Databases

A scalability model for Cassandra

One thing that struck me when reading up on Cassandra is that there is a very strong mindset in the Cassandra community around linear scalability and therefore on primary key based data models. So de-normalizing your data, such as by using materialized views is considered a best practice.

However, de-normalization has some challenges of its own. Both Cassandra-managed materialized views or any other application side managed denormalization run the risk of becoming inconsistent. And of course it does mean you're multiplying your database size.

RUM Conjecture for Beginners hingo Sun, 2020-07-26 10:07

The paper stating the RUM conjecture was published by a group of Harvard DASLab researchers in 2016. They also have created a more easily digestable RUM conjecture home page with graphics. Yet, in this blog post I try to describe the idea in even simpler terms than that page.

What's in a database storage engine

I overheard - over-read, really - an internet discussion about database storage engines. The discussion was about what functionality is considered part of a storage engine, and what functionality is in the common parts of the database server. My first reaction was something like "how isn't this obvious?" Then I realized for a lot of the database functionality it isn't obvious at all and the answer really is that it could be either way.

My conference talks on Youtube

My kids watch a lot of youtube. They follow the famous Finnish youtubers every week. At some point my son had realized there are many videos on youtube with his father doing conference talks. Some of them have a thousand viewers. I've never gotten so much adoration and respect from my son as that day!

I've created a playlist of all my conference talks that have been published on youtube.

Paper review: Strong and Efficient Consistency with Consistency-Aware Durability

Mark Callaghan pointed me to a paper for my comments: Strong and Efficient Consistency with Consistency-Aware Durability by Ganesan, Alagappan and Arpaci-Dusseau ^2. It won Best Paper award at the Usenix Fast '20 conference. The paper presents a new consistency level for distributed databases where reads are causally consistent with other reads but not (necessarily) with writes.

My comments are mostly on section 2 of the paper, which describes current state of the art and a motivation for their work.

Writing a data loader for database benchmarks

A task that I've done many times in my career in databases is to load data into a database as a first step in some benchmark. To do it efficiently you want to use multiple threads. Dividing the work onto many threads requires good comprehension of third grade math, yet can be surprisingly hard to get right.

The typical setup is often like this:

  1. The benchmark framework launches N independent threads. For example in Sysbench these are completely isolated Lua environments with no shared data structures or communication possible between the threads.
  2. Each thread gets as input its thread id i and the total number of threads launched N.
About the bookAbout this siteAcademicAmazonBeginnersBooksBuildBotBusiness modelsbzrCassandraCloudcloud computingclsCommunitycommunityleadershipsummitConsistencycoodiaryCopyrightCreative CommonscssDatabasesdataminingDatastaxDevOpsDrizzleDrupalEconomyelectronEthicsEurovisionFacebookFrosconFunnyGaleraGISgithubGnomeGovernanceHandlerSocketHigh AvailabilityimpressionistimpressjsInkscapeInternetJavaScriptjsonKDEKubuntuLicensingLinuxMaidanMaker cultureMariaDBmarkdownMEAN stackMepSQLMicrosoftMobileMongoDBMontyProgramMusicMySQLMySQL ClusterNerdsNodeNoSQLodbaOpen ContentOpen SourceOpenSQLCampOracleOSConPAMPPatentsPerconaperformancePersonalPhilosophyPHPPiratesPlanetDrupalPoliticsPostgreSQLPresalespresentationsPress releasesProgrammingRed HatReplicationSeveralninesSillySkySQLSolonStartupsSunSybaseSymbiansysbenchtalksTechnicalTechnologyThe making ofTungstenTwitterUbuntuvolcanoWeb2.0WikipediaWork from HomexmlYouTube