If you are using Git Submodules, is very probable that at some point you will create you own libraries. Probably those libraries will have their own structure, even with their own tests/ folder and you’re adding into a subfolder into your new project and maybe you have problems using relative imports.
This is a trick you can use to add the relevant root folder of your project to the System Path, so the libraries are found, specially when you call by command line from anywhere in the filesystem. This works for Python2 and Python3.
#!/usr/bin/env python3
import sys
import os
s_path_program = os.path.dirname(__file__)
sys.path.append(s_path_program + '../../')
from clib.src.argsutils import ArgsUtils
from clib.src.datetimeutils import DateTimeUtils
from clib.src.fileutils import FileUtils
This is a shell script I made long time ago and I use it to monitor in real time what’s the total or individual bandwidth and maximum bandwidth achieved, for READ and WRITE, of Hard drives and NMVe devices.
It uses iostat to capture the metrics, and then processes the maximum values, the combined speed of all the drives… has also an interesting feature to let out the booting device. That’s very handy for Rack Servers where you boot from an SSD card or and SD, and you want to monitor the speed of the other (SAS probably) devices.
I used it to monitor the total bandwidth achieved by our 4U60 and 4U90 Servers, the All-Flash-Arrays 2U and the NVMe 1U units in Sanmina and the real throughput of IOC (Input Output Controllers).
I used also to compare what was the real data written to ZFS and mdraid RAID systems, and to disks and the combined speed with different pool configurations, as well as the efficiency of iSCSI and NFS from clients to the Servers.
You can specify how many times the information will be printed, whether you want to keep the max speed of each device per separate, and specify a drive to exclude. Normally it will be the boot drive.
If you want to test performance metrics you should make sure that other programs are not running or using the swap, to prevent bias. You should disable the boot drive if it doesn’t form part of your tests (like in the 4U60 with an SSD boot drive in a card, and 60 hard drive bays SAS or SATA).
You may find useful tools like iotop.
You can find the code here, and in my gitlab repo:
#!/usr/bin/env bash
AUTHOR="Carles Mateo"
VERSION="1.4"
# Changelog
# 1.4
# Added support for NVMe drives
# 1.3
# Fixed Decimals in KB count that were causing errors
# 1.2
# Added new parameter to output per drive stats
# Counting is performed in KB
# Leave boot device empty if you want to add its activity to the results
# Specially thinking about booting SD card or SSD devices versus SAS drives bandwidth calculation.
# Otherwise use i.e.: s_BOOT_DEVICE="sdcv"
s_BOOT_DEVICE=""
# If this value is positive the loop will be kept n times
# If is negative ie: -1 it will loop forever
i_LOOP_TIMES=-1
# Display all drives separatedly
i_ALL_SEPARATEDLY=0
# Display in KB or MB
s_DISPLAY_UNIT="M"
# Init variables
i_READ_MAX=0
i_WRITE_MAX=0
s_READ_MAX_DATE=""
s_WRITE_MAX_DATE=""
i_IOSTAT_READ_KB=0
i_IOSTAT_WRITE_KB=0
# Internal variables
i_NUMBER_OF_DRIVES=0
s_LIST_OF_DRIVES=""
i_UNKNOWN_OPTION=0
# So if you run in screen you see colors :)
export TERM=xterm
# ANSI colors
s_COLOR_RED='\033[0;31m'
s_COLOR_BLUE='\033[0;34m'
s_COLOR_NONE='\033[0m'
for i in "$@"
do
case $i in
-b=*|--boot_device=*)
s_BOOT_DEVICE="${i#*=}"
shift # past argument=value
;;
-l=*|--loop_times=*)
i_LOOP_TIMES="${i#*=}"
shift # past argument=value
;;
-a=*|--all_separatedly=*)
i_ALL_SEPARATEDLY="${i#*=}"
shift # past argument=value
;;
*)
# unknown option
i_UNKNOWN_OPTION=1
;;
esac
done
if [[ "${i_UNKNOWN_OPTION}" -eq 1 ]]; then
echo -e "${s_COLOR_RED}Unknown option${s_COLOR_NONE}"
echo "Use: [-b|--boot_device=sda -l|--loop_times=-1 -a|--all-separatedly=1]"
exit 1
fi
if [ -z "${s_BOOT_DEVICE}" ]; then
i_NUMBER_OF_DRIVES=`iostat -d -m | grep "sd\|nvm" | wc --lines`
s_LIST_OF_DRIVES=`iostat -d -m | grep "sd\|nvm" | awk '{printf $1" ";}'`
else
echo -e "${s_COLOR_BLUE}Excluding Boot Device:${s_COLOR_NONE} ${s_BOOT_DEVICE}"
# Add an space after the name of the device to prevent something like booting with sda leaving out drives like sdaa sdab sdac...
i_NUMBER_OF_DRIVES=`iostat -d -m | grep "sd\|nvm" | grep -v "${s_BOOT_DEVICE} " | wc --lines`
s_LIST_OF_DRIVES=`iostat -d -m | grep "sd\|nvm" | grep -v "${s_BOOT_DEVICE} " | awk '{printf $1" ";}'`
fi
AR_DRIVES=(${s_LIST_OF_DRIVES})
i_COUNTER_LOOP=0
for s_DRIVE in ${AR_DRIVES};
do
AR_DRIVES_VALUES_AVG[i_COUNTER_LOOP]=0
AR_DRIVES_VALUES_READ_MAX[i_COUNTER_LOOP]=0
AR_DRIVES_VALUES_WRITE_MAX[i_COUNTER_LOOP]=0
i_COUNTER_LOOP=$((i_COUNTER_LOOP+1))
done
echo -e "${s_COLOR_BLUE}Bandwidth for drives:${s_COLOR_NONE} ${i_NUMBER_OF_DRIVES}"
echo -e "${s_COLOR_BLUE}Devices:${s_COLOR_NONE} ${s_LIST_OF_DRIVES}"
echo ""
while [ "${i_LOOP_TIMES}" -lt 0 ] || [ "${i_LOOP_TIMES}" -gt 0 ] ;
do
s_READ_PRE_COLOR=""
s_READ_POS_COLOR=""
s_WRITE_PRE_COLOR=""
s_WRITE_POS_COLOR=""
# In MB
# s_IOSTAT_OUTPUT_ALL_DRIVES=`iostat -d -m -y 1 1 | grep "sd\|nvm"`
# In KB
s_IOSTAT_OUTPUT_ALL_DRIVES=`iostat -d -y 1 1 | grep "sd\|nvm"`
if [ -z "${s_BOOT_DEVICE}" ]; then
s_IOSTAT_OUTPUT=`printf "${s_IOSTAT_OUTPUT_ALL_DRIVES}" | awk '{sum_read += $3} {sum_write += $4} END {printf sum_read"|"sum_write"\n"}'`
else
# Add an space after the name of the device to prevent something like booting with sda leaving out drives like sdaa sdab sdac...
s_IOSTAT_OUTPUT=`printf "${s_IOSTAT_OUTPUT_ALL_DRIVES}" | grep -v "${s_BOOT_DEVICE} " | awk '{sum_read += $3} {sum_write += $4} END {printf sum_read"|"sum_write"\n"}'`
fi
if [ "${i_ALL_SEPARATEDLY}" -eq 1 ]; then
i_COUNTER_LOOP=0
for s_DRIVE in ${AR_DRIVES};
do
s_IOSTAT_DRIVE=`printf "${s_IOSTAT_OUTPUT_ALL_DRIVES}" | grep $s_DRIVE | head --lines=1 | awk '{sum_read += $3} {sum_write += $4} END {printf sum_read"|"sum_write"\n"}'`
i_IOSTAT_READ_KB=`printf "%s" "${s_IOSTAT_DRIVE}" | awk -F '|' '{print $1;}'`
i_IOSTAT_WRITE_KB=`printf "%s" "${s_IOSTAT_DRIVE}" | awk -F '|' '{print $2;}'`
if [ "${i_IOSTAT_READ_KB%.*}" -gt ${AR_DRIVES_VALUES_READ_MAX[i_COUNTER_LOOP]%.*} ]; then
AR_DRIVES_VALUES_READ_MAX[i_COUNTER_LOOP]=${i_IOSTAT_READ_KB}
echo -e "New Max Speed Reading for ${s_COLOR_BLUE}$s_DRIVE${s_COLOR_NONE} at ${s_COLOR_RED}${i_IOSTAT_READ_KB} KB/s${s_COLOR_NONE}"
echo
fi
if [ "${i_IOSTAT_WRITE_KB%.*}" -gt ${AR_DRIVES_VALUES_WRITE_MAX[i_COUNTER_LOOP]%.*} ]; then
AR_DRIVES_VALUES_WRITE_MAX[i_COUNTER_LOOP]=${i_IOSTAT_WRITE_KB}
echo -e "New Max Speed Writing for ${s_COLOR_BLUE}$s_DRIVE${s_COLOR_NONE} at ${s_COLOR_RED}${i_IOSTAT_WRITE_KB} KB/s${s_COLOR_NONE}"
fi
i_COUNTER_LOOP=$((i_COUNTER_LOOP+1))
done
fi
i_IOSTAT_READ_KB=`printf "%s" "${s_IOSTAT_OUTPUT}" | awk -F '|' '{print $1;}'`
i_IOSTAT_WRITE_KB=`printf "%s" "${s_IOSTAT_OUTPUT}" | awk -F '|' '{print $2;}'`
# CAST to Integer
if [ "${i_IOSTAT_READ_KB%.*}" -gt ${i_READ_MAX%.*} ]; then
i_READ_MAX=${i_IOSTAT_READ_KB%.*}
s_READ_PRE_COLOR="${s_COLOR_RED}"
s_READ_POS_COLOR="${s_COLOR_NONE}"
s_READ_MAX_DATE=`date`
i_READ_MAX_MB=$((i_READ_MAX/1024))
fi
# CAST to Integer
if [ "${i_IOSTAT_WRITE_KB%.*}" -gt ${i_WRITE_MAX%.*} ]; then
i_WRITE_MAX=${i_IOSTAT_WRITE_KB%.*}
s_WRITE_PRE_COLOR="${s_COLOR_RED}"
s_WRITE_POS_COLOR="${s_COLOR_NONE}"
s_WRITE_MAX_DATE=`date`
i_WRITE_MAX_MB=$((i_WRITE_MAX/1024))
fi
if [ "${s_DISPLAY_UNIT}" == "M" ]; then
# Get MB
i_IOSTAT_READ_UNIT=${i_IOSTAT_READ_KB%.*}
i_IOSTAT_WRITE_UNIT=${i_IOSTAT_WRITE_KB%.*}
i_IOSTAT_READ_UNIT=$((i_IOSTAT_READ_UNIT/1024))
i_IOSTAT_WRITE_UNIT=$((i_IOSTAT_WRITE_UNIT/1024))
fi
# When a MAX is detected it will be displayed in RED
echo -e "READ ${s_READ_PRE_COLOR}${i_IOSTAT_READ_UNIT} MB/s ${s_READ_POS_COLOR} (${i_IOSTAT_READ_KB} KB/s) Max: ${i_READ_MAX_MB} MB/s (${i_READ_MAX} KB/s) (${s_READ_MAX_DATE})"
echo -e "WRITE ${s_WRITE_PRE_COLOR}${i_IOSTAT_WRITE_UNIT} MB/s ${s_WRITE_POS_COLOR} (${i_IOSTAT_WRITE_KB} KB/s) Max: ${i_WRITE_MAX_MB} MB/s (${i_WRITE_MAX} KB/s) (${s_WRITE_MAX_DATE})"
if [ "$i_LOOP_TIMES" -gt 0 ]; then
i_LOOP_TIMES=$((i_LOOP_TIMES-1))
fi
done
I wanted to automate certain operations that we do very often, and so I decided to do a PoC of how handy will it be to create GUI applications that can automate tasks.
As locating information in several repositories of information (ldap, databases, websites, etc…) can be tedious I decided to create a small program that queries LDAP for the information I’m interested, in this case a Location. This small program can very easily escalated to launch the VPN, to query a Database after querying LDAP if no results are found, etc…
I share with you the basic application as you may find interesting to create GUI applications in Python, compatible with Windows, Linux and Mac.
I’m super Linux fan but this is important, as many multinationals still use Windows or Mac even for Engineers and SRE positions.
With the article I provide a Dockerfile and a docker-compose.yml file that will launch an OpenLDAP Docker Container preloaded with very basic information and a PHPLDAPMIN Container.
This article is more an exercise, like a game, so you get to know certain things about Linux, and follow my mental process to uncover this. Is nothing mysterious for the Senior Engineers but Junior Sys Admins may enjoy this reading. :)
Ok, so the first thing is I wrote an script in order to completely backup my NVMe hard drive to a gziped file and then I will use this, as a motivation to go deep into investigations to understand.
So basically, we are going to restart the computer, boot with Linux Live USB Key, mount the Seagate Hard Drive, and run the script.
We are booting with a Live Linux Cd in order to have our partition unmounted and unmodified while we do the backup. This is in order to avoid corruption or data loss as a live Filesystem is getting modifications as we read it.
The problem with this first script is that it will generate a big gzip file.
By big I mean much more bigger than 2GB. Not all physical supports support files bigger than 2GB or 4GB, but even if they do, it’s a pain to transfer this over the Network, or in USB files, so we are going to do a slight modification.
Then one may ask himself, wait, if pipes use STDOUT and STDIN and dd is displaying into the screen, then will our gz file get corrupted?.
I like when people question things, and investigate, so let’s answer this question.
If it was a young member of my Team I would ask:
Ok, try,it. Check the output file to see if is corrupted.
So they can do zcat or zless to inspect the file, see if it has errors, and to make sure:
gzip -v -t nvme.img.gz
nvme.img.gz: OK
Ok, so what happened?, because we were seeing output in the screen.
Assuming the young Engineer does not know the answer I would had told:
Ok, so you know that if dd would print to STDOUT, then you won’t see it, cause it would be sent to the pipe, so there is something more you’re missing. Let’s check the source code of dd to see what status=progress does
And then look for “progress”.
Soon you’ll find things like everywhere:
if (progress_time)
fputc ('\r', stderr);
Ok, pay attention to where is the data written: stderr. So basically the answer is: dd status=progress does not corrupt STDOUT and prints into the screen because it uses STDERR.
Other funny ways to get the progress would be to use:
So you would see in real time what was the advance and finally 512GB where compressed to around 336GB in 336 files of 1 GB each (except the last one)
Another funny way would had been sending USR1 signal to the dd process:
Hope you enjoyed this little exercise about the importance of going deep, to the end, to understand what’s going on on the system. :)
Instead of gzip you can use bzip2 or pixz. pixz is very handy if you want to just compress a file, as it uses multiple processors in parallel for the tasks.
xz or lrzip are other compressors. lrzip aims to compress very large files, specially source code.
This is a trick to restart a Service that is running on a immutable Docker, with some change, and you need to refresh the values very quickly without having to roll the CI/CD Jenkins Pipeline and uploading a new image.
So why would you need to do that?.
I can think about possible scenarios like:
Need to roll out an urgent fix in a time critical manner
Jenkins is broken
Somebody screw it on the git master branch
Docker Hub is down
GitHub is down
Your artifactory is down
The lines between your jumpbox or workstation and the secure Server are down and you have really few bandwidth
You have to fix something critical and you only have a phone with you and SSH only
Maybe the Dockerfile had latest, and the latest image has changed
FROM os:latest
The ideal is that if you work with immutable images, you roll out a new immutable image and that’s it.
But if for whatever reason you need to update this super fast, this trick may become really handy.
Let’s go for it!.
Normally you’ll start your container with a command similar to this:
docker run -d --rm -p 5000:5000 api_carlesmateo_com:v7 prod
The first thing we have to do is to stop the container.
So:
docker ps
Locate your container across the list of running containers and stop it, and then restart without the –rm:
docker stop container_name
docker run -d -p 5000:5000 api_carlesmateo_com:v7 prod
the –rm makes the container to cleanup. By default a container’s file system persists even after the container exits. So don’t start it with –rm.
Ok, so login to the container:
docker exec -it container_name /bin/sh
Edit the config you require to change, for example config.yml
If what you have to update is a password, and is encoded in base64, encode it:
echo -n "ThePassword" | base64
VGhlUGFzc3dvcmQ=
Stop the container. You can do it by stopping the container with docker stop or from inside the container, killing the listening process, probably a Python Flask.
If your Dockerfile ends with something like:
ENTRYPOINT ["./webservice.py"]
And webservice.py has Python Flask code similar to this:
#!/usr/bin/python3
#
# webservice.py
#
# Author: Carles Mateo
# Creation Date: 2020-05-10 20:50 GMT+1
# Description: A simple Flask Web Application
# Part of the samples of https://leanpub.com/pythoncombatguide
# More source code for the book at https://gitlab.com/carles.mateo/python_combat_guide
#
from flask import Flask, request
import logging
# Initialize Flask
app = Flask(__name__)
# Sample route so http://127.0.0.1/carles
@app.route('/carles', methods=['GET'])
def carles():
logging.critical("A connection was established")
return "200"
logging.info("Initialized...")
if __name__ == "__main__":
app.run(host='0.0.0.0', port=5000, debug=True)
Then you can kill the process, and so ending the container, from inside the container by doing:
This will finish the container the same way as docker stop container_name.
Then start the container (not run)
docker start container_name
You can now test from outside or from inside the container. If from insise:
/opt/webservice # wget localhost:5000/carles
Connecting to localhost:5000 (127.0.0.1:5000)
carles 100% |**************************************************************************************************************| 3 0:00:00 ETA
/opt/webservice # cat debug.log
2020-05-06 20:46:24,349 Initialized...
2020-05-06 20:46:24,359 * Running on http://0.0.0.0:5000/ (Press CTRL+C to quit)
2020-05-06 20:46:24,360 * Restarting with stat
2020-05-06 20:46:24,764 Initialized...
2020-05-06 20:46:24,771 * Debugger is active!
2020-05-06 20:46:24,772 * Debugger PIN: 123-456-789
2020-05-07 13:18:43,890 127.0.0.1 - - [07/May/2020 13:18:43] "GET /carles HTTP/1.1" 200 -
if you don’t use YAML files or what you need is to change the code, all this can be avoided as when you update the Python code, Flash realizes that and reloads. See this line in the logs:
2020-05-07 13:18:40,431 * Detected change in '/opt/webservice/wwebservice.py', reloading
You can also start a container with shell directly:
After some work reviewing it and ensuring it has the expected quality, I finally published my book Python Combat Guide.
Is an atypical creation. Is more a Master Class to my best friend, it could be a SDM, TL leading a small Software Development department, a Coder or a Scientist wanting to join IT as programmer and to learn a lot of stuff very quickly, than rather a formal Python Book for learning. Absolutely is not for beginners.
If you want to buy it, to explore the TOC, extended description…
This small script will count repeated patterns in the Logs.
Ideal for checking if there are errors that you’re missing while developing.
#!/usr/bin/env bash
# count_repeated_pattern_in_logs.sh
# By Carles Mateo
# Helps to find repeated lines in Logs
LOGFILE_MESSAGES="/var/log/messages"
LOGFILE_SYSLOG="/var/log/syslog"
if [[ -f "${LOGFILE_MESSAGES}" ]]; then
LOGFILE=${LOGFILE_MESSAGES}
else
LOGFILE=${LOGFILE_SYSLOG}
fi
echo "Using Logfile: ${LOGFILE}"
CMD_OUTPUT=`cat ${LOGFILE} | awk '{ $1=$2=$3=$4=""; print $0 }' | sort | uniq --
count | sort --ignore-case --reverse --numeric-sort`
echo -e "$CMD_OUTPUT"
Basically it takes out the non relevant fields that can prevent from detecting repetition, like the time, and prints the rest. Then you will launch it like this:
count_repeated_pattern_in_logs.sh | head -n20
If you are checking a machine with Ubuntu UFW (Firewall) and want to skip those likes:
And this makes me think about all the Architects that are using Memcached and Redis in different Servers, in Networks of 1Gbps and makes me want to share with you what a nonsense, is often, that.
So the idea of having Memcache or Redis is just to cache the queries and unload the Database from those queries.
But 1Gbps is equivalent to 125MB (Megabytes) per second.
Local RAM Memory in Servers can perform at 24GB and more (24,000,000 Megabytes) per second, even more.
A PCIE NVMe drive at 3.5GB per second.
A local SSD drive without RAID 550 MB/s.
A SSD in the Cloud, varies a lot on the provider, number of drives, etc… but I’ve seen between 200 MB/s and 2.5GB/s aggregated in RAID.
In fact I have worked with Servers equipped with several IO Controllers, that were delivering 24GB/s of throughput writing or reading to HDD spinning drives.
If you’re in the Cloud. Instead of having 2 Load Balancers, 100 Front Web servers, with a cluster of 5 Redis with huge amount of RAM, and 1 MySQL Master and 1 Slave, all communicating at 1Gbps, probably you’ll get a better performance having the 2 LBs, and 11 Front Web with some more memory and having the Redis instance in the same machine and saving the money of that many small Front and from the 5 huge dedicated Redis.
The same applies if you’re using Docker or K8s.
Even if you just cache the queries to drive, speed will be better than sending everything through 1 Gbps.
This will matter for you if your site is really under heavy load. Most of the sites just query the MySQL Server using 1 Gbps lines, or 2 Gbps in bonding, and that’s enough.
Ok, so you can take a look at my video, and hopefully it makes spark your motivation to learn by yourself. :)
So all the people that are at home, closed down for coronavirus, you have a chance now to start learning Python and from there get a live as programmer.
You can download the code for this lesson 0, from:
I’m teaching Unit Testing, Refactors, Quality Code and moving from Procedural to OOP to some colleagues, you can find source code for our classes here (please, be aware that there are some error made on purpose to show why and why not do things and hot to apply proper unit testing)
First you have to understand that Python, Java and PHP are worlds completely different.
In Python you’ll probably use Flask, and listen to the port you want, inside Docker Container.
In PHP you’ll use a Frameworks like Laravel, or Symfony, or Catalonia Framework (my Framework) :) and a repo or many (as the idea is that the change in one microservice cannot break another it is recommended to have one git repo per Service) and split the requests with the API Gateway and Filters (so /billing/ goes to the right path in the right Server, is like rewriting URLs). You’ll rely in Software to split your microservices. Usually you’ll use Docker, but you have to add a Web Server and any other tools, as the source code is not packet with a Web Server and other Dependencies like it is in Java Spring Boot.
In Java you’ll use Spring Cloud and Spring Boot, and every Service will be auto-contained in its own JAR file, that includes Apache Tomcat and all other Dependencies and normally running inside a Docker. Tcp/Ip listening port will be set at start via command line, or through environment. You’ll have many git repositories, one per each Service.
Using many repos, one per Service, also allows to deploy only that repository and to have better security, with independent deployment tokens.
It is not unlikely that you’ll use one language for some of your Services and another for other, as well as a Database or another, as each Service is owner of their data.
In any case, you will be using CI/CD and your pipeline will be something like this:
Pull the latest code for the Service from the git repository
Compile the code (if needed)
Run the Unit and Integration Tests
Compile the service to an executable artifact (f.e. Java JAR with Tomcat server and other dependencies)
Generate a Machine image with your JAR deployed (for Java. Look at Spotify Docker Plugin to Docker build from Maven), or with Apache, PHP, other dependencies, and the code. Normally will be a Docker image. This image will be immutable. You will probably use Dockerhub.
Machine image will be started. Platform test are run.
If platform tests pass, the service is promoted to the next environment (for example Dev -> Test -> PreProd -> Prod), the exact same machine is started in the next environment and platform tests are repeated.
Before deploying to Production the new Service, I recommend running special Application Tests / Behavior-driven. By this I mean, to conduct tests that really test the functionality of everything, using a real browser and emulating the acts of a user (for example with BeHat, Cucumber or with JMeter). I recommend this specially because Microservices are end-points, independent of the implementation, but normally they are API that serve to a whole application. In an Application there are several components, often a change in the Front End can break the application. Imagine a change in Javascript Front End, that results in a call a bit different, for example, with an space before a name. Imagine that the Unit Tests for the Service do not test that, and that was not causing a problem in the old version of the Service and so it will crash when the new Service is deployed. Or another example, imagine that our Service for paying with Visa cards generates IDs for the Payment Gateway, and as a result of the new implementation the IDs generated are returned. With the mocked objects everything works, but when we deploy for real is when we are going to use the actual Bank Payment. This is also why is a good idea to have a PreProduction environment, with PreProduction versions of the actual Services we use (all banks or the GDS for flights/hotel reservation like Galileo or Amadeus have a Test, exactly like Production, Gateway)
If you work with Microsoft .NET, you’ll probably use Azure DevOps.
We IT Engineers, CTOs and Architects, serve the Business. We have to develop the most flexible approaches and enabling the business to release as fast as their need.
Take in count that Microservices is a tool, a pattern. We will use it to bring more flexibility and speed developing, resilience of the services, and speed and independence deploying. However this comes at a cost of complexity.
Microservices is more related to giving flexibility to the Business, and developing according to the Business Domains. Normally oriented to suite an API. If you have an API that is consumed by third party you will have things like independence of Services (if one is down the others will still function), gradual degradation, being able to scale the Services that have more load only, being able to deploy a new version of a Service which is independent of the rest of the Services, etc… the complexity in the technical solution comes from all this resilience, and flexibility.
If your Dev Team is up to 10 Developers or you are writing just a CRUD Web Application, a PoC, or you are an Startup with a critical Time to Market you probably you will not want to use Microservices approach. Is like killing flies with laser cannons. You can use typical Web services approach, do everything in one single Https request, have transactions, a single Database, etc…
But if your team is 100 Developer, like a big eCommerce, you’ll have multiple Teams between 5 and 10 Developers per Business Domain, and you need independence of each Service, having less interdependence. Each Service will own their own Data. That is normally around 5 to 7 tables. Each Service will serve a Business Domain. You’ll benefit from having different technologies for the different needs, however be careful to avoid having Teams with different knowledge that can have hardly rotation and difficult to continue projects when the only 2 or 3 Devs that know that technology leave. Typical benefit scenarios can be having MySql for the Billing Services, but having NoSQL Database for the image catalog, or to store logs of account activity. With Microservices, some services will be calling other Services, often asynchronously, using Queues or Streams, you’ll have Callbacks, Databases for reading, you’ll probably want to have gradual and gracefully failure of your applications, client load balancing, caches and read only databases/in-memory databases… This complexity is in order to protect one Service from the failure of others and to bring it the necessary speed under heavy load.
Here you can find a PDF Document of the typical resources I use for Microservice Projects.
