We use Apache Ignite to update prices of stocks continuously. Since these prices are stored in RAM in Ignite, is there some way to determine the memory address of a specific stock price, and update that address directly? That way we can bypass the indexing, query parsing, etc, and update Ignite much faster! We have enough RAM to keep entire DB in the memory.
The way to update a single column in a record is to use an entry processor. There's no way to directly get a memory address.
Related
I have a site where you can list your car for sale. There is a list and a map with filtering on car types and other car specifications. My idea was to cache cars table and use that to filter on when user is searching for a car on the website. Currently, especially when zooming in/out on the map, each time user does that, http request is made and it's querying the database, and that can be slow and heavy on the server.
As an experiment with 1 000 items, I have cached map data (trimmed data with only basic info) and it's working fine. I was thinking of doing a basically copy of cars table instead with all needed joins added in Memory Cache and use that instead of querying the DB every request for both list and the map. I would have Cron Job every 5 minutes (as data can change, but it doesn't have to be immediate) to update Memory Cache with latest cars data from DB.
What would be the cons of using this approach in long term and for using it for example storing 100 000 records? Beside server needing more RAM, would there be any concerns about scalability or usability of this approach? Would it be better to use Redis instead?
I do have in place now "search as you type" service, but I don't really need that functionality as filtering is pretty exact, I have added it more as a caching server but I think I would be better off just using Memory Cache until a real need for that kind of service is required.
Thank you
Since memory isn’t infinite, we need to limit the number of items stored in the In-Memory cache.
MemoryCache VS Redis
MemoryCache
MemoryCache is embedded in the process , hence can only be used as a plain key-value store from that process.
Redis
Redis is a remote data structure server. It is certainly slower than just storing the data in local memory.
I conclude that MemoryCache is running in the web server of the current application, and it is limited by the performance of the web server. Of course, it will be very fast under the same configuration. I think the disadvantage is that the stored data cannot be shared with other applications.
If redis is used, reading data directly from memory is not as fast as memorycache, but it has high reliability and high scalability.
Related Post:
1. How to update redis after updating database?
2. how to keep caching up to date
3. How can MySQL update data in real time in redis cache?
Could you please answer these 2 questions and correct me if wrong.
I assume Both Redis Database and Redis Cache are stored in Memory and not in Disk. Am I correct?
If Yes, What are the major difference between both. I am assuming both are stored in memory and it should not make much difference between them both. I mean the speed should be the same as they are in memory only. Do we still need Cache again?
Could you please tell me what are the differences and advantages between the both.
Second Question: Can the server restart remove all data in the Redis database? Cache must be deleted for sure I believe.
Thanks
Not sure what do you mean?
Redis is a product first of all - its an in-memory data structures store.
Depending on its configurations it can be targeted to different use cases:
Database
Cache
Even message broker
If you're coming from the cloud world, cloud providers can call this "Cache" and this means that they offer a redis that is pre-configured to be used as a cache (remove the oldest records when the memory becomes next to be fully utilized, etc).
But after you'll you will work with some kind of redis client that will interact with remote redis server.
I understand the native persistence mode of Apache Ignite allows to store as much as possible data in memory - and the potential remaining data on disk.
Is it possible to manually choose which table I want to store in memory and which I want to store EXCLUSIVELY on disk? If I want to save costs, should I just give Ignite a lot of disk space and just a small amount of memory? What if I know some tables should return results as fast as possible while other tables have lower priorities in terms of speed (even if they are accessed more often)? Is there any feature to prioritize data storage into memory at table level or any other level?
You can define two different data regions - one with small amount of memory and enabled persistence and second without persistence, but with bigger max memory size: https://apacheignite.readme.io/docs/memory-configuration
You can't have a cache (which contains rows for a table) to be stored exclusively on disk.
When you add a row to table it gets stored in Durable Memory, which is always located in RAM. Later it may be flushed to disk via Checkpointing process, which will use checkpoint page buffer, which is also in RAM. So you can have a separate region with low memory usage (see another answer) but you can't have data exclusively on disk.
When you access data it will always be pulled from disk to Durable Memory, too.
I have multiple servers that all store set members in a shared Redis cache. When the cache fills up, I need to persist the data to disk to free up RAM. I then plan to parse the dumped data such that I will be able to combine all of the values that belong to a given key in MongoDB.
My first plan was to have each server process attempt an sadd operation. If the request fails because Redis has reached maxmemory, I planned to query for each of my set keys, and write each to disk.
However, I am wondering if there is a way to use one of the inbuilt persistence methods in Redis to write the Redis data to disk and delete the key/value pairs after writing. If this is possible I could just parse the rdb dump and work with the data in that fashion. I'd be grateful for any help others can offer on this question.
Redis' persistence is meant to be used for whatever's in the RAM. Put differently, you can't persist what ain't in RAM.
To answer your question: no, you can't use persistence to "offload" data from RAM.
I'm writing a crawler. I keep the visited urls in redis set,and maintain the job queue using redis list. As data grows,memory is used up, my memory is 4G. How to maintain these without redis? I have no idea,if I store these in files,they also need to be in memory.
If I use a mysql to store that,I think it maybe much slower than redis.
I have 5 machines with 4G memory,if anyone has some material to set up a redis cluster,it also helps a lot. I have some material to set up a cluster to be failover ,but what I need is to set a load balanced cluster.
thx
If you are just doing the basic operations of adding/removing from sets and lists, take a look at twemproxy/nutcracker. With it you can use all of the nodes.
Regarding your usage pattern itself, are you removing or expiring jobs and URLs? How much repetition is there in the system? For example, are you repeatedly crawling the same URLs? If so, perhaps you only need a mapping of URLs to their last crawl time, and instead of a job queue you pull URLs that are new or outside a given window since their last run.
Without the details on how your crawler actually runs or interacts with Redis, that is about what I can offer. If memory grows continually, it likely means you aren't cleaning up the DB.