Monitr

monitr

Mathilda by Mary Wollstonecraft Shelley is part of the HackerNoon Books Series. You can jump to any chapter in this book . CHAPTER. I here CHAP. I Florence. Nov. 9th 1819 It is only four o’clock; but it is winter and the sun has already set: there are no clouds in the clear, frosty sky to reflect its slant beams, but the air itself is tinged with a slight roseate colour which is again reflected on the snow that covers the ground. I live in a lone cottage on a solitary, wide heath: no voice of life reaches me. I see the desolate plain covered with white, save a few black patches that the noonday sun has made at the top of those sharp pointed hillocks from which the snow, sliding as it fell, lay thinner than on the plain ground: a few birds are pecking at the hard ice that covers the pools—for the frost has been of long continuance. I am in a strange state of mind. I am alone—quite alone—in the world—the blight of misfortune has passed over me and withered me; I know that I am about to die and I feel happy—joyous.—I feel my pulse; it beats fast: I place my thin hand on my cheek; it burns: there is a slight, quick spirit within me which is now emitting its last sparks. I shall never see the snows of another winter—I do believe that I shall never again feel the vivifying warmth of another summer sun; and it is in this persuasion that I begin to write my tragic history. Perhaps a history such as mine had better die with me, but a feeling that I cannot define leads me on and I am too weak both in body and mind to resist the slightest impulse. While life was strong within me I thought indeed that there was a sacred horror in my tale that rendered it unfit for utterance, and now about to die I pollute its mystic terrors. It is as the wood of the Eumenides none but the dying may enter; and Oedipus is about to die. What am I writing?—I must collect my thoughts. I do not know that any will peruse these pages except you, my friend, who will receive them at my death. I do not address them to you alone because it will give me pleasure to dwell upon our friendship in a way that would be needless if you alone read what I shall write. I shall relate my tale therefore as if I wrote for strangers. You have often asked me the cause of my solitary life; my tears; and above all of my impenetrable and unkind silence. In life I dared not; in death I unveil the mystery. Others will toss these pages lightly over: to you, Woodville, kind, affectionate friend, they will be dear—the precious memorials of a heart-broken girl who, dying, is still warmed by gratitude towards you: your tears will fall on the words that record my misfortunes; I know they will—and while I have life I thank you for your sympathy. But enough of this. I will begin my tale: it is my last task, and I hope I have strength sufficient to fulfill it. I record no crimes; my faults may easily be pardoned; for they proceeded not from evil motive but from want of judgement; and I believe few would say that they could, by a different conduct and superior wisdom, have avoided the misfortunes to which I am the victim. My fate has been governed by necessity, a hideous necessity. It required hands stronger than mine; stronger I do believe than any human force to break the thick, adamantine chain that has bound me, once breathing nothing but joy, ever possessed by a warm love & delight in goodness,—to misery only to be ended, and now about to be ended, in death. But I forget myself, my tale is yet untold. I will pause a few moments, wipe my dim eyes, and endeavour to lose the present obscure but heavy feeling of unhappiness in the more acute emotions of the past. I was born in England. My father was a man of rank: he had lost his father early, and was educated by a weak mother with all the indulgence she thought due to a nobleman of wealth. He was sent to Eton and afterwards to college; & allowed from childhood the free use of large sums of money; thus enjoying from his earliest youth the independance which a boy with these advantages, always acquires at a public school. Under the influence of these circumstances his passions found a deep soil wherein they might strike their roots and flourish either as flowers or weeds as was their nature. By being always allowed to act for himself his character became strongly and early marked and exhibited a various surface on which a quick sighted observer might see the seeds of virtues and of misfortunes. His careless extravagance, which made him squander immense sums of money to satisfy passing whims, which from their apparent energy he dignified with the name of passions, often displayed itself in unbounded generosity. Yet while he earnestly occupied himself about the wants of others his own desires were gratified to their fullest extent. He gave his money, but none of his own wishes were sacrifised to his gifts; he gave his time, which he did not value, and his affections which he was happy in any manner to have called into action. I do not say that if his own desires had been put in competition with those of others that he would have displayed undue selfishness, but this trial was never made. He was nurtured in prosperity and attended by all its advantages; every one loved him and wished to gratify him. He was ever employed in promoting the pleasures of his companions—but their pleasures were his; and if he bestowed more attention upon the feelings of others than is usual with schoolboys it was because his social temper could never enjoy itself if every brow was not as free from care as his own. While at school, emulation and his own natural abilities made him hold a conspicuous rank in the forms among his equals; at college he discarded books; he believed that he had other lessons to learn than those which they could teach him. He was now to enter into life and he was still young enough to consider study as a school-boy shackle, employed merely to keep the unruly out of mischief but as having no real connexion with life—whose wisdom of riding—gaming &c. he considered with far deeper interest—So he quickly entered into all college follies although his heart was too well moulded to be contaminated by them—it might be light but it was never cold. He was a sincere and sympathizing friend—but he had met with none who superior or equal to himself could aid him in unfolding his mind, or make him seek for fresh stores of thought by exhausting the old ones. He felt himself superior in quickness of judgement to those around him: his talents, his rank and wealth made him the chief of his party, and in that station he rested not only contented but glorying, conceiving it to be the only ambition worthy for him to aim at in the world. By a strange narrowness of ideas he viewed all the world in connexion only as it was or was not related to his little society. He considered queer and out of fashion all opinions that were exploded by his circle of intimates, and he became at the same time dogmatic and yet fearful of not coinciding with the only sentiments he could consider orthodox. To the generality of spectators he appeared careless of censure, and with high disdain to throw aside all dependance on public prejudices; but at the same time that he strode with a triumphant stride over the rest of the world, he cowered, with self disguised lowliness, to his own party, and although its chief never dared express an opinion or a feeling until he was assured that it would meet with the approbation of his companions. Yet he had one secret hidden from these dear friends; a secret he had nurtured from his earliest years, and although he loved his fellow collegiates he would not trust it to the delicacy or sympathy of any one among them. He loved. He feared that the intensity of his passion might become the subject of their ridicule; and he could not bear that they should blaspheme it by considering that trivial and transitory which he felt was the life of his life. There was a gentleman of small fortune who lived near his family mansion who had three lovely daughters. The eldest was far the most beautiful, but her beauty was only an addition to her other qualities—her understanding was clear & strong and her disposition angelically gentle. She and my father had been playmates from infancy: Diana, even in her childhood had been a favourite with his mother; this partiality encreased with the years of this beautiful and lively girl and thus during his school & college vacations they were perpetually together. Novels and all the various methods by which youth in civilized life are led to a knowledge of the existence of passions before they really feel them, had produced a strong effect on him who was so peculiarly susceptible of every impression. At eleven years of age Diana was his favourite playmate but he already talked the language of love. Although she was elder than he by nearly two years the nature of her education made her more childish at least in the knowledge and expression of feeling; she received his warm protestations with innocence, and returned them unknowing of what they meant. She had read no novels and associated only with her younger sisters, what could she know of the difference between love and friendship? And when the development of her understanding disclosed the true nature of this intercourse to her, her affections were already engaged to her friend, and all she feared was lest other attractions and fickleness might make him break his infant vows. But they became every day more ardent and tender. It was a passion that had grown with his growth; it had become entwined with every faculty and every sentiment and only to be lost with life. None knew of their love except their own two hearts; yet although in all things else, and even in this he dreaded the censure of his companions, for thus truly loving one inferior to him in fortune, nothing was ever able for a moment to shake his purpose of uniting himself to her as soon as he could muster courage sufficient to meet those difficulties he was determined to surmount. Diana was fully worthy of his deepest affection. There were few who could boast of so pure a heart, and so much real humbleness of soul joined to a firm reliance on her own integrity and a belief in that of others. She had from her birth lived a retired life. She had lost her mother when very young, but her father had devoted himself to the care of her education—He had many peculiar ideas which influenced the system he had adopted with regard to her—She was well acquainted with the heroes of Greece and Rome or with those of England who had lived some hundred years ago, while she was nearly ignorant of the passing events of the day: she had read few authors who had written during at least the last fifty years but her reading with this exception was very extensive. Thus although she appeared to be less initiated in the mysteries of life and society than he her knowledge was of a deeper kind and laid on firmer foundations; and if even her beauty and sweetness had not fascinated him her understanding would ever have held his in thrall. He looked up to her as his guide, and such was his adoration that he delighted to augment to his own mind the sense of inferiority with which she sometimes impressed him. When he was nineteen his mother died. He left college on this event and shaking off for a while his old friends he retired to the neighbourhood of his Diana and received all his consolation from her sweet voice and dearer caresses. This short seperation from his companions gave him courage to assert his independance. He had a feeling that however they might express ridicule of his intended marriage they would not dare display it when it had taken place; therefore seeking the consent of his guardian which with some difficulty he obtained, and of the father of his mistress which was more easily given, without acquainting any one else of his intention, by the time he had attained his twentieth birthday he had become the husband of Diana. He loved her with passion and her tenderness had a charm for him that would not permit him to think of aught but her. He invited some of his college friends to see him but their frivolity disgusted him. Diana had torn the veil which had before kept him in his boyhood: he was become a man and he was surprised how he could ever have joined in the cant words and ideas of his fellow collegiates or how for a moment he had feared the censure of such as these. He discarded his old friendships not from fickleness but because they were indeed unworthy of him. Diana filled up all his heart: he felt as if by his union with her he had received a new and better soul. She was his monitress as he learned what were the true ends of life. It was through her beloved lessons that he cast off his old pursuits and gradually formed himself to become one among his fellow men; a distinguished member of society, a Patriot; and an enlightened lover of truth and virtue.—He loved her for her beauty and for her amiable disposition but he seemed to love her more for what he considered her superior wisdom. They studied, they rode together; they were never seperate and seldom admitted a third to their society. Thus my father, born in affluence, and always prosperous, clombe without the difficulty and various disappointments that all human beings seem destined to encounter, to the very topmost pinacle of happiness: Around him was sunshine, and clouds whose shapes of beauty made the prospect divine concealed from him the barren reality which lay hidden below them. From this dizzy point he was dashed at once as he unawares congratulated himself on his felicity. Fifteen months after their marriage I was born, and my mother died a few days after my birth. A sister of my father was with him at this period. She was nearly fifteen years older than he, and was the offspring of a former marriage of his father. When the latter died this sister was taken by her maternal relations: they had seldom seen one another, and were quite unlike in disposition. This aunt, to whose care I was afterwards consigned, has often related to me the effect that this catastrophe had on my father’s strong and susceptible character. From the moment of my mother’s death untill his departure she never heard him utter a single word: buried in the deepest melancholy he took no notice of any one; often for hours his eyes streamed tears or a more fearful gloom overpowered him. All outward things seemed to have lost their existence relatively to him and only one circumstance could in any degree recall him from his motionless and mute despair: he would never see me. He seemed insensible to the presence of any one else, but if, as a trial to awaken his sensibility, my aunt brought me into the room he would instantly rush out with every symptom of fury and distraction. At the end of a month he suddenly quitted his house and, unatteneded by any servant, departed from that part of the country without by word or writing informing any one of his intentions. My aunt was only relieved of her anxiety concerning his fate by a letter from him dated Hamburgh. sic How often have I wept over that letter which untill I was sixteen was the only relick I had to remind me of my parents. “Pardon me,” it said, “for the uneasiness I have unavoidably given you: but while in that unhappy island, where every thing breathes spirit whom I have lost for ever, a spell held me. It is broken: I have quitted England for many years, perhaps for ever. But to convince you that selfish feeling does not entirely engross me I shall remain in this town untill you have made by letter every arrangement that you judge necessary. When I leave this place do not expect to hear from me: I must break all ties that at present exist. I shall become a wanderer, a miserable outcast—alone! alone!”—In another part of the letter he mentioned me—“As for that unhappy little being whom I could not see, and hardly dare mention, I leave her under your protection. Take care of her and cherish her: one day I may claim her at your hands; but futurity is dark, make the present happy to her.” her My father remained three months at Hamburgh; when he quitted it he changed his name, my aunt could never discover that which he adopted and only by faint hints, could conjecture that he had taken the road of Germany and Hungary to Turkey. Thus this towering spirit who had excited interest and high expectation in all who knew and could value him became at once, as it were, extinct. He existed from this moment for himself only. His friends remembered him as a brilliant vision which would never again return to them. The memory of what he had been faded away as years passed; and he who before had been as a part of themselves and of their hopes was now no longer counted among the living. About HackerNoon Book Series: We bring you the most important technical, scientific, and insightful public domain books. This book is part of the public domain. Mary Wollstonecraft Shelley (2005). Mathilda. Urbana, Illinois: Project Gutenberg. Retrieved https://www.gutenberg.org/cache/epub/15238/pg15238-images.html This eBook is for the use of anyone anywhere at no cost and with almost no restrictions whatsoever. You may copy it, give it away or re-use it under the terms of the Project Gutenberg License included with this eBook or online at , located at . www.gutenberg.org https://www.gutenberg.org/policy/license.html

I am in a strange state of mind

These days a lot of effort is being put into designing systems that are highly available, reliable, fault tolerant etc. A lot of alerting tools are also being used to notify about any sort of mishaps like servers crashing, performance degradation etc. There are dedicated DevOps teams, SRE teams and a bunch of other folks who ensure production systems are always running smoothly & without any downtime. More often than not a lot of paid tools are being used for achieving this. This is a must have setup for any service running in production environment. It is not feasible to replicate the same setup for your local dev environment where most of the feature development work is done and a single developer is responsible for maintaining and running it. Lets take a small example where an application uses Redis and DynamoDB for storing and caching data. So Redis and DynamoDb needs to be running locally for developing this application. Most common approach is to open multiple terminal windows and run each local server in a separate dedicated tab. But adding a new tab for each server becomes complex and difficult when the number of servers increase. To simplify this we are going to configure . Monit Setup Guide: This is a setup guide for MacOS. Setup Terminal Notifier brew install terminal-notifier Enable Notifications from System Preferences: Run the following command on your terminal: terminal-notifier -message "Test Notification" Output: This is how the notification looks. Note: Looks like notify-send can be used on linux, haven’t tried it myself. **Setup Redis**brew install redis Lets make some changes in the redis config file: vim /usr/local/etc/redis.conf Set default pid path to To run redis as daemon: /usr/local/var/run/redis-server.pid daemonize yes Create a wrapper script for starting and stopping redis-server file: vim /usr/local/etc/redis-init With the following content: #!/bin/bashcase $1 instart)exec redis-server /usr/local/etc/redis.conf;;stop)redis-cli shutdown ;;*)echo "usage: redis-init {start|stop}" ;;esacexit 0 Lets make it executable: chmod +x redis-init Setup DynamoDB Download the latest version of DynamoDb local. cd /usr/local/etcmkdir dynamo copy DynamoDBLocal.jar and DynamoDBLocal_lib/ in /usr/local/etc/dynamo/ Create a wrapper script for DynamoDB like we did for redis: vim /usr/local/etc/dynamo/dynamo-init With the following content: case $1 instart)cd /usr/local/etc/dynamo;nohup java -Djava.library.path=./DynamoDBLocal_lib -jar DynamoDBLocal.jar -port $2 -sharedDb 1> dynamo_$2.out & echo $! > /usr/local/var/run/dynamo_$2.pid;;stop)kill `cat /usr/local/var/run/dynamo_$2.pid`;rm /usr/local/var/run/dynamo_$2.pid;;;*)echo "usage: dynamo-init {start|stop} port";;esacexit 0 Lets make it executable: chmod +x dynamo-init **Setup httpd(Apache http server program):**sudo suapachectl startTo Verify try accessing http://localhost Setup Monit: Create monitrc file vim /usr/local/etc/monitrc With the following content: set daemon 30set log /usr/local/etc/monit.log # monit logsset httpd port 2812 anduse address localhost  # only accept connection from localhostallow localhost        # allow localhost to connect to the server and check process redis-server  with pidfile /usr/local/var/run/redis-server.pidstart program = "/usr/local/etc/redis-init start"stop program = "/usr/local/etc/redis-init stop"if does not exist then exec "/bin/bash terminal-notifier -message 'redis does not exist'" check process dynamo8001 with pidfile /usr/local/var/run/dynamo_8001.pidstart program = "/bin/bash /usr/local/etc/dynamo/dynamo-init start 8001"stop program = "/bin/bash /usr/local/etc/dynamo/dynamo-init stop 8001"if does not exist then exec "/bin/bash terminal-notifier -message 'Dynamo 8001 does not exist'" This runs dynamoDb on port 8001. Change it to some other value if you want to run it on some other port. Run monit -t to valid monitrc file. To start monit: monit Open in your browser and you will see something like this localhost:2812 Type in terminal monit summary To start the processes type monit start all Check the status using monit summary monit summary Clicking on a process will give a detailed info about that process:redis-server: Monit Commands: monit -h # usage monit summarymonit status monit start all     # start all processesmonit stop  all     # stop all processesmonit restart all   # restart all processes # For single process monit start redis-servermonit stop redis-servermonit restart redis-server monit start dynamo8001monit stop dynamo8001monit restart dynamo8001 Here is how the Alerts look: Now if any of the process crashes or gets killed monit will send us an alert.To try it lets kill the redis-server process. kill -9 `cat /usr/local/var/run/redis-server.pid` And There it is: To start redis-server again run: monit start redis-server Note: Monitrc file can be modified to restart the process automatically, if it does not exist. Conclusion If a system is being booted/rebooted can be used to start all the services.To restart all services is enough. There is no need to individually restart all the services. monit start all monit restart all Monit can be used to monitor process on development environment. This can be useful when there are a lot of servers or micro-services running.If a standard installation way is devised, can be used in a team. More where this came from This story is published in , where thousands come every day to learn about the people & ideas shaping the products we love. Noteworthy Follow our publication to see more stories featured by the team. Journal

Monitoring Development Environment with Monit

Monit monitors Geth, autorestarts it on crashes and sends email alerts. Good, good Monit! I’m running geth and back-end on a low-cost Digital Ocean (DO) Ubuntu droplet. I use to autorestart geth and send email alerts when something goes wrong with the processes. TheMillionEtherHomepage.com monit Here I’ll cover a working solution and a beautiful one which isn’t working (hope somebody will eventually fix it). Install and setup monit $ sudo apt-get update $ sudo apt-get install monit Backup the config file and start editing it with nano: $ sudo cp /etc/monit/monitrc /etc/monit/monitrc-orig # backup $ sudo nano /etc/monit/monitrc Allow access to monit status. Uncomment these lines: set daemon 120 # number of seconds between checkups set httpd port 2812 and     use address localhost # only accept connection from localhost    allow localhost # allow localhost to connect to the server Let monit include *.cfg files from its conf.d directory. Uncomment (or add at the very end): include /etc/monit/conf.d/*.cfg Set up mail server to send alerts. Add these lines if you are using gmail: # GMAIL set mailserver smtp.gmail.com port 587     username "username@gmail.com" password "12345678"     using tlsv1     with timeout 30 seconds 

set alert username@gmail.com # monit will send alerts to this address Exit nano editor: Ctrl+X to save, type Y, press enter, and enter again when it asks for a filename (leave the same filename). Set up permossions as monitrc now has very sensitive data. $ sudo chmod 600 /etc/monit/monitrc For better understanding of monit: Video — Monitoring Linux Systems and Services with Monit Digital Ocean: How To Install and Configure Monit Setup geth monitoring — the working solution Create and start editing geth.cfg file with nano: $ sudo nano /etc/monit/monitrc/geth.cfg Paste this code: # GETH CHECK PROCESS geth MATCHING "[g]eth.*fast"     start program = "/bin/bash -c 'geth --fast --rpc --rpcport 8545 --rpccorsdomain localhost --cache=16 >/dev/null 2> /home/my-logs-folder/geth.log'" as uid username as gid username     stop program = "/bin/bash -c 'kill -HUP $(ps aux | grep '[g]eth' | awk '{print $2}')'" as uid username as gid username     if not exist then alert     if not exist then restart Ctrl+X to save, type Y, press enter, and hit enter again when it asks for a filename. We use >/dev/null here because geth outputs everything to the stderr (standard error output). And we save all stderr output (i.e. all logs) to the /home/my-logs-folder/geth.log file. We use -HUP signal because -SIGINT (which you typically send with Ctrl+C) will not reach geth here. Restart monit: $ sudo monit reload This config will perfectly send an email to you when geth crashes and will perfectly auto-restart geth But. The disadvantage here is that it cannot send “graceful” -SIGINT signal to geth through monit. And have to send -HUP instead, which is presumably not safe ( ). I tried it and it works, but I prefer to send -SIGINT manually instead: Is it safe to kill geth with SIGTERM? $ sudo monit unmonitor geth # so that monit won't restart geth again $ PID_OF_GETH=`pidof geth` # get geth's process ID $ kill -2 $PID_OF_GETH # KILL IT NOW!... but do it gracefully. This is clumsy. I’ve tried to make it more graceful but failed. Posted this question at stackexchange — How to monitor and auto-restart geth with monit? Setup geth monitoring — the right way I know that the config should look very similar to this. geth.cfg # GETH CHECK PROCESS geth with pidfile "/home/username/monit/geth.pid"     start program = "/bin/bash -c '/home/username/monit/geth.sh start'" as uid username as gid username     stop program = "/bin/bash -c '/home/username/monit/geth.sh stop'" as uid username as gid username     if not exist then alert     if not exist then restart geth.sh #!/bin/bash PID='/home/username/monit/geth.pid' LOGS='/home/username/monit/geth.log' KEYS='\--fast --rpc --rpcport 8545 --rpccorsdomain localhost --ipcpath /home/username/.ethereum/geth.ipc --cache=16' case $1 in     start)         setsid geth "$KEYS" &> "$LOGS" &         echo $! > "$PID"     ;;     stop)         kill -SIGINT `cat "$PID"`         rm "$PID"     ;;     *)     echo "usage: geth.sh {start|stop}"     ;; esac exit 0 Beautiful isn’t it? If you know how to make it work please submit your answer here — How to monitor and auto-restart geth with monit? Get monit status After you restart monit, you can start geth with the following command: $ sudo monit start geth This will run geth, autorestart it on crashes and notify you via email if something goes wrong. You can also check the monit status by the following command: $ sudo monit status And have a screen like this (mlneth here is python back-end of my ): project Monitoring geth Happy monitoting and no alerts to you all!

How to monitor geth and autorestart it on crashes with monit

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Optimizing Nginx for High-Traffic Systems: A Guide to Configuration & Monitoring

How to Monitor Blockchain Transactions in Realtime With dRPC and Web3.js

Logging is essential for successful DevSecOps teams. Logs are filled with the information needed to monitor and understand systems. Tracking down a defect? Trying to understand a sudden burst in questionable logins from a new region? Need to figure out why an app is crawling? Logs are that single source of truth for understanding what’s really happening. But there’s a problem that comes along with logs: the sheer amount of data. The information logged by services and applications just keeps on growing. And growing. It doesn’t take long for it to become more — much more — than can be managed. The data becomes overwhelming. Alert fatigue sets in. Data keeps growing. Human resources can’t. But there’s hope on the horizon. Innovations in AI have revolutionized the process of continuous log monitoring. AI algorithms can analyze and detect patterns within vast datasets, translate raw logs into actionable insights, and proactively alert teams to problems — all at a scale and at higher precision to assist humans. Let’s look at 8 ways that AI can maximize the value of logs. #1: Handling massive amounts of data First, the most obvious. Cloud-native environments, with their dozens (or hundreds) of distributed components, emit a massive volume of log data. In turn, this data requires high levels of expertise to sift through and analyze it all. But most organizations already face a shortage of people with the skills needed to tease out the insights from this data. Companies could train more people — but training is slow. And the growing complexity of the data tends to outpace any new skills. Here is where AI shines: It’s a scalable way to handle log data, no matter the volume and no matter the time. Humans need breaks; they clock out for the day, get sick, take PTO, and play Galaga when bosses aren’t looking. But log data still arrives in massive volumes, regardless of the time of the day or day of the week. An AI-based system with analysis, detection, and alerting is always on. #2: Automated security and access control When dealing with logs, it’s important to protect any sensitive user information and ensure that the data is only accessible to authorized team members. SecOps managers often implement fine-grained access control to ensure that the approved team members have access to (and only to) the data or metrics intended for use. AI systems can automatically identify and redact sensitive information — such as personal identifiers, financial details, or confidential business information — from log data before it’s accessed by humans. Or, as part of automated preprocessing, AI can de-identify or mask sensitive parts of data. #3: Collating data from disparate sources Merging data from various sources is a complex task. But it’s essential for effective security and operations. When logs are properly aggregated and correlated, the resulting data and metrics can give the context needed for better visibility and better troubleshooting. But this is a menial and time-consuming task … which makes it perfect for AI. AI can automatically gather information from various sources and identify patterns within the data, making its analysis easier. AI correlates data from different sources far more efficiently than a human. Modern log analytics tools leverage AI to gather log data from cloud services and on-premises environments. Log analysis and issue resolution become proactive, preventing negative impacts on the health of applications and systems. #4: Transforming raw log data Organizations depend on skilled professionals to handle and analyze log data, yet they often face overwhelming resource constraints. This is where AI can contribute significantly, by automating repetitive tasks and enhancing human capabilities. Before analysis, log data often required cleaning and preprocessing to remove errors, duplicates, or irrelevant information. AI can automate this process, ensuring the accuracy and standardization of all the data. AI can also organize log data into clusters based on similarities or classify them into predefined categories. This helps manage data more efficiently, making it easier for humans to understand and act upon the insights derived. #5: Analyzing log data A clear use case for AI within a DevSecOps strategy is the automation of repetitive and time-consuming tasks — such as data cleaning, feature selection, and model training. With AI taking on these tasks, developers can focus on other tasks. AI can sift through a mountain of data to spot duplication and anomalies — like subtle signs of a cyberattack or unusual traffic patterns — that might easily slip past human scrutiny. This yields enhanced security and operational insights. It’s more than just about handling the volume; AI is adept at detecting patterns that are too complex or too faint for the human eye to catch. For example, let’s consider logging and monitoring for a network to catch signs of data exfiltration. This kind of anomaly might manifest as an unusually high volume of data being sent to an unfamiliar external IP address during off-hours; that’s a pattern that might not immediately raise flags for a human amid thousands of legitimate data transfers happening every day. On the other hand, an AI-based system that’s trained on vast datasets of normal and malicious network behavior can identify this subtle pattern by correlating different indicators: The timing of the data transfer
The volume of data
The destination IP address
The type of data being transferred For a human, recognizing such a complex pattern requires painstaking analysis and might be missed entirely due to the sheer volume of log data. But an AI system can continuously monitor for these patterns across the entire network, detecting potential threats with precision and speed that far surpasses human capability. #6: Reducing alert fatigue Traditional infrastructure and service monitoring solutions are notoriously noisy, often generating alerts for events that don’t signify a genuine threat. Excessive unactionable alerts lead to alert fatigue. AI-based alerting intelligently filters alerts and reduces the noise, ensuring that the alerts generated are relevant and actionable. For example, traditional monitoring can’t adjust for seasonality — so a crossed threshold in the middle of a high-season weekday afternoon gets as much attention as one in the middle of the night during what should be a slow week. AI-based alerting uses historical data to continually train its models, factoring seasonality into its baselines. The result is fewer false positives and no more alert fatigue. Of course, organizations using AI-driven alerting need to rigorously test results in order to tune specificity and sensitivity. This ensures that critical events are captured effectively. #7: Proactive monitoring Given the large amounts of data an organization generates, teams often struggle to monitor all the organization’s resources proactively. AI is well-equipped to address this issue at scale. By continuously monitoring and aggregating logs from across entire environments, an AI-based tool can identify anomalies before they become widespread, allowing teams to detect potential threats in their initial stages. For example, the threat detection and investigation from Sumo Logic provides the visibility to address advanced threats before they affect operations. AI features such as this enable real-time monitoring, alerting, and data analysis across security tools, cloud infrastructures, and SaaS applications. This enables a DevSecOps team to investigate and respond to cyber threats swiftly. #8: Efficient incident response AI-driven alerting improves incident response by facilitating automatic resource allocation and gathering contextual information about an incident. This helps identify potential security threats faster, which in turn helps organizations respond more quickly. When AI-powered logging and observability platforms provide automated remediation features, teams can connect the dots: from continuously monitored logs to incident detection to remediation playbooks. Automated playbook execution means near-immediate response to an incident, whether that’s eliminating the root cause or alerting an engineer to begin an investigation. Remember, with every delay in responding to a security incident, the window for impact widens. Minimizing that delay with AI directly minimizes the impact of an incident. Privacy concerns related to AI adoption One last note: as we’ve seen, AI shows great promise as a tool for improving logging. But remember that it’s still an emerging technology. As a recent GitLab report makes clear, there are serious privacy concerns around AI adoption. While 83% of the teams surveyed said implementing AI in their development process was essential, 79% said they were highly concerned about privacy and IP when dealing with AI. But in many business contexts, AI tools need access to that private data for analysis. So take advantage of AI, but be aware and guard against any privacy concerns. Drowning in log data? AI is here to help. Logs are crucial. They’re a rich source of data for monitoring applications and infrastructure. But the multiple data sources and volume of log data — along with the sensitivity of some of that data in the logs — lead to some big challenges in managing log data, security, and privacy. AI is here to help. Modern log management and SIEM solutions are leveraging AI to automate analysis, enhance monitoring, and improve incident response. AI is making DevSecOps more efficient. And as AI solutions evolve, their role in log analysis will only grow, offering smarter, faster insights into logs. Logging is essential for successful DevSecOps teams. Logs are filled with the information needed to monitor and understand systems. Tracking down a defect? Trying to understand a sudden burst in questionable logins from a new region? Need to figure out why an app is crawling? Logs are that single source of truth for understanding what’s really happening. filled But there’s a problem that comes along with logs: the sheer amount of data. The information logged by services and applications just keeps on growing. And growing. It doesn’t take long for it to become more — much more — than can be managed. The data becomes overwhelming. Alert fatigue sets in. Data keeps growing. Human resources can’t. But there’s hope on the horizon. Innovations in AI have revolutionized the process of continuous log monitoring . AI algorithms can analyze and detect patterns within vast datasets, translate raw logs into actionable insights, and proactively alert teams to problems — all at a scale and at higher precision to assist humans. continuous log monitoring Let’s look at 8 ways that AI can maximize the value of logs. #1: Handling massive amounts of data First, the most obvious. Cloud-native environments, with their dozens (or hundreds) of distributed components, emit a massive volume of log data. In turn, this data requires high levels of expertise to sift through and analyze it all. massive But most organizations already face a shortage of people with the skills needed to tease out the insights from this data. Companies could train more people — but training is slow. And the growing complexity of the data tends to outpace any new skills. could Here is where AI shines: It’s a scalable way to handle log data, no matter the volume and no matter the time. Humans need breaks; they clock out for the day, get sick, take PTO, and play Galaga when bosses aren’t looking . But log data still arrives in massive volumes, regardless of the time of the day or day of the week. An AI-based system with analysis, detection, and alerting is always on. play Galaga when bosses aren’t looking #2: Automated security and access control When dealing with logs, it’s important to protect any sensitive user information and ensure that the data is only accessible to authorized team members. SecOps managers often implement fine-grained access control to ensure that the approved team members have access to (and only to) the data or metrics intended for use. AI systems can automatically identify and redact sensitive information — such as personal identifiers, financial details, or confidential business information — from log data before it’s accessed by humans. Or, as part of automated preprocessing, AI can de-identify or mask sensitive parts of data. #3: Collating data from disparate sources Merging data from various sources is a complex task. But it’s essential for effective security and operations. When logs are properly aggregated and correlated, the resulting data and metrics can give the context needed for better visibility and better troubleshooting. But this is a menial and time-consuming task … which makes it perfect for AI. AI can automatically gather information from various sources and identify patterns within the data, making its analysis easier. AI correlates data from different sources far more efficiently than a human. Modern log analytics tools leverage AI to gather log data from cloud services and on-premises environments. Log analysis and issue resolution become proactive, preventing negative impacts on the health of applications and systems. #4: Transforming raw log data Organizations depend on skilled professionals to handle and analyze log data, yet they often face overwhelming resource constraints. This is where AI can contribute significantly, by automating repetitive tasks and enhancing human capabilities. Before analysis, log data often required cleaning and preprocessing to remove errors, duplicates, or irrelevant information. AI can automate this process, ensuring the accuracy and standardization of all the data. AI can also organize log data into clusters based on similarities or classify them into predefined categories. This helps manage data more efficiently, making it easier for humans to understand and act upon the insights derived. #5: Analyzing log data A clear use case for AI within a DevSecOps strategy is the automation of repetitive and time-consuming tasks — such as data cleaning, feature selection, and model training. With AI taking on these tasks, developers can focus on other tasks. AI can sift through a mountain of data to spot duplication and anomalies — like subtle signs of a cyberattack or unusual traffic patterns — that might easily slip past human scrutiny. This yields enhanced security and operational insights. duplication It’s more than just about handling the volume; AI is adept at detecting patterns that are too complex or too faint for the human eye to catch. For example, let’s consider logging and monitoring for a network to catch signs of data exfiltration. This kind of anomaly might manifest as an unusually high volume of data being sent to an unfamiliar external IP address during off-hours; that’s a pattern that might not immediately raise flags for a human amid thousands of legitimate data transfers happening every day. On the other hand, an AI-based system that’s trained on vast datasets of normal and malicious network behavior can identify this subtle pattern by correlating different indicators: The timing of the data transfer The volume of data The destination IP address The type of data being transferred The timing of the data transfer The volume of data The destination IP address The type of data being transferred For a human, recognizing such a complex pattern requires painstaking analysis and might be missed entirely due to the sheer volume of log data. But an AI system can continuously monitor for these patterns across the entire network, detecting potential threats with precision and speed that far surpasses human capability. #6: Reducing alert fatigue Traditional infrastructure and service monitoring solutions are notoriously noisy, often generating alerts for events that don’t signify a genuine threat. Excessive unactionable alerts lead to alert fatigue. AI-based alerting intelligently filters alerts and reduces the noise, ensuring that the alerts generated are relevant and actionable. For example, traditional monitoring can’t adjust for seasonality — so a crossed threshold in the middle of a high-season weekday afternoon gets as much attention as one in the middle of the night during what should be a slow week. AI-based alerting uses historical data to continually train its models, factoring seasonality into its baselines. The result is fewer false positives and no more alert fatigue. Of course, organizations using AI-driven alerting need to rigorously test results in order to tune specificity and sensitivity. This ensures that critical events are captured effectively. #7: Proactive monitoring Given the large amounts of data an organization generates, teams often struggle to monitor all the organization’s resources proactively. AI is well-equipped to address this issue at scale. By continuously monitoring and aggregating logs from across entire environments, an AI-based tool can identify anomalies before they become widespread, allowing teams to detect potential threats in their initial stages. For example, the threat detection and investigation from Sumo Logic provides the visibility to address advanced threats before they affect operations. AI features such as this enable real-time monitoring, alerting, and data analysis across security tools, cloud infrastructures, and SaaS applications. This enables a DevSecOps team to investigate and respond to cyber threats swiftly. threat detection and investigation #8: Efficient incident response AI-driven alerting improves incident response by facilitating automatic resource allocation and gathering contextual information about an incident. This helps identify potential security threats faster, which in turn helps organizations respond more quickly. When AI-powered logging and observability platforms provide automated remediation features, teams can connect the dots: from continuously monitored logs to incident detection to remediation playbooks. Automated playbook execution means near-immediate response to an incident, whether that’s eliminating the root cause or alerting an engineer to begin an investigation. Remember, with every delay in responding to a security incident, the window for impact widens. Minimizing that delay with AI directly minimizes the impact of an incident. Privacy concerns related to AI adoption One last note: as we’ve seen, AI shows great promise as a tool for improving logging. But remember that it’s still an emerging technology. As a recent GitLab report makes clear, there are serious privacy concerns around AI adoption. While 83% of the teams surveyed said implementing AI in their development process was essential, 79% said they were highly concerned about privacy and IP when dealing with AI. But in many business contexts, AI tools need access to that private data for analysis. GitLab report privacy and IP So take advantage of AI, but be aware and guard against any privacy concerns. Drowning in log data? AI is here to help. Logs are crucial. They’re a rich source of data for monitoring applications and infrastructure. But the multiple data sources and volume of log data — along with the sensitivity of some of that data in the logs — lead to some big challenges in managing log data, security, and privacy. AI is here to help. Modern log management and SIEM solutions are leveraging AI to automate analysis, enhance monitoring, and improve incident response. AI is making DevSecOps more efficient. And as AI solutions evolve, their role in log analysis will only grow, offering smarter, faster insights into logs.

Maximizing Log Value with AI: 8 Ways to Revolutionize DevSecOps Monitoring

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In our last two articles, we explored how to configure CI/CD for Heroku using Heroku pipelines. When viewing a pipeline within the Heroku dashboard, you can easily start a deployment or promote your code from one environment to the next with the click of a button. From the dashboard, you can monitor the deployment and view its progress. This all works really well, assuming that you have Heroku open in your browser. But, what if you wanted to do it all from Slack? Software engineers use a lot of apps at work. Throughout the day, we are constantly bouncing between Zoom meetings, Jira tasks, Slack conversations, GitHub, email, our calendar, and our IDE. This context switching can be exhausting and also lead to a lot of visual clutter on our monitors. Sometimes, it’s nice to just live in Slack, and that’s why many tools offer Slack integrations. With these Slack integrations, you can monitor various processes and even use shortcut commands to trigger actions. Heroku ChatOps, the Heroku Slack integration, allows you to start and monitor deployments directly from Slack. In this article, we’ll explore some of the Slack commands it offers. Getting Started If you’d like to follow along throughout this tutorial, you’ll need a Heroku account and a GitHub account. You can create a Heroku account here, and you can create a GitHub account here. The demo app that we will use with our Heroku pipeline in this article is deployed to Heroku, and the code is hosted on GitHub. Create Our Heroku Pipeline We won’t go through the step-by-step process of creating a Heroku pipeline in this article. Refer to these articles for a walkthrough of creating a Heroku pipeline: How to create a Heroku pipeline with a staging and production app and a single main branch
How to create a Heroku pipeline with a staging and production app with a dev branch and a main branch You can also read the Heroku docs for Heroku pipelines. Configuring your Heroku pipeline includes the following steps: Create a GitHub repo.
Create a Heroku pipeline.
Connect the GitHub repo to the Heroku pipeline.
Add a staging app to the pipeline.
Add a production app to the pipeline. The other activities that you’ll see in those articles, such as configuring review apps, Heroku CI, or automated deployments are optional. In fact, for the purposes of this demo, I recommend not configuring automated deployments, since we’ll be using some Slack commands to start the deployments. When you’re done, you should have a Heroku pipeline that looks something like this: Connect to Slack Now that you have your Heroku pipeline created, it’s time for the fun part: integrating with Slack. You can install the Heroku ChatOps Slack app here. Clicking that link will prompt you to grant the Heroku ChatOps app permission to access your Slack workspace: After that, you can add the Heroku ChatOps app to any Slack channel in your workspace. After adding the app, type /h login and hit Enter. This will prompt you to connect your Heroku and GitHub accounts. You’ll see several Heroku OAuth and GitHub OAuth screens where you confirm connecting these accounts. (As a personal anecdote, I found that it took me several tries to connect my Heroku account and my GitHub account. It may be due to having several Slack workspaces to choose from, but I’m not sure.) After connecting your Heroku account and your GitHub account, you’re ready to start using Heroku in Slack. View All Pipelines To view all deployable pipelines, you can type /h pipelines: View Pipeline Info To see information about any given pipeline, type /h info <PIPELINE_NAME>. (Anything you see in angle brackets throughout this article should be replaced by an actual value. In this case, the value would be the name of a pipeline — for example, “heroku-flow-demo-pipeline”.) View Past Releases To view a history of past releases for any given pipeline, type /h releases <PIPELINE_NAME>. This command defaults to showing you past releases for the production app, so if you want to see the past releases for the staging app, you can type /h releases <PIPELINE_NAME> in <STAGE_NAME>, where <STAGE_NAME> is “staging.” Deploy to Staging Now that we know which pipelines are available, we can see information about any given pipeline along with when the code was last released for that pipeline. We’re ready to trigger a deployment. Most engineering organizations have a Slack channel (or channels) where they monitor deployments. Imagine being able to start a deployment right from that channel and monitor it as it goes out! That’s exactly what we’ll do next. To start a deployment to your staging environment, type /h deploy <PIPELINE_NAME> to <STAGE_NAME>, where <STAGE_NAME> is “staging.” After running that command, an initial message is posted to communicate that the app is being deployed. Shortly after, you’ll also see several more messages; this time, in a Slack thread on the original message: If you want to verify what you’re seeing in Slack, you can always check the Heroku pipeline in your Heroku dashboard. You’ll see the same information: The staging app has been deployed! Promote to Production Now, let’s promote our app to production. Without the Slack commands, we could navigate to our Heroku pipeline, click the “Promote to production” button, and then confirm that action in the modal dialog that appears. However, we’d prefer to stay in Slack. To promote the app to production from Slack, type /h promote <PIPELINE_NAME>. Just like with the staging deployment, an initial Slack message will be sent, followed by several other messages as the production deployment goes out: And — voilà — the latest changes to the app are now in production! Conclusion Now, you can start and monitor Heroku app deployments all from Slack — no need to context switch or move between multiple apps. For more use cases and advanced setups, you can also check out the docs. Happy deploying! In our last two articles, we explored how to configure CI/CD for Heroku using Heroku pipelines . When viewing a pipeline within the Heroku dashboard, you can easily start a deployment or promote your code from one environment to the next with the click of a button. From the dashboard, you can monitor the deployment and view its progress. configure CI/CD for Heroku using Heroku pipelines start a deployment or promote your code from one environment to the next This all works really well, assuming that you have Heroku open in your browser. But, what if you wanted to do it all from Slack? Software engineers use a lot of apps at work. Throughout the day, we are constantly bouncing between Zoom meetings, Jira tasks, Slack conversations, GitHub, email, our calendar, and our IDE. This context switching can be exhausting and also lead to a lot of visual clutter on our monitors. Sometimes, it’s nice to just live in Slack, and that’s why many tools offer Slack integrations. With these Slack integrations, you can monitor various processes and even use shortcut commands to trigger actions. Heroku ChatOps , the Heroku Slack integration, allows you to start and monitor deployments directly from Slack. In this article, we’ll explore some of the Slack commands it offers. Heroku ChatOps Getting Started If you’d like to follow along throughout this tutorial, you’ll need a Heroku account and a GitHub account. You can create a Heroku account here , and you can create a GitHub account here . create a Heroku account here create a GitHub account here The demo app that we will use with our Heroku pipeline in this article is deployed to Heroku, and the code is hosted on GitHub . the code is hosted on GitHub Create Our Heroku Pipeline We won’t go through the step-by-step process of creating a Heroku pipeline in this article. Refer to these articles for a walkthrough of creating a Heroku pipeline: How to create a Heroku pipeline with a staging and production app and a single main branch How to create a Heroku pipeline with a staging and production app with a dev branch and a main branch How to create a Heroku pipeline with a staging and production app and a single main branch How to create a Heroku pipeline with a staging and production app and a single main branch How to create a Heroku pipeline with a staging and production app with a dev branch and a main branch How to create a Heroku pipeline with a staging and production app with a dev branch and a main branch You can also read the Heroku docs for Heroku pipelines . Heroku docs for Heroku pipelines Configuring your Heroku pipeline includes the following steps: Create a GitHub repo. Create a Heroku pipeline. Connect the GitHub repo to the Heroku pipeline. Add a staging app to the pipeline. Add a production app to the pipeline. Create a GitHub repo. Create a Heroku pipeline. Connect the GitHub repo to the Heroku pipeline. Add a staging app to the pipeline. Add a production app to the pipeline. The other activities that you’ll see in those articles, such as configuring review apps, Heroku CI, or automated deployments are optional. In fact, for the purposes of this demo, I recommend not configuring automated deployments, since we’ll be using some Slack commands to start the deployments. not When you’re done, you should have a Heroku pipeline that looks something like this: Connect to Slack Now that you have your Heroku pipeline created, it’s time for the fun part: integrating with Slack. You can install the Heroku ChatOps Slack app here . install the Heroku ChatOps Slack app here Clicking that link will prompt you to grant the Heroku ChatOps app permission to access your Slack workspace: After that, you can add the Heroku ChatOps app to any Slack channel in your workspace. After adding the app, type /h login and hit Enter. This will prompt you to connect your Heroku and GitHub accounts. You’ll see several Heroku OAuth and GitHub OAuth screens where you confirm connecting these accounts. /h login (As a personal anecdote, I found that it took me several tries to connect my Heroku account and my GitHub account. It may be due to having several Slack workspaces to choose from, but I’m not sure.) (As a personal anecdote, I found that it took me several tries to connect my Heroku account and my GitHub account. It may be due to having several Slack workspaces to choose from, but I’m not sure.) After connecting your Heroku account and your GitHub account, you’re ready to start using Heroku in Slack. View All Pipelines To view all deployable pipelines, you can type /h pipelines : /h pipelines View Pipeline Info To see information about any given pipeline, type /h info <PIPELINE_NAME> . (Anything you see in angle brackets throughout this article should be replaced by an actual value. In this case, the value would be the name of a pipeline — for example, “heroku-flow-demo-pipeline”.) /h info <PIPELINE_NAME> View Past Releases To view a history of past releases for any given pipeline, type /h releases <PIPELINE_NAME> . /h releases <PIPELINE_NAME> This command defaults to showing you past releases for the production app, so if you want to see the past releases for the staging app, you can type /h releases <PIPELINE_NAME> in <STAGE_NAME> , where <STAGE_NAME> is “staging.” /h releases <PIPELINE_NAME> in <STAGE_NAME> <STAGE_NAME> Deploy to Staging Now that we know which pipelines are available, we can see information about any given pipeline along with when the code was last released for that pipeline. We’re ready to trigger a deployment. Most engineering organizations have a Slack channel (or channels) where they monitor deployments. Imagine being able to start a deployment right from that channel and monitor it as it goes out! That’s exactly what we’ll do next. To start a deployment to your staging environment, type /h deploy <PIPELINE_NAME> to <STAGE_NAME> , where <STAGE_NAME> is “staging.” /h deploy <PIPELINE_NAME> to <STAGE_NAME> <STAGE_NAME> After running that command, an initial message is posted to communicate that the app is being deployed. Shortly after, you’ll also see several more messages; this time, in a Slack thread on the original message: If you want to verify what you’re seeing in Slack, you can always check the Heroku pipeline in your Heroku dashboard. You’ll see the same information: The staging app has been deployed! Promote to Production Now, let’s promote our app to production. Without the Slack commands, we could navigate to our Heroku pipeline, click the “Promote to production” button, and then confirm that action in the modal dialog that appears. However, we’d prefer to stay in Slack. could To promote the app to production from Slack, type /h promote <PIPELINE_NAME> . /h promote <PIPELINE_NAME> Just like with the staging deployment, an initial Slack message will be sent, followed by several other messages as the production deployment goes out: And — voilà — the latest changes to the app are now in production! Conclusion Now, you can start and monitor Heroku app deployments all from Slack — no need to context switch or move between multiple apps. For more use cases and advanced setups, you can also check out the docs . check out the docs Happy deploying!

Heroku for ChatOps: How to Start and Monitor Deployments From Slack

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