I'm using Firebase Realtime database, for my flutter project. I am curious about the storage which is limited to 1GB, I want to know how the used up storage appears, say for example if I store 1mb data it would show me 1mb storage occupied of GB storage, soo if I delete the 1MB data from the RTDB will it show 0MB used in 1Gb storage or it'll show 1MB because I already used it previously. I know this is a simple question, I just want to be clear.
Storage limit is what u store not what u read/write. So if u remove 1mb u have 1 GB to use.
For more info
Firebase bills for the data you store in your database and all outbound network traffic at the session layer (layer 5) of the OSI model. Storage is billed at $5 for each GB/month, evaluated daily. Outbound traffic includes connection and encryption overhead from all database operations and data downloaded through database reads. Both database reads and writes can lead to connection costs on your bill. All traffic to and from your database, including operations denied by security rules, leads to billable costs.
https://firebase.google.com/docs/database/usage/billing
Related
I have a question regarding the freeable memory for AWS Aurora Postgres.
We recently wanted to create an index on one of our dbs and the db died and made a failover to the slave which all worked fine. It looks like the freeable memory dropped by the configured 500mb of maintenance_work_mem and by that went to around 800mb of memory - right after that the 32gig instance died.
1) I am wondering if the memory that is freeable is the overall system memory and if a low memory here could invoke the system oom killer on the AWS Aurora instance? So we may want to plan in more head room for operational tasks and the running of autovacuum jobs to not encounter this issue again?
2) The actual work of the index creation should then have used the free local storage as far as I understood, so the size of the index shouldn't have mattered, right?
Thanks in advance,
Chris
Regarding 1)
Freeable Memory from (https://forums.aws.amazon.com/thread.jspa?threadID=209720)
The freeable memory includes the amount of physical memory left unused
by the system plus the total amount of buffer or page cache memory
that are free and available.
So it's freeable memory across the entire system. While MySQL is the
main consumer of memory on the host we do have internal processes in
addition to the OS that use up a small amount of additional memory.
If you see your freeable memory near 0 or also start seeing swap usage
then you may need to scale up to a larger instance class or adjust
MySQL memory settings. For example decreasing the
innodb_buffer_pool_size (by default set to 75% of physical memory) is
one way example of adjusting MySQL memory settings.
That also means that if the memory gets low its likely to impact the process in some form. In this thread (https://forums.aws.amazon.com/thread.jspa?messageID=881320󗊨) e.g. it was mentioned that it caused the mysql server to restart.
Regarding 2)
This is like it is described in the documentation (https://aws.amazon.com/premiumsupport/knowledge-center/postgresql-aurora-storage-issue/) so I guess its right and the size shouldn't have mattered.
Storage used for temporary data and logs (local storage). All DB
temporary files (for example, logs and temporary tables) are stored in
the instance local storage. This includes sorting operations, hash
tables, and grouping operations that are required by queries.
Each Aurora instance contains a limited amount of local storage that
is determined by the instance class. Typically, the amount of local
storage is twice the amount of memory on the instance. If you perform
a sort or index creation operation that requires more memory than is
available on your instance, Aurora uses the local storage to fulfill
the operation.
I have an entry plan instance of DB2 Warehouse on Cloud that I'm looking to use for development of a streaming application.
If I keep the data to <= 1GB, it will cost me $50/month. I'm worried that I could easily fill the database up with 20GB and the cost jumps up to $1000/month.
Is there a way that I can limit the amount of data in my DB2 Warehouse on Cloud to < 1GB?
As per this link
Db2 Warehouse pricing plans
You will not be charged anything as long as your data usage does not exceed 1 GB. From 1 GB to 20 GB the price will vary based on the data used.
You should be able to see the current % of usage at any time in your console. Other than that I am not aware of any method to automatically restrict the usage to less than 1 GB at this time.
One of the problem would be the data compression which determines the actual amount of data stored and it can vary based on the type of data stored.
Hope this helps.
Regards
Murali
I've got a task to do and some limited hardware resources, as always.
I need to setup postgres server with single database, with a table of largeobjects (3TB+) and a few small, heavily accessed tables (<10 GB).
I've got old physical server with ~5 TB of harddisk space, with limited CPU and RAM, I can also use much faster (in CPU and RAM) virtual server - but limited in storage.
I won't have much DELETE statements, most SELECT statements will be to recent data. There will be one simultanous connection doing all the job, client on one host only.
I see a few scenarios:
Postgres on virtual machine with remote storage (single instance)
Postgres on old hardware with local storage (single instance)
Postgres on both, with some kind of replication (high speed virtual machine for new data, low speed for older data on the old hardware)
Any other ideas?
Is it even possible to replicate just the most recent part of the postgres database?
90% of SELECT queries will be to the most recent ~5-10 gigabytes of data, but I need seamless access to the rest 2,990 TB.
What should I do? (except buying appropriate hardware;)
It doesn't really matter as long as you have enough RAM to buffer the 10GB of heavily accessed data.
You'll need some additional RAM to read large objects without pushing the 10GB out of the cache, but that shouldn't be a problem on today's machines.
If all your work is done on one connection, that sounds like there will be no high load on the database.
So I wouldn't really worry about scaling with requirements like that.
Your biggest worry should probably be how to backup 3TB of data in a reasonable time.
Edit: If you have much less memory, you should take the machine with the faster storage.
Finally I've checked several different scenarios and decided not to keep files/largeobjects in database.
Postgres with database location mounted over NFS (v4) had some lags - It was faster but it was choking for a few seconds periodically, i decided to store plain files over NFS which is significantly slower but more stable.
I'm sure there was a way to tune it, but this solution is fine too.
Postgres is used for file index and keeps their files on local harddisk.
I'm using pymongo to insert a big amount of jsons to MongoDB gridFS + some data to collection.
What I noticed some time ago is that MongoDB consumes just crazy amount of RAM within using single connection. As soon as I close this connection it releases it.
RAM consumption is like 10-12GB in total within connection and 200MB without. The actual size of collection is actually ~300MB with 10-18GB gridFS storage.
Why does it happen? How can opening new connection for any bulky operation can be lot less resource-dependent than using one single connection for everything?
Is it somehow related to Journaling?
I will have to break down this problem into multiple smaller problems for ease of understanding:
It is well known that MongoDB is RAM hungry, it will try to use as much RAM as possible.
GridFS tends to store files in collection fs.chunks and corresponding meta-data in fs.files. The files stored in GridFS are split into chunks of 256KB each.
When you read GridFS data by opening a connection, the chunks belonging to file(s) have to be loaded into the RAM from the disk(if it is not already present in RAM). So , RAM usage is directly proportional to the amount of data stored and importantly frequency of GridFS data access. Just to re-iterate GridFS data gets pulled into RAM if the query references it.
If you have a active connection for large amounts of GridFS data then you should expect heavy RAM usage. But if your query frequency is low(just write, but read rarely) then RAM usage will be relatively lower.If you are mostly writing data, then ensure the connection is closed after the operation in done.
The more the number of open connections, your RAM usage will increase.
This is no-way related to journaling.
Note: GridFS also supports sharding which will tend to solve your problem of excessive RAM usage.
Hope this clarifies.
Since MongoDB 2.0. each connection consumes about 1MB of RAM.
You can read more here.
What is the maximum storage capacity of Core Data?
Is there any capacity limit defined at the app level? Like, out of total available Core Data space, my app can consume only X amount?
What if my app tries to exceed the X?
Core Data has no hard storage capacity, so you're pretty much just limited by the available disk space on the system. On iOS (and most of the time on OS X) Core Data is backed by SQLite, so if there's any restriction, it's in the size of the backing SQLite database. Unsaved data is stored in RAM, and iOS does not support paging out memory, so your unsaved data set is limited by the available RAM on the device. If you try to exceed the RAM, your app will be shut down. If you try to exceed disk space, I imagine the save action will fail with an appropriate NSError.