<antirez>

This is one of this trivial changes in Redis that can make a big difference for users. Basically in the unstable branch I added some code that has the following effect, when running Redis on Linux systems:

[32741] 19 Nov 12:00:55.019 * Background saving started by pid 391
[391] 19 Nov 12:01:00.663 * DB saved on disk
[391] 19 Nov 12:01:00.673 * RDB: 462 MB of memory used by copy-on-write

As you can see now the amount of additional memory used by the saving child is reported (it is also reported for AOF rewrite operations).

Memory errors in computers are so common that you can register domain names similar to very famous domain names, but altered in one bit, and get a number of requests per hour:

http://dinaburg.org/bitsquatting.html

On Twitter:

@War3zRub1 "Hahaha it's silly how people use Redis when they need a reverse proxy"
@C4ntc0de "ZOMG! Use a real message queue, Redis is not a queue!"
@L4m3tr00l "My face when Redis BLABLABLA..."

Meanwhile *at* Twitter:

OP1: "Hey guys, there is a spike in the number of lame messages today, load is increasing..."
OP2: "Yep noticed, it's the usual troll fiesta trowing shit at Redis, 59482 messages per second right now."
OP1: "Ok, no prob, let's spawn two additional Redis nodes to serve their timelines as smooth as usually".

This post by Peter Bailis is a must read. "Safety and Liveness: Eventual consistency is not safe" [1].

[1] http://www.bailis.org/blog/safety-and-liveness-eventual-consistency-is-not-safe/

An extreme TL;DR of this is.

1) In an eventually consistent system, when all the nodes will agree again after a partition?
2) In an eventually consistent system, HOW the nodes will agree about inconsistencies?
3) In immediately consistent systems, when I'm no longer able to write? When I'm no longer able to read?

There is a new DB option out there, I know it took a long time to be developed. While I don't know very well how it works I hope it will be an interesting player in the database landscape.

My initial feeling is that it will compete closely with Riak and MongoDB (the system seems more similar to MongoDB itself, but if it can scale well multi-nodes people that don't need high write availability may pick an immediate consistent database such as RethinkDB instead of Riak for certain applications).

While I consider the Amazon Dynamo design, and its original paper, one of the most interesting things produced in the field of databases in recent times, in the Redis world eventual consistency was never particularly discussed.

Redis Cluster for instance is a system biased towards consistency than availability. Redis Sentinel itself is an HA solution with the dogma of consistency and master slave setups.

This bias for consistent systems over more available but eventual consistent systems has some good reasons indeed, that I can probably reduce to three main points:

You can follow the commits in the next days in the "psync" branch at github:

https://github.com/antirez/redis/commits/psync

From an HN comment[1]

"(Geek note: In the late nineties I worked briefly with a D&D fanatic ops team lead. He threw a D100 when he came in every morning. Anything >90 he picked a random machine to failover 'politely'. If he threw a 100 he went to the machine room and switched something off or unplugged something. A human chaos monkey)."

[1] http://news.ycombinator.com/item?id=4736220

I'm pretty surprised no one tried to write a wrapper for redis-rb or other clients implementing a Dynamo-style system on top of Redis primitives.

Basically something like that:

1) You have a list of N Redis nodes.
2) On write, use consistent hashing and write the same thing to M nodes (M configurable).
3) On reads, read from M nodes and pick the most common reply to return to the client. For all the non-matching replies, use DUMP / RESTORE in Redis 2.6 to update the value of nodes that are in the minority.