What is Sysbench?
Sysbench is designed for evaluating the performance of systems by executing CPU, memory, file I/O, and database benchmarks. It is highly customizable, and the results can be used for performance tuning, comparing different configurations, or validating hardware upgrades.
Installing Sysbench on CentOS and Ubuntu.
Step 1: Install Dependencies
Run the following command to install the required dependencies:
sudo yum install mysql-devel libtool automake autoconf
Step 2: Download Sysbench
For example, to download Sysbench from GitHub, you can use curl:
curl -LO https://github.com/akopytov/sysbench/releases/download/1.0.20/sysbench-1.0.20.tar.gz
If the .tar.gz file is downloaded, extract it:
tar -xzvf sysbench-1.0.20.tar.gz
cd sysbench-1.0.20/
Step 3: Build Sysbench from Source
./autogen.sh ; ./configure ; make -j ; make install
Step 4: Verify the Installation
sysbench --version
sysbench 1.0.20
Running the CPU Benchmark Test:
Sysbench provides a number of tests, but here, we will focus on testing the CPU performance. Sysbench’s CPU test calculates prime numbers up to a specified limit and reports the number of events processed during the benchmark run.
Here’s how you can run the benchmark on the CPU:
Running Sysbench CPU Test with 1 Thread:
sysbench --test=cpu --threads=1 --time=60 run
Sample Output:
WARNING: the --test option is deprecated. You can pass a script name or path on the command line without any options.
sysbench 1.0.20 (using bundled LuaJIT 2.1.0-beta2)
Running the test with following options:
Number of threads: 1
Initializing random number generator from current time
Prime numbers limit: 10000
Initializing worker threads...
Threads started!
CPU speed:
events per second: 2955.46
General statistics:
total time: 60.0002s
total number of events: 177333
Latency (ms):
min: 0.33
avg: 0.34
max: 0.39
95th percentile: 0.35
sum: 59978.41
Threads fairness:
events (avg/stddev): 177333.0000/0.00
execution time (avg/stddev): 59.9784/0.00
Running Sysbench CPU Test with 2 Threads:
sysbench --test=cpu --threads=2 --time=60 run
Sample Output:
CPU speed:
events per second: 5903.08
General statistics:
total time: 60.0002s
total number of events: 354195
Latency (ms):
min: 0.33
avg: 0.34
max: 1.29
95th percentile: 0.35
sum: 119915.68
Threads fairness:
events (avg/stddev): 177097.5000/89.50
execution time (avg/stddev): 59.9578/0.00
As you can see, with more threads, the events per second increase as well, which indicates better CPU utilization.
Running Sysbench CPU Test with 4 Threads:
Increasing the number of threads even more tests the system's scalability:
sysbench --test=cpu --threads=4 --time=60 run
Sample Output:
CPU speed:
events per second: 11819.27
General statistics:
total time: 60.0003s
total number of events: 709178
Latency (ms):
min: 0.33
avg: 0.34
max: 1.28
95th percentile: 0.35
sum: 239860.45
Threads fairness:
events (avg/stddev): 177294.5000/122.97
execution time (avg/stddev): 59.9651/0.00
Running Sysbench CPU Test with 8 Threads:
sysbench --test=cpu --threads=8 --time=60 run
Sample Output:
CPU speed:
events per second: 23637.61
General statistics:
total time: 60.0004s
total number of events: 1418301
Latency (ms):
min: 0.33
avg: 0.34
max: 1.28
95th percentile: 0.35
sum: 479730.48
Threads fairness:
events (avg/stddev): 177287.6250/42.17
execution time (avg/stddev): 59.9663/0.00
Running Sysbench CPU Test with 16 Threads:
sysbench --test=cpu --threads=16 --time=60 run
Sample Output:
CPU speed:
events per second: 47267.52
General statistics:
total time: 60.0004s
total number of events: 2836140
Latency (ms):
min: 0.33
avg: 0.34
max: 1.42
95th percentile: 0.35
sum: 959459.02
Threads fairness:
events (avg/stddev): 177287.6250/42.17
execution time (avg/stddev): 59.9662/0.00
Observations:
Threads and Performance: As the number of threads increases, the events per second increase proportionally. This shows how well your CPU can handle multi-threaded workloads.
Latency: The latency remains fairly constant across different thread counts, which suggests that the CPU's ability to process each event is relatively consistent.
Thread Fairness: The fairness remains stable, indicating that Sysbench is distributing tasks evenly across the threads.
Friday, 27 December 2024
Friday, 22 November 2024
Prometheus vs InfluxDB: Choosing the Best Time-Series Database for Monitoring
When it comes to monitoring the performance and health of your applications, systems, and infrastructure, time-series data plays a key role. A time-series database is essential for managing and analyzing this data effectively.
InfluxDB and Prometheus are two of the most popular open-source tools for handling time-series data. They are both widely used, but each serves a different purpose and has its own advantages. InfluxDB has a broad range of time-series data storage capabilities, including system metrics and IoT data, while Prometheus is popular for monitoring real-time metrics and cloud-native environments.
What Is Prometheus?
Prometheus is an open-source monitoring and alerting toolkit developed by SoundCloud and later contributed to the Cloud Native Computing Foundation (CNCF). It is widely adopted for monitoring the health of applications, microservices, containers, and infrastructure, particularly in Kubernetes-based environments.
Prometheus collects and stores metrics in a time-series format, where each time-series is identified by a metric name and associated labels (key-value pairs). Prometheus uses a pull-based model to scrape data from various sources like application endpoints, servers, or exporters.
Key Features of Prometheus:
Pull-based model: Prometheus scrapes metrics from configured endpoints, which allows for a decentralized and flexible architecture.
PromQL: A powerful query language designed specifically for time-series data. PromQL allows for aggregating, filtering, and visualizing metrics.
Alerting: Built-in alerting capabilities through Alertmanager, enabling users to define alert rules based on metric values.
Data retention: Prometheus stores data on disk using a custom, time-series optimized format and allows you to configure retention periods manually.
Integration with Grafana: Prometheus integrates seamlessly with Grafana to visualize metrics on customizable dashboards.
What Is InfluxDB?
InfluxDB is another popular open-source time-series database developed by InfluxData. Unlike Prometheus, which is primarily focused on monitoring and alerting, InfluxDB is a more general-purpose time-series database that can handle various types of time-series data, including metrics, events, logs, and IoT data.
InfluxDB follows a push-based model, where data is written to the database using an HTTP API or other ingestion methods like Telegraf (an open-source agent for collecting, processing, and sending metrics).
Key Features of InfluxDB:
Push-based model: Data is pushed to InfluxDB either via its API or through Telegraf agents, making it suitable for scenarios where the data is generated by external systems or devices.
InfluxQL and Flux: InfluxDB uses InfluxQL, a SQL-like query language, for querying time-series data. Flux is a more powerful, functional query language that enables complex transformations, aggregations, and analytics.
Continuous queries: InfluxDB supports continuous queries to automatically downsample and aggregate data over time, making it ideal for long-term data retention and historical analysis.
Retention policies: InfluxDB allows users to define automatic retention policies, meaning older data can be automatically dropped or downsampled as needed.
Clustering and High Availability: InfluxDB Enterprise provides support for clustering, data replication, and high availability (HA), enabling horizontal scaling for large-scale environments.
Integration with Grafana: Like Prometheus, InfluxDB integrates with Grafana for visualizing time-series data on interactive dashboards.
Prometheus vs InfluxDB: A Detailed Comparison
Saturday, 16 November 2024
HAProxy Log Rotation Not Working? Here’s How to Fix It
When running HAProxy in production, it's crucial that log files are rotated properly to prevent excessive disk usage and system slowdowns. If HAProxy logs are not rotating as expected, it could lead to your disk filling up, affecting the performance and reliability of your system.
If your HAProxy logs are not rotating, it could be due to several possible reasons.
In this post, we'll walk through the most common causes of log rotation issues, how to troubleshoot them, and provide a real-world use case with a solution.
HAProxy typically uses logrotate to handle log file rotation. If your log files are not rotating, it could be due to a missing or misconfigured logrotate configuration.
How to Check Logrotate Configuration:
Ensure there is a logrotate configuration file for HAProxy in /etc/logrotate.d/
It should look similar to the following:
/var/log/haproxy.log {
daily
missingok
rotate 7
compress
notifempty
create 0640 haproxy adm
sharedscripts
postrotate
/etc/init.d/haproxy reload > /dev/null 2>/dev/null || true
endscript
}
Explanation of Directives:
daily: Rotate the log files daily. You can also use weekly, monthly, etc., depending on your requirements.
rotate 7: Keep 7 backup log files before deleting the oldest.
compress: Compress old log files to save disk space.
create 0640 haproxy adm: This ensures that new log files are created with proper permissions (0640), and the owner is set to haproxy, with the group as adm.
postrotate: This ensures that HAProxy is reloaded after log rotation to begin writing to the new log file. If HAProxy is still writing to the old log file, logrotate will not be able to rename the rotated file.
Troubleshooting:
If the logrotate configuration is missing or incorrectly configured, you can either create or update the configuration file as shown above.
To check if logrotate is working correctly, run the following command to simulate the log rotation process:
sudo logrotate -d /etc/logrotate.conf
This command will display what logrotate would do, but will not actually rotate any logs. This is useful for troubleshooting.
2. Permissions Issues
If the HAProxy log files are not being written to or rotated due to permission issues, you need to verify that HAProxy has write access to its log file and the directory.
Check the permissions of /var/log/haproxy.log and ensure the user HAProxy runs as (usually haproxy) has the correct permissions:
ls -l /var/log/haproxy.log
Check that the logrotate user (usually root) has the necessary permissions to rotate the file.
If permissions are incorrect, adjust them with chown and chmod:
sudo chown haproxy:adm /var/log/haproxy.log
sudo chmod 0640 /var/log/haproxy.log
3. Log Output Configuration in HAProxy
HAProxy must be configured to log to a file (e.g., /var/log/haproxy.log). Ensure your HAProxy configuration includes proper logging directives:
In /etc/haproxy/haproxy.cfg, make sure you have something like the following:
global
log /dev/log local0
defaults
log global
option httplog
This tells HAProxy to log to the syslog facility local0, which is often associated with the HAProxy logs. If this is not set correctly, HAProxy may not be logging to the expected location.
4. Logfile Being Open by HAProxy Process
If the HAProxy process is holding the log file open (e.g., if HAProxy is still running with the old log file after rotation), logrotate might fail to rename the file. You can ensure that HAProxy is properly reloading by sending a SIGHUP signal to HAProxy, or by using the postrotate script in the logrotate config (mentioned above).
To manually reload HAProxy, you can:
sudo systemctl reload haproxy
or
sudo service haproxy reload
5. Logrotate Not Running
If logrotate is not running automatically (e.g., if the cron job for logrotate is not configured or working), the logs will not rotate.
Check cron jobs: Ensure that the logrotate cron job is enabled. You can check cron jobs by listing them with:
crontab -l
Alternatively, check if the logrotate service is running (on systems that use systemd):
systemctl status logrotate
To test logrotate manually, run:
sudo logrotate /etc/logrotate.conf
6. Disk Space Issues
If your disk is full, logrotate may not be able to create new log files or rotate old ones. You can check disk usage with:
df -h
If the disk is full, free up some space or increase the disk size.
Monday, 28 October 2024
Kubernetes Engine (OKE) supports the following versions of Kubernetes for new clusters
Container Engine for Kubernetes now supports Kubernetes version 1.28.10, in addition to versions 1.30.1 and 1.29.1. Please be aware that with the addition of support for version 1.28.10, Container Engine for Kubernetes will officially discontinue support for version 1.28.2 starting October 8, 2024.
Kubernetes Engine (OKE) supports the following versions of Kubernetes for new clusters:
Ref: https://docs.oracle.com/en-us/iaas/releasenotes/conteng/conteng-K8s-1-28-10-support.htm
Tuesday, 27 August 2024
Understanding and Fixing the IndentationError: expected an indented block after 'if' statement in Python
Python is renowned for its clean and readable syntax, which includes its use of indentation to define blocks of code. One common issue beginners (and sometimes even experienced developers) encounter is the IndentationError: expected an indented block after 'if' statement. This error can be frustrating, but understanding its cause and how to fix it can save a lot of time and hassle.
In this blog, we’ll explore what triggers this error and how to resolve it effectively.
What is the IndentationError: expected an indented block after 'if' statement?
The IndentationError in Python occurs when the interpreter expects an indented block of code but doesn’t find one. Specifically, when you use control structures like if, for, while, or try, Python expects an indented block following these statements to define the scope of the code that should be executed. If this indentation is missing, Python raises an IndentationError.
Example of the Error
x = 10
if x > 5:
print("x is greater than 5")
When you run this code, Python will throw an IndentationError like this:
ERROR!
Traceback (most recent call last):
File "<main.py>", line 3
print("x is greater than 5")
^^^^^
IndentationError: expected an indented block after 'if' statement on line 2
=== Code Exited With Errors ===
Why Does This Error Occur?
In Python, indentation is used to define blocks of code. For example, after an if statement, you need to indent the code that should execute if the condition is true. This indentation can be spaces or tabs, but it must be consistent. The error occurs because Python expects an indented block to follow the if statement, and if it doesn’t find it, it raises an error.
How to Fix the Error
To resolve this error, you need to provide an indented block of code following the if statement. Here’s how you can correct the example:
x = 10
if x > 5:
print("x is greater than 5")
In this corrected version, the print statement is indented with four spaces (or a tab, depending on your editor's configuration). This indentation indicates that print("x is greater than 5") is part of the if block and should be executed when x > 5 evaluates to True.
Best Practices for Indentation in Python
Consistent Indentation: Use either spaces or tabs for indentation, but do not mix them. The Python community typically prefers 4 spaces per indentation level.
Editor Configuration: Most modern code editors and IDEs can be configured to automatically insert spaces when you press the tab key, which helps avoid mixing tabs and spaces.
Check Indentation: If you encounter indentation errors, double-check that all blocks of code are properly indented and that your editor is set up correctly.
Use Linters: Tools like flake8 or pylint can help catch indentation issues and other coding standards violations before you run your code.
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