Productive data engineer
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I both love it and hate Hadoop - I love it because it provides and commodify easy to use, engineer friendly and scalable abstraction layer over cluster of machines. I hate it because of all the gotchas and vast knowledge it requires to be productive throughout the full Hadoop stack. In this talk I will focus, and share the knowledge necessary to be productive data engineer.
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Productive data engineer
- 1. How to Be Productive Data Engineer Rafal Wojdyla - rav@spotify.com Note: My views are my own and don't necessarily represent those of Spotify.
- 2. • Operations • Development • Organization • Culture
- 3. What is Spotify? For everyone: • Streaming Service • Launched in October 2008 • 60 Million Monthly Users • 15 Million Paid Subscribers + and for me: • 1.3K nodes Hadoop cluster
- 4. Automation
- 5. ME ADAM
- 7. + Puppet
- 10. Wild Wild West
- 11. Apache Bigtop
- 13. To enable log aggregation yarn.log-aggregation-enable = true yarn.log-aggregation.retain-seconds = ?
- 14. + <property> + <name>yarn.log-aggregation-enable</name> + <value>true</value> + </property> + + <property> + <name>yarn.log-aggregation.retain-seconds</name> + <value>315569260</value> + <!--retention: 10 years--> + </property>
- 15. Heap Memory used is 97%
- 16. Hellelephant
- 17. Custom logs • Profiling • Garbage collection
- 18. Right tool for the job
- 21. Right abstraction for the job
- 22. Scaling machines is easy, scaling people is hard
- 23. • Map split size • Number of reducers • HDFS data retention • User feedback (ongoing) Automation
- 24. Organization
- 26. Ownerless
- 27. Ownerless Squad
- 30. Culture
- 33. Spark spark.storage.memoryFraction (0.6) spark.shuffle.memoryFraction (0.2) In shuffle heavy algorithms reduce cache fraction in favour of shuffle.
- 34. Spark spark.executor.heartbeatInterval (10K) spark.core.connection.ack.wait.timeout (60) Increase in case of long GC pauses.
- 35. Learnings • Operations Automation • Development Abstraction • Organization Team • Culture Experiment
- 36. Join the band Engineers wanted in NYC & Stockholm http://spotify.com/jobs
Notas do Editor
- Hi – my name is Rafal and I’m an engineer at Spotify, in this presentation I will talk about how to be a productive data engineer. I will combine knowledge of multiple productive engineers at Spotify and touch different areas our your daily work life. I will use real world examples, failures, success stories – but mostly failures. So if you are or want to be a data engineer hopefully after this presentation every single one of you will take at least one ‘ahh’ moment with you – the moment when you learn something now. I hope this learning will improve you productivity , bring new feature to your infrastructure or maybe spark a discussion inside your team.
- We will go through lessons of productive data engineer and cover 4 different areas – operations, development, organization and culture. So we will kinda work our way from low level admin tips and spectacular disasters, after hard core operation we will talk about development on Hadoop – what to avoid, how Spotify is overcoming huge problem of legacy Hadoop tools. After development part we will take a look on how organization structure can affect your productivity and how one can tackle this problem, and finish with ubiquitous culture – how culture can help with in being productive. So in a way we start with low level scope or you productivity, how the cluster itself can affect you, how operators can help you out, to talk later talk about your development decision, structure of company and finish with how environment can influence your work. There will be time for questions at the end so please keep them till the end.
- Before we go deep into presentation let’s first talk about What spotify is. Spotify is a streaming service, launched in 2008 in beautiful Stockholm, Sweden. Current public numbers are that we have 60M monthly users, 15M subscribers. And what’s unique about Spotify service is that it can play a perfect song for every single moment, and some of this is powered through Hadoop which makes it even cooler! For my Spotify is also 1.3 Hadoop nodes – which is like a baby for a team of 4 people. A baby that is sometimes very frustrating, shit happens all the time and you have to wake up in the middle of the night and clean it up, but it’s our baby and we love it. Without further ado let’s move to the core of the topic and start with operations. If there’s one lesson that comes from operating hadoop clusters from a handful of nodes in the corner of the office to 1300 nodes – it will be AUTOMATION.
- Automation is crucial – especially if talking about Hadoop. Hadoop is huge beast to manage, there is loads of moving parts, loads of new stuff coming in and there’s always a reason for hadoop clusters to go down for some reason. If hadoop was not enough there’s always something that your company will push on poor operators – whether it’s a new linux distro or maybe there’s a bug in libc and you need to restart all the daemons and so on and so on.
- You want to be proactive and do as little as possible – without automation even coffee won’t help you. You want to be Adam on – be happy and work on new features, enhance hadoop – bring joy to hadoop users. You don’t want to be the poor operator on the left, focused primarily on putting out fires, exhausted. Btw this is the picture of Adam and me after 40 hours of hadoop ugrade from Hadoop 1 to 2 in 2013.
- So how to reach good enough automation of your cluster – let’s take spotify as example first – Spotify started with hadoop in 2009, very early, then the was a couple of tiny expansions, a short episode of hadoop in EMR, and we went back to on premise with shinny new 60 nodes – at that point we had to make a decision on how to manage hadoop – and because back then CM was limited and Ambari didn’t exist and cause Spotify loves puppet we have decided to use Puppet for this use case. It was rather big effort and took time, during which had had to drop some work, put out fires and work on Puppet, but it was a great investment. Today after a few iteration we like our puppet – as an example – most recent ongoing expansion is rather easy – we name the machines using proper naming convention and puppet kicks in installs all the services, configuration and keeps the machines in normalized state – very very important piece of our infrastructure. But wait – the slide says something about Ambari and CM – yes – cause if we were to set up a cluster today, we would most likely evaluate at least these two solution. Like I said Spotify basically didn’t have a choice and we settled on puppet and we are happy about it right now, but there’s huge leverage you can gain out of using these tools, loads of features that you get out of the box, that we need to implement ourselves. So if you are considering building hadoop cluster – make sure to give these tools a good try, them may not solve all your issues and use cases but for sure will bring loads of value and in time your will get even more features just from community – which is great, and is something that we are missing.
- That said – even if you decided to use Ambari of CM – mostly likely you will still need some kind of configuration management tool – whether it will be puppet, or chef or salt or whatever is your favorite – you will need one, there will always be some extra library that you need to install and configure or some user to create, ldap configuration and so on. There’s another interesting outcome of us building our own puppet infrastructure – we know exactly how our hadoop is configured – every single piece of it – which comes in handy in case of trouble shooting and so on. In this case we touched a little bit a problem of 3rd party solutions vs let’s implement our own tailored solutions. How many of you are aware of NIH problem?
- I will argue that there’s a number of cases and teams where this problem occurs at Spotify. NIH problem is an nutshell when you undervalue 3rd party solutions and convince others to implement your own solutions – in most cases this is a huge problem. The lesson that we have learned is that you need to give external tools a try, experiment but don’t expect something to solve all your problems – preferable define metrics of acceptance priori evaluation of tools.
- But what is actually very interesting in case of data areas is kinda sibling problem on NIH – which is NeIH – a problem described I believe by Michael O Church – it’s kinda opposite approach it’s when you overvalue 3rd party solutions, and end you in a messy place of glue implementation madness. There’s loads of great tools in BigData areas – not all of the work well with each other, not all of them do well what them meant to be doing. I urge you to be critical, sometimes implementation of your own tool or postponing a new, shinny framework from infrastructure may be a good thing to do – but it has to be a data driven decision that bring value. Think about this two problems, and ask yourself are there examples of such solutions at your company?
- To illustrate this I will tell you a real story – a story of great failure and success at the end. So we had an external consultant at Spotify – and his goal was to certify our cluster – basically 4 days of looking at different corner of our infrastructure. First two days went really smooth, we went through our configuration, state of the cluster and so on, we could not find a way to improve our cluster easily – which made as feel like proud, cause you know, we have this world class hadoop expert over and he can’t find a way to improve our cluster right. But oh boy was that a big mistake – so on day number 3, we are sitting in a room, whole team and consultant and due to miscommunication and misconfiguration our standby NN and RM go down – but that is still fine, cause RM starts in minute or two, standby can start in background – but unfortunately during the troubleshooting by mistake we have killed our active NN – at this point basically whole infrastructure was down – at our scale that means about 2 hours of downtime. It was bad! But wait for day number 4 – so next day we are sitting in the room, again whole time, consultant but also our managers and we listen to consultant saying that our testing and deployment procedures are like Wild Wild West and we act like cowboys – it was hard to listen to but he was right and we knew it. Next thing we do, was to go to a room with the team and come up something too solve this issue, we came up with something that may be obvious – a preproduction cluster – a cluster made out of the same machine profile and almost identical configuration that we will use for testing. But how to test was a real question. We went into research mode and started reading and watching presentation – we were especially impressed by tool called HIT by Yahoo, so we contacted the creator, unfortunately there was no plan to open source it – but they gave us a nice tip – look at apache bigtop.
- So apache bigtop primarily facilities building and deployment of hadoop distribution – but you can also us it in a slightly different way – you can point you bigtop at your preproduction cluster and use its smoke tests to test the infrastructure. So our current flow of testing and deployment is to first deploy to preproduction cluster, run bigtop tests – get instant feedback about the change, if feedback is fine deploy to production if not – there’s something wrong with the change and we know that before it is deployed to production. Some finding from using BigTop is that it’s actually very easy to extend so we were able to add smoke tests for our own tools like snakebite and luigi but also what is very important we also run some production workloads as part of smoke tests – which actually makes us feel sure about the change. So in case of Apache BigTop are problem was testing of hadoop infrastructure – even tho BigTop is not perfect for this it provides loads of value just out of the box and thus it’s a great example of preventing NIH problem.
- As an operator there are many ways to help yourself and also delegate some of the work to developers themselves – one disabled by default, but great feature is log aggregation – how many of you have log aggregation enable on your cluster? Cool. So in a nutshell this feature will aggregate yarn logs from workers and store them on HDFS for inspection, very useful for troubleshooting. So most of you probably know how to enable it right?
- It’s dead simple. But there’s one question – how long should we keep the logs for? So we thought about it for a while, talked with HW a little bit and since we have huge cluster why not story it for long time – maybe we will need these logs for some analytics etc.
- This is our initial change to configuration – 10 years. Does anyone know what bad can happen if you do that?
- If you run enough jobs after some time you will see something like this in your NN – and when you see something like this in your NN then you end up with hellelepahant!
- It’s a situation when your hadoop cluster spectacularly goes down. What happen is that log aggregation will create many many files, very important consequence of many files on HDFS is growing heap size, and when you get out of memory on NN. The lesson is that it’s good idea to alert on heap usage for your master daemons but also understand your configuration and its consequences, keep yourself up to data on the changes in configuration, read code of hadoop configuration keys and how they are connected between each other – no all configuration parameters all documented.
- While on the topic of log aggregation – it’s good to know that aggregation will take log directory and aggregate its content – so if you put extra information on task in there you can get aggregation for free thus likely bring value for developers – think about profiling and garbage collection logs. While talking about developer – let’s move to development and how it works at spotify.
- There is a couple of interesting lessons about productive development – first arguable most important is to pick right tool for the job – what is the current most important value to bring. Let’s talk Spotify – in 2009 Spotify started with hadoop streaming as a supported framework for MR development – hadoop streaming basically enables your to implement MR jobs in languages different than java – for many years it was THE framework – because Spotify loved python and it enabled us to iterate faster thus provide knowledge for our business. Time as passing and our hadoop cluster was growing – in time we needed something different something better when it comes to performance but also maturity. After long evaluation and I encourage you to watch presentations by David Whiting about different frameworks, we have decided to use Apache Crunch as supported framework for batch MR. Why – a couple of reasons – first ease of testing and type safety.
- This graph shows number of successful and failed jobs divided by framework for 6 month – and these are production jobs – as you can see two most popular frameworks are hadoop streaming and crunch – but the difference between failed and successful jobs is crucial. Crunch jobs act much better and have better testing. Type safety helps to discover problem at compile time and testing framework that comes with crunch that we were able to enhance with hadoop minicluster helps users to easily test their jobs – basically makes testing easy something that we missed for our hadoop streaming jobs. But performance is another thing -
- On this graph we can see map throughput in for apache crunch and hadoop streaming – there’s huge difference and again we are talking about production jobs here. Crunch turns out to be on average 8 times faster, 75% of all crunch jobs are much faster than all hadoop streaming jobs. What is more interesting is that we actually see higher utilization of our cluster the more crunch jobs we see on the cluster – which makes us super happy.
- Another thing that crunch provides is great abstraction – and that is another thing that productive developer need to keep in mind – pick the right abstraction for the jobs. In case of crunch we can start thinking in terms of high level operations like filter, groupby, joins and so on instead of old map/reduce legacy. This makes implementation more intuitive and simply pleasant – thus make developer experience much better. The interesting thing that we have observed is that higher abstraction may remove some of the opportunities for optimization thus it’s not as easy to implement the best performing job – but on the other hand it reduces problem with premature optimization but also on average performs really well – there’s very few people that actually know how to optimize pure MR jobs or Hadoop streaming jobs at Spotify – but average optimization that we get from crunch turns out to be really good as you could see on the performance graph.
- We do have loads of nodes – and we have scaling machines nailed down, crunch scales very well. But there’s big problem that we currently have: scaling people. How you scale support and best practices – we constantly see problems with code repetition, HDFS mess, lack of data management, YARN resource contention – all this bring our productivity down. There’s not enough time to go through all of them but some of this problems we are trying to tackle with nothing different then our beloved automation. Let’s see examples:
- We automate map split size calculation thus number of map tasks, but also number of reducers therefor number and size of output files – all this is done by estimation and historical data using our workflow manager Luigi – that I encourage you to take a look at! We about to finish our second iteration of HDFS retention policy that we automatically remove data therefor reduce HDFS usage and in long term hopefully reduce HDFS legacy mess. Another ongoing effort is second iteration of automatic user feedback – we already expose database with aggregated information about all MR jobs that our users can query and learn how their jobs are performing – but we also plan another iteration very simple iteration, focused on Crunch – that right after to workflow pipeline is done will provide user with instant feedback – memory usage, garbage collection and so on, very simple tweaks users can apply to improve their jobs – for example if a user gives a pipeline 8GB of memory for each task and after going though counters we see that tasks are actually using only max 3GB, instant feedback to reduce memory could improve multitenancy of your cluster thus improve productivity.
- With that let’s talk how organization structure can improve your performance – but before that let’s take a look at this graph.
- This graph show Hadoop availability by Quarter at Spotify, higher is of course better – ok – so let’s see what happen here:
- First part is hadoop cluster being ownerless, it was best effort support by team of people that mostly didn’t even want to do operations of hadoop, therefor multiple days of downtime happen, and infrastructure was in bad shape, denormalized – overall terrible state to be in. But there was a light at the end of the tunnel – in Q3 we have decided to create a squad – 3 people focused solely on hadoop infrastructure.
- There was instant right after squad was created – users were happy and infrastructure was getting in shape, one of the first decisions we have made was to move to yarn in Q4.
- Q4 and beginnings of ‘14 we again saw drop in availability mostly due to huge upgrade – and it’s consequences thereafter. The upgrade itself took whole weekend, and after the upgrade we saw many issues and fires that we had to put out, during this time we were mostly reactive but also working on polishing our puppet manifests. Whole situation stabilized after most fires were gone and puppet was in good shape.
- Our goal is too keep hadoop at 3 nines of availability – and we are getting there since Q2 2014, hadoop squad is receiving constant feedback from users and its common that hear that availability was drastically improved which improved productivity and overall experience, which is great and makes us want to work even harder to achieve better results. As you see there’s a small drop at the beginning on Q1 2015 – does anyone of you know why and can guess?
- With that lets now talk about what surrounds us – the culture – I strongly believe the culture at Spotify has huge influence on productivity - there are tree main pillars of culture.
- Experiment, fail fast and embrace failure. We love to experiment and we time to experiment whether it’s company wide hack week, R&D days – if only one wish to experiment there’s time to do that and there’s loads of curious people at Spotify there’s always something going on. The most successful data based experiments are Luigi – hadoop workflow manager and snakebite – pure python hdfs client – I encourage you to take a look at them. Fail fast - don’t be afraid to admit failure, keep it as part of learning process to the point of embracing it. Talk about your failures, share them publicly for example through presentations both internally and externally – it will make experimentation thus innovation flow much smoother. To back this up by example let’s talk about two most recent ongoing experiments.
- Another experiment is Spark – it pretty much ongoing experiment that we come back to every now and then, but officially it’s not welcome on production cluster due to immaturity and poor multitenancy support – that said most recent releases are very promising and we are constantly playing with it and have high hopes for it, especially about most recent dynamic resource allocation feature. There’s not so much time left but I would like to share with you two important lessons from our evaluation of a heavy spark job.
- First hint is about memory settings – there are two important settings that can improve stability of your heavy Spark jobs – memory available for caching – storage.memoryFraction and memory available for shuffle – shuffle.memoryFraction. The default settings are .6 and .2 leaving .2 for runtime. In our case we had a heave machine learning job that was doing almost terabyte of shuffle – but very little caching – initally we had issues with shuffle step, but reducing storage memory and leaving extra memory for shuffle and runtime improved stability.
- Another issue that we hit was long GC pauses – thus executors would disappear which in turns triggers recomputation and in the end potentially application failure. After tweaking hearbeat interval and ack.wait timeout was saw improvement in stability and even tho GC pauses still occurred they were less harmful.
- We will go through lessons of productive data engineer and cover 4 different areas – operations, development, organization and culture. So we will kinda work our way from low level admin tips and spectacular disasters, after hard core operation we will talk about development on Hadoop – what to avoid, how Spotify is overcoming huge problem of legacy Hadoop tools. After development part we will take a look on how organization structure can affect your productivity and how one can tackle this problem, and finish with obiqious culture – how you attitude can help with in being productive. Ok – let’s get this started with operations.
- Ok – if you were sleep the whole time – please wake up now for a few minutes