My company use redis pub/sub and find that RedisConsumer consumer slowly, I want to check slow log to know what happen about redis, can someone tell me how to config redis to record redis pub/sub log ? or other solution ?
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I'm researching about Redis Architecture. and I don't understand some point about it. please, you explain for me.
follow some document says: "Client will only write data to Master Instance and read-only from Slave instances.". Is it right? if true, in case, Slave is failed, how will read data redis?
About process start a Redis instance, how happened it?
Thanks,
Use Redis sentinel to monitor several redis instances. When the master fails, Sentinels will elect a new master.
We're running a flask application and we do all our heavy processing with celery. We use a redis instance from amazon to be our message broker. We just had a fail, causing much pain and bleeding, so we're looking into fail-over strategies.
The first project that appeared to us was Celery sentinel. https://github.com/dealertrack/celery-redis-sentinel
Would this be something that would give us a fail-over capability?
We've been doing some tests, and it seems not to be working as anticipated.
In your case maybe moving the celery backend to RabbitMQ would be better, as RabbitMQ is a lot more persistent with its data
I'm wondering what are the pros and cons of using redis as a broker in an infrastructure?
At the moment, all my agents are sending to a central NXLog server which proxies the requests to logstash --> ES.
What would I gain by using a redis server in between my nxlog collector and logstash? To me, it seems pointless as nxlog has already good mem and disk buffers in case logstash is down.
What would I gain?
Thank you
On a heavy load : calling ES (HTTP) directly can be dangerous and you can have problems if ES break down .
Redis can handle More (Much more) Write request and send it in asynch logic to ES(HTTP).
I started using redis because I felt that it would separat the input and the filter part.
At least during periodes in which I change the configuration a lot.
As you know if you change the logstash configuration you have to restart the thing. All clients (in my case via syslog) are doomed to reconnect to the logstash daemon when he is back in business.
By putting an indexer in front which holds the relativly static input configuration and pusing everything to redis I am able to restart logstash without causing hickups throughout the datacenter.
I encountered some issues, because our developers hadn't found time (yet) to reduce the amount of useless logs send to syslog, thus overflowing the server. Before we had logstash they overflowed the disk space for logs - more general issue though... :)
When used with Logstash, Redis acts as a message queue. You can have multiple writers and multiple readers.
By using Redis (or any other queueing service) allows you to scale Logstash horizontaly by adding more servers to the 'cluster'. This will not matter for small operations but can be extremely useful for larger installations.
When using Logstash with Redis, you can configure Redis to only store all the log entries in memory which would like a in memory queue (like memcache).
You mat come to the point where the number of logs sent will not be processed by Logstash and it can bring down your system on constant basis (observed in our environment).
If you feel Redis is an overhead for your disk, you can configure it to store all the logs in memory until they are processed by logstash.
As we built our ELK infrastructure, we originally had a lot of problems with the logstash indexer (reading from redis). Redis would back up and eventually die. I believe this was because, in the hope of not losing log files, redis was configured to persist the cache to disk once in a while. When the queue got "too large" (but still within available disk space), redis would die, taking all of the cached entries with it.
If this is the best redis can do, I wouldn't recommend it.
Fortunately, we were able to resolve the issues with the indexer, which typically kept the redis queue empty. We set our monitoring to alert quickly when the queue did back up, and it was a good sign that the indexer was unhappy again.
Hope that helps.
Is this reliable to using Redis Pub/Sub for message-queue compare to RabbitMQ ?
Because configure and maintain a RabbitMQ message-queue system seems more complicated than Redis.
Redis pub/sub is not meant to be reliable. It does not even queue the messages. Any crash of a subscriber will result in message losses.
There are ways to implement a more secure distributed mechanism with Redis, but it is not with pub/sub.
Assuming I have a Master-Slave deployment of Redis (1 master, 1 slave) and a client (webapp) that will manage Publish-Subscribe.
Can I Publish messages to the slave and will they be "seen" by the master?
Or should I use only the Master for Publish and the Slave for Subscribe commands?
I've been looking around but couldn't find the answer. Anyone knows?
EDIT: As #jameshfisher pointed out, the link below is regarding Redis Cluster. The comment from #lionello seems to be the correct answer:
Publishing to a slave will not propagate to the master, only the other way around.
The answer is on the cluster-spec docs:
Publish/Subscribe
In a Redis Cluster clients can subscribe to every node, and can also publish to every other node. The cluster will make sure that published messages are forwarded as needed.
The current implementation will simply broadcast each published message to all other nodes, but at some point this will be optimized either using Bloom filters or other algorithms.
For the typical data you store in Redis, you should only write to the master.
From http://redis.io/topics/replication:
...writes [to slaves] will be discarded if the slave and the master will [sic] resynchronize, or if the slave is restarted...
In fact, starting from v2.6, you can put slaves in slave-read-only mode which would prevent the mistake of writing data to a slave.
The documentation does go on to mention a potential use case for writing data to slaves:
...often there is ephemeral data that is unimportant that can be stored
into slaves. For instance clients may take information about
reachability of master in the slave instance to coordinate a fail over
strategy.