Ricon East 2013 Talk Summaries

By anders pearson 18 May 2013

This week I attended Ricon East, a conference organized by Basho, makers of the Riak distributed database. Riak was featured prominently at the conference but it was intended more as a conference on distributed systems in general spanning academia and industry.

For once, I brought a laptop and actually took full notes of every talk I went to (there were two tracks going on, so I could unfortunately only be at half of them). Last weekend, I even wrote myself a little toy wiki in Go (with Riak as the backend) to take notes in and really get in the spirit.

I’m a fan of Richard Feynman’s basic technique for learning, which can be summarized as “write down a summary of the topic as if you were explaining it to a new student”. It’s simple, but forces you to identify the areas that you don’t really understand as well as you think. Then you can go off and learn those areas.

Since distributed systems are an area that I’m deeply interested in and trying to learn as much as I can about, I decided to use this as an excuse to apply the Feynman technique and write up summaries of all the talks I saw. The following summaries are explanations largely from memory, informed by my notes, of my understanding of the content of each talk. I’m sure I got some stuff wrong. A lot of the content was in areas that I’m not experienced with and only loosely understand. I was typing notes as fast as I could while trying to listen at the same time, and I probably misheard some things and mistyped others. If you were at the talks (or just know the subjects well) and have corrections, please let me know. As I find links to speakers’ slide decks, I’ll try to add those.

First though, a few words about the conference overall. This was one of the best run conferences I’ve been to in a long time. Everything mostly ran on time, there were only a few minor technical problems (though the projection screens in Stage 2 were pretty rough looking), the venue and catering were excellent, it was full but not overcrowded, and the talks were consistently well-executed with a good range of accessibility and depth. If my “summaries” seem long, it’s because all of the talks were really that information dense. I’m leaving out a lot of detail.

Automatically Scalable Computation

by Dr. Margo Seltzer, Harvard SEAS

video, slides

Margo Seltzer and her grad students at Harvard have been working on a new technique for automatically parallelizing sequential code at the VM/runtime level.

The idea begins with looking at a running machine in terms of the full set of registers and memory as a single (very large) bit vector. If you know the full state of the machine at a given time, it should be completely deterministic (we are excluding all external IO from this model). An instruction executing is just a function on the input state that produces another state as the output and computation is just this process repeated. You can view computation as a trajectory through this large-dimensional state space. A naive approach to thinking about making that computation parallel is to pick N points on the trajectory and have each processor start at those points (this makes more sense with a diagram, sorry). Of course, that presupposes that you know what the final computation trajectory would look like, which is clearly not realistic (and if you did know it, you might as well jump straight to the end) as it would require having pre-computed every state transition in the entire state-space ahead of time (and having it all stored where the outputs can be retrieved).

Seltzer’s approach is a variation on this naive version. The proposal is to say: instead of being able to have each processor pick up at exactly the points on the trajectory that we know the program will hit, what if we can come up with reasonable guesses that put us in close to the same area? If that’s possible, then parallel processes could work to refine from there and speed up the computation. Indeed, it turns out that real world programs tend to follow predictable enough trajectories through the state space that this might be reasonable.

The overall system involves a few components. There’s a VM, which takes the input state vector, knows how to find the instruction pointer in that vector, will look up the current instruction, run it, and generate an output state vector. Then there are components that look at pieces of input vectors and use different strategies to try to predict pieces of output vectors. Eg, some operate at a single bit level and make predictions like: if a bit was 1 last time, it will be 1 next time. Others work at a 32-bit word level and do, eg linear regression.

Aggregators combine those individual predictors and make larger predictions by running a given input vector though N instructions and adjusting the weights of each of the predictors with each round. Basically, very simple machine learning. There is a trajectory cache which maps input vectors to known output vectors.

A key insight here is that instead of caching the entire state vector each time, the system pays attention to which bits in the inputs are read and written to during the course of execution. So while a given state vector may be huge (since it encompasses all of the program’s memory), in the course of running 1000 instructions from a given start state, only a small subset of that memory will be read or written and the vast majority of the memory and registers will remain whatever they were before. So the cache takes advantage of this and stores the masks along with the values of the bit vectors in those masks. On a lookup, it can see if the current state matches any cached mask+value pairs. If it does, it can change only the bits that the cache entry says get changed, leaving the rest of the vector intact. What that is analogous to is taking a linear transformation of the cached trajectory fragment and applying it to the input vector.

Implementation of their system is still immature, but Seltzer shows that they are able to get pretty good speedups on real physics problems written in C and compiled with normal GCC. It’s nowhere near linear speedup (with the number of additional CPUs) and is not as good as a programmer could do manually (all their examples are for embarassingly parallel problems). A 10x speedup from 128 cores sounds bad but she points out that at some point in the future your laptop will have that many cores and this kind of automatic parallelizing will be the only way that you’ll be able to make use of most of that. She’s also happy to note that there are still a large number of “stupid” things that the implementation does, so there’s plenty of room for it to improve.

ZooKeeper for the Skeptical Architect

by Camille Fournier, VP of Technical Architecture, Rent the Runway

video slides

Camille presented ZooKeeper from the perspective of an architect who is a ZooKeeper committer, has done large deployments of it at her previous employer (Goldman Sachs), left to start her own company, and that company doesn’t use ZooKeeper. In other words, taking a very balanced engineering view of what ZooKeeper is appropriate for and where you might not want to use it.

ZooKeeper is a distributed consensus and distributed locking system that came out of Hadoop and is now safely part of the Apache project. In the last few years, it seems to have worked its way into many places: neo4j, Apache Kafka, Storm, Netflix, OpenStack, ubiquitous in cloud computing, SaaS, PaaS, and at any organization that deploys very large distributed systems. This makes the systems that don’t use Zookeeper notable: Riak, Cassandra, MongoDB, etc.

A few reasons that ZooKeeper isn’t used there: 1) from a CAP theorem perspective, ZooKeeper favors consistency over availability. It requires a quorum of nodes and will not run in a reduced capacity. That makes it an inappropriate match with systems like those mentioned above that favor availability via eventual consistency. 2) ZooKeeper has a very high operational complexity. It requires a hard minimum of three nodes, takes a fair amount of resources to run (Java…), and the ZooKeeper project strongly advises against colocating ZooKeeper nodes on servers that are running other services, or running them on virtual servers. Ie, you really should have at least three dedicated bare metal servers just for ZooKeeper to use it properly. If you are running a Hadoop cluster or a big SaaS operation, that’s probably not a problem. But if you’re a vendor like Basho trying to get people to use your product, adding a ZooKeeper dependency makes it a hard sell. She has also experienced first-hand the problem that an out of control client that doesn’t understand ZooKeeper can write too much data to it, filling memory (ZooKeeper needs to keep all its data resident in memory), and taking down the entire ZooKeeper cluster. Facebook and Google (Chubby is their equivalent to ZooKeeper) both deal with that problem by enforcing strict code review policies on any code that goes near the distributed locking. But at some level, you need to be able to trust any developers that are accessing it.

Why would you use ZooKeeper though? Why is it so popular? She divides the functionality into two main categories: distributed lock management, and service management/discovery. Distributed locking lets you do things like failover of nodes in a large system safely. If you have traditional database servers with master/slave replicas and a master goes offline, you need to promote one of the slaves to be the new master. If your system can’t agree on which slave to promote and two try to claim the crown, it’s a disaster. Likewise, if a slave gets promoted and then the original master comes back online because it was just a network partition and it doesn’t realize that it’s been replaced, it will be disaster. ZooKeeper can be used to absolutely guarantee that only one of the database servers holds the “master” title at a given time. Service management/discovery would be, eg, the rest of your system being able to be notified that there is a new master that they should direct their writes to. Ie, having that be able to happen automatically based on events in the system rather than someone manually changing a config file settings and doing rolling restarts on the cluster to get that change reflected. Once a distributed system scales past a certain point, doing that kind of thing automatically rather than manually becomes crucial.

Distributed locking is typically needed if you have components that are not designed with a distributed, eventual consistency or immutable data kind of mindset from the beginning (eg, scaling out traditional databases rather than something like Riak). Service management can often be achieved to acceptable levels with other techniques like load balancing proxies or DNS (see a blog post from Spotify on how they are using DNS instead of ZooKeeper), or by storing service information in a shared database. Those all have their own scaling issues and potential problems, but are often simpler than running ZooKeeper). Ultimately, she argues that you should run ZooKeeper if you have one of the above use cases as a requirement, you have the operational resources to justify it, and you have rapid growth and need the dynamic resource allocation.

Scaling Happiness Horizontally

by Mark Wunsch, Gilt Group

video slides

Mark’s talk was focused on Conway’s Law, roughly “organizations which design systems are constrained to produce designs which are copies of the communication structures of these organizations” and its converse, that the software architecture at a development shop influences the communication structures of the organization in turn. Gilt’s development team wanted to optimize for developer happiness and used this approach as their fundamental philosophy.

When most companies start out, they have one monolithic architecture, platform, framework, etc. That gets them quite a ways and then they begin to feel pain as they outgrow it. Eg, Twitter’s beginnings on RoR, moving to JVM, Scala, etc. That pain manifests in development but also in a monolithic organizational structure. Eg, only one engineer might understand a crucial part of the system and they become a bottleneck for the rest of the organization. It’s a classic concurrency problem and managers become thread schedulers.

They view developer happiness as a dining philosophers problem. At any given time, a developer can either code or think. A developer who is only able to code and has no time to think burns out and is unhappy. A developer who only thinks and can not code (due to organizational bottlenecks like above) gets frustrated and is unhappy. These are deadlock and livelock problems at an organizational level. Gilt sought to eliminate them the way a distributed systems programmer would. Find the mutex and eliminate it. Make it a proper lock-free distributed system from the ground up.

What they ended up doing was breaking up into small autonomous teams and changing their architecture to “LOSA”, “Lots Of Small Applications”, basically SOA for every component in use. Teams then have “initiatives”, “ingredients”, and “KPIs”. Initiatives are projects that they expect to work on for a while. Key Performance Indicators, or KPIs are metrics used to track the progress of those initiatives. They must be agreed on between the team and the management and simple to calculate. “Mission statements are an antipattern; they’re just a less useful version of a metric”. Teams are otherwise autonomous. They self-select and pick initiatives to work on. Teams are organizationally flat and made up of “ingredients” like designer, architect, developer, ops, etc. “Job titles are an antipattern; they let people say ‘that’s not my job’”.

There is a spectrum of communication styles from “hive minds” with tightly coupled, synchronous, informal communication to “pen pals” with loosely coupled, asynchronous, formal communication. A team should be a hive mind and teams should interact with other teams as pen pals. They apply CAP theorem to their organizational design and choose A over C. The result is that there is sometimes inconsistency between teams. Eg, it’s OK for their microsites to each have slightly different UI elements temporarily as one team advances that component faster than another team is keeping up with. Eventual consistency will bring things in line in a while and in the meantime, it’s better than deadlocking until the teams can achieve consistency. Other organizations like Apple, or small startups probably would value consistency over availability.

Gilt’s preferred communication tool is code reviews. They use Gerrit. A code review gives them a permalink associated with every change along with a history of the communication and decision making process associated with that change.

Firefighting Riak At Scale

by Michajlo Matijkiw, Sr. Software Engineer at Comcast Interactive Media


This talk was basically “we did a very large Riak deployment when Riak was very young, found a bunch of problems, solved them, and kept everything running”.

Comcast wanted to use Riak to expose a simple, easy to use key/value store to their developers. They weren’t necessarily interested in massive scale or performance. They built a small object wrapper around the Riak client that hid complexity like vector clocks, added monitoring and logging, and made it very simple to store objects at keys. It was an enterprise system, so they did enterprise capacity planning and load testing. Everything worked well until they started getting a lot of HTTP 412 errors. Those occur when you do a sequence of GETs and conditional PUTs based on eTags and a pre-condition fails. What they discovered was that overall latency in the cluster had gone way up. Not enough to trip their alarms, but enough that it was causing other problems. The analogy is “if you leave your apartment door unlocked while you run out quickly to switch your laundry, it’s probably ok, but if you leave it unlocked for a week while you go on vacation, you’ll come home to find everything gone”. High latency was leaving a large window for other inconsistencies to show up. Machines were getting so overloaded that they were overheating the machines next to them causing cascading failures that way.

When hardware failed, they figured out that they could abandon a bad node, then reintroduce it to the cluster as a new node and that worked as a quick fix, so they did that a lot. That became their ops solution to all problems. They started getting some failures that were not hardware failures, but corruption to the bitcask data stores. They decided to use their usual fix even though Basho kept telling them not to do that. When they rejoined the bad nodes, they found that not all the data was getting read-repaired. Their N-values weren’t set correctly and the old nodes that they had been replacing hadn’t been replicating data. It had been fine up until the point where they reached a threshold and all the replicas of some pieces were gone. They ended up having to do surgery on the bitcask files, which are append-only log structures. So they went in and replayed them up until the point of the corruption, getting back all the data from before that point, and then forcing the cluster to read-repair everything after that. Riak has since built in detection and correction for that situation.

Retrospective lessons: better monitoring of utilization and latency were needed. They needed to spend more time with clients showing them proper ways to use (and ways not to use) the system, and they needed to introduce proper choke points so they could throttle traffic to limit damage.

Bloom: Big Systems From Small Programs

by Neil Conway, Berkely PhD candidate.

video, slides

This talk was a continuation of Neil’s talk BashoTalk on CALM computing and his professor’s talk from the first Ricon, which was a big hit. This one reviewed the basic ideas and introduced the Bloom language that he’s been working on which implements the ideas.

Distributed computing is the new normal (between mobile devices and cloud computing). You are all building distributed systems whether you want to admit it or not. But our tools are still built for traditional problems. The kinds of optimizations in distributed systems are new (global coordination, locality, etc) and compilers don’t know about them. They can’t detect deadlocks, replica divergence, or race conditions. We have no tools for debugging an entire system. We know how to build distributed systems, but we don’t know how to make developers productive and efficient at writing them. We can do better, but we need new approaches.

The two main approaches to date are either: enforce global order at all nodes with distributed locks and consensus, allowing us to only have to consider one possible event ordering, but paying the price in terms of availability and increased latency penalties. Or, we can ensure correct behavior for any event ordering with “weak consistency”, but that makes race conditions and deadlocks hard to reason about and is a major burden on developers.

His approach involves Bounded Join Semilattices, which are just a mathematical way of talking about objects that only grow over time. The core is a monotonic merge() function. The classic example is Sets with union as the merge() function. Eg, if you start with {a}, {b}, {c}, you can merge() those into {a,b}, {a,c}, {b,c}, and merge() again into {a,b,c}. The order that any of those merge’s happened in doesn’t matter; you will end up at the same result eventually. Ie, merge() is commutative. You can also think about Integers with max(), Booleans with OR(), etc. These lattices form the basis of “convergent replicated data types” like sets, counters, graphs, and trees. If you can express your computation in terms of CRDTs, you can build systems that are easy to reason about mathematically and do not depend on the ordering of events. Neil calls this “disorderly data” and “disorderly computation”.

CRDTs and asynchronous messaging form a core model that eliminates inconsistency. However, not everything can be expressed in terms of monotone functions. So they’ve been working on a language, Bloom, that has those primitives and has syntax to explicitly mark non-monotone parts, allowing the compiler to reason about the overall program and point out to the user which parts are safe and which are not (and would require some kind of synchronization).

One of the key techniques is to use immutable data wherever possible. Neil showed the example of a shopping cart model. There would be multiple SessionApps involved and a Client that adds and removes a number of items, possibly hitting a different SessionApp each time, eventually checking out. Instead of having a shared mutable state representing the cart, each SessionApp keeps a log of add and remove operations; adding entries to those logs and merging logs are monotonic. They can be completely inconsistent during most of the process, only synchronizing at the final checkout point, which is non-monotonic, but it’s intuitive that that is the point where synchronization needs to happen.

Bloom is implemented as a Ruby DSL (but they really don’t care about Ruby) with async message passing actors added and rule triple syntax. Each triple is a left-hand side, an operation, and a right-hand side. The engine looks at the RHS values, and if they are true, sets the LHS values to true, using the operator to track how the two are related in terms of time and location. Eg, same location, same timestamp means that it is a traditional computation. Same location, next timestamp means that it’s some kind of persistence operation like storing data or deleting something. Different location, non-deterministic timestamp implies that the rule application represents communication between parts of the system. The result is concise high level programs where state update, asynchrony, and non-monotonicity are explicit in the syntax.

Just Open A Socket – Connecting Applications To Distributed Systems

by Sean Cribbs, Software Engineer at Basho

video, slides

Sean Cribbs has written many of the client libraries for Riak and presented on techniques and lessons learned writing client libraries for distributed systems.

Riak supports streaming operations, which are very good for performance. The client can ask for a set of results and get individual notifications for each result as the cluster sends it back. This lets it act on each ASAP instead of having to wait for the full set, which is a huge win. You would normally do that by setting up an iterator that runs over the results. The Ruby client does that with code blocks and uses libcurl for the HTTP communication. Each block is a closure that will execute on each response that comes in. That worked well until he started getting lots of errors. Eventually tracked it down to connection caching in libcurl to avoid setup/teardown overhead. That turned out to be implicit global state and made the blocks non-reentrant. The fix was to use multiple connections (which you ought to be doing when building a distributed system anyway) and pooling them.

His second example involved a customer complaining that Riak 1.2 was performing 20% slower than the previous release on their queries. They were pretty sure that all the changes they made would only improve performance and couldn’t duplicate the problem. The customer was using the Python client and ProtocolBuffers instead of HTTP and making mapReduce requests over secondary indices. They were eventually able to duplicate the problem in test clusters when they used the same clients access it. Did a DTrace and optimized a bunch of things but still didn’t understand why it was slowing down so much. Eventually noticed that it was making a LOT of calls to gen_tcp:send() with very small payloads, just a couple bytes each, but each call was taking ~30ms. Essentially, since it was doing mapreduce over index data rather than the main data, the queries and responses were both very small and they were running into Nagle’s algorithm. Nagle’s algorithm is a low level optimization in the TCP/IP stack that tries to maximize network throughput by bunching up traffic to/from the same hosts into large enough packets to be efficient. In a nutshell, it sets a threshold of a few KB and collects smaller packets, holding off on firing them all off until it passes the threshold or a timeout passes. For most network traffic, that makes much more efficient use of the network and saves a lot of switching overhead that would be incurred by sending out every small packet as soon as it can. But in this case, they were hitting a pathological case and it was killing their performance. Their solution was to buffer the requests themselves so they could control the latency instead.

Distributed systems fail in very interesting ways. Clients often have a hard time telling all the different types of failure apart. Sean recommends dividing it into two main classes: system related or network errors such as network failures, connection refused, etc. and unexpected results such as quorum failures, key not found, bad request, server side error. The first class can usually be dealt with by doing a retry, preferably with an exponential backoff. The second type can be handled with a retry only if the request is idempotent, but you need to be very careful to make sure that requests are really idempotent. A third class of failures are timeouts and those are the trickiest kind to deal with. He doesn’t have a good solution for what to do then. Often a retry, but not always.

Overall, he suggests opening multiple connections, keeping them isolated, connect to as many peers as you can (rather than rely on intermediate load balancers), know the structure of your entire network stack well, and keep in mind that clients are not passive observers of the system, but active participants whose presence changes the system.

Nobody ever got fired for picking Java: evaluating emerging programming languages for business-critical systems

by Alex Payne, Co-Founder at Breather, wrote the O’Reilly Scala book

video, slides

Alex Payne wrote the O’Reilly Scala book, but considers himself a true polyglot programmer and also organizes the Emerging Languages Conference every year. That conference has seen 48 new languages presented since 2010. So the problem is that there is so much to build, so many languages, and no good way to choose what language is appropriate for a given task.

There are existing “crappy” solutions to the problem. You can make a totally arbitrary selection. You can make it a popularity contest (slightly better than the first option). Or you can do “Design By Hacker News” and pick the language and technologies that seem to have the most buzz online.

He presents, instead, an evidence based approach to language selection. The first problem with that approach is that you need to avoid subjective criteria. He includes in that category: readability, terseness, productivity, agility, and mindshare. He does argue though that there are a few subjective criteria which have some value when evaluating emerging languages. Developer appeal: some programmers are just excited by novelty. Strategic novelty: you get to invent your own design patterns for the language. This promotes sort of a zen beginner mind mentality, which can be good for problem-solving. Homesteading: you may get to write the basic, fundamental libraries. This is fairly easy since you have examples from other languages to copy and improve upon. Malleable roadmap: emerging languages are often still in search of their ideal problem domain and if you’re an early adopter, you can help move it in a direction that’s useful to you. Finally, accessible community: you will probably be able to gain easy access to the small number of people involved in writing and driving the library.

He then sets out the actual objective criteria that can be used for language choice: performance (mostly), library breadth, available primitives (eg, Actors and CSP in languages like Erlang and Go), stability of syntax, security history, and development cycle (how often are new versions of the language released and how is backwards compatibility, etc handled). With those criteria, he recommends taking the very boring MBA like approach of setting up a weighted matrix and analyzing each option with it. Programmers are disappointed when he gives them that advice, but he doesn’t know of a better approach.

Once a language is chosen, he does suggest three strategies for succeeding with an emerging language: First, acknowledge the risk, and hedge against it by also working in a more traditional, “safe” language. Eg, start up a new project in the new language, but keep your old code going alongside it. Next, involve yourself in the language community. You can help shape it, you will be on top of potentially dangerous language changes, and you will have more ready access to the language experts to call upon if you have trouble. Finally, play to your pick’s strengths and use another language where it’s weak. He points out that if you look at any large development organization like Google, Facebook, Apple, or even Microsoft, they are all polyglot environments. He doesn’t know of any even moderately large organization that has managed to truly standardize on only one language for development.

High Availability with Riak and PostgreSQL

by Shawn Gravelle and Sam Townsend, developers from State Farm

Two developers from State Farm talking about rolling out a nationwide platform to be allow all their customers to interact with all their services through all channels (mobile, web, phone, and at terminals in their offices). Their priorities are availability, scalability, performance, supportability, and cost in that order. Their applications layer requires three nines of uptime (about eight hours of downtime per year allowed) and the infrastructure layer itself should perform at four nines (52 minutes a year of downtime).

At a high level, they had a greenfield project. Planning three sites active, each would get 100% replicas of the data. All infrastructure would be virtualized, use automatic provisioning, and would be oriented around services. Application and service development is Spring and Java, using PostgreSQL for relational data and Riak for non-relational, all running on top of RHEL.

For the Riak part, they had a seven person team, needed to hook it into their existing monitoring, security, and testing setups. The needed to get replication latency between data centers low and consistent. Each site would have a customer facing cluster, the business area would get their own full silo starting at a five node cluster and growing as needed, an internal-facing cluster with read-only replication and lower availability requirements that would be used for running batch analysis jobs that they didn’t want impacting the customer facing clusters, and a backup cluster that would get a full replica set, then be shut down and the filesystem snapshotted once per day for off-site backups. They wrapped Basho’s client jar in their own which they added monitoring, security, and some data type restrictions to which was then the single interface that the application developers would have to access the cluster.

They had very strict security requirements which they addressed by running every node behind Apache so they could re-use their existing security infrastructure. Their custom jars also had strict logging requirements and ensured that all operations were logged with all the relevant client info. The downside was that they only had per-node security granularity. So all of their internal applications could potentially access the data stored by other applications.

Their main challenges deploying Riak were that they did not have much experience using non-relational data storage and had to do a lot of internal education to teach application developers how to make best use of it, when to use relational datastores instead, etc. Eg, developers would contact them asking them to create buckets for them because they were used to the RDBMS model where they would define a DDL and hand it off to DBAs to create the table structure for them before they could use it. They had to explain to them that with Riak, you just start saving data in the structure you want and it is created on the fly. Developers also had a lot of doubts about performance characteristics which they addressed by doing heavy load testing and benchmarking ahead of time so they could preempt most of those. They maintain three full copies of the ENTIRE cluster setup that code has to pass through on a deployment pipeline: testing, load testing, and pre-production.

They used both PostgreSQL and Riak alongside each other to play to each database’s strengths. Their background is heavily relational and PostgreSQL is very smart and has a lot of functionality that they find extremely useful like spatial data awareness. Riak lines up perfectly with their five priorities but tries to be “dumb” about the data that’s stored in it (in a good way). PostgreSQL has issues with size, both in terms of scaling the entire database up and also with individual row sizes (if you go over about 8KB, it works but can negatively affect performance) and a lot of their data objects are large forms and documents that are acceptable to be opaque to the database but can grow quite large. PostgreSQL doesn’t scale writes out as far as they’d like with a standard master-standby replication scheme, making it a single point of failure. Auto sharding tools exist for PostgreSQL but they aren’t as useful for them since they are acting as a service provider and don’t necessarily know much about the schema or access patterns of the application data. Riak is transactional internally but does not participate with the applications’ transactions the way a RDBMS does. They developed a “pseudo-transactional” approach where they start a transaction in Postgres, write data to Riak, wait for it to succeed/fail and then commit or rollback the Postgres transaction. That works though it’s not ideal in all situations.

They highly recommend logging and automating everything. If you have 52 minutes a year of downtime allowed, you can’t waste any of that doing things manually or risk human error during those short windows. However, be careful to not automate decision making. Eg, automate failover, but make recovery after the failover be manually initiated. That helps prevent cascading failures where there are bugs in your automated processes. They found that planned outages were harder to deal with than the unplanned ones. Postgres upgrades w/ replication involve locking both sites temporarily and dealing with all the traffic backing up. On unplanned, it crashes so there’s no traffic. Plus, on unplanned outages, people expect some data loss and rollback; on planned ones, they expect none.

How Do You Eat An Elephant?

by Theo Schlossnagle and Robert Treat, at OmniTI

video slides

Two consultants at OmniTI, who focus on web scalability and actually implement systems for their clients instead of just advising. Existing monitoring tools were not sufficient for the deployments they were doing so they decided to build their own to handle the large scale that they work at. In particular, they deal with multiple datacenters which don’t necessarily have connections to each other so most fully centralized monitoring systems like Nagios don’t work. RRD backed monitoring like Munin (Graphite and its Whisper data store didn’t exist at the time) typically roll old data out after a year but their clients are largely interested in looking at multi-year trends (“how have our traffic patterns changed over the last four years?”) so that wasn’t a good solution.

They picked Postgres to back their system because they had a Postgres contributor on staff and had experience scaling Postgres out to databases in the hundreds of terabyte range. They built it and it worked well, but it was hard to install, with quite a few components that had to be installed and configured correctly on each instance. Plus, once it was installed, their clients didn’t necessarily understand how to make the best use of it: “when you download the tool, you don’t get the philosophy.” So they decided that the best solution was to “shove it down their throats as an SaaS”.

They rebuilt it as a SaaS, still on Postgres and it was wildly successful. Which means it was a nightmare. Since anyone could hook up new systems to be monitored without going through them, growth was unpredictable and faster than they expected. They discovered that if you have monitoring as a service, it’s far more sensitive to downtime than just about any other system you could imagine. When all your clients are using it to track their uptime, if you go down, all of their system graphs have a gap where you were down, so everyone notices. The CEO comes in on Monday morning, looks at the traffic graphs for the weekend, sees the gap and panics. So the tolerance for downtime was now much lower than before.

They planned on storing data rolled up every five minutes for each metric, averaging about 50 metrics per server, and storing that data for up to ten years (their definition of “forever” for internet companies). That comes out to 50 million rows of data per server. They went through quite a few steps to get this manageable, and developed some clever tricks with Postgres array data types and schemas to let them mix real-time metric collection with batch-mode rollups of the data. Storage growth and performance were basically handled.

The major issue became fault tolerance. PostgreSQL is rock solid, but the hardware it runs on would still fail sometimes and they wanted to be able to do updates. In a cluster as large as they were running, these failures happen regularly. PostgreSQL can do reasonably good fault tolerance with a replication setup but it fundamentally expects you to be able to pause traffic while it does some of the more sensitive failover operations (because PG’s focus is on guaranteeing ACID and consistency rather than availability). With their incredibly low tolerance for downtime, this was becoming seriously difficult.

They decided that long term they needed to switch to a column store of some type that was simpler to deal with from a fault tolerance perspective. They ruled out commercial offerings like Oracle RAC and Teradata (would not fit with their pricing model and require dedicated DBAs), Hadoop (their data is heavily structured), Cassandra (column-store, which is good, but apparently Cassandra insists on being able to delete old data when it feels like it? I’m not too familiar with Cassandra, so I’m not sure what they were referring to), and took a close look at Riak. Riak was a very close fit for what they needed but at the time it didn’t have a column-store backend (LevelDB wasn’t implemented yet) and it had more overhead than they needed (all their data operations were append-only, so Riak’s entire model for eventual consistency would be wasted on them) and eventually decided to just design their own simple column-store database for their service.

They built “Snowth” in eight programmer weeks over three months (and put four programmer-months more into it over the next three years), and managed to achieve a 93% reduction in storage usage and 50-60% reduction in IOPs and it no longer had a single point of failure. They ran the two systems in parallel for 14 months, slowly shifting more and more of the traffic to Snowth via feature flags as they gained confidence in it, eventually shutting off PostgreSQL.

Realtime Systems for Social Data Analysis

by Hilary Mason, chief data scientist at Bit.ly

video slides

Most of us are familiar with bit.ly and use it regularly. The basic business model is that one person takes a long link, puts it into Bitly to get a shorter version, then shares that link with their friends on various social networks. Bitly sees what links people are putting in to share, and then they can follow that link as it is shared and other people click on it. From this they can do basic demographics and learn a lot about what people share, how links propagate through social networks, and they can sell that insight to advertisers and marketers.

A typical link’s traffic spikes quickly soon after being posted and then trails off over time. This pattern has caused bitly to be very focused on real-time or near real-time analysis and they’ve been doing more experiments with real-time visualization and summarizing of the data that comes through their system.

Hillary Mason’s job at Bitly is to guide the analysis and guide their strategies for taking a stream of raw data and telling stories with it. The talk was peppered with frequent fun tidbits of things that she’s learned about peoples’ link sharing and consuming habits. Eg, the internet is more than half robots (shouldn’t surprise anyone), pictures of dogs were shared more than pictures of cats (the internet, apparently, is not made of cats), people share links that make their identity look good (news, technology, science) but they consume links about gossip, sports, and sex (every employee that she’s seen start there eventually goes through a phase of getting really depressed about humanity based on how much traffic articles about the Kardashians get).

On a technical level, she went through a few different real-time components of their stack. First is a simple classifier to determine the language of a link. Basic “is this English or Japanese?” kinds of questions. Originally, they used Google’s service for this heavily until Google shut it off, but it was always less than perfect anyway. The standard machine learning approach to this problem is supervised learning. Get a large corpus of text that’s been marked out by language (these are readily available), train your classifier on it, and get a set of weights for a Bayesian network that can classify new content. This works well for standard text, but fails on short bits like tweets, social network status updates, and image sharing. They realized that they have access to more than just the content though and started feeding the browser request headers and server response headers in as well, paying close attention to character set encodings, etc and using those to adjust the weights. They did something similar for detecting robots vs humans but instead of using the content, they found that the best indicator was actually the spike and trail pattern of the traffic over time. Any link that has a different pattern tends to be something other than a human sharing links with other humans (eg, people wrap JavaScript embed links in Bitly or the links to avatar images and those get automatically included all over the place).

The other side of their experimentation has been around the visualization and real-time mining of different kinds of trends. Eg, supporting real-time search, which means “show me links mentioning term X that people are sharing right now”, automatically identifying bursts from the traffic patterns (eg, on the day of the talk, the algorithms could easily tell that Angelina Jolie was in the news), and how to group articles together into related “stories”. They built real-time search with the Zoie Solr plugin running alongside a Redis cluster that held scoring info. When a search comes in, they search Solr to find links that match the term, then query Redis for scoring info to sort the results. Links that hadn’t been shared in the last 24 hours automatically fall out of those databases, so they stay a pretty steady size.

Bit.ly was originally written in PHP but that didn’t last very long. Now, typically as new features are developed, she describes them as going through one phase of badly written Python, then a phase of much better Python, then, when they are proven, well-understood and obviously need to be faster, they get rewritten in C or Go.

Using Datomic with Riak

by Rich Hickey, creator of Clojure + Datomic


Datomic is the distributed database developed by Rich Hickey, the creator of the Clojure programming language. Datomic is not open source (boo!), but integrates tightly with a number of open source applications like Riak.

Datomic is a fairly radical new approach to databases and at some level aims to operate in a space that isn’t really exactly what other databases do. Hence, he views Datomic as complementing RDBMSs and distributed databases like Riak rather than competing with them. That idea is reinforced by Datomic’s implementation which doesn’t have its own storage model and instead uses Riak or any number of other databases as the underlying storage, treating them as opaque datastores in the same way that a regular database treats the filesystem as opaque block storage.

Datomic aims to be a sound model of information with time and brings declarative programming to applications, reducing complexity. In Rich’s view, where you typically see an impedence mismatch between databases and applications, the application is wrong. Declarative systems like SQL are right and the application is wrong. Datomic wants you to be able to treat the entire database as a single value that you can calculate on.

Datomic built around a database of atomic facts (called “datoms”). You can assert and retract datoms. Datomic keeps all of those assertions and retractions and gives you a consistent view of the world according those datoms. In this way, it is very analogous to an RDF triple store kind of view of the world, but hopefully not as painful. Datomic guarantees transactional writes (so it chooses C over A in CAP and you must accept the availability constraints that that leaves you with).

He defines some terms: “value”: something immutable, magnitude, quantity, number, or immutable composite thereof, “identity”: a putative entity we associate with a series of causally related values (states) over time, “state”: value of an identity at a moment in time, “time”: something that is relative. A is either before or after B, and “epochal time model”: identity (succession of states). Apply functional transformation from value to value to move along time. All data in Datomic is immutable and it is built around Okasaki’s Purely Functional Data Structures, particularly trees of the immutable data.

The core components are a Transactor, which does transaction coordination and accepts writes, which it applies serially in an append-only structure like LevelDB or BigTable, an App Server, which runs on each node and does querying and indexing. App Servers all have access to the same underlying (pluggable) storage, so distributed storage like Riak is ideal, and the top level nodes of the tree structure are stored in a distributed, consistent store, which is usually Zookeeper (particularly in deployments using Riak for the data storage). Essentially, the “values” go into Riak and the “identities” go into Zookeeper. Those top level nodes are very few and very small but consistency on them is crucial to holding the whole thing together, hence Zookeeper. Since data is immutable, the app servers can always do consistent reads against the distributed storage and never have to worry about coordination. Only the writes have to be synchronized through the Transactor + Zookeeper. Further, because the data is immutable, they can use Riak in “R=1” mode, meaning that Riak, on a read, doesn’t need to verify that the data exists on more than one node (how it would normally detect conflicts) and that is the most efficient way possible to query Riak, avoiding all the overhead of vector clocks and siblings. Datomic automatically generates a few spanning indexes for the data and allows you to specify additional ones, those end up stored in the same structure as the data with a key or two at the top in zookeeper and most of the index as immutable data in Riak.

Lessons Learned and Questions Raised from Building Distributed Systems

by Andy Gross, chief architect at Basho

video slides

This talk was the closing Keynote and is very hard to summarize; I highly recommend just looking up video of the talk and watching it. Definitely worth it.

Andy Gross is the chief architect at Basho and one of the current big names in distributed systems. He is a self-taught programmer (starting with a VIC20), college dropout, has written databases, filesystems, CDNs, and according to legend, wrote the first version of Riak on a plane to Boston to interview at Basho. He considered the prototype a failure but they hired him anyway. His career advice is “BS or charm your way into jobs you aren’t qualified for, quit before it gets boring, and repeat”.

Herb Sutter published a paper in 2005 called “The Free Lunch is Over” that pointed out the fundamental shift towards concurrency and distributed systems. Moore’s law had stopped giving us faster and faster single-core processors and the clear direction was going to be more and more cores running in parallel. So we could no longer rely on software getting faster just by virtue of the underlying hardware clock speed going up. Instead it had to be able to take advantage of more and more parallel cores and that required a fundamental change in how software was designed (unless Dr. Seltzer has her way). The software industry was not ready for what was going to happen to them and probably still isn’t.

For part of the industry though, it has meant a renaissance in distributed systems research. We are all writing distributed systems now whether we want to acknowledge it or not. Distributed systems are one of those areas where the key concepts keep recurring and being rediscovered. In the late 70’s Leslie Lamport dug up an (already old) Dijkstra paper on self-stabilizing systems and hailed it as Dijkstra’s most important work. Programmers now are just rediscovering Lamport’s work and finding it relevant and cutting edge when applied to modern technology. Andy put a hack in his browser that made it so if he tried to go to Reddit or Hacker News, it redirects him to a random article of Lamport’s.

He stressed the importance of abstractions as how we move computer science and engineering forward and wants to see more common abstractions for distributed systems.

He asks, “Where’s my libPaxos?” If Linux can have literally hundreds of toy filesystems implemented, why can’t we just import a standard library for distributed consensus? “Where’s my libARIES?” (for write-ahead logging). Why do we have to keep reimplementing these fundamental abstractions that recur over and over in distributed systems. Languages that support immutable data structures and have good concurrency primitives (Actors in Erlang/Scala/Rust, CSP in Go, STM in Clojure/Haskell) are a step forward but not nearly enough. These things should all be reusable primitives that we just have at our disposal so we can move on to the next layer of the problem. He’s worked on solving this by writing Riak Core, which is a reusable implementation of distributed consensus, gossip protocols, consistent hashing, etc. Riak itself is just a set of plugins on top of Riak Core, RiakCS is a different set of plugins on the same core, and Riak Core has a couple large deployments at other organizations (Yahoo!, OpenMX, and Stackmob) but otherwise hasn’t seen much adoption.

The other major deficiency is in testing tools for distributed systems. Unit tests are woefully insufficient. Distributed systems fail in more creative and interesting ways than other software and traditional tools for testing, debugging, and monitoring aren’t good enough. Many of the algorithms used in distributed systems can be proven correct but still prove to be buggy when implemented (insert relevant Knuth quote). Something in between formal verification and unit tests is needed. A good start in that direction is a tool called QuickCheck which generates, like a fuzzer, unit tests for your code based on static analysis of the source code, then it runs millions of them covering the entire domain of every function rather than just a few discrete values and automatically reduces failures into minimal test cases. Run it overnight and you’ll find heisenbugs that normal unit tests wouldn’t have found. Unfortunately “Quick”check is very complicated, requiring a lot of training to use, takes a long time to set up for each codebase, and the value of the tests that it creates decays quickly, so it takes a massive investment in time to keep the quickcheck config up to date and useful. There’s also a tool called “PULSE” which works with Erlang to spoof the scheduler and run your code under pathological scheduling conditions, making sure that you get hit with every weird edge case around out-of-order message delivery and overloaded or deadlocked components.

He finished with a list of the remaining “Hard Problems” he sees in distributed systems:

  • Admission control / overload prevention. How do you provide back pressure? Most distributed systems have unbounded queues everywhere. Especially in an Erlang/actor system with unbounded mailboxes. You want to accept load until you’re overloaded, then shed work. How do you enforce fairness in multi-tenant situations?
  • Multi-tenancy and the noisy neighbor problem. You can solve it at the OS/VM layer, but it leaves your resources under-utilized.
  • Security. “Whatever…”
  • Dynamic membership. Theoretically, it’s “just another round of consensus” but in practice, every distributed system seems to have issues with members joining and leaving at high rates.
  • Garbage collection. Immutability is great. It’s a great match for eventual consistent systems. But it leaves garbage around. Now you have a big distributed garbage collection problem. At least that pushes the problem back to the system developers, not leaving the burden on the client.