the Memory-Optimized-Table for openGauss is purely in-memory table? or it's can persist the data when data is larger that the server memory? or at least persist when server shutdown?
MOT storage engine is a pure in-memory engine, so yes the whole data must fit into the memory. But MOT tables are strictly durable with persistence capabilities (redo log and checkpoint).
The Memory-Optimized-Table for openGauss is an memory table.When the total memory usage is close to the selected memory limit.MOT no longer allows additional data to be inserted.But It can also persist data when the server is shut down(persistence capabilities).
Related
After the MOT transaction in the openGauss database is complete, the local memory is not released. How can I find the cause?
OpenGauss provides two functions to check the memory usage of MOT transactions. You are advised to use the function to query:
mot_session_memory_detail(): checks the MOT memory usage of all sessions.
mot_local_memory_detail(): checks the size of the MOT local memory, including data and indexes.
thanks
Is it a good practice to store your data in ehcache to improve the performance of a web application when lots of update operation on data regularly?
It all depends on how much reads you have over writes. Your updates will be costlier. So the time gain by reading should offset that.
Ehcache handles concurrent access. However, it is atomic, not transactional. So if you are getting multiple values from different caches, you can get updates in-between. But that's the same for a database. Also, you can use XA to make sure your writes are in sync with the database.
I was wondering if I could get an explanation between the differences between In-Memory cache(redis, memcached), In-Memory data grids (gemfire) and In-Memory database (VoltDB). I'm having a hard time distinguishing the key characteristics between the 3.
Cache - By definition means it is stored in memory. Any data stored in memory (RAM) for faster access is called cache. Examples: Ehcache, Memcache Typically you put an object in cache with String as Key and access the cache using the Key. It is very straight forward. It depends on the application when to access the cahce vs database and no complex processing happens in the Cache. If the cache spans multiple machines, then it is called distributed cache. For example, Netflix uses EVCAche which is built on top of Memcache to store the users movie recommendations that you see on the home screen.
In Memory Database - It has all the features of a Cache plus come processing/querying capabilities. Redis falls under this category. Redis supports multiple data structures and you can query the data in the Redis ( examples like get last 10 accessed items, get the most used item etc). It can span multiple machine and is usually very high performant and also support persistence to disk if needed. For example, Twitter uses Redis database to store the timeline information.
I don't know about gemfire and VoltDB, but even memcached and redis are very different. Memcached is really simple caching, a place to store variables in a very uncomplex fashion, and then retrieve them so you don't have to go to a file or database lookup every time you need that data. The types of variable are very simple. Redis on the other hand is actually an in memory database, with a very interesting selection of data types. It has a wonderful data type for doing sorted lists, which works great for applications such as leader boards. You add your new record to the data, and it gets sorted automagically.
So I wouldn't get too hung up on the categories. You really need to examine each tool differently to see what it can do for you, and the application you're building. It's kind of like trying to draw comparisons on nosql databases - they are all very different, and do different things well.
I would add that things in the "database" category tend to have more features to protect and replicate your data than a simple "cache". Cache is temporary (usually) where as database data should be persistent. Many cache solutions I've seen do not persist to disk, so if you lost power to your whole cluster, you'd lose everything in cache.
But there are some cache solutions that have persistence and replication features too, so the line is blurry.
An in-memory Cache is a common query store therefore relieves DB of read Workloads. Common examples of in-memory cache are Redis cache. An example could be Web site storing popular searches made by clients thereby relieving the DB of some load.
In-memory Cache provides query functionality on top of caching (storing session data in RAM (temporary storage)).
Memcache falls in the temp store caching category.
Does Couchbase store documents in-memory first before moving the data to filestore? Is there any configuration available to specify how long the data has to be store in-memory before it can be flushed to file store?
Couchbase architecture is Memory first\Cache thru.
You can't decide if using memory or not, and it write the data to disk as soon as possible.
Part of that is that you need to have enough memory for the amount of data you have.
You do have some policies like Full or Value eviction but again you don't have the control.
But what you can do is in the SDK wait until the data is replicated\persisted to disk.
Couchbase stores data both on disk and in RAM. The default behavior is to write the document to disk at some arbitrary time (usually quickly) after storing in RAM. This leaves a short window where node failure can result in loss of data. I can't find anything in the documentation for the current version of Couchbase, but it used to be that you could request the "set" method to only complete once the data has been persisted to disk (default is to RAM only).
In any case, after writing to RAM, the document will eventually be written to disk. Couchbase keeps a disk write queue which you can check on the metrics report page in the management console. Now, CB does synchronize writes across the cluster, and I believe a write will be synchronized across a cluster before Couchbase will acknowledge that the write happened (e.g. before the write method returns to the caller). Again, the documentation is hard to determine on this, as prior versions the documentation was much more detailed.
If you have more documents than available RAM, only the most-frequently accessed documents will be stored in RAM for quick retrieval, with all others being "evicted" to disk.
Any idea about loading the data from database to shared memory, the idea is to fasten the data retrieval from frequently used tables?
the server will automatically cache frequently used tables. So I would no optimize from the server side. Now, if the client is querying remotely you might consider coying the data to a local database (like the free SQL Express).
You are talking about cache.
it is easily implemented.
but there are some tricks you need to remember:
You will need to log changes in the underlying table - and reload the cache when they happens.
(poll a change table).
Some operation might be faster inside the database then in your own memory structure.
(If you intereseted in a fast data access with no work at all there are some in-memory Databases that can do the trick for you).