Advanced prefetching and caching of models with PrefetchML

Abstract

Caching and prefetching techniques have been used for decades in database engines and file systems to improve the performance of I/O intensive application. A prefetching algorithm typically benefits from the system's latencies by loading into main memory elements that will be needed in the future, speeding-up data access. While these solutions can bring a significant improvement in terms of execution time, prefetching rules are often defined at the data-level, making them hard to understand, maintain, and optimize. In addition, low-level prefetching and caching components are difficult to align with scalable model persistence frameworks because they are unaware of potential optimizations relying on the analysis of metamodel-level information, and are less present in NoSQL databases, a common solution to store large models. To overcome this situation we propose PrefetchML, a framework that executes prefetching and caching strategies over models. Our solution embeds a DSL to configure precisely the prefetching rules to follow, and a monitoring component providing insights on how the prefetching execution is working to help designers optimize his performance plans. Our experiments show that PrefetchML is a suitable solution to improve query execution time on top of scalable model persistence frameworks. Tool support is fully available online as an open-source Eclipse plugin.

Bivariate, cluster, and suitability analysis of NoSQL solutions for big graph applications

“…‗DMFP' represents execution results when 6 features (Document-oriented, Graph, Key-Value, Wide-Column, Free and Proprietary) were considered for clustering. [67] AllegroGraph [65] Amazon Neptune [68] ArangoDB [70] Accumulo [66] BerkeleyDB [73] Cassandra [77] Cache [76] AnzoGraph [69] BaseX [72] Clusterpoint Database [80] BigTable [75] CDB or Constant Database [78] Cloudant [79] Azure Tables [71] CouchDB [83] CouchBase Server [82] GridGain Systems [93] etcd [90] Coherence [81] DataStax Enterprise Graph [86] CrateIO [84] HBase [4] NoSQLz [117] FoundationDB [92] CosmosDB [85] Dynamo [88] ElasticSearch [89] HyperTable [98] OpenLink Virtuoso [119] GT.M [54] DocumentDB [87] Hazelcast [95] eXist [91] MongoDB [6] Hibari [96] IBM Informix [99] HyperGraphDB [97] Jackrabbit [104] RethinkDB [128] IBM Informix C-ISAM [100] Lotus Domino [110] InfiniteGraph [102] OrientDB…”

Model Sensemaking Strategies: Exploiting Meta-Model Patterns to Understand Large Models

“…These include model fragmentation according to user-defined strategies [14], [15]; partial model loading [16]; and model decomposition into smaller valid submodels [17]. Moreover, indexers [18] (similar to those in relational databases) and caching techniques for queries [19], [20] have been proposed for faster model element retrieval.…”

Partial Loading of Repository-Based Models through Static Analysis

“…In Epsilon's architecture, there is an Epsilon Connectivity Layer (EMC) 3 ) which enables Epsilon programs to interact with models in different modelling technologies in a uniform manner by defining the drivers (e. g., EMF, CDO, NeoEMF).…”

“…In [3], Daniel et al propose PrefetchML, a domain-specific language that describes prefetching and caching rules over models. PrefetchML is a suitable solution to improve query execution time on top of scalable model persistence frameworks.…”