6.824 2022 Lecture 1: Introduction 6.824: Distributed Systems Engineering What I mean by "distributed system": a group of computers cooperating to provide a service this class is mostly about infrastructure services e.g. storage for big web sites, MapReduce, peer-to-peer sharing lots of important infrastructure is distributed Why do people build distributed systems? to increase capacity via parallel processing to tolerate faults via replication to match distribution of physical devices e.g. sensors to achieve security via isolation But it's not easy: many concurrent parts, complex interactions must cope with partial failure tricky to realize performance potential Why study this topic? interesting -- hard problems, powerful solutions widely used -- driven by the rise of big Web sites active research area -- important unsolved problems challenging to build -- you'll do it in the labs COURSE STRUCTURE http://pdos.csail.mit.edu/6.824 Course staff: Robert Morris, lecturer Cel Skeggs, TA Assel Ismoldayeva, TA Anish Athalye, TA Course components: lectures papers two exams labs final project (optional) Lectures: big ideas, paper discussion, lab guidance will be video-taped, available online Papers: there's a paper assigned for almost every lecture research papers, some classic, some new problems, ideas, implementation details, evaluation please read papers before class! each paper has a short question for you to answer and we ask you to send us a question you have about the paper submit answer and question before start of lecture Exams: Mid-term exam in class Final exam during finals week Mostly about papers and labs Labs: goal: deeper understanding of some important ideas goal: experience with distributed programming first lab is due a week from Friday one per week after that for a while Lab 1: distributed big-data framework (like MapReduce) Lab 2: fault tolerance library using replication (Raft) Lab 3: a simple fault-tolerant database Lab 4: scalable database performance via sharding Optional final project at the end, in groups of 2 or 3. The final project substitutes for Lab 4. You think of a project and clear it with us. Code, short write-up, demo on last day. Warning: debugging the labs can be time-consuming start early ask questions on Piazza use the TA office hours We grade the labs using a set of tests we give you all the tests; none are secret MAIN TOPICS This is a course about infrastructure for applications. * Storage. * Communication. * Computation. A big goal: hide the complexity of distribution from applications. Topic: fault tolerance 1000s of servers, big network -> always something broken We'd like to hide these failures from the application. "High availability": service continues despite failures Big idea: replicated servers. If one server crashes, can proceed using the other(s). Labs 2 and 3 Topic: consistency General-purpose infrastructure needs well-defined behavior. E.g. "Get(k) yields the value from the most recent Put(k,v)." Achieving good behavior is hard! "Replica" servers are hard to keep identical. Topic: performance The goal: scalable throughput Nx servers -> Nx total throughput via parallel CPU, disk, net. Scaling gets harder as N grows: Load imbalance. Slowest-of-N latency. Some things don't speed up with N: initialization, interaction. Labs 1, 4 Topic: tradeoffs Fault-tolerance, consistency, and performance are enemies. Fault tolerance and consistency require communication e.g., send data to backup e.g., check if my data is up-to-date communication is often slow and non-scalable Many designs provide only weak consistency, to gain speed. e.g. Get() does *not* yield the latest Put()! Painful for application programmers but may be a good trade-off. We'll see many design points in the consistency/performance spectrum. Topic: implementation RPC, threads, concurrency control, configuration. The labs... This material comes up a lot in the real world. All big web sites and cloud providers are expert at distributed systems. Many big open source projects are built around these ideas. We'll read multiple papers from industry. And industry has adopted many ideas from academia. CASE STUDY: MapReduce Let's talk about MapReduce (MR) a good illustration of 6.824's main topics hugely influential the focus of Lab 1 MapReduce overview context: multi-hour computations on multi-terabyte data-sets e.g. build search index, or sort, or analyze structure of web only practical with 1000s of computers applications not written by distributed systems experts overall goal: easy for non-specialist programmers programmer just defines Map and Reduce functions often fairly simple sequential code MR manages, and hides, all aspects of distribution! Abstract view of a MapReduce job -- word count Input1 -> Map -> a,1 b,1 Input2 -> Map -> b,1 Input3 -> Map -> a,1 c,1 | | | | | -> Reduce -> c,1 | -----> Reduce -> b,2 ---------> Reduce -> a,2 1) input is (already) split into M files 2) MR calls Map() for each input file, produces set of k2,v2 "intermediate" data each Map() call is a "task" 3) when Maps are don, MR gathers all intermediate v2's for a given k2, and passes each key + values to a Reduce call 4) final output is set of pairs from Reduce()s Word-count-specific code Map(k, v) split v into words for each word w emit(w, "1") Reduce(k, v_set) emit(len(v_set)) MapReduce scales well: N "worker" computers (might) get you Nx throughput. Maps()s can run in parallel, since they don't interact. Same for Reduce()s. Thus more computers -> more throughput -- very nice! MapReduce hides many details: sending app code to servers tracking which tasks have finished "shuffling" intermediate data from Maps to Reduces balancing load over servers recovering from failures However, MapReduce limits what apps can do: No interaction or state (other than via intermediate output). No iteration No real-time or streaming processing. Input and output are stored on the GFS cluster file system MR needs huge parallel input and output throughput. GFS splits files over many servers, in 64 MB chunks Maps read in parallel Reduces write in parallel GFS also replicates each file on 2 or 3 servers GFS is a big win for MapReduce Some details (paper's Figure 1): one coordinator, that hands out tasks to workers and remembers progress. 1. coordinator gives Map tasks to workers until all Maps complete Maps write output (intermediate data) to local disk Maps split output, by hash, into one file per Reduce task 2. after all Maps have finished, coordinator hands out Reduce tasks each Reduce fetches its intermediate output from (all) Map workers each Reduce task writes a separate output file on GFS What will likely limit the performance? We care since that's the thing to optimize. CPU? memory? disk? network? In 2004 authors were limited by network capacity. What does MR send over the network? Maps read input from GFS. Reduces read Map intermediate output. Often as large as input, e.g. for sorting. Reduces write output files to GFS. [diagram: servers, tree of network switches] In MR's all-to-all shuffle, half of traffic goes through root switch. Paper's root switch: 100 to 200 gigabits/second, total 1800 machines, so 55 megabits/second/machine. 55 is small: much less than disk or RAM speed. Today: networks are much faster How does MR minimize network use? Coordinator tries to run each Map task on GFS server that stores its input. All computers run both GFS and MR workers So input is read from local disk (via GFS), not over network. Intermediate data goes over network just once. Map worker writes to local disk. Reduce workers read from Map worker disks over the network. Storing it in GFS would require at least two trips over the network. Intermediate data partitioned into files holding many keys. R is much smaller than the number of keys. Big network transfers are more efficient. How does MR get good load balance? Wasteful and slow if N-1 servers have to wait for 1 slow server to finish. But some tasks likely take longer than others. Solution: many more tasks than workers. Coordinator hands out new tasks to workers who finish previous tasks. So no task is so big it dominates completion time (hopefully). So faster servers do more tasks than slower ones, finish abt the same time. What about fault tolerance? I.e. what if a worker crashes during a MR job? We want to hide failures from the application programmer! Does MR have to re-run the whole job from the beginning? Why not? MR re-runs just the failed Map()s and Reduce()s. Suppose MR runs a Map twice, one Reduce sees first run's output, another Reduce sees the second run's output? Correctness requires re-execution to yield exactly the same output. So Map and Reduce must be pure deterministic functions: they are only allowed to look at their arguments/input. no state, no file I/O, no interaction, no external communication. What if you wanted to allow non-functional Map or Reduce? Worker failure would require whole job to be re-executed, or you'd need to roll back to some kind of global checkpoint. Details of worker crash recovery: * a Map worker crashes: coordinator notices worker no longer responds to pings coordinator knows which Map tasks ran on that worker those tasks' intermediate output is now lost, must be re-created coordinator tells other workers to run those tasks can omit re-running if all Reduces have fetched the intermediate data * a Reduce worker crashes: finished tasks are OK -- stored in GFS, with replicas. coordinator re-starts worker's unfinished tasks on other workers. Other failures/problems: * What if the coordinator gives two workers the same Map() task? perhaps the coordinator incorrectly thinks one worker died. it will tell Reduce workers about only one of them. * What if the coordinator gives two workers the same Reduce() task? they will both try to write the same output file on GFS! atomic GFS rename prevents mixing; one complete file will be visible. * What if a single worker is very slow -- a "straggler"? perhaps due to flakey hardware. coordinator starts a second copy of last few tasks. * What if a worker computes incorrect output, due to broken h/w or s/w? too bad! MR assumes "fail-stop" CPUs and software. * What if the coordinator crashes? Current status? Hugely influential (Hadoop, Spark, &c). Probably no longer in use at Google. Replaced by Flume / FlumeJava (see paper by Chambers et al). GFS replaced by Colossus (no good description), and BigTable. Conclusion MapReduce made big cluster computation popular. - Not the most efficient or flexible. + Scales well. + Easy to program -- failures and data movement are hidden. These were good trade-offs in practice. We'll see some more advanced successors later in the course. Have fun with Lab 1!