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 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.

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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.

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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.

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