I'm figuring out about in-memory databases. Some articles said that an in-memory database is a database that keeps the whole dataset in RAM.
What does that mean? It means that each time you query a database or update data in a database, you only access the main memory. I’m confused about how database keeps the whole dataset in RAM?
I'm understanding that when I do select * from table1, in-memory database automatically load this table into memory and after this I'm able to use table very fast.
Now assume that I just have 8GB RAM and a very large table (100TB), how can database load large data table into memory?
It does not. An in memory database is where your entire dataset must fit in ram. You can get servers with 256gb of ram which allows many datasets to comfortable fit in ram
Paging things in and out of memory is what a regular database does.
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My dataset had around 30000 tables. I have archived them all into 300 partitioned tables now. I Have deleted 29700 tables. The data volume is same as deleted tables were all archived first. Will it affect processing time of python scripts that use this dataset for creating new tables daily?
PS: I am not concerned about processes that use the archived tables. I am concerned about the processes that only uses the same dataset to create their new tables.
BigQuery doesn't mind if you have 3 tables or 30,000 tables. That shouldn't affect querying speed.
But! Imagine if a UI tries to list all tables in one dataset, or similar operations in other environments. That will be slower for sure.
I expect to have 10-25 TBs of data in my MPP DW in nearest few years. The size of one dataset could be up to 500GB csv. I want to do interactive querying against that data in anaytical tools (Power BI).
I'm looking for a way to achieve interactive querying with reasonable billing.
I know Azure Analytics Services (AAS) Multidimensional Model can be used for that data volumes. But it will give me less performance as tabular model. In other hand, even with 10x compression rate I can't keep everything in RAM simultaneously due to AAS pricing.
So I'm wondering if there is an possibility to keep all tabular models inside AAS in detached state (disk only) within minimal AAS cluster size (minimal billing), while on request do scale out (increase number of nodes) and attach specific dataset (load from disk to RAM)? Is there any other option to use AAS tabular model and not keeping all 10-25 TBs in RAM simultaneously?
I assume this option with small amount of concurrent users will have better performance that multidimensional model, while not require keep everything in RAM (less expensive).
I have an in memory database in SQLite written in c++. Data is being inserted into the in-memory datbase at a very fast rate. I want to persist the database to disk for recovery purposes but I want to do it periodically after every 10mins and don't want to flush the in-memory database(to support faster queries). Is there a way I can copy from the in-memory database from where I left of earlier and append to the file on disk periodically.
I need some guidance on our strategy for loading data into a Redshift Data Warehouse for analytics. We have ~40 SQL databases, each represents one customer and each database is identical. I have a SQL database with the same table structure as the 40 but each table has an additional column called "customer" that will capture where that record came from. We do some additional ETL processing with the records as they come in.
In total we have about 50 GB of data across all 40 DBs. Looking into the recommended processes for Updating / Inserting data on AWS's site they recommend creating the scratch table then merging data. I could do this but I could also just drop all the data from a table and re-load it since I am reading from the source every time. What is the recommended way to handle this?
I am trying to load data from oracle to spark in juypter notebook.But each time I try to pot graph the time taken is huge. How do I make it faster?
query = "(select * from db.schema where lqtime between trunc(sysdate)-30 and trunc(sysdate) )"
%time df = sqlContext.read.format('jdbc').options(url="jdbc:oracle:thin:useradmin/pass12#//localhost:1521/aldb",dbtable=query,driver="oracle.jdbc.OracleDriver").load()
Now I group by node:
%time fo_node = df.select('NODE').groupBy('NODE').count().sort('count',ascending=False)
%time fo_node.show(10)
The load time is 4m or more each time I run this.
Using hadoop or apache spark against relational database is an anti-pattern, as database receives too many connections at once and tries to respond to too many requests at once. The disk most certainly is overwhelmed - even with index and partitioning, it reads data for each partition at once. I bet you have HDD there and I'd say that's the reason why it's really slow.
To speed loading up you can try
to actually reduce number of partitions for loading. Later on you can reshuffle it and increase parallelism.
to select specific fields instead of *. Even if you need all, but one, it can make a difference.
if you run few actions on the Dataframe, it make sense to cache that. If you don't have enough memory on cluster, then use options to cache on disk, too.
to export everything from Oracle to disk and read it as a plain csv file, processing it as you do right now in a query on a cluster.