Using Storm for real time distributed computations has become a widely adopted approach, and today one can easily find more than a few posts on Storm’s architecture, internals, and what have you (e.g., Storm wiki, Understanding the parallelism of a storm topology, Understanding storm internal message buffers, etc).
So you read all these posts and and got yourself a running Storm cluster. You even wrote a topology that does something you need, and managed to get it deployed. “How cool is this?”, you think to yourself. “Extremely cool”, you reply to yourself sipping the morning coffee. The next step would probably be writing some sort of a validation procedure, to make sure your distributed Storm computation does what you think it does, and does it well. Here at Outbrain we have these validation processes running hourly, making sure our realtime layer data is consistent with our batch layer data – which we consider to be the source of truth.
It was when the validation of a newly written computation started failing, that we embarked on a great journey to the land of “How does one go about debugging a distributed Storm computation?”, true story. The validation process was reporting intermittent inconsistencies when, intermittent being the operative word here, since it was not like the new topology was completely and utterly messed up, rather, it was failing to produce correct results for some of the input, all the time (by correct results I mean such that match our source of truth).
Earlier today, Outbrain was the victim of a hacking attack by the Syrian Electronic Army. Below is a description of how the attack unfolded to help others protect against similar attempts. Updates will continue to be posted to this blog.
On the evening of August 14th, a phishing email was sent to all employees at Outbrain purporting to be from Outbrain’s CEO. It led to a page asking Outbrain employees to input their credentials to see the information. Once an employee had revealed their information, the hackers were able to infiltrate our email systems and identify other credentials for accessing some of our internal systems.
At 10:23am EST SEA took responsibility for hack of CNN.com, changing a setting through Outbrain’s admin console to label Outbrain recommendations as “Hacked by SEA.”
At 10:34am Outbrain internal staff became aware of the breach.
By 10:40am Outbrain network operations began investigating and decided to shut down all serving systems, degrade gracefully and block all external access to the system.
By 11:03am Outbrain finished turning off its service from all sites where we operate.
We are continuing to review all systems before re-initiating service.
We are aware that Outbrain was hacked earlier today and we took down service as soon as it was apparent. The breach now seems to be secured and the hackers blocked out, but we are keeping the service down for a little longer until we can be sure it’s safe to turn it back on securely. Please stayed tuned here or to our Twitter feed for updates.
Yes it’s been a long time since we last updated this blog – Shame on us!
A lot has happened since our last blog post, which came while we we dealing the effects of hurricane Sandy. In the end, our team handled it bravely and effectively, with no downtime and no business impact. However, a storm is still a storm, and did have to do an emergency evacuation from our old New York data center and move to a new one.
More things have happened since and today I want to focus on one major aspect of our life in the last year. We have made some cultural decisions that somehow changed the way we treat our work. Yes, the Devops movement has its influence here. When we stood in front of the decision of “NOC or NOT”, Basically, we adopted the theme of “You build it, You run it!”.
Instead of hiring 10 students, attempting to train them on the “moving target” of a continuously changing production setup , we decided to hire 2 engineers and concentrate effort on building strong monitoring system that will allow engineers to take ownership on monitoring their systems
Now, Outbrain is indeed a high scale system. Building a monitoring system that enables more then 1000 machines and more then 100 services to report metrics every minute is quite a challenge. We chose the stack of Logstash, RabbitMQ and Graphite for that mission. In addition we developed an open source project called Graphitus which enables us to build dashboards from graphite metrics. Since adopting it we have more then 100 dashboards the teams are using daily. We also developed Dashanty which enables each team to develop an operational dashboard for itself.
On the alerting front we stayed with Nagios but improved it’s data sources. Instead of Nagios polling metrics by itself, we developed a Nagios/Graphite plugin where Nagios querys Graphite for the latest metrics and according to thresholds shoots appropriate alerts to relevant people. On top of that, the team developed an application called RedAlert that enable each and every team/engineer to configure their own alerts on their own owned services, configure when alerts are critical and when such alert should be pushed to them. This data goes into Nagios that start monitoring the metric in Graphite and will fire an alert if something goes wrong. “Push” alerts are configured to go to PagerDuty that will be able to locate the relevant engineer, email, text or call him as needed.
Now that’s on the technical part. What is more important to make it happen is the cultural side that this technology supports:
We truly believe in ”End to End Ownership”. “You build it, You run it!” is one way to say that. In an environment where everybody can (and should) change production at any moment , putting someone else to watch the systems makes it impossible. We were also very keen about MTTR (Mean Time To Recover). We don’t promise our business people 100% fault free environment, but we do promise fast recovery time. When we put these two themes in front of us, we came to the conclusion it is best that alerts will be directed to owner engineers as fast as we can, with fewer mediators on the way. So, we came up with the following:
- We put a baseline of monitoring systems to support the procedure – and we continuously improve it.
- Engineers/teams are owners of services (very SOA architecture). Lets use the term “Owner”. We try to eliminate services without clear owners.
- Owners push metrics into graphite using calls on code or other collectors.
- Owners define alerts on these metrics using RedAlert system.
- Each team defined “on call schedule” on PagerDuty. “On call” engineer is the point of contact for any alerting service under the team ownership.
- Ops are owners for the infrastructure (Servers/Network/software infra) – they also have “Ops on shift” – awake 24/7 (we use the team distribution between NY and IL for that).
- Non push alerts that does not require immediate action are gathered along non working hours and treated during working hours.
- Push Alerts are routed via PagerDuty the following way: Ops on shift get them and if he can address them or correlate them with infrastructure issue – he acknowledge them. In case Ops on Shift doesn’t know what to do with it, Pager duty continues and rout the alerts to the engineer on call.
- Usually the next thing that will happen is that both of then will jump on the HipChat and start tackling the issue to shorten MTTR and resolve it.
The biggest benefit of this method is increased sense of “ownership” for everyone in the team. The virtual wall between Ops and Dev (which was initially somehow low in Outbrain) was completely removed. Everybody is more “Production sensitive”.
Few things that helped us through it:
- Our team. As management we encouraged it and formalized it but the motivation came from the team. It is very rare to see engineers that want (not to say hardly push) to take more ownership on their products and to really “Own them”. I feel lucky that we have such team. It made our decisions much simpler.
- Being so tech-ish and pushing our monitoring capabilities to such edges instead of going to the easy, labor intensive, half ass solution (AKA NOC).
- A 2 week “Quality Time” of all engineering that was devoted to improving MTTR and building all necessary to support this procedure. – All Credits to Erez Mazor for running this week.
Hi all As Hurricane Sandy is about to hit the east coast US, and as Outbrain’s main Datacenter is located in downtown Manhattan, we are taking measures to make as little service interruption as possible for our partners and customers. Outbrain is normally serving from 3 data centers and in case of NY data center loss, we will supply the service from one the other data centers. On this page, below – we will update on any service interruption and ETAs for problem solving. We assume all will go well and we will not have to update but… just in case
[UPDATE - Nov 3rd 3:45pm EST] - At this time Utility power is back to all our datacenters and HQ office. It is now time to restore the service from NY and get the office back to work. This will take some time but systems will gradually be put back up over the next week or so. There should be no effect on users, publishers or clients.
Our HQ will also start working gradually depending on the availability of public transportation.
We are here closing this reporting post – if you see any issues, please report to firstname.lastname@example.org or your rep.
I hope the storm of the century will be the last one for the next century (at least).
[UPDATE - Nov 1st 9:30am EST] - Our HQ, located on 13th between 5th and 6th in downtown New York City is still without power and therefore closed. Thankfully, our NY-based team is safe and in dry locations, and will continue to try and work as best they can. We highly appreciate the concern and best wishes we received from our partners and clients across the globe; thank you!
We are doing our best to continue to provide the best in class service, one we hope you’ve come to expect from us. As an update, our datacenter in NY is still without power and we expect it to be down for a few more days. We will continue to serve from our other datacenters located in Chicago and Los Angeles. To reiterate, our service did not go down, and we are currently still serving across our client’s sites. As of this morning, we recovered and updated all our reporting capabilities, so we should be back to 100%.
If you are experiencing any difficulties or seeing different, please reach out to your respective contacts. We’ll also continue to operate under emergency mode until Monday, you can reach us 24/7 at am-emergency-support@outbrain.
[UPDATE - Oct 31st 6:46am EST] - Serving still holds strong from our LA and Chicago data centers and we are not aware of any disruption to our service. We are working hard to recover our dashboard reporting capabilities, but it will probably take a couple more days before we’re able to get back to normal mode. Sorry for any inconvenience caused by this. Send us a note to am-emergency-support@outbrain.
[UPDATE - 6:51pm EST] - Again, not much to update – All is stable with both LA and Chicago datacenters. It’s the end of the day here in Israel and we are trying to get some rest. Our team mates in the US are keeping an eye on the system and will alert us if there is anything wrong. Good night.
[UPDATE - 3:35am EST] - Actually not much to update about the service. All is pretty much stable. we are safely serving from LA and Chicago. most back-end services are running in LA Datacenter and our tech team in Israel and NY are monitoring and handling issues as they raise. Our Datacenter vendors in NY are working with FDNY to pump the water from the flooded generator room so it will take a while to recover this datacenter
[UPDATE - 10:50am EST] - The clients dashboard is back up.
[UPDATE - 10am EST] – The clients dashboard on our site is periodically down – we are handling the issues there and will update soon.
[UPDATE - 5am EST] Our NY Data center went down. Our service is fully operational and we are serving through our Chicago and LA Data centers. If you’re accessing your Outbrain dashboard you may experience some delays in data freshness. We are working to resolve this issue and will continue to update.
[UPDATE - 2am EST] – Our NY Data center went completely off – We are fully serving from our Chicago and LA Data centers. External reports on our site are still down but we are working to fail over all services from the LA Datacenter. – we will follow with updates.
[Update - 12:50am EST] – power just went all off in our NY Datacenter and provider has evacuated the facility – we are taking our measures to move all functionality to other datacenters.
[UPDATE] - at 9pm EST] commercial power went down on our NY Datacenter. Provider failed over to generator and we continue to serve smoothly from this Datacenter. We continue to monitor the service closely and ready to take actions if needed.
Many of our internal applications were developed using the Extjs framework.
It is very difficult to write automated test to Ext application with selenium because Ext generates many <div> and <span> tags with an automatically-generated ID (something like “ext-comp-11xx”). Accessing these tags through Selenium is the big challenge we are trying to solve. We wanted to find a way to get these automatically-generated IDs automatically.
How do we approach this?
Ext has a component manager, where all of the developers’ components are being saved. We can “ask” the component manager for the component ID by sending it a descriptor of the component. To simplify – we (the selenium server) tell the component manager “I need the ID of the current visible window which, btw, is labeled as ‘campaign editor’”.
This will look something like:
ComponentLocatorFactory extjsCmpLoc = new ComponentLocatorFactory(selenuim);
Window testWin = new Window(extjsCmpLoc.createLocator(”campaign editor”Xtype.WINDOW));
Then we can to use Ext window method like close -> testWin.close();
Anther Example :
ComponentLocatorFactory extjsCmpLoc = new ComponentLocatorFactory(selenuim);
Button newButton = new Button(extjsCmpLoc.createLocator(“Add Campaign”, ExtjsUtils.Xtype.BUTTON));
You can ask for all of the visible components by type, by label or both:
TextField flyfromdate = new TextField( extjsCmpLoc.createLocator(ExtjsUtils.Xtype.DATEFIELD, 0));
TextField flytodate = new TextField(extjsCmpLoc.createLocator(ExtjsUtils.Xtype.DATEFIELD, 1));
Here’s a simple diagram of our solution:
link to project in git-hub : https://github.com/simbal/SelenuimExtend
This solution is Open Source. In the meantime, if you have any questions, feel free to contact me directly. Asaf at outbrain dot com.
At outbrain, we like things that are awesome.
Cassandra is awesome.
Ergo, we like Cassandra.
We’ve had it in production for a few years now.
I won’t delve into why the developers like it, but as a Sysadmin on-call in the evenings, I can tell you straight out I’m glad it has my back.
We have MySQL deployed pretty heavily, and it is fantastic at what it does. However, MySQL has a bit of an administrative overhead compared to a lot of the new alternative data stores out there, especially when making MySQL work in a large geographically distributed environment.
If you can model your data in Cassandra, are educated about the trade-offs, and have an undying wish not to have to worry too deeply about managing replication and sharding, it is a no-brainer.
Us Sysadmins fear change, because it is our butt on the line if there is an outage. With executives anxiously pacing behind us and revenue flushing down the drain, we’re the last line of defense if there is an issue and we’re the ones who will be torn away from families in the evenings to handle an outage.
So, yeah… we’re a conservative lot
That being said, change and progress can be good, especially when it frees you up. Cassandra is resilient, fault-graceful and elastic. Once you understand how so, you’ll be slightly less surly. Your developers might not even recognize you!
These slides are for the Sys Admin, noble fellow, to assuage his fears and get him started with Cassandra.
The Annotated Timeline
This graph may seem intimidating at first, so don’t be scared and let’s dive right into it… BTW, you may click on the image to enlarge it.
In this graph the x axis shows the time (date and time of day) and the y axis shows the svn revision number. Each colored line represents a single module (so we have one line for www and one line for the BehavioralEngine etc).
What you would usually see is for each line (representing a module) a monotonically increasing value over time, a line from the bottom left corner towards the top right corner, however, in relatively rare cases where a developer wants to deploy an older version of his module, then you clearly see it by the line suddenly dropping down a bit instead of climbing up; this is really nice, helps find unusual events.
In the next graph you see an overview of deployments per day. (click to enlarge)
This is more of a holistic view of how things went the last couple of days, it just shows how many deployments took place each day (counts production clusters only) and colors the successful ones in green and the failed ones in red.
This graph is like an executive summary that can tell the story of – in case there are too many reds (or there are reds at all), then someone needs to take that seriously and figure out what needs to be fixed (usually that someone is me…) – or in case the bars aren’t high enough, then someone needs to kick developer’s buts and get them deploying somethin already…
Like many other graphs from Google’s library (this one’s a Stacked Column Chart, BTW), it shows nice tooltips when hovering over any of the columns with their x values (the date) and their y value (number of successful/failed deployments)
Versions DNA Mapping
The following graph shows the current variety of versions that we have in our production systems for each and every module. It was attributed as a DNA mapping by one of our developers b/c of the similarity in how they look but that’s how far this similarity goes…
The x axis lists the different modules that we have (names were intentionally left out, but you can imaging having www and other folks there). The y axis shows the svn versions of them in production. It uses glu’s live model as reported by glu’s agents to zookeeper.
Let’s zoom in a bit:
What this diagram tells us is that the module www has versions starting from 41268 up to 41463 in production. This is normal as we don’t necessarily deploy everything to all servers at once, but this graph helps us easily find hosts that are left behind for too long, so for example if one of the modules had not been deployed in a while then you’d see it falling behind low on the graph. Similarly, if a module has a large variability in versions in production, chances are that you want to close that gap pretty soon. The following graph illustrates both cases:
To implement this graph I used a crippled version of the Candle Stick Chart, which is normally used for showing stock values; it’s not ideal for this use case but it’s the closest I could find.
That’s all, three charts is enough for now and there are other news regarding our evolving deployment system, but they are not as visual; if you have any questions or suggestions for other types of graphs that could be useful don’t be shy to comment or tweet (@rantav).
Recently we had to implement an active-passive redundancy of a singleton service in our production environment where the general rule is always have “more than one of anything”. The main motivation is to alleviate the need to manually monitor and manage these services, whose presence is crucial to the overall health of the site.
This means that we sometime have a service installed on several machines for redundancy, but only one of the is active at any given moment. If the active services goes down for some reason, another service rises to do its work. This is actually called leader election. One of the most prominent open source implementation facilitating the process of leader election is Zookeeper. So what is Zookeeper?
Originally developed by Yahoo reasearch, Zookeepr acts as a service providing reliable distributed coordination. It is highly concurrent, very fast and suitable mainly for read-heavy access patterns. Reads can be done against any node of a Zookeeper cluster while writes a quorum-based. To reach a quorum, Zookeeper utilizes an atomic broadcast protocol. So how does it work?
I recently participated in the ILTechTalk week. Most of the talks discussed issues like Scalability, Software Quality, Company Culture, and Continuous Deployment (CD). Since the talks were hosted at Outbrain, we got many direct questions about our concrete implementations. Some of the questions and statements claimed that Feature Flags complicate your code. What bothered most participants was that committing code directly to trunk requires addition of feature flags in some cases and that it may make their code base more complex.
While in some cases, feature flags may make the code slightly more complicated, it shouldn’t be so in most cases. The main idea I’m presenting here is that conditional logic can be easily replaced with polymorphic code. In fact, conditional logic can always be replaced by polymorphism.
Enough with the abstract talk…
Suppose we have an application that contains some imaginary feature, and we want to introduce a feature flag. Below is a code snippet that developers normally come up with:
While this is a legitimate implementations in some cases, it does complicate your code base by increasing the cyclomatic complexity of your code. In some cases, the test for activation of the feature may recur in many place in the code, so this approach can quickly turn into a maintenance nightmare.
Luckily, implementing a feature flag using polymorphism is pretty easy. First, let’s define an interface for the imaginary feature and two implementations (old and new):
Now, let’s use the feature in our application, selecting the implementation at runtime:
Here, we initialized the imaginary feature member by reflection, using a class name specified as a system property. The createImaginaryFeature() method above is usually abstracted into a factory but kept as is here for brevity. But we’re still not done. Most of the readers would probably say that the introduction of a factory and reflection makes the code less readable and less maintainable. I have to agree — and apart from that, adding dependencies to the concrete implementations will complicate the code even more. Luckily, I have a secret weapon at my disposal. It is called IoC, (or DI). When using an IoC container such as Spring or Guice, your code can be made extremely flexible, and implementing feature flags becomes a walk in the park.
Below is a rewrite of the PolymorphicApplication using Spring dependency injection:
The spring code above defines an application and 2 imaginary feature implementations. By default, the application is initialized with the oldImaginaryFeature, but this behavior can be overridden by specifying a -DimaginaryFeature.implementation.bean=newImaginaryFeature command line argument. Only a single feature implementation will be initialized by Spring, and the implementations may have dependencies.
Bottom line is: with a bit of extra preparation and correct design decisions, feature flags shouldn’t be a burden on your code base. By extra preparation, I mean extracting interfaces for your domain objects, using an IoC container, etc, which is something we should be doing in most cases anyway.
Eran Harel is a Senior Software Developer at Outbrain.