How does Redis handle multiple threads (from different clients) updating the same data structure in Redis ? What is the recommended best practice for such a use case?
if you read the Little redis book at some point this sentence comes.
"You might not know it, but Redis is actually single-threaded, which is how every command is guaranteed to be atomic.
While one command is executing, no other command will run."
Have a look in http://openmymind.net/2012/1/23/The-Little-Redis-Book/ for more information
Regards
Related
I'm creating a new service, and for that I have database entries (Mongo) that have a state field, which I need to update based on a current time, so, for instance, the start time was set to two hours from now, I need to change state from CREATED -> STARTED in database, and there can be multiple such states.
Approaches I've thought of:
Keep querying database entries that are <= current time and then change their states accordingly. This causes extra reads for no reason and half the time empty reads, and it will get complicated fast with more states coming in.
I write a job scheduler (I am using go, so that'd be not so hard), and schedule all the jobs, but I might lose queue data in case of a panic/crash.
I use some products like celery, have found a go implementation for it https://github.com/gocelery/gocelery
Another task scheduler I've found is on Google Cloud https://cloud.google.com/solutions/reliable-task-scheduling-compute-engine, but I don't want to get stuck in proprietary technologies.
I wanted to use some PubSub service for this, but I couldn't find one that has delayed messages (if that's a thing). My problem is mainly not being able to find an actual name for this problem, to be able to search for it properly, I've even tried searching Microsoft docs. If someone can point me in the right direction or if any of the approaches I've written are the ones I should use, please let me know, that would be a great help!
UPDATE:
Found one more solution by Netflix, for the same problem
https://medium.com/netflix-techblog/distributed-delay-queues-based-on-dynomite-6b31eca37fbc
I think you are right in that the problem you are trying to solve is the job or task scheduling problem.
One approach that many companies use is the system you are proposing: jobs are inserted into a datastore with a time to execute at and then that datastore can be polled for jobs to be run. There are optimizations that prevent extra reads like polling the database at a regular interval and using exponential back-off. The advantage of this system is that it is tolerant to node failure and the disadvantage is added complexity to the system.
Looking around, in addition to the one you linked (https://github.com/gocelery/gocelery) there are other implementations of this model (https://github.com/ajvb/kala or https://github.com/rakanalh/scheduler were ones I found after a quick search).
The other approach you described "schedule jobs in process" is very simple in go because goroutines which are parked are extremely cheap. It's simple to just spawn a goroutine for your work cheaply. This is simple but the downside is that if the process dies, the job is lost.
go func() {
<-time.After(expirationTime.Sub(time.Now()))
// do work here.
}()
A final approach that I have seen but wouldn't recommend is the callback model (something like https://gitlab.com/andreynech/dsched). This is where your service calls to another service (over http, grpc, etc.) and schedules a callback for a specific time. The advantage is that if you have multiple services in different languages, they can use the same scheduler.
Overall, before you decide on a solution, I would consider some trade-offs:
How acceptable is job loss? If it's ok that some jobs are lost a small percentage of the time, maybe an in-process solution is acceptable.
How long will jobs be waiting? If it's longer than the shutdown period of your host, maybe a datastore based solution is better.
Will you need to distribute job load across multiple machines? If you need to distribute the load, sharding and scheduling are tricky things and you might want to consider using a more off-the-shelf solution.
Good luck! Hope that helps.
A few years ago I read ODL recommendation not to use READ operation but instead use Data Change Listener or some of its variation. Is it still valid recommendation?
Looking at the ODL code, I got impression that each transaction commit is applied to “In Memory Data Store” immediately during the commit simultaneously with sending notification to the listener. Is it correct?
Why in this case, reading is not as efficient as using the notification?
Where did you read this recommendation? It depends on your use case. Using a data tree change listener (DTCL) with your own cache is going to have faster access than issuing a read operation, especially in a clustered environment if the shard leader is remote. However maintaining your own cache via a DTCL is eventually consistent, meaning your cache may not have up-to-date data. This has to be considered for the use case. If you need strong consistency, then you must use read operations.
Can someone please confirm whether CacheManager.Net supports redis pipelining?
I could not find it in the documentation
Thanks a lot.
Cheers,
U
Kind of.
CacheManager does not have support any batch operations directly.
But in case of Redis you can use cache.Put which internally uses the fire and forget flag of StackExchange.Redis. This is one kind of pipelining as the client doesn't wait for one operation to complete before you can excecute the next one.
If you use cache.Add (or Update and such) instead, CacheManager has to wait for the reply, e.g. if the operation was successful or not, maybe the item did exist already etc...
So, if you just want to push a lot of data into the cache, use Put.
What I'd like to achieve is as follows (pseudocode):
f, t = select(files, threads)
if f
<read from files>
elsif t
<do something else>
end
Where select is a method similar to IO.select. But it seems unlikely to be possible.
The big picture is I'm trying to write a program which has to perform several types of jobs. The idea was to pass job data using database. But also inform the program about new jobs using pipes (by sending just type of the job). So that it wouldn't need to poll for jobs. So I was planning to create a loop waiting for either new notifications from pipes, or for worker threads to finish. After thread finishes I check if there were at least one notification about this particular type of job and run the worker thread again if needed. I'm not really sure what's is the best route to take here, so if you've got suggestions I'd like to hear them out.
Don't reinvent the wheel mate :) check out https://github.com/eventmachine/eventmachine (IO lib based on reactor pattern like node.js etc) or (perhaps preferably) https://github.com/celluloid/celluloid-io (IO lib based on actor pattern, better docs and active maintainers)
OPTION 1 - use EM or Celluloid to handle non-blocking sockets
EM and Celluloid are quite different, EM is reactor pattern ("same thing" as node.js, with a threadpool as workaround for blocking calls) and Celluloid is actor pattern (an actor thread pool).
Both can do non-blocking IO to/from a lot of sockets and delegate work to a lot of threads, depending on how you go about to do it. Both libs are very robust, efficient and battle tested, EM has more history but seems to have fallen slightly out of maintenance (https://www.youtube.com/watch?v=mPDs-xQhPb0), celluloid has nicer API and more active community (http://www.youtube.com/watch?v=KilbFPvLBaI).
Best advice I can give is to play with code samples that both projects provide and see what feels best. I'd go with celluloid for a new project, but that's a personal opinion - you may find that EM has more IO-related features (such as handling files, keyboard, unix sockets, ...)
OPTION 2 - use background job queues
I may have been misguided by the low level of your question :) Have you considered using some of the job queues available under ruby? There's a TON of decent and different options available, see https://www.ruby-toolbox.com/categories/Background_Jobs
OPTION 3 - DIY (not recommended)
There is a pure ruby implementation of EM, it uses IO selectables to handle sockets so it offers a pattern for what you're trying to do, check it out: https://github.com/eventmachine/eventmachine/blob/master/lib/em/pure_ruby.rb#L311 (see selectables handling).
However, given the amount of other options, hopefully you shouldn't need to resort to such low level coding.
I'm writing a web-crawler that should be able to parse multiple pages at the same time. I use Nokogiri for parsing which is quiet good and solve all my tasks, but I don't know how to achieve better perfomance.
I use threads to make many open-uri requests at the same time and it makes the process quicker, but it seems that it's still far from the potential that I can achieve from a single server. Should I use multiple processes? What are the limits of the threads and processes that can be launched for a single ruby application?
By the other words: how to achieve the best performance in this case.
I really like Typhoeus and Hydra for handling multiple requests at once.
Typhoeus is the http client side, and Hydra is the part that handles multiple requests. The examples are good so go through them and see.
While it sounds like you're not looking for something quite so complex I found this thesis an interesting read awhile ago: Building blocks of a scalable webcrawler - Marc Seeger.
In terms of threading/process limits Ruby has very low threading potential. Standard Ruby (MRI/YARV) and Rubinius don't support simultaneous thread execution, unless using an extension specifically built to support it. Depending on how much of your performance trouble is in the IO and how much is in the processing I could suggest using EventMachine.
Multi process however Ruby works very well, as long as you've got a good manager/database for all the processes to communicate with then running multiple processes should scale as well as your processing power allows.
Hey another way is to use a combination of Nokogiri and IronWorker (IronMQ and IronCache).
See a full blog entry on the Topic here
We use a combination of ActiveMQ/Active Messaging, Event Machine, and multi-threading for this problem. We start off with a big list of URL's to fetch. We then break them down into batches of 100 URL's per batch. Each batch is then pushed into ActiveMQ. Then, we have an array of poller/consumer processes listening to the queue. These consumers can all be on one computer, or they can be spread across multiple computers. The array of consumers can grow arbitrarily large to support as much parallelism as we want. The consumers use Active Messaging, which is a nice Ruby integration with ActiveMQ.
When a consumer receives a message to process a batch of 100 URL's, it kicks off Event Machine to create a thread pool that can process multiple messages in multiple threads. Like you, we use Nokogiri to process each URL.
So, there are three levels of parallelism:
1) Multiple concurrent requests per consumer process, supported by Event Machine and threads.
2) Multiple consumer processes per computer.
3) Multiple computers.
If you want something easy go for http://anemone.rubyforge.org/
If you want something fast, code something with eventmachine/em-http-request
I found redis to be a great multi purpose tool for queue management, caching and so on. You could also use specialized things like beanstalkd/active mq/... but at least in my use case, I didn't really find them to be a big advantage compared to redis.
Especially the load on the backend system could be a bottleneck, so chose your database carefully and pay attention to what you save