This is the source code for our (Tobias Ziegler, Carsten Binnig and Viktor Leis) published paper at SIGMOD’22: ScaleStore: A Fast and Cost-Efficient Storage Engine using DRAM, NVMe, and RDMA. Paper can be found here: Paper Link
In this paper, we propose ScaleStore, a novel distributed storage engine that exploits DRAM caching, NVMe storage, and RDMA networking to achieve high performance, cost-efficiency, and scalability at the same time. Using low latency RDMA messages, ScaleStore implements a transparent memory abstraction that provides access to the aggregated DRAM memory and NVMe storage of all nodes. In contrast to existing distributed RDMA designs such as NAM-DB or FaRM, ScaleStore integrates seamlessly with NVMe SSDs, lowering the overall hardware cost significantly. The core of ScaleStore is a distributed caching strategy that dynamically decides which data to keep in memory (and which on SSDs) based on the workload. The caching protocol also provides strong consistency in the presence of concurrent data modifications. In our YCSB-based evaluation, we show that ScaleStore can provide high performance for various types of workloads (read/write-dominated, uniform/skewed) even when the data size is larger than the aggregated memory of all nodes. We further show that ScaleStore can efficiently handle dynamic workload changes and support elasticity.
@inproceedings{DBLP:conf/sigmod/0001BL22,
author = {Tobias Ziegler and
Carsten Binnig and
Viktor Leis},
title = {ScaleStore: {A} Fast and Cost-Efficient Storage Engine using DRAM,
NVMe, and {RDMA}},
booktitle = {{SIGMOD} '22: International Conference on Management of Data, Philadelphia,
PA, USA, June 12 - 17, 2022},
pages = {685--699},
publisher = {{ACM}},
year = {2022},
url = {https://doi.org/10.1145/3514221.3526187},
doi = {10.1145/3514221.3526187}
}
All experiments were conducted on a 5-node cluster running Ubuntu 18.04.1 LTS, with Linux 4.15.0 kernel. Each node is equipped with two Intel(R) Xeon(R) Gold 5120 CPUs (14 cores), 512 GB main-memory split between both sockets, and four Samsung SSD 980 Pro M.2 1 TB connected via PCIe by one ASRock Hyper Quad M.2 PCIe card. The nodes of the cluster are connected with an InfiniBand network using one Mellanox ConnectX-5 MT27800 NICs (InfiniBand EDR 4x, 100 Gbps) per node.
We used the following Mellanox OFED installation:
MLNX_OFED_LINUX-5.1-2.5.8.0 (OFED-5.1-2.5.8):
Installed Packages:
-------------------
ii ar-mgr 1.0-0.3.MLNX20200824.g8577618.51258 amd64 Adaptive Routing Manager
ii dapl2-utils 2.1.10.1.mlnx-OFED.51258 amd64 Utilities for use with the DAPL libraries
ii dpcp 1.1.0-1.51258 amd64 Direct Packet Control Plane (DPCP) is a library to use Devx
ii dump-pr 1.0-0.3.MLNX20200824.g8577618.51258 amd64 Dump PathRecord Plugin
ii hcoll 4.6.3125-1.51258 amd64 Hierarchical collectives (HCOLL)
ii ibacm 51mlnx1-1.51258 amd64 InfiniBand Communication Manager Assistant (ACM)
ii ibdump 6.0.0-1.51258 amd64 Mellanox packets sniffer tool
ii ibsim 0.9-1.51258 amd64 InfiniBand fabric simulator for management
ii ibsim-doc 0.9-1.51258 all documentation for ibsim
ii ibutils2 2.1.1-0.126.MLNX20200721.gf95236b.51258 amd64 OpenIB Mellanox InfiniBand Diagnostic Tools
ii ibverbs-providers:amd64 51mlnx1-1.51258 amd64 User space provider drivers for libibverbs
ii ibverbs-utils 51mlnx1-1.51258 amd64 Examples for the libibverbs library
ii infiniband-diags 51mlnx1-1.51258 amd64 InfiniBand diagnostic programs
ii iser-dkms 5.1-OFED.5.1.2.5.3.1 all DKMS support fo iser kernel modules
ii isert-dkms 5.1-OFED.5.1.2.5.3.1 all DKMS support fo isert kernel modules
ii kernel-mft-dkms 4.15.1-100 all DKMS support for kernel-mft kernel modules
ii knem 1.1.4.90mlnx1-OFED.5.1.2.5.0.1 amd64 userspace tools for the KNEM kernel module
ii knem-dkms 1.1.4.90mlnx1-OFED.5.1.2.5.0.1 all DKMS support for mlnx-ofed kernel modules
ii libdapl-dev 2.1.10.1.mlnx-OFED.51258 amd64 Development files for the DAPL libraries
ii libdapl2 2.1.10.1.mlnx-OFED.51258 amd64 The Direct Access Programming Library (DAPL)
ii libibmad-dev:amd64 51mlnx1-1.51258 amd64 Development files for libibmad
ii libibmad5:amd64 51mlnx1-1.51258 amd64 Infiniband Management Datagram (MAD) library
ii libibnetdisc5:amd64 51mlnx1-1.51258 amd64 InfiniBand diagnostics library
ii libibumad-dev:amd64 51mlnx1-1.51258 amd64 Development files for libibumad
ii libibumad3:amd64 51mlnx1-1.51258 amd64 InfiniBand Userspace Management Datagram (uMAD) library
ii libibverbs-dev:amd64 51mlnx1-1.51258 amd64 Development files for the libibverbs library
ii libibverbs1:amd64 51mlnx1-1.51258 amd64 Library for direct userspace use of RDMA (InfiniBand/iWARP)
ii libibverbs1-dbg:amd64 51mlnx1-1.51258 amd64 Debug symbols for the libibverbs library
ii libopensm 5.7.3.MLNX20201102.e56fd90-0.1.51258 amd64 Infiniband subnet manager libraries
ii libopensm-devel 5.7.3.MLNX20201102.e56fd90-0.1.51258 amd64 Developement files for OpenSM
ii librdmacm-dev:amd64 51mlnx1-1.51258 amd64 Development files for the librdmacm library
ii librdmacm1:amd64 51mlnx1-1.51258 amd64 Library for managing RDMA connections
ii mlnx-ethtool 5.4-1.51258 amd64 This utility allows querying and changing settings such as speed,
ii mlnx-iproute2 5.6.0-1.51258 amd64 This utility allows querying and changing settings such as speed,
ii mlnx-ofed-kernel-dkms 5.1-OFED.5.1.2.5.8.1 all DKMS support for mlnx-ofed kernel modules
ii mlnx-ofed-kernel-utils 5.1-OFED.5.1.2.5.8.1 amd64 Userspace tools to restart and tune mlnx-ofed kernel modules
ii mpitests 3.2.20-5d20b49.51258 amd64 Set of popular MPI benchmarks and tools IMB 2018 OSU benchmarks ver 4.0.1 mpiP-3.3 IPM-2.0.6
ii mstflint 4.14.0-3.51258 amd64 Mellanox firmware burning application
ii openmpi 4.0.4rc3-1.51258 all Open MPI
ii opensm 5.7.3.MLNX20201102.e56fd90-0.1.51258 amd64 An Infiniband subnet manager
ii opensm-doc 5.7.3.MLNX20201102.e56fd90-0.1.51258 amd64 Documentation for opensm
ii perftest 4.4+0.5-1 amd64 Infiniband verbs performance tests
ii rdma-core 51mlnx1-1.51258 amd64 RDMA core userspace infrastructure and documentation
ii rdmacm-utils 51mlnx1-1.51258 amd64 Examples for the librdmacm library
ii sharp 2.2.2.MLNX20201102.b26a0fd-1.51258 amd64 SHArP switch collectives
ii srp-dkms 5.1-OFED.5.1.2.5.3.1 all DKMS support fo srp kernel modules
ii srptools 51mlnx1-1.51258 amd64 Tools for Infiniband attached storage (SRP)
ii ucx 1.9.0-1.51258 amd64 Unified Communication X4x 512 GB main-memory split between both sockets, and four Samsung SSD 980 Pro M.2 1 TB connected via PCIe by one ASRock Hyper Quad M.2 PCIe card. All SSDs are used as block device and organized as a RAID 0 via
sudo mdadm --create /dev/md0 --auto md --level=0 --raid-devices=4 /dev/nvme0n1 /dev/nvme1n1 /dev/nvme2n1 /dev/nvme3n1We are using huge pages for the memory buffers:
echo N | sudo tee /sys/devices/system/node/node0/hugepages/hugepages-2048kB/nr_hugepages To build ScaleStore we use CMake. First we create a build folder in the top level folder of scalestore:
mkdir build
cd buildAfterwards, we can build the executable with either in debug mode with address sanitizers enabled:
cmake -D CMAKE_C_COMPILER=gcc-10 -D CMAKE_CXX_COMPILER=g++-10 -DCMAKE_BUILD_TYPE=Debug -DSANI=On .. && make -jor in release mode:
cmake -D CMAKE_C_COMPILER=gcc-10 -D CMAKE_CXX_COMPILER=g++-10 -DCMAKE_BUILD_TYPE=Release .. && make -j- gflags
- lib_aio
- ibverbs
- tabulate
- rdma cm
All executables can be found in scalestore/build/frontend.
For instance, the follwoing command can be used to run ycsb in a single node setup:
make -j && numactl --membind=0 --cpunodebind=0 ./ycsb -ownIp=172.18.94.80 -nodes=1 -YCSB_all_workloads -worker=20 -YCSB_tuple_count=1000000000 -dramGB=150 -csvFile=singlenode_oom_scalestore_ycsb_zipf.csv -YCSB_run_for_seconds=60 -ssd_path=/dev/md0 --ssd_gib=400 -pageProviderThreads=4 -YCSB_all_zipfThe main configuration file in order to execute ScaleStore can be found in shared-headers/Defs.hpp.
To configure the servers and their ips the following configuration needs to be adapted:
const std::vector<std::vector<std::string>> NODES{
{""}, // 0 to allow direct offset
{"172.18.94.80"}, // 1
{"172.18.94.80", "172.18.94.70"}, // 2
{"172.18.94.80", "172.18.94.70", "172.18.94.10"}, // 3
{"172.18.94.80", "172.18.94.70", "172.18.94.10", "172.18.94.20"}, // 4
{"172.18.94.80", "172.18.94.70", "172.18.94.10", "172.18.94.20", "172.18.94.40"}, // 5
{"172.18.94.80", "172.18.94.70", "172.18.94.10", "172.18.94.20", "172.18.94.40", "172.18.94.30"}, // 6
};We implemented a very simple CoreManager which can be found in (scalestore/backend/threads/CoreManager.hpp).
All configurations are hard-coded to fit our servers (2 NUMA nodes) and might need to be adapted to fit yours.
Besides the Defs.hpp file there are gflags parameters.
Most of them are stored in backend/ScaleStore/Config.hpp.
However, some are attached to the main executable file, e.g. ycsb has the YCSB_tuple_count flag.
To see all (custom) gflags parameters and their description one can run:
./exe --helpThe paper benchmark implementations can be found in frontend/ycsb.
The distributed experiment runner scripts can be found in distexperiments/experiments.
In order to run them please consult the following github page: https://github.com/mjasny/distexprunner
- YCSB runner
- OLAP scan queries
- consistency checks
- TPC-C consistency checks
If you see the following exception at the startup of ScaleStore:
"Consider adjusting BATCH_SIZE and PARTITIONS" in /home/tziegler/ScaleStore/backend/scalestore/storage/buffermanager/Buffermanager.cpp:62
You would need to change the PARTITIONS and BATCH_SIZE variable in the Defs.hpp file.
The reason is that we use a partitioned queue of batches to reduce contention in the free lists and accesses to the latch.
To calculate the right number of batches per partition we use.
NUMBER_BATCHES = (DRAM_SIZE / PAGE_SIZE) / PARTITIONS / BATCH_SIZE
Therefore, this may be needed if the DRAM_SIZE is too small or the page size has been changed.
