Fauna Blog

Three months of civic engagement

In February, along with several other startups, we announced our new civic engagement policy.

The policy was inspired by the outpouring of political action after the presidential inauguration. But it was a natural extension of our existing family, diversity, and community focus, and reflected staff engagement that was already happening. It’s been about three months now and we are excited to share how the policy has empowered our team.

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Spanner vs. Calvin: distributed consistency at scale

Daniel J. Abadi is an Associate Professor at Yale University. He does research primarily in database system architecture and implementation. He received a Ph.D. from MIT and a M.Phil from Cambridge.

Introduction

In 2012, two research papers were published that described the design of geographically replicated, consistent, ACID compliant, transactional database systems. Both papers criticized the proliferation of NoSQL database systems that compromise replication consistency and transactional support, and argue that it is possible to build extremely scalable, geographically-replicated systems without giving up consistency and transactional support.

It is possible to build extremely scalable, geographically-replicated systems without giving up consistency and transactional support.

The first of these papers was the Calvin paper, published in SIGMOD 2012. A few months later, Google published their Spanner paper in OSDI 2012. Both of these papers have been cited many hundreds of times and have influenced the design of several modern “NewSQL” systems, including FaunaDB.

Recently, Google released a beta version of their Spanner implementation, available to customers who use Google Cloud Platform. This development has excited many users seeking to build on Google’s cloud, since they now have a reliably scalable and consistent transactional database system to use as a foundation.

However, the availability of Spanner outside of Google has also brought it more scrutiny: what are its technical advantages, and what are its costs? Even though it has been five years since the Calvin paper was published, it is only now that the database community is asking me to directly compare and contrast the technical designs of Calvin and Spanner.

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Dive into FaunaDB with our technical white paper

How do you build a database like FaunaDB?

Since our serverless cloud launch a few weeks ago, the community has been asking for deep technical insight into how FaunaDB is designed and implemented. A comment by mdasen on Hacker News summed it up:

“They seem to understand more than their marketing lets on.”

We are happy to release the first draft of our technical white paper in response. Download it here.

The paper explains the foundations of FaunaDB and the motivations for creating an adaptive operational database.

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Escape the cloud database trap with serverless

If you rely on any cloud infrastructure, you know it is complex. It promises to free you from hardware—but you still have to worry about regions, zones, volumes, memory, software versions, and CPUs. Migrating from one service to another, or even simply changing capacity, is often a manual, error-prone process.

When I cofounded the company that became Couchbase, there was no cloud. Databases were built for physical infrastructure.

Databases were built for physical infrastructure.

Now the world has moved on, but databases have not. Cloud databases are still constrained by provisioning choices. But when demand spikes, shouldn’t your database have your back? Isn’t that what the cloud is for?

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Launch day

I’m excited to announce that today we are opening FaunaDB Serverless Cloud to the public.

Even though we only started talking about ourselves a few months ago, we have been blown away by the response, including a constant stream of inquiries from both the developer community and enterprises looking to escape legacy systems and defeat cloud lock-in. Early customers have launched their projects and now FaunaDB serves millions of end users every day.

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120,000 distributed consistent writes per second with Calvin

As we prepare for the general availability release of FaunaDB, we’re happy to begin sharing performance data. I’m a big fan of ACID-compliant distributed transactions, so we’ll start there.

Our benchmarks show that FaunaDB can easily exceed 120,000 distributed, consistent writes per second, per logical database, on 15 machines.

FaunaDB can easily exceed 120,000 distributed, consistent writes per second, per logical database.

Unlike other distributed databases that rely on hardware clocks or multi-phase commits, FaunaDB’s transaction consistency algorithm is inspired by Calvin. Calvin is designed for high throughput regardless of network latency, and was the work of Alexander Thomson and others from Daniel Abadi’s lab at Yale.

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