Some more GCP Flowcharts

Grace
Grace
Feb 9 · 4 min read

Update: As of June 2019: I am maintaining my collection of flowcharts here

I love flowcharts as those of you who have read the previous entries in the series are aware. This is the 3rd collection of GCP related flowcharts in my series. The first of which can be found here and part II here.

I am gobsmacked by how popular the series has been to date and would like to thank you for reading them . I hope this addition to the collection proves to be as useful to you as the others in the series seem to have been!

Attribution: Graphics & flowcharts apart from Sara’s & the Cloud Storage one cheerfully copied from the Google Cloud platform or blog site

Need an identity mgt product?

How you manage your identities depends on the use case. Need to manage users who will have direct access to GCP resources versus users who need access to an application that you’re hosting on GCP? Different requirements and thus different solutions required. Here’s a flowchart to help you figure out out the right solution for your use case

The words that go with the flowchart can be found here.

Choosing a Load balancer

Load balancing is great it allows you to treat a group of compute resources as a single entity providing an entry point that has in the case of GCP load balancing services a single anycast IP address. Combining GCP Load balancers with autoscaling you can scale the resources up and down according to metrics you configure. There are loads more cool features but you get the idea. So what type of load balancing service do you need? Layer 7, layer 4, global , regional? Maybe you need an internal load balancer well there’s a flowchart for helping you decide ( Okay you knew that was coming didn’t you? 😃)

Here are the words to go with the flowchart. Once you have figured out what load balancing option is likely to address your needs have a look at the load balancing overview page as a first stop before diving in.

Choosing a Cloud Storage class for your use case

Cloud Storage (GCS) is a fantastic service which is suitable for a variety of use cases. The thing is it has different classes and each class is optimised to address different use cases. All the storage classes offer low latency (time to first byte typically tens of milliseconds) and high durability. You can use the same APiIs , lifecycle rules etc . Basically the classes differ by their availability, minimum storage durations, and charges for storage and access.

There are 4 classes that you need to care about .

Multi regional — geo redundant storage optimised for storing data that is frequently accessed (“hot” objects) for example web site serving and multi media streaming.

Regional — Data can be stored at lower cost, with the trade-off of data being stored in a specific regional location, instead of having redundancy distributed over a large geographic area. This is ideal for when you need the data to be close to the computing resources that process the data say for when using Dataproc.

Nearline — Nearline Storage is ideal for data you plan to read or modify on average once a month or less. Nearline Storage data stored in multi-regional locations is redundant across multiple regions, providing higher availability than Nearline Storage data stored in regional locations. This is great for backups . You should be carrying out regular DR fire drills at least once a month which includes recovering your data from your backups !

Coldline- a very low cost, highly durable storage service. It is the best choice for data that you plan to access at most once a year, due to its slightly lower availability, 90-day minimum storage duration, costs for data access, and higher per-operation costs. This is ideal for long term archiving use cases

Here’s a flow chart that helps you decide which storage class is appropriate for your use case when you don’t feel like reading too many words to figure out your choices ( which after all is what flowcharts are for ) .

For an overview of the GCS storage classes see here

ML or SQL ?

Always wanted to know whether you really need to use ML or whether a SQL query will suffice well Sara Robinson tweeted this flow chart

From https://twitter.com/SRobTweets/status/1053273512079699968

She then wrote some words to augment the flowchart here and then wrote some more words walking you through figuring out if ML is a good fit for your prediction task. A SQL query may be all you need. Use the right tool for the job . I love these two posts well I do get to look at the flowchart twice !

Google Cloud Platform - Community

A collection of technical articles published or curated by Google Cloud Platform Developer Advocates. The views expressed are those of the authors and don't necessarily reflect those of Google.

Grace

Written by

Grace

Chocolate addict - I have it under control really I do. I do stuff involving cloudy tech. Tweets my own so only me to blame, except for retweets.

Google Cloud Platform - Community

A collection of technical articles published or curated by Google Cloud Platform Developer Advocates. The views expressed are those of the authors and don't necessarily reflect those of Google.

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