heavydb

Cool project, congratulations. How would you contrast it against HeavyDB? https://github.com/heavyai/heavydb

For those wanting to try it for themselves, we recently released a preview of our full stack for Mac (containing both OmniSciDB as well as our Immerse frontend for interactive visual analytics), available for free here: https://www.omnisci.com/mac-preview. This is a bit of an experiment for us, so we'd love your feedback! Note that the Mac preview doesn't yet have the scalable rendering capabilities our platform is known for, but stay tuned. You can also install the open source version of OmniSciDB, either via tar/deb/rpm/Docker for Linux (https://www.omnisci.com/platform/downloads/open-source) or by following the build instructions for Mac in our git repo: https://github.com/omnisci/omniscidb (hopefully will have standalone builds for Mac up soon). You can also run a Dockerized version on your Mac, but as a disclaimer the performance, pa

Also worth checking out OmniSci (formerly MapD), which features an LLVM query compiler to gain large speedups executing SQL on both CPU and GPU: https://github.com/omnisci/omniscidb . And here's a link to a blog post giving a high level overview of the advantages of JIT compilation of queries over an interpreter: https://devblogs.nvidia.com/mapd-massive-throughput-database... .

LLVM actually has a native PTX backend, NVPTX, but since the Nvidia ISA is proprietary we use the CUDA driver API to generate the binary for the target GPU arch. (see here in MapD where it generates the PTX from the LLVM IR: https://github.com/omnisci/mapd-core/blob/ae88c486d1d790db54..., and here for where it calls the driver API to generate the native code: https://github.com/omnisci/mapd-core/blob/568e77d9c9706049ee...)

Hi Todd - Has your team looked at GraphBLAS [1] yet for MapD [2] now that Tim Davis has released a 2.0 reference C implementation (included in SuiteSparse 5.3) [3]? There is a GraphBLAS 2.0 CPU implementation in RedisGraph now [4] and official GraphBLAS 2.x GPU implementations are in the works. [1] GraphBLAS http://graphblas.org [2] OmniSci MapD DB https://github.com/omnisci/mapd-core [3] SuiteSparse::GraphBLAS http://faculty.cse.tamu.edu/davis/suitesparse.html [4] Previous Discussion https://news.ycombinator.com/item?id=18099520

http://www.timestored.com/time-series-data/column-oriented-d... > Open Source Column-Oriented Databases: There are three available open source column databases, all were based on works of research groups that later saw commercial spinoffs. C-Store produced vertica, MonetDB spawned Vectorwise and LucidDB was DynamoBI. Each project has stagnated as the team around them moved on to commercial endeavours. Only MonetDB appears to be still actively developed, it's also the one that seems most feature complete as we'll see later. Is the above an accurate characterization of OSS column-oriented databases? There is also MapD which uses GPUs, https://github.com/mapd/mapd-core > MapD Core is an in-memory, column store, SQL relational database that was designed from the ground up to run on GPUs.

I see they are supporting Apache Arrow (http://arrow.apache.org/) in their result set converter, which will be nice for interoperability with other data system that use Arrow: https://github.com/mapd/mapd-core/blob/21fc39fab9e1dc2c1682b... (I'm on the Arrow PMC)