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Best Practices

Debunking Myths about the VoltDB in-memory database

Monday, May 12, 2014 - 12:00am

written by John Hugg on May 12, 2014

I’d like to take a quick moment to address some myths and misconceptions about VoltDB. Many people selling products who view VoltDB as competition seem to be repeating them. As you’ll read, much of what’s said is just plain FUD.

VoltDB is an in-memory database that has benchmarked at over 3 million transactions asecond on bare metal, and recently crushed previous performance records in the cloud, posting eye-popping YCSB (Yahoo! Cloud Service Benchmark) numbers on AWS, Amazon’s cloud platform.

In-memory database sizing – throw out conventional wisdom

Monday, June 2, 2014 - 12:00am

written by John Piekos on June 2, 2014 

Sizing an in-memory database does not follow conventional database sizing rules.

For traditional databases, you buy a decent server machine, likely one with many CPU cores and reasonable memory, and then focus on application IOPS (I/O Operations per Second). If you are really going to stress the database, you must choose disks that can support the I/O needs of your application, today and in the future. Because these systems often use many disks to achieve high I/O performance, capacity is usually an afterthought.

With in-memory databases, throw out

Scaling with VoltDB: The Clustered Database

Wednesday, January 9, 2013 - 12:00am

written by John Piekos on January 9, 2013

This is part 2 (of 2) of my Programming VoltDB – Easy, Flexible and Ultra-fast series. Blog post, part 1, showed how to build a VoltDB application using ad hoc queries and achieving thousands of transactions a second. It also showed how converting that logic to use VoltDB stored procedures allowed you to parallelize query execution and achieve 100,000+ transactions a second on a single node. In this blog post I’ll talk about scaling beyond 100,000 transactions per second by creating a VoltDB clustered database.

There are primarily two reasons why you

Simplify Your Stored Procedure Logic with Expectations

Monday, December 3, 2012 - 12:00am

written by Andrew Wilson on December 3, 2012

John Hugg was talking with me today about a way to reduce the complexity of error checking in a stored procedure and how rarely it is used. VoltDB’s stored procedures let you set “expectations” on each SQL statement. Those expectations can eliminate several lines of code leading to shorter, readable and more reliable stored procedures.

Consider the following sample:

Example.DDL

CREATE TABLE user_table (

user_name        varchar(200)        UNIQUE NOT NULL,

  password         varchar(100)        NOT NULL,

  CONSTRAINT user_name_idx PRIMARY KEY

 

Stonebraker Live! – Tonight!

Tuesday, January 29, 2013 - 12:00pm

written by VoltDB Team on January 29, 2013

Register nowto join us via live streaming!

Can’t make it to Santa Clara tonight? That doesn’t mean you can’t attend Stonebraker Live!

 

Join us via live stream at 6:30 p.m. Pacific to hear from database legend Mike Stonebraker, as well as VoltDB’s VP of Market Strategy, Mark Hydar, and Co-founder, Scott Jarr.

 

Upserts in VoltDB

Friday, November 16, 2012 - 12:00am

written by Andrew Wilson on November 16, 2012

Upserts in VoltDB

The idea behind an upsert is that you try an update or an insert query first and if it fails, you then do the other query.
Why do an upsert? Upserts tend to be very fast in traditional databases because they can execute in as little as one query or as many as two.