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Analytic workloads on terabyte data-sets are often run in the cloud, where application and storage servers are separate and connected via network. In order to saturate the storage bandwidth and to hide the long storage latency, such a solution requires an expensive server cluster with sufficient aggregate DRAM capacity and hardware threads. An alternative solution is to push the query computation into storage servers. In this paper we present an in-storage Analytics QUery Offloading MAchiNe (AQUOMAN) to “offload” most SQL operators, including multi-way joins, to SSDs. AQUOMAN executes Table Tasks, which apply a static dataflow graph of SQL operators to relational tables to produce an output table. Table Tasks use a streaming computation model, which allows AQUOMAN to process queries with a reasonable amount of DRAM for intermediate results. AQUOMAN is a general analytic query processor, which can be integrated in the database software stack transparently. We have built a prototype of AQUOMAN in FPGAs, and using TPC-H benchmarks on 1TB data sets, shown that a single instance of 1TB AQUOMAN disk, on average, can free up 70% CPU cycles and reduce DRAM usage by 60%. One way to visualize this saving is to think that if we run queries sequentially and ignore inter-query page cache reuse, MonetDB running on a 4-core, 16GB-DRAM machine with AQUOMAN augmented SSDs performs, on average, as well as a MonetDB running on a 32-core, 128GB-DRAM machine with standard SSDs. 

Key-value store based on a log-structured merge-tree (LSMtree) is preferable to hash-based KV store because an LSMtree can support a wider variety of operations and show better performance, especially for writes. However, LSM-tree is difficult to implement in the resource constrained environment of a key-value SSD (KV-SSD) and consequently, KV-SSDs typically use hash-based schemes. We present PinK, a design and implementation of an LSM-tree-based KV-SSD, which compared to a hash-based KV-SSD, reduces 99th percentile tail latency by 73%, improves average read latency by 42% nd shows 37% higher throughput. The key idea in improving the performance of an LSM-tree in a resource constrained environment is to avoid the use of Bloom filters and instead, use a small amount of DRAM to keep/pin the top levels of the LSM-tree. 

We describe GraFBoost, a flash-based architecture with hardware acceleration for external analytics of multi-terabyte graphs. We compare the performance of GraFBoost with 1 GB of DRAM against various state-of-the-art graph analytics software including FlashGraph, running on a 32-thread Xeon server with 128 GB of DRAM. We demonstrate that despite the relatively small amount of DRAM, GraFBoost achieves high performance with very large graphs no other system can handle, and rivals the performance of the fastest software platforms on sizes of graphs that existing platforms can handle. Unlike in-memory and semi-external systems, GraFBoost uses a constant amount of memory for all problems, and its performance decreases very slowly as graph sizes increase, allowing GraFBoost to scale to much larger problems than possible with existing systems while using much less resources on a single-node system. The key component of GraFBoost is the sort-reduce accelerator, which implements a novel method to sequentialize fine-grained random accesses to flash storage. The sort-reduce accelerator logs random update requests and then uses hardware-accelerated external sorting with interleaved reduction functions. GraFBoost also stores newly updated vertex values generated in each superstep of the algorithm lazily with the old vertex values to further reduce I/O traffic. We evaluate the performance of GraFBoost for PageRank, breadth-first search and betweenness centrality on our FPGA-based prototype (Xilinx VC707 with 1 GB DRAM and 1 TB flash) and compare it to other graph processing systems including a pure software implementation of GrapFBoost.