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Welcome to the second of a two-part blog series on getting a simple TiDB cluster up and running on the Google Cloud Platform. The goal is to let you quickly set up TiDB and become familiar with its benefits. In Part 1, we created a GCP instance. Here in Part 2, we will install, configure, and run TiDB on that instance.
By the end of this article, your architecture may look similar to the following:
I am using NixOs as a Development environment that is running on Google Cloud VM.
NixOs official Web site does not provide an official GCP image. On the NixOs Wiki page, you can find an article how you can build your GCP NixOs Image that didn’t work well in the past but now seems all build image issue was resolved and now works well. NixOS Wiki page article also provides two GCP storage that you can find outdated GCP VM images that seem not actively maintained anymore.
In the below "HowTo," we will cover how you can build your images in GCP by using Google Cloud VM and store the images in your Google storage bucket.
This post is the first in a series on Google Cloud VMware Engine and Google Cloud Platform. This post walks through planning, prerequisites, and the process of deploying a Software-Defined Data Center (SDDC) using HCX in Google Cloud VMware Engine, as well as configuring a VPN gateway for initial access to the environment.
Before we dive into this, I want to set expectations for this blog series. My goal when working in the cloud is to create, modify and destroy resources programmatically. My tool of choice is Terraform, but I will also use CLI-based tools like gcloud. Occasionally I will inspect API calls directly and perform API calls with Python or cURL. I have found that learning a product's API is an excellent way to master it. Cloud consoles (GUIs) are adequate when getting started, but interfacing with the API, whether through Terraform or an SDK, is how these platforms are designed to work.
AWS and Google Cloud are two key rivals in the world of cloud computing and storage. While the first one is winning people’s hearts with its amazing flexibility and ample features, Google Cloud has managed to firm its feet in the industry with its superb backup services and cost-effectiveness. These two are performing such wonderfully at their respective fronts that it’s tough to pick one out of these two.
If you’re also at the crossroads of picking one, let’s help you decide. In this blog post of AWS VS Google Cloud 2021, we have covered few key differences between these co-workers cloud computing giants. Before we get into the details, let’s figure out the basics of these two.
The Google Cloud Pub/Sub is a fully managed real-time messaging service and it helps our applications/services to send and receive messages independently between them. It helps us to build robust and scalable applications by integrating them asynchronously. It provides scalability, resilience and handles millions of messages simultaneously.
Google Cloud Pub/Sub can be used for a lot of use cases. In general, If you want to process large amounts of data for analytics or do you want to simplify the event-driven microservices development?, then the Google Cloud Pub-Sub is the right choice.
In Q1 2021, Zoom reported that its gross margin widened to 73.9% from 69.4% in the previous quarter, primarily thanks to the optimization of public cloud resources. And Zoom is certainly not the only company that realized the value of optimizing the cloud infrastructure. As businesses migrate their workloads to the cloud and build cloud-native applications, they’re starting to realize that overprovisioning and cloud sprawl aren’t just urban legends.
For startups, the cloud is an essential technology because of its unparalleled support for scalability. But the cloud may quickly turn into a struggle because of growing costs. Here's what a16z wrote in a recent analysis:
Google has put over a decade’s worth of work into formulating the newly released Anthos. Anthos is the bold culmination and expansion of many admired container products, including Linux Containers, Kubernetes, GKE (Google Kubernetes Engine), and GKE On-Prem. It has been over a year since the general availability of Anthos was announced, and the platform marks Google’s official step into enterprise data center management.
Anthos is the first next-gen tech multi-cloud platform supporter designed by a mainstream cloud provider. The platform’s unique selling point lies in application deployment capability across multiple environments, whether on-premises data centers, Google cloud, other clouds, or even existing Kubernetes clusters.
The Jamstack architecture is rapidly gaining popularity with web developers everywhere. Jam stands for “JavaScript, APIs, and Markup.” Jamstack sites are static sites built with HTML, JavaScript, and CSS; they’re text files, so they can be served up fast without requiring the overhead of traditional compiled or interpreted architectures.
Tim Berners-Lee created the first static website in 1991— in fact, it was the first website ever built. In the decades after, developers have moved to using interpreted and compiled code for websites and applications, requiring complicated configuration and additional overhead.
In order to meet the needs of the growing demand of businesses, many global enterprises are choosing cloud infrastructure from multiple providers like AWS, Azure, Google, or private data centers. According to a Gartner survey of public cloud users, 81% of respondents are already using multiple clouds. Leveraging multiple clouds to support data infrastructure provides these benefits:
Google Cloud is used by many of the world’s top organizations, and provides an array of top-shelf solutions from developer tools to databases. Among these resources is Redis Enterprise Cloud, a robust in-memory data platform built by the same group that developed open source Redis.
Join Google Customer Engineer Matt Cowger and Redis Labs Senior Solution Architect Jake Angerman as they discuss how technology teams employ Redis Enterprise Cloud on Google Cloud to maximize the value of the cloud.
There are many facets that go into the leading API portals and ProgrammableWeb has created a series of articles that help you understand what best practices are being used by real-world API providers.
In the current highly volatile market trend the cost optimization, infinite scalability, and agile development tools are very vital for any enterprise digital roadmap. The fast-growing trend of FaaS world with AWS Lambda, Google Cloud function, and Azure Fn has already received recognition of small, medium, and large enterprises and this indicates that the move from the iPaaS to FaaS is natural and unavoidable. This move opens a new world of Serverless Integration.
The key features of serverless integration are:
With the advent of agile and continuous integration and deployment, continuous testing has become a requirement. There is no alternative to that. The emphasis on shorter release cycles to give the product an edge in the competitive market is crucial.
In the faster release cycles, maintaining the quality of delivery is a real challenge.
Let’s assume you build an accounts screen for a highly secured banking app. Now some of the users might be using Chrome to access the application, some might use Internet Explorer, some might use mobile apps, etc. Now in the mobile app also, some users may have an Android phone and some may have iOS. So while deploying the code on production, developers and testers have to make sure that the feature is compatible with all different OS, browsers, platforms.
Native cloud applications use services and infrastructure provided by AWS, Microsoft Azure, and Google Cloud among others. Essentially, this means you can code, test, maintain, and run your app all in the cloud.
This approach gives developers an opportunity to:
With the competition heating up in the public cloud service vendors, the addition of new features and a regular drop in the price will decide who the winner is. In this article, we intend to throw light on the mounting competition between AWS, Microsoft’s Azure, and GCP. AWS already has a head start of years over the others, but there is no denying the other two public cloud service providers are not far behind. It is important to compare them to understand which one suits your project the most.
Our comparison guide is a thorough take on all the three cloud service providers based on parameters like:
Notebooks are becoming the standard for prototyping and analysis for data scientists. Many cloud providers offer machine learning and deep learning services in the form of Jupyter notebooks. Other players have now begun to offer cloud-hosted Jupyter environments, with similar storage, compute and pricing structures. One of the main differences can be multi-language support and version control options that allow data scientists to share their work in one place.
Jupyter notebook environments are now becoming the first destination in the journey to productizing your data science project. The notebook environment allows us to keep track of errors and maintain clean code. One of the best features although simple is that the notebook would stop compiling your code if it spots an error. Regular IDE’s do not stop compilation even if an error is detected and, depending on the amount of code, it can be a waste of time to go back and manually detect where the error is located.
When you create a new user account in your Google Cloud MySQL database, it has the same privileges as a root user. It is, therefore, a good idea to limit the admin privileges of the new MySQL user with the REVOKE command and explicitly grant the required user privileges with the GRANT statement.
You can use MySQL Workbench or Sequel Pro to connect to your Cloud SQL database with the root user. Make sure that your database has a public IP and your computer’s IP address is added as an authorized network in the Connections tab of your Database console.
SHOW GRANTS FOR db_user
If your MySQL user has root privileges, the statement will output the following:
GRANT SELECT, INSERT, UPDATE, DELETE, CREATE, DROP, RELOAD, SHUTDOWN, PROCESS, REFERENCES, INDEX, ALTER, SHOW DATABASES, CREATE TEMPORARY TABLES, LOCK TABLES, EXECUTE, REPLICATION SLAVE, REPLICATION CLIENT, CREATE VIEW, SHOW VIEW, CREATE ROUTINE, ALTER ROUTINE, CREATE USER, EVENT, TRIGGER, CREATE TABLESPACE ON *.* TO 'db_user'@'%' WITH GRANT OPTIONAs a first step, you can revoke all privileges from the user account. You can either specify individual permissions, separated by commas, but since the root user has many privileges, we can revoke them all and grant the required one in another statement.
REVOKE ALL PRIVILEGES, GRANT OPTION FROM db_user
Next, we tell the server to reload the privileges from the grant tables in the MySQL system schema.
FLUSH PRIVILEGES
Finally, grant the required privileges to the user. In our case, the user should only be able to read, insert, view and delete rows from all tables in a specific database.
GRANT SELECT, UPDATE, INSERT, DELETE ON db_name.* TO db_user
Execute the Flush Privileges statement again to apply the changes.
FLUSH PRIVILEGES
You may also run the SHOW GRANTS statement to verify that the correct privileges have been applied to the user.
SHOW GRANTS FOR db_user