Google Cloud Database – With no less than five (5) different database options to choose from, along with the importance of choosing the right single option or pairs, choosing the right database in Google Cloud Platform (GCP) is one of the regular tasks that cloud computing professionals. to undertake. One will have to consider many things, among which there is; data type, data size, latency, throughput, IOPs, scalability, etc. If you’ve been an avid reader of my articles, you might be wondering, “Why on earth is this guy suddenly an expert in cloud computing services?’ Well, for starters, I’m no expert. I started my journey to cloud computing last month, thanks to Andela Learning Community in partnership with Google and Pluralsight.
Now, why would someone who is barely a month old in cloud computing write on choosing the right databases for a project? Well, let’s just say that Pluralsight dealt me with a lot of content. And if you’re new like I am at the time of writing this, you’ll probably learn more as I would using languages that newbies – like me – understand. Also, I find it easy to learn while writing. So yes, I’m writing on choosing the right GCP databases because I want to learn.
Google Cloud Database
1. Latency, Throughput, IOPs: Latency is simply the time it takes to exchange data between networks. Throughput is how much data is successfully transferred over a given period of time – it used to be bits per second (bps), then it became Kbps, then Mbps, with 5G on the way, we’ll be looking at Gbps. Anyway, IOPs (pronounced
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) is a unit of measurement for the maximum number of reads and writes (Input, Output) per second in computer storage devices such as HDD, SSD and SAN.
As I promised, I will write in languages that newbies like me understand. Let’s get rid of the abbreviations we have there.
HDD – Hard Disk Drive, SSD – Solid State Drive, SAN – Storage Area Networks. And before I forget, IOPS – Input/Output Operations Per Second
2. Database: A database is a deliberate organization of data, so that these data can be easily accessed, managed and updated. There are different types of databases, but simplifying them to the database management offered by GCP, I like to group them as either structured or unstructured, or relational and non-relational. Ok, maybe I need to write on databases after this article.
How To Choose The Right Google Cloud Platform Database
So, away from definitions, what are the options that GCP offers related to databases? At the time of writing, GCP offers the following database management tools; Cloud SQL, Cloud Spanner, Cloud Bigtable, Cloud Firestore (Formrly Cloud Datastore), Cloud MemoryStore, Firebase Realtime and Database. However, Cloud Memorystore, Firebase Realtime and Database are beyond my learning path at Pluralsight. So, if you happen to have read to this path, hoping to find useful information on the two databases listed 28 words ago, I apologize. So, let’s begin…
Or cloud sequel) is a serverless, fully managed database service that makes it easy for users to administer, manage and maintain a relational database. It is integrated with three database languages; PostgreSQL, MySQL, and SQL, which makes it somewhat flexible for people like me who only recently became familiar with SQL. With Cloud SQL hosted on GCP, you don’t need to worry yourself about infrastructure; all you need to do is focus on your application.
With a storage capacity of 10TB, 416GB of RAM for each instance and IOPS of 40,000, Cloud SQL is your number one choice if you are looking at storing databases for E-commerce applications, Customer Relationship Management (CRM) tools , WordPress websites, and basically any other program that integrates a MySQL, PostgreSQL or SQL server.
Cloud SQL is highly scalable (vertically) with 99.95% availability guaranteed. 99.95% might be insignificant in mathematical terms but in cloud computing, that equates to 263 minutes per year — 4 hours, 38 minutes per year.
Enterprise Database Migration
If your project requires full relational database capability, with the required storage capacity not exceeding 10TB, and planned concurrent connections to these databases will not exceed 4000, and your organization is cool with on-site management, then SQL cloud is for you. For me, I think one of the most important things to consider here is maximum storage capacity and concurrent connections. This is because Cloud SQL is vertically scalable. If you believe otherwise, I am ready to learn from you.
Speaking of scalability, Cloud Spanner is a very interesting database system. According to its documentation, ‘…is the first scalable, enterprise-grade, globally distributed and highly consistent database.’ relational, and horizontal scalability which is related to non-relational database systems. The concept of horizontal and vertical scalability is quite basic. When an instance is said to be vertically scalable, it means that resources can be added to increase or decrease its capacity. Horizontal scalability on the other meant that other cases could be created if needed.
Having established this, it will therefore come as no surprise to say that Cloud Spanner has a storage capacity running into Petabytes. In terms of reliability and availability, Cloud Spanner data is stored in n-Zones, regardless of regions. This is the database that runs most of the services offered by Google. Talk about Search, Gmail, Youtube, and much more.
If your project is expected to use a large amount of data that will eventually exceed the 10TB provided by Cloud SQL with transaction consistency, and you want to break these databases into pieces in order to achieve good throughput with good global accessibility, then Cloud Spanner is for you. More likely, if you are considering a project that will store billions of data per day, this database system is for you.
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So far, we have considered two databases for structured data that are primarily based on a network of servers. Cloud Firestore is an unstructured document (NoSQL) database that runs on a serverless platform. It is a database system that makes it easy to store, synchronize and query data for either mobile applications, web applications or IoT applications, and it does this on a global scale. It was previously – and still is – Cloud Datastore but with its recent integration with Firebase’s security features, it’s called Cloud Firestore – I still think Datastore is a cool name though.
Cloud Firestore is a database specifically designed to facilitate app development processes with live sync availability and offline support. You don’t need to set up a server to access your data. It reproduces data in multi-regions with strong consistency and availability of 99.95%. Its capacity is Terabytes and it is mainly used for storing NoSQL data for mobile and web applications, collaborative multi-user applications, retail product catalogs, gaming leaderboards, social user profiles and much more.
Cloud Bigtable belongs to the NoSQL class, it is what you consider when your project contains large-scale single-key data and you want low latency as well as good data processing throughput. It is built for big data and its capacity is Petabytes. Cloud Bigtable’s architecture is somewhat complex as the processing done through the front-end server is separated from the storage. Because of how large the data could be, tables in Bigtable are divided into tablets with the aim of balancing the workload on the entire database. If I want to point out a few differences between Firestore and Bigtable, apart from the storage capacity, I will say that Firestore is good at scaling down, and Bigtable is good at scaling up. I also want to say that Bigtable is compatible with the HBase API but really, let’s not go to define what HBase is. But in general, Cloud Big Table is mainly used by Google in its analytics, search, maps and Google earth services.
I hope you learned as I did. In case you want more details, you can always visit the documentation page for GCP Datastore.
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Cloud Architect | DevOps Evangelist | CKA, CKAD | I mostly write things here so I can read them again when I get lost – eventually. A few weeks ago, I took the Google Cloud Professional Cloud Architect Certification exam. I found the exam to be a good test of your knowledge of the products and services offered on Google Cloud, and how best to use them in software architecture and design. When studying for the exam, I could never find a good study guide on how to prepare for the exam, so I decided to put this together for how to successfully prepare to pass the exam. This is more of a guide on what and how to study for the exam and not a comprehensive repo with all the study material you will need. Instead, I will link important resources you can use while studying here.
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