Run 1,000 Docker Redis Containers in Less Than 15 Minutes on a Cluster of 5 Cloud Servers with 2GB of Memory Each
// DCHQ.io | Linux Containers | Application Deployment
While application portability (i.e. being able to run the same application on any Linux host) is still the leading driver for the adoption of Linux Containers, another key advantages is being able to optimize server utilization so that you can use every bit of compute. Of course, for upstream environments, like PROD, you may still want to dedicate more than enough CPU & Memory for your workload – but in DEV/TEST environments, which typically represent the majority of compute resource consumption in an organization, optimizing server utilization can lead to significant cost savings.
This all sounds good on paper — but DevOps engineers and infrastructure operators still struggle with the following questions:
- How can I group servers across different clouds into clusters that map to business groups, development teams, or application projects?
- How do I monitor these clusters and get insight into the resource consumption by different groups or users?
- How do I set up networking across servers in a cluster so that containers across multiple hosts can communicate with each other?
- How do I define my own capacity-based placement policy so that I can use every bit of compute in a cluster?
- How can I automatically scale out the cluster to meet the demands of the developers for new container-based application deployments?
DCHQ, available in hosted and on-premise versions, addresses all of these challenges and provides the most advanced infrastructure provisioning, auto-scaling, clustering and placement policies for infrastructure operators or DevOps engineers.
- A user can register any Linux host running anywhere by running an auto-generated script to install the DCHQ agent, along with Docker and the software-defined networking layer (optional). This task can be automated programmatically using our REST API’s for creating “Docker Servers” (https://dchq.readme.io/docs/dockerservers)
- Alternatively, DCHQ integrates with 13 cloud providers, allowing users to automatically spin up virtual infrastructure on vSphere, OpenStack, CloudStack, Amazon Elastic Cloud Computing, Google Compute Engine, Rackspace, DigitalOcean, SoftLayer, Microsoft Azure, and many others.
Caching a MongoDB Database with Redis
Today, performance is one of the most important metrics you need to evaluate when developing a web service. Keeping customers engaged is critical to any company, especially startups, and for this reason it is extremely important to improve the performances and reduce page load times.
When running a web server that interacts with a database, its operations may become a bottleneck. MongoDB is no exception here, and as your MongoDB database scales up, things can really slow down.
This issue can even get worse if the database server is detached from the web server. In such systems, the communication with the database can cause a big overhead.
Luckily, you can use a method called caching to speed things up. In this tutorial we’ll introduce this method and see how you can use it to enhance the performance of your Node.js web service.
Everyone knows promises can help flatten the JS pyramid of death. But promises aren’t the only solution available. I’ll discuss some more advanced techniques surrounding the flow of information through your application through the use of libraries such as AsyncJS and RxJS. I’ll also talk about how embracing streams can not only alleviate control flow issues, but also improve performance. Finally, we’ll look into how tools such as ZeroMQ and Redis can help to foster asynchronous and event-driven APIs.
via Using Redis as Your Main Superfast Persistent Database in Node & Express.
Redis is a great database. I love it, I really do. It’s easy to use. It’s super powerful with its useful and easy to use structures. I can do anything with it. And above all it is FAST, extremely fast. Nothing else comes even close to it.
Most of this speed comes from the fact that Redis is an in-memory database. Every data, every structure, everything is always in the memory.
As a result, we often think that it is only good for storing temporary data. Everybody uses it for caching the information from their “real” database.
We just expect that it cannot have the persistence of MySQL, Mongo or any other database out there. But it is not true.
Let’s have a look at two very important facts that we are going to use today.
- Redis is a great database for storing your data persistently and just as safely and securely as any other.
- Many applications don’t need that kind of persistence.
For more infomration on why the first point is true, you should read the following post by Redis creator Redis persistence demystified
I have been working on understanding how IPython works, the kernels, the client etc. I have managed to figure out how the zmq client and server mechanism works and makes it so simple to add so many types of clients. Its really awesome. Not content with that I wanted to see if I would write […]