#Your project ID PROJECT_ID= #Your Zone. E.g. us-west1-c ZONE= #New service account we will create. Can be any string that isn't an existing service account. E.g. min-priv-sa SA_NAME= #Name for your cluster we will create or modify. E.g. example-secure-cluster CLUSTER_NAME= #Name for a node-pool we will create. Can be any string that isn't an existing node-pool. E.g. example-node-pool NODE_POOL=
gcloud iam service-accounts create "${SA_NAME}" \ --display-name="${SA_NAME}" gcloud projects add-iam-policy-binding "${PROJECT_ID}" \ --member "serviceAccount:${SA_NAME}@${PROJECT_ID}.iam.gserviceaccount.com" \ --role roles/logging.logWriter gcloud projects add-iam-policy-binding "${PROJECT_ID}" \ --member "serviceAccount:${SA_NAME}@${PROJECT_ID}.iam.gserviceaccount.com" \ --role roles/monitoring.metricWriter gcloud projects add-iam-policy-binding "${PROJECT_ID}" \ --member "serviceAccount:${SA_NAME}@${PROJECT_ID}.iam.gserviceaccount.com" \ --role roles/monitoring.viewer #if your cluster already exists, you can now create a new node pool with this new service account. gcloud container node-pools create "${NODE_POOL}" \ --service-account="${SA_NAME}@${PROJECT_ID}.iam.gserviceaccount.com" \ --cluster="${CLUSTER_NAME}"
gcloud container clusters update "${CLUSTER_NAME}" \ --update-addons=KubernetesDashboard=DISABLED
gcloud container clusters update "${CLUSTER_NAME}" \ --no-enable-legacy-authorization
gcloud container clusters create "${CLUSTER_NAME}" \ --service-account="${SA_NAME}@${PROJECT_ID}.iam.gserviceaccount.com" \ --no-enable-legacy-authorization \ --disable-addons=KubernetesDashboard
--enable-network-policy
gcloud beta
gcloud beta container clusters create "${CLUSTER_NAME}" \ --project="${PROJECT_ID}" \ --zone="${ZONE}" \ --enable-network-policy
gcloud beta compute instances create-with-container nginx-vm \ --container-image gcr.io/cloud-marketplace/google/nginx1:1.12 \ --tags http-server
gcloud compute firewall-rules create allow-http \ --allow=tcp:80 --target-tags=http-server
gcloud beta compute instances update-container nginx-vm \ --container-image gcr.io/cloud-marketplace/google/nginx1:1.13
gcloud beta compute instance-templates create-with-container nginx-it \ --container-image gcr.io/cloud-marketplace/google/nginx1:1.12 \ --tags http-server
gcloud compute instance-groups managed create nginx-mig \ --template nginx-it \ --size 3
“[Google Kubernetes Engine] gives us elasticity and scalable performance for our Kubernetes clusters. It’s fully supported and managed by Google, which makes it more attractive to us than elastic container services from other cloud providers”
— Arya Asemanfar, Engineering Manager at Mixpanel
“Our engineering team manages several hundred projects within GCP, and the resource hierarchy makes it easy to handle the growing complexity of our environment. We classify projects based on criteria such as department, geography, product, and data sensitivity to ensure the right people have access to the right information. With folders, we have the flexibility we need to organize our resources and manage access control policies based on those criteria.”
— Alex Olivier, Technical Product Manager, Qubit
Where we got unlucky There were various production inconsistencies caused by past outages and experiments. These weren’t cleaned up properly and made it difficult to reason about the state of production.
“A solid cloud infrastructure is the foundation for building a smart city and helping businesses succeed in the digital economy. We are glad that Google is launching the Hong Kong cloud region, a recognition of Hong Kong's edge and strengths as a data hub. This means businesses in Hong Kong, whether big or small, can leverage the latest, well-established technology infrastructure to expand and succeed in the region and globally.”
— Nicholas W Yang, Secretary for Innovation & Technology, HKSAR Government
“Using Google Cloud Platform, our Machine Learning (ML) training times have improved by 15 percent. We were able to build a Kubernetes cluster of 150 64-core Skylake processors in 15 minutes.”
— Michael Bishop, CTO, Alpha Vertex Inc
“By using Skylake with larger core machines we've been able to process more data faster, enabling our artists to be more productive, creative and cost effective. With preemptible machines we've cut the cost even more, so much so that we're already seeing savings made in such a short timeframe. More importantly, for the past 12 weeks since we started rendering all 3D on the GCP, we have met our deadlines without any late nights or weekend work and everyone is really happy.”
— Dave Goodbourn, Head of Systems, Milk Visual Effects
“Our work at QuantConnect is constantly pushing the boundaries of cloud computing. When we learned of the addition of Skylake processors to the Google compute platform, we quickly joined as one of the early beta testers and converted infrastructure to harness it. The Skylake vCPUs improved our web-compiler speeds by 10 to 15 percent, making a measurable improvement to our user coding experience and increasing user satisfaction overall.”
— Jared Broad, Founder, QuantConnect
“Our shopping chatbot was developed using Dialogflow to offer a new type of shopping experience through a messaging interface, with responses continually being improved through machine learning. Going forward, we’re also looking to expand the functionality to include voice recognition and multiple languages.”
— Shinya Matsuyama, Director of Global Digital Commerce, Uniqlo
“Dialogflow is by far the best platform for text-based conversational chatbots. With it, we derive all the benefits of machine learning without restrictions on the frontend. Through our chatbot, we are now closing over 13,000 sales totaling a premium of nearly $2 million (USD) every month and growing at a 30% month-over-month rate.”
— Ashish Gupta, CTO & CPO, Policybazaar.com
SELECT resource_type, TO_JSON_STRING(labels) as labels, cost as cost FROM `project.dataset.table`;
SELECT TO_JSON_STRING(labels) as labels, sum(cost) as cost FROM `project.dataset.table` GROUP BY labels;
“We’re delighted that Google Cloud Platform users will experience the best of Intel® Xeon® Scalable processors using the Intel® Distribution for Python and the Intel performance libraries Intel® MKL, Intel® DAAL, Intel® TBB, Intel® IPP and Intel® MPI. These software tools are carefully tuned to deliver the workload-optimized performance benefits of the advanced processors that Google has deployed, including 96 vCPUs and workload-optimized vector capabilities provided by Intel® AVX-512.”
— Sanjiv Shah, VP and GM, Software Development tools for technical, enterprise, and cloud computing at Intel
"It was essential for us to have order sequence in our app logic, and with Cloud Spanner, it’s built in. When we started looking at GCP, we quickly identified Cloud Spanner as the solution, as it provided relational semantics and incredible scalability within a managed service. We hadn’t found a Cloud Spanner-like product in other clouds. We ran a successful POC and plan to move several massive services to Cloud Spanner. We look forward to Multi-Region configurations, as they give us the ability to expand globally and reduce latencies for customers on the other side of the world"
— Manoj Goyal, Marketo Chief Product Officer
"At our size, problems such as scalability and reliability don't have a simple answer, Cloud Spanner is a transformational technology choice for us. It will give us a regionally distributed database storage layer for our customers’ data that can scale as we continue to grow. Our whole technology team is excited to bring this into production in the coming months."
— Ben McCormack, Evernote Vice President of Operations
“We want to move from our current on-prem per-customer deployment model to the cloud to improve performance and reliability, which is extremely important to us and our customers. With Cloud Spanner, we can process ten times more transactions per second (using a current benchmark of 55k transactions per second), allowing us to better serve customers, with a dramatically reduced total cost of ownership."
— Danielle Royston, CEO, Redknee
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