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Achieving high performance OLAP over large graphs is a challenging problem and has received great attention recently because of its broad spectrum of applications. Existing systems have various performance bottlenecks due to limitations such as low parallelism and high network overheads. This Demo presents Grasper, an RDMA-enabled distributed graph OLAP system, which adopts a series of new system designs to overcome the challenges of OLAP on graphs. The take-aways for Demo attendees are: (1)~a good understanding of the challenges of processing graph OLAP queries; (2)~useful insights about where Grasper's good performance comes from; (3)~inspirations about how to design an efficient graph OLAP system by comparing Grasper with existing systems.

The property graph (PG) model is one of the most general graph data model and has been widely adopted in many graph analytics and processing systems. However, existing systems suffer from poor performance in terms of both latency and throughput for processing online analytical workloads on PGs due to their design defects such as expensive interactions with external databases, low parallelism, and high network overheads. In this paper, we propose Grasper, a high performance distributed system for OLAP on property graphs. Grasper adopts RDMA-aware system designs to reduce the network communication cost. We propose a novel query execution model, called Expert Model, which supports adaptive parallelism control at the fine-grained query operation level and allows tailored optimizations for different categories of query operators, thus achieving high parallelism and good load balancing. Experimental results show that Grasper achieves low latency and high throughput on a broad range of online analytical workloads.

Pregel-like systems are popular for iterative graph processing thanks to their user-friendly vertex-centric programming model. However, existing Pregel-like systems only adopt a naive checkpointing approach for fault tolerance, which saves a large amount of data about the state of computation and significantly degrades the failure-free execution performance. Advanced fault tolerance/recovery techniques are left unexplored in the context of Pregel-like systems. This paper proposes a non-invasive lightweight checkpointing (LWCP) scheme which minimizes the data saved to each checkpoint, and additional data required for recovery are generated online from the saved data. This improvement results in 10x speedup in checkpointing, and an integration of it with a recently proposed log-based recovery approach can further speed up recovery when failure occurs. Extensive experiments verified that our proposed LWCP techniques are able to significantly improve the performance of both checkpointing and recovery in a Pregel-like system.

This paper presents GraphRex, an efficient, robust, scalable, and easy-to-program framework for graph processing on datacenter infrastructure. To users, GraphRex presents a declarative, Datalog-like interface that is natural and expressive. Underneath, it compiles those queries into efficient implementations. A key technical contribution of GraphRex is the identification and optimization of a set of global operators whose efficiency is crucial to the good performance of datacenter-based, large graph analysis. Our experimental results show that GraphRex significantly outperforms existing frameworks---both high- and low-level---in scenarios ranging across a wide variety of graph workloads and network conditions, sometimes by two orders of magnitude.

This Demo presents G-Miner, a distributed system for graph mining. The take-aways for Demo attendees are: (1) a good understanding of the challenges of various graph mining workloads; (2) useful insights on how to design a good system for graph mining by comparing G-Miner with existing systems on performance, expressiveness and user-friendliness; and (3) how to use G-Miner for interactive graph analytics.

Graph mining is one of the most important areas in data mining. However, scalable solutions for graph mining are still lacking as existing studies focus on sequential algorithms. While many distributed graph processing systems have been proposed in recent years, most of them were designed to parallelize computations such as PageRank and Breadth-First Search that keep states on individual vertices and propagate updates along edges. Graph mining, on the other hand, may generate many subgraphs whose number can far exceed the number of vertices. This inevitably leads to much higher computational and space complexity rendering existing graph systems inefficient. We propose G-Miner, a distributed system with a new architecture designed for general graph mining. G-Miner adopts a unified programming framework for implementing a wide range of graph mining algorithms. We model subgraph processing as independent tasks, and design a novel task pipeline to streamline task processing for better CPU, network and I/O utilization. Our extensive experiments validate the efficiency of G-Miner for a range of graph mining tasks.

Graph analytics systems have gained significant popularity due to the prevalence of graph data. Many of these systems are designed to run in a shared-nothing architecture whereby a cluster of machines can process a large graph in parallel. In more recent proposals, others have argued that a single-machine system can achieve better performance and/or is more cost-effective. There is however no clear consensus which approach is better. In this paper, we classify existing graph analytics systems into four categories based on the architectural differences, i.e., processing infrastructure (centralized vs distributed), and memory consumption (in-memory vs out-of-core). We select eight open-source systems to cover all categories, and perform a comparative measurement study to compare their performance and cost characteristics across a spectrum of input data, applications, and hardware settings. Our results show that the best performing configuration can depend on the type of applications and input graphs, and there is no dominant winner across all categories. Based on our findings, we summarize the trends in performance and cost, and provide several insights that help to illuminate the performance and resource cost tradeoffs across different graph analytics systems and categories.