Syntax: `ZMSCORE KEY MEMBER [MEMBER ...]`
This is an extension of #2359
amended by Tyson Andre to work with the changed unstable API,
add more tests, and consistently return an array.
- It seemed as if it would be more likely to get reviewed
after updating the implementation.
Currently, multi commands or lua scripting to call zscore multiple times
would almost definitely be less efficient than a native ZMSCORE
for the following reasons:
- Need to fetch the set from the string every time instead of reusing the C
pointer.
- Using pipelining or multi-commands would result in more bytes sent by
the client for the repeated `ZMSCORE KEY` sections.
- Need to specially encode the data and decode it from the client
for lua-based solutions.
- The fastest solution I've seen for large sets(thousands or millions)
involves lua and a variadic ZADD, then a ZINTERSECT, then a ZRANGE 0 -1,
then UNLINK of a temporary set (or lua). This is still inefficient.
Co-authored-by: Tyson Andre <tysonandre775@hotmail.com>
When the element new score is the same of prev/next node, the
lexicographical order kicks in, so we can safely update the node in
place only when the new score is strictly between the adjacent nodes
but never equal to one of them.
Technically speaking we could do extra checks to make sure that even if the
score is the same as one of the adjacent nodes, we can still update on
place, but this rarely happens, so probably not a good deal to make it
more complex.
Related to #5179.
Usually blocking operations make a lot of sense with multiple keys so
that we can listen to multiple queues (or whatever the app models) with
a single connection. However in the synchronous case it is more useful
to be able to ask for N elements. This is a change that I also wanted to
perform soon or later in the blocking list variant, but here it is more
natural since there is no reply type difference.
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.
