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efd96a448c
"These patches introduce execution stages to Scylla in order to improve
icache friendliness. The places were stages are added are not chosen
very carefully and rather introduced at between different subsystems:
cql, storage proxy and database. This already results in a rather
significant improvement and can be tuned later if necessary.
Performance results:
perf_simple_query -c4 --duration 60
(medians)
before after diff
write 83017.75 242876.04 +192.6%
read 61709.16 168258.26 +172.7%
The real life improvements aren't as good because it is much harder
to collect sufficiently high number of operations in a batch."
Additional benchmarking from Paweł:
"I did some tests on my local setup.
* Latency at light loads
Scylla running on 16 logical CPUs (8 cores) with 64 GB of RAM.
cassandra-stress -rate threads=32
write latency
master seda
median 1.2 0.6
95th 1.6 0.8
99th 1.7 0.9
99.9th 2.5 1.3
max 26.4 24.2
Flags '--poll-mode' and '--defragment-memory-on-idle false' didn't improve situation for master.
See also attached graph write_99.svg and write_999.svg.
read latency
master seda
median 0.8 0.6
95th 1.0 0.9
99th 1.1 1.0
99.9th 1.4 1.2
max 18.5 18.0
See also attached graph read_99.svg and read_999.svg.
* Server 100% loaded, dataset fitting in memory (throughput)
Scylla running on 2 cores with 64 GB of RAM.
4x scylla-bench with the uniform workload
(concurrency of each s-b: 512 for writes, 256 for reads).
There were no cache misses during reads.
master seda diff
writes 107722.4 168482.26 +56.4%
reads 51049.48 76158.19 +49.2%
* Server 100% loaded, writes being flushed and compacted (throughput)
Scylla running on 2 cores with 4 GB of RAM.
4x scylla-bench with the uniform workload, concurrency 256 each.
master seda diff
writes 79575.77 114206.11 +43.5%
See attached graph: writes_with_flushes_and_compaction.png (first run: master, second: seda)."
* tag 'pdziepak/scylla-execution-stages/v1-rebased' of github.com:cloudius-systems/seastar-dev:
transport: make process_request_one() an execution stage
mutation_query: add an execution stage
db: make database::query() an execution stage
db: make apply an execution stage
storage_proxy: make mutate() an execution stage
cql3: make batch statement an execution stage
cql3: make modification statement an execution stage
cql3: make select statement an execution stage
mutation_reader: make mutation_source nothrow movable
Scylla
Building Scylla
In addition to required packages by Seastar, the following packages are required by Scylla.
Submodules
Scylla uses submodules, so make sure you pull the submodules first by doing:
git submodule init
git submodule update --init --recursive
Building and Running Scylla on Fedora
- Installing required packages:
sudo dnf install yaml-cpp-devel lz4-devel zlib-devel snappy-devel jsoncpp-devel thrift-devel antlr3-tool antlr3-C++-devel libasan libubsan gcc-c++ gnutls-devel ninja-build ragel libaio-devel cryptopp-devel xfsprogs-devel numactl-devel hwloc-devel libpciaccess-devel libxml2-devel python3-pyparsing lksctp-tools-devel protobuf-devel protobuf-compiler systemd-devel libunwind-devel
- Build Scylla
./configure.py --mode=release --with=scylla --disable-xen
ninja-build build/release/scylla -j2 # you can use more cpus if you have tons of RAM
- Run Scylla
./build/release/scylla
- run Scylla with one CPU and ./tmp as data directory
./build/release/scylla --datadir tmp --commitlog-directory tmp --smp 1
- For more run options:
./build/release/scylla --help
Building Fedora RPM
As a pre-requisite, you need to install Mock on your machine:
# Install mock:
sudo yum install mock
# Add user to the "mock" group:
usermod -a -G mock $USER && newgrp mock
Then, to build an RPM, run:
./dist/redhat/build_rpm.sh
The built RPM is stored in /var/lib/mock/<configuration>/result directory.
For example, on Fedora 21 mock reports the following:
INFO: Done(scylla-server-0.00-1.fc21.src.rpm) Config(default) 20 minutes 7 seconds
INFO: Results and/or logs in: /var/lib/mock/fedora-21-x86_64/result
Building Fedora-based Docker image
Build a Docker image with:
cd dist/docker
docker build -t <image-name> .
Run the image with:
docker run -p $(hostname -i):9042:9042 -i -t <image name>