has been reduced by nearly 60%, boosting productivity and accelerating the daily operating rhythm. A thoughtfully curated technology stack together with a modern BI solution allows us to stay at the vanguard of the industry. While the DTC sales channel is not designed to surpass the core business of wholesale for Constellation in terms of size, the approach enables unparalleled insights and measurement abilities that will pay dividends for the entire business for years to come.

Over one third of organizations believe that data analytics and machine learning have the most potential to significantly alter the way they run business over the next 3 to 5 years. However, only 26% of organizations are data driven. One of the biggest reasons for this gap is that a major portion of the data generated today is unstructured, which includes images, documents, and videos. It is estimated to cover roughly up to 80% of all data, which has so far remained untapped by organizations.

One of the goals of Google’s data cloud is to help customers realize value from data of all types and formats. Earlier this year, we announced BigLake, which unifies data lakes and warehouses under a single management framework, enabling you to analyze, search, secure, govern and share unstructured data using BigQuery.

At Next ‘22, we announced the preview of object tables, a new table type in BigQuery that provides a structured record interface for unstructured data stored in Google Cloud Storage. This enables you to directly run analytics and machine learning on images, audio, documents and other file types using existing frameworks like SQL and remote functions natively in BigQuery itself. Object tables also extend our best practices of securing, sharing and governing structured data to unstructured, without needing to learn or deploy new tools.

Directly process unstructured data using BigQuery ML

Object tables contain metadata such as URI (Uniform Resource Identifier), content type, and size that can be queried just like other BigQuery tables. You can then derive inferences using machine learning models on unstructured data with BigQuery ML. As part of preview, you can import open source TensorFlow Hub image models, or your own custom models to annotate the images. Very soon, we plan to enable this for audio, video, text and many other formats, and pre-trained models to enable out-of-the box analysis. Check out this video to learn more and watch a demo.

Create an object table

CREATE EXTERNAL TABLE my_dataset.object_table
WITH CONNECTION us.my_connection
OPTIONS(uris=["gs://mybucket/images/*.jpg"],
object_metadata="SIMPLE", metadata_cache_mode="AUTOMATIC");
​
# Generate inferences with BQML
SELECT * FROM ML.PREDICT(
MODEL my_dataset.vision_model,
(SELECT ML.DECODE_IMAGE(data) AS img FROM my_dataset.object_table)
);

By analyzing unstructured data natively in BigQuery, businesses can

• Eliminate manual effort as pre-processing steps such as tuning image sizes to model requirements are automated
• Leverage the simple and familiar SQL interface to quickly gain insights
• Save costs by utilizing existing BigQuery slots without needing to provision new forms of compute

Adswerve is a leading Google Marketing, Analytics and Cloud partner on a mission to humanize data. Twiddy & Co. is Adswerve’s client – a vacation rental company in North Carolina. By combining structured and unstructured data, Twiddy and Adswerve used BigQuery ML to analyze images of rental listings and predict the click-through rate, enabling data-driven photo editorial decisions.

“Twiddy now has the capability to use advanced image analysis to stay competitive in an ever changing landscape of vacation rental providers – and can do this using their in-house SQL skills.” said Pat Grady, Technology Evangelist, Adswerve

Process unstructured data using remote functions

Customers today use remote functions (UDFs) to process structured data for languages and libraries that are not supported in BigQuery. We are extending this capability to process unstructured data using object tables.

Object tables provide signed URLs to allow remote UDFs running on Cloud Functions or Cloud Run to process the object table content. This is particularly useful for running Google’s pre-trained AI models, including Vision AI, Speech-to-Text, Document AI, open source libraries such as Apache Tika, or deploying your own custom models where performance SLAs are important.

Here’s an example of an object table being created over PDF files that are parsed using an open source library running as a remote UDF.

SELECT uri, extract_title(samples.parse_tika(signed_url)) AS title<br>FROM EXTERNAL_OBJECT_TRANSFORM(TABLE pdf_files_object_table,<br>["SIGNED_URL"]);

Extending more BigQuery capabilities to unstructured data

Business intelligence – The results of analyzing unstructured data either directly in BigQuery ML or via UDFs can be combined with your structured data to build unified reports using Looker Studio (at no charge), Looker or any of your preferred BI solutions. This allows you to gain more comprehensive business insights. For example, online retailers can analyze product return rates by correlating them with the images of defective products. Similarly, digital advertisers can correlate ad performance with various attributes of ad creatives to make more informed decisions.

BigQuery search index – Customers are increasingly using the search functionality of BigQuery to power search use cases. These capabilities now extend to unstructured data analytics as well. Whether you use BigQueryML to produce inference on images or use remote UDFs with Doc AI to produce document extraction, the results can now be search indexed and used to support search access patterns.

Here’s an example of search index on data that is parsed from PDF files:

CREATE SEARCH INDEX my_index ON pdf_text_extract(ALL COLUMNS);
​
SELECT * FROM pdf_text_extract WHERE SEARCH(pdf_text, "Google");

Security and governance – We are extending BigQuery’s row-level security capabilities to help you secure objects in Google Cloud Storage. By securing specific rows in an object table, you can restrict the ability of end users to retrieve the signed URLs of corresponding URIs present in the table. This is a shared responsibility security model, for which administrators need to ensure that end users don’t have direct access to Google Cloud Storage, and use signed URLs from object tables as the only access mechanism.

Here’s an example of a policy for PII images that are secured to be first processed through a blur pipeline:

CREATE ROW ACCESS POLICY pii_data ON object_table_images
GRANT TO ("group:admin@example.com")
FILTER USING (ARRAY_LENGTH(metadata)=1 AND
metadata[OFFSET(0)].name="face_detected")

Soon, Dataplex will support object tables, allowing you to automatically create object tables in BigQuery and manage and govern unstructured data at scale.

Data sharing – You can now use Analytics Hub to share unstructured data with partners, customers and suppliers while not compromising on security and governance. Subscribers can consume the rows of object tables that are shared with them, and use signed URLs for unstructured data objects.

Getting Started

Submit this form to try these new capabilities that unlock the power of your unstructured data in BigQuery. Watch this demo to learn more about these new capabilities.

Special thanks to engineering leaders Amir Hormati, Justin Levandoski and Yuri Volobuev for contributing to this post.

There are just under 8 billion people on Earth, depending on the source. Here at Recruit, our work is to develop and maintain an email marketing system that sends personalized emails to tens of millions of customers of hundreds of web services, all with the goal of providing the best, most relevant customer experience.

When Recruit was founded in 1960, the company was focused on helping match graduates to jobs. Over the years, we’ve expanded to help providers that deal with almost every personal moment and event a person encounters in their life. From travel plans to real estate, restaurant choices to haircuts, we offer software and services to help providers deliver on virtually everything and connect to their end-customers.

Recruit depends on email as a key marketing vehicle to end-customers and to provide a communications channel to clients and advertisers across its services. To maximize the impact of these emails, we customize each email we send. To help power this business objective, we developed a proprietary system named “Soup” that we host on Google Cloud Platform (GCP). Making use of Google Cloud Spanner, Soup is the connective tissue that manages the complex customization data needed for this system.

Of course, getting from idea to functioning product is easier said than done. We have massive datasets so requirements like high availability and serving data in real-time are particularly tricky. Add in a complex existing on-premises environment, some of which we had to maintain in our journey to the cloud creating a hybrid environment, and the project became even more challenging.

A Soup Primer

First, why the name “Soup”? The name of the app is actually “dashi-wake” in Japanese, from “dashi,” a type of soup. In theory, Soup is a fairly simple application: its API returns recommendation results based on the data we compute about a user via the user’s user ID. Soup ingests pre-computed recommendations and then serves those recommendations to the email generation engine and tracks metrics. While Soup doesn’t actually send the customer emails, it manages the entire volume of personalization and customization data for tens of millions of users. It also manages the computed metrics associated with these email sends such as opens, clicks, and other metadata.

Soup leverages other GCP services such as App Engine Flex (Node.js), BigQuery, Data Studio, and Stackdriver in addition to Cloud Spanner.

Soup Requirements

High availability

If the system is unavailable when a user decides to open an email they see a white screen with no content at all. Not only is that lost revenue for that particular email, it makes customers less likely to open future emails from us.

Low latency

Given a user ID, the system needs to search all its prediction data and generate the appropriate content—an HTML file, an image, multiple images, or other content—and deliver it, all very quickly.

Real-time log ingestion and fast JOINs

In today’s marketing environment, tracking user activity and being able to make dynamic recommendations based on it is a must-have. We live in an increasingly real-time world. In the past, it might have been OK to take a week or longer to adapt content based on customer behavior. Now? A delay of even a day can make the difference between a conversion and a lost opportunity.

The Problem

Pushing out billions of personalized emails to tens of millions of customers comes with some unique challenges. Our previous on-premises system was based on Apache HBase, the open-source NoSQL database, and Hive data warehouse software. This setup presented three major obstacles:

Cluster sizing

Email marketing is a bursty workload. You typically send a large batch of emails, which requires a lot of compute, and then there’s a quiet period. For our email workloads, we pre-compute a large set of recommendations and then serve those recommendations dynamically upon email open. On-premises, there wasn’t much flexibility and we had to resize clusters manually. We were plagued by errors whenever loads of email opens and the resulting requests to the system outpaced the traffic we could handle, because the cluster size of our HBase/Hive system couldn’t keep up.

Performance

The next issue was optimizing the schema model for performance. Soup has a couple of main functions: services write customer tracking data to it, and downstream “customers” read that data from it to create the personalized emails. On the write side, after the data is written to Soup, the writes need to be aggregated. We initially did this on-premises, which was quite difficult and time consuming because Hbase’s doesn’t offer aggregation queries, and because it was hard to scale in response to traffic bursts.

Transfer delays

Finally, every time we needed to generate a recommendation model for a personalized email blast, we needed to transfer the necessary data from HBase to Hive to create the model, then back to HBase. These complex data transfers were taking two-to-three days. Needless to say, this didn’t allow for the type of agility that we need to provide the best service to our customers.

Cloud Spanner allows us to store all our data in one place, and simply join the data tables and do aggregates; there’s no need for a time-intensive data transfer. Using this model, we believe we can cut the recommendation generation time from days to under a minute, bringing real-time back into the equation.

Why Cloud Spanner?

Compared to the previous application running on-premises, Cloud Spanner offers lower cost, lower operations requirements and higher availability. Most critically, we wanted to calculate metrics (KPIs) in real time without data transfer. Cloud Spanner allows us to do this by pumping SQL queries into a custom dashboard that monitors KPIs in real time.

Soup now runs on GCP, although the recommendations themselves are still generated in an on-premise Hadoop cluster. The computed recommendations are stored in Cloud Spanner for the reasons mentioned above. After moving to GCP and architecting for the cloud, we see an error rate of .005% per second vs. a previous rate of 4% per second, an improvement of 1/800. This means that for an email blast sent to all users in Japan, one user won’t be able to see one image in one email. Since these emails often contain 10 images or more, this error rate is acceptable.

Cloud Spanner also solved our scaling problem. In the future, Soup will have to support one million concurrent users in different geographical areas. Likewise, Soup has to perform 5,000 queries per second (QPS) at peak times on the read side, and will expand this requirement to 20,000 to 30,000 QPS in the near future. Cloud Spanner can handle all the different, complex transactions Soup has to run, while scaling horizontally with ease.

Takeaways

In migrating our database to Cloud Spanner, we learned many things that are worth taking note of, whether you have 10 or 10 million users.

Be prepared to scale

We took scaling into account from Day One, sketching out specific requirements for speed, high availability, and other metrics. Only by having these requirements specifically laid out were we able to choose—and build—a solution that could meet them. We knew we needed elastic scale.

With Cloud Spanner, we didn’t have to make any of the common trade-offs between the relational database structure we wanted, and the scalability and availability needed to keep up with the business requirements. Likewise, with a growing company, you don’t want to place any artificial limits on growth, and Cloud Spanner’s ability to scale to “arbitrarily large” database sizes eliminates this cap, as well as the need to rewrite or migrate in the future as our data needs grow.

Be realistic about downtime

For us, any downtime can result in literally thousands of lost opportunities. That meant that we had to demand virtually zero downtime from any solution, to avoid serving up errors to our users. This was an important realization. Google Cloud provides an SLA guarantee for Cloud Spanner. This solution is more available and resistant to outages than anything we would build on our own.

Don’t waste time on management overhead

When you’re worrying about millions of users and billions of emails, the last thing you have time to do is all the maintenance and administrative tasks required to keep a database system healthy and running. Of course, this is true for the smallest installations, as well. Nobody has a lot of extra time to do things that should be taken care of automatically.

Don’t be afraid of hybrid

We found that a hybrid architecture that leverages the cloud for fast data access but still using our existing on-premises investments for batch processing to be effective. In the future, we may move the entire workload to the cloud but data has gravity, and we currently have lots of data stored on-premises.

Aim for real-time

At this time, we can only move data in and out of Cloud Spanner in small volumes. This prevents us from making real-time changes to recommendations. Once Cloud Spanner supports batch and streaming connections, we’ll be able to enable an implementation to provide more real-time recommendations to deliver even more relevant results and outcomes.

Overall, we’re extremely happy with Cloud Spanner and GCP. Google Cloud has been a great partner in our move to the cloud, and the unique services provided enable us to offer the best service to our customers and stay competitive.

Intro to Cloud Spanner

Cloud Spanner is an enterprise-grade, globally distributed, externally consistent database that offers unlimited scalability and industry-leading 99.999% availability. It requires no maintenance windows and offers a familiar PostgreSQL interface. It combines the benefits of relational databases with the unmatched scalability and availability of non-relational databases. 

As organizations modernize and simplify their tech stack, Spanner provides a unique opportunity to transform the way they think about and use databases as part of building new applications and customer experiences.

But choosing a database for your workload can be challenging; there are so many options in the market and each one has a different onboarding and operating experience. At Google Cloud we know it’s hard to navigate this choice and are here to help you. In this blog post, I want to bust the seven most common misconceptions that I regularly hear about Spanner so that you can confidently make your decision.

Myth #1 Only use Spanner if you have a massive workload

The truth is that Spanner powers Google’s most popular, globally available products, like YouTube, Drive, and Gmail, and has enabled many large scale transformations including that of Uber, Niantic and Sharechat. It is also true that Spanner processes more than 1 Billion queries per second at peak.

At the same time, many customers also use Spanner for their smaller workloads (both in terms of transactions per second and storage size) for availability and scalability reasons. For example, Google Password Manager has small workloads that run on Spanner. These customers cannot tolerate downtime, require high availability to power their applications and seek scale insurance for future growth scenarios.

Limitless scalability with the highest availability is critical in many industry verticals such as gaming and retail, especially when a newly launched game goes viral and becomes an overnight success or when a retailer has to handle a sudden surge in traffic due to a  Black Friday/Cyber Monday sale.  

Regardless of workload size, every customer on the journey to the cloud wants the benefits of scalability and availability while reducing the operational burden and the costs associated with patching, upgrades and other maintenance.

Myth #2 Spanner is too expensive

The truth is, when looking at the cost of a database, it is better to consider Total Cost of Ownership (TCO) and the value it offers rather than the raw list price. We deliver significant value to our customers starting at this price including critical things like availability, price performance, and reduced operational costs. 

• Availability: Spanner provides high availability and reliability by synchronously replicating data. When it comes to Disaster Recovery, Spanner offers 0-RPO and 0-RTO for zonal failures in case of a regional instance and regional failure in case of multi-regional instances. Less downtime, more revenue!
• Price-performance: Spanner offers one of the industry’s leading price-performance ratios which makes it a great choice if you are running a demanding, performance sensitive application. Great customer experiences require consistent, optimal latencies!
• Reduced operational cost: With Spanner, customers enjoy zero downtime upgrades and schema changes, and no maintenance windows. Sharding is automatically handled so the challenges associated with scaling up traditional databases don’t exist. Spend more time innovating, and less time administering!
• Security & Compliance: By default, Spanner already offers encryption for data-in-transit via its client libraries and for data-at-rest using Google-managed encryption keys. CMEK support for Spanner lets you now have complete control of the encryption keys. Spanner also provides VPC Service Controls support and has compliance certifications and necessary approvals so that it can be used for workloads requiring ISO 27001, 27017, 27018, PCI DSS, SOC1|2|3, HIPAA and FedRAMP.

With Spanner, you have peace of mind knowing that your data’s security, availability and reliability won’t be compromised.

And best of all, with the introduction of Granular Instance Sizing, you can now get started for as little as $65/month and unlock the tremendous value spanner offers.

Pro tip : Use the auto-scaler to right size your Spanner instances. Take advantage of TTL to reduce the amount of data stored.

Myth #3 You have to make a trade off between scale, consistency, and latency

The truth is, depending on the use case and instance configuration, users can use Spanner such that they don’t have to pick between consistency, latency and scale.

To provide strong data consistency, Spanner uses a synchronous, Paxos-based replication scheme, in which replicas acknowledge every write request. A write is committed when a majority of the replicas (e.g 2 out of 3), called a quorum, agree to commit the write. In the case of regional instances, the replicas are within the region and hence the writes are faster than in the case of multi-region instances, where the replicas are distributed across multiple regions. In the latter case, forming a quorum on writes can result in slightly higher latency. Nevertheless, Spanner multi-regions are carefully designed in geographical configurations that ensure that the replicas can communicate fast enough and write latencies are acceptably low.

A read can be served strong (by default) or stale. A strong read is a read at a current timestamp and is guaranteed to see all the data that has been committed up until the start of the read. A stale read is a read executed at a timestamp in the past. In case of a strong read, the serving replica ​​will guarantee that you will see all data that has been committed up until the start of the read. In some cases, this means that the serving replica has to contact the leader to ensure that it has the latest data. In case of a multi-region instance where the read is served from a non-leader replica, this would mean that read latency can be slightly higher than if it was served from a leader region. Stale reads are performed over data that was committed at a  timestamp in the past and can, therefore, be served at very low latencies by the closest replica that is caught up until that timestamp. If your application is latency sensitive, stale reads may be a good option and we recommend using a stale read value of 15 seconds. 

Myth #4 Spanner does not have a familiar interface

The truth is that Spanner offers the flexibility to interact with the database via a SQL dialect based on ANSI 2011 standard as well as via a REST or gRPC API interface, which are optimized for performance and ease-of-use. In addition to Spanner’s interface, we recently introduced a PostgreSQL interface for Spanner, that leverages the ubiquity of PostgreSQL to meet development teams using an interface that they are familiar with. The PostgreSQL interface provides a rich subset of the open-source PostgreSQL SQL dialect, including common query syntax, functions, and operators. It supports a core collection of open-source PostgreSQL data types, DDL syntax, and information schema views. You get the PostgreSQL familiarity, and relational semantics at Spanner scale. 

Learn more about our PostgreSQL interface here.

Myth #5 The only way to get observability data is via the Spanner Console

​​The truth is that Spanner client libraries support OpenCensus Tracing and Metrics, which gives insight into the client internals and aids in debugging production issues. For instance, client-side traces and metrics include sessions and transactions related information. 

Spanner also supports the OpenTelemetery receiver, which provides an easy way for you to process and visualize metrics from Cloud Spanner System tables, and export these to the Application Monitoring (APM) tool of your choice. This could be either an open source combination of a time-series database like Prometheus coupled with a Grafana dashboard, or it could be a commercial offering like Splunk, Datadog, Dynatrace, NewRelic or AppDynamics. We’ve also published reference Grafana dashboards, so that you can debug the most common user journeys such as “Why is my tail latency high” or “Why do I see a CPU spike when my workload did not change”. Here is a sample docker service, to show how the Cloud Spanner receiver can work with Prometheus exporter and Grafana dashboards.

We are continuing to embrace open standards, and continuing to integrate with our partner ecosystem. We also continue to evolve the observability experience offered by the Google console so that our customers get the best experience wherever they are. 

Myth #6 Spanner is only for global workloads requiring copies in multiple regions 

The truth is that, while Spanner offers a range of multi-region instance configurations, it also offers regional configuration in each GCP region. Each regional node is replicated in 3 zones within the region, while a multi-regional node is replicated at least 5 times across multiple regions. A regional configuration offers 4 nines of availability and protection against zonal failures.

Typically, multi-regional instance configurations are indicated if your application runs workloads in multiple geographical locations or your business needs 99.999% of availability and protection against regional failures. Learn more here.

Myth #7 Spanner schema changes require expensive locks

The truth is that Spanner never has table level locks. Spanner uses a multi-version concurrency control architecture to manage concurrent versions of schema and  data allowing ad-hoc and online qualified schema changes that do not require any downtime, additional tools, migration pipelines or complex rollback/backup plans. When issuing a schema update you can continue writing and reading from the database without interruption while Spanner backfills the update, whether you have 10 rows or 10 billion rows in your table.

The same mechanism can be used for Point-in-time recovery (PITR) and snapshot queries using stale reads to restore both schema and the state of data at a given query-condition and timestamp up to a maximum of seven days.

Now that we’ve learned the truth about Cloud Spanner, I invite you to get started – visit our website.