Oct. 27, 2021
Here is the link.
6.824 2021 Lecture 2: Infrastructure: RPC and threads
Today:
Threads and RPC in Go, with an eye towards the labs
Why Go?
good support for threads
convenient RPC
type safe
garbage-collected (no use after freeing problems)
threads + GC is particularly attractive!
relatively simple
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 an ordinary non-threaded program
the threads share memory
each thread includes some per-thread state:
program counter, registers, stack
Why threads?
They express concurrency, which you need in distributed systems
I/O concurrency
Client sends requests to many servers in parallel and waits for replies.
Server processes multiple 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 gets 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:
shared data
e.g. what if two threads do n = n + 1 at the same time?
or one thread reads while another increments?
this is a "race" -- and is usually a bug
-> use locks (Go's sync.Mutex)
-> or avoid sharing mutable data
coordination between threads
e.g. 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 WaitGroup
deadlock
cycles via locks and/or communication (e.g. RPC or Go channels)
Let's look at the tutorial's web crawler as a threading example.
What is a web crawler?
goal is to fetch all web pages, e.g. to feed to an indexer
web pages and links form a graph
multiple links to some pages
graph has cycles
Crawler challenges
Exploit I/O concurrency
Network latency is more limiting than network capacity
Fetch many URLs at the same time
To increase URLs fetched per second
=> Need threads for concurrency
Fetch each URL only *once*
avoid wasting network bandwidth
be nice to remote servers
=> Need to remember which URLs visited
Know when finished
We'll look at two styles of solution [crawler.go on schedule page]
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
but: fetches only one page at a time
can we just put a "go" in front of the Serial() call?
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 "fetched" map
So only one thread will fetch any given page
Why the Mutex (Lock() and Unlock())?
One reason:
Two different web pages contain links to the same URL
Two threads simultaneously fetch those two pages
T1 reads fetched[url], T2 reads fetched[url]
Both see that url hasn't been fetched (already == false)
Both fetch, which is wrong
The lock causes the check and update to be atomic
So only one thread sees already==false
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
What if I comment out Lock() / Unlock()?
go run crawler.go
Why does it work?
go run -race crawler.go
Detects races even when output is correct!
How does the ConcurrentMutex crawler decide it is done?
sync.WaitGroup
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 this crawler create?
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
for y := range ch
a receiver waits until some goroutine sends
channels both communicate and synchronize
several threads can send and receive on a channel
channels are cheap
remember: sender blocks until the receiver receives!
"synchronous"
watch out for deadlock
ConcurrentChannel master()
master() 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
master() reads URL slices from the channel
At what line does the master wait?
Does the master use CPU time while it waits?
No need to lock the fetched map, because it isn't shared!
How does the master know it is done?
Keeps count of workers in n.
Each worker sends exactly one item on channel.
Why is it not a race that multiple threads use the same channel?
Is there a race when worker thread writes into a slice of URLs,
and master thread reads that slice, without locking?
* worker only writes slice *before* sending
* master only reads slice *after* receiving
So they can't use the slice at the same time.
When to use sharing and locks, versus channels?
Most 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"
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 server 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
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"
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 do nothing if repeated
e.g. DB checks if record has already been inserted
Better RPC behavior: "at most once"
idea: 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 lab 3
what if two clients use the same XID?
big random number?
combine unique client ID (ip address?) with sequence #?
server must eventually discard info about old RPCs
when is discard safe?
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
or only allow client one outstanding RPC at a time
arrival of seq+1 allows server to discard all <= seq
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 3
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