How to analyse FIO output

Storage performance testing plays a vital role in understanding the capabilities of your storage system. When using tools like Fio (Flexible I/O Tester) to evaluate performance, analyzing the generated output is crucial. This guide will walk you through the process of analyzing Fio output to gain insights into your storage system's performance.

Step 1: Run Fio Test: Begin by running the Fio test using your desired configuration file. For instance, if your configuration file is named random-read.fio, execute the following command:

fio random-read.fio

 

Step 2: Understand the Output: Fio provides both human-readable and machine-readable output. While the human-readable output is displayed in the terminal during testing, the machine-readable output is saved in a JSON file. This JSON output is well-suited for detailed analysis.

Step 3: Parsing JSON Output: To extract specific metrics from the JSON output, tools like jq can be helpful. For instance, to retrieve the mean IOPS of read operations, use:

fio random-read.fio --output=result.json
jq '.jobs[0].read.iops_mean' result.json

 

Step 4: Metrics to Analyze: Key metrics to analyze include:

• iops: Input/Output Operations Per Second.
• bw: Bandwidth in bytes per second.
• lat: Latency metrics, such as lat_ns for nanosecond latency.
• slat, clat, and latency percentiles: These provide insights into different latency components.

 

Step 5: Graphing and Visualization: Visualizing metrics over time using tools like Excel or Gnuplot can reveal performance trends. This helps identify potential bottlenecks or improvements in your storage system.

 

Step 6: Comparing Tests: When conducting multiple tests with varying configurations, comparing their outputs can highlight performance differences. This aids in pinpointing optimization opportunities.

 

Step 7: Experimentation and Iteration: Fio offers numerous configuration options. Experiment with different settings to understand how your storage system behaves under various workloads.

In conclusion, effectively analyzing Fio output involves running tests, extracting relevant metrics, visualizing data, and making informed decisions based on your storage system's behavior. By following these steps, you can unlock valuable insights into your storage system's performance and make informed decisions about optimizations and configurations.

Create Desktop Environment in Suse Linux on AWS

Having a Desktop environment on a Cloud Instance is helpful in many ways. You can troubleshoot application connectivity, take proper HAR files and so on. Even having a desktop is cool!

Here is how you can install GNOME on any SUSE Linux instances in any Cloud Environments. Remember, once you install GNOME (or KDE or any desktop environment as a matter of fact), you need to use VNC to connect to it.

The same steps can be used on any Cloud environments like Oracle Cloud (OCI), AWS, Azure, GCP and so on.

 

Requirements:

- SSH client that allows X11 forwarding

- TightVNC Server and Client

* Here are the steps I took to install GNOME desktop:

1. ssh into the instance with root username

2. type 'yast2' to get into YaST2 Control Center

3. Select "Software" on the left side bar, select "Online Update" on the right side bar, and then hint Enter key. This step is to update the repository of the system

4. Select "Software" on the left side bar, select "software Management" on the right side bar, and then hint Enter key.

5. In the "Search Phrase" textbox in the Filter Search session, type "gnome", and then hint Enter key

6. Install everything that listed on the right side bar, if the error page about "Package Dependencies"pops up, select the first option under "possible solutions", and then click "OK -- Try Again"

7. Select "Accept" on the bottom right of the page, hint Enter key. It will install all the packages you selected.

8. After installing the packages, click "F9" key twice to exit out YaST2 Control Center

Here are the steps to install and configure VNCServer:

1. Open TCP port 5901 in the security group that the instance belongs.

2. In the instance, type "zypper install vnc"

3. After installing VNCServer, type "vncpasswd" to set the access password

4. type "vncserver :1" to start a vnc session

5. sudo vim /root/.vnc/xstartup

6. comment out the "twm &" by typing # in front of the phrase, and then add "/usr/bin/gnome &" to the next line

7. save and exit out the xstartup file

8. type "vncserver -kill :1"

9. type "vncserver :1" to start a new session to load the modified xstartup file

10. In your local host, download and install tightvnc: http://www.tightvnc.com/download.php

11. Open "TightVNC Viewer"

12. For the Remote Host, type your DNS for the instance, and then add "::5901" at the end of the line

13. Click "Connect"

14. Type your password you set by vncpasswd

15. Now you can access to your instance via VNC connection

 

Hope this helps.

 

How to create 1000s of small files in Linux for troubleshooting

 

You can use the following one liner to create thousands of smaller random size files on local block volume.

 

 

In this example we are creating 21099 smaller files of multiple KBs.

for n in {1..21099}; do dd if=/dev/urandom of=file$( printf %03d "$n" ).txt bs=1 count=$(( RANDOM + 1024 )); done

How to do faster copy and delete operations on EFS file systems

 

Issue: How to do faster copy and delete operations on EFS file systems.


Environment:

Amazon Linux
Ubuntu Server
Amazon EFS


Solution:


To optimize copy and delete operations on EFS file systems, you can use the GNU Parallel shell tool for executing jobs in parallel. By doing this you will be able to complete these tasks faster than using the normal serial method.


1.a. Install the NFS utilities and the GNU parallel package on Amazon Linux.

[ec2-user ~]$ sudo yum install nfs-utils -y
[ec2-user ~]$ sudo yum install parallel
 

1.b. Install the NFS utilities and the GNU parallel package on Ubuntu Server.
[ubuntu ~]$ sudo apt-get install nfs-common parallel -y


1.c. Install from source:

[ec2-user ~]$ cd /tmp; wget http://ftp.gnu.org/gnu/parallel/parallel-latest.tar.bz2
[ec2-user ~]$ tar -xvf parallel-20120122.tar.bz2; cd parallel-20170822
[ec2-user ~]$ sudo yum groupinstall 'development tools' -y
[ec2-user ~]$ make; ./configure && sudo make install


2. Create a temporary directory and mount the EFS filesystem.
[ec2-user ~]$ sudo mkdir /mnt/efs; sudo mount -t nfs4 -o nfsvers=4.1,rsize=1048576,wsize=1048576,hard,timeo=600,retrans=2 fs-XXXXXXXX.efs.REGION.amazonaws.com:/ /mnt/efs


3. Create ten thousand small files local in your instance.
[ec2-user ~]$ mkdir /tmp/efs; for each in $(seq 1 10000); do SUFFIX=$(mktemp -u _XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX); sudo dd if=/dev/zero of=/tmp/efs/${SUFFIX} bs=64k count=1; done


4. Copy the files from your instance to the EFS file system using the parallel method.
[ec2-user ~]$ cd /tmp/efs; find -L . -maxdepth 1 -type f | sudo parallel rsync -avR {} /mnt/efs/

5. Delete the files from the EFS file system using the parallel method.
[ec2-user ~]$ cd /mnt/efs; find -L . -maxdepth 1 -type f | sudo parallel rm -rfv {}

 

Test:
The following output is from my tests, using an EFS file system (General Purpose) mounted on a t2.micro instance type:


1. Copy ten thousand files from my EC2 instance to my EFS file system, using the normal serial method.
[ec2-user ~]$ cd /tmp/efs; time sudo find -L . -maxdepth 1 -type f -exec rsync -avR '{}' /mnt/efs/ \;

real 20m8.947s
user 0m0.060s
sys 0m0.980s

2. Copy ten thousand files from my EC2 instance to my EFS file system, using the parallel method.
[ec2-user ~]$ cd /tmp/efs; time find -L . -maxdepth 1 -type f | sudo parallel rsync -avR {} /mnt/efs/
real 5m34.264s
user 0m8.308s
sys 0m6.904s


3. Delete ten thousand files from my EFS file system, using the normal serial method.
[ec2-user ~]$ cd /mnt/efs; time sudo find -L . -maxdepth 1 -exec rm -rfv {} \;
real 2m24.799s
user 0m0.124s
sys 0m1.240s


4. Delete ten thousand files from my EFS file system, using the parallel method.
[ec2-user ~]$ cd /mnt/efs; find -L . -maxdepth 1 -type f | sudo parallel rm -rfv {}
real 1m55.153s
user 0m7.988s
sys 0m6.972s

Recursively copy :
To add to this amazing article as most us are using this as a sample for our customers I would like to mention that the examples above will copy all files from SRC only, not recursively into /SRC. If you need recursively copy you will have two option with rsync:

Loop:
find /SRC/ -type d | while read -r c; do cd "$c"; find -L . -maxdepth 1 -type f | parallel rsync --avR {} /DST; done

As the above allows you the possibility to run parallel copy and recursively copy the data from SRC/ to DST it also introduces a performance penalty as the loop have to read to each folder recursively and fire parallel copies only of the content of that location.

List Creation:
Create List
rsync -avR --dry-run /SRC /DST > lsit.log
 

Run the command:
cat list.log | parallel --will-cite -j 100 rsync -avR {} /DST/


The above is a much simpler approach, what basically do is to create a list of all files/folders recursively on /SRC and fire 100 parallel copies reading the path of the files to copy from the list. This allows the copies to be much more efficient as having less over the head.


[2] http://www.gnu.org/software/parallel/man.html#EXAMPLE:-Parallelizing-rsync

Block Volume Performance calculation

In the realm of modern computing, where data storage and retrieval speed are paramount, understanding the performance of storage solutions is crucial. One of the fundamental components of this landscape is Linux block volume performance calculation. Whether you're a system administrator, a developer, or an enthusiast, delving into the intricacies of block volume performance, including Fio-based tests, can empower you to make informed decisions about storage setups. In this blog post, we'll demystify the concepts behind Linux block volume performance calculation and explore the key factors that influence it, along with practical Fio-based tests.
 

Understanding Block Volumes:
Block volumes are a type of storage solution commonly used in modern IT infrastructures. They provide raw storage space that can be partitioned and formatted according to the user's needs. These volumes are often found in virtual machines, cloud instances, and even physical servers. They are characterized by their ability to handle data at the block level, meaning data is read from and written to storage in fixed-size blocks.
 

Factors Influencing Block Volume Performance:
Several factors play a pivotal role in determining the performance of Linux block volumes. Understanding these factors helps optimize storage systems for better efficiency and responsiveness.

1. I/O Operations Per Second (IOPS): IOPS refers to the number of input/output operations a storage device can handle in a second. It is a key metric in assessing storage responsiveness. The higher the IOPS, the faster the storage system can read from or write to the block volume.

2. Throughput: Throughput measures the amount of data that can be transferred between the storage device and the system in a given period. It's usually measured in megabytes or gigabytes per second. Throughput is a crucial metric when dealing with large data transfers.

3. Latency: Latency is the delay between initiating a data request and receiving the first byte of data. Lower latency indicates a more responsive storage system. Excessive latency can lead to delays in data-intensive operations.

4. Queue Depth: Queue depth refers to the number of I/O requests that can be in the queue to the storage device at a given time. A higher queue depth can lead to improved performance, especially in scenarios with concurrent I/O operations.

Calculating Block Volume Performance:
While calculating precise block volume performance can be intricate, here's a simplified approach:

1. IOPS Calculation: Determine the total IOPS required by considering the application's read and write demands. Divide this total by the number of block volumes to distribute the load. It's important to consider peak I/O requirements.

2. Throughput Calculation: Calculate the required throughput by estimating the data transfer needs of the application. Divide this by the number of block volumes for load distribution.

3. Latency Estimation: Latency is affected by various factors, including the speed of the storage media and the efficiency of the underlying technology. Faster media and optimized configurations lead to lower latency.