Google Cloud Platform Blog

Use labels to gain visibility into GCP resource usage and spending

Tuesday, December 12, 2017

By Chris Crall and Marco Cavalli, Product Managers At Descartes Labs, we process petabytes of satellite imagery on a daily basis that we store in GCP. We use labels to tag our Google Cloud Storage buckets by functional business area so that we have better visibility into how resource usage looks across departments. We also use labels with Google Compute Engine to identify the processing pipelines across different areas of our environment so that we have an accurate view of how resource costs map to our business. We can easily track any cost changes by exporting resource billing details to Big Query and using Data Studio Dashboards. —Tim Kelton, Co-Founder and Cloud Architect, Descartes Labs
Labels provide a convenient way for developers and administrators to organize resources at scale costcenter=c23543service=playlistenvironment=test

Labels can be used to associate your GCP resources with your cost center of choice.

playlistcomponent=frontendcomponent=storage

Use labels to search and filter on specific GCP resources.

Using labels to understand costsexport of billing dataBigQuery

What does the shopping cart service in my application cost to run?

How much do I spend on developer test machines?

costcenter=c23543, service=playlist, environment=test

Here, Billing export to BigQuery has been enabled. "Labels-demo-prj" is the source of your resource usage and "labels_demo_bqexport" is the destination dataset where usage data is stored.

Here, you can see Google Compute Engine usage and its associated cost in BigQuery.

documentation page

Extracting value from your logs with Stackdriver logs-based metrics

Monday, December 11, 2017

By Mary Koes, Product ManagerStackdriver Logging’s improved analytics tools became available in betaLogs-based metrics Filter on labels Logs-based metric labels

logs based metric labels Extract values from log entries with distribution metricsdistribution metrics

Alert on matching log entriesvideoGoogle Cloud Platformherewe’d love to hear from you

Expanding our partner ecosystem with managed services providers

Monday, December 11, 2017

By Zoltan Szabadi, Head of MSP Partners, Google Cloud our first managed services provider (MSP) partnerGoogle Cloud Platform

Consulting, assessment, implementation, monitoring and optimization services

24x7x365 support with enterprise-grade SLAs

L1, L2, L3 tiered support models

Certified support engineers

Meet the Google Cloud MSPs

Accenture - Accenture is a global leader in helping organizations move to cloud. Their managed service capability is multi-mode and multi-cloud, and they work with GCP to help customers with security, data, application, infrastructure or business process operations.

Cascadeo - As a long-time Google partner, Cascadeo specializes in managed services, cloud operations and automation. Their services are designed to allow customers to bring their own tooling, monitoring systems, and accounts — while leveraging best in breed services and engineers.

Claranet - With more than 1,800 employees and 24 offices across Europe and Brazil, Claranet specializes in helping customers migrate non-'cloud native' workloads into GCP, and then iteratively improve the infrastructure to take advantage of all of GCP’s tools and services.

Cloudreach - Cloudreach has transitioned some of the largest enterprises from traditional IT to the power of GCP. They've been named a Gartner Magic Quadrant Leader for Hyperscale Cloud Managed Services and offer software enabled services including migration, app modernization, financial, security and governance management.

DoiT International - DoIT’s team of GCP experts provides state-of-the-art managed services for startups. They focus on helping some of the most innovative startups in the world design best in class architecture, cloud operations, security protection and advanced big data analytics.

Go Reply - Go Reply, the Reply group company specialized on Google Cloud technology, offers design and implementation on GCP. They help enterprises build and optimize their cloud strategy, migration, infrastructure hosting, big data, machine learning, PCI/ISO compliance and security management.

Pythian - For over 20 years, Pythian has been delivering managed services. They feature flexible subscription packages, with mix and match services, that help control costs while maximizing performance, availability, efficiency and security — ensuring customers get the most from their GCP environments.

Rackspace - Rackspace helps customers of all sizes plan, architect and operate projects at scale leveraging its 150+ GCP-certified experts. Rackspace recently acquired Datapipe, a next-gen cloud MSP, and both were named as leaders in the 2017 Gartner Magic Quadrant for Public Cloud Infrastructure Managed Service Providers, Worldwide.

RightScale - RightScale provides managed services to help organizations successfully and cost-effectively deploy and manage applications. Through the RightScale Cloud Management Platform and RightScale Optima for cost management, they enable enterprises, other MSPs, and system integrators to grow their cloud business on GCP.

SADA Systems - SADA Systems works with GCP customers at various stages of their cloud adoption strategy. They assist with deploying new cloud-native applications, augmenting existing applications to run more efficiently in the cloud or the hybrid cloud — and offer security services, billing management and automation via Orbitera.

Sutherland - As a unique Support Partner for Google Cloud, Sutherland brings domain expertise, process transformation, design knowledge and combined machine learning and artificial intelligence application management to GCP customers.

Taos - Taos delivers a robust portfolio of cloud, on-premise and hybrid managed services, assessments and migrations — plus end-to-end customer support for the full GCP journey. They leverage GCP to create future-proof infrastructure, data and analytics to mitigate concerns about reliability, capacity or performance.

partner page

Getting the most out of shared postmortems — CRE life lessons

Friday, December 8, 2017

By Adrian Hilton and Gwendolyn Stockman, Customer Reliability Engineers, and Dave Rensin, Director of Customer Reliability Engineeringour previous post
Postmortems should fix your customer tooHow to get outages to benefit everyoneMTTD and MTTRquite a lotWhat went well

The shared monitoring implemented with CustomerName showed a clear single-region latency hit which resulted in a quick escalation to storage oncall.

What went wrong

A new release of the storage frontend suffered from a performance regression for uncached reads that was not detected during testing or rollout.

Where we got lucky

Only reads of objects between 4KB and 32KB in size were materially affected.

Action items

Add explicit read/write latency testing in testing for both cached and uncached objects in buckets of 1KB, 4KB, 32KB, …

Have paging alerts for latency over SLO limits, aggregated by Cloud region, for both cached and uncached objects, in buckets of 1KB, 4KB, 32KB, ...

What went well

We had anticipated a generic single-region platform failure and had the capability to fail over out of an affected region.

What went wrong

Although the latency increase was detected quickly, we didn’t have accessible thru-stack monitoring that could show us that it was coming from platform storage-service rather than our own backends.

Our decision to fail out of the affected region took nearly 30 minutes to complete because we had not practiced it for one year and our playbook instructions were out of date.

Where we got lucky

This happened during business hours so our development team was on hand to help diagnose the cause.

Action items

Add explicit dashboard monitoring for aggregate read and write latency to and from platform storage-service.

Run periodic (at least once per quarter) test failovers out of a region to validate that the failover instructions still work and increase ops team confidence with the process.

Prioritize and track your action items A postmortem isn’t complete until the root causes have been fixed Other benefits Opening up is an opportunity Joint postmortemsIf you want joint operations, you need joint postmortems
Summary

Gain a better understanding of the impact and consequences of your outages

Increase the reliability of your customers’ service

Give customers confidence in continuing to run on your platform even after an outage.

an example of an external postmortemSRE Book postmortem template

How to get real-time, actionable insights from your Fastly logs with Looker and BigQuery

Thursday, December 7, 2017

By Simon Wistow, Fastly Co-founder and VP of Product Strategy and Keenan Rice, Looker VP of Alliances Editor’s note: Fastly, whose edge cloud platform offers content delivery, streaming, security and load-balancing, recently integrated its platform with Looker, a business intelligence tool. Using Google BigQuery as its analytics engine, you can use Fastly plus Looker to do things like improve your operations, analyze the effectiveness of marketing programs — even identify attack trends.announcedGoogle Cloud PlatformDevOps

Looker dashboard, troubleshooting using Fastly log data

Marketing/Digital Advertising

Looker dashboard, analysis of user engagement with Google Ad data

Security

Looker dashboard, Fastly's WAF top offenders

Getting started with Fastly and Looker on GCPeasyset uprequest a demothe documentationLog Analytics by Fastly Blockthe directionscontact us

OAuth whitelisting can now control access to GCP services and data

Wednesday, December 6, 2017

By Christiaan Brand, Product Manager, Security and IdentityGoogle Cloud PlatformOAuth apps whitelisting

Cloud Platform - a whitelist that covers GCP services like Google Cloud Storage and BigQuery, but excludes Cloud Machine Learning and Cloud Billing

Machine Learning - a dedicated whitelist for machine learning services that includes Cloud Video Intelligence, Cloud Speech API, Cloud Natural Language API, Cloud Translation API, and Cloud Vision API

Cloud Billing - a dedicated whitelist for the Cloud Billing API

OAuth API access controls

Whitelisting trusted applications (click to enlarge)

— — get started

With Google Kubernetes Engine regional clusters, master nodes are now highly available

Tuesday, December 5, 2017

By Weston Hutchins, Product Manager, Google Kubernetes EngineGoogle Kubernetes Enginealpha launch of regional clusters

Resilience from single zone failure - Because your masters and nodes are available across a region rather than a single zone, your Kubernetes cluster is still fully functional if a zone goes down.

No downtime during master upgrades - Kubernetes Engine minimizes downtime during all Kubernetes master upgrades, but with a single master, some downtime is inevitable. By using regional clusters, the control plane remains online and available, even during upgrades.

How regional clusters work--num-nodes

$ gcloud beta container clusters create my-regional-cluster --region=us-central1 --num-nodes=2

the documentation.gke-regional-clusters-feedback@google.com
Meet the Kubernetes Engine team at #KubeCong.co/kubecon