Giving up on Logstash

splunk-logoMore than six months away, I've put both Splunk and Logstash (plus ElasticSearch and Kibana) to trial. Since the beginning of my experiment, the difference between both products was pretty clear. I was hoping that ELK would be a nice solution to aggregate GBs of logs every day and more importantly to allow me and my team to dig into those logs.
Unfortunately, ELK failed hard on me. Mostly because of ElasticSearch: java threads on the loose eating my CPU, weird issue where a restart would fail and ES would become unavailable, no real storage tiering solution, and so on.
ElastickSearch looks like a very nice developer playground to me. It's quite badly - if at all - documented on the sysadmin side, meaning if you're not a developer and don't have weeks to spend reading API documentation you will have a hard time figuring out how to simply use/tune the product.
Also, this data storage backend has absolutely no security features (at the time I was testing Logstash). Just imagine you put valuable data into a remotely available database/storage/whatever and you're told that you are the one that should create a security layer around the storage. Imagine a product like SMB, NFS, Oracle DB, Postgres, etc. with zero access control feature, no role. Anyone who can display a pie chart in Kibana can gain full control of your ElasticSearch cluster and wipe all your data.
Logstash too has important issues: almost every setting changes require an application restart. Writing grok pattern to extract data is an horrendous process, and a new pattern won't be applied on past data. Splunk on the other hand will happily use a brand new pattern to extract values from past logs, no restart required.
Logstash misses also pipes, functions, triggers… Search in Kibana is not great. The syntax is a bit weird and you can easily find nothing just because you've forgot to enclose your search string with quotes… or is it because you put those quotes? And of course there is this strange limit that prevent you from searching in more than seven or eight months of data…

I've tried, hard. I've registered to not-so-helpful official mailing lists for Logstash and ElasticSearch and simply reading them will show you how far those products are far from "production approval". I've used it alongside with Splunk, it boils down to this statement: Kibana is pretty, you can put together many fancy pie charts, tables, maps and Splunk is useful, reliable, efficient.

Yes, I'm moving to Splunk, @work and @home. My own needs are very easily covered by a Free license, and we are buying a 10 GB/day license for our +350 servers/switches/firewalls.

You might say that Splunk is expensive. That's true. But it's way less expensive to me than paying a full time experienced Java developer to dive into ElasticSearch and Logstash for at least a year. Splunk has proper ACL that will allow me to abide by regulations, a good documentation, and a large community. It support natively storage tiering, too.

ELK is a fast moving beast, it grows, evolves, but it's way behind Splunk in terms of maturity. You can't just deploy ELK like you would do for MySQL, Apache, Postfix, Oracle DB, or Splunk. Lets wait few years to see how it gets better.

L4D2: comparative benchmark between Mac OS X and Windows

Back in december 2012 I've benchmarked (shortly) native and virtualized Mac OS X against virtualized Windows.
Few days ago, I've dedicated a 250G B SSD to a Windows 7 installation, inside my Mac Pro. Weird thing for me to go back and forth between Mac OS X and Windows. I'm more accustomed to +50 days long uptime. Admittedly my various attempts to put Mac OS X into deep sleep, reboot on Windows, and go back later to a fully restored Mac OS X session right out from deep sleep, are failing. That's another story.
Nevertheless, I'm using this Windows system as a playground.

Inside this Mac Pro model 2010, I've one Xeon quad core 2.8 GHz with 24 GB RAM, and a Radeon HD 5770. One SSD is dedicated to Mac OS X 10.6.8, and one SSD is dedicated to Windows 7 Pro 64 bits (with latest stable Catalyst drivers). Both systems are using the latest Steam client with a fully updated and clean Left 4 Dead 2 install.

I've recorded a demo, and played back this file on both systems with identical video settings, recording fps numbers during the playback. The demo is 17827 frames long, and video settings are "MSAA x4", "Anisotropic 8x", "vertical sync triple", "resolution 1920x1200", "shader detail very high', "effect detail high", "model/texture detail high".

The playback is a bit laggy on Mac OS X, especially when the player is looking at fire. It would be playable, but not a very smooth experience. The playback is better on Windows.
Here is the plot of numbers of frames calculated at a given fps rate. For example, on Mac OS X (black line) a total of 4 frames were calculated at a frame rate of 10 fps. On Windows, 90 frames where calculated at a frame rate of 47 fps.

click plot to display full size

click plot to display full size

Windows 7 has better drivers, and may be the game itself is coded better. The fact is some situations in the game are not handled very well by the GPU on Mac OS X. The huge spike around 30 fps means that ~2500 frames were computed at about 30 fps. Not good. But more importantly the global shape of the plot shows a spread of fps values from as low as 10 fps to 60 fps. Note that the log scale on Y does mask isolated frames (Y=1).
Windows does a better job here, with only a handful of frames below 40 fps.

Fortunately L4D2 is an old game, and my hardware is enough to handle it nicely even on Mac OS X (I usually play at 1600x1000), but being able to push it a little further with full quality on Windows is a nice thing. I hope L4D3 will run ok too, some day, in a not too distant future.


To complete the comparison, I've made a Cinebench R15 benchmark. The OpenGL score on Windows 7 is ~64 fps, and the same test on Mac OS X 10.6.8 is ~53 fps. On CPU side both OSes score around 440.

S’envoyer des SMS multilignes via l’API de free-mobile

Free vient de mettre à disposition de ses abonnés Free-mobile un moyen simple de générer via une API accessible en ligne des SMS à soi-même. L'utilisation est globalement très simple et tient presque toutes ses promesses (actuellement il ne semble pas possible de faire du POST, seul le GET fonctionne).
Il suffit aux intéressés de se rendre sur leur interface de gestion de compte Free-mobile, dans la section "Mes options", et d'y activer (vers le bas de la page) l'option ad-hoc. Une fois l'option activée, Free vous gratifie d'un mot de passe dédié à cet usage, et vous renseigne un peu sur le fonctionnement du service :


Une fois l'option activée, l'utilisation se résume à une requête HTTP de cette forme :

curl '***&pass=***&msg=coucou'

Vous recevez alors sur votre téléphone Free-mobile le SMS contenant le message "coucou".

La création d'un message multiligne est un peu plus délicate. La plupart des plateforme Unix/Linux envoie en guise de séparateur de ligne un \n qui se traduit par %0a une fois "url-encodé". Malheureusement, l'API n'accepte pas ce %0a et le message sera tronqué à la première occurrence.
Par contre, le séparateur de ligne \r, encodé en %0d est bien accepté par l'API Free-mobile. Il faut donc, avant d'envoyer un message sur plusieurs lignes, traduire les \n en \r.

J'ai conçu un script shell pour mes besoins personnels. Il prend ses données en entrée standard, par exemple via un pipe :

echo "mon message" |

Ce qui est plus souple pour moi dans bien des situations, que de devoir invoquer le script et de lui servir le message comme argument, par exemple : mon message

Voici donc le code de mon script qui prend les messages via stdin, et qui converti les \n en \r. Notez que le SMS à l'arrivée ne contient pas de saut de ligne, donc prévoyez des espaces en fin de ligne si vous ne voulez pas que la fin de ligne soit collée au début de la ligne suivante.

# push notification via
LOGGEROPT="-p security.notice -t SMS"

report() {
        [ ${REPLOGG} -eq 1 ] && echo $@ | logger ${LOGGEROPT}
        [ ${REPMAIL} -eq 1 ] && echo $@ | mail -s "SMS $@" ${MAILTO}

eval $(${CURL} --insecure -G --write-out "c=%{http_code} u=%{url_effective}" \
        -o /dev/null -L -d user=${USR} -d pass=${PSW} --data-urlencode msg="$(cat|tr '\n' '\r')" ${URL} 2>/dev/null | tr '&' ';')

echo ${c} ${u}\&pass=***\&msg=${msg} | logger ${LOGGEROPT}

case ${c} in
                exit 0
                report "parameter missing" ; exit 400
                report "too many SMS..." ; exit 402
                report "service unavailable for user, or wrong credentials" ; exit 403
                report "server error, try later" ; exit 500
                report "unexpected result" ; exit 600

Utilisation :

echo "mon message 
sur plusieurs 
lignes" |

Il reste à faire :
- ajouter l'enregistrement dans les log système de chaque utilisation du script
- ajouter la gestion des codes de retour HTTP autres que 200.

Pour bénéficier d'une gestion d'erreur et d'une trace dans les log de la machine j'ai utilisé une grosse ruse très sale avec eval. Cela permet de créer des variables à la volée à partir de données fournie par l'utilisateur. C'est éminemment risqué car tout ce est qui généré par c=%{http_code} u=%{url_effective} sera évalué sans distinction (avec potentiellement des exécutions de code).
Pour un système de monitoring c'est un risque négligeable : les messages sont connus et fixés par le logiciel qui les génère, sans interaction avec l'utilisateur. Pour une utilisation dans tout autre mécanisme la prudence est requise.

Une adresse email pour chaque contact ?

Depuis de nombreuses années j'ai pris l'habitude de créer une adresse email spéciale pour (presque) chaque site web sur le quel je m'inscris. D'une part cela apporte un bénéfice de sécurité immédiat car il est courant que l'adresse email soit aussi le login pour un système d'authentification sur le site en question. Donc ce login est différent pour chaque site.
D'autre part, cela me permet de cloisonner le spam, et de détruire sans aucun état d'âme toute adresse email qui serait un peu trop "publique". Au quotidien, c'est aussi un excellent moyen de savoir qui distribue votre adresse email, et qui la garde pour lui comme promis.

Le cas s'est présenté précédemment avec l'adresse email que j'utilisais pour l'association des anciens de mon école d'ingénieurs. Quand j'ai commencé à recevoir de la publicité pour du vin, j'ai détruit l'adresse. Maintenant il ne leur reste que mon adresse postale, ce qui leur pose quelques soucis. Il fallait y penser avant de faire n'importe quoi avec l'annuaire.
Le cas que je souhaite évoquer ici est un peu plus grave. Il s'agit du photographe Zack Arias et de sa liste OnelightWorkshop.
Il a créé sa liste de diffusion d'information au sujet de ses ateliers fin 2009 ou début 2010. Et je m'y suis inscrit presque aussitôt. Il annonce d'ailleurs sur son site :

Sign up on our email list for news about the workshop. We promise not to spam you or pass your information on to any third party. You can expect about 4 emails a year from us… at most.

4 emails par an, c'est à peu prêt vrai, si on s'en tient aux messages légitimes. Par contre, pour le reste, il y a comme un souci. J'hésite entre négligence criminelle, "je-m'en-foutisme", ou grossière escroquerie. Quoi qu'il en soit, moins de 15 jours après le premier message légitime, le spam a commencé à arriver. Les chiffres sont, ces derniers temps, plutôt alarmants :

spam volume per month

Le nombre de messages légitimes est juste invisible sur ce graphique, tellement la masse de spam est importante. Je ne m'en suis aperçu que très tardivement, car mon antispam fait très bien son travail, mais je vais de ce pas détruire cette adresse email.

Maintenant on peut se demander comment ce photographe protège ses clients, leurs données personnelles bien sûr, mais aussi les travaux en cours, les photo confidentielles ou qui devraient rester à jamais privées. Savoir gérer un business de nos jours, c'est aussi savoir répondre efficacement à toutes ces problématiques de sécurité des informations, de protection des données personnelles.
Je suis sûr d'une chose : je ne fournirai plus aucune donnée personnelle à M. Arias.

Log aggregation and analysis: logstash

Logstash is free software, as in beer and speech. It can use many different backends, filters, etc. It comes packaged with Elasticsearch as a backend, and Kibana as user interface, by default. It makes a pleasant package to start with, as it's readily available for the user to start feeding logs. For your personal use, demo, or testing, the package is enough. But if you want to seriously use LS+ES you must have at least a dedicated Elasticsearch cluster.


Starting with Logstash 1.4.0, the release is no longer a single jar file. It's now a fully browsable directory tree allowing you to manipulate files more easily.
ELK (Elasticsearch+Logstash+Kibana) is quite easy to deploy, but unlike Splunk, you'll have to install prerequisites yourself (Java, for example). No big deal. But the learning curve of ELK is harder. It took me almost a week to get some interesting results. I can blame the 1.4.0 release that is a bit buggy and won't start-up agent and web as advertised, the documentation that is light years away from what Splunk provides, the modularity of the solution that makes you wonder where to find support (is this an Elasticsearch question? a Kibana problem? some kind of grok issue?), etc.

Before going further with functionalities lets take a look at how ELK works. Logstash is the log aggregator tool. It's the piece of software in the middle of the mess, taking logs, filtering them, and sending them to any output you choose. Logstash takes logs through about 40 different "inputs" advertised in the documentation. You can think of file and syslog, of course, stdin, snmptrap, and so on. You'll also find some exotic inputs like twitter. That's in Logstash that you will spend the more time initially, tuning inputs, and tuning filters.
Elasticsearch is your storage backend. It's where Logstash outputs its filtered data. Elasticsearch can be very complex and needs a bit of work if you want to use it for production. It's more or less a clustered database system.
Kibana is the user interface to Elasticsearch. Kibana does not talk to your Logstash install. It will only talk to your Elasticsearch cluster. The thing I love the most about Kibana, is that it does not require any server-side processing. Kibana is entirely HTML and Javascript. You can even use a local copy of Kibana on your workstation to send request to a remote Elasticsearch cluster. This is important. Because Javascript is accessing your Elasticsearch server directly, it means that your Elasticsearch server has to be accessible from where you stand. This is not a good idea to let the world browse your indexed logs, or worse, write into your Elasticsearch cluster.

To avoid security complications the best move is to hide your ELK install behind an HTTP proxy. I'm using Apache, but anything else is fine (Nginx for example).
Knowing that is served by "logstash web" command, and is default Elasticsearch socket, your can use those Apache directives to get remote access based on IP addresses. Feel free to use any other access control policy.

ProxyPass /KI 
ProxyPassReverse /KI 
ProxyPass /ES 
ProxyPassReverse /ES 
<Location /KI>
	Order Allow,Deny
	Allow from YOUR-IP
<Location /ES>
	Order Allow,Deny
	Allow from YOUR-IP

original data in µs, result in µs. Impossible to convert in hours (17h09)

original data in µs, result in µs. Impossible to convert in hours (17h09)

On the user side, ELK looks a lot like Splunk. Using queries to search through indexed logs is working the same, even if syntax is different. But Splunk allows you to pipe results into operators and math/stats/presentation functions… ELK is not really built for complex searches and the user cannot transform data with functions. The philosophy around Kibana is all about dashboards, with a very limited set of functions. You can build histograms, geoip maps, counters, compute some basic stats. You cannot make something as simple as rounding a number, or dynamically get a geolocation for an IP address. Everything has to be computed through Logstash filters, before reaching the Elasticsearch backend. So everything has to be computed before you know you need it.
Working with Logstash requires a lot of planing: breakdown of data with filters, process the result (geoip, calculation, normalization…), inject into Elasticsearch, taylor your request in Kibana, create the appropriate dashboard. And in the end, it won't allow you to mine your data as deep as I would want.
Kibana makes it very easy to save, store, share your dashboards/searches but is not very friendly with clear analysis needs.

Elasticsearch+Logstash+Kibana is an interesting product, for sure. It's also very badly documented. It looks like a free Splunk, but its only on the surface. I've been testing both for more than a month now, and I can testify they don't have a lot in common when it comes to use them on the field.

If you want pretty dashboards, and a nice web-based grep, go for ELK. It can also help a lot your command-line-illeterate colleagues. You know, those who don't know how to compute human-readable stats with a grep/awk one-liner and who gratefully rely on a dashboard printing a 61 billions microseconds figure.
If you want more than that, if you need some analytics, or even forensic, then odds are that ELK will let you down, and it makes me sad.

Metasploit hearbleed scanner reliably crashes (some) HP ILO

Disclaimer: HP ILO Firmware involved here is NOT the latest available. ILO 3 cards were not affected.

Using Metasploit to scan my network for OpenSSL "Heartbleed" vulnerability I've been quite shocked to get a handful of alerts in my mailbox. Our HP C7000 was no longer able to talk to some of our HP Proliant blades' ILO.


Production was all good, and service was still delivered, but blade management was impossible: Metasploit's auxiliary/scanner/ssl/openssl_heartbleed.rb probe has just crashed six HP ILO 2 cards. That's funny, because the ILO firmware is not vulnerable to heartbleed, it's only vulnerable to the scanner...


I've made some tests to repeat the problem. It happens with a 100% reliability. Quite impressive.

Software versions:

Framework: 4.8.2-2013121101
Console  : 4.8.2-2013121101.15168
openssl_heartbleed.rb : downloaded on April the 22th.

HP ProLiant BL490c G6 (product ID 509316-B21), ILO 2 firmware undisclosed for security reasons.

Log aggregation and analysis: splunk

As soon as you have one server, you might be tempted to do some log analysis. That can be to get some metrics from your Apache logs, your spam filter, or whatever time-stamped data your server collects. You can easily find small tools, or even create a home-made solution to extract info from these files.
Now imagine you have 100, 200, or even thousands of servers. This home-made solution you've created no longer suits your needs.
Different powerful products exist, but I'll focus on two of them: Splunk on one side, Logstash+Elasticsearch+Kibana on the other side. This post is dedicated to Splunk. Logstash will come later.

Both softwares are tools. These are not all-in-one solution. Exactly like a spreadsheet software which is unable to calculate your taxes unless you design a specific table to do so, you must use the software to create value from your logs. Installing and feeding logs into the software is not the end of your work, it's the very beginning.

Splunk is a commercial product. It's incredibly powerful out of the box, and its documentation is very good. Every aspect of the software is covered in depth with numerous examples. It also has an official support. Unfortunately Splunk is very (very) expensive, and no official rates are available online. Hiding the cost of a software is often the promise you won't be able to afford it.
Splunk is well packaged and will run effortlessly on many common systems. At least for testing. Scaling up requires some work. I've been told that apparently, scaling up to more than few TB of daily logs can be difficult, but I don't have enough technical details to make a definitive statement about this.
Rest assured that Splunk is a very nice piece of software. It took me only an hour from the time I've installed it on a FreeBSD server, to the time I've produced a world map showing spam filter action breakdown by location:


One hour. This is very short, almost insane. The map is fully interactive, and you can click any pie chart to display the table of values and the search request allowing you to create this table:


The query syntax is quite pleasant and almost natural. The search box is very helpful and suggests "Common next commands", or "Command history" alongside with documentation and example:


Splunk has some other killing features like users management and access control, assisted (almost automated) regex design for field extraction, or its plugin system. The Field extraction "wizard" is quite impressive, as it allows you to extract new fields out of already indexed logs, without writing any regex nor re-indexing your logs. You just browse your logs, paste samples of data you want to extract, and it builds the filter for you.
Transactions are also a pretty damn great feature: they make correlation of different events possible (login and logout, for example), so that you can track complex behaviors.

More importantly, Splunk appears to be simple enough so any sysadmin wants to use it and does not get rebutted by it's complexity. It's a matter of minute(s) to get, for example, the total CPU time involved in spam filtering last month (~573 hours here). Or if you want, the total CPU time your antispam wasted analyzing incoming emails from facebook (~14.5 hours). But it's definitively a very complex software, and you have to invest a great deal of time in order to get value (analytics designed for you) from what you paid for (the license fees).

I love Splunk, but it's way too expensive for me (and for the tax payers whose I use the money). So I'm currently giving Logstash a try and I'm quite happy about it.

Le PC dans mon téléphone

Précédemment, j'ai évoqué l'ordinateur de demain. J'ai fait un test grandeur nature façon corporate. Comprendre : vous aurez du mal à faire pareil chez vous. J'utilise en effet quelques artifices comme un accès VPN au réseau de mon employeur, et au travers de cet accès une machine virtuelle VMware View.
Au final, j'ai le bureau d'une machine Windows 7 affiché sur la TV du salon, j'ai un clavier et une souris bluetooth, le tout servi par mon téléphone malin, via un tunnel VPN dans une connexion WIFI. La connexion avec la machine virtuelle se fait via le client officiel View, de VMware (donc en PCoIP).

Windows dans ma TV sur mon téléphone

Ci dessus une photo de la TV affichant le bureau de la VM dans laquelle est lancé le client VSphere (outil d'administration de datacenter VMware).

À 2m50, le texte dans l'interface n'est pas hyper lisible sur l"écran 80cm, donc pour le PC dans le canapé, on repassera, ou alors on fera des réglages spéciaux sur la machine virtuelle.

Le client View se comporte relativement bien, néanmoins, il y a comme un petit souci avec le clavier. Ce dernier fonctionne parfaitement en azerty partout, sauf à l'intérieur du Windows, où il fonctionne en qwerty. Le client View dispose d'un champs de saisie qui permet de taper du texte à l'extérieur de la VM et de l'envoyer ensuite vers cette dernière. À cet endroit, c'est bien de l'azerty. Mais le même texte tapé directement dans la VM passe en qwerty. C'est peut être du à mon téléphone, qui est paramétré tout en anglais.

Aussi, suite à un blocage logiciel sur ma VM, j'ai tenté un ctrl-alt-suppr via mon clavier bluetooth. Le programme n'a pas rendu la main, la VM n'a pas rebooté. Non. C'est le téléphone qui a rebooté. Ça m'a fait rire.