Skip to main content

How to construct a File System that lives in Shared Memory.



Shared Memory File System Goals

1. MOUNTED IN SHARED MEMORY

The result is a very fast, real time file system.
We use Shared Memory so that the file system is public and not private.

2. PERSISTS TO DISK

When the file system is unmounted, what happens to it?
We need to be able to save the file system so that a system reboot does not destroy it.
A great way to achieve this is to save the file system to disk.

3. EXTENSIBLE IN PLACE

We want to be able to grow the file system in place.

4. SUPPORTS CONCURRENCY

We want multiple users to be able to access the file system at the same time.
In fact, we want multiple users to be able to access the same file at the same time.
With the goals now in mind we can now talk about the major design issues:

FAT File System & Design Issues

The FAT File System has been around for quite some time. Basically it provides a pretty good file structure. But I have two problems with it:

1. FAT IS NOT EXTENSIBLE IN PLACE.

That is, you cannot shutdown the file system and then add space to the end.
You have to create a new file system and then copy in the old data.
What a pain.

2. FAT DOES NOT PROVIDE FILE LOCKING.

That is, you cannot control file concurrency access.

Preview of Part 2

Drupal ModulesThat is enough for today though. This blog post has presented the background for a Memory File System that is FAT based. Next time I’ll cover the rest of the Memory File System story. I will discuss the following subjects:
*) FAT Design.
Boot Block.
Disk Block Table.
Directory Blocks.
*) How to make the file system Extensible.
*) File Locking.

Comments

Post a Comment

Popular posts from this blog

Python and Parquet Performance

In Pandas, PyArrow, fastparquet, AWS Data Wrangler, PySpark and Dask. This post outlines how to use all common Python libraries to read and write Parquet format while taking advantage of  columnar storage ,  columnar compression  and  data partitioning . Used together, these three optimizations can dramatically accelerate I/O for your Python applications compared to CSV, JSON, HDF or other row-based formats. Parquet makes applications possible that are simply impossible using a text format like JSON or CSV. Introduction I have recently gotten more familiar with how to work with  Parquet  datasets across the six major tools used to read and write from Parquet in the Python ecosystem:  Pandas ,  PyArrow ,  fastparquet ,  AWS Data Wrangler ,  PySpark  and  Dask . My work of late in algorithmic trading involves switching between these tools a lot and as I said I often mix up the APIs. I use Pandas and PyArrow for in-RAM comput...
Post a Comment
Read more

Kubernetes Configuration Provider to load data from Secrets and Config Maps

Using Kubernetes Configuration Provider to load data from Secrets and Config Maps When running Apache Kafka on Kubernetes, you will sooner or later probably need to use Config Maps or Secrets. Either to store something in them, or load them into your Kafka configuration. That is true regardless of whether you use Strimzi to manage your Apache Kafka cluster or something else. Kubernetes has its own way of using Secrets and Config Maps from Pods. But they might not be always sufficient. That is why in Strimzi, we created Kubernetes Configuration Provider for Apache Kafka which we will introduce in this blog post. Usually, when you need to use data from a Config Map or Secret in your Pod, you will either mount it as volume or map it to an environment variable. Both methods are configured in the spec section or the Pod resource or in the spec.template.spec section when using higher level resources such as Deployments or StatefulSets. When mounted as a volume, the contents of the Secr...
Post a Comment
Read more

Design of Large-Scale Services on Cloud Services PART 2

Decompose the Application by Workload Applications are typically composed of multiple workloads. Different workloads can, and often do, have different requirements, different levels of criticality to the business, and different levels of financial consideration associated with them. By decomposing an application into workloads, an organization provides itself with valuable flexibility. A workload-centric approach provides better controls over costs, more flexibility in choosing technologies best suited to the workload, workload specific approaches to availability and security, flexibility and agility in adding and deploying new capabilities, etc. Scenarios When thinking about resiliency, it’s sometimes helpful to do so in the context of scenarios. The following are examples of typical scenarios: Scenario 1 – Sports Data Service  A customer provides a data service that provides sports information. The service has two primary workloads. The first provides statistics for th...
Post a Comment
Read more