6.5840 2024 Lecture 2: Threads and RPC Topic: implementing distributed systems ... and Go programming for the labs Go threads, and the web crawler Go RPC Why Go? good support for threads convenient RPC type- and memory- safe garbage-collected (no use after freeing problems) threads + GC is particularly attractive! not too complex Go is often used in distributed systems After the tutorial, use https://golang.org/doc/effective_go.html Threads a useful structuring tool, but can be tricky Go calls them goroutines; everyone else calls them threads Thread = "thread of execution" threads allow one program to do many things at once each thread executes serially, just like a non-threaded program the threads share memory each thread includes some per-thread state: program counter, registers, stack Why threads? I/O concurrency Client sends requests to many servers in parallel and waits for replies. Server processes many simultaneous client requests. Each request may block. While waiting for the disk to read data for client X, process a request from client Y. Multicore performance Execute code in parallel on several cores. Convenience In background, once per second, check whether each worker is still alive. Is there an alternative to threads? Yes: write code that explicitly interleaves activities, in a single thread. Usually called "event-driven." Keep a table of state about each activity, e.g. each client request. One "event" loop that: checks for new input for each activity (e.g. arrival of reply from server), does the next step for each activity, updates state. Event-driven can get you I/O concurrency, and eliminates thread costs (which can be substantial), but doesn't get multi-core speedup, and is painful to program. Threading challenges: sharing data safely what if two threads do n = n + 1 at the same time? or one thread reads while another increments? this is a "race" = two threads use same memory at same time, one (or both) writes often a bug -> use locks (Go's sync.Mutex) -> or avoid sharing mutable data coordination between threads one thread is producing data, another thread is consuming it how can the consumer wait (and release the CPU)? how can the producer wake up the consumer? -> use Go channels or sync.Cond or sync.WaitGroup deadlock a cycle of threads waiting for each other via locks, or channels, or RPC Let's look at the tutorial's web crawler as a threading example. What is a web crawler? goal: fetch all web pages, e.g. to feed to an indexer you give it a starting web page it recursively follows all links [diagram: pages, links, a DAG, a cycle] but don't fetch a given page more than once and don't get stuck in cycles Crawler challenges Exploit I/O concurrency Network latency is more limiting than network capacity internet latency: maybe 0.1 seconds, due to speed of light &c internet throughput: maybe MB/sec or GB/sec Fetch many pages in parallel To increase URLs fetched per second => Use threads for concurrency Fetch each URL only *once* avoid wasting network bandwidth avoid link cycles be nice to remote servers => Need to remember which URLs visited Know when finished We'll look at three solutions [crawler.go on schedule page] Serial Concurrent, coordination via shared data Concurrent, coordination via channels Serial crawler: performs depth-first exploration via recursive Serial calls the "fetched" map avoids repeats, breaks cycles a single map, passed by reference, caller sees callee's updates finished when all [recursive] links are explored: easy but: fetches only one page at a time -- slow can we just put a "go" in front of the Serial() call? what will happen? let's try it... what happened? ConcurrentMutex crawler: Creates a thread for each page fetch Many concurrent fetches, higher fetch rate the "go func" creates a goroutine and starts it running func... is an "anonymous function" The threads share the fs.fetched map So only one thread will fetch any given page Why the Mutex (Lock() and Unlock()) in testAndSet()? One reason: Two threads make simultaneous calls to ConcurrentMutex() with same URL Due to two different pages containing link to same URL T1 reads fetched[url], T2 reads fetched[url] Both see that url hasn't been fetched (fetched[url] = false) Both fetch, which is wrong The mutex causes one to wait while the other does both check and set So only one thread sees fetched[url]==false We say "the lock protects fs.fetched[]" But note Go does not enforce any relationship between locks and data! The code between lock/unlock is often called a "critical section" Another reason: Internally, map is a complex data structure (tree? expandable hash?) Concurrent update/update may wreck internal invariants Concurrent update/read may crash the read defer... What if I comment out Lock() / Unlock()? go run crawler.go Does it always work? Always fail? Why? go run -race crawler.go Detects races even when output is correct! What if I forget to Unlock()? deadlock How does the ConcurrentMutex crawler decide it is done? sync.WaitGroup -- it's basically a counter Wait() waits for all Add()s to be balanced by Done()s i.e. waits for all child threads to finish [diagram: tree of goroutines, overlaid on cyclic URL graph] there's a WaitGroup per node in the tree How many concurrent threads might there be? ConcurrentChannel crawler a Go channel: a channel is an object ch := make(chan int) a channel lets one thread send an object to another thread ch <- x the sender waits until some goroutine receives y := <- ch a receiver waits until some goroutine sends also: for y := range ch channels both communicate and synchronize several threads can send and receive on a channel send+recv takes less than a microsecond -- fairly cheap remember: sender blocks until the receiver receives! "synchronous" watch out for deadlock ConcurrentChannel coordinator() coordinator() creates a worker goroutine to fetch each page worker() sends slice of page's URLs on a channel multiple workers send on the single channel coordinator() reads URL slices from the channel At what line does the coordinator wait? Does the coordinator use CPU time while it waits? Note: there is no recursion here; coordinator() creates all workers. Note: no need to lock the fetched map, because it isn't shared! How does the coordinator know it is done? Keeps count of workers in n. Each worker sends exactly one item on channel. The channel does two things: 1. communication of values. 2. notification of events (e.g. thread termination). Why is it safe for multiple threads use the same channel? Is this a race: Worker thread modifies (creates) url slice, coordinator uses it? * worker only writes slice *before* sending * coordinator only reads slice *after* receiving So they can't use the slice at the same time, so there's no race. Why does ConcurrentChannel() create a goroutine just for "ch <- ..."? Let's get rid of the goroutine... When to use sharing and locks, versus channels? Most (all?) problems can be solved in either style What makes the most sense depends on how the programmer thinks state -- sharing and locks communication -- channels For the 6.824 labs, I recommend sharing+locks for state, and sync.Cond or channels or time.Sleep() for waiting/notification. Remote Procedure Call (RPC) a key piece of distributed system machinery; all the labs use RPC goal: easy-to-program client/server communication hide details of network protocols convert data (strings, arrays, maps, &c) to "wire format" portability / interoperability RPC message diagram: Client Server request---> <---response Software structure client app handler fns stub fns dispatcher RPC lib RPC lib net ------------ net Go example: kv.go on schedule page A toy key/value storage server -- Put(key,value), Get(key)->value Uses Go's RPC library Common: Declare Args and Reply struct for each server handler. Client: connect()'s Dial() creates a TCP connection to the server get() and put() are client "stubs" Call() asks the RPC library to perform the call you specify connection, function name, arguments, place to put reply library marshalls args, sends request, waits, unmarshalls reply return value from Call() indicates whether it got a reply usually you'll also have a reply.Err indicating service-level failure Server: Go requires server to declare an object with methods as RPC handlers Server then registers that object with the RPC library Server accepts TCP connections, gives them to RPC library The RPC library reads each request creates a new goroutine for this request unmarshalls request looks up the named object (in table create by Register()) calls the object's named method (dispatch) marshalls reply writes reply on TCP connection The server's Get() and Put() handlers Must lock, since RPC library creates a new goroutine for each request read args; modify reply A few details: Binding: how does client know what server computer to talk to? For Go's RPC, server name/port is an argument to Dial Big systems have some kind of name or configuration server Marshalling: format data into packets Go's RPC library can pass strings, arrays, objects, maps, &c Go passes pointers by copying the pointed-to data Cannot pass channels or functions Marshals only exported fields (i.e., fields w/ CAPITAL letter) RPC problem: what to do about failures? e.g. lost packet, broken network, slow server, crashed server What does a failure look like to the client RPC library? Client never sees a response from the server Client does *not* know if the server saw the request! [diagram of losses at various points] Maybe server never saw the request Maybe server executed, crashed just before sending reply Maybe server executed, but network died just before delivering reply Simplest failure-handling scheme: "best-effort RPC" Call() waits for response for a while If none arrives, re-send the request Do this a few times Then give up and return an error Q: is "best effort" easy for applications to cope with? A particularly bad situation: client executes Put("k", 10); Put("k", 20); both succeed what will Get("k") yield? [diagram, timeout, re-send, original arrives late] Q: is best effort ever OK? read-only operations operations that it's harmless to repeat e.g. DB checks if record has already been inserted Better RPC behavior: "at-most-once RPC" idea: client re-sends if no answer; server RPC code detects duplicate requests, returns previous reply instead of re-running handler Q: how to detect a duplicate request? client includes unique ID (XID) with each request uses same XID for re-send server: if seen[xid]: r = old[xid] else r = handler() old[xid] = r seen[xid] = true some at-most-once complexities this will come up in labs 2 and 4 what if two clients use the same XID? big random number? how to avoid a huge seen[xid] table? idea: each client has a unique ID (perhaps a big random number) per-client RPC sequence numbers client includes "seen all replies <= X" with every RPC much like TCP sequence #s and acks then server can keep O(# clients) state, rather than O(# XIDs) server must eventually discard info about old RPCs or old clients when is discard safe? how to handle dup req while original is still executing? server doesn't know reply yet idea: "pending" flag per executing RPC; wait or ignore What if an at-most-once server crashes and re-starts? if at-most-once duplicate info in memory, server will forget and accept duplicate requests after re-start maybe it should write the duplicate info to disk maybe replica server should also replicate duplicate info Go RPC is a simple form of "at-most-once" open TCP connection write request to TCP connection Go RPC never re-sends a request So server won't see duplicate requests Go RPC code returns an error if it doesn't get a reply perhaps after a timeout (from TCP) perhaps server didn't see request perhaps server processed request but server/net failed before reply came back What about "exactly once"? unbounded retries plus duplicate detection plus fault-tolerant service Lab 4