Google Cloud Platform Releases New Database Storage Tools

By Chris Preimesberger  |  Posted 2016-08-16 Print this article Print

Cloud SQL Second Generation, Cloud Datastore, Cloud Bigtable and Google Compute Engine all get new tools and/or upgrades as announced earlier this year.

Google Cloud Platform on Aug. 16 released to general availability the database storage solutions (Cloud SQL Second Generation, Cloud Datastore and Cloud Bigtable) it announced a few months ago.

The company also released several updates to Google Compute Engine that will bring improvements for database customers. These include support for Microsoft SQL licensing, new data encryption features, improved price-performance and lowered latency.

"From our managed database services to self-managed versions of your favorite relational or NoSQL database, we want enterprises with databases of all sizes and types to experience the best price-performance with the least amount of friction," Google's Dominic Preuss, lead product manager for storage and databases, wrote in the corporate blog.

"All of our database storage products are now generally available and covered by corresponding service-level agreements (SLAs). We're also releasing new performance and security support for Google Compute Engine.

"Whether you're running a WordPress application with a Cloud SQL backend or building a petabyte-scale monitoring system, Cloud Platform is secure, reliable and able to store databases of all types."

Cloud SQL Second Generation, the fully managed database service offering easy-to-use MySQL instances, has completed a successful beta and is now generally available. Since beta, Google has added a number of enterprise features, such as support for MySQL 5.7, point-in-time-recovery (PITR), automatic storage resizing and setting up failover replicas with a single click.

Cloud Bigtable is the company's scalable, fully managed NoSQL wide-column database service with Apache HBase client compatibility, and this is also now generally available. Since beta, customers such as Spotify, Energyworx and FIS (formerly Sungard) have built scalable applications on top of Cloud Bigtable for workloads, such as monitoring, financial and geospatial data analysis, Preuss wrote.

Cloud Datastore, Google's scalable, fully managed NoSQL document database serves 15 trillion requests a month, and its v1 API for applications outside of Google App Engine also has reached general availability. The Cloud Datastore SLA of 99.95 percent monthly uptime demonstrates high confidence in the scalability and availability of this cross-region, replicated service for your toughest web and mobile workloads, Preuss said.

Customers that include Snapchat, Workiva and Khan Academy have built web and mobile applications with Cloud Datastore.

Google Compute Engine (GCE) added the following improvements, according to Preuss:

--Microsoft SQL Server images now available on Google Compute Engine: Enterprise customers emphasize the importance of continuity for their mission-critical applications. The strengths of Google Compute Engine make it a good environment to run Microsoft SQL Server, featuring images with built-in licenses (in beta), as well as the ability to bring your existing application licenses.

--Increased IOPS for persistent disk volumes: Database workloads are dependent on strong block storage performance, so Google is increasing the maximum read and write IOPS for SSD-backed persistent disk volumes from 15,000 to 25,000 at no additional cost, servicing the needs of the most demanding databases.

--Custom encryption for Google Cloud Storage: When you need to store your database backups, you now have the added option of using customer-supplied encryption keys (CSEK). This feature allows Cloud Storage to be a zero-knowledge system without access to the keys.

--Low-latency for Google Cloud Storage Nearline storage: This is a cost-effective way to store your database backups. Google Cloud Storage Nearline offers object storage at costs less than tape. Prior to today, retrieving data from Nearline incurred 3 to 5 seconds of latency per object.

"We've been continuously improving Nearline performance, and now it enables access times and throughput similar to Standard class objects," Preuss wrote. "These faster access times and throughput give customers the ability to leverage big data tools, such as Google BigQuery, to run federated queries across your stored data."



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