#bigmemory

[nosql]

While Hadoop is great for batch processing and storage of very large data sets, it can take hours to produce results. […] To address this challenge, Terracotta recently announced the > BigMemory-Hadoop Connector, a game-changing solution that lets Hadoop jobs write data directly into BigMemory, Terracotta’s in-memory data management platform. This enables downstream applications to get instant access to Hadoop results by reading from BigMemory. Hadoop jobs also execute faster, as they can now write to memory instead of disk (HDFS). The result can be a significant boost in competitive advantage and enterprise profitability.

Think about online applications. When the database gets slow you add a caching layer. It looks like a similar direction is very tempting for the majority of in-memory data grid-like solutions.

✚ The top speed of an african bush elephant is 24.9mph/40kmh. According to this.

Original title and link: Hadoop + Terracotta BigMemory: Run, Elephant, Run! (NoSQL database©myNoSQL)

[reco4j]

Reco4j Terracotta Cache Test

Starting from #Neo4j 1.9M4 it is really simple and straightforward to implement a custom cache for nodes and relationships. We deploy a cache based on #Terracotta #BigMemory. We start testing it, but our test shows that the best performance are reached using internal cache. BigMemory adds little overhead and we can't use off-heap storage (since at the moment NodeImpl and RelationshipImpl can't be serialized) to use all BigMemory functionalities. In any case we will use it as Reco4J cache, since it is a really powerful tool for second level cache.