in-memory-database

I am trying to store my data in memory here is what i have right now //sq lite driver Class.forName("org.sqlite.JDBC"); //database path, if it's new data base it will be created in project folder con = DriverManager.getConnection("jdbc:sqlite:mydb.db"); Statement stat = con.createStatement(); stat.executeUpdate("drop table if exists weights"); //creating table stat.executeUpdate("create table weights(id integer," + "firstName varchar(30)," + "age INT," + "sex varchar(15)," + "weight INT," + "height INT," + "idealweight INT, primary key (id));"); Now where should i put this statement[ ":memory:

There are quite a few independent and not-so-independent studies comparing traditional RDBMSs but I haven't managed to find any good material on in-memory databases. I am primarily interested in ones specialized for OLTP. So far, I managed to find generic white papers on TimesTen and MySQL Cluster, but I have yet to see a head-to-head comparison. There are other alternatives (e.g. from IBM), but there's even less easily available material. The information is scattered all over the web, but here's what I found out: Introduction to database benchmarking The first thing that you need to do is

I'm using derby as an embedded database. Furthermore, I'm using it's in-memory database option for my unit tests. What I can't figure out is how to properly shut down (A quick look at the code) the Derby database. I beleive I have it working for a standard database but I'm getting different exceptions when attempt similar code on a in-memory database. I'm going to omit details, I'll add them if other feel are needed. Basically, I'm trying to shut down my database in these two fashions where my in-memory database is consistently called "eh": DriverManager.getConnection("jdbc:derby:memory:eh

Quoting the Spark DataFrames, Datasets and SQL manual : A handful of Hive optimizations are not yet included in Spark. Some of these (such as indexes) are less important due to Spark SQL’s in-memory computational model. Others are slotted for future releases of Spark SQL. Being new to Spark, I'm a bit baffled by this for two reasons: Spark SQL is designed to process Big Data, and at least in my use case the data size far exceeds the size of available memory. Assuming this is not uncommon, what is meant by "Spark SQL’s in-memory computational model"? Is Spark SQL recommended only for cases

My integration tests would run much faster if I used in-memory-database instead of PostgreSQL. I use JPA (Hibernate) and I need an in-memory-database that would be easy to switch to using JPA, easy to setup, and reliable. It needs to support JPA and Hibernate (or vice verse if you will) rather extensively since I have no desire to adopt my data access code for tests. What database is the best choice given requirements above? For integration testing , I now use H2 (from the original author of HSQLDB) that I prefer over HSQLDB. It is faster (and I want my tests to be as fast as possible), it has

I just found " Has anyone published a detailed comparison between different in-memory RDBMSs ?" that is related to my question. TimesTen (see also ) is a In-Memory Database from oracle. It has a lot going for it including. Fast, consistent response time High transaction throughput Standard SQL, no application rewrite Persistent and recoverable High availability and no data loss However it is priced out of the reach of most people ( $41,500.00 / Processor ). So what alternatives are there, and what are there pros and cons. (I am using .NET if it changes your answer) A popular in-memory database