GraQL: A Query Language for High-Performance Attributed Graph Databases

Chavarŕıa-Miranda, Daniel; Castellana, Vito Giovanni; Morari, Alessandro; Haglin, David J.; Feo, John

doi:10.1109/ipdpsw.2016.216

Cited by 12 publications

(2 citation statements)

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“…Query models over property graphs and associated query engines have been popular for semantic graphs [19,20]. Languages like SPARQL offer a highly flexible declarative syntax, but are costly to execute in practice for large graphs.…”

Section: Property and Temporal Graph Queryingmentioning

confidence: 99%

A Distributed Path Query Engine for Temporal Property Graphs

Ramesh,

Baranawal,

Simmhan

2020

Preprint

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Property graphs are a common form of linked data, with path queries used to traverse and explore them for enterprise transactions and mining. Temporal property graphs are a recent variant where time is a first-class entity to be queried over, and properties and structure varying over time. These are seen in social, telecom and transit networks. However, current graph databases and query engines have limited support for temporal relations among graph entities, no support for time-varying entities and/or do not scale on distributed resources. We address this gap by extending a linear path query model over property graphs to include intuitive temporal predicates that operate over temporal graphs. We design a distributed execution model for these temporal path queries using the interval-centric computing model, and develop a novel cost model to select an efficient execution plan from several. We perform detailed experiments of our Granite distributed query engine using temporal property graphs as large as 52M vertices, 218M edges, and 118M properties, and a 800-query workload, derived from the LDBC benchmark. We offer sub-second query latencies in most cases, which is 154×-1786× faster compared to industry-leading Neo4J shared-memory graph database and the JanusGraph/Spark distributed graph query engine. Further, our cost model selects a query plan that is within 10% of the optimal execution time in 90% of the cases. We also scale well, and complete 100% of the query workload for all graphs, as opposed to only 32-90% completion by the baseline systems.

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Section: Property and Temporal Graph Queryingmentioning

confidence: 99%

A Distributed Path Query Engine for Temporal Property Graphs

Ramesh,

Baranawal,

Simmhan

2020

Preprint

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“…Our preliminary work has focused on DSLs for graph methods. We implemented a SPARQL compiler (a query language for datasets in the Resource Description Framework) [10] that generates graph pattern matching routines in C, and defined GraQL [12] an extension of SQL to support graph walks when tables are combined to provide a graph view of the data. Reconfigurable hardware must be programmed spatially.…”

Section: Programming Abstractionsmentioning

confidence: 99%

Software defined architectures for data analytics

Castellana

Minutoli

Tumeo

et al. 2019

Proceedings of the 24th Asia and South Pacific Design Automation Conference

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Data analytics applications increasingly are complex workflows composed of phases with very different program behaviors (e.g., graph algorithms and machine learning, algorithms operating on sparse and dense data structures, etc). To reach the levels of efficiency required to process these workflows in real time, upcoming architectures will need to leverage even more workload specialization. If, at one end, we may find even more heterogenous processors composed by a myriad of specialized processing elements, at the other end we may see novel reconfigurable architectures, composed of sets of functional units and memories interconnected with (re)configurable on-chip networks, able to adapt dynamically to adapt the workload characteristics. Field Programmable Gate Arrays are more and more used for accelerating various workloads and, in particular, inferencing in machine learning, providing higher efficiency than other solutions. However, their fine-grained nature still leads to issues for the design software and still makes dynamic reconfiguration impractical. Future, more coarse-grained architectures could offer the features to execute diverse workloads at high efficiency while providing better reconfiguration mechanisms for dynamic adaptability. Nevertheless, we argue that the challenges for reconfigurable computing remain in the software. In this position paper, we describe a possible toolchain for reconfigurable architectures targeted at data analytics.

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