Cluster of bigger sizes tend to have a lot of traffic in the cluster bus
just for failure detection: a node will try to get a ping reply from
another node no longer than when the half the node timeout would elapsed,
in order to avoid a false positive.
However this means that if we have N nodes and the node timeout is set
to, for instance M seconds, we'll have to ping N nodes every M/2
seconds. This N*M/2 pings will receive the same number of pongs, so
a total of N*M packets per node. However given that we have a total of N
nodes doing this, the total number of messages will be N*N*M.
In a 100 nodes cluster with a timeout of 60 seconds, this translates
to a total of 100*100*30 packets per second, summing all the packets
exchanged by all the nodes.
This is, as you can guess, a lot... So this patch changes the
implementation in a very simple way in order to trust the reports of
other nodes: if a node A reports a node B as alive at least up to
a given time, we update our view accordingly.
The problem with this approach is that it could result into a subset of
nodes being able to reach a given node X, and preventing others from
detecting that is actually not reachable from the majority of nodes.
So the above algorithm is refined by trusting other nodes only if we do
not have currently a ping pending for the node X, and if there are no
failure reports for that node.
Since each node, anyway, pings 10 other nodes every second (one node
every 100 milliseconds), anyway eventually even trusting the other nodes
reports, we will detect if a given node is down from our POV.
Now to understand the number of packets that the cluster would exchange
for failure detection with the patch, we can start considering the
random PINGs that the cluster sent anyway as base line:
Each node sends 10 packets per second, so the total traffic if no
additioal packets would be sent, including PONG packets, would be:
Total messages per second = N*10*2
However by trusting other nodes gossip sections will not AWALYS prevent
pinging nodes for the "half timeout reached" rule all the times. The
math involved in computing the actual rate as N and M change is quite
complex and depends also on another parameter, which is the number of
entries in the gossip section of PING and PONG packets. However it is
possible to compare what happens in cluster of different sizes
experimentally. After applying this patch a very important reduction in
the number of packets exchanged is trivial to observe, without apparent
impacts on the failure detection performances.
Actual numbers with different cluster sizes should be published in the
Reids Cluster documentation in the future.
Related to #3929.
If a thread unblocks a client blocked in a module command, by using the
RedisMdoule_UnblockClient() API, the event loop may not be awaken until
the next timeout of the multiplexing API or the next unrelated I/O
operation on other clients. We actually want the client to be served
ASAP, so a mechanism is needed in order for the unblocking API to inform
Redis that there is a client to serve ASAP.
This commit fixes the issue using the old trick of the pipe: when a
client needs to be unblocked, a byte is written in a pipe. When we run
the list of clients blocked in modules, we consume all the bytes
written in the pipe. Writes and reads are performed inside the context
of the mutex, so no race is possible in which we consume the bytes that
are actually related to an awake request for a client that should still
be put into the list of clients to unblock.
It was verified that after the fix the server handles the blocked
clients with the expected short delay.
Thanks to @dvirsky for understanding there was such a problem and
reporting it.
Testing with Solaris C compiler (SunOS 5.11 11.2 sun4v sparc sun4v)
there were issues compiling due to atomicvar.h and running the
tests also failed because of "tail" usage not conform with Solaris
tail implementation. This commit fixes both the issues.
Slow systems like the original Raspberry PI need more time
than 5 seconds to start the script and detect writes.
After fixing the Raspberry PI can pass the unit without issues.
For performance reasons we use a reduced rounds variant of
SipHash. This should still provide enough protection and the
effects in the hash table distribution are non existing.
If some real world attack on SipHash 1-2 will be found we can
trivially switch to something more secure. Anyway it is a
big step forward from Murmurhash, for which it is trivial to
generate *seed independent* colliding keys... The speed
penatly introduced by SipHash 2-4, around 4%, was a too big
price to pay compared to the effectiveness of the HashDoS
attack against SipHash 1-2, and considering so far in the
Redis history, no such an incident ever happened even while
using trivially to collide hash functions.
1. Refactor memory overhead computation into a function.
2. Every 10 keys evicted, check if memory usage already reached
the target value directly, since we otherwise don't count all
the memory reclaimed by the background thread right now.
This change attempts to switch to an hash function which mitigates
the effects of the HashDoS attack (denial of service attack trying
to force data structures to worst case behavior) while at the same time
providing Redis with an hash function that does not expect the input
data to be word aligned, a condition no longer true now that sds.c
strings have a varialbe length header.
Note that it is possible sometimes that even using an hash function
for which collisions cannot be generated without knowing the seed,
special implementation details or the exposure of the seed in an
indirect way (for example the ability to add elements to a Set and
check the return in which Redis returns them with SMEMBERS) may
make the attacker's life simpler in the process of trying to guess
the correct seed, however the next step would be to switch to a
log(N) data structure when too many items in a single bucket are
detected: this seems like an overkill in the case of Redis.
SPEED REGRESION TESTS:
In order to verify that switching from MurmurHash to SipHash had
no impact on speed, a set of benchmarks involving fast insertion
of 5 million of keys were performed.
The result shows Redis with SipHash in high pipelining conditions
to be about 4% slower compared to using the previous hash function.
However this could partially be related to the fact that the current
implementation does not attempt to hash whole words at a time but
reads single bytes, in order to have an output which is endian-netural
and at the same time working on systems where unaligned memory accesses
are a problem.
Further X86 specific optimizations should be tested, the function
may easily get at the same level of MurMurHash2 if a few optimizations
are performed.
GCC will produce certain unaligned multi load-store instructions
that will be trapped by the Linux kernel since ARM v6 cannot
handle them with unaligned addresses. Better to use the slower
but safer implementation instead of generating the exception which
should be anyway very slow.
I'm not sure how much test Jemalloc gets on ARM, moreover
compiling Redis with Jemalloc support in not very powerful
devices, like most ARMs people will build Redis on, is extremely
slow. It is possible to enable Jemalloc build anyway if needed
by using "make MALLOC=jemalloc".
However note that in architectures supporting 64 bit unaligned
accesses memcpy(...,...,8) is likely translated to a simple
word memory movement anyway.
After investigating issue #3796, it was discovered that MIGRATE
could call migrateCloseSocket() after the original MIGRATE c->argv
was already rewritten as a DEL operation. As a result the host/port
passed to migrateCloseSocket() could be anything, often a NULL pointer
that gets deferenced crashing the server.
Now the socket is closed at an earlier time when there is a socket
error in a later stage where no retry will be performed, before we
rewrite the argument vector. Moreover a check was added so that later,
in the socket_err label, there is no further attempt at closing the
socket if the argument was rewritten.
This fix should resolve the bug reported in #3796.
Ziplists had a bug that was discovered while investigating a different
issue, resulting in a corrupted ziplist representation, and a likely
segmentation foult and/or data corruption of the last element of the
ziplist, once the ziplist is accessed again.
The bug happens when a specific set of insertions / deletions is
performed so that an entry is encoded to have a "prevlen" field (the
length of the previous entry) of 5 bytes but with a count that could be
encoded in a "prevlen" field of a since byte. This could happen when the
"cascading update" process called by ziplistInsert()/ziplistDelete() in
certain contitious forces the prevlen to be bigger than necessary in
order to avoid too much data moving around.
Once such an entry is generated, inserting a very small entry
immediately before it will result in a resizing of the ziplist for a
count smaller than the current ziplist length (which is a violation,
inserting code expects the ziplist to get bigger actually). So an FF
byte is inserted in a misplaced position. Moreover a realloc() is
performed with a count smaller than the ziplist current length so the
final bytes could be trashed as well.
SECURITY IMPLICATIONS:
Currently it looks like an attacker can only crash a Redis server by
providing specifically choosen commands. However a FF byte is written
and there are other memory operations that depend on a wrong count, so
even if it is not immediately apparent how to mount an attack in order
to execute code remotely, it is not impossible at all that this could be
done. Attacks always get better... and we did not spent enough time in
order to think how to exploit this issue, but security researchers
or malicious attackers could.
The original jemalloc source tree was modified to:
1. Remove the configure error that prevents nested builds.
2. Insert the Redis private Jemalloc API in order to allow the
Redis fragmentation function to work.
