Prometheus seeks to be a new generation within open source monitoring tools. A different approach with no legacies from the past.

For years, many monitoring tools have been linked to Nagios for its architecture and philosophy or directly for being a complete fork (CheckMk, Centreon, OpsView, Icinga, Naemon, Shinken, Vigilo NMS, NetXMS, OP5 and others).

Prometheus software, however, is true to the “Open” spirit: if you want to use it, you will have to put together several different parts.

Somehow, like Nagios, we can say that it is a kind of monitoring Ikea: you will be able to do many things with it, but you will need to put the pieces together yourself and devote a lot of time to it.

Prometheus network monitoring architecture

Prometheus, written in the go programming language, has an architecture based on the integration of third-party free technologies:

Unlike other well-known systems, which also have many plugins and parts to present maps, Prometheus needs third parties to, for example, display data (Grafana) or execute notifications (Pagerduty).

All those high-level elements can be replaced by other pieces, but Prometheus is part of an ecosystem, not a single tool. That’s why it has exporters and key pieces that in the background are other Opensource projects:

• HAProxy
• StatsD
• Graphite
• Grafana
• Pagerduty
• OpsGenie
• and we could go on and on.

What is Prometheus monitoring?

As we mentioned, Prometheus is an open-source monitoring tool.

The main difference with the rest of the tools we have, is that Prometheus is conceived as a framework for collecting data of undefined structure (key value), rather than as a monitoring tool. This allows you to define a syntax for your evaluation and thus store only in case of change events.

If you’re familiar with RRD, you’re thinking the right thing.

Prometheus does not store data in an SQL database.

Like Graphite, which does something similar, like other systems from another generation that store numerical series in RRD files, Prometheus stores each data series in a special file. 

If you are looking for a Time series database information gathering tool, you should take a look at OpenTSBD, InfluxDB or Graphite.

What to use Prometheus for?

Or rather, why NOT use Prometheus.

They themselves say it on their website: if you are going to use this tool to collect logs, DO NOT DO it, they propose ELK instead.

If you want to use Prometheus to monitor applications, servers or remote computers using SNMP, you may do so and generate beautiful graphics with Grafana, but first of all…

Prometheus Settings

All Prometheus software configuration is done in YAML text files, with a rather complex syntax. In addition, each employed exporter has its own independent configuration file.

In the event of a configuration change, you will need to restart the service to make sure it takes the changes.

Prometheus reports

By default, Prometheus monitoring has no report type.

You will have to program them yourself using their API to retrieve data.

Of course, there are some independent projects to achieve this.

Dashboards and visual displays

To have a dashboard in Prometheus, you’ll need to integrate it with Grafana.

There is documentation of how to do this, as Grafana and Prometheus coexist amicably.

Scalability in Prometheus

If you need to process more data sources in Prometheus, you may always add more servers.

Each server processes its own workload, because each Prometheus server is independent and can work even if its peers fail. 

Of course, you will have to “divide” the servers by functional areas to be able to differentiate them, e.g.: “service A, service B”. So that each server is independent.

There does not seem to be a way to “scale” as we understand it, since there is no way to synchronize, recover data and it does not have high availability or a common access framework to information on different independent servers.

But as we warned at the beginning, this is not a “closed” solution but a framework for designing your own final solution.

Of course, there is no doubt that Prometheus is able to absorb a lot of information, following another order of magnitude than other better known tools.

Monitoring with Prometheus: exporters and collectors

Somehow, each different “way” of obtaining information with this tool, needs a piece of software that they call “exporter”.

It is still a binary with its own YAML configuration file that must be managed independently (with its own daemon, configuration file, etc.).

It would be the equivalent of a “plugin” in Nagios.

So, for example, Prometheus has exporters for SNMP (snmp_exporter), log monitoring (grok_exporter), and so on.

Example of configuring an snmp exporter as a service:

To get information from a host, you may install a “node_exporter” that works as a conventional agent, similar to those of Nagios.

These “node_exporters” collect metrics of different types, in what they call “collectors”.

By default, Prometheus has activated dozens of these collectors. You may check them all by navigating to Annex 1: active collectors.

And, in addition, there are a multitude of “exporters” or plugins, to obtain information from different hardware and software systems.

Although the number of exporters is relevant (about 200), it does not reach the level of plugins available for Nagios (more than 2000).

Here we have included an example of an Oracle exporter.

Conclusion

Prometheus’ approach for modern monitoring is much more flexible than that of older tools. Thanks to its philosophy, you may integrate it into hybrid environments more easily.

However, you will miss reports, dashboards and a centralized configuration management system.

That is, an interface that allows observing and monitoring information grouped in services / hosts.

Because Prometheus is a data processing ecosystem, not a common IT monitoring system.

Its power in data processing is far superior, but the use of that data for day-to-day use makes it extremely complex to manage, as it requires many configuration files, many distributed external commands and everything must be maintained manually.

Appendix 1: Collectors active in Prometheus

Here are the collectors that Prometheus has active by default:

These “node_exporter” collect metrics of different types, in what they call “collectors”, these are the serial collectors that are activated:

arp	Exposes ARP statistics from /proc/net/arp.
bcache	Exposes bcache statistics from /sys/fs/bcache/.
bonding	Exposes the number of configured and active slaves of Linux bonding interfaces.
btrfs	Exposes btrfs statistics
boottime	Exposes system boot time derived from the kern.boottime sysctl.
conntrack	Shows conntrack statistics (does nothing if no /proc/sys/net/netfilter/ present).
cpu	Exposes CPU statistics
cpufreq	Exposes CPU frequency statistics
diskstats	Exposes disk I/O statistics.
dmi	Expose Desktop Management Interface (DMI) info from /sys/class/dmi/id/
edac	Exposes error detection and correction statistics.
entropy	Exposes available entropy.
exec	Exposes execution statistics.
fibrechannel	Exposes fibre channel information and statistics from /sys/class/fc_host/.
filefd	Exposes file descriptor statistics from /proc/sys/fs/file-nr.
filesystem	Exposes filesystem statistics, such as disk space used.
hwmon	Expose hardware monitoring and sensor data from /sys/class/hwmon/.
infiniband	Exposes network statistics specific to InfiniBand and Intel OmniPath configurations.
ipvs	Exposes IPVS status from /proc/net/ip_vs and stats from /proc/net/ip_vs_stats.
loadavg	Exposes load average.
mdadm	Exposes statistics about devices in /proc/mdstat (does nothing if no /proc/mdstat present).
meminfo	Exposes memory statistics.
netclass	Exposes network interface info from /sys/class/net/
netdev	Exposes network interface statistics such as bytes transferred.
netstat	Exposes network statistics from /proc/net/netstat. This is the same information as netstat -s.
nfs	Exposes NFS client statistics from /proc/net/rpc/nfs. This is the same information as nfsstat -c.
nfsd	Exposes NFS kernel server statistics from /proc/net/rpc/nfsd. This is the same information as nfsstat -s.
nvme	Exposes NVMe info from /sys/class/nvme/
os	Expose OS release info from /etc/os-release or /usr/lib/os-release
powersupplyclass	Exposes Power Supply statistics from /sys/class/power_supply
pressure	Exposes pressure stall statistics from /proc/pressure/.
rapl	Exposes various statistics from /sys/class/powercap.
schedstat	Exposes task scheduler statistics from /proc/schedstat.
sockstat	Exposes various statistics from /proc/net/sockstat.
softnet	Exposes statistics from /proc/net/softnet_stat.
stat	Exposes various statistics from /proc/stat. This includes boot time, forks and interrupts.
tapestats	Exposes statistics from /sys/class/scsi_tape.
textfile	Exposes statistics read from local disk. The –collector.textfile.directory flag must be set.
thermal	Exposes thermal statistics like pmset -g therm.
thermal_zone	Exposes thermal zone & cooling device statistics from /sys/class/thermal.
time	Exposes the current system time.
timex	Exposes selected adjtimex(2) system call stats.
udp_queues	Exposes UDP total lengths of the rx_queue and tx_queue from /proc/net/udp and /proc/net/udp6.
uname	Exposes system information as provided by the uname system call.
vmstat	Exposes statistics from /proc/vmstat.
xfs	Exposes XFS runtime statistics.
zfs	Exposes ZFS performance statistics.

Appendix 2: Oracle exporter example

This is an example of the type of information that an Oracle exporter returns, which is invoked by configuring a file and a set of environment variables that define credentials and SID:

• oracledb_exporter_last_scrape_duration_seconds
• oracledb_exporter_last_scrape_error
• oracledb_exporter_scrapes_total
• oracledb_up
• oracledb_activity_execute_count
• oracledb_activity_parse_count_total
• oracledb_activity_user_commits
• oracledb_activity_user_rollbacks
• oracledb_sessions_activity
• oracledb_wait_time_application
• oracledb_wait_time_commit
• oracledb_wait_time_concurrency
• oracledb_wait_time_configuration
• oracledb_wait_time_network
• oracledb_wait_time_other
• oracledb_wait_time_scheduler
• oracledb_wait_time_system_io
• oracledb_wait_time_user_io
• oracledb_tablespace_bytes
• oracledb_tablespace_max_bytes
• oracledb_tablespace_free
• oracledb_tablespace_used_percent
• oracledb_process_count
• oracledb_resource_current_utilization
• oracledb_resource_limit_value

To get an idea of how an exporter is configured, let’s look at an example, with an JMX exporter configuration file:

---
startDelaySeconds: 0
hostPort: 127.0.0.1:1234
username: 
password: 
jmxUrl: service:jmx:rmi:///jndi/rmi://127.0.0.1:1234/jmxrmi
ssl: false
lowercaseOutputName: false
lowercaseOutputLabelNames: false
whitelistObjectNames: ["org.apache.cassandra.metrics:*"]
blacklistObjectNames: ["org.apache.cassandra.metrics:type=ColumnFamily,*"]
rules:
  - pattern: 'org.apache.cassandra.metrics<type=(\w+), name=(\w+)><>Value: (\d+)'
    name: cassandra_$1_$2
    value: $3
    valueFactor: 0.001
    labels: {}
    help: "Cassandra metric $1 $2"
    cache: false
    type: GAUGE
    attrNameSnakeCase: false

When you leave the faculty with a smile on your face and after the undertow of the graduation celebration, you hope that the great multinationals approach you with hundreds and varied jobs. “Take this huge sum of money and work on what you always dreamed of”…

But nothing could be further from the truth.

For that reason, today in Pandora FMS blog, we give you our sincere condolences for facing that load of hunting for a job related to “your stuff” and a couple of pages totally necessary to find an IT job.

*We know that there are millions of specialized people that look for an article like this, from water stocker in IT to those who prepare a megalomaniac IA in their garage, but this time we have wanted to focus on looking for an IT job. 

** Even so, these pages are very versatile and are helpful for many more specialties. Look among them for a job that suits your specialties.

Do you know where you have to look for an IT job?

Ticjob

Good stuff: Ticjob. We dive right into it with one of the most valued portals of IT jobs in Spain. 

Go in, thread between the offers with enough precision, since you can choose among role categories, development, system, business… Choose and forget about it. Soon you will find something!

If I were you, I would sign up immediately, because you may find companies that usually do not appear in other more well-known platforms. 

TalentHackers

Talent Hackers. We already explained to you why you don’t have to fear the word “hacker”, because it can have positive connotations and, of course, it has them here.

We face here a very singular platform for job hunting: 

Its aim is to catch talents within the technological scope through one distributed network. That is, by means of searching and picking up professionals through references later repaid. 

What does this mean?
It means that if the candidate which you recommend for a position is the selected one you can take up to 3,000 bucks.

Manfred

Manfred: “We manage talent, not selection processes”. With this quote, the company makes clear that it is not a common portal.

Rather, Manfred claims to be a platform that offers “IT recruitment” and gives the candidate an experience totally different from that we are used to with the rest of this type of services.

Manfred takes less into account the necessities of the companies and worries more about the programmers that look for a job.

1. You sign up.
2. You are assigned a person that will be in charge of you, who will inform you about the most interesting opportunities that comply with the profile which you previously detailed.
3. You are advised with the utmost respect.
4. You realize everything is for free for IT profiles and they only charge companies that hire them.

TekkieFinder

“We are the ONLY job portal that PAYS you whenever a company contacts you.” This is what TekkieFinder promises. Do you like the idea?

Is very easy: You register, fill in your profile happily, they get you in their database and, here’s the good stuff, when a company is interested in you, it buys your profile from TekkieFinder to be able contact you, and whether you are interested in the offer or not, you get paid!

There is such a shortage of IT professionals that it is changing the way to take control over them. They are like exotic legendary pokemon hidden behind an ancient glitch. What IT professional wouldn’t be thrilled with this platform?

Circular

Looking for something truly individualized and round? Get in Circular

Circular is similar to the previous employment portal mentioned: Manfred. Although it gives you a less personal feeling than Manfred, among the Spanish platforms, it is the best one in this feature.

Circular, like the dating application Tinder, it gathers companies and applicants all together. 

First, you sign up, then a friend of yours/contact within the platform recommends you, since if they do not do it, you will not be able to contact the companies, and that’s it!

GeeksHubs

GeeksHubs is without a doubt one of the best options if you look for an IT job in Spain. 

Systems/DevOps, Back-end, Front-end, Mobile, FullStack,… These are some of the categories that you will be able to find in your sector. In addition to enough information on each vacancy, so that it becomes clear whether it interests you or not. 

And, in addition, they say how much they are willing to pay you, which is the most interesting part and it is what many hide. 

Growara

 Growara gets in your shoes and it never offers to its users a project in which they themselves would not work. In fact, it seems that they only work with companies that are actually worth it.

They never ghost you, since they seem to feed on the feedback that you can offer them.

The best thing? They don’t bother spamming you with thousands of offers that do not have anything to do with your professional development. They look for precise and elegant matches that meet your values and capacities.

Tecnoempleo

Tecnoempleo is that portal specialized in computer science, telecommunications and technology that you’re looking for.

More than half a million candidates and 27 thousand companies guarantee its 20 years of professional expertise in the sector.

Although just for having its own mobile app, and specific sections for working abroad or remotely, or looking for your first job, I would choose it hands down.

Primer Empleo

If you are a newbie this is your site, Primer Empleo.

A job portal founded in 2002 and directed specifically to students and recent graduates without labor experience.

So if you have a junior profile and you want to check it out, go ahead. Even if you have not even finished your grade and you are only looking for an internship, it is quite interesting.

Jooble 

Jooble and Jooble Mexico are websites that take you to many and a wide range of existing job offers in other pages.  Perhaps you lose some time signing up to each one of them, but it may be worth it if you end up getting your way. 

It is worth pointing out that, if you get a job thanks to this article, you should treat us to something, even if it’s just a coffee. Always depending on the job you got and its consequent remuneration, of course!

Conclusions

Looking for a job is a task that is already too ungrateful for you to not accept our help through this article and these links. After all, we have been there and we know how lost and frustrated one can feel.

Good luck and take courage in your job hunting!

In our blog we have posted a few articles about data centers. We like them. They have grown on us. It is a branch of technology that interests us as much as bitcoin interests brothers-in-law or neighborhood projects interest retirees. For that reason, today, in our blog, we will deal with data management as a service or DMaaS.

Do you already know what DMaaS is and why you need it in your life?

We have talked about it in countless after-dinner conversations with cigars in hand: Data centers are centralized physical facilities used by companies to host their information and applications. Although data centers help us meet the requirements of sending data in real time, there can be problems with outages, and these are an expensive business for companies. On the other hand, the Data center infrastructure management (DCIM) is in charge of monitoring and giving us information about the IT components and facilities of our structure. That includes servers and storage to power distribution units or cooling equipment. The goal of a DCIM initiative is to provide managers with a comprehensive view of data center performance so that power, equipment, and space are used as efficiently as possible. Well, so far we knew everything and we had no rival until the desserts arrived. 

However, one might add (while stirring a cup of tea) that today’s data centers are becoming increasingly complex and sophisticated, and as they evolve, they ask for features in DCIM solutions to increase. For that reason, DCIM has to transcend the well-known Cloud and bring its capabilities. So, in order to improve the way data centers operate,Data Management-as-a-Service or DMaaS emerged.

DMaaS, definition and advantages

DMaaS is a type of cloud service that provides companies with centralized storage for different data sources. It enables the optimization of the IT layer by simplifying, monitoring and servicing the physical data center infrastructure for the company.

*Data of vital importance: DMaaS is not DCIM nor a SaaS version of DCIM.

Thanks to the DMaaS service you may analyze large sets of anonymous customer data and improve with machine learning. In no case, I give you my word, will a company using DCIM receive better information than it can get with a DMaaS approach. Not to mention cost savings, downtime reduction and overall performance improvement.

Easy to use and low cost, DMaaS makes it easy for IT professionals to increasingly monitor their data center infrastructure, receiving information in real time and with the additional ability to prevent possible failures as a seer octopus.

Still, in the midst of so much profit, it is very likely that if you were to do a worldwide survey of professionals and entrepreneurs, you would find that cost savings is the most important chosen feature of DMaaS. And it is that, thanks to DMaaS, companies only have to ask their users to register, while informing providers about the specific needs of the organization and the number of registered users. So the provider indeed provides, and manages the infrastructure based on what you have requested.  

In a somewhat modest third position among the advantages we would find the protection of a company’s data assets and the additional value obtained from them. As an example, for the data center, DMaaS allows you to maximize hardware security through smart alarms and remote troubleshooting.

One of the main differences to highlight with DCIM is that it is limited to a single data center, while DMaas can help analyze a much larger set, thus providing a more complete view. Furthermore, aside from providing us with analytical insights, the service continually learns and improves based on data collected from users. 

Conclusion

Although it is true that we could judge that DMaaS is still in an early stage, work is already being done to solve the main challenges it faces: data encryption, data management functions, data center reduction or performance increase.

Resources

Monitoring as a Service (MaaS)

At the end of the last century I had the opportunity to help in a very ambitious computer project: the search for radio messages emitted by extraterrestrial civilizations… And what the hell does it have to do with Distributed Systems?

Recently my colleagues wrote an interesting article on distributed network visibility, which I really liked and I came up with the idea of taking it to the next level. If this post tries to offer full knowledge of the different components in operation within our network, Distributed Systems go “further”; they reach where we lack control over the devices that comprise it.

I am going to exemplify both at the social science level, comparing a union versus a confederation (as a central of workers and unioI am going to exemplify both at the social science level, comparing a union versus a confederation (as a central of workers and unions and not from a political point of view).

*Confederacy

According to Merriam-Webster

1. A group of people, countries, organizations, etc. joined together for a common purpose or by a common interest: LEAGUE, ALLIANCE

Distributed computing, distributed systems, are they the same?

Distributed Systems

If you look for the concept of Distributed Systems on Wikipedia (that magical place), you will be redirected to the article called Distributed Computing and, I quote:

“Distributed computing also refers to the use of distributed systems to solve computational problems. In distributed computing, a problem is divided into many tasks, each of which is solved by one or more computers, which communicate with each other via message passing.”

Without going any further: Wikipedia, if we consider ourselves as computers, it is a very high-level Distributed System, since we comply with its intrinsic characteristics… And what are they?

Features of Distributed Systems

A Distributed System (or Distributed Computing) has:

•   Concurrence: Which in the case of computers is a distributed program and in Wikipedia they are people… who use specialized software distributed by web browsers.

•   Asynchronous: Each computer (or Wikipedian) works independently without waiting for a result from the other, when it finishes its batch of work, it delivers it and it is taken in and saved.

•   Resilience: A computer device that breaks down or loses connection, or a person who dies, withdraws or is expelled from Wikipedia, in both environments does not mean stopping the work or global task. There will always be new resources, machines or humans, ready to join the Distributed System.

The aliens

Right, I started this article talking about them. In today’s -unfortunately- destroyed radio telescope in Arecibo, Puerto Rico, astronomers Carl Sagan and Frank Drake sent a message to the Hercules cluster, a group of galaxies 25,000 light years away from our planet.

“Hercules Globular Cluster (https://commons.wikimedia.org/wiki/File:Hercules_Globular_Cluster,_EVscope-20211008.jpg) ”

That means that it will take 50 thousand years to get an answer, if there is life out there, but what if it is us who were already sent messages thousands or millions of years ago?

Well, this was the program Seti@home  about: it collected radio signals and chopped them into two-minute pieces that were sent to each person who wanted to collaborate in the analysis with their own computer. At the end of the calculation according to a special algorithm, the result was sent and a new piece of code was requested. If a computer after a reasonable time did not return an answer, then the same piece was sent to another computer that wanted to collaborate: the “prize” consisted in publicly recognizing the collaborator as a discoverer of life and intelligence outside this world.

I installed the program and put it as a screensaver, so I calculated while I was working on something else or resting.

“Seti@home (imagen de setiathome.berkeley.edu) ”

There you have it! A distributed system for analyzing the radio signals of the universe!

Distributed monitoring

Distributed monitoring depends on the network topology used, and I bring it up as an introduction or approach to monitoring a distributed system. If you are new to Pandora FMS, I recommend you take some time to read this post.

Essentially it is about distributed environments that give service to a company or organization but do not execute a common software and have very different areas or purposes between departments, supported in communication with a distributed network topology accompanied by a well planned security architecture in monitoring.

Pandora FMS offers in this field service monitoring, very well described in the official documentation.

Observability

It would be an attribute of a system, and the topic is worth a full blog post, but, in summary, I expose observability as a global concept that includes more alert monitoring and alert management activities, visualization and trace analysis for distributed systems, and log analysis.

Companies like Twitter have taken observability very seriously and, as you may have guessed, that addictive social network is a distributed system but with a diffuse end product (increase our knowledge and facts about the real world).

Transaction monitoring

How can we monitor a distributed system if it consists of very heterogeneous components and, as we saw, can reach any part of our known universe?

Pandora FMS has Business Transactional Monitoring, a tool that I consider the most appropriate for distributed systems since we can configure transactions, as many as we need, and then use the necessary transactional agents to do so.

It is a difficult topic to take in but our documentation starts with a simple and practical example, with which, as you experiment, you may add “blocks” of more complex transactions until you reach a point where you can have a panorama of the distributed system.

All this is possible with Pandora FMS since it has standard monitoring, remote checks, transaction synthetic monitoring and the Satellite server for distributed environments that can be used with transactional monitoring for distributed systems.

Present and future

The question is no longer whether we need distributed systems. That is a fact. Today people use distributed systems in computing services in the cloud or in data centers and the Internet.

Distributed systems can offer impossible functions in monolithic systems or take advantage of computer processes, such as performing restorations from backups by asking other systems for chunks that are missing or have deteriorated in the local system.

For all these cases, and in any case, the flexibility of Pandora FMS will always be useful and adaptable for current or future challenges.

There are different positions on whether observability and monitoring are two sides of the same coin.

We will analyze and explain what the observability of a system is, what it has to do with monitoring and why it is important to understand the differences between the two.

What is observability?

Following the exact definition of the concept of observability, observability is nothing more than the measure that determines how internal states can be inferred through external outputs.

That is, you may guess the status of the system at a given time if you only know the outputs of that system.

But let’s look at it better with an example.

Observability vs monitoring: a practical example

Some say that monitoring provides situational awareness and the capacity for observation (observability) helps determine what is happening and what needs to be done about it.

So what about the root cause analysis that has been provided by monitoring systems for more than a decade?

What about the event correlation that gave us so many headaches?

Both concepts were essentially what observability promises, which is nothing more than adding dimensions to our understanding of the environment. Be able to see (or observe) its complexity as a whole and understand what is happening.

Let’s look at it with an example:

Suppose our business depends on an apple tree. We sell apples, and our tree needs to be healthy.

We can measure the soil pH, humidity, tree temperature and even the existence of bad insects for the plant.

Measuring each of these parameters is monitoring the health of the tree, but individually they are only data, without context, at most with thresholds that delimit what is right or what is wrong.

When we look at that tree, and we also see those metrics on paper, we know that it’s healthy because we have that picture of what a healthy tree is like and we compare it with things that we don’t see.

That is the difference between observing and monitoring.

You may have blood tests, but you will only see a few specific metrics of your blood.

If you have doubts about your health, you will go to a doctor to look at you and help you with the analysis data, do more tests or send you home with a pat on your back.

Monitoring is what nourishes observation.

We’re not talking about a new concept, we’re rediscovering gunpowder.

Although being fair, gunpowder can be a powerful weapon or just used for fireworks.

The path to observability

One of the endemic problems with monitoring is verticality.

Have isolated “silos” of knowledge and technology that barely have contact with each other.

Networks, applications, servers, storage.

Not only do they not have much to do with each other, but sometimes the tools and equipment that handle them are independent. 

Returning to our example, it is as if our apple tree were dying and we asked each expert separately:

• Our soil expert would tell us it’s okay.
• Our insect expert would tell us it’s okay.
• Our expert meteorologist would tell us that everything is fine.

Perhaps the worm eating the tree reflected a strange spike in soil pH and it all happened on a day of subtropical storm.

By themselves the data did not trigger the alarms, or if they did, they corrected themselves, but the ensemble of all the signals should have portended something worse.

The first step to achieving observability is to be able to put together metrics from different domains/environments in one place. So you may analyze them, compare them, mix them and interpret them.

Basically what we’ve been saying at Pandora FMS for almost a decade: a single monitoring tool to see it all.

But it’s only the first step, let’s move on.

Is Doctor House wrong when he says everyone is lying?

Or rather, everyone tells what they think they know.

If you ask a server at network level if it’s okay, it will say yes.

If there is no network connectivity and the application is in perfect condition, and you ask at application level whether it is OK, it will tell you that it is OK.

In both cases, no service is provided.

And we’ll say, but how is it okay? it doesn’t work!

Therein lies the reason that observability and monitoring are not the same.

It is processing all the signals what produces a diagnosis and a diagnosis is something that brings much more value than data.

Is it better to observe or monitor?

Wrong.

If you’re asking yourself that question, we haven’t been able to understand each other.

Is it better to go to the doctor or just have an analysis?

It depends on what you’re risking.

If it is important, you should observe with all available data.

If what you’re worried about is something very specific and you know well what you’re talking about, it might be worthwhile to monitor a group of isolated data.Although, are you sure you can afford only to monitor?

Finding the needle in the haystack

Among so many data, with thousands of metrics, the question is how to get relevant information among so many shrouds. Right?

AIOPS, correlation, Big Data, root cause analysis…

Are we looking at another concocted word to sell us more of the same?

It may, but deep down it is a deeper and more meaningful reflection:

What is the use of so much data (Big Data) if I don’t have the capacity for its analysis to be useful to me for something practical?

What good is technology like AIOPS if we can’t have all the data together from all our systems, together and accessible?

Before developing black magic, the ingredients must first be obtained, if not, everything remains in promises and expensive investments that entail wasting time and the unpleasant feeling of having been deceived.

From monitoring to observability

In order to elevate monitoring to the new observability paradigm, we must gather all possible data for analysis.

But how do we get them?

With a monitoring tool.

Yes, a tool like Pandora FMS that can gather all the information together, in one piece, without different parts that make up a Frankenstein that we do not know either what it costs or how it is assembled.

And we’re not talking about a monitoring IKEA, made up of hundreds of pieces that require time and… a lot of time.

This is not new.

Nor is it new that we need a monitoring tool that can collect data from any domain.

For example, switch data, crossed with SAP concurrent user data.

Latency data with session times of a web transaction. 

Temperature in Kelvin dancing next to euro cents, positive heartbeats looking closely at the number of slots waiting in a message queue.

LThe only thing that matters is business.

Just the final view.

Observe, understand and above all, resolve that everything is okay, and if it is wrong, know exactly who to call.

What is real observability?

We call it service views.

It is not difficult, we provide tools so that you, who know your business, can identify the critical elements and form a service map that gets feedback from the available information, wherever it comes from.

FMS means for us FLEXIBLE Monitoring System, and it was designed to get information from any system, in any situation, however complex it was and store it to be able to do things with it.

Today our best customers are those who have such a large amount of information that other manufacturers do not know what to do with it.

We don’t know what to do with it either, I won’t fool you, but our customers with our simple technology do.

We help them process it and make sense of it. Make it observable. 

We would like to say that we have a kind of magic that others do not, but the truth is that we have no secret.

We take the information from wherever it comes from, whatever it is, and make it available to design service maps.

Some are semi-automatic, but customers who know what to do with it prefer to define very well how to implement them. I insist, they do it themselves, they don’t even ask us for help.

If you want to observe, you need to monitor everything first. 

And there we can help you.