Data collation of memory technology

Let's take a look at a panorama of technology, in which the technology that involves memory computing is labeled red.

1 transaction processing: mainly divided into caches (Memcached, Redis, GemFire),RDBMS,newsql( voltdb The three -part, Cache and newsql databases are the focus of attention.

2 streaming:Storm itself is only the framework of computation, and spark-streaming realizes the flow processing of the memory computation.

3 ) Comparison of the analysis phase:

? General processing:MapReduce,Spark

? query:Hive,Pig,spark-shark

? Data mining:Mahout,spark-mllib,spark-graphx

As you can see, theSpark Ecosystem's sub-projects, as well as Impala , are a noteworthy focus.

theory

Search " paper name PDF" directly from Google or bing.com

"implementation techniques for Main Memory Database Systems"

"Main Memory Database systems:an Overview"

"TheRevolution in Database Architecture" –jim Gary

"A Study of Index structures for Main Memory Database Management Systems"

"high-performance Concurrency Control mechanisms for main-memory Databases"

"A bridging model for parallel computation" (BSP model)

"Seda:an Architecture for scalable, well-conditioned Internet Services"

Real-time processing and stream processing

Parallel computation of relational algebra

Concurrent computing Model BSP and SEDA

From NSM to parquet: The derivation of the storage structure

Product

GemFire ,voltdb:

Introduction to Distributed Cache GemFire architecture

Newsql Introduction to Database Voltdb features

Spark :

Spark Research on distributed computing and RDD model

Spark development status and fronts

Distributed memory File system Tachyon

Impala (Dremel) :

Google Dremel Detailed Data Model ( top )

Google Dremel Detailed Data Model ( below )

Impala The code generation technology in

Data collation of memory technology